python /home/admin/mtr/script_for_cron.py -j default -m 20 -a 'python3 ~/workarea/git/Velours/python/prod/datou.py -j batch_current -C 2529344' -s traitement_4234 -M 0 -S 0 -U 100,80,95 import MySQLdb succeeded Import error (python version) ['/Users/moilerat/Documents/Fotonower/install/caffe/distribute/python', '/home/admin/workarea/git/Velours/python/prod', '/home/admin/workarea/install/darknet', '/home/admin/workarea/git/Velours/python', '/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python', '/home/admin/mtr/.credentials', '/home/admin/workarea/install/caffe/python', '/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools', '/home/admin/workarea/git/fotonowerpip', '/home/admin/workarea/install/segment-anything', '/home/admin/workarea/git/pyfvs', '/home/admin/workarea/git/apy', '/usr/lib/python38.zip', '/usr/lib/python3.8', '/usr/lib/python3.8/lib-dynload', '/home/admin/.local/lib/python3.8/site-packages', '/usr/local/lib/python3.8/dist-packages', '/usr/lib/python3/dist-packages'] process id : 2208836 load datou : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! Unexpected type for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? load thcls load pdts Running datou job : batch_current TODO datou_current to load to do maybe to take outside batchDatouExec updating current state to 1 list_input_json: [] Current got : datou_id : 4234, datou_cur_ids : ['2529344'] with mtr_portfolio_ids : ['20068969'] and first list_photo_ids : [] new path : /proc/2208836/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, brightness, blur_detection, rle_unique_nms_with_priority, crop_condition, thcl, ventilate_hashtags_in_portfolio, final, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 21 list_input_json : [] origin We have 1 , WARNING: data may be incomplete, need to offset and complete ! BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 21 ; length of list_pids : 21 ; length of list_args : 21 time to download the photos : 7.178339242935181 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 11 step1:mask_detect Tue Feb 11 10:53:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6776 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-11 10:53:21.842038: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-11 10:53:21.850875: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-11 10:53:21.852607: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fca80000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-11 10:53:21.852661: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-11 10:53:21.856074: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-11 10:53:22.013194: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4185c790 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-11 10:53:22.013265: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-11 10:53:22.014375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-11 10:53:22.014973: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 10:53:22.018068: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 10:53:22.021797: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 10:53:22.022808: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 10:53:22.026517: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 10:53:22.028446: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 10:53:22.034845: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 10:53:22.036519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 10:53:22.036683: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 10:53:22.037472: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-11 10:53:22.037502: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-11 10:53:22.037518: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-11 10:53:22.039001: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6227 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-02-11 10:53:22.370218: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-11 10:53:22.370367: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 10:53:22.370399: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 10:53:22.370436: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 10:53:22.370461: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 10:53:22.370489: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 10:53:22.370512: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 10:53:22.370538: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 10:53:22.372176: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 10:53:22.377745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-11 10:53:22.377990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-11 10:53:22.378091: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 10:53:22.378190: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-11 10:53:22.378286: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-11 10:53:22.378392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-11 10:53:22.378495: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-11 10:53:22.378619: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-11 10:53:22.380889: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-11 10:53:22.380971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-11 10:53:22.380991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-11 10:53:22.381007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-11 10:53:22.382884: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6227 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-02-11 10:53:31.595784: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-11 10:53:31.804966: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 21 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.77813 max: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 0.00011968612670898438 nb_pixel_total : 165 time to create 1 rle with old method : 0.00043272972106933594 length of segment : 25 time for calcul the mask position with numpy : 0.00010132789611816406 nb_pixel_total : 55 time to create 1 rle with old method : 0.00018644332885742188 length of segment : 22 time for calcul the mask position with numpy : 8.988380432128906e-05 nb_pixel_total : 417 time to create 1 rle with old method : 0.001115560531616211 length of segment : 57 time for calcul the mask position with numpy : 8.7738037109375e-05 nb_pixel_total : 307 time to create 1 rle with old method : 0.0008130073547363281 length of segment : 45 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 306 time to create 1 rle with old method : 0.0006461143493652344 length of segment : 37 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.0004551410675048828 length of segment : 30 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0006146430969238281 length of segment : 35 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.00040411949157714844 length of segment : 39 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.64141 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 33 time to create 1 rle with old method : 9.822845458984375e-05 length of segment : 6 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 30 time to create 1 rle with old method : 6.937980651855469e-05 length of segment : 6 time for calcul the mask position with numpy : 9.560585021972656e-05 nb_pixel_total : 2433 time to create 1 rle with old method : 0.0032536983489990234 length of segment : 52 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.00044918060302734375 length of segment : 50 time for calcul the mask position with numpy : 0.0005590915679931641 nb_pixel_total : 27498 time to create 1 rle with old method : 0.036512136459350586 length of segment : 371 time for calcul the mask position with numpy : 0.00011277198791503906 nb_pixel_total : 2538 time to create 1 rle with old method : 0.0036644935607910156 length of segment : 50 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.97734 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 3 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 806 time to create 1 rle with old method : 0.001127481460571289 length of segment : 64 time for calcul the mask position with numpy : 0.0001544952392578125 nb_pixel_total : 6323 time to create 1 rle with old method : 0.008745908737182617 length of segment : 104 time for calcul the mask position with numpy : 0.00010156631469726562 nb_pixel_total : 2011 time to create 1 rle with old method : 0.002908468246459961 length of segment : 53 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.82500 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.00028228759765625 nb_pixel_total : 16800 time to create 1 rle with old method : 0.019365787506103516 length of segment : 264 time for calcul the mask position with numpy : 0.00022459030151367188 nb_pixel_total : 11621 time to create 1 rle with old method : 0.013386011123657227 length of segment : 105 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002968311309814453 length of segment : 16 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 343 time to create 1 rle with old method : 0.0005764961242675781 length of segment : 22 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 395 time to create 1 rle with old method : 0.0005397796630859375 length of segment : 22 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.68984 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 3 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.00027871131896972656 length of segment : 13 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 731 time to create 1 rle with old method : 0.0010352134704589844 length of segment : 38 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 311 time to create 1 rle with old method : 0.0004394054412841797 length of segment : 21 Processing 1 images image shape: (400, 400, 3) min: 20.00000 max: 217.00000 molded_images shape: (1, 640, 640, 3) min: -79.67266 max: 87.63750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -113.95000 max: 150.45156 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.0005898475646972656 length of segment : 14 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 1273 time to create 1 rle with old method : 0.0018541812896728516 length of segment : 46 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 294 time to create 1 rle with old method : 0.0005428791046142578 length of segment : 17 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.00026154518127441406 length of segment : 19 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.00040650367736816406 length of segment : 20 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 2925 time to create 1 rle with old method : 0.004032135009765625 length of segment : 65 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1191 time to create 1 rle with old method : 0.0018837451934814453 length of segment : 33 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 1391 time to create 1 rle with old method : 0.002269268035888672 length of segment : 35 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.19609 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 0.0002498626708984375 nb_pixel_total : 13295 time to create 1 rle with old method : 0.019547462463378906 length of segment : 118 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 92 time to create 1 rle with old method : 0.00020241737365722656 length of segment : 14 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 939 time to create 1 rle with old method : 0.0015604496002197266 length of segment : 41 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.00029015541076660156 length of segment : 16 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.00029850006103515625 length of segment : 13 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 322 time to create 1 rle with old method : 0.0005638599395751953 length of segment : 18 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 1326 time to create 1 rle with old method : 0.0025053024291992188 length of segment : 26 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.0002655982971191406 length of segment : 12 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 768 time to create 1 rle with old method : 0.0014333724975585938 length of segment : 33 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 747 time to create 1 rle with old method : 0.0010902881622314453 length of segment : 36 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 1427 time to create 1 rle with old method : 0.0018415451049804688 length of segment : 31 time for calcul the mask position with numpy : 0.0003790855407714844 nb_pixel_total : 25926 time to create 1 rle with old method : 0.029395341873168945 length of segment : 145 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 379 time to create 1 rle with old method : 0.0005958080291748047 length of segment : 20 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 163 time to create 1 rle with old method : 0.0002524852752685547 length of segment : 12 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.48906 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 106 time to create 1 rle with old method : 0.00018548965454101562 length of segment : 10 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 795 time to create 1 rle with old method : 0.0009799003601074219 length of segment : 48 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.00027751922607421875 length of segment : 20 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 356 time to create 1 rle with old method : 0.0005171298980712891 length of segment : 22 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 1150 time to create 1 rle with old method : 0.0014350414276123047 length of segment : 40 time for calcul the mask position with numpy : 0.0005023479461669922 nb_pixel_total : 14252 time to create 1 rle with old method : 0.022513866424560547 length of segment : 344 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 821 time to create 1 rle with old method : 0.001047372817993164 length of segment : 37 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 373 time to create 1 rle with old method : 0.0005340576171875 length of segment : 35 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 576 time to create 1 rle with old method : 0.0008268356323242188 length of segment : 56 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.44766 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00028777122497558594 length of segment : 11 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 1229 time to create 1 rle with old method : 0.0016551017761230469 length of segment : 59 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 1499 time to create 1 rle with old method : 0.0018992424011230469 length of segment : 74 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003209114074707031 length of segment : 50 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 1228 time to create 1 rle with old method : 0.001665353775024414 length of segment : 51 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 896 time to create 1 rle with old method : 0.0011849403381347656 length of segment : 51 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 941 time to create 1 rle with old method : 0.001428842544555664 length of segment : 58 time for calcul the mask position with numpy : 9.989738464355469e-05 nb_pixel_total : 673 time to create 1 rle with old method : 0.0010650157928466797 length of segment : 59 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0007407665252685547 length of segment : 53 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 959 time to create 1 rle with old method : 0.0012216567993164062 length of segment : 55 time for calcul the mask position with numpy : 0.00018072128295898438 nb_pixel_total : 2400 time to create 1 rle with old method : 0.0033779144287109375 length of segment : 167 time for calcul the mask position with numpy : 7.104873657226562e-05 nb_pixel_total : 522 time to create 1 rle with old method : 0.0009248256683349609 length of segment : 56 Processing 1 images image shape: (280, 400, 3) min: 13.00000 max: 199.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 80.30312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009284019470214844 nb_pixel_total : 106929 time to create 1 rle with old method : 0.14028668403625488 length of segment : 283 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.93594 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 482 time to create 1 rle with old method : 0.0008032321929931641 length of segment : 22 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.00037789344787597656 length of segment : 15 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.0007998943328857422 length of segment : 28 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 513 time to create 1 rle with old method : 0.0008463859558105469 length of segment : 45 time for calcul the mask position with numpy : 0.00012111663818359375 nb_pixel_total : 1942 time to create 1 rle with old method : 0.003776073455810547 length of segment : 65 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 1422 time to create 1 rle with old method : 0.0020360946655273438 length of segment : 52 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 1062 time to create 1 rle with old method : 0.0016789436340332031 length of segment : 47 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 1774 time to create 1 rle with old method : 0.002554178237915039 length of segment : 50 time for calcul the mask position with numpy : 9.489059448242188e-05 nb_pixel_total : 2275 time to create 1 rle with old method : 0.003266572952270508 length of segment : 101 time for calcul the mask position with numpy : 0.00017499923706054688 nb_pixel_total : 4269 time to create 1 rle with old method : 0.006089925765991211 length of segment : 141 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 1000 time to create 1 rle with old method : 0.0017428398132324219 length of segment : 59 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 2146 time to create 1 rle with old method : 0.0032198429107666016 length of segment : 89 time for calcul the mask position with numpy : 0.00012350082397460938 nb_pixel_total : 3632 time to create 1 rle with old method : 0.004937410354614258 length of segment : 70 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 1575 time to create 1 rle with old method : 0.0027806758880615234 length of segment : 62 time for calcul the mask position with numpy : 0.00011944770812988281 nb_pixel_total : 1245 time to create 1 rle with old method : 0.003287076950073242 length of segment : 47 time for calcul the mask position with numpy : 0.00021028518676757812 nb_pixel_total : 3531 time to create 1 rle with old method : 0.008157014846801758 length of segment : 127 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 34 time for calcul the mask position with numpy : 0.00010132789611816406 nb_pixel_total : 549 time to create 1 rle with old method : 0.0012001991271972656 length of segment : 33 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 285 time to create 1 rle with old method : 0.0006322860717773438 length of segment : 16 time for calcul the mask position with numpy : 8.559226989746094e-05 nb_pixel_total : 668 time to create 1 rle with old method : 0.0014846324920654297 length of segment : 34 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0009758472442626953 length of segment : 36 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 314 time to create 1 rle with old method : 0.0007333755493164062 length of segment : 25 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 444 time to create 1 rle with old method : 0.001020193099975586 length of segment : 24 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.0007081031799316406 length of segment : 37 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 987 time to create 1 rle with old method : 0.001978158950805664 length of segment : 42 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.00037860870361328125 length of segment : 17 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 252 time to create 1 rle with old method : 0.0005784034729003906 length of segment : 17 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 140 time to create 1 rle with old method : 0.0003771781921386719 length of segment : 15 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 201 time to create 1 rle with old method : 0.00031375885009765625 length of segment : 16 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.0003943443298339844 length of segment : 10 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 203 time to create 1 rle with old method : 0.00031685829162597656 length of segment : 17 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 716 time to create 1 rle with old method : 0.0009746551513671875 length of segment : 34 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 1186 time to create 1 rle with old method : 0.001705169677734375 length of segment : 44 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1121 time to create 1 rle with old method : 0.0015633106231689453 length of segment : 45 time for calcul the mask position with numpy : 8.20159912109375e-05 nb_pixel_total : 916 time to create 1 rle with old method : 0.0013859272003173828 length of segment : 38 time for calcul the mask position with numpy : 9.131431579589844e-05 nb_pixel_total : 2113 time to create 1 rle with old method : 0.0030546188354492188 length of segment : 90 time for calcul the mask position with numpy : 8.511543273925781e-05 nb_pixel_total : 908 time to create 1 rle with old method : 0.0013751983642578125 length of segment : 38 time for calcul the mask position with numpy : 9.083747863769531e-05 nb_pixel_total : 1933 time to create 1 rle with old method : 0.002820253372192383 length of segment : 65 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 442 time to create 1 rle with old method : 0.0006949901580810547 length of segment : 22 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 290 time to create 1 rle with old method : 0.000499725341796875 length of segment : 17 time for calcul the mask position with numpy : 9.036064147949219e-05 nb_pixel_total : 1545 time to create 1 rle with old method : 0.002236604690551758 length of segment : 50 time for calcul the mask position with numpy : 8.58306884765625e-05 nb_pixel_total : 1580 time to create 1 rle with old method : 0.0024132728576660156 length of segment : 51 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 490 time to create 1 rle with old method : 0.0007240772247314453 length of segment : 32 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 462 time to create 1 rle with old method : 0.0007073879241943359 length of segment : 28 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 442 time to create 1 rle with old method : 0.0006947517395019531 length of segment : 36 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 731 time to create 1 rle with old method : 0.0010330677032470703 length of segment : 39 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 715 time to create 1 rle with old method : 0.0010843276977539062 length of segment : 32 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 1600 time to create 1 rle with old method : 0.0024971961975097656 length of segment : 57 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 862 time to create 1 rle with old method : 0.0013227462768554688 length of segment : 38 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 591 time to create 1 rle with old method : 0.0009579658508300781 length of segment : 39 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 410 time to create 1 rle with old method : 0.0007021427154541016 length of segment : 21 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 1136 time to create 1 rle with old method : 0.0014464855194091797 length of segment : 69 time for calcul the mask position with numpy : 0.00012421607971191406 nb_pixel_total : 6405 time to create 1 rle with old method : 0.007633209228515625 length of segment : 238 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.00034546852111816406 length of segment : 18 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 278 time to create 1 rle with old method : 0.0005450248718261719 length of segment : 27 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.0003941059112548828 length of segment : 12 time for calcul the mask position with numpy : 0.00016617774963378906 nb_pixel_total : 6281 time to create 1 rle with old method : 0.011034488677978516 length of segment : 150 time for calcul the mask position with numpy : 0.00010180473327636719 nb_pixel_total : 89 time to create 1 rle with old method : 0.0002524852752685547 length of segment : 11 time for calcul the mask position with numpy : 8.20159912109375e-05 nb_pixel_total : 1410 time to create 1 rle with old method : 0.002580404281616211 length of segment : 82 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 576 time to create 1 rle with old method : 0.0011026859283447266 length of segment : 41 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.0006575584411621094 length of segment : 33 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 449 time to create 1 rle with old method : 0.0006611347198486328 length of segment : 19 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 149 time to create 1 rle with old method : 0.00020575523376464844 length of segment : 27 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.0003008842468261719 length of segment : 29 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 137.03750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.0004734992980957031 length of segment : 12 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 521 time to create 1 rle with old method : 0.0009822845458984375 length of segment : 38 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.00018262863159179688 length of segment : 10 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 558 time to create 1 rle with old method : 0.0012214183807373047 length of segment : 111 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 81 time to create 1 rle with old method : 0.00017762184143066406 length of segment : 10 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.20781 max: 149.55312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 188 time to create 1 rle with old method : 0.00029158592224121094 length of segment : 27 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.00011515617370605469 length of segment : 19 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 277 time to create 1 rle with old method : 0.00040149688720703125 length of segment : 34 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 193 time to create 1 rle with old method : 0.0002796649932861328 length of segment : 24 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.000240325927734375 length of segment : 8 time for calcul the mask position with numpy : 0.0003485679626464844 nb_pixel_total : 15348 time to create 1 rle with old method : 0.022666454315185547 length of segment : 207 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.0003483295440673828 length of segment : 28 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.28984 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 19 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 530 time to create 1 rle with old method : 0.0008175373077392578 length of segment : 48 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 562 time to create 1 rle with old method : 0.0008723735809326172 length of segment : 22 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 776 time to create 1 rle with old method : 0.0011518001556396484 length of segment : 51 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 467 time to create 1 rle with old method : 0.0007498264312744141 length of segment : 42 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.0003390312194824219 length of segment : 26 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 652 time to create 1 rle with old method : 0.000946044921875 length of segment : 44 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.00028586387634277344 length of segment : 20 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 512 time to create 1 rle with old method : 0.0007483959197998047 length of segment : 31 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 57 time to create 1 rle with old method : 0.00011038780212402344 length of segment : 15 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 827 time to create 1 rle with old method : 0.0012619495391845703 length of segment : 67 time for calcul the mask position with numpy : 0.00010371208190917969 nb_pixel_total : 584 time to create 1 rle with old method : 0.0010085105895996094 length of segment : 60 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 314 time to create 1 rle with old method : 0.0005125999450683594 length of segment : 23 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.00029540061950683594 length of segment : 21 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 2250 time to create 1 rle with old method : 0.003103494644165039 length of segment : 117 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 25 time to create 1 rle with old method : 8.702278137207031e-05 length of segment : 6 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 614 time to create 1 rle with old method : 0.0010311603546142578 length of segment : 50 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 670 time to create 1 rle with old method : 0.0009815692901611328 length of segment : 50 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 824 time to create 1 rle with old method : 0.0012204647064208984 length of segment : 38 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 300 time to create 1 rle with old method : 0.00043201446533203125 length of segment : 23 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.52031 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 609 time to create 1 rle with old method : 0.0007901191711425781 length of segment : 44 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 3788 time to create 1 rle with old method : 0.004638671875 length of segment : 78 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 2045 time to create 1 rle with old method : 0.0026998519897460938 length of segment : 41 time for calcul the mask position with numpy : 0.00037932395935058594 nb_pixel_total : 23623 time to create 1 rle with old method : 0.026231050491333008 length of segment : 412 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.14922 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 0.0001761913299560547 nb_pixel_total : 12264 time to create 1 rle with old method : 0.016846418380737305 length of segment : 94 time for calcul the mask position with numpy : 0.00023674964904785156 nb_pixel_total : 10540 time to create 1 rle with old method : 0.012523412704467773 length of segment : 262 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 210 time to create 1 rle with old method : 0.0003082752227783203 length of segment : 17 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.0003578662872314453 length of segment : 21 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 308 time to create 1 rle with old method : 0.0005340576171875 length of segment : 26 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 981 time to create 1 rle with old method : 0.0012047290802001953 length of segment : 58 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 238 time to create 1 rle with old method : 0.000370025634765625 length of segment : 16 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 210 time to create 1 rle with old method : 0.0003294944763183594 length of segment : 14 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.07266 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.00045299530029296875 length of segment : 17 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 897 time to create 1 rle with old method : 0.0012366771697998047 length of segment : 46 time for calcul the mask position with numpy : 0.0006749629974365234 nb_pixel_total : 58519 time to create 1 rle with old method : 0.06383419036865234 length of segment : 299 time for calcul the mask position with numpy : 0.0005960464477539062 nb_pixel_total : 43779 time to create 1 rle with old method : 0.04757118225097656 length of segment : 232 Processing 1 images image shape: (400, 400, 3) min: 15.00000 max: 219.00000 molded_images shape: (1, 640, 640, 3) min: -85.86797 max: 89.51250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -113.64141 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1352 time to create 1 rle with old method : 0.002235889434814453 length of segment : 31 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 341 time to create 1 rle with old method : 0.00046825408935546875 length of segment : 24 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 327 time to create 1 rle with old method : 0.000461578369140625 length of segment : 24 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 2943 time to create 1 rle with old method : 0.005479097366333008 length of segment : 58 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.00022363662719726562 length of segment : 16 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 1238 time to create 1 rle with old method : 0.0016834735870361328 length of segment : 33 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.95000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 113 time to create 1 rle with old method : 0.00017690658569335938 length of segment : 17 time for calcul the mask position with numpy : 0.00020933151245117188 nb_pixel_total : 12670 time to create 1 rle with old method : 0.014328479766845703 length of segment : 126 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 778 time to create 1 rle with old method : 0.0010750293731689453 length of segment : 34 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 939 time to create 1 rle with old method : 0.0012149810791015625 length of segment : 39 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0016398429870605469 length of segment : 49 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.0003676414489746094 length of segment : 14 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 413 time to create 1 rle with old method : 0.0006515979766845703 length of segment : 19 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 457 time to create 1 rle with old method : 0.0007746219635009766 length of segment : 25 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0015053749084472656 length of segment : 40 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 359 time to create 1 rle with old method : 0.0005421638488769531 length of segment : 26 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 280 time to create 1 rle with old method : 0.0005693435668945312 length of segment : 16 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.44609 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 586 time to create 1 rle with old method : 0.0007731914520263672 length of segment : 49 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 581 time to create 1 rle with old method : 0.0007758140563964844 length of segment : 37 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 3075 time to create 1 rle with old method : 0.003846406936645508 length of segment : 88 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 381 time to create 1 rle with old method : 0.0004949569702148438 length of segment : 25 time for calcul the mask position with numpy : 0.0006368160247802734 nb_pixel_total : 18831 time to create 1 rle with old method : 0.021713733673095703 length of segment : 340 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 667 time to create 1 rle with old method : 0.0009343624114990234 length of segment : 85 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 1288 time to create 1 rle with old method : 0.0014908313751220703 length of segment : 41 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 449 time to create 1 rle with old method : 0.0006425380706787109 length of segment : 28 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 743 time to create 1 rle with old method : 0.0009284019470214844 length of segment : 37 time for calcul the mask position with numpy : 0.00010514259338378906 nb_pixel_total : 1104 time to create 1 rle with old method : 0.0014417171478271484 length of segment : 124 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.53359 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 925 time to create 1 rle with old method : 0.0011341571807861328 length of segment : 51 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 1347 time to create 1 rle with old method : 0.0016150474548339844 length of segment : 55 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 965 time to create 1 rle with old method : 0.0012769699096679688 length of segment : 85 time for calcul the mask position with numpy : 0.00015687942504882812 nb_pixel_total : 2623 time to create 1 rle with old method : 0.0032994747161865234 length of segment : 184 time for calcul the mask position with numpy : 7.104873657226562e-05 nb_pixel_total : 1703 time to create 1 rle with old method : 0.0020461082458496094 length of segment : 85 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0007638931274414062 length of segment : 30 Processing 1 images image shape: (280, 400, 3) min: 14.00000 max: 199.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 81.74844 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009205341339111328 nb_pixel_total : 106909 time to create 1 rle with old method : 0.1139519214630127 length of segment : 282 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.66641 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002570152282714844 length of segment : 16 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 1475 time to create 1 rle with old method : 0.0017807483673095703 length of segment : 52 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 440 time to create 1 rle with old method : 0.0006008148193359375 length of segment : 34 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 535 time to create 1 rle with old method : 0.0007112026214599609 length of segment : 43 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 308 time to create 1 rle with old method : 0.00046324729919433594 length of segment : 26 time for calcul the mask position with numpy : 0.00011110305786132812 nb_pixel_total : 4378 time to create 1 rle with old method : 0.005175590515136719 length of segment : 132 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 2265 time to create 1 rle with old method : 0.0028276443481445312 length of segment : 45 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 911 time to create 1 rle with old method : 0.0011487007141113281 length of segment : 51 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 101 time to create 1 rle with old method : 0.00016736984252929688 length of segment : 13 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00020766258239746094 length of segment : 14 time for calcul the mask position with numpy : 0.00014257431030273438 nb_pixel_total : 5468 time to create 1 rle with old method : 0.0063974857330322266 length of segment : 128 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 1942 time to create 1 rle with old method : 0.0020537376403808594 length of segment : 98 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 508 time to create 1 rle with old method : 0.0006570816040039062 length of segment : 24 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 805 time to create 1 rle with old method : 0.0009717941284179688 length of segment : 55 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 236 time to create 1 rle with old method : 0.00032973289489746094 length of segment : 25 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 30 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.00024390220642089844 length of segment : 15 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 922 time to create 1 rle with old method : 0.0011665821075439453 length of segment : 47 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 973 time to create 1 rle with old method : 0.0012090206146240234 length of segment : 39 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 268 time to create 1 rle with old method : 0.0003974437713623047 length of segment : 15 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 1567 time to create 1 rle with old method : 0.002046346664428711 length of segment : 57 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 605 time to create 1 rle with old method : 0.0008184909820556641 length of segment : 34 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 499 time to create 1 rle with old method : 0.0006906986236572266 length of segment : 23 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.0004930496215820312 length of segment : 37 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 287 time to create 1 rle with old method : 0.00042366981506347656 length of segment : 16 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 1544 time to create 1 rle with old method : 0.0018455982208251953 length of segment : 73 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00020885467529296875 length of segment : 11 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 928 time to create 1 rle with old method : 0.0012302398681640625 length of segment : 39 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 266 time to create 1 rle with old method : 0.0003731250762939453 length of segment : 24 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 1344 time to create 1 rle with old method : 0.0016908645629882812 length of segment : 49 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 730 time to create 1 rle with old method : 0.0009632110595703125 length of segment : 25 time for calcul the mask position with numpy : 8.392333984375e-05 nb_pixel_total : 1570 time to create 1 rle with old method : 0.0019540786743164062 length of segment : 107 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 238 time to create 1 rle with old method : 0.00031447410583496094 length of segment : 23 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 549 time to create 1 rle with old method : 0.0007004737854003906 length of segment : 29 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005471706390380859 length of segment : 31 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 756 time to create 1 rle with old method : 0.0009233951568603516 length of segment : 41 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 338 time to create 1 rle with old method : 0.00043964385986328125 length of segment : 20 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 508 time to create 1 rle with old method : 0.0006253719329833984 length of segment : 47 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 1116 time to create 1 rle with old method : 0.0013475418090820312 length of segment : 53 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 1288 time to create 1 rle with old method : 0.0015382766723632812 length of segment : 63 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 209 time to create 1 rle with old method : 0.00033164024353027344 length of segment : 11 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 220 time to create 1 rle with old method : 0.0003063678741455078 length of segment : 19 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 359 time to create 1 rle with old method : 0.00047326087951660156 length of segment : 38 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 916 time to create 1 rle with old method : 0.0011243820190429688 length of segment : 38 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.0002911090850830078 length of segment : 18 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 954 time to create 1 rle with old method : 0.0011742115020751953 length of segment : 40 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 17 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 1240 time to create 1 rle with old method : 0.0015993118286132812 length of segment : 65 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 90 time to create 1 rle with old method : 0.00015354156494140625 length of segment : 23 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.00032901763916015625 length of segment : 27 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.0003108978271484375 length of segment : 26 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 88 time to create 1 rle with old method : 0.00015974044799804688 length of segment : 22 time for calcul the mask position with numpy : 0.00017642974853515625 nb_pixel_total : 7124 time to create 1 rle with old method : 0.008257389068603516 length of segment : 175 time for calcul the mask position with numpy : 0.00032019615173339844 nb_pixel_total : 10459 time to create 1 rle with old method : 0.013119697570800781 length of segment : 118 time for calcul the mask position with numpy : 0.00016641616821289062 nb_pixel_total : 10370 time to create 1 rle with old method : 0.010797262191772461 length of segment : 121 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 61 time to create 1 rle with old method : 0.00013685226440429688 length of segment : 5 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 428 time to create 1 rle with old method : 0.0005373954772949219 length of segment : 36 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 802 time to create 1 rle with old method : 0.0011372566223144531 length of segment : 29 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 546 time to create 1 rle with old method : 0.0007736682891845703 length of segment : 22 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 469 time to create 1 rle with old method : 0.0006253719329833984 length of segment : 34 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.00026106834411621094 length of segment : 36 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 532 time to create 1 rle with old method : 0.0007650852203369141 length of segment : 22 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.00019073486328125 length of segment : 26 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 489 time to create 1 rle with old method : 0.0006201267242431641 length of segment : 33 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 134.35000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 583 time to create 1 rle with old method : 0.0008058547973632812 length of segment : 47 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 537 time to create 1 rle with old method : 0.0007221698760986328 length of segment : 53 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.00031447410583496094 length of segment : 13 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 82 time to create 1 rle with old method : 0.00016236305236816406 length of segment : 11 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 67 time to create 1 rle with old method : 0.00011801719665527344 length of segment : 9 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.20000 max: 146.26406 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 290 time to create 1 rle with old method : 0.00043320655822753906 length of segment : 35 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.00045871734619140625 length of segment : 32 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 75 time to create 1 rle with old method : 0.0002002716064453125 length of segment : 21 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 265 time to create 1 rle with old method : 0.0006306171417236328 length of segment : 31 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 80 time to create 1 rle with old method : 0.0002205371856689453 length of segment : 20 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 287 time to create 1 rle with old method : 0.0006792545318603516 length of segment : 35 time for calcul the mask position with numpy : 8.20159912109375e-05 nb_pixel_total : 1557 time to create 1 rle with old method : 0.0033926963806152344 length of segment : 50 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 177 time to create 1 rle with old method : 0.0004553794860839844 length of segment : 29 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.09063 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 18 time for calcul the mask position with numpy : 0.00010967254638671875 nb_pixel_total : 2395 time to create 1 rle with old method : 0.003348112106323242 length of segment : 46 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0006835460662841797 length of segment : 24 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 434 time to create 1 rle with old method : 0.0007276535034179688 length of segment : 33 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 45 time to create 1 rle with old method : 0.0001010894775390625 length of segment : 6 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 28 time to create 1 rle with old method : 6.771087646484375e-05 length of segment : 6 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 25 time to create 1 rle with old method : 7.05718994140625e-05 length of segment : 5 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 227 time to create 1 rle with old method : 0.0003802776336669922 length of segment : 49 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 116 time to create 1 rle with old method : 0.000225067138671875 length of segment : 14 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 35 time to create 1 rle with old method : 8.368492126464844e-05 length of segment : 9 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 78 time to create 1 rle with old method : 0.0002143383026123047 length of segment : 6 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 122 time to create 1 rle with old method : 0.00021958351135253906 length of segment : 19 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 24 time to create 1 rle with old method : 6.389617919921875e-05 length of segment : 5 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 1011 time to create 1 rle with old method : 0.0012712478637695312 length of segment : 57 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 97 time to create 1 rle with old method : 0.00021791458129882812 length of segment : 19 time for calcul the mask position with numpy : 0.00012421607971191406 nb_pixel_total : 523 time to create 1 rle with old method : 0.0007948875427246094 length of segment : 19 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 59 time to create 1 rle with old method : 0.00010418891906738281 length of segment : 23 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 303 time to create 1 rle with old method : 0.0005695819854736328 length of segment : 24 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 803 time to create 1 rle with old method : 0.0011010169982910156 length of segment : 38 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.29766 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 8.511543273925781e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.0003514289855957031 length of segment : 14 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 783 time to create 1 rle with old method : 0.0011420249938964844 length of segment : 43 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 611 time to create 1 rle with old method : 0.0009551048278808594 length of segment : 41 time for calcul the mask position with numpy : 0.00013971328735351562 nb_pixel_total : 5964 time to create 1 rle with old method : 0.007108211517333984 length of segment : 104 time for calcul the mask position with numpy : 0.00013589859008789062 nb_pixel_total : 3998 time to create 1 rle with old method : 0.0047948360443115234 length of segment : 80 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0006687641143798828 length of segment : 37 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 507 time to create 1 rle with old method : 0.0006852149963378906 length of segment : 30 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 163 time to create 1 rle with old method : 0.0002579689025878906 length of segment : 15 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 475 time to create 1 rle with old method : 0.0006279945373535156 length of segment : 38 time for calcul the mask position with numpy : 0.00017380714416503906 nb_pixel_total : 5425 time to create 1 rle with old method : 0.0068645477294921875 length of segment : 125 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 1291 time to create 1 rle with old method : 0.0015590190887451172 length of segment : 80 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 132 time to create 1 rle with old method : 0.00023484230041503906 length of segment : 16 time for calcul the mask position with numpy : 0.00010752677917480469 nb_pixel_total : 3401 time to create 1 rle with old method : 0.005452871322631836 length of segment : 43 time for calcul the mask position with numpy : 6.794929504394531e-05 nb_pixel_total : 2599 time to create 1 rle with old method : 0.00406646728515625 length of segment : 57 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -113.30156 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 0.00016689300537109375 nb_pixel_total : 10934 time to create 1 rle with old method : 0.012370586395263672 length of segment : 88 time for calcul the mask position with numpy : 0.0003380775451660156 nb_pixel_total : 9566 time to create 1 rle with old method : 0.011326789855957031 length of segment : 212 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.00030303001403808594 length of segment : 16 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.0005359649658203125 length of segment : 20 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 112 time to create 1 rle with old method : 0.0002033710479736328 length of segment : 12 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.0002884864807128906 length of segment : 11 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.90469 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002684593200683594 length of segment : 12 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 657 time to create 1 rle with old method : 0.0009284019470214844 length of segment : 39 time for calcul the mask position with numpy : 0.0001556873321533203 nb_pixel_total : 1891 time to create 1 rle with old method : 0.003077983856201172 length of segment : 192 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 192 time to create 1 rle with old method : 0.0003905296325683594 length of segment : 22 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 719 time to create 1 rle with old method : 0.0010797977447509766 length of segment : 36 time for calcul the mask position with numpy : 0.0007600784301757812 nb_pixel_total : 45658 time to create 1 rle with old method : 0.06674599647521973 length of segment : 298 time for calcul the mask position with numpy : 0.0008642673492431641 nb_pixel_total : 58929 time to create 1 rle with old method : 0.06546449661254883 length of segment : 331 Processing 1 images image shape: (400, 400, 3) min: 28.00000 max: 223.00000 molded_images shape: (1, 640, 640, 3) min: -84.39531 max: 98.63359 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -111.37969 max: 150.51406 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 366 time to create 1 rle with old method : 0.0005133152008056641 length of segment : 21 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 597 time to create 1 rle with old method : 0.0007786750793457031 length of segment : 30 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 237 time to create 1 rle with old method : 0.0003070831298828125 length of segment : 26 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 1369 time to create 1 rle with old method : 0.0017552375793457031 length of segment : 37 time for calcul the mask position with numpy : 0.000263214111328125 nb_pixel_total : 8722 time to create 1 rle with old method : 0.009783267974853516 length of segment : 153 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 100 time to create 1 rle with old method : 0.0002071857452392578 length of segment : 14 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 381 time to create 1 rle with old method : 0.0005438327789306641 length of segment : 18 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 346 time to create 1 rle with old method : 0.0005414485931396484 length of segment : 19 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 605 time to create 1 rle with old method : 0.0008320808410644531 length of segment : 26 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 425 time to create 1 rle with old method : 0.0006237030029296875 length of segment : 47 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 149 time to create 1 rle with old method : 0.00022554397583007812 length of segment : 16 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1964 time to create 1 rle with old method : 0.002671480178833008 length of segment : 49 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.55547 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 0.00022292137145996094 nb_pixel_total : 13843 time to create 1 rle with old method : 0.016039371490478516 length of segment : 121 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 1511 time to create 1 rle with old method : 0.0025484561920166016 length of segment : 35 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 918 time to create 1 rle with old method : 0.0011944770812988281 length of segment : 39 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 326 time to create 1 rle with old method : 0.0006225109100341797 length of segment : 24 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 341 time to create 1 rle with old method : 0.0007116794586181641 length of segment : 16 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 528 time to create 1 rle with old method : 0.000835418701171875 length of segment : 33 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 1281 time to create 1 rle with old method : 0.0018630027770996094 length of segment : 27 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 741 time to create 1 rle with old method : 0.0012156963348388672 length of segment : 35 time for calcul the mask position with numpy : 9.250640869140625e-05 nb_pixel_total : 773 time to create 1 rle with old method : 0.001499176025390625 length of segment : 47 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.00038909912109375 length of segment : 14 time for calcul the mask position with numpy : 0.0001316070556640625 nb_pixel_total : 2249 time to create 1 rle with old method : 0.0035140514373779297 length of segment : 123 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 380 time to create 1 rle with old method : 0.0008740425109863281 length of segment : 22 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0006868839263916016 length of segment : 16 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 910 time to create 1 rle with old method : 0.0015568733215332031 length of segment : 37 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.59063 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 0.00012087821960449219 nb_pixel_total : 4655 time to create 1 rle with old method : 0.007131814956665039 length of segment : 112 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 699 time to create 1 rle with old method : 0.0009171962738037109 length of segment : 58 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.000209808349609375 length of segment : 12 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 642 time to create 1 rle with old method : 0.0008187294006347656 length of segment : 34 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 1285 time to create 1 rle with old method : 0.00157928466796875 length of segment : 40 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 297 time to create 1 rle with old method : 0.00043582916259765625 length of segment : 24 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.79141 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 1514 time to create 1 rle with old method : 0.0022025108337402344 length of segment : 58 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.00028061866760253906 length of segment : 10 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 966 time to create 1 rle with old method : 0.001451730728149414 length of segment : 52 time for calcul the mask position with numpy : 0.00012350082397460938 nb_pixel_total : 873 time to create 1 rle with old method : 0.0014941692352294922 length of segment : 97 time for calcul the mask position with numpy : 9.465217590332031e-05 nb_pixel_total : 1245 time to create 1 rle with old method : 0.0020279884338378906 length of segment : 76 time for calcul the mask position with numpy : 0.00010895729064941406 nb_pixel_total : 1507 time to create 1 rle with old method : 0.0024635791778564453 length of segment : 78 time for calcul the mask position with numpy : 0.00027370452880859375 nb_pixel_total : 2887 time to create 1 rle with old method : 0.004947662353515625 length of segment : 184 time for calcul the mask position with numpy : 0.00012111663818359375 nb_pixel_total : 333 time to create 1 rle with old method : 0.000946044921875 length of segment : 52 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 619 time to create 1 rle with old method : 0.0009834766387939453 length of segment : 42 time for calcul the mask position with numpy : 0.00014781951904296875 nb_pixel_total : 2389 time to create 1 rle with old method : 0.0028853416442871094 length of segment : 164 Processing 1 images image shape: (280, 400, 3) min: 14.00000 max: 203.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 86.56094 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0011539459228515625 nb_pixel_total : 106943 time to create 1 rle with old method : 0.13684916496276855 length of segment : 282 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.05312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.0003561973571777344 length of segment : 17 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 381 time to create 1 rle with old method : 0.0005240440368652344 length of segment : 33 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1495 time to create 1 rle with old method : 0.0019183158874511719 length of segment : 51 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 523 time to create 1 rle with old method : 0.0006501674652099609 length of segment : 44 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 510 time to create 1 rle with old method : 0.0007276535034179688 length of segment : 24 time for calcul the mask position with numpy : 0.00030350685119628906 nb_pixel_total : 13211 time to create 1 rle with old method : 0.015438556671142578 length of segment : 219 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 95 time to create 1 rle with old method : 0.00017642974853515625 length of segment : 13 time for calcul the mask position with numpy : 9.799003601074219e-05 nb_pixel_total : 3147 time to create 1 rle with old method : 0.0037069320678710938 length of segment : 137 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 299 time to create 1 rle with old method : 0.0004305839538574219 length of segment : 24 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1197 time to create 1 rle with old method : 0.0014688968658447266 length of segment : 60 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 2176 time to create 1 rle with old method : 0.0027725696563720703 length of segment : 51 time for calcul the mask position with numpy : 0.00011730194091796875 nb_pixel_total : 3749 time to create 1 rle with old method : 0.005553483963012695 length of segment : 73 time for calcul the mask position with numpy : 0.00014352798461914062 nb_pixel_total : 2282 time to create 1 rle with old method : 0.003793001174926758 length of segment : 88 time for calcul the mask position with numpy : 9.703636169433594e-05 nb_pixel_total : 2163 time to create 1 rle with old method : 0.003763437271118164 length of segment : 93 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 34 time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 662 time to create 1 rle with old method : 0.0008955001831054688 length of segment : 35 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 573 time to create 1 rle with old method : 0.0007181167602539062 length of segment : 31 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 968 time to create 1 rle with old method : 0.0012049674987792969 length of segment : 39 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 1580 time to create 1 rle with old method : 0.002072572708129883 length of segment : 56 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 307 time to create 1 rle with old method : 0.0004715919494628906 length of segment : 16 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.00023627281188964844 length of segment : 15 time for calcul the mask position with numpy : 9.393692016601562e-05 nb_pixel_total : 1750 time to create 1 rle with old method : 0.002062082290649414 length of segment : 83 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.0003952980041503906 length of segment : 23 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 349 time to create 1 rle with old method : 0.0005042552947998047 length of segment : 36 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 1354 time to create 1 rle with old method : 0.0016732215881347656 length of segment : 60 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 580 time to create 1 rle with old method : 0.0011157989501953125 length of segment : 32 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 356 time to create 1 rle with old method : 0.0007276535034179688 length of segment : 24 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 498 time to create 1 rle with old method : 0.0007727146148681641 length of segment : 22 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.00030875205993652344 length of segment : 33 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 1290 time to create 1 rle with old method : 0.002196073532104492 length of segment : 67 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 895 time to create 1 rle with old method : 0.0012061595916748047 length of segment : 39 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.00046825408935546875 length of segment : 20 time for calcul the mask position with numpy : 0.00011277198791503906 nb_pixel_total : 2185 time to create 1 rle with old method : 0.0037288665771484375 length of segment : 57 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.00031113624572753906 length of segment : 12 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 877 time to create 1 rle with old method : 0.0015914440155029297 length of segment : 38 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 232 time to create 1 rle with old method : 0.0004508495330810547 length of segment : 18 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 628 time to create 1 rle with old method : 0.0011434555053710938 length of segment : 35 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.00029778480529785156 length of segment : 17 time for calcul the mask position with numpy : 8.58306884765625e-05 nb_pixel_total : 2290 time to create 1 rle with old method : 0.0032367706298828125 length of segment : 90 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 457 time to create 1 rle with old method : 0.0006289482116699219 length of segment : 29 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 1437 time to create 1 rle with old method : 0.002373933792114258 length of segment : 47 time for calcul the mask position with numpy : 7.939338684082031e-05 nb_pixel_total : 1434 time to create 1 rle with old method : 0.0023348331451416016 length of segment : 49 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 998 time to create 1 rle with old method : 0.0017960071563720703 length of segment : 39 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 322 time to create 1 rle with old method : 0.0005829334259033203 length of segment : 36 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 667 time to create 1 rle with old method : 0.0010304450988769531 length of segment : 36 time for calcul the mask position with numpy : 8.249282836914062e-05 nb_pixel_total : 1655 time to create 1 rle with old method : 0.0025734901428222656 length of segment : 70 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 1086 time to create 1 rle with old method : 0.0016808509826660156 length of segment : 42 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 195 time to create 1 rle with old method : 0.0004010200500488281 length of segment : 16 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 523 time to create 1 rle with old method : 0.0009431838989257812 length of segment : 37 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 24 time for calcul the mask position with numpy : 9.703636169433594e-05 nb_pixel_total : 1413 time to create 1 rle with old method : 0.0017018318176269531 length of segment : 66 time for calcul the mask position with numpy : 0.0003218650817871094 nb_pixel_total : 10673 time to create 1 rle with old method : 0.015615224838256836 length of segment : 120 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.00041985511779785156 length of segment : 13 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 308 time to create 1 rle with old method : 0.0005431175231933594 length of segment : 30 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 60 time to create 1 rle with old method : 0.00014066696166992188 length of segment : 21 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 230 time to create 1 rle with old method : 0.0003142356872558594 length of segment : 28 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 1354 time to create 1 rle with old method : 0.002239227294921875 length of segment : 69 time for calcul the mask position with numpy : 0.00013065338134765625 nb_pixel_total : 486 time to create 1 rle with old method : 0.0010578632354736328 length of segment : 47 time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.0003008842468261719 length of segment : 24 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 413 time to create 1 rle with old method : 0.0008068084716796875 length of segment : 27 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.00025272369384765625 length of segment : 29 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0005509853363037109 length of segment : 42 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 484 time to create 1 rle with old method : 0.0008978843688964844 length of segment : 23 time for calcul the mask position with numpy : 0.00026035308837890625 nb_pixel_total : 8192 time to create 1 rle with old method : 0.010574817657470703 length of segment : 171 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.00043511390686035156 length of segment : 22 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 1388 time to create 1 rle with old method : 0.002474546432495117 length of segment : 58 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 514 time to create 1 rle with old method : 0.0009267330169677734 length of segment : 32 time for calcul the mask position with numpy : 8.344650268554688e-05 nb_pixel_total : 1571 time to create 1 rle with old method : 0.0027229785919189453 length of segment : 36 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00020170211791992188 length of segment : 8 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 144 time to create 1 rle with old method : 0.00028705596923828125 length of segment : 17 time for calcul the mask position with numpy : 0.0001308917999267578 nb_pixel_total : 2206 time to create 1 rle with old method : 0.0034322738647460938 length of segment : 123 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 63 time to create 1 rle with old method : 0.00014734268188476562 length of segment : 6 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005633831024169922 length of segment : 52 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.0002474784851074219 length of segment : 12 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 135.72500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 620 time to create 1 rle with old method : 0.001129150390625 length of segment : 50 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 493 time to create 1 rle with old method : 0.0009796619415283203 length of segment : 54 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0004494190216064453 length of segment : 13 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 76 time to create 1 rle with old method : 0.000186920166015625 length of segment : 9 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 69 time to create 1 rle with old method : 0.0002167224884033203 length of segment : 9 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 31 time to create 1 rle with old method : 0.00014901161193847656 length of segment : 6 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.59844 max: 145.43984 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.00035762786865234375 length of segment : 27 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 44 time to create 1 rle with old method : 9.608268737792969e-05 length of segment : 18 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002663135528564453 length of segment : 36 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 281 time to create 1 rle with old method : 0.0004780292510986328 length of segment : 11 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 222 time to create 1 rle with old method : 0.000308990478515625 length of segment : 28 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 205 time to create 1 rle with old method : 0.00031256675720214844 length of segment : 31 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 249 time to create 1 rle with old method : 0.00035643577575683594 length of segment : 31 time for calcul the mask position with numpy : 0.00018310546875 nb_pixel_total : 4093 time to create 1 rle with old method : 0.005063295364379883 length of segment : 107 time for calcul the mask position with numpy : 7.104873657226562e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.0003266334533691406 length of segment : 27 time for calcul the mask position with numpy : 9.036064147949219e-05 nb_pixel_total : 1448 time to create 1 rle with old method : 0.0018999576568603516 length of segment : 52 time for calcul the mask position with numpy : 0.0003452301025390625 nb_pixel_total : 5630 time to create 1 rle with old method : 0.006974458694458008 length of segment : 141 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.66875 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00019288063049316406 length of segment : 8 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.00020384788513183594 length of segment : 16 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 971 time to create 1 rle with old method : 0.0015552043914794922 length of segment : 35 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.0003292560577392578 length of segment : 20 time for calcul the mask position with numpy : 0.00014519691467285156 nb_pixel_total : 3647 time to create 1 rle with old method : 0.005633831024169922 length of segment : 60 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 83 time to create 1 rle with old method : 0.00021409988403320312 length of segment : 9 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 331 time to create 1 rle with old method : 0.00042057037353515625 length of segment : 46 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 2267 time to create 1 rle with old method : 0.002719402313232422 length of segment : 48 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 30 time to create 1 rle with old method : 6.556510925292969e-05 length of segment : 6 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 1003 time to create 1 rle with old method : 0.0013096332550048828 length of segment : 28 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 25 time to create 1 rle with old method : 6.079673767089844e-05 length of segment : 5 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 715 time to create 1 rle with old method : 0.0008256435394287109 length of segment : 34 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 993 time to create 1 rle with old method : 0.0012772083282470703 length of segment : 84 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.0002474784851074219 length of segment : 16 time for calcul the mask position with numpy : 6.794929504394531e-05 nb_pixel_total : 2227 time to create 1 rle with old method : 0.002695798873901367 length of segment : 44 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.28984 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 1359 time to create 1 rle with old method : 0.002160310745239258 length of segment : 59 time for calcul the mask position with numpy : 0.0001347064971923828 nb_pixel_total : 6209 time to create 1 rle with old method : 0.008939027786254883 length of segment : 103 time for calcul the mask position with numpy : 0.0001125335693359375 nb_pixel_total : 2707 time to create 1 rle with old method : 0.0041675567626953125 length of segment : 56 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 934 time to create 1 rle with old method : 0.0013365745544433594 length of segment : 58 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 2346 time to create 1 rle with old method : 0.0039594173431396484 length of segment : 64 time for calcul the mask position with numpy : 0.00017905235290527344 nb_pixel_total : 4540 time to create 1 rle with old method : 0.006609439849853516 length of segment : 128 time for calcul the mask position with numpy : 0.00011849403381347656 nb_pixel_total : 2595 time to create 1 rle with old method : 0.004681110382080078 length of segment : 44 time for calcul the mask position with numpy : 7.82012939453125e-05 nb_pixel_total : 1001 time to create 1 rle with old method : 0.0017838478088378906 length of segment : 66 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.86016 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 0.00016951560974121094 nb_pixel_total : 11493 time to create 1 rle with old method : 0.01385951042175293 length of segment : 93 time for calcul the mask position with numpy : 0.0002639293670654297 nb_pixel_total : 10036 time to create 1 rle with old method : 0.013849735260009766 length of segment : 221 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.00038242340087890625 length of segment : 16 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 112 time to create 1 rle with old method : 0.00027251243591308594 length of segment : 12 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.0002758502960205078 length of segment : 9 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 1196 time to create 1 rle with old method : 0.0018947124481201172 length of segment : 58 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 1171 time to create 1 rle with old method : 0.0020134449005126953 length of segment : 60 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 1130 time to create 1 rle with old method : 0.0021126270294189453 length of segment : 57 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.08437 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.00039958953857421875 length of segment : 12 time for calcul the mask position with numpy : 0.00011396408081054688 nb_pixel_total : 1965 time to create 1 rle with old method : 0.0032608509063720703 length of segment : 181 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 830 time to create 1 rle with old method : 0.0010852813720703125 length of segment : 41 time for calcul the mask position with numpy : 0.00012946128845214844 nb_pixel_total : 5829 time to create 1 rle with old method : 0.007220029830932617 length of segment : 68 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 874 time to create 1 rle with old method : 0.0011649131774902344 length of segment : 43 Processing 1 images image shape: (400, 400, 3) min: 23.00000 max: 220.00000 molded_images shape: (1, 640, 640, 3) min: -84.70000 max: 94.16094 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -109.69609 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.00034356117248535156 length of segment : 12 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 368 time to create 1 rle with old method : 0.0005457401275634766 length of segment : 20 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 315 time to create 1 rle with old method : 0.0004961490631103516 length of segment : 17 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 2417 time to create 1 rle with old method : 0.0030918121337890625 length of segment : 51 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 324 time to create 1 rle with old method : 0.0004451274871826172 length of segment : 23 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.000186920166015625 length of segment : 11 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 1322 time to create 1 rle with old method : 0.0017502307891845703 length of segment : 36 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.21563 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 8.821487426757812e-05 nb_pixel_total : 197 time to create 1 rle with old method : 0.0003349781036376953 length of segment : 14 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 906 time to create 1 rle with old method : 0.0012288093566894531 length of segment : 38 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 1400 time to create 1 rle with old method : 0.0019059181213378906 length of segment : 31 time for calcul the mask position with numpy : 0.0003554821014404297 nb_pixel_total : 16273 time to create 1 rle with old method : 0.01942896842956543 length of segment : 148 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 274 time to create 1 rle with old method : 0.00038170814514160156 length of segment : 16 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 532 time to create 1 rle with old method : 0.0008065700531005859 length of segment : 32 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 407 time to create 1 rle with old method : 0.0006747245788574219 length of segment : 18 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 542 time to create 1 rle with old method : 0.0006771087646484375 length of segment : 33 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.0002155303955078125 length of segment : 20 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 891 time to create 1 rle with old method : 0.0012285709381103516 length of segment : 54 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 731 time to create 1 rle with old method : 0.0008449554443359375 length of segment : 34 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 1266 time to create 1 rle with old method : 0.0015370845794677734 length of segment : 28 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 370 time to create 1 rle with old method : 0.0008587837219238281 length of segment : 19 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.81328 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 1110 time to create 1 rle with old method : 0.001646280288696289 length of segment : 54 time for calcul the mask position with numpy : 9.751319885253906e-05 nb_pixel_total : 3526 time to create 1 rle with old method : 0.005187034606933594 length of segment : 70 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 318 time to create 1 rle with old method : 0.00044155120849609375 length of segment : 21 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 652 time to create 1 rle with old method : 0.0009686946868896484 length of segment : 42 time for calcul the mask position with numpy : 0.0004246234893798828 nb_pixel_total : 14915 time to create 1 rle with old method : 0.017941951751708984 length of segment : 227 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 740 time to create 1 rle with old method : 0.0010328292846679688 length of segment : 24 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 647 time to create 1 rle with old method : 0.0007402896881103516 length of segment : 34 time for calcul the mask position with numpy : 2.86102294921875e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.00019669532775878906 length of segment : 12 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 1275 time to create 1 rle with old method : 0.0014655590057373047 length of segment : 40 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.61172 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 1362 time to create 1 rle with old method : 0.0020346641540527344 length of segment : 58 time for calcul the mask position with numpy : 0.00010228157043457031 nb_pixel_total : 1859 time to create 1 rle with old method : 0.002821683883666992 length of segment : 104 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 244 time to create 1 rle with old method : 0.0005221366882324219 length of segment : 56 time for calcul the mask position with numpy : 0.00017404556274414062 nb_pixel_total : 3024 time to create 1 rle with old method : 0.0038292407989501953 length of segment : 186 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 1411 time to create 1 rle with old method : 0.0023071765899658203 length of segment : 47 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 914 time to create 1 rle with old method : 0.00144195556640625 length of segment : 49 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00020170211791992188 length of segment : 16 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 1000 time to create 1 rle with old method : 0.0014090538024902344 length of segment : 61 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 874 time to create 1 rle with old method : 0.0013718605041503906 length of segment : 52 time for calcul the mask position with numpy : 0.0001690387725830078 nb_pixel_total : 2390 time to create 1 rle with old method : 0.0032601356506347656 length of segment : 162 Processing 1 images image shape: (280, 400, 3) min: 17.00000 max: 200.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 79.83047 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009198188781738281 nb_pixel_total : 106694 time to create 1 rle with old method : 0.11181402206420898 length of segment : 283 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.000301361083984375 length of segment : 15 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005507469177246094 length of segment : 32 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 517 time to create 1 rle with old method : 0.0006961822509765625 length of segment : 22 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 1110 time to create 1 rle with old method : 0.0012772083282470703 length of segment : 43 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 1076 time to create 1 rle with old method : 0.0012500286102294922 length of segment : 69 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 488 time to create 1 rle with old method : 0.0005834102630615234 length of segment : 41 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.00043654441833496094 length of segment : 27 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 3208 time to create 1 rle with old method : 0.003885984420776367 length of segment : 67 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 2818 time to create 1 rle with old method : 0.003620624542236328 length of segment : 86 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 1453 time to create 1 rle with old method : 0.0016508102416992188 length of segment : 51 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 36 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 814 time to create 1 rle with old method : 0.0009598731994628906 length of segment : 42 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 287 time to create 1 rle with old method : 0.00036978721618652344 length of segment : 22 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 270 time to create 1 rle with old method : 0.0004076957702636719 length of segment : 15 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 459 time to create 1 rle with old method : 0.0006215572357177734 length of segment : 28 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 440 time to create 1 rle with old method : 0.0005967617034912109 length of segment : 25 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 678 time to create 1 rle with old method : 0.0008821487426757812 length of segment : 33 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 579 time to create 1 rle with old method : 0.0014121532440185547 length of segment : 34 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 892 time to create 1 rle with old method : 0.001207113265991211 length of segment : 36 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 1565 time to create 1 rle with old method : 0.0017580986022949219 length of segment : 52 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 1217 time to create 1 rle with old method : 0.0015158653259277344 length of segment : 49 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 1384 time to create 1 rle with old method : 0.001649618148803711 length of segment : 57 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 1449 time to create 1 rle with old method : 0.0016727447509765625 length of segment : 48 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 195 time to create 1 rle with old method : 0.00026798248291015625 length of segment : 17 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 150 time to create 1 rle with old method : 0.00023651123046875 length of segment : 15 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 324 time to create 1 rle with old method : 0.00042319297790527344 length of segment : 31 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 197 time to create 1 rle with old method : 0.0003082752227783203 length of segment : 33 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 685 time to create 1 rle with old method : 0.0008194446563720703 length of segment : 37 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 883 time to create 1 rle with old method : 0.0010585784912109375 length of segment : 35 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 624 time to create 1 rle with old method : 0.0008127689361572266 length of segment : 34 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 352 time to create 1 rle with old method : 0.0004572868347167969 length of segment : 35 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1656 time to create 1 rle with old method : 0.0019478797912597656 length of segment : 65 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 1374 time to create 1 rle with old method : 0.001567840576171875 length of segment : 52 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 358 time to create 1 rle with old method : 0.000457763671875 length of segment : 37 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.0002613067626953125 length of segment : 32 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.0003097057342529297 length of segment : 10 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 420 time to create 1 rle with old method : 0.0005142688751220703 length of segment : 29 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 1663 time to create 1 rle with old method : 0.0019371509552001953 length of segment : 66 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 816 time to create 1 rle with old method : 0.0009620189666748047 length of segment : 37 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 1577 time to create 1 rle with old method : 0.0018513202667236328 length of segment : 60 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.00025010108947753906 length of segment : 20 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 1594 time to create 1 rle with old method : 0.0021436214447021484 length of segment : 73 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 1520 time to create 1 rle with old method : 0.001720428466796875 length of segment : 86 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 1478 time to create 1 rle with old method : 0.0017044544219970703 length of segment : 52 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 589 time to create 1 rle with old method : 0.00079345703125 length of segment : 35 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 1380 time to create 1 rle with old method : 0.0016083717346191406 length of segment : 49 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 562 time to create 1 rle with old method : 0.0007638931274414062 length of segment : 32 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 24 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 866 time to create 1 rle with old method : 0.0010743141174316406 length of segment : 39 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 1089 time to create 1 rle with old method : 0.005639076232910156 length of segment : 56 time for calcul the mask position with numpy : 8.821487426757812e-05 nb_pixel_total : 304 time to create 1 rle with old method : 0.0006053447723388672 length of segment : 27 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 492 time to create 1 rle with old method : 0.0009026527404785156 length of segment : 52 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.0003132820129394531 length of segment : 16 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0010750293731689453 length of segment : 47 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.0004913806915283203 length of segment : 32 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 529 time to create 1 rle with old method : 0.0010001659393310547 length of segment : 41 time for calcul the mask position with numpy : 0.0001480579376220703 nb_pixel_total : 3139 time to create 1 rle with old method : 0.003976583480834961 length of segment : 152 time for calcul the mask position with numpy : 0.0001990795135498047 nb_pixel_total : 10768 time to create 1 rle with old method : 0.012199878692626953 length of segment : 116 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.0003466606140136719 length of segment : 18 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.00031566619873046875 length of segment : 27 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 579 time to create 1 rle with old method : 0.000858306884765625 length of segment : 24 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 898 time to create 1 rle with old method : 0.001165151596069336 length of segment : 37 time for calcul the mask position with numpy : 0.0001609325408935547 nb_pixel_total : 9039 time to create 1 rle with old method : 0.010249853134155273 length of segment : 107 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.0001957416534423828 length of segment : 28 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.0003046989440917969 length of segment : 25 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 451 time to create 1 rle with old method : 0.0006668567657470703 length of segment : 83 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.0002887248992919922 length of segment : 13 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 145 time to create 1 rle with old method : 0.0002281665802001953 length of segment : 30 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 474 time to create 1 rle with old method : 0.0006995201110839844 length of segment : 24 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 241 time to create 1 rle with old method : 0.0006318092346191406 length of segment : 19 time for calcul the mask position with numpy : 6.794929504394531e-05 nb_pixel_total : 423 time to create 1 rle with old method : 0.0010924339294433594 length of segment : 23 time for calcul the mask position with numpy : 8.821487426757812e-05 nb_pixel_total : 1920 time to create 1 rle with old method : 0.0023419857025146484 length of segment : 87 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 131.78750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 604 time to create 1 rle with old method : 0.0012538433074951172 length of segment : 49 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 545 time to create 1 rle with old method : 0.0009427070617675781 length of segment : 51 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.0004315376281738281 length of segment : 14 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 74 time to create 1 rle with old method : 0.000186920166015625 length of segment : 10 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.0005505084991455078 length of segment : 24 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 55 time to create 1 rle with old method : 0.00017547607421875 length of segment : 8 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 28 time to create 1 rle with old method : 0.0001125335693359375 length of segment : 6 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.83672 max: 147.08437 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 63 time to create 1 rle with old method : 0.0001049041748046875 length of segment : 16 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003197193145751953 length of segment : 40 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.0002772808074951172 length of segment : 27 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 272 time to create 1 rle with old method : 0.0004913806915283203 length of segment : 29 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.0004425048828125 length of segment : 36 time for calcul the mask position with numpy : 0.00015544891357421875 nb_pixel_total : 3520 time to create 1 rle with old method : 0.00455474853515625 length of segment : 143 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 128 time to create 1 rle with old method : 0.00031304359436035156 length of segment : 44 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 268 time to create 1 rle with old method : 0.0004725456237792969 length of segment : 37 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.0003237724304199219 length of segment : 27 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -112.87969 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 20 time for calcul the mask position with numpy : 9.369850158691406e-05 nb_pixel_total : 2509 time to create 1 rle with old method : 0.0031218528747558594 length of segment : 54 time for calcul the mask position with numpy : 0.0001461505889892578 nb_pixel_total : 2614 time to create 1 rle with old method : 0.0035033226013183594 length of segment : 47 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.000331878662109375 length of segment : 20 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 28 time to create 1 rle with old method : 7.700920104980469e-05 length of segment : 10 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.0002124309539794922 length of segment : 19 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 83 time to create 1 rle with old method : 0.00022721290588378906 length of segment : 16 time for calcul the mask position with numpy : 2.7418136596679688e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.00018548965454101562 length of segment : 8 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 66 time to create 1 rle with old method : 0.00010323524475097656 length of segment : 31 time for calcul the mask position with numpy : 2.5510787963867188e-05 nb_pixel_total : 27 time to create 1 rle with old method : 5.555152893066406e-05 length of segment : 6 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 900 time to create 1 rle with old method : 0.0011289119720458984 length of segment : 40 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.0002167224884033203 length of segment : 10 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 860 time to create 1 rle with old method : 0.0010881423950195312 length of segment : 40 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.0001659393310546875 length of segment : 16 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 1868 time to create 1 rle with old method : 0.0024080276489257812 length of segment : 82 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 21 time to create 1 rle with old method : 6.008148193359375e-05 length of segment : 4 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001633167266845703 length of segment : 22 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.0002090930938720703 length of segment : 15 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 32 time to create 1 rle with old method : 7.867813110351562e-05 length of segment : 6 time for calcul the mask position with numpy : 2.8371810913085938e-05 nb_pixel_total : 59 time to create 1 rle with old method : 0.0001404285430908203 length of segment : 7 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 1581 time to create 1 rle with old method : 0.0019114017486572266 length of segment : 77 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.28203 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 3078 time to create 1 rle with old method : 0.0037310123443603516 length of segment : 46 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 1151 time to create 1 rle with old method : 0.0013706684112548828 length of segment : 53 time for calcul the mask position with numpy : 0.000102996826171875 nb_pixel_total : 6221 time to create 1 rle with old method : 0.00762629508972168 length of segment : 103 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 662 time to create 1 rle with old method : 0.0008018016815185547 length of segment : 63 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 91 time to create 1 rle with old method : 0.0002913475036621094 length of segment : 7 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 847 time to create 1 rle with old method : 0.0015065670013427734 length of segment : 26 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.64531 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.00022411346435546875 nb_pixel_total : 11749 time to create 1 rle with old method : 0.014245033264160156 length of segment : 241 time for calcul the mask position with numpy : 0.00018548965454101562 nb_pixel_total : 11597 time to create 1 rle with old method : 0.013898849487304688 length of segment : 93 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 389 time to create 1 rle with old method : 0.0008833408355712891 length of segment : 24 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 825 time to create 1 rle with old method : 0.0016481876373291016 length of segment : 53 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 193 time to create 1 rle with old method : 0.0004036426544189453 length of segment : 17 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 101 time to create 1 rle with old method : 0.00024390220642089844 length of segment : 9 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 1166 time to create 1 rle with old method : 0.0021474361419677734 length of segment : 57 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0007174015045166016 length of segment : 23 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 808 time to create 1 rle with old method : 0.001033782958984375 length of segment : 40 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.00017499923706054688 length of segment : 11 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 1286 time to create 1 rle with old method : 0.0014760494232177734 length of segment : 151 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 962 time to create 1 rle with old method : 0.0012063980102539062 length of segment : 40 time for calcul the mask position with numpy : 0.0001316070556640625 nb_pixel_total : 7323 time to create 1 rle with old method : 0.008301258087158203 length of segment : 64 time for calcul the mask position with numpy : 0.0004687309265136719 nb_pixel_total : 36943 time to create 1 rle with old method : 0.04285073280334473 length of segment : 235 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00018262863159179688 length of segment : 10 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 801 time to create 1 rle with old method : 0.0010335445404052734 length of segment : 38 time for calcul the mask position with numpy : 0.0008835792541503906 nb_pixel_total : 57712 time to create 1 rle with old method : 0.06287765502929688 length of segment : 266 Processing 1 images image shape: (400, 400, 3) min: 28.00000 max: 224.00000 molded_images shape: (1, 640, 640, 3) min: -85.20781 max: 98.44219 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0012860298156738281 nb_pixel_total : 152761 time to create 1 rle with new method : 0.0018086433410644531 length of segment : 396 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -110.84844 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00025391578674316406 length of segment : 11 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 244 time to create 1 rle with old method : 0.00034165382385253906 length of segment : 26 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.0004673004150390625 length of segment : 15 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 63 time to create 1 rle with old method : 0.00013113021850585938 length of segment : 9 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 333 time to create 1 rle with old method : 0.00046443939208984375 length of segment : 23 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 550 time to create 1 rle with old method : 0.0007224082946777344 length of segment : 40 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0004563331604003906 length of segment : 21 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.40313 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 909 time to create 1 rle with old method : 0.0012307167053222656 length of segment : 39 time for calcul the mask position with numpy : 0.00020432472229003906 nb_pixel_total : 14010 time to create 1 rle with old method : 0.01641535758972168 length of segment : 127 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 1867 time to create 1 rle with old method : 0.0024390220642089844 length of segment : 63 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 380 time to create 1 rle with old method : 0.0017647743225097656 length of segment : 19 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 85 time to create 1 rle with old method : 0.0001475811004638672 length of segment : 10 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 277 time to create 1 rle with old method : 0.000415802001953125 length of segment : 16 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004401206970214844 length of segment : 17 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 839 time to create 1 rle with old method : 0.0011599063873291016 length of segment : 34 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 258 time to create 1 rle with old method : 0.0003662109375 length of segment : 18 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 1329 time to create 1 rle with old method : 0.0018091201782226562 length of segment : 29 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 1444 time to create 1 rle with old method : 0.0018546581268310547 length of segment : 32 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 410 time to create 1 rle with old method : 0.0006792545318603516 length of segment : 24 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 532 time to create 1 rle with old method : 0.0007064342498779297 length of segment : 46 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.98125 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 681 time to create 1 rle with old method : 0.0008635520935058594 length of segment : 55 time for calcul the mask position with numpy : 0.0005216598510742188 nb_pixel_total : 19996 time to create 1 rle with old method : 0.022872209548950195 length of segment : 381 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 447 time to create 1 rle with old method : 0.0006389617919921875 length of segment : 26 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 1091 time to create 1 rle with old method : 0.0014925003051757812 length of segment : 71 time for calcul the mask position with numpy : 0.00038552284240722656 nb_pixel_total : 9739 time to create 1 rle with old method : 0.011539697647094727 length of segment : 294 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 823 time to create 1 rle with old method : 0.0010526180267333984 length of segment : 37 time for calcul the mask position with numpy : 9.274482727050781e-05 nb_pixel_total : 2540 time to create 1 rle with old method : 0.003635406494140625 length of segment : 96 time for calcul the mask position with numpy : 0.0003383159637451172 nb_pixel_total : 1288 time to create 1 rle with old method : 0.0018317699432373047 length of segment : 40 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 788 time to create 1 rle with old method : 0.0009930133819580078 length of segment : 37 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 728 time to create 1 rle with old method : 0.0009028911590576172 length of segment : 37 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.15078 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 1375 time to create 1 rle with old method : 0.0017457008361816406 length of segment : 58 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 719 time to create 1 rle with old method : 0.00101470947265625 length of segment : 72 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 961 time to create 1 rle with old method : 0.0012080669403076172 length of segment : 52 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 1632 time to create 1 rle with old method : 0.0020313262939453125 length of segment : 80 time for calcul the mask position with numpy : 7.605552673339844e-05 nb_pixel_total : 993 time to create 1 rle with old method : 0.001219034194946289 length of segment : 75 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 1239 time to create 1 rle with old method : 0.0015130043029785156 length of segment : 77 time for calcul the mask position with numpy : 0.00015354156494140625 nb_pixel_total : 2322 time to create 1 rle with old method : 0.0028693675994873047 length of segment : 165 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 902 time to create 1 rle with old method : 0.0011970996856689453 length of segment : 46 time for calcul the mask position with numpy : 0.00013136863708496094 nb_pixel_total : 2118 time to create 1 rle with old method : 0.002532482147216797 length of segment : 162 Processing 1 images image shape: (280, 400, 3) min: 22.00000 max: 205.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 83.94375 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0012440681457519531 nb_pixel_total : 106904 time to create 1 rle with old method : 0.1360623836517334 length of segment : 284 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.00028014183044433594 length of segment : 16 time for calcul the mask position with numpy : 9.894371032714844e-05 nb_pixel_total : 5226 time to create 1 rle with old method : 0.009311676025390625 length of segment : 70 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 494 time to create 1 rle with old method : 0.0006434917449951172 length of segment : 44 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 317 time to create 1 rle with old method : 0.0004677772521972656 length of segment : 25 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006852149963378906 length of segment : 44 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 96 time to create 1 rle with old method : 0.00016546249389648438 length of segment : 13 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 205 time to create 1 rle with old method : 0.0003325939178466797 length of segment : 31 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 2229 time to create 1 rle with old method : 0.0027663707733154297 length of segment : 91 time for calcul the mask position with numpy : 0.00010395050048828125 nb_pixel_total : 3969 time to create 1 rle with old method : 0.004960060119628906 length of segment : 156 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 996 time to create 1 rle with old method : 0.0012874603271484375 length of segment : 56 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 904 time to create 1 rle with old method : 0.0011341571807861328 length of segment : 49 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 31 time for calcul the mask position with numpy : 0.00010251998901367188 nb_pixel_total : 604 time to create 1 rle with old method : 0.0008192062377929688 length of segment : 32 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 934 time to create 1 rle with old method : 0.001135110855102539 length of segment : 41 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 278 time to create 1 rle with old method : 0.00036454200744628906 length of segment : 19 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 861 time to create 1 rle with old method : 0.0010597705841064453 length of segment : 43 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005469322204589844 length of segment : 26 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 673 time to create 1 rle with old method : 0.0008087158203125 length of segment : 42 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 208 time to create 1 rle with old method : 0.0002799034118652344 length of segment : 17 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 317 time to create 1 rle with old method : 0.00043392181396484375 length of segment : 16 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 1108 time to create 1 rle with old method : 0.0013332366943359375 length of segment : 48 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 694 time to create 1 rle with old method : 0.0008716583251953125 length of segment : 33 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0004956722259521484 length of segment : 37 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005171298980712891 length of segment : 31 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0004756450653076172 length of segment : 39 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.00023984909057617188 length of segment : 16 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 128 time to create 1 rle with old method : 0.00018715858459472656 length of segment : 17 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.0003008842468261719 length of segment : 12 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 466 time to create 1 rle with old method : 0.000644683837890625 length of segment : 22 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 144 time to create 1 rle with old method : 0.0002384185791015625 length of segment : 29 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 803 time to create 1 rle with old method : 0.0009548664093017578 length of segment : 37 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 1332 time to create 1 rle with old method : 0.0015769004821777344 length of segment : 56 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 1056 time to create 1 rle with old method : 0.001294851303100586 length of segment : 43 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1647 time to create 1 rle with old method : 0.0018842220306396484 length of segment : 73 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 710 time to create 1 rle with old method : 0.0008392333984375 length of segment : 34 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0005049705505371094 length of segment : 23 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0005638599395751953 length of segment : 26 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 885 time to create 1 rle with old method : 0.0010879039764404297 length of segment : 37 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 116 time to create 1 rle with old method : 0.00024080276489257812 length of segment : 31 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 1444 time to create 1 rle with old method : 0.0015819072723388672 length of segment : 58 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 791 time to create 1 rle with old method : 0.0010030269622802734 length of segment : 37 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 1318 time to create 1 rle with old method : 0.0015156269073486328 length of segment : 48 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 1332 time to create 1 rle with old method : 0.0015845298767089844 length of segment : 49 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 25 time for calcul the mask position with numpy : 0.00011539459228515625 nb_pixel_total : 109 time to create 1 rle with old method : 0.0003254413604736328 length of segment : 24 time for calcul the mask position with numpy : 0.0003154277801513672 nb_pixel_total : 11080 time to create 1 rle with old method : 0.014981508255004883 length of segment : 115 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 279 time to create 1 rle with old method : 0.0003752708435058594 length of segment : 29 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 998 time to create 1 rle with old method : 0.0011794567108154297 length of segment : 43 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 431 time to create 1 rle with old method : 0.0005221366882324219 length of segment : 39 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 380 time to create 1 rle with old method : 0.0004856586456298828 length of segment : 29 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.0002601146697998047 length of segment : 29 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.00018358230590820312 length of segment : 23 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 954 time to create 1 rle with old method : 0.001505136489868164 length of segment : 61 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 754 time to create 1 rle with old method : 0.0010540485382080078 length of segment : 33 time for calcul the mask position with numpy : 0.0002334117889404297 nb_pixel_total : 10120 time to create 1 rle with old method : 0.011315345764160156 length of segment : 182 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.00011992454528808594 length of segment : 12 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 979 time to create 1 rle with old method : 0.0011324882507324219 length of segment : 67 time for calcul the mask position with numpy : 8.511543273925781e-05 nb_pixel_total : 634 time to create 1 rle with old method : 0.0009243488311767578 length of segment : 124 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 571 time to create 1 rle with old method : 0.0008265972137451172 length of segment : 22 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 141 time to create 1 rle with old method : 0.00021028518676757812 length of segment : 16 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 114 time to create 1 rle with old method : 0.0001678466796875 length of segment : 16 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.00022840499877929688 length of segment : 19 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 541 time to create 1 rle with old method : 0.0006272792816162109 length of segment : 36 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 584 time to create 1 rle with old method : 0.0007224082946777344 length of segment : 23 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002512931823730469 length of segment : 21 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 180 time to create 1 rle with old method : 0.00024080276489257812 length of segment : 22 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 294 time to create 1 rle with old method : 0.00045371055603027344 length of segment : 20 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006301403045654297 length of segment : 34 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.0002799034118652344 length of segment : 25 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 133.85000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 521 time to create 1 rle with old method : 0.0008714199066162109 length of segment : 55 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 595 time to create 1 rle with old method : 0.0008907318115234375 length of segment : 48 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0003616809844970703 length of segment : 14 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.0003364086151123047 length of segment : 29 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.00013685226440429688 length of segment : 10 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 26 time to create 1 rle with old method : 8.249282836914062e-05 length of segment : 6 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 42 time to create 1 rle with old method : 0.00018525123596191406 length of segment : 7 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.52422 max: 145.47500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.00024509429931640625 length of segment : 33 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 32 time to create 1 rle with old method : 7.414817810058594e-05 length of segment : 10 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 215 time to create 1 rle with old method : 0.00031757354736328125 length of segment : 26 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003209114074707031 length of segment : 39 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 192 time to create 1 rle with old method : 0.0002582073211669922 length of segment : 31 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.00026726722717285156 length of segment : 32 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.00033020973205566406 length of segment : 31 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 232 time to create 1 rle with old method : 0.00030612945556640625 length of segment : 30 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 1717 time to create 1 rle with old method : 0.0021033287048339844 length of segment : 53 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -112.74297 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 9.322166442871094e-05 nb_pixel_total : 2437 time to create 1 rle with old method : 0.003064393997192383 length of segment : 46 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 25 time to create 1 rle with old method : 6.723403930664062e-05 length of segment : 7 time for calcul the mask position with numpy : 0.00011515617370605469 nb_pixel_total : 38 time to create 1 rle with old method : 9.059906005859375e-05 length of segment : 11 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 48 time to create 1 rle with old method : 8.58306884765625e-05 length of segment : 20 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 21 time to create 1 rle with old method : 5.9604644775390625e-05 length of segment : 4 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 940 time to create 1 rle with old method : 0.0011932849884033203 length of segment : 56 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 405 time to create 1 rle with old method : 0.0005514621734619141 length of segment : 42 time for calcul the mask position with numpy : 8.344650268554688e-05 nb_pixel_total : 786 time to create 1 rle with old method : 0.0010523796081542969 length of segment : 75 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 81 time to create 1 rle with old method : 0.0001666545867919922 length of segment : 14 time for calcul the mask position with numpy : 7.939338684082031e-05 nb_pixel_total : 875 time to create 1 rle with old method : 0.0016748905181884766 length of segment : 41 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 329 time to create 1 rle with old method : 0.000499725341796875 length of segment : 35 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0005652904510498047 length of segment : 31 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 781 time to create 1 rle with old method : 0.001031637191772461 length of segment : 37 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 915 time to create 1 rle with old method : 0.0011570453643798828 length of segment : 40 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.45000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 583 time to create 1 rle with old method : 0.0008153915405273438 length of segment : 75 time for calcul the mask position with numpy : 0.0001049041748046875 nb_pixel_total : 4498 time to create 1 rle with old method : 0.005573272705078125 length of segment : 60 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 1770 time to create 1 rle with old method : 0.0023195743560791016 length of segment : 48 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 1547 time to create 1 rle with old method : 0.0019099712371826172 length of segment : 33 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.67656 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.0002498626708984375 nb_pixel_total : 11331 time to create 1 rle with old method : 0.012628316879272461 length of segment : 251 time for calcul the mask position with numpy : 0.0001888275146484375 nb_pixel_total : 11352 time to create 1 rle with old method : 0.01443791389465332 length of segment : 97 time for calcul the mask position with numpy : 0.00010538101196289062 nb_pixel_total : 373 time to create 1 rle with old method : 0.00093841552734375 length of segment : 23 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.0003151893615722656 length of segment : 16 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.00039005279541015625 length of segment : 19 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.92812 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.0003838539123535156 length of segment : 13 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 733 time to create 1 rle with old method : 0.0011751651763916016 length of segment : 38 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.0003504753112792969 length of segment : 21 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 752 time to create 1 rle with old method : 0.0010628700256347656 length of segment : 39 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.0005285739898681641 length of segment : 29 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 698 time to create 1 rle with old method : 0.0011320114135742188 length of segment : 36 Processing 1 images image shape: (400, 400, 3) min: 32.00000 max: 224.00000 molded_images shape: (1, 640, 640, 3) min: -82.61797 max: 95.75078 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -111.18438 max: 150.98281 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 9.226799011230469e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.00035381317138671875 length of segment : 17 time for calcul the mask position with numpy : 9.5367431640625e-05 nb_pixel_total : 948 time to create 1 rle with old method : 0.0024127960205078125 length of segment : 37 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 311 time to create 1 rle with old method : 0.0007772445678710938 length of segment : 23 time for calcul the mask position with numpy : 8.821487426757812e-05 nb_pixel_total : 308 time to create 1 rle with old method : 0.0008769035339355469 length of segment : 16 time for calcul the mask position with numpy : 9.5367431640625e-05 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006010532379150391 length of segment : 24 time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 817 time to create 1 rle with old method : 0.0016739368438720703 length of segment : 42 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 65 time to create 1 rle with old method : 0.0001766681671142578 length of segment : 9 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.0003542900085449219 length of segment : 12 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 2845 time to create 1 rle with old method : 0.00519108772277832 length of segment : 57 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.0007328987121582031 length of segment : 20 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.93047 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 0.00012612342834472656 nb_pixel_total : 892 time to create 1 rle with old method : 0.00189971923828125 length of segment : 39 time for calcul the mask position with numpy : 0.00012159347534179688 nb_pixel_total : 1222 time to create 1 rle with old method : 0.0030672550201416016 length of segment : 35 time for calcul the mask position with numpy : 0.00010967254638671875 nb_pixel_total : 427 time to create 1 rle with old method : 0.0012624263763427734 length of segment : 28 time for calcul the mask position with numpy : 0.00047397613525390625 nb_pixel_total : 13768 time to create 1 rle with old method : 0.03281521797180176 length of segment : 122 time for calcul the mask position with numpy : 8.511543273925781e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00019478797912597656 length of segment : 15 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 1313 time to create 1 rle with old method : 0.0019249916076660156 length of segment : 26 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 291 time to create 1 rle with old method : 0.00045418739318847656 length of segment : 28 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 431 time to create 1 rle with old method : 0.0007371902465820312 length of segment : 24 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 312 time to create 1 rle with old method : 0.0006563663482666016 length of segment : 24 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 411 time to create 1 rle with old method : 0.0009436607360839844 length of segment : 23 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 240 time to create 1 rle with old method : 0.0005545616149902344 length of segment : 18 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.0006818771362304688 length of segment : 17 time for calcul the mask position with numpy : 0.0006535053253173828 nb_pixel_total : 18836 time to create 1 rle with old method : 0.022123336791992188 length of segment : 132 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.28984 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 719 time to create 1 rle with old method : 0.0008914470672607422 length of segment : 50 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 673 time to create 1 rle with old method : 0.000934600830078125 length of segment : 45 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.00039315223693847656 length of segment : 19 time for calcul the mask position with numpy : 9.107589721679688e-05 nb_pixel_total : 3672 time to create 1 rle with old method : 0.0045986175537109375 length of segment : 94 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 312 time to create 1 rle with old method : 0.00045680999755859375 length of segment : 20 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005295276641845703 length of segment : 26 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 1243 time to create 1 rle with old method : 0.0021893978118896484 length of segment : 40 time for calcul the mask position with numpy : 8.988380432128906e-05 nb_pixel_total : 878 time to create 1 rle with old method : 0.001752614974975586 length of segment : 28 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 378 time to create 1 rle with old method : 0.0007290840148925781 length of segment : 22 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.000270843505859375 length of segment : 11 time for calcul the mask position with numpy : 0.00013780593872070312 nb_pixel_total : 604 time to create 1 rle with old method : 0.0011143684387207031 length of segment : 64 time for calcul the mask position with numpy : 0.00011229515075683594 nb_pixel_total : 778 time to create 1 rle with old method : 0.0018284320831298828 length of segment : 35 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 714 time to create 1 rle with old method : 0.001180887222290039 length of segment : 36 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.63906 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 9.131431579589844e-05 nb_pixel_total : 1334 time to create 1 rle with old method : 0.0021698474884033203 length of segment : 59 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 611 time to create 1 rle with old method : 0.001085519790649414 length of segment : 57 time for calcul the mask position with numpy : 0.00014591217041015625 nb_pixel_total : 769 time to create 1 rle with old method : 0.0014622211456298828 length of segment : 63 time for calcul the mask position with numpy : 0.00011420249938964844 nb_pixel_total : 870 time to create 1 rle with old method : 0.001619577407836914 length of segment : 49 time for calcul the mask position with numpy : 0.00016355514526367188 nb_pixel_total : 1539 time to create 1 rle with old method : 0.003555774688720703 length of segment : 77 time for calcul the mask position with numpy : 0.0001461505889892578 nb_pixel_total : 117 time to create 1 rle with old method : 0.000461578369140625 length of segment : 10 time for calcul the mask position with numpy : 0.00018548965454101562 nb_pixel_total : 1217 time to create 1 rle with old method : 0.0026540756225585938 length of segment : 99 time for calcul the mask position with numpy : 0.00038051605224609375 nb_pixel_total : 3077 time to create 1 rle with old method : 0.006036043167114258 length of segment : 187 time for calcul the mask position with numpy : 8.678436279296875e-05 nb_pixel_total : 616 time to create 1 rle with old method : 0.0013861656188964844 length of segment : 45 time for calcul the mask position with numpy : 0.00010848045349121094 nb_pixel_total : 1013 time to create 1 rle with old method : 0.0016644001007080078 length of segment : 60 Processing 1 images image shape: (280, 400, 3) min: 19.00000 max: 197.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 76.31875 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0010859966278076172 nb_pixel_total : 106984 time to create 1 rle with old method : 0.1243736743927002 length of segment : 280 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.05312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.0002884864807128906 length of segment : 16 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0007483959197998047 length of segment : 21 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 555 time to create 1 rle with old method : 0.001514434814453125 length of segment : 44 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0009257793426513672 length of segment : 32 time for calcul the mask position with numpy : 9.393692016601562e-05 nb_pixel_total : 1499 time to create 1 rle with old method : 0.0034072399139404297 length of segment : 53 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 319 time to create 1 rle with old method : 0.0008184909820556641 length of segment : 25 time for calcul the mask position with numpy : 0.00013303756713867188 nb_pixel_total : 4479 time to create 1 rle with old method : 0.005349159240722656 length of segment : 117 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 100 time to create 1 rle with old method : 0.00018930435180664062 length of segment : 13 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 307 time to create 1 rle with old method : 0.0004608631134033203 length of segment : 35 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 1028 time to create 1 rle with old method : 0.0012860298156738281 length of segment : 57 time for calcul the mask position with numpy : 0.00014591217041015625 nb_pixel_total : 4462 time to create 1 rle with old method : 0.005335330963134766 length of segment : 149 time for calcul the mask position with numpy : 0.00011348724365234375 nb_pixel_total : 3813 time to create 1 rle with old method : 0.005151033401489258 length of segment : 81 time for calcul the mask position with numpy : 0.00022363662719726562 nb_pixel_total : 4397 time to create 1 rle with old method : 0.00876617431640625 length of segment : 139 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 2445 time to create 1 rle with old method : 0.0037865638732910156 length of segment : 52 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.0002951622009277344 length of segment : 26 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 1301 time to create 1 rle with old method : 0.0021026134490966797 length of segment : 53 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 41 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 665 time to create 1 rle with old method : 0.0009748935699462891 length of segment : 36 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 1519 time to create 1 rle with old method : 0.0020101070404052734 length of segment : 48 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 800 time to create 1 rle with old method : 0.0012309551239013672 length of segment : 44 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 972 time to create 1 rle with old method : 0.0013496875762939453 length of segment : 42 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 996 time to create 1 rle with old method : 0.0013511180877685547 length of segment : 46 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006496906280517578 length of segment : 25 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.0004379749298095703 length of segment : 27 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.0003046989440917969 length of segment : 17 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 656 time to create 1 rle with old method : 0.0008652210235595703 length of segment : 34 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 420 time to create 1 rle with old method : 0.0006945133209228516 length of segment : 34 time for calcul the mask position with numpy : 7.82012939453125e-05 nb_pixel_total : 978 time to create 1 rle with old method : 0.0015234947204589844 length of segment : 36 time for calcul the mask position with numpy : 0.00010633468627929688 nb_pixel_total : 1614 time to create 1 rle with old method : 0.0033266544342041016 length of segment : 56 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.0002536773681640625 length of segment : 17 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 964 time to create 1 rle with old method : 0.0019307136535644531 length of segment : 36 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.00035762786865234375 length of segment : 18 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 540 time to create 1 rle with old method : 0.0007469654083251953 length of segment : 43 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 384 time to create 1 rle with old method : 0.0005402565002441406 length of segment : 39 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.00031757354736328125 length of segment : 31 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.00045037269592285156 length of segment : 16 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 692 time to create 1 rle with old method : 0.0009143352508544922 length of segment : 33 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.00041961669921875 length of segment : 14 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 769 time to create 1 rle with old method : 0.0010197162628173828 length of segment : 50 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 1319 time to create 1 rle with old method : 0.0017402172088623047 length of segment : 48 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.0003113746643066406 length of segment : 19 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.00029969215393066406 length of segment : 33 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 1151 time to create 1 rle with old method : 0.0014803409576416016 length of segment : 50 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 249 time to create 1 rle with old method : 0.0003662109375 length of segment : 17 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 421 time to create 1 rle with old method : 0.000583648681640625 length of segment : 24 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 386 time to create 1 rle with old method : 0.0005390644073486328 length of segment : 39 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0007092952728271484 length of segment : 22 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 1843 time to create 1 rle with old method : 0.0022754669189453125 length of segment : 65 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003662109375 length of segment : 18 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.00029850006103515625 length of segment : 16 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 426 time to create 1 rle with old method : 0.0005686283111572266 length of segment : 22 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 737 time to create 1 rle with old method : 0.0009474754333496094 length of segment : 36 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0007069110870361328 length of segment : 26 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 1387 time to create 1 rle with old method : 0.0017573833465576172 length of segment : 48 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.00032258033752441406 length of segment : 17 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 213 time to create 1 rle with old method : 0.00031685829162597656 length of segment : 17 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 747 time to create 1 rle with old method : 0.0009679794311523438 length of segment : 39 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1383 time to create 1 rle with old method : 0.001718759536743164 length of segment : 48 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 23 time for calcul the mask position with numpy : 0.0002396106719970703 nb_pixel_total : 8087 time to create 1 rle with old method : 0.01725292205810547 length of segment : 169 time for calcul the mask position with numpy : 0.00010633468627929688 nb_pixel_total : 1546 time to create 1 rle with old method : 0.002996683120727539 length of segment : 79 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 140 time to create 1 rle with old method : 0.0002994537353515625 length of segment : 29 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0008087158203125 length of segment : 39 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 281 time to create 1 rle with old method : 0.00037217140197753906 length of segment : 30 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 889 time to create 1 rle with old method : 0.0012254714965820312 length of segment : 77 time for calcul the mask position with numpy : 9.179115295410156e-05 nb_pixel_total : 11 time to create 1 rle with old method : 8.392333984375e-05 length of segment : 6 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 219 time to create 1 rle with old method : 0.0004496574401855469 length of segment : 17 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.00038051605224609375 length of segment : 24 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.0008187294006347656 length of segment : 25 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 496 time to create 1 rle with old method : 0.0006377696990966797 length of segment : 36 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.00021314620971679688 length of segment : 7 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.0003039836883544922 length of segment : 24 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 55 time to create 1 rle with old method : 0.000118255615234375 length of segment : 24 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 563 time to create 1 rle with old method : 0.0008633136749267578 length of segment : 22 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 659 time to create 1 rle with old method : 0.0009329319000244141 length of segment : 78 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 496 time to create 1 rle with old method : 0.0006594657897949219 length of segment : 34 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 1309 time to create 1 rle with old method : 0.0016868114471435547 length of segment : 35 time for calcul the mask position with numpy : 8.559226989746094e-05 nb_pixel_total : 436 time to create 1 rle with old method : 0.0007996559143066406 length of segment : 40 time for calcul the mask position with numpy : 0.0001323223114013672 nb_pixel_total : 1494 time to create 1 rle with old method : 0.001940011978149414 length of segment : 128 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.00017523765563964844 length of segment : 26 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 564 time to create 1 rle with old method : 0.0008139610290527344 length of segment : 21 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0007045269012451172 length of segment : 33 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 130.03750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 522 time to create 1 rle with old method : 0.0013842582702636719 length of segment : 56 time for calcul the mask position with numpy : 0.00010132789611816406 nb_pixel_total : 664 time to create 1 rle with old method : 0.002041339874267578 length of segment : 53 time for calcul the mask position with numpy : 8.440017700195312e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.0007317066192626953 length of segment : 13 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 239 time to create 1 rle with old method : 0.0005924701690673828 length of segment : 32 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.00018978118896484375 length of segment : 10 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.96953 max: 145.45547 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 63 time to create 1 rle with old method : 0.0002155303955078125 length of segment : 21 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 274 time to create 1 rle with old method : 0.00036215782165527344 length of segment : 31 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 228 time to create 1 rle with old method : 0.0003383159637451172 length of segment : 40 time for calcul the mask position with numpy : 0.00040841102600097656 nb_pixel_total : 17332 time to create 1 rle with old method : 0.022676706314086914 length of segment : 194 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 322 time to create 1 rle with old method : 0.00046563148498535156 length of segment : 37 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.00046539306640625 length of segment : 40 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 265 time to create 1 rle with old method : 0.00044608116149902344 length of segment : 30 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.0004165172576904297 length of segment : 29 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -113.88750 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 0.00013756752014160156 nb_pixel_total : 2673 time to create 1 rle with old method : 0.004436969757080078 length of segment : 62 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 835 time to create 1 rle with old method : 0.0013508796691894531 length of segment : 37 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.00015735626220703125 length of segment : 10 length of segment : 0 time for calcul the mask position with numpy : 0.0001125335693359375 nb_pixel_total : 2460 time to create 1 rle with old method : 0.003966569900512695 length of segment : 53 time for calcul the mask position with numpy : 9.655952453613281e-05 nb_pixel_total : 476 time to create 1 rle with old method : 0.0011293888092041016 length of segment : 38 time for calcul the mask position with numpy : 8.678436279296875e-05 nb_pixel_total : 920 time to create 1 rle with old method : 0.0016851425170898438 length of segment : 53 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 24 time to create 1 rle with old method : 9.083747863769531e-05 length of segment : 5 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 45 time to create 1 rle with old method : 0.0001575946807861328 length of segment : 14 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 401 time to create 1 rle with old method : 0.0008597373962402344 length of segment : 15 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 371 time to create 1 rle with old method : 0.000774383544921875 length of segment : 14 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.86016 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 1198 time to create 1 rle with old method : 0.002183198928833008 length of segment : 36 time for calcul the mask position with numpy : 0.00013685226440429688 nb_pixel_total : 3499 time to create 1 rle with old method : 0.007127046585083008 length of segment : 63 time for calcul the mask position with numpy : 0.00011229515075683594 nb_pixel_total : 777 time to create 1 rle with old method : 0.0019109249114990234 length of segment : 77 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 2157 time to create 1 rle with old method : 0.0031554698944091797 length of segment : 53 time for calcul the mask position with numpy : 0.00021982192993164062 nb_pixel_total : 6314 time to create 1 rle with old method : 0.009661436080932617 length of segment : 106 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 605 time to create 1 rle with old method : 0.0007779598236083984 length of segment : 82 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.55156 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 0.00015401840209960938 nb_pixel_total : 11050 time to create 1 rle with old method : 0.012981414794921875 length of segment : 103 time for calcul the mask position with numpy : 0.0002498626708984375 nb_pixel_total : 9723 time to create 1 rle with old method : 0.01200556755065918 length of segment : 212 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002796649932861328 length of segment : 15 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 415 time to create 1 rle with old method : 0.000659942626953125 length of segment : 26 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.70937 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 1603 time to create 1 rle with old method : 0.0018835067749023438 length of segment : 181 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 303 time to create 1 rle with old method : 0.0004138946533203125 length of segment : 19 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 900 time to create 1 rle with old method : 0.0011839866638183594 length of segment : 40 time for calcul the mask position with numpy : 0.00014591217041015625 nb_pixel_total : 2047 time to create 1 rle with old method : 0.0026001930236816406 length of segment : 178 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 912 time to create 1 rle with old method : 0.00121307373046875 length of segment : 39 Processing 1 images image shape: (400, 400, 3) min: 24.00000 max: 218.00000 molded_images shape: (1, 640, 640, 3) min: -84.39531 max: 91.61406 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -110.17266 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.00042748451232910156 length of segment : 15 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 708 time to create 1 rle with old method : 0.0009553432464599609 length of segment : 45 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 935 time to create 1 rle with old method : 0.0013127326965332031 length of segment : 33 time for calcul the mask position with numpy : 0.00011467933654785156 nb_pixel_total : 2143 time to create 1 rle with old method : 0.0037844181060791016 length of segment : 50 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 56 time to create 1 rle with old method : 0.00010013580322265625 length of segment : 13 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 115 time to create 1 rle with old method : 0.00027823448181152344 length of segment : 12 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.0003552436828613281 length of segment : 18 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 122 time to create 1 rle with old method : 0.00026488304138183594 length of segment : 17 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 430 time to create 1 rle with old method : 0.0007500648498535156 length of segment : 20 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.00043654441833496094 length of segment : 15 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 220 time to create 1 rle with old method : 0.0005097389221191406 length of segment : 19 time for calcul the mask position with numpy : 8.654594421386719e-05 nb_pixel_total : 1682 time to create 1 rle with old method : 0.003158092498779297 length of segment : 47 time for calcul the mask position with numpy : 9.918212890625e-05 nb_pixel_total : 758 time to create 1 rle with old method : 0.0014011859893798828 length of segment : 41 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 796 time to create 1 rle with old method : 0.0012316703796386719 length of segment : 36 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.75859 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 915 time to create 1 rle with old method : 0.0011661052703857422 length of segment : 39 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 490 time to create 1 rle with old method : 0.0008065700531005859 length of segment : 25 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 114 time to create 1 rle with old method : 0.0001819133758544922 length of segment : 15 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 1123 time to create 1 rle with old method : 0.001462697982788086 length of segment : 27 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 295 time to create 1 rle with old method : 0.00042057037353515625 length of segment : 16 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 1308 time to create 1 rle with old method : 0.0016863346099853516 length of segment : 27 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 707 time to create 1 rle with old method : 0.0008981227874755859 length of segment : 32 time for calcul the mask position with numpy : 0.0002162456512451172 nb_pixel_total : 13020 time to create 1 rle with old method : 0.01486825942993164 length of segment : 120 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 555 time to create 1 rle with old method : 0.0008690357208251953 length of segment : 89 time for calcul the mask position with numpy : 0.00010251998901367188 nb_pixel_total : 471 time to create 1 rle with old method : 0.0007650852203369141 length of segment : 18 time for calcul the mask position with numpy : 0.00018715858459472656 nb_pixel_total : 3753 time to create 1 rle with old method : 0.004828929901123047 length of segment : 73 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.57891 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 701 time to create 1 rle with old method : 0.001039266586303711 length of segment : 53 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 433 time to create 1 rle with old method : 0.0006442070007324219 length of segment : 27 time for calcul the mask position with numpy : 0.000102996826171875 nb_pixel_total : 3312 time to create 1 rle with old method : 0.0045855045318603516 length of segment : 135 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 1262 time to create 1 rle with old method : 0.0019092559814453125 length of segment : 40 time for calcul the mask position with numpy : 0.000102996826171875 nb_pixel_total : 1468 time to create 1 rle with old method : 0.002384185791015625 length of segment : 71 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 731 time to create 1 rle with old method : 0.0010972023010253906 length of segment : 35 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.07656 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 1273 time to create 1 rle with old method : 0.002255678176879883 length of segment : 56 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 369 time to create 1 rle with old method : 0.0007233619689941406 length of segment : 67 time for calcul the mask position with numpy : 8.440017700195312e-05 nb_pixel_total : 507 time to create 1 rle with old method : 0.0009679794311523438 length of segment : 52 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 964 time to create 1 rle with old method : 0.0017380714416503906 length of segment : 53 time for calcul the mask position with numpy : 0.00013828277587890625 nb_pixel_total : 1010 time to create 1 rle with old method : 0.001804351806640625 length of segment : 91 time for calcul the mask position with numpy : 0.00010275840759277344 nb_pixel_total : 469 time to create 1 rle with old method : 0.0009553432464599609 length of segment : 27 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 447 time to create 1 rle with old method : 0.0008738040924072266 length of segment : 27 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 1202 time to create 1 rle with old method : 0.0020372867584228516 length of segment : 64 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 562 time to create 1 rle with old method : 0.0010471343994140625 length of segment : 46 time for calcul the mask position with numpy : 0.0002338886260986328 nb_pixel_total : 2205 time to create 1 rle with old method : 0.0037326812744140625 length of segment : 156 time for calcul the mask position with numpy : 0.00024271011352539062 nb_pixel_total : 2591 time to create 1 rle with old method : 0.003271341323852539 length of segment : 178 time for calcul the mask position with numpy : 0.00010657310485839844 nb_pixel_total : 1452 time to create 1 rle with old method : 0.0019125938415527344 length of segment : 79 Processing 1 images image shape: (280, 400, 3) min: 12.00000 max: 200.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 79.23281 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.00103759765625 nb_pixel_total : 106861 time to create 1 rle with old method : 0.1310877799987793 length of segment : 282 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 216 time to create 1 rle with old method : 0.00031638145446777344 length of segment : 16 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.000579833984375 length of segment : 32 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 486 time to create 1 rle with old method : 0.0006906986236572266 length of segment : 22 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 314 time to create 1 rle with old method : 0.0004787445068359375 length of segment : 26 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 1608 time to create 1 rle with old method : 0.0019392967224121094 length of segment : 52 time for calcul the mask position with numpy : 0.00014543533325195312 nb_pixel_total : 4939 time to create 1 rle with old method : 0.0060672760009765625 length of segment : 128 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 969 time to create 1 rle with old method : 0.0012941360473632812 length of segment : 71 time for calcul the mask position with numpy : 9.560585021972656e-05 nb_pixel_total : 2544 time to create 1 rle with old method : 0.0030312538146972656 length of segment : 115 time for calcul the mask position with numpy : 0.00012230873107910156 nb_pixel_total : 5031 time to create 1 rle with old method : 0.006551265716552734 length of segment : 100 time for calcul the mask position with numpy : 0.000110626220703125 nb_pixel_total : 1513 time to create 1 rle with old method : 0.0018887519836425781 length of segment : 66 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 1339 time to create 1 rle with old method : 0.0018002986907958984 length of segment : 70 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 40 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005900859832763672 length of segment : 24 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 613 time to create 1 rle with old method : 0.0008413791656494141 length of segment : 35 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.0002560615539550781 length of segment : 18 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 731 time to create 1 rle with old method : 0.0010974407196044922 length of segment : 37 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 868 time to create 1 rle with old method : 0.0013103485107421875 length of segment : 41 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 337 time to create 1 rle with old method : 0.0005631446838378906 length of segment : 26 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 254 time to create 1 rle with old method : 0.00048089027404785156 length of segment : 15 time for calcul the mask position with numpy : 0.000118255615234375 nb_pixel_total : 2634 time to create 1 rle with old method : 0.003297567367553711 length of segment : 70 time for calcul the mask position with numpy : 8.58306884765625e-05 nb_pixel_total : 1610 time to create 1 rle with old method : 0.002017974853515625 length of segment : 57 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0006289482116699219 length of segment : 20 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 369 time to create 1 rle with old method : 0.0004956722259521484 length of segment : 39 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 1031 time to create 1 rle with old method : 0.001703500747680664 length of segment : 42 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 930 time to create 1 rle with old method : 0.0021855831146240234 length of segment : 38 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.0002944469451904297 length of segment : 17 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 167 time to create 1 rle with old method : 0.0002536773681640625 length of segment : 15 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 803 time to create 1 rle with old method : 0.0010516643524169922 length of segment : 37 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 683 time to create 1 rle with old method : 0.0008943080902099609 length of segment : 27 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 1485 time to create 1 rle with old method : 0.0019545555114746094 length of segment : 49 time for calcul the mask position with numpy : 0.0005345344543457031 nb_pixel_total : 1712 time to create 1 rle with old method : 0.0026574134826660156 length of segment : 64 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 193 time to create 1 rle with old method : 0.00029349327087402344 length of segment : 16 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007843971252441406 length of segment : 33 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 269 time to create 1 rle with old method : 0.00040841102600097656 length of segment : 18 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003533363342285156 length of segment : 17 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 114 time to create 1 rle with old method : 0.00017309188842773438 length of segment : 21 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1653 time to create 1 rle with old method : 0.0020294189453125 length of segment : 65 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.0005161762237548828 length of segment : 33 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 241 time to create 1 rle with old method : 0.00036144256591796875 length of segment : 21 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 268 time to create 1 rle with old method : 0.00038361549377441406 length of segment : 38 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 430 time to create 1 rle with old method : 0.0006148815155029297 length of segment : 33 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0007081031799316406 length of segment : 31 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 1545 time to create 1 rle with old method : 0.0019278526306152344 length of segment : 50 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 636 time to create 1 rle with old method : 0.0008580684661865234 length of segment : 31 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 417 time to create 1 rle with old method : 0.0006184577941894531 length of segment : 36 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 799 time to create 1 rle with old method : 0.0010294914245605469 length of segment : 35 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 343 time to create 1 rle with old method : 0.0004820823669433594 length of segment : 37 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.0002715587615966797 length of segment : 19 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 1424 time to create 1 rle with old method : 0.0017881393432617188 length of segment : 50 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 1786 time to create 1 rle with old method : 0.0022935867309570312 length of segment : 83 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 473 time to create 1 rle with old method : 0.0006561279296875 length of segment : 45 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 1417 time to create 1 rle with old method : 0.0019087791442871094 length of segment : 49 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 23 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 341 time to create 1 rle with old method : 0.0005207061767578125 length of segment : 26 time for calcul the mask position with numpy : 0.0002086162567138672 nb_pixel_total : 9855 time to create 1 rle with old method : 0.011796236038208008 length of segment : 148 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 297 time to create 1 rle with old method : 0.00039839744567871094 length of segment : 30 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 62 time to create 1 rle with old method : 0.00012803077697753906 length of segment : 21 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 991 time to create 1 rle with old method : 0.001249074935913086 length of segment : 47 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0006685256958007812 length of segment : 40 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.00018668174743652344 length of segment : 12 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 293 time to create 1 rle with old method : 0.0004024505615234375 length of segment : 19 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 602 time to create 1 rle with old method : 0.0008449554443359375 length of segment : 46 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 195 time to create 1 rle with old method : 0.0002987384796142578 length of segment : 26 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 140 time to create 1 rle with old method : 0.00021982192993164062 length of segment : 28 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0002620220184326172 length of segment : 16 time for calcul the mask position with numpy : 0.0001304149627685547 nb_pixel_total : 1439 time to create 1 rle with old method : 0.0018210411071777344 length of segment : 93 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 328 time to create 1 rle with old method : 0.0006282329559326172 length of segment : 31 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 540 time to create 1 rle with old method : 0.0008358955383300781 length of segment : 43 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 748 time to create 1 rle with old method : 0.0010712146759033203 length of segment : 73 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 55 time to create 1 rle with old method : 0.0001347064971923828 length of segment : 15 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1696 time to create 1 rle with old method : 0.002557516098022461 length of segment : 38 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 594 time to create 1 rle with old method : 0.0009906291961669922 length of segment : 25 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 145 time to create 1 rle with old method : 0.000247955322265625 length of segment : 19 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.00019788742065429688 length of segment : 27 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 365 time to create 1 rle with old method : 0.0005471706390380859 length of segment : 24 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 481 time to create 1 rle with old method : 0.0006225109100341797 length of segment : 29 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 130.47500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 591 time to create 1 rle with old method : 0.0007421970367431641 length of segment : 51 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 538 time to create 1 rle with old method : 0.0007171630859375 length of segment : 53 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.00029730796813964844 length of segment : 13 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 258 time to create 1 rle with old method : 0.0004515647888183594 length of segment : 32 time for calcul the mask position with numpy : 0.00013518333435058594 nb_pixel_total : 13 time to create 1 rle with old method : 6.461143493652344e-05 length of segment : 8 time for calcul the mask position with numpy : 2.9087066650390625e-05 nb_pixel_total : 77 time to create 1 rle with old method : 0.00013709068298339844 length of segment : 10 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 87 time to create 1 rle with old method : 0.0002009868621826172 length of segment : 11 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.08672 max: 145.24453 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 221 time to create 1 rle with old method : 0.00034737586975097656 length of segment : 37 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 35 time to create 1 rle with old method : 7.796287536621094e-05 length of segment : 16 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 380 time to create 1 rle with old method : 0.0005762577056884766 length of segment : 65 length of segment : 0 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003056526184082031 length of segment : 44 time for calcul the mask position with numpy : 0.00017642974853515625 nb_pixel_total : 2607 time to create 1 rle with old method : 0.0031404495239257812 length of segment : 85 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00020051002502441406 length of segment : 38 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00016617774963378906 length of segment : 17 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1642 time to create 1 rle with old method : 0.0022895336151123047 length of segment : 51 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.27422 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 291 time to create 1 rle with old method : 0.0005691051483154297 length of segment : 29 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 29 time to create 1 rle with old method : 7.891654968261719e-05 length of segment : 6 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 2346 time to create 1 rle with old method : 0.003323793411254883 length of segment : 50 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.000347137451171875 length of segment : 28 time for calcul the mask position with numpy : 0.0001392364501953125 nb_pixel_total : 157 time to create 1 rle with old method : 0.00039887428283691406 length of segment : 7 time for calcul the mask position with numpy : 9.72747802734375e-05 nb_pixel_total : 2418 time to create 1 rle with old method : 0.004202604293823242 length of segment : 46 time for calcul the mask position with numpy : 0.000507354736328125 nb_pixel_total : 19135 time to create 1 rle with old method : 0.03148770332336426 length of segment : 338 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 520 time to create 1 rle with old method : 0.0007073879241943359 length of segment : 34 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 365 time to create 1 rle with old method : 0.0007805824279785156 length of segment : 25 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 237 time to create 1 rle with old method : 0.0005154609680175781 length of segment : 27 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.72344 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 2 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 750 time to create 1 rle with old method : 0.001209259033203125 length of segment : 103 time for calcul the mask position with numpy : 0.0001671314239501953 nb_pixel_total : 6003 time to create 1 rle with old method : 0.011197328567504883 length of segment : 99 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.06328 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.00023818016052246094 nb_pixel_total : 11224 time to create 1 rle with old method : 0.01746392250061035 length of segment : 90 time for calcul the mask position with numpy : 0.00028252601623535156 nb_pixel_total : 10019 time to create 1 rle with old method : 0.014898300170898438 length of segment : 222 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 349 time to create 1 rle with old method : 0.00079345703125 length of segment : 21 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 275 time to create 1 rle with old method : 0.00044798851013183594 length of segment : 21 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 188 time to create 1 rle with old method : 0.0002791881561279297 length of segment : 16 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.00234 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 803 time to create 1 rle with old method : 0.001102447509765625 length of segment : 37 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 95 time to create 1 rle with old method : 0.000186920166015625 length of segment : 10 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.000263214111328125 length of segment : 11 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 1892 time to create 1 rle with old method : 0.0027353763580322266 length of segment : 177 Processing 1 images image shape: (400, 400, 3) min: 23.00000 max: 220.00000 molded_images shape: (1, 640, 640, 3) min: -82.61797 max: 95.95391 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0015747547149658203 nb_pixel_total : 151996 time to create 1 rle with new method : 0.0024945735931396484 length of segment : 396 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.01641 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 908 time to create 1 rle with old method : 0.0013051033020019531 length of segment : 33 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.00020074844360351562 length of segment : 12 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 82 time to create 1 rle with old method : 0.0001392364501953125 length of segment : 11 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 294 time to create 1 rle with old method : 0.0004360675811767578 length of segment : 16 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 272 time to create 1 rle with old method : 0.0003933906555175781 length of segment : 16 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.000278472900390625 length of segment : 21 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 503 time to create 1 rle with old method : 0.0007152557373046875 length of segment : 31 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.00032520294189453125 length of segment : 14 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 451 time to create 1 rle with old method : 0.0006763935089111328 length of segment : 24 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.63750 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 913 time to create 1 rle with old method : 0.0011649131774902344 length of segment : 39 time for calcul the mask position with numpy : 0.00027370452880859375 nb_pixel_total : 14018 time to create 1 rle with old method : 0.015973329544067383 length of segment : 127 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 958 time to create 1 rle with old method : 0.0012392997741699219 length of segment : 31 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 995 time to create 1 rle with old method : 0.0012896060943603516 length of segment : 44 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 1363 time to create 1 rle with old method : 0.0017671585083007812 length of segment : 40 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 114 time to create 1 rle with old method : 0.00017786026000976562 length of segment : 16 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 364 time to create 1 rle with old method : 0.0005059242248535156 length of segment : 17 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 1277 time to create 1 rle with old method : 0.0016608238220214844 length of segment : 60 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 796 time to create 1 rle with old method : 0.0009603500366210938 length of segment : 35 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 426 time to create 1 rle with old method : 0.0007214546203613281 length of segment : 23 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 371 time to create 1 rle with old method : 0.0005116462707519531 length of segment : 23 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 1176 time to create 1 rle with old method : 0.0016243457794189453 length of segment : 36 time for calcul the mask position with numpy : 0.00042366981506347656 nb_pixel_total : 24772 time to create 1 rle with old method : 0.02630329132080078 length of segment : 147 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 751 time to create 1 rle with old method : 0.0010373592376708984 length of segment : 23 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0007131099700927734 length of segment : 21 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.81719 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0007467269897460938 length of segment : 59 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 252 time to create 1 rle with old method : 0.0004978179931640625 length of segment : 17 time for calcul the mask position with numpy : 0.00014281272888183594 nb_pixel_total : 4298 time to create 1 rle with old method : 0.007492780685424805 length of segment : 108 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 419 time to create 1 rle with old method : 0.0007653236389160156 length of segment : 25 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 432 time to create 1 rle with old method : 0.0008018016815185547 length of segment : 25 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 725 time to create 1 rle with old method : 0.0012459754943847656 length of segment : 36 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 1306 time to create 1 rle with old method : 0.002185821533203125 length of segment : 41 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.0003261566162109375 length of segment : 32 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 650 time to create 1 rle with old method : 0.0012960433959960938 length of segment : 21 time for calcul the mask position with numpy : 0.0007634162902832031 nb_pixel_total : 19718 time to create 1 rle with old method : 0.026980876922607422 length of segment : 261 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 383 time to create 1 rle with old method : 0.0007131099700927734 length of segment : 41 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.87734 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 7.343292236328125e-05 nb_pixel_total : 1419 time to create 1 rle with old method : 0.0020885467529296875 length of segment : 56 time for calcul the mask position with numpy : 0.00010323524475097656 nb_pixel_total : 1103 time to create 1 rle with old method : 0.0015361309051513672 length of segment : 121 time for calcul the mask position with numpy : 6.794929504394531e-05 nb_pixel_total : 969 time to create 1 rle with old method : 0.001436471939086914 length of segment : 54 time for calcul the mask position with numpy : 0.0002048015594482422 nb_pixel_total : 3178 time to create 1 rle with old method : 0.003828287124633789 length of segment : 195 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 1134 time to create 1 rle with old method : 0.001462697982788086 length of segment : 62 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00022220611572265625 length of segment : 10 time for calcul the mask position with numpy : 8.20159912109375e-05 nb_pixel_total : 1501 time to create 1 rle with old method : 0.0023355484008789062 length of segment : 74 time for calcul the mask position with numpy : 0.00013709068298339844 nb_pixel_total : 822 time to create 1 rle with old method : 0.0010478496551513672 length of segment : 71 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 452 time to create 1 rle with old method : 0.0006997585296630859 length of segment : 33 time for calcul the mask position with numpy : 0.00017404556274414062 nb_pixel_total : 2293 time to create 1 rle with old method : 0.002737283706665039 length of segment : 165 Processing 1 images image shape: (280, 400, 3) min: 6.00000 max: 199.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 74.36172 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0010826587677001953 nb_pixel_total : 106903 time to create 1 rle with old method : 0.13702678680419922 length of segment : 280 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.05312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.00028777122497558594 length of segment : 15 time for calcul the mask position with numpy : 0.00011587142944335938 nb_pixel_total : 5126 time to create 1 rle with old method : 0.006206512451171875 length of segment : 82 time for calcul the mask position with numpy : 0.00010824203491210938 nb_pixel_total : 1174 time to create 1 rle with old method : 0.0015177726745605469 length of segment : 56 time for calcul the mask position with numpy : 0.00014591217041015625 nb_pixel_total : 3092 time to create 1 rle with old method : 0.0038983821868896484 length of segment : 88 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 529 time to create 1 rle with old method : 0.0007107257843017578 length of segment : 43 time for calcul the mask position with numpy : 0.0001163482666015625 nb_pixel_total : 4586 time to create 1 rle with old method : 0.005392551422119141 length of segment : 89 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 1501 time to create 1 rle with old method : 0.0018033981323242188 length of segment : 54 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 360 time to create 1 rle with old method : 0.000530242919921875 length of segment : 19 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 467 time to create 1 rle with old method : 0.00061798095703125 length of segment : 35 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 1880 time to create 1 rle with old method : 0.0024595260620117188 length of segment : 46 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 946 time to create 1 rle with old method : 0.001239776611328125 length of segment : 55 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001881122589111328 length of segment : 11 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 35 time for calcul the mask position with numpy : 0.00011992454528808594 nb_pixel_total : 357 time to create 1 rle with old method : 0.0007572174072265625 length of segment : 35 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0009245872497558594 length of segment : 32 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 836 time to create 1 rle with old method : 0.0013518333435058594 length of segment : 38 time for calcul the mask position with numpy : 0.00011277198791503906 nb_pixel_total : 2235 time to create 1 rle with old method : 0.0035512447357177734 length of segment : 71 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 295 time to create 1 rle with old method : 0.0003960132598876953 length of segment : 18 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 871 time to create 1 rle with old method : 0.001119852066040039 length of segment : 38 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 420 time to create 1 rle with old method : 0.0005645751953125 length of segment : 31 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 605 time to create 1 rle with old method : 0.000881195068359375 length of segment : 34 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 435 time to create 1 rle with old method : 0.0007467269897460938 length of segment : 29 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 1739 time to create 1 rle with old method : 0.0021805763244628906 length of segment : 75 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 216 time to create 1 rle with old method : 0.0003020763397216797 length of segment : 18 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002524852752685547 length of segment : 18 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 253 time to create 1 rle with old method : 0.00046253204345703125 length of segment : 18 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004229545593261719 length of segment : 25 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 1371 time to create 1 rle with old method : 0.0016160011291503906 length of segment : 49 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 752 time to create 1 rle with old method : 0.0009293556213378906 length of segment : 33 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 537 time to create 1 rle with old method : 0.0006623268127441406 length of segment : 38 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.0003323554992675781 length of segment : 16 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 777 time to create 1 rle with old method : 0.0012814998626708984 length of segment : 34 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 1814 time to create 1 rle with old method : 0.002841949462890625 length of segment : 82 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 1405 time to create 1 rle with old method : 0.0017130374908447266 length of segment : 51 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 608 time to create 1 rle with old method : 0.0010235309600830078 length of segment : 39 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.0004367828369140625 length of segment : 20 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 410 time to create 1 rle with old method : 0.0005156993865966797 length of segment : 24 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 942 time to create 1 rle with old method : 0.0012004375457763672 length of segment : 56 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 1066 time to create 1 rle with old method : 0.0012929439544677734 length of segment : 44 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 751 time to create 1 rle with old method : 0.0008828639984130859 length of segment : 33 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0005116462707519531 length of segment : 22 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 456 time to create 1 rle with old method : 0.0005786418914794922 length of segment : 36 time for calcul the mask position with numpy : 0.00010704994201660156 nb_pixel_total : 1796 time to create 1 rle with old method : 0.002240419387817383 length of segment : 81 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 277 time to create 1 rle with old method : 0.0004127025604248047 length of segment : 18 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 436 time to create 1 rle with old method : 0.0005390644073486328 length of segment : 40 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 621 time to create 1 rle with old method : 0.0009124279022216797 length of segment : 31 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 192 time to create 1 rle with old method : 0.0002853870391845703 length of segment : 19 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 732 time to create 1 rle with old method : 0.000965118408203125 length of segment : 31 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 20 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 419 time to create 1 rle with old method : 0.0005338191986083984 length of segment : 39 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 492 time to create 1 rle with old method : 0.0006310939788818359 length of segment : 34 time for calcul the mask position with numpy : 0.0001571178436279297 nb_pixel_total : 10851 time to create 1 rle with old method : 0.01214456558227539 length of segment : 118 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.00031685829162597656 length of segment : 13 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.00020813941955566406 length of segment : 27 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 910 time to create 1 rle with old method : 0.0011072158813476562 length of segment : 41 time for calcul the mask position with numpy : 8.916854858398438e-05 nb_pixel_total : 1718 time to create 1 rle with old method : 0.0025336742401123047 length of segment : 34 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 333 time to create 1 rle with old method : 0.0005083084106445312 length of segment : 24 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.00030684471130371094 length of segment : 16 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0005660057067871094 length of segment : 47 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 89 time to create 1 rle with old method : 0.00015592575073242188 length of segment : 22 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 222 time to create 1 rle with old method : 0.000339508056640625 length of segment : 28 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 528 time to create 1 rle with old method : 0.0009016990661621094 length of segment : 21 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 1284 time to create 1 rle with old method : 0.0020682811737060547 length of segment : 44 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 514 time to create 1 rle with old method : 0.0009713172912597656 length of segment : 36 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 544 time to create 1 rle with old method : 0.0008428096771240234 length of segment : 21 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 303 time to create 1 rle with old method : 0.0005271434783935547 length of segment : 23 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 416 time to create 1 rle with old method : 0.0007233619689941406 length of segment : 38 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.0001971721649169922 length of segment : 6 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 322 time to create 1 rle with old method : 0.0004603862762451172 length of segment : 25 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 131.72500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 569 time to create 1 rle with old method : 0.0016405582427978516 length of segment : 47 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006670951843261719 length of segment : 50 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 195 time to create 1 rle with old method : 0.00033974647521972656 length of segment : 12 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 75 time to create 1 rle with old method : 0.00013756752014160156 length of segment : 9 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.00031304359436035156 length of segment : 12 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.07891 max: 145.51406 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 346 time to create 1 rle with old method : 0.0004475116729736328 length of segment : 29 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002951622009277344 length of segment : 42 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 56 time to create 1 rle with old method : 0.00010251998901367188 length of segment : 18 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 261 time to create 1 rle with old method : 0.0003924369812011719 length of segment : 42 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0003821849822998047 length of segment : 34 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.0002772808074951172 length of segment : 29 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 233 time to create 1 rle with old method : 0.0002892017364501953 length of segment : 40 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.14922 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.0005731582641601562 length of segment : 28 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 561 time to create 1 rle with old method : 0.0010704994201660156 length of segment : 34 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.00040650367736816406 length of segment : 31 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 2378 time to create 1 rle with old method : 0.002646207809448242 length of segment : 50 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 193 time to create 1 rle with old method : 0.00036454200744628906 length of segment : 30 time for calcul the mask position with numpy : 2.9087066650390625e-05 nb_pixel_total : 34 time to create 1 rle with old method : 7.367134094238281e-05 length of segment : 6 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 904 time to create 1 rle with old method : 0.0010557174682617188 length of segment : 43 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 241 time to create 1 rle with old method : 0.00031948089599609375 length of segment : 26 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.49688 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 1044 time to create 1 rle with old method : 0.0013437271118164062 length of segment : 59 time for calcul the mask position with numpy : 0.00011301040649414062 nb_pixel_total : 4017 time to create 1 rle with old method : 0.005045890808105469 length of segment : 53 time for calcul the mask position with numpy : 0.00011920928955078125 nb_pixel_total : 5776 time to create 1 rle with old method : 0.00686955451965332 length of segment : 97 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 570 time to create 1 rle with old method : 0.0006756782531738281 length of segment : 43 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.06328 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 0.00027823448181152344 nb_pixel_total : 12608 time to create 1 rle with old method : 0.013942956924438477 length of segment : 316 time for calcul the mask position with numpy : 0.0002040863037109375 nb_pixel_total : 11883 time to create 1 rle with old method : 0.013610124588012695 length of segment : 92 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.00029158592224121094 length of segment : 17 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 361 time to create 1 rle with old method : 0.0005195140838623047 length of segment : 23 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.36172 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 3 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 728 time to create 1 rle with old method : 0.001294851303100586 length of segment : 35 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 1634 time to create 1 rle with old method : 0.001787424087524414 length of segment : 180 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.00019216537475585938 length of segment : 11 Processing 1 images image shape: (400, 400, 3) min: 26.00000 max: 217.00000 molded_images shape: (1, 640, 640, 3) min: -84.39531 max: 93.01641 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.24688 max: 150.82656 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00029850006103515625 length of segment : 12 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 1168 time to create 1 rle with old method : 0.0021851062774658203 length of segment : 34 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 395 time to create 1 rle with old method : 0.0006885528564453125 length of segment : 21 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 405 time to create 1 rle with old method : 0.0007302761077880859 length of segment : 19 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.0006575584411621094 length of segment : 19 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002620220184326172 length of segment : 18 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 326 time to create 1 rle with old method : 0.0004954338073730469 length of segment : 17 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 606 time to create 1 rle with old method : 0.0011365413665771484 length of segment : 33 time for calcul the mask position with numpy : 0.00012612342834472656 nb_pixel_total : 3002 time to create 1 rle with old method : 0.004987001419067383 length of segment : 65 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.71953 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 0.00013756752014160156 nb_pixel_total : 905 time to create 1 rle with old method : 0.002827167510986328 length of segment : 39 time for calcul the mask position with numpy : 0.00011515617370605469 nb_pixel_total : 1640 time to create 1 rle with old method : 0.004379749298095703 length of segment : 33 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 1114 time to create 1 rle with old method : 0.0020949840545654297 length of segment : 34 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 330 time to create 1 rle with old method : 0.0006592273712158203 length of segment : 17 time for calcul the mask position with numpy : 0.00037789344787597656 nb_pixel_total : 13488 time to create 1 rle with old method : 0.022424697875976562 length of segment : 117 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1049 time to create 1 rle with old method : 0.001842498779296875 length of segment : 28 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.00032520294189453125 length of segment : 17 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 490 time to create 1 rle with old method : 0.001001119613647461 length of segment : 25 time for calcul the mask position with numpy : 9.870529174804688e-05 nb_pixel_total : 866 time to create 1 rle with old method : 0.001623392105102539 length of segment : 54 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 766 time to create 1 rle with old method : 0.0010383129119873047 length of segment : 34 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 462 time to create 1 rle with old method : 0.0006062984466552734 length of segment : 75 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.0006816387176513672 length of segment : 30 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0006976127624511719 length of segment : 20 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.00859 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 444 time to create 1 rle with old method : 0.0006318092346191406 length of segment : 69 time for calcul the mask position with numpy : 0.00011658668518066406 nb_pixel_total : 3599 time to create 1 rle with old method : 0.004278421401977539 length of segment : 129 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 704 time to create 1 rle with old method : 0.0008904933929443359 length of segment : 35 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.0002231597900390625 length of segment : 12 time for calcul the mask position with numpy : 0.0005390644073486328 nb_pixel_total : 23096 time to create 1 rle with old method : 0.025529861450195312 length of segment : 382 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 60 time to create 1 rle with old method : 0.0001251697540283203 length of segment : 9 time for calcul the mask position with numpy : 9.465217590332031e-05 nb_pixel_total : 664 time to create 1 rle with old method : 0.0008215904235839844 length of segment : 80 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 1296 time to create 1 rle with old method : 0.0015788078308105469 length of segment : 40 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 944 time to create 1 rle with old method : 0.0011072158813476562 length of segment : 65 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 1300 time to create 1 rle with old method : 0.0015375614166259766 length of segment : 40 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.13906 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 1305 time to create 1 rle with old method : 0.0015785694122314453 length of segment : 54 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.00034046173095703125 length of segment : 44 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 879 time to create 1 rle with old method : 0.0011076927185058594 length of segment : 48 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 614 time to create 1 rle with old method : 0.0007805824279785156 length of segment : 40 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 1075 time to create 1 rle with old method : 0.0013124942779541016 length of segment : 62 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 560 time to create 1 rle with old method : 0.0007719993591308594 length of segment : 57 time for calcul the mask position with numpy : 8.988380432128906e-05 nb_pixel_total : 1884 time to create 1 rle with old method : 0.0022673606872558594 length of segment : 110 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 1615 time to create 1 rle with old method : 0.001979827880859375 length of segment : 77 time for calcul the mask position with numpy : 0.0002079010009765625 nb_pixel_total : 2163 time to create 1 rle with old method : 0.002684354782104492 length of segment : 161 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 1407 time to create 1 rle with old method : 0.001657724380493164 length of segment : 57 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 814 time to create 1 rle with old method : 0.0010743141174316406 length of segment : 64 time for calcul the mask position with numpy : 9.822845458984375e-05 nb_pixel_total : 1094 time to create 1 rle with old method : 0.0012969970703125 length of segment : 91 Processing 1 images image shape: (280, 400, 3) min: 15.00000 max: 194.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 77.54141 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0010118484497070312 nb_pixel_total : 106521 time to create 1 rle with old method : 0.1528329849243164 length of segment : 281 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.0002770423889160156 length of segment : 16 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 1561 time to create 1 rle with old method : 0.0017633438110351562 length of segment : 54 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 495 time to create 1 rle with old method : 0.0006837844848632812 length of segment : 21 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0006060600280761719 length of segment : 34 time for calcul the mask position with numpy : 0.00011372566223144531 nb_pixel_total : 5289 time to create 1 rle with old method : 0.006231069564819336 length of segment : 142 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 923 time to create 1 rle with old method : 0.0010597705841064453 length of segment : 54 time for calcul the mask position with numpy : 0.00010204315185546875 nb_pixel_total : 4215 time to create 1 rle with old method : 0.00468897819519043 length of segment : 123 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 623 time to create 1 rle with old method : 0.0010156631469726562 length of segment : 54 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 1361 time to create 1 rle with old method : 0.002035856246948242 length of segment : 58 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 872 time to create 1 rle with old method : 0.001384735107421875 length of segment : 48 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 41 time for calcul the mask position with numpy : 9.512901306152344e-05 nb_pixel_total : 624 time to create 1 rle with old method : 0.0008292198181152344 length of segment : 34 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1472 time to create 1 rle with old method : 0.0018532276153564453 length of segment : 46 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 351 time to create 1 rle with old method : 0.0004601478576660156 length of segment : 25 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 633 time to create 1 rle with old method : 0.0008077621459960938 length of segment : 37 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 895 time to create 1 rle with old method : 0.0010700225830078125 length of segment : 40 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 268 time to create 1 rle with old method : 0.0004069805145263672 length of segment : 15 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1643 time to create 1 rle with old method : 0.0018961429595947266 length of segment : 56 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 623 time to create 1 rle with old method : 0.0007512569427490234 length of segment : 35 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 815 time to create 1 rle with old method : 0.0009813308715820312 length of segment : 32 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 151 time to create 1 rle with old method : 0.0002162456512451172 length of segment : 16 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.00033473968505859375 length of segment : 17 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 884 time to create 1 rle with old method : 0.0010714530944824219 length of segment : 37 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 239 time to create 1 rle with old method : 0.0003349781036376953 length of segment : 18 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 1272 time to create 1 rle with old method : 0.0015554428100585938 length of segment : 46 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 1287 time to create 1 rle with old method : 0.0015420913696289062 length of segment : 44 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.0003590583801269531 length of segment : 19 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 369 time to create 1 rle with old method : 0.00047016143798828125 length of segment : 38 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 1317 time to create 1 rle with old method : 0.0014863014221191406 length of segment : 55 time for calcul the mask position with numpy : 0.00010633468627929688 nb_pixel_total : 1487 time to create 1 rle with old method : 0.0018575191497802734 length of segment : 91 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 381 time to create 1 rle with old method : 0.000507354736328125 length of segment : 22 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005543231964111328 length of segment : 27 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003483295440673828 length of segment : 17 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 561 time to create 1 rle with old method : 0.0006883144378662109 length of segment : 36 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 420 time to create 1 rle with old method : 0.0008702278137207031 length of segment : 24 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 431 time to create 1 rle with old method : 0.0008344650268554688 length of segment : 32 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.00027298927307128906 length of segment : 13 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1607 time to create 1 rle with old method : 0.001972198486328125 length of segment : 69 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 445 time to create 1 rle with old method : 0.0008099079132080078 length of segment : 41 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 1547 time to create 1 rle with old method : 0.002100229263305664 length of segment : 74 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 404 time to create 1 rle with old method : 0.0008020401000976562 length of segment : 39 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 1173 time to create 1 rle with old method : 0.0018236637115478516 length of segment : 50 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 1628 time to create 1 rle with old method : 0.002384662628173828 length of segment : 73 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 474 time to create 1 rle with old method : 0.0007543563842773438 length of segment : 36 time for calcul the mask position with numpy : 0.0001010894775390625 nb_pixel_total : 1333 time to create 1 rle with old method : 0.0018656253814697266 length of segment : 50 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 705 time to create 1 rle with old method : 0.0012371540069580078 length of segment : 43 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 726 time to create 1 rle with old method : 0.001230478286743164 length of segment : 34 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.0003693103790283203 length of segment : 25 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 1390 time to create 1 rle with old method : 0.002237558364868164 length of segment : 47 time for calcul the mask position with numpy : 0.00010967254638671875 nb_pixel_total : 1633 time to create 1 rle with old method : 0.002934694290161133 length of segment : 55 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 689 time to create 1 rle with old method : 0.0013082027435302734 length of segment : 30 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 450 time to create 1 rle with old method : 0.0008742809295654297 length of segment : 30 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 20 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 1497 time to create 1 rle with old method : 0.0016417503356933594 length of segment : 74 time for calcul the mask position with numpy : 0.0001919269561767578 nb_pixel_total : 10745 time to create 1 rle with old method : 0.011191129684448242 length of segment : 120 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 177 time to create 1 rle with old method : 0.00025463104248046875 length of segment : 12 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.0004928112030029297 length of segment : 31 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 472 time to create 1 rle with old method : 0.0007832050323486328 length of segment : 37 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 416 time to create 1 rle with old method : 0.0006747245788574219 length of segment : 29 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 95 time to create 1 rle with old method : 0.00017905235290527344 length of segment : 22 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 267 time to create 1 rle with old method : 0.00041222572326660156 length of segment : 33 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.0003552436828613281 length of segment : 15 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0007119178771972656 length of segment : 53 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004794597625732422 length of segment : 29 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0008966922760009766 length of segment : 44 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00025081634521484375 length of segment : 26 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.00046896934509277344 length of segment : 13 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 528 time to create 1 rle with old method : 0.0009849071502685547 length of segment : 31 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 402 time to create 1 rle with old method : 0.0007648468017578125 length of segment : 32 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 606 time to create 1 rle with old method : 0.0011165142059326172 length of segment : 22 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 56 time to create 1 rle with old method : 0.00018525123596191406 length of segment : 9 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.00034928321838378906 length of segment : 19 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 570 time to create 1 rle with old method : 0.0010797977447509766 length of segment : 30 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 128.85000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0005941390991210938 length of segment : 53 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 600 time to create 1 rle with old method : 0.0007081031799316406 length of segment : 48 time for calcul the mask position with numpy : 2.86102294921875e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002949237823486328 length of segment : 12 time for calcul the mask position with numpy : 2.5987625122070312e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.0001163482666015625 length of segment : 9 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.96953 max: 148.99062 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0003845691680908203 length of segment : 28 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 13 time to create 1 rle with old method : 5.340576171875e-05 length of segment : 7 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.0003039836883544922 length of segment : 33 time for calcul the mask position with numpy : 0.00017142295837402344 nb_pixel_total : 4402 time to create 1 rle with old method : 0.005064964294433594 length of segment : 105 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 235 time to create 1 rle with old method : 0.00029850006103515625 length of segment : 29 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 266 time to create 1 rle with old method : 0.00036072731018066406 length of segment : 33 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 246 time to create 1 rle with old method : 0.0003185272216796875 length of segment : 33 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 298 time to create 1 rle with old method : 0.000377655029296875 length of segment : 31 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 227 time to create 1 rle with old method : 0.0002968311309814453 length of segment : 29 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.19609 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 0.000118255615234375 nb_pixel_total : 2268 time to create 1 rle with old method : 0.003528594970703125 length of segment : 69 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.00041961669921875 length of segment : 39 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 835 time to create 1 rle with old method : 0.0013403892517089844 length of segment : 33 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 2331 time to create 1 rle with old method : 0.003432750701904297 length of segment : 46 time for calcul the mask position with numpy : 2.86102294921875e-05 nb_pixel_total : 23 time to create 1 rle with old method : 7.200241088867188e-05 length of segment : 5 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1635 time to create 1 rle with old method : 0.002070188522338867 length of segment : 41 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 646 time to create 1 rle with old method : 0.0008401870727539062 length of segment : 85 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 311 time to create 1 rle with old method : 0.0003864765167236328 length of segment : 28 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 1031 time to create 1 rle with old method : 0.001394033432006836 length of segment : 39 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 992 time to create 1 rle with old method : 0.0011441707611083984 length of segment : 45 time for calcul the mask position with numpy : 0.00010538101196289062 nb_pixel_total : 246 time to create 1 rle with old method : 0.00035309791564941406 length of segment : 17 time for calcul the mask position with numpy : 8.440017700195312e-05 nb_pixel_total : 763 time to create 1 rle with old method : 0.0010235309600830078 length of segment : 67 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 318 time to create 1 rle with old method : 0.0004189014434814453 length of segment : 28 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 1001 time to create 1 rle with old method : 0.0013606548309326172 length of segment : 98 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 446 time to create 1 rle with old method : 0.0006203651428222656 length of segment : 26 time for calcul the mask position with numpy : 7.271766662597656e-05 nb_pixel_total : 1584 time to create 1 rle with old method : 0.001961231231689453 length of segment : 106 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.14922 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 1074 time to create 1 rle with old method : 0.0012934207916259766 length of segment : 49 time for calcul the mask position with numpy : 9.751319885253906e-05 nb_pixel_total : 5861 time to create 1 rle with old method : 0.006163120269775391 length of segment : 100 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 66 time to create 1 rle with old method : 0.00013494491577148438 length of segment : 11 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 1806 time to create 1 rle with old method : 0.00237274169921875 length of segment : 43 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -112.96953 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 0.0002624988555908203 nb_pixel_total : 12995 time to create 1 rle with old method : 0.015237808227539062 length of segment : 241 time for calcul the mask position with numpy : 0.0003056526184082031 nb_pixel_total : 11467 time to create 1 rle with old method : 0.01364278793334961 length of segment : 90 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002682209014892578 length of segment : 17 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 94 time to create 1 rle with old method : 0.00019669532775878906 length of segment : 11 time for calcul the mask position with numpy : 0.00011301040649414062 nb_pixel_total : 440 time to create 1 rle with old method : 0.0008645057678222656 length of segment : 26 time for calcul the mask position with numpy : 0.00014328956604003906 nb_pixel_total : 768 time to create 1 rle with old method : 0.001405477523803711 length of segment : 78 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 238 time to create 1 rle with old method : 0.00047850608825683594 length of segment : 15 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 278 time to create 1 rle with old method : 0.00058746337890625 length of segment : 16 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.98281 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.0001494884490966797 nb_pixel_total : 4022 time to create 1 rle with old method : 0.006502389907836914 length of segment : 86 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 854 time to create 1 rle with old method : 0.0014886856079101562 length of segment : 39 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 1688 time to create 1 rle with old method : 0.0019693374633789062 length of segment : 182 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 180 time to create 1 rle with old method : 0.00032830238342285156 length of segment : 13 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001735687255859375 length of segment : 11 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 876 time to create 1 rle with old method : 0.0011167526245117188 length of segment : 39 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002739429473876953 length of segment : 12 Processing 1 images image shape: (400, 400, 3) min: 28.00000 max: 220.00000 molded_images shape: (1, 640, 640, 3) min: -81.70391 max: 95.22344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.30547 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 6.866455078125e-05 nb_pixel_total : 284 time to create 1 rle with old method : 0.0005795955657958984 length of segment : 16 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0006268024444580078 length of segment : 18 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1089 time to create 1 rle with old method : 0.001901388168334961 length of segment : 36 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 441 time to create 1 rle with old method : 0.0007944107055664062 length of segment : 29 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 271 time to create 1 rle with old method : 0.0005922317504882812 length of segment : 20 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 228 time to create 1 rle with old method : 0.00039315223693847656 length of segment : 24 time for calcul the mask position with numpy : 2.8133392333984375e-05 nb_pixel_total : 123 time to create 1 rle with old method : 0.00019788742065429688 length of segment : 12 time for calcul the mask position with numpy : 2.9087066650390625e-05 nb_pixel_total : 62 time to create 1 rle with old method : 0.00011420249938964844 length of segment : 8 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0008990764617919922 length of segment : 25 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.49297 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 930 time to create 1 rle with old method : 0.0012030601501464844 length of segment : 39 time for calcul the mask position with numpy : 0.00025582313537597656 nb_pixel_total : 13184 time to create 1 rle with old method : 0.014314889907836914 length of segment : 132 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 1327 time to create 1 rle with old method : 0.0016477108001708984 length of segment : 29 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 658 time to create 1 rle with old method : 0.0009062290191650391 length of segment : 51 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.000179290771484375 length of segment : 15 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 289 time to create 1 rle with old method : 0.00036263465881347656 length of segment : 16 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 1303 time to create 1 rle with old method : 0.0017123222351074219 length of segment : 43 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 1461 time to create 1 rle with old method : 0.0017628669738769531 length of segment : 31 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 391 time to create 1 rle with old method : 0.0007338523864746094 length of segment : 19 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005869865417480469 length of segment : 21 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 451 time to create 1 rle with old method : 0.0006937980651855469 length of segment : 25 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.55938 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 9.489059448242188e-05 nb_pixel_total : 3796 time to create 1 rle with old method : 0.004402875900268555 length of segment : 126 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 1269 time to create 1 rle with old method : 0.0015468597412109375 length of segment : 40 time for calcul the mask position with numpy : 0.0005123615264892578 nb_pixel_total : 18793 time to create 1 rle with old method : 0.020536422729492188 length of segment : 275 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 697 time to create 1 rle with old method : 0.0009083747863769531 length of segment : 36 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001761913299560547 length of segment : 11 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 313 time to create 1 rle with old method : 0.00043010711669921875 length of segment : 37 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 1844 time to create 1 rle with old method : 0.0027587413787841797 length of segment : 53 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 473 time to create 1 rle with old method : 0.0006089210510253906 length of segment : 30 time for calcul the mask position with numpy : 7.557868957519531e-05 nb_pixel_total : 1005 time to create 1 rle with old method : 0.0016901493072509766 length of segment : 31 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 420 time to create 1 rle with old method : 0.0006558895111083984 length of segment : 28 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.17422 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 491 time to create 1 rle with old method : 0.0006451606750488281 length of segment : 79 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 907 time to create 1 rle with old method : 0.001055002212524414 length of segment : 49 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 1321 time to create 1 rle with old method : 0.0015287399291992188 length of segment : 56 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.0002753734588623047 length of segment : 9 time for calcul the mask position with numpy : 8.940696716308594e-05 nb_pixel_total : 1650 time to create 1 rle with old method : 0.002511262893676758 length of segment : 83 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 464 time to create 1 rle with old method : 0.000804901123046875 length of segment : 28 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.0006053447723388672 length of segment : 30 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 977 time to create 1 rle with old method : 0.0011010169982910156 length of segment : 64 time for calcul the mask position with numpy : 0.0002186298370361328 nb_pixel_total : 2959 time to create 1 rle with old method : 0.0033197402954101562 length of segment : 192 Processing 1 images image shape: (280, 400, 3) min: 15.00000 max: 203.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 79.43984 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0010483264923095703 nb_pixel_total : 106089 time to create 1 rle with old method : 0.11244869232177734 length of segment : 283 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 204 time to create 1 rle with old method : 0.00045561790466308594 length of segment : 16 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0009038448333740234 length of segment : 33 time for calcul the mask position with numpy : 0.00010609626770019531 nb_pixel_total : 1558 time to create 1 rle with old method : 0.003033876419067383 length of segment : 56 time for calcul the mask position with numpy : 9.655952453613281e-05 nb_pixel_total : 566 time to create 1 rle with old method : 0.0013055801391601562 length of segment : 43 time for calcul the mask position with numpy : 7.939338684082031e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0010330677032470703 length of segment : 22 time for calcul the mask position with numpy : 8.511543273925781e-05 nb_pixel_total : 886 time to create 1 rle with old method : 0.0018494129180908203 length of segment : 51 time for calcul the mask position with numpy : 0.00025010108947753906 nb_pixel_total : 4568 time to create 1 rle with old method : 0.007928133010864258 length of segment : 147 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.0002803802490234375 length of segment : 11 time for calcul the mask position with numpy : 0.00021195411682128906 nb_pixel_total : 6793 time to create 1 rle with old method : 0.009613513946533203 length of segment : 213 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 92 time to create 1 rle with old method : 0.0001494884490966797 length of segment : 13 time for calcul the mask position with numpy : 9.298324584960938e-05 nb_pixel_total : 3656 time to create 1 rle with old method : 0.0043141841888427734 length of segment : 68 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.06484 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 39 time for calcul the mask position with numpy : 0.0001533031463623047 nb_pixel_total : 515 time to create 1 rle with old method : 0.0008249282836914062 length of segment : 32 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.0004742145538330078 length of segment : 15 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 999 time to create 1 rle with old method : 0.001447439193725586 length of segment : 56 time for calcul the mask position with numpy : 0.0001552104949951172 nb_pixel_total : 3440 time to create 1 rle with old method : 0.005048990249633789 length of segment : 80 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 970 time to create 1 rle with old method : 0.0014274120330810547 length of segment : 41 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 1254 time to create 1 rle with old method : 0.0016684532165527344 length of segment : 48 time for calcul the mask position with numpy : 7.104873657226562e-05 nb_pixel_total : 760 time to create 1 rle with old method : 0.0011544227600097656 length of segment : 36 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 1601 time to create 1 rle with old method : 0.0023698806762695312 length of segment : 52 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 616 time to create 1 rle with old method : 0.0010044574737548828 length of segment : 30 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 266 time to create 1 rle with old method : 0.000377655029296875 length of segment : 21 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 1987 time to create 1 rle with old method : 0.0021772384643554688 length of segment : 85 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.0001819133758544922 length of segment : 17 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 482 time to create 1 rle with old method : 0.0007658004760742188 length of segment : 27 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0005521774291992188 length of segment : 37 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 282 time to create 1 rle with old method : 0.0005066394805908203 length of segment : 23 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 1149 time to create 1 rle with old method : 0.0018000602722167969 length of segment : 58 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 1321 time to create 1 rle with old method : 0.0028388500213623047 length of segment : 45 time for calcul the mask position with numpy : 9.369850158691406e-05 nb_pixel_total : 359 time to create 1 rle with old method : 0.0009424686431884766 length of segment : 37 time for calcul the mask position with numpy : 0.00013017654418945312 nb_pixel_total : 1718 time to create 1 rle with old method : 0.004123210906982422 length of segment : 59 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 458 time to create 1 rle with old method : 0.0010352134704589844 length of segment : 48 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0003857612609863281 length of segment : 15 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 548 time to create 1 rle with old method : 0.0007715225219726562 length of segment : 35 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 638 time to create 1 rle with old method : 0.0009691715240478516 length of segment : 33 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.0002803802490234375 length of segment : 8 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 477 time to create 1 rle with old method : 0.0007517337799072266 length of segment : 31 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.0004172325134277344 length of segment : 18 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 147 time to create 1 rle with old method : 0.0002689361572265625 length of segment : 19 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 678 time to create 1 rle with old method : 0.0009763240814208984 length of segment : 32 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003554821014404297 length of segment : 17 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 624 time to create 1 rle with old method : 0.0009367465972900391 length of segment : 34 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005593299865722656 length of segment : 33 time for calcul the mask position with numpy : 8.368492126464844e-05 nb_pixel_total : 572 time to create 1 rle with old method : 0.00089263916015625 length of segment : 36 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005147457122802734 length of segment : 29 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005328655242919922 length of segment : 25 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 554 time to create 1 rle with old method : 0.0007157325744628906 length of segment : 32 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0004718303680419922 length of segment : 26 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.0003418922424316406 length of segment : 19 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 219 time to create 1 rle with old method : 0.00032639503479003906 length of segment : 19 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 496 time to create 1 rle with old method : 0.000637054443359375 length of segment : 36 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 26 time for calcul the mask position with numpy : 0.0001246929168701172 nb_pixel_total : 261 time to create 1 rle with old method : 0.0006008148193359375 length of segment : 17 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.000400543212890625 length of segment : 25 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 504 time to create 1 rle with old method : 0.0008559226989746094 length of segment : 38 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 412 time to create 1 rle with old method : 0.0006778240203857422 length of segment : 29 time for calcul the mask position with numpy : 9.036064147949219e-05 nb_pixel_total : 1046 time to create 1 rle with old method : 0.0017018318176269531 length of segment : 52 time for calcul the mask position with numpy : 0.00024247169494628906 nb_pixel_total : 10439 time to create 1 rle with old method : 0.01171422004699707 length of segment : 116 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 980 time to create 1 rle with old method : 0.001163482666015625 length of segment : 45 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 454 time to create 1 rle with old method : 0.0006184577941894531 length of segment : 34 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 32 time to create 1 rle with old method : 6.771087646484375e-05 length of segment : 11 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 50 time to create 1 rle with old method : 0.00011229515075683594 length of segment : 20 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0002951622009277344 length of segment : 28 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002300739288330078 length of segment : 17 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 281 time to create 1 rle with old method : 0.00038123130798339844 length of segment : 33 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 250 time to create 1 rle with old method : 0.000335693359375 length of segment : 35 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 58 time to create 1 rle with old method : 0.00011491775512695312 length of segment : 10 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.00024509429931640625 length of segment : 20 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.00023746490478515625 length of segment : 19 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 449 time to create 1 rle with old method : 0.000667572021484375 length of segment : 26 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.00019311904907226562 length of segment : 28 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 930 time to create 1 rle with old method : 0.0013217926025390625 length of segment : 42 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006213188171386719 length of segment : 37 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.00021076202392578125 length of segment : 14 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 849 time to create 1 rle with old method : 0.0015518665313720703 length of segment : 38 time for calcul the mask position with numpy : 8.916854858398438e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.0007355213165283203 length of segment : 21 time for calcul the mask position with numpy : 0.00012159347534179688 nb_pixel_total : 1250 time to create 1 rle with old method : 0.002293825149536133 length of segment : 80 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 519 time to create 1 rle with old method : 0.0008952617645263672 length of segment : 32 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 131.91250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 576 time to create 1 rle with old method : 0.0007476806640625 length of segment : 51 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 539 time to create 1 rle with old method : 0.0006046295166015625 length of segment : 53 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.00028967857360839844 length of segment : 13 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 67 time to create 1 rle with old method : 0.00014257431030273438 length of segment : 9 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 136 time to create 1 rle with old method : 0.0002682209014892578 length of segment : 13 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.00026679039001464844 length of segment : 25 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.00017762184143066406 length of segment : 9 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.65703 max: 146.22891 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.00015115737915039062 length of segment : 24 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.00032448768615722656 length of segment : 33 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 157 time to create 1 rle with old method : 0.0002989768981933594 length of segment : 21 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.0002980232238769531 length of segment : 30 time for calcul the mask position with numpy : 0.00021076202392578125 nb_pixel_total : 2906 time to create 1 rle with old method : 0.004467964172363281 length of segment : 108 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 221 time to create 1 rle with old method : 0.00040078163146972656 length of segment : 11 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.02031 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 31 time to create 1 rle with old method : 7.772445678710938e-05 length of segment : 7 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 2352 time to create 1 rle with old method : 0.0026788711547851562 length of segment : 74 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.00023651123046875 length of segment : 46 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.0002808570861816406 length of segment : 23 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 1025 time to create 1 rle with old method : 0.00121307373046875 length of segment : 77 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.0002892017364501953 length of segment : 30 time for calcul the mask position with numpy : 2.5510787963867188e-05 nb_pixel_total : 26 time to create 1 rle with old method : 6.198883056640625e-05 length of segment : 6 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 774 time to create 1 rle with old method : 0.0009398460388183594 length of segment : 55 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 1755 time to create 1 rle with old method : 0.0021610260009765625 length of segment : 43 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 1520 time to create 1 rle with old method : 0.0017545223236083984 length of segment : 61 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1922 time to create 1 rle with old method : 0.0022089481353759766 length of segment : 123 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 630 time to create 1 rle with old method : 0.0007843971252441406 length of segment : 32 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 1049 time to create 1 rle with old method : 0.0012235641479492188 length of segment : 39 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.46172 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 3 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 2225 time to create 1 rle with old method : 0.002747774124145508 length of segment : 44 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1418 time to create 1 rle with old method : 0.0018570423126220703 length of segment : 36 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.0005116462707519531 length of segment : 39 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -110.64922 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.00025653839111328125 nb_pixel_total : 13922 time to create 1 rle with old method : 0.015009880065917969 length of segment : 279 time for calcul the mask position with numpy : 0.0002052783966064453 nb_pixel_total : 11956 time to create 1 rle with old method : 0.015561103820800781 length of segment : 92 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.00031566619873046875 length of segment : 16 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 87 time to create 1 rle with old method : 0.00020074844360351562 length of segment : 9 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 800 time to create 1 rle with old method : 0.0011513233184814453 length of segment : 64 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 881 time to create 1 rle with old method : 0.0011684894561767578 length of segment : 40 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.0002391338348388672 length of segment : 11 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1632 time to create 1 rle with old method : 0.002187490463256836 length of segment : 171 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 723 time to create 1 rle with old method : 0.0010323524475097656 length of segment : 35 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 170 time to create 1 rle with old method : 0.00024437904357910156 length of segment : 19 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 913 time to create 1 rle with old method : 0.0014083385467529297 length of segment : 41 Processing 1 images image shape: (400, 400, 3) min: 26.00000 max: 224.00000 molded_images shape: (1, 640, 640, 3) min: -80.53594 max: 99.32109 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -110.50859 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.00031375885009765625 length of segment : 10 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 255 time to create 1 rle with old method : 0.000331878662109375 length of segment : 25 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 447 time to create 1 rle with old method : 0.0005481243133544922 length of segment : 34 time for calcul the mask position with numpy : 0.0001385211944580078 nb_pixel_total : 3241 time to create 1 rle with old method : 0.004854917526245117 length of segment : 61 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 296 time to create 1 rle with old method : 0.0004477500915527344 length of segment : 17 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 1009 time to create 1 rle with old method : 0.0012803077697753906 length of segment : 42 time for calcul the mask position with numpy : 2.8371810913085938e-05 nb_pixel_total : 71 time to create 1 rle with old method : 0.00011920928955078125 length of segment : 11 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 112 time to create 1 rle with old method : 0.00019693374633789062 length of segment : 11 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 869 time to create 1 rle with old method : 0.001096487045288086 length of segment : 40 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.90313 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 896 time to create 1 rle with old method : 0.0012021064758300781 length of segment : 38 time for calcul the mask position with numpy : 0.00023889541625976562 nb_pixel_total : 14281 time to create 1 rle with old method : 0.015605688095092773 length of segment : 140 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 254 time to create 1 rle with old method : 0.0003879070281982422 length of segment : 15 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.0001685619354248047 length of segment : 17 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 1365 time to create 1 rle with old method : 0.0016331672668457031 length of segment : 42 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 1263 time to create 1 rle with old method : 0.0015988349914550781 length of segment : 28 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 314 time to create 1 rle with old method : 0.0004296302795410156 length of segment : 17 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 1362 time to create 1 rle with old method : 0.001811981201171875 length of segment : 28 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.00017976760864257812 length of segment : 16 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0007524490356445312 length of segment : 23 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.55938 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.0004086494445800781 nb_pixel_total : 20939 time to create 1 rle with old method : 0.0251617431640625 length of segment : 317 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 278 time to create 1 rle with old method : 0.0006051063537597656 length of segment : 49 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 2686 time to create 1 rle with old method : 0.004503011703491211 length of segment : 105 time for calcul the mask position with numpy : 0.00010228157043457031 nb_pixel_total : 268 time to create 1 rle with old method : 0.0005996227264404297 length of segment : 35 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 700 time to create 1 rle with old method : 0.0010914802551269531 length of segment : 36 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 375 time to create 1 rle with old method : 0.0006797313690185547 length of segment : 25 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 1259 time to create 1 rle with old method : 0.0018451213836669922 length of segment : 40 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.16250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 1495 time to create 1 rle with old method : 0.0018091201782226562 length of segment : 56 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 915 time to create 1 rle with old method : 0.001161336898803711 length of segment : 49 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 564 time to create 1 rle with old method : 0.0007874965667724609 length of segment : 57 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 267 time to create 1 rle with old method : 0.0004374980926513672 length of segment : 49 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 475 time to create 1 rle with old method : 0.0006778240203857422 length of segment : 29 time for calcul the mask position with numpy : 0.0002357959747314453 nb_pixel_total : 1939 time to create 1 rle with old method : 0.0024785995483398438 length of segment : 136 time for calcul the mask position with numpy : 0.0001399517059326172 nb_pixel_total : 927 time to create 1 rle with old method : 0.0012431144714355469 length of segment : 75 time for calcul the mask position with numpy : 0.00010395050048828125 nb_pixel_total : 1532 time to create 1 rle with old method : 0.0020024776458740234 length of segment : 74 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 643 time to create 1 rle with old method : 0.0008683204650878906 length of segment : 42 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 851 time to create 1 rle with old method : 0.0011436939239501953 length of segment : 43 Processing 1 images image shape: (280, 400, 3) min: 14.00000 max: 201.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 83.75234 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0012078285217285156 nb_pixel_total : 106314 time to create 1 rle with old method : 0.11743569374084473 length of segment : 280 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.05312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.0002765655517578125 length of segment : 15 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 486 time to create 1 rle with old method : 0.00055694580078125 length of segment : 40 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 1554 time to create 1 rle with old method : 0.0019443035125732422 length of segment : 54 time for calcul the mask position with numpy : 0.00010991096496582031 nb_pixel_total : 4537 time to create 1 rle with old method : 0.005078554153442383 length of segment : 98 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.0002079010009765625 length of segment : 10 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 503 time to create 1 rle with old method : 0.0007181167602539062 length of segment : 24 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 874 time to create 1 rle with old method : 0.0010793209075927734 length of segment : 52 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 1425 time to create 1 rle with old method : 0.0016295909881591797 length of segment : 59 time for calcul the mask position with numpy : 0.00010228157043457031 nb_pixel_total : 3786 time to create 1 rle with old method : 0.004565238952636719 length of segment : 81 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 329 time to create 1 rle with old method : 0.0004639625549316406 length of segment : 25 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0005738735198974609 length of segment : 32 time for calcul the mask position with numpy : 0.00010776519775390625 nb_pixel_total : 4512 time to create 1 rle with old method : 0.0050427913665771484 length of segment : 128 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 313 time to create 1 rle with old method : 0.00042724609375 length of segment : 29 time for calcul the mask position with numpy : 0.00015664100646972656 nb_pixel_total : 6104 time to create 1 rle with old method : 0.006772041320800781 length of segment : 75 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 39 time for calcul the mask position with numpy : 9.632110595703125e-05 nb_pixel_total : 896 time to create 1 rle with old method : 0.0011315345764160156 length of segment : 39 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.0003743171691894531 length of segment : 16 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 814 time to create 1 rle with old method : 0.0010333061218261719 length of segment : 39 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 561 time to create 1 rle with old method : 0.0007345676422119141 length of segment : 34 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 345 time to create 1 rle with old method : 0.0004744529724121094 length of segment : 37 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 455 time to create 1 rle with old method : 0.0005750656127929688 length of segment : 33 time for calcul the mask position with numpy : 9.274482727050781e-05 nb_pixel_total : 1667 time to create 1 rle with old method : 0.00211334228515625 length of segment : 50 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0003993511199951172 length of segment : 22 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 752 time to create 1 rle with old method : 0.0010232925415039062 length of segment : 66 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 1170 time to create 1 rle with old method : 0.0015478134155273438 length of segment : 45 time for calcul the mask position with numpy : 0.00010204315185546875 nb_pixel_total : 1623 time to create 1 rle with old method : 0.002249002456665039 length of segment : 55 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 679 time to create 1 rle with old method : 0.0011746883392333984 length of segment : 32 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 275 time to create 1 rle with old method : 0.0005102157592773438 length of segment : 25 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 1303 time to create 1 rle with old method : 0.0022089481353759766 length of segment : 62 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 257 time to create 1 rle with old method : 0.0005311965942382812 length of segment : 27 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.00045609474182128906 length of segment : 19 time for calcul the mask position with numpy : 0.00010085105895996094 nb_pixel_total : 1812 time to create 1 rle with old method : 0.003041505813598633 length of segment : 62 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 520 time to create 1 rle with old method : 0.0009667873382568359 length of segment : 37 time for calcul the mask position with numpy : 0.00014328956604003906 nb_pixel_total : 1517 time to create 1 rle with old method : 0.002640247344970703 length of segment : 51 time for calcul the mask position with numpy : 9.512901306152344e-05 nb_pixel_total : 912 time to create 1 rle with old method : 0.0016472339630126953 length of segment : 39 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 642 time to create 1 rle with old method : 0.0011363029479980469 length of segment : 52 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0008504390716552734 length of segment : 22 time for calcul the mask position with numpy : 0.00010228157043457031 nb_pixel_total : 741 time to create 1 rle with old method : 0.001041412353515625 length of segment : 36 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.0002689361572265625 length of segment : 16 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 86 time to create 1 rle with old method : 0.00012755393981933594 length of segment : 17 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 239 time to create 1 rle with old method : 0.0003676414489746094 length of segment : 16 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 151 time to create 1 rle with old method : 0.0002562999725341797 length of segment : 31 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 395 time to create 1 rle with old method : 0.0005214214324951172 length of segment : 28 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 1889 time to create 1 rle with old method : 0.002436399459838867 length of segment : 82 time for calcul the mask position with numpy : 0.00011205673217773438 nb_pixel_total : 1599 time to create 1 rle with old method : 0.0020143985748291016 length of segment : 44 time for calcul the mask position with numpy : 8.749961853027344e-05 nb_pixel_total : 876 time to create 1 rle with old method : 0.0012402534484863281 length of segment : 37 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 227 time to create 1 rle with old method : 0.0003445148468017578 length of segment : 21 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.00031113624572753906 length of segment : 21 time for calcul the mask position with numpy : 8.392333984375e-05 nb_pixel_total : 1419 time to create 1 rle with old method : 0.0018527507781982422 length of segment : 55 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.00036072731018066406 length of segment : 23 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 426 time to create 1 rle with old method : 0.0005865097045898438 length of segment : 25 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 828 time to create 1 rle with old method : 0.0011365413665771484 length of segment : 42 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 896 time to create 1 rle with old method : 0.0011868476867675781 length of segment : 39 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 1567 time to create 1 rle with old method : 0.001992464065551758 length of segment : 48 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 261 time to create 1 rle with old method : 0.00037741661071777344 length of segment : 20 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005955696105957031 length of segment : 36 time for calcul the mask position with numpy : 0.00023126602172851562 nb_pixel_total : 10775 time to create 1 rle with old method : 0.01272726058959961 length of segment : 112 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 89 time to create 1 rle with old method : 0.00018167495727539062 length of segment : 22 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 231 time to create 1 rle with old method : 0.0003209114074707031 length of segment : 29 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 1043 time to create 1 rle with old method : 0.0014119148254394531 length of segment : 49 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006153583526611328 length of segment : 33 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 551 time to create 1 rle with old method : 0.0008230209350585938 length of segment : 22 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 825 time to create 1 rle with old method : 0.0011904239654541016 length of segment : 42 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.0001938343048095703 length of segment : 28 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 962 time to create 1 rle with old method : 0.001215219497680664 length of segment : 39 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.0001938343048095703 length of segment : 27 time for calcul the mask position with numpy : 0.0002608299255371094 nb_pixel_total : 10174 time to create 1 rle with old method : 0.012054204940795898 length of segment : 183 time for calcul the mask position with numpy : 0.00010514259338378906 nb_pixel_total : 399 time to create 1 rle with old method : 0.0009407997131347656 length of segment : 41 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.0004024505615234375 length of segment : 20 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 507 time to create 1 rle with old method : 0.0006785392761230469 length of segment : 34 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 131.72500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 537 time to create 1 rle with old method : 0.0006949901580810547 length of segment : 46 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 504 time to create 1 rle with old method : 0.0006833076477050781 length of segment : 52 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0015518665313720703 length of segment : 12 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 74 time to create 1 rle with old method : 0.00014519691467285156 length of segment : 9 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 27 time to create 1 rle with old method : 8.106231689453125e-05 length of segment : 6 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.75859 max: 150.63125 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.00028061866760253906 length of segment : 33 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 39 time to create 1 rle with old method : 7.081031799316406e-05 length of segment : 18 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 252 time to create 1 rle with old method : 0.00033354759216308594 length of segment : 36 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.0004127025604248047 length of segment : 11 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0002334117889404297 length of segment : 23 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.00023102760314941406 length of segment : 31 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 1119 time to create 1 rle with old method : 0.0015463829040527344 length of segment : 69 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.00030684471130371094 length of segment : 33 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.83672 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 8.988380432128906e-05 nb_pixel_total : 2416 time to create 1 rle with old method : 0.002724885940551758 length of segment : 89 time for calcul the mask position with numpy : 0.00010371208190917969 nb_pixel_total : 2773 time to create 1 rle with old method : 0.0033178329467773438 length of segment : 108 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.0001232624053955078 length of segment : 15 time for calcul the mask position with numpy : 2.86102294921875e-05 nb_pixel_total : 15 time to create 1 rle with old method : 4.57763671875e-05 length of segment : 3 time for calcul the mask position with numpy : 8.845329284667969e-05 nb_pixel_total : 3279 time to create 1 rle with old method : 0.003799915313720703 length of segment : 66 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 1358 time to create 1 rle with old method : 0.001684427261352539 length of segment : 76 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 28 time to create 1 rle with old method : 6.699562072753906e-05 length of segment : 6 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 1068 time to create 1 rle with old method : 0.001390218734741211 length of segment : 75 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 66 time to create 1 rle with old method : 0.00019431114196777344 length of segment : 24 time for calcul the mask position with numpy : 9.441375732421875e-05 nb_pixel_total : 772 time to create 1 rle with old method : 0.0010409355163574219 length of segment : 63 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.14531 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 2 time for calcul the mask position with numpy : 0.00013875961303710938 nb_pixel_total : 5861 time to create 1 rle with old method : 0.0068645477294921875 length of segment : 100 time for calcul the mask position with numpy : 8.797645568847656e-05 nb_pixel_total : 2483 time to create 1 rle with old method : 0.0031566619873046875 length of segment : 45 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -112.83672 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 0.00035309791564941406 nb_pixel_total : 10724 time to create 1 rle with old method : 0.013025999069213867 length of segment : 223 time for calcul the mask position with numpy : 0.00029778480529785156 nb_pixel_total : 11727 time to create 1 rle with old method : 0.014010429382324219 length of segment : 92 time for calcul the mask position with numpy : 0.0001010894775390625 nb_pixel_total : 1515 time to create 1 rle with old method : 0.0020456314086914062 length of segment : 67 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.0002834796905517578 length of segment : 17 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0005948543548583984 length of segment : 22 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 91 time to create 1 rle with old method : 0.0001475811004638672 length of segment : 13 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 793 time to create 1 rle with old method : 0.001028299331665039 length of segment : 63 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 1294 time to create 1 rle with old method : 0.00164031982421875 length of segment : 64 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.13906 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 821 time to create 1 rle with old method : 0.0011043548583984375 length of segment : 39 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0003781318664550781 length of segment : 12 time for calcul the mask position with numpy : 9.179115295410156e-05 nb_pixel_total : 1351 time to create 1 rle with old method : 0.0016088485717773438 length of segment : 163 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 116 time to create 1 rle with old method : 0.0003261566162109375 length of segment : 14 time for calcul the mask position with numpy : 0.0007288455963134766 nb_pixel_total : 48915 time to create 1 rle with old method : 0.06935501098632812 length of segment : 264 Processing 1 images image shape: (400, 400, 3) min: 19.00000 max: 223.00000 molded_images shape: (1, 640, 640, 3) min: -87.34063 max: 95.85000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -109.36016 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 344 time to create 1 rle with old method : 0.0005116462707519531 length of segment : 18 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 219 time to create 1 rle with old method : 0.0002942085266113281 length of segment : 23 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 476 time to create 1 rle with old method : 0.0006818771362304688 length of segment : 18 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.0002510547637939453 length of segment : 12 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 756 time to create 1 rle with old method : 0.001268625259399414 length of segment : 35 time for calcul the mask position with numpy : 0.0007956027984619141 nb_pixel_total : 3531 time to create 1 rle with old method : 0.00542902946472168 length of segment : 72 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1308 time to create 1 rle with old method : 0.0015485286712646484 length of segment : 47 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 80 time to create 1 rle with old method : 0.00014281272888183594 length of segment : 11 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.21172 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 949 time to create 1 rle with old method : 0.0012722015380859375 length of segment : 39 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 873 time to create 1 rle with old method : 0.001308441162109375 length of segment : 50 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 351 time to create 1 rle with old method : 0.0005559921264648438 length of segment : 26 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 1284 time to create 1 rle with old method : 0.0016927719116210938 length of segment : 28 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 1049 time to create 1 rle with old method : 0.001468658447265625 length of segment : 41 time for calcul the mask position with numpy : 0.0004029273986816406 nb_pixel_total : 16852 time to create 1 rle with old method : 0.01958942413330078 length of segment : 215 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 295 time to create 1 rle with old method : 0.00044536590576171875 length of segment : 16 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0007874965667724609 length of segment : 23 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00019121170043945312 length of segment : 17 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 1353 time to create 1 rle with old method : 0.0017952919006347656 length of segment : 29 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0007991790771484375 length of segment : 26 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.31719 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.0005178451538085938 length of segment : 53 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 719 time to create 1 rle with old method : 0.0010285377502441406 length of segment : 44 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 265 time to create 1 rle with old method : 0.0003743171691894531 length of segment : 19 time for calcul the mask position with numpy : 8.797645568847656e-05 nb_pixel_total : 2713 time to create 1 rle with old method : 0.0033354759216308594 length of segment : 144 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 139 time to create 1 rle with old method : 0.00023865699768066406 length of segment : 13 time for calcul the mask position with numpy : 0.00010371208190917969 nb_pixel_total : 2980 time to create 1 rle with old method : 0.0036613941192626953 length of segment : 83 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 1259 time to create 1 rle with old method : 0.0016303062438964844 length of segment : 39 time for calcul the mask position with numpy : 9.417533874511719e-05 nb_pixel_total : 1069 time to create 1 rle with old method : 0.0014405250549316406 length of segment : 78 time for calcul the mask position with numpy : 0.00061798095703125 nb_pixel_total : 17773 time to create 1 rle with old method : 0.021198511123657227 length of segment : 276 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 128 time to create 1 rle with old method : 0.000217437744140625 length of segment : 11 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 712 time to create 1 rle with old method : 0.0008795261383056641 length of segment : 37 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.01797 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 0.00010204315185546875 nb_pixel_total : 1014 time to create 1 rle with old method : 0.00196075439453125 length of segment : 55 time for calcul the mask position with numpy : 0.000118255615234375 nb_pixel_total : 1329 time to create 1 rle with old method : 0.002569437026977539 length of segment : 56 time for calcul the mask position with numpy : 0.0001361370086669922 nb_pixel_total : 835 time to create 1 rle with old method : 0.0019292831420898438 length of segment : 73 time for calcul the mask position with numpy : 0.00010085105895996094 nb_pixel_total : 1619 time to create 1 rle with old method : 0.0020208358764648438 length of segment : 80 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 579 time to create 1 rle with old method : 0.0009219646453857422 length of segment : 61 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 270 time to create 1 rle with old method : 0.0004661083221435547 length of segment : 27 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00015211105346679688 length of segment : 13 time for calcul the mask position with numpy : 9.894371032714844e-05 nb_pixel_total : 428 time to create 1 rle with old method : 0.0005850791931152344 length of segment : 76 time for calcul the mask position with numpy : 0.00017213821411132812 nb_pixel_total : 2035 time to create 1 rle with old method : 0.002542734146118164 length of segment : 150 time for calcul the mask position with numpy : 0.0002181529998779297 nb_pixel_total : 2151 time to create 1 rle with old method : 0.0026869773864746094 length of segment : 148 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 201.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 84.13125 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.001214742660522461 nb_pixel_total : 106685 time to create 1 rle with old method : 0.13433599472045898 length of segment : 279 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.00029969215393066406 length of segment : 17 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0007007122039794922 length of segment : 42 time for calcul the mask position with numpy : 0.0001316070556640625 nb_pixel_total : 4996 time to create 1 rle with old method : 0.007984161376953125 length of segment : 93 time for calcul the mask position with numpy : 8.20159912109375e-05 nb_pixel_total : 1490 time to create 1 rle with old method : 0.0025069713592529297 length of segment : 53 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0009596347808837891 length of segment : 24 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.0002567768096923828 length of segment : 10 time for calcul the mask position with numpy : 0.00012636184692382812 nb_pixel_total : 4224 time to create 1 rle with old method : 0.007222414016723633 length of segment : 83 time for calcul the mask position with numpy : 7.343292236328125e-05 nb_pixel_total : 916 time to create 1 rle with old method : 0.0011043548583984375 length of segment : 56 time for calcul the mask position with numpy : 0.0001270771026611328 nb_pixel_total : 3350 time to create 1 rle with old method : 0.004144906997680664 length of segment : 78 time for calcul the mask position with numpy : 0.0001704692840576172 nb_pixel_total : 5005 time to create 1 rle with old method : 0.005937814712524414 length of segment : 132 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 45 time for calcul the mask position with numpy : 9.322166442871094e-05 nb_pixel_total : 255 time to create 1 rle with old method : 0.0003993511199951172 length of segment : 16 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 856 time to create 1 rle with old method : 0.0010724067687988281 length of segment : 41 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 1004 time to create 1 rle with old method : 0.0012540817260742188 length of segment : 51 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 215 time to create 1 rle with old method : 0.00032520294189453125 length of segment : 30 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 671 time to create 1 rle with old method : 0.0009176731109619141 length of segment : 32 time for calcul the mask position with numpy : 9.083747863769531e-05 nb_pixel_total : 1458 time to create 1 rle with old method : 0.001970052719116211 length of segment : 48 time for calcul the mask position with numpy : 0.00010180473327636719 nb_pixel_total : 1913 time to create 1 rle with old method : 0.0025560855865478516 length of segment : 66 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 292 time to create 1 rle with old method : 0.0004475116729736328 length of segment : 25 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 833 time to create 1 rle with old method : 0.0010797977447509766 length of segment : 37 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 337 time to create 1 rle with old method : 0.00048351287841796875 length of segment : 28 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 675 time to create 1 rle with old method : 0.0009059906005859375 length of segment : 36 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 617 time to create 1 rle with old method : 0.0007798671722412109 length of segment : 35 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.0001933574676513672 length of segment : 24 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 586 time to create 1 rle with old method : 0.0008275508880615234 length of segment : 47 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 309 time to create 1 rle with old method : 0.0004470348358154297 length of segment : 27 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 274 time to create 1 rle with old method : 0.0003974437713623047 length of segment : 22 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 1397 time to create 1 rle with old method : 0.0017147064208984375 length of segment : 65 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 308 time to create 1 rle with old method : 0.00047135353088378906 length of segment : 34 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 527 time to create 1 rle with old method : 0.0006816387176513672 length of segment : 35 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 163 time to create 1 rle with old method : 0.00025773048400878906 length of segment : 16 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002911090850830078 length of segment : 13 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 594 time to create 1 rle with old method : 0.0008099079132080078 length of segment : 35 time for calcul the mask position with numpy : 8.440017700195312e-05 nb_pixel_total : 1847 time to create 1 rle with old method : 0.0023193359375 length of segment : 82 time for calcul the mask position with numpy : 0.00010180473327636719 nb_pixel_total : 1399 time to create 1 rle with old method : 0.0017609596252441406 length of segment : 51 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 666 time to create 1 rle with old method : 0.0009992122650146484 length of segment : 37 time for calcul the mask position with numpy : 8.988380432128906e-05 nb_pixel_total : 1673 time to create 1 rle with old method : 0.002039194107055664 length of segment : 56 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 386 time to create 1 rle with old method : 0.00054168701171875 length of segment : 27 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 513 time to create 1 rle with old method : 0.0006566047668457031 length of segment : 27 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 834 time to create 1 rle with old method : 0.0011143684387207031 length of segment : 37 time for calcul the mask position with numpy : 9.107589721679688e-05 nb_pixel_total : 1084 time to create 1 rle with old method : 0.0014770030975341797 length of segment : 55 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 954 time to create 1 rle with old method : 0.0012204647064208984 length of segment : 42 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 591 time to create 1 rle with old method : 0.0007162094116210938 length of segment : 52 time for calcul the mask position with numpy : 9.226799011230469e-05 nb_pixel_total : 1836 time to create 1 rle with old method : 0.002205371856689453 length of segment : 70 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.00028777122497558594 length of segment : 15 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 512 time to create 1 rle with old method : 0.00066375732421875 length of segment : 34 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.00020074844360351562 length of segment : 23 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 1634 time to create 1 rle with old method : 0.002025604248046875 length of segment : 51 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 243 time to create 1 rle with old method : 0.0005581378936767578 length of segment : 18 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.0006647109985351562 length of segment : 35 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 688 time to create 1 rle with old method : 0.0012536048889160156 length of segment : 40 time for calcul the mask position with numpy : 0.00012159347534179688 nb_pixel_total : 1616 time to create 1 rle with old method : 0.0022246837615966797 length of segment : 54 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0008046627044677734 length of segment : 20 time for calcul the mask position with numpy : 9.965896606445312e-05 nb_pixel_total : 1592 time to create 1 rle with old method : 0.0023543834686279297 length of segment : 53 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 236 time to create 1 rle with old method : 0.00034236907958984375 length of segment : 16 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 1501 time to create 1 rle with old method : 0.0020461082458496094 length of segment : 49 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 25 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 52 time to create 1 rle with old method : 0.00015163421630859375 length of segment : 19 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 964 time to create 1 rle with old method : 0.0011444091796875 length of segment : 39 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 371 time to create 1 rle with old method : 0.0004851818084716797 length of segment : 34 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 466 time to create 1 rle with old method : 0.0005753040313720703 length of segment : 36 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 128 time to create 1 rle with old method : 0.0002002716064453125 length of segment : 14 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 488 time to create 1 rle with old method : 0.0006594657897949219 length of segment : 38 time for calcul the mask position with numpy : 0.00016450881958007812 nb_pixel_total : 7096 time to create 1 rle with old method : 0.008141756057739258 length of segment : 180 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0005102157592773438 length of segment : 25 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 1071 time to create 1 rle with old method : 0.0013370513916015625 length of segment : 60 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 781 time to create 1 rle with old method : 0.0012640953063964844 length of segment : 36 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 388 time to create 1 rle with old method : 0.0005207061767578125 length of segment : 26 time for calcul the mask position with numpy : 0.0002789497375488281 nb_pixel_total : 7831 time to create 1 rle with old method : 0.009074926376342773 length of segment : 180 time for calcul the mask position with numpy : 0.00015234947204589844 nb_pixel_total : 1516 time to create 1 rle with old method : 0.0027115345001220703 length of segment : 91 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.0005354881286621094 length of segment : 38 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 522 time to create 1 rle with old method : 0.0009932518005371094 length of segment : 34 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 516 time to create 1 rle with old method : 0.0009441375732421875 length of segment : 44 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 113 time to create 1 rle with old method : 0.0001678466796875 length of segment : 28 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1752 time to create 1 rle with old method : 0.002246856689453125 length of segment : 37 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 434 time to create 1 rle with old method : 0.0005626678466796875 length of segment : 33 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 442 time to create 1 rle with old method : 0.0006821155548095703 length of segment : 26 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 242 time to create 1 rle with old method : 0.00044989585876464844 length of segment : 25 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 77 time to create 1 rle with old method : 0.00014281272888183594 length of segment : 22 time for calcul the mask position with numpy : 0.0001590251922607422 nb_pixel_total : 5729 time to create 1 rle with old method : 0.0071833133697509766 length of segment : 154 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006935596466064453 length of segment : 26 time for calcul the mask position with numpy : 0.00020241737365722656 nb_pixel_total : 6576 time to create 1 rle with old method : 0.009494543075561523 length of segment : 184 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 134.53750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 9.441375732421875e-05 nb_pixel_total : 641 time to create 1 rle with old method : 0.00103759765625 length of segment : 52 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 542 time to create 1 rle with old method : 0.0009377002716064453 length of segment : 54 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0005965232849121094 length of segment : 13 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 74 time to create 1 rle with old method : 0.0002257823944091797 length of segment : 9 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.64922 max: 148.37344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 8.487701416015625e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.00042939186096191406 length of segment : 29 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 244 time to create 1 rle with old method : 0.00043201446533203125 length of segment : 10 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 233 time to create 1 rle with old method : 0.00048804283142089844 length of segment : 26 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.0003173351287841797 length of segment : 35 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 50 time to create 1 rle with old method : 0.00011134147644042969 length of segment : 14 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 1252 time to create 1 rle with old method : 0.001692056655883789 length of segment : 79 time for calcul the mask position with numpy : 0.0002677440643310547 nb_pixel_total : 3860 time to create 1 rle with old method : 0.004929065704345703 length of segment : 79 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 266 time to create 1 rle with old method : 0.0003972053527832031 length of segment : 30 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 246 time to create 1 rle with old method : 0.00035953521728515625 length of segment : 27 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 167 time to create 1 rle with old method : 0.00025177001953125 length of segment : 30 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 304 time to create 1 rle with old method : 0.00041556358337402344 length of segment : 38 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.03594 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 0.00010824203491210938 nb_pixel_total : 3032 time to create 1 rle with old method : 0.0035681724548339844 length of segment : 116 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 234 time to create 1 rle with old method : 0.00036072731018066406 length of segment : 30 time for calcul the mask position with numpy : 9.655952453613281e-05 nb_pixel_total : 2060 time to create 1 rle with old method : 0.002680540084838867 length of segment : 85 time for calcul the mask position with numpy : 0.0001418590545654297 nb_pixel_total : 3234 time to create 1 rle with old method : 0.004247426986694336 length of segment : 57 time for calcul the mask position with numpy : 8.58306884765625e-05 nb_pixel_total : 657 time to create 1 rle with old method : 0.000904083251953125 length of segment : 65 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 1398 time to create 1 rle with old method : 0.001760244369506836 length of segment : 40 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 22 time to create 1 rle with old method : 6.365776062011719e-05 length of segment : 5 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 346 time to create 1 rle with old method : 0.0004935264587402344 length of segment : 46 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 726 time to create 1 rle with old method : 0.000911712646484375 length of segment : 47 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.40313 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 3 time for calcul the mask position with numpy : 0.00012111663818359375 nb_pixel_total : 6148 time to create 1 rle with old method : 0.007363319396972656 length of segment : 105 time for calcul the mask position with numpy : 0.0001068115234375 nb_pixel_total : 2563 time to create 1 rle with old method : 0.0032165050506591797 length of segment : 42 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 1660 time to create 1 rle with old method : 0.002223491668701172 length of segment : 42 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -112.90703 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.0002579689025878906 nb_pixel_total : 13394 time to create 1 rle with old method : 0.015363931655883789 length of segment : 232 time for calcul the mask position with numpy : 8.249282836914062e-05 nb_pixel_total : 203 time to create 1 rle with old method : 0.00037789344787597656 length of segment : 17 time for calcul the mask position with numpy : 0.00023555755615234375 nb_pixel_total : 11363 time to create 1 rle with old method : 0.016167879104614258 length of segment : 90 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.00017881393432617188 length of segment : 13 time for calcul the mask position with numpy : 8.487701416015625e-05 nb_pixel_total : 912 time to create 1 rle with old method : 0.0015797615051269531 length of segment : 55 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.27187 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 170 time to create 1 rle with old method : 0.00034046173095703125 length of segment : 12 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 694 time to create 1 rle with old method : 0.0010383129119873047 length of segment : 38 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.00021600723266601562 length of segment : 13 time for calcul the mask position with numpy : 0.0005006790161132812 nb_pixel_total : 41458 time to create 1 rle with old method : 0.053457021713256836 length of segment : 276 time for calcul the mask position with numpy : 0.00011563301086425781 nb_pixel_total : 1540 time to create 1 rle with old method : 0.001802206039428711 length of segment : 186 Processing 1 images image shape: (400, 400, 3) min: 26.00000 max: 222.00000 molded_images shape: (1, 640, 640, 3) min: -87.08672 max: 95.99297 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.21172 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 78 time to create 1 rle with old method : 0.00018143653869628906 length of segment : 10 time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 2784 time to create 1 rle with old method : 0.0036988258361816406 length of segment : 57 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.0003390312194824219 length of segment : 21 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 475 time to create 1 rle with old method : 0.0008525848388671875 length of segment : 39 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 407 time to create 1 rle with old method : 0.0008356571197509766 length of segment : 19 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 111 time to create 1 rle with old method : 0.00025844573974609375 length of segment : 12 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 272 time to create 1 rle with old method : 0.0005853176116943359 length of segment : 15 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 976 time to create 1 rle with old method : 0.0017762184143066406 length of segment : 39 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.56719 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 916 time to create 1 rle with old method : 0.0012378692626953125 length of segment : 39 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 1454 time to create 1 rle with old method : 0.0020198822021484375 length of segment : 44 time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00025963783264160156 length of segment : 14 time for calcul the mask position with numpy : 8.296966552734375e-05 nb_pixel_total : 455 time to create 1 rle with old method : 0.0011179447174072266 length of segment : 25 time for calcul the mask position with numpy : 9.202957153320312e-05 nb_pixel_total : 986 time to create 1 rle with old method : 0.0019032955169677734 length of segment : 24 time for calcul the mask position with numpy : 0.00045609474182128906 nb_pixel_total : 14261 time to create 1 rle with old method : 0.021378755569458008 length of segment : 128 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 355 time to create 1 rle with old method : 0.0005431175231933594 length of segment : 17 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 1150 time to create 1 rle with old method : 0.002282857894897461 length of segment : 35 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.62188 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.00010776519775390625 nb_pixel_total : 339 time to create 1 rle with old method : 0.0006914138793945312 length of segment : 52 time for calcul the mask position with numpy : 0.0006508827209472656 nb_pixel_total : 19192 time to create 1 rle with old method : 0.02103734016418457 length of segment : 300 time for calcul the mask position with numpy : 0.00011873245239257812 nb_pixel_total : 1273 time to create 1 rle with old method : 0.0016009807586669922 length of segment : 41 time for calcul the mask position with numpy : 0.00015234947204589844 nb_pixel_total : 3470 time to create 1 rle with old method : 0.00450444221496582 length of segment : 119 time for calcul the mask position with numpy : 0.00011086463928222656 nb_pixel_total : 2387 time to create 1 rle with old method : 0.0033974647521972656 length of segment : 47 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 667 time to create 1 rle with old method : 0.0008356571197509766 length of segment : 35 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 243 time to create 1 rle with old method : 0.00043010711669921875 length of segment : 27 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.52969 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 1364 time to create 1 rle with old method : 0.0017139911651611328 length of segment : 57 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 883 time to create 1 rle with old method : 0.0011696815490722656 length of segment : 47 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 894 time to create 1 rle with old method : 0.00116729736328125 length of segment : 68 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 571 time to create 1 rle with old method : 0.0007455348968505859 length of segment : 58 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 565 time to create 1 rle with old method : 0.0007915496826171875 length of segment : 43 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 973 time to create 1 rle with old method : 0.0012311935424804688 length of segment : 63 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.00022745132446289062 length of segment : 30 time for calcul the mask position with numpy : 9.918212890625e-05 nb_pixel_total : 1575 time to create 1 rle with old method : 0.0018634796142578125 length of segment : 78 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00019121170043945312 length of segment : 9 time for calcul the mask position with numpy : 0.0002396106719970703 nb_pixel_total : 2250 time to create 1 rle with old method : 0.003149747848510742 length of segment : 158 time for calcul the mask position with numpy : 0.00011134147644042969 nb_pixel_total : 164 time to create 1 rle with old method : 0.0003559589385986328 length of segment : 29 Processing 1 images image shape: (280, 400, 3) min: 4.00000 max: 203.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 80.50625 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009512901306152344 nb_pixel_total : 106846 time to create 1 rle with old method : 0.11615657806396484 length of segment : 280 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.00625 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.000396728515625 length of segment : 17 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0008852481842041016 length of segment : 22 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 572 time to create 1 rle with old method : 0.0007946491241455078 length of segment : 42 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 476 time to create 1 rle with old method : 0.0007727146148681641 length of segment : 42 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.0002815723419189453 length of segment : 12 time for calcul the mask position with numpy : 0.00013375282287597656 nb_pixel_total : 4651 time to create 1 rle with old method : 0.0067560672760009766 length of segment : 111 time for calcul the mask position with numpy : 0.0003523826599121094 nb_pixel_total : 12240 time to create 1 rle with old method : 0.01702713966369629 length of segment : 229 time for calcul the mask position with numpy : 0.00020813941955566406 nb_pixel_total : 4238 time to create 1 rle with old method : 0.00590825080871582 length of segment : 116 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 1573 time to create 1 rle with old method : 0.0025396347045898438 length of segment : 55 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 814 time to create 1 rle with old method : 0.0014886856079101562 length of segment : 46 time for calcul the mask position with numpy : 0.00012373924255371094 nb_pixel_total : 4431 time to create 1 rle with old method : 0.005501985549926758 length of segment : 131 time for calcul the mask position with numpy : 0.0001347064971923828 nb_pixel_total : 5063 time to create 1 rle with old method : 0.006247997283935547 length of segment : 99 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 1874 time to create 1 rle with old method : 0.002453327178955078 length of segment : 49 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 841 time to create 1 rle with old method : 0.001588582992553711 length of segment : 50 time for calcul the mask position with numpy : 0.0001232624053955078 nb_pixel_total : 1484 time to create 1 rle with old method : 0.0037720203399658203 length of segment : 53 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 39 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 852 time to create 1 rle with old method : 0.0010564327239990234 length of segment : 38 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 761 time to create 1 rle with old method : 0.0013883113861083984 length of segment : 43 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 626 time to create 1 rle with old method : 0.0009388923645019531 length of segment : 36 time for calcul the mask position with numpy : 8.916854858398438e-05 nb_pixel_total : 1262 time to create 1 rle with old method : 0.002011537551879883 length of segment : 47 time for calcul the mask position with numpy : 9.989738464355469e-05 nb_pixel_total : 1940 time to create 1 rle with old method : 0.003042936325073242 length of segment : 60 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 261 time to create 1 rle with old method : 0.0004019737243652344 length of segment : 14 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 298 time to create 1 rle with old method : 0.0004410743713378906 length of segment : 25 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 518 time to create 1 rle with old method : 0.0006678104400634766 length of segment : 40 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.00048089027404785156 length of segment : 37 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 801 time to create 1 rle with old method : 0.0010683536529541016 length of segment : 40 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 242 time to create 1 rle with old method : 0.00036334991455078125 length of segment : 20 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 1221 time to create 1 rle with old method : 0.0016314983367919922 length of segment : 49 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 594 time to create 1 rle with old method : 0.0008270740509033203 length of segment : 38 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 769 time to create 1 rle with old method : 0.0009436607360839844 length of segment : 45 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 216 time to create 1 rle with old method : 0.0003399848937988281 length of segment : 32 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 832 time to create 1 rle with old method : 0.0011417865753173828 length of segment : 37 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 251 time to create 1 rle with old method : 0.00038909912109375 length of segment : 29 time for calcul the mask position with numpy : 9.489059448242188e-05 nb_pixel_total : 1655 time to create 1 rle with old method : 0.002043485641479492 length of segment : 54 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.0002684593200683594 length of segment : 19 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 615 time to create 1 rle with old method : 0.0007946491241455078 length of segment : 32 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 2033 time to create 1 rle with old method : 0.002481698989868164 length of segment : 82 time for calcul the mask position with numpy : 6.866455078125e-05 nb_pixel_total : 336 time to create 1 rle with old method : 0.0005052089691162109 length of segment : 28 time for calcul the mask position with numpy : 9.72747802734375e-05 nb_pixel_total : 1553 time to create 1 rle with old method : 0.0019407272338867188 length of segment : 50 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 224 time to create 1 rle with old method : 0.00034427642822265625 length of segment : 18 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 342 time to create 1 rle with old method : 0.00048732757568359375 length of segment : 27 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 1307 time to create 1 rle with old method : 0.0016541481018066406 length of segment : 43 time for calcul the mask position with numpy : 7.557868957519531e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.00023937225341796875 length of segment : 10 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.0003063678741455078 length of segment : 21 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 174 time to create 1 rle with old method : 0.000274658203125 length of segment : 16 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 219 time to create 1 rle with old method : 0.00031447410583496094 length of segment : 16 time for calcul the mask position with numpy : 9.870529174804688e-05 nb_pixel_total : 1844 time to create 1 rle with old method : 0.002896547317504883 length of segment : 49 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006444454193115234 length of segment : 21 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 1295 time to create 1 rle with old method : 0.0014903545379638672 length of segment : 64 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 461 time to create 1 rle with old method : 0.0006363391876220703 length of segment : 36 time for calcul the mask position with numpy : 0.00011539459228515625 nb_pixel_total : 1666 time to create 1 rle with old method : 0.0029327869415283203 length of segment : 59 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 557 time to create 1 rle with old method : 0.0007967948913574219 length of segment : 29 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 1514 time to create 1 rle with old method : 0.0018897056579589844 length of segment : 51 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 845 time to create 1 rle with old method : 0.0011534690856933594 length of segment : 37 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005450248718261719 length of segment : 28 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 23 time for calcul the mask position with numpy : 0.000331878662109375 nb_pixel_total : 11066 time to create 1 rle with old method : 0.0146484375 length of segment : 117 time for calcul the mask position with numpy : 0.00010442733764648438 nb_pixel_total : 1213 time to create 1 rle with old method : 0.0016162395477294922 length of segment : 63 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002841949462890625 length of segment : 23 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 272 time to create 1 rle with old method : 0.0003445148468017578 length of segment : 31 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.00018334388732910156 length of segment : 15 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 35 time to create 1 rle with old method : 0.00011682510375976562 length of segment : 14 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0008485317230224609 length of segment : 41 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.0002193450927734375 length of segment : 28 time for calcul the mask position with numpy : 0.00022530555725097656 nb_pixel_total : 9475 time to create 1 rle with old method : 0.01097726821899414 length of segment : 184 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 132 time to create 1 rle with old method : 0.0002810955047607422 length of segment : 11 time for calcul the mask position with numpy : 0.00012826919555664062 nb_pixel_total : 367 time to create 1 rle with old method : 0.0012691020965576172 length of segment : 32 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 35 time to create 1 rle with old method : 0.0001494884490966797 length of segment : 19 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0008575916290283203 length of segment : 29 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 455 time to create 1 rle with old method : 0.0008471012115478516 length of segment : 25 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.0003600120544433594 length of segment : 27 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 295 time to create 1 rle with old method : 0.0004067420959472656 length of segment : 20 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 495 time to create 1 rle with old method : 0.0007555484771728516 length of segment : 17 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.00016617774963378906 length of segment : 6 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 141 time to create 1 rle with old method : 0.0002219676971435547 length of segment : 23 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 5 time to create 1 rle with old method : 0.00012373924255371094 length of segment : 4 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 564 time to create 1 rle with old method : 0.0007243156433105469 length of segment : 36 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.0002467632293701172 length of segment : 27 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 723 time to create 1 rle with old method : 0.0011243820190429688 length of segment : 31 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 128.85000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 584 time to create 1 rle with old method : 0.0008962154388427734 length of segment : 48 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 525 time to create 1 rle with old method : 0.00083160400390625 length of segment : 53 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.0003333091735839844 length of segment : 13 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 74 time to create 1 rle with old method : 0.00019240379333496094 length of segment : 10 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 31 time to create 1 rle with old method : 0.00011372566223144531 length of segment : 6 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.00015044212341308594 length of segment : 10 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.45391 max: 147.11562 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.0002791881561279297 length of segment : 30 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 42 time to create 1 rle with old method : 0.00012946128845214844 length of segment : 18 time for calcul the mask position with numpy : 0.0003719329833984375 nb_pixel_total : 6530 time to create 1 rle with old method : 0.011796236038208008 length of segment : 125 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 1433 time to create 1 rle with old method : 0.0024788379669189453 length of segment : 49 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 370 time to create 1 rle with old method : 0.0006582736968994141 length of segment : 53 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 491 time to create 1 rle with old method : 0.0010950565338134766 length of segment : 37 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.000331878662109375 length of segment : 8 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.0003180503845214844 length of segment : 24 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.00028395652770996094 length of segment : 37 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003197193145751953 length of segment : 32 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.21563 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 0.00012803077697753906 nb_pixel_total : 1839 time to create 1 rle with old method : 0.0027844905853271484 length of segment : 61 time for calcul the mask position with numpy : 0.00011801719665527344 nb_pixel_total : 2528 time to create 1 rle with old method : 0.0037641525268554688 length of segment : 99 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 244 time to create 1 rle with old method : 0.0004515647888183594 length of segment : 27 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 869 time to create 1 rle with old method : 0.001096487045288086 length of segment : 48 time for calcul the mask position with numpy : 0.00012612342834472656 nb_pixel_total : 3235 time to create 1 rle with old method : 0.003962993621826172 length of segment : 56 time for calcul the mask position with numpy : 0.00014519691467285156 nb_pixel_total : 987 time to create 1 rle with old method : 0.0016074180603027344 length of segment : 59 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 1325 time to create 1 rle with old method : 0.0017359256744384766 length of segment : 39 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 770 time to create 1 rle with old method : 0.0011172294616699219 length of segment : 21 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 881 time to create 1 rle with old method : 0.0012042522430419922 length of segment : 39 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.0002980232238769531 length of segment : 17 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 757 time to create 1 rle with old method : 0.0011906623840332031 length of segment : 81 time for calcul the mask position with numpy : 0.00011730194091796875 nb_pixel_total : 1957 time to create 1 rle with old method : 0.0026128292083740234 length of segment : 85 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003495216369628906 length of segment : 47 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 376 time to create 1 rle with old method : 0.0005042552947998047 length of segment : 49 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.43438 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 3 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 1758 time to create 1 rle with old method : 0.0023527145385742188 length of segment : 39 time for calcul the mask position with numpy : 0.00013899803161621094 nb_pixel_total : 6147 time to create 1 rle with old method : 0.007334709167480469 length of segment : 101 time for calcul the mask position with numpy : 0.00012063980102539062 nb_pixel_total : 2109 time to create 1 rle with old method : 0.0029489994049072266 length of segment : 57 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.29375 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.0003294944763183594 nb_pixel_total : 11240 time to create 1 rle with old method : 0.015699148178100586 length of segment : 222 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.00036215782165527344 length of segment : 17 time for calcul the mask position with numpy : 0.00025343894958496094 nb_pixel_total : 12156 time to create 1 rle with old method : 0.017351627349853516 length of segment : 96 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.00023555755615234375 length of segment : 13 time for calcul the mask position with numpy : 8.392333984375e-05 nb_pixel_total : 802 time to create 1 rle with old method : 0.0014140605926513672 length of segment : 57 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.79531 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 93 time to create 1 rle with old method : 0.00029659271240234375 length of segment : 10 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 882 time to create 1 rle with old method : 0.0011749267578125 length of segment : 41 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.000278472900390625 length of segment : 11 time for calcul the mask position with numpy : 0.00014543533325195312 nb_pixel_total : 4006 time to create 1 rle with old method : 0.005143165588378906 length of segment : 54 time for calcul the mask position with numpy : 9.012222290039062e-05 nb_pixel_total : 242 time to create 1 rle with old method : 0.0003814697265625 length of segment : 18 time for calcul the mask position with numpy : 0.00013017654418945312 nb_pixel_total : 1523 time to create 1 rle with old method : 0.002257823944091797 length of segment : 166 time for calcul the mask position with numpy : 0.0010159015655517578 nb_pixel_total : 44903 time to create 1 rle with old method : 0.12205028533935547 length of segment : 262 Processing 1 images image shape: (400, 400, 3) min: 23.00000 max: 227.00000 molded_images shape: (1, 640, 640, 3) min: -92.57109 max: 100.48516 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.77813 max: 150.63125 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 9.036064147949219e-05 nb_pixel_total : 288 time to create 1 rle with old method : 0.0005049705505371094 length of segment : 16 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0015442371368408203 length of segment : 42 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 1261 time to create 1 rle with old method : 0.0017397403717041016 length of segment : 56 time for calcul the mask position with numpy : 0.0001430511474609375 nb_pixel_total : 3121 time to create 1 rle with old method : 0.005254507064819336 length of segment : 62 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 696 time to create 1 rle with old method : 0.001378774642944336 length of segment : 34 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.00029206275939941406 length of segment : 12 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.0003123283386230469 length of segment : 19 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 565 time to create 1 rle with old method : 0.0008623600006103516 length of segment : 23 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 406 time to create 1 rle with old method : 0.0006308555603027344 length of segment : 34 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 76 time to create 1 rle with old method : 0.0001513957977294922 length of segment : 10 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 76 time to create 1 rle with old method : 0.00016188621520996094 length of segment : 10 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 417 time to create 1 rle with old method : 0.0006611347198486328 length of segment : 20 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 476 time to create 1 rle with old method : 0.0007202625274658203 length of segment : 21 time for calcul the mask position with numpy : 9.298324584960938e-05 nb_pixel_total : 1097 time to create 1 rle with old method : 0.0014362335205078125 length of segment : 31 time for calcul the mask position with numpy : 0.0001957416534423828 nb_pixel_total : 4454 time to create 1 rle with old method : 0.00608062744140625 length of segment : 132 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.68047 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 891 time to create 1 rle with old method : 0.0011703968048095703 length of segment : 38 time for calcul the mask position with numpy : 8.797645568847656e-05 nb_pixel_total : 1144 time to create 1 rle with old method : 0.002438783645629883 length of segment : 22 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 316 time to create 1 rle with old method : 0.0004355907440185547 length of segment : 24 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 101 time to create 1 rle with old method : 0.00016260147094726562 length of segment : 14 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 355 time to create 1 rle with old method : 0.0005161762237548828 length of segment : 18 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0014386177062988281 length of segment : 34 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 657 time to create 1 rle with old method : 0.0009403228759765625 length of segment : 43 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 1311 time to create 1 rle with old method : 0.001802682876586914 length of segment : 27 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 440 time to create 1 rle with old method : 0.0006730556488037109 length of segment : 20 time for calcul the mask position with numpy : 0.0003247261047363281 nb_pixel_total : 14122 time to create 1 rle with old method : 0.019748210906982422 length of segment : 133 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 201 time to create 1 rle with old method : 0.00033783912658691406 length of segment : 15 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 443 time to create 1 rle with old method : 0.0007336139678955078 length of segment : 24 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.72734 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 9.107589721679688e-05 nb_pixel_total : 1078 time to create 1 rle with old method : 0.002029895782470703 length of segment : 76 time for calcul the mask position with numpy : 0.0005118846893310547 nb_pixel_total : 16192 time to create 1 rle with old method : 0.01947331428527832 length of segment : 313 time for calcul the mask position with numpy : 0.0001678466796875 nb_pixel_total : 1918 time to create 1 rle with old method : 0.0032660961151123047 length of segment : 105 time for calcul the mask position with numpy : 9.226799011230469e-05 nb_pixel_total : 495 time to create 1 rle with old method : 0.0007274150848388672 length of segment : 29 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 74 time to create 1 rle with old method : 0.00016546249389648438 length of segment : 10 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 1280 time to create 1 rle with old method : 0.001560211181640625 length of segment : 40 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.0003132820129394531 length of segment : 11 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 327 time to create 1 rle with old method : 0.0005929470062255859 length of segment : 42 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 675 time to create 1 rle with old method : 0.0008461475372314453 length of segment : 36 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.00234 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 9.965896606445312e-05 nb_pixel_total : 1485 time to create 1 rle with old method : 0.003013134002685547 length of segment : 56 time for calcul the mask position with numpy : 0.00011515617370605469 nb_pixel_total : 1681 time to create 1 rle with old method : 0.0032720565795898438 length of segment : 85 time for calcul the mask position with numpy : 0.00011396408081054688 nb_pixel_total : 909 time to create 1 rle with old method : 0.0016276836395263672 length of segment : 50 time for calcul the mask position with numpy : 0.0001804828643798828 nb_pixel_total : 1337 time to create 1 rle with old method : 0.0024995803833007812 length of segment : 81 time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 545 time to create 1 rle with old method : 0.001191854476928711 length of segment : 55 time for calcul the mask position with numpy : 0.0002734661102294922 nb_pixel_total : 2081 time to create 1 rle with old method : 0.004501819610595703 length of segment : 142 time for calcul the mask position with numpy : 0.00015497207641601562 nb_pixel_total : 513 time to create 1 rle with old method : 0.001878499984741211 length of segment : 30 time for calcul the mask position with numpy : 9.989738464355469e-05 nb_pixel_total : 223 time to create 1 rle with old method : 0.0008378028869628906 length of segment : 39 time for calcul the mask position with numpy : 0.0001246929168701172 nb_pixel_total : 857 time to create 1 rle with old method : 0.0017313957214355469 length of segment : 73 time for calcul the mask position with numpy : 0.00032520294189453125 nb_pixel_total : 2013 time to create 1 rle with old method : 0.006368160247802734 length of segment : 146 Processing 1 images image shape: (280, 400, 3) min: 11.00000 max: 200.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 76.56094 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0010046958923339844 nb_pixel_total : 106967 time to create 1 rle with old method : 0.11469721794128418 length of segment : 281 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.08828 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.00035309791564941406 length of segment : 16 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 431 time to create 1 rle with old method : 0.0007150173187255859 length of segment : 32 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 462 time to create 1 rle with old method : 0.0006055831909179688 length of segment : 40 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 311 time to create 1 rle with old method : 0.0004546642303466797 length of segment : 25 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 473 time to create 1 rle with old method : 0.0006825923919677734 length of segment : 21 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1435 time to create 1 rle with old method : 0.0018901824951171875 length of segment : 52 time for calcul the mask position with numpy : 0.00010251998901367188 nb_pixel_total : 3188 time to create 1 rle with old method : 0.003763914108276367 length of segment : 121 time for calcul the mask position with numpy : 0.00013065338134765625 nb_pixel_total : 4441 time to create 1 rle with old method : 0.00680088996887207 length of segment : 103 time for calcul the mask position with numpy : 0.00036787986755371094 nb_pixel_total : 4326 time to create 1 rle with old method : 0.012287378311157227 length of segment : 96 time for calcul the mask position with numpy : 7.271766662597656e-05 nb_pixel_total : 383 time to create 1 rle with old method : 0.0007958412170410156 length of segment : 36 time for calcul the mask position with numpy : 0.00010323524475097656 nb_pixel_total : 113 time to create 1 rle with old method : 0.0004372596740722656 length of segment : 11 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 100 time to create 1 rle with old method : 0.00023221969604492188 length of segment : 13 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 887 time to create 1 rle with old method : 0.0013997554779052734 length of segment : 52 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 345 time to create 1 rle with old method : 0.0006341934204101562 length of segment : 18 time for calcul the mask position with numpy : 0.0001304149627685547 nb_pixel_total : 3996 time to create 1 rle with old method : 0.004937887191772461 length of segment : 133 time for calcul the mask position with numpy : 9.655952453613281e-05 nb_pixel_total : 1795 time to create 1 rle with old method : 0.002175569534301758 length of segment : 45 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.02969 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 44 time for calcul the mask position with numpy : 0.00016736984252929688 nb_pixel_total : 644 time to create 1 rle with old method : 0.0009160041809082031 length of segment : 35 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 877 time to create 1 rle with old method : 0.0011303424835205078 length of segment : 51 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 273 time to create 1 rle with old method : 0.0003840923309326172 length of segment : 24 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004734992980957031 length of segment : 35 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.00041103363037109375 length of segment : 15 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 926 time to create 1 rle with old method : 0.001201629638671875 length of segment : 40 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 478 time to create 1 rle with old method : 0.0007088184356689453 length of segment : 37 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.0007035732269287109 length of segment : 21 time for calcul the mask position with numpy : 0.00010323524475097656 nb_pixel_total : 1493 time to create 1 rle with old method : 0.001981019973754883 length of segment : 48 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.0004889965057373047 length of segment : 29 time for calcul the mask position with numpy : 0.00010323524475097656 nb_pixel_total : 1739 time to create 1 rle with old method : 0.002107381820678711 length of segment : 68 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 254 time to create 1 rle with old method : 0.00041937828063964844 length of segment : 17 time for calcul the mask position with numpy : 9.202957153320312e-05 nb_pixel_total : 1445 time to create 1 rle with old method : 0.0018002986907958984 length of segment : 65 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 282 time to create 1 rle with old method : 0.0004181861877441406 length of segment : 23 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 201 time to create 1 rle with old method : 0.0002875328063964844 length of segment : 17 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.00039386749267578125 length of segment : 16 time for calcul the mask position with numpy : 9.703636169433594e-05 nb_pixel_total : 1849 time to create 1 rle with old method : 0.002269268035888672 length of segment : 63 time for calcul the mask position with numpy : 0.00012135505676269531 nb_pixel_total : 1586 time to create 1 rle with old method : 0.0022399425506591797 length of segment : 53 time for calcul the mask position with numpy : 0.00010371208190917969 nb_pixel_total : 994 time to create 1 rle with old method : 0.0013439655303955078 length of segment : 41 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 401 time to create 1 rle with old method : 0.0005707740783691406 length of segment : 21 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 1557 time to create 1 rle with old method : 0.0020341873168945312 length of segment : 52 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 268 time to create 1 rle with old method : 0.0004067420959472656 length of segment : 15 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 329 time to create 1 rle with old method : 0.00047135353088378906 length of segment : 36 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 476 time to create 1 rle with old method : 0.0006585121154785156 length of segment : 38 time for calcul the mask position with numpy : 0.00010657310485839844 nb_pixel_total : 630 time to create 1 rle with old method : 0.0008335113525390625 length of segment : 37 time for calcul the mask position with numpy : 9.822845458984375e-05 nb_pixel_total : 961 time to create 1 rle with old method : 0.0012722015380859375 length of segment : 39 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 203 time to create 1 rle with old method : 0.00032019615173339844 length of segment : 36 time for calcul the mask position with numpy : 8.749961853027344e-05 nb_pixel_total : 1169 time to create 1 rle with old method : 0.0014846324920654297 length of segment : 59 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0005371570587158203 length of segment : 26 time for calcul the mask position with numpy : 0.00010752677917480469 nb_pixel_total : 979 time to create 1 rle with old method : 0.0018968582153320312 length of segment : 43 time for calcul the mask position with numpy : 0.0001475811004638672 nb_pixel_total : 2098 time to create 1 rle with old method : 0.0036478042602539062 length of segment : 62 time for calcul the mask position with numpy : 0.0001671314239501953 nb_pixel_total : 962 time to create 1 rle with old method : 0.0017824172973632812 length of segment : 57 time for calcul the mask position with numpy : 0.00013017654418945312 nb_pixel_total : 1617 time to create 1 rle with old method : 0.0027399063110351562 length of segment : 58 time for calcul the mask position with numpy : 8.20159912109375e-05 nb_pixel_total : 580 time to create 1 rle with old method : 0.001050710678100586 length of segment : 29 time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 966 time to create 1 rle with old method : 0.0017082691192626953 length of segment : 62 time for calcul the mask position with numpy : 8.487701416015625e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0007340908050537109 length of segment : 27 time for calcul the mask position with numpy : 0.00010991096496582031 nb_pixel_total : 828 time to create 1 rle with old method : 0.0015513896942138672 length of segment : 49 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 411 time to create 1 rle with old method : 0.0007946491241455078 length of segment : 24 time for calcul the mask position with numpy : 0.00012063980102539062 nb_pixel_total : 1602 time to create 1 rle with old method : 0.0027980804443359375 length of segment : 50 time for calcul the mask position with numpy : 8.845329284667969e-05 nb_pixel_total : 521 time to create 1 rle with old method : 0.0009381771087646484 length of segment : 37 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 145 time to create 1 rle with old method : 0.0003230571746826172 length of segment : 12 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.00038552284240722656 length of segment : 12 time for calcul the mask position with numpy : 0.00010371208190917969 nb_pixel_total : 1547 time to create 1 rle with old method : 0.0028676986694335938 length of segment : 50 time for calcul the mask position with numpy : 8.630752563476562e-05 nb_pixel_total : 651 time to create 1 rle with old method : 0.001306295394897461 length of segment : 35 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 23 time for calcul the mask position with numpy : 0.00027632713317871094 nb_pixel_total : 9611 time to create 1 rle with old method : 0.015758752822875977 length of segment : 147 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.00022530555725097656 length of segment : 16 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 287 time to create 1 rle with old method : 0.0003764629364013672 length of segment : 29 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 1535 time to create 1 rle with old method : 0.0021011829376220703 length of segment : 81 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.00045013427734375 length of segment : 22 time for calcul the mask position with numpy : 7.939338684082031e-05 nb_pixel_total : 263 time to create 1 rle with old method : 0.0005588531494140625 length of segment : 30 time for calcul the mask position with numpy : 0.00010466575622558594 nb_pixel_total : 299 time to create 1 rle with old method : 0.0006389617919921875 length of segment : 22 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 431 time to create 1 rle with old method : 0.0009522438049316406 length of segment : 16 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 518 time to create 1 rle with old method : 0.001016855239868164 length of segment : 25 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.00046896934509277344 length of segment : 25 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0008420944213867188 length of segment : 29 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 23 time to create 1 rle with old method : 8.249282836914062e-05 length of segment : 11 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 180 time to create 1 rle with old method : 0.000400543212890625 length of segment : 26 time for calcul the mask position with numpy : 9.632110595703125e-05 nb_pixel_total : 679 time to create 1 rle with old method : 0.0010106563568115234 length of segment : 44 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.0002789497375488281 length of segment : 39 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 151 time to create 1 rle with old method : 0.00024080276489257812 length of segment : 23 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 59 time to create 1 rle with old method : 0.00015592575073242188 length of segment : 10 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 508 time to create 1 rle with old method : 0.0012750625610351562 length of segment : 34 time for calcul the mask position with numpy : 0.0001499652862548828 nb_pixel_total : 186 time to create 1 rle with old method : 0.0004918575286865234 length of segment : 29 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 208 time to create 1 rle with old method : 0.0003032684326171875 length of segment : 24 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 518 time to create 1 rle with old method : 0.0007522106170654297 length of segment : 36 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0005702972412109375 length of segment : 43 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 667 time to create 1 rle with old method : 0.00098419189453125 length of segment : 33 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 134.53750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.0001690387725830078 nb_pixel_total : 594 time to create 1 rle with old method : 0.0016179084777832031 length of segment : 49 time for calcul the mask position with numpy : 0.0001049041748046875 nb_pixel_total : 554 time to create 1 rle with old method : 0.0011692047119140625 length of segment : 54 time for calcul the mask position with numpy : 0.00010919570922851562 nb_pixel_total : 184 time to create 1 rle with old method : 0.0005056858062744141 length of segment : 13 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 75 time to create 1 rle with old method : 0.00021409988403320312 length of segment : 10 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 51 time to create 1 rle with old method : 0.00018286705017089844 length of segment : 9 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.35625 max: 148.37344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.00014257431030273438 nb_pixel_total : 47 time to create 1 rle with old method : 0.0002243518829345703 length of segment : 15 time for calcul the mask position with numpy : 8.368492126464844e-05 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006012916564941406 length of segment : 35 time for calcul the mask position with numpy : 0.00045609474182128906 nb_pixel_total : 13325 time to create 1 rle with old method : 0.025012969970703125 length of segment : 210 time for calcul the mask position with numpy : 0.000125885009765625 nb_pixel_total : 322 time to create 1 rle with old method : 0.0011496543884277344 length of segment : 32 time for calcul the mask position with numpy : 0.00013017654418945312 nb_pixel_total : 244 time to create 1 rle with old method : 0.0006725788116455078 length of segment : 31 time for calcul the mask position with numpy : 0.00010752677917480469 nb_pixel_total : 213 time to create 1 rle with old method : 0.0005598068237304688 length of segment : 39 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 342 time to create 1 rle with old method : 0.0007226467132568359 length of segment : 29 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.23906 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 8.606910705566406e-05 nb_pixel_total : 1871 time to create 1 rle with old method : 0.0024046897888183594 length of segment : 80 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 237 time to create 1 rle with old method : 0.0004291534423828125 length of segment : 23 time for calcul the mask position with numpy : 0.00011038780212402344 nb_pixel_total : 2875 time to create 1 rle with old method : 0.0039064884185791016 length of segment : 112 time for calcul the mask position with numpy : 8.845329284667969e-05 nb_pixel_total : 1812 time to create 1 rle with old method : 0.0024569034576416016 length of segment : 43 time for calcul the mask position with numpy : 0.000141143798828125 nb_pixel_total : 3238 time to create 1 rle with old method : 0.0038614273071289062 length of segment : 56 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 309 time to create 1 rle with old method : 0.0005779266357421875 length of segment : 13 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 684 time to create 1 rle with old method : 0.0009720325469970703 length of segment : 64 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 21 time to create 1 rle with old method : 0.00010418891906738281 length of segment : 4 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 32 time to create 1 rle with old method : 0.00010514259338378906 length of segment : 7 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 867 time to create 1 rle with old method : 0.0017342567443847656 length of segment : 41 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 345 time to create 1 rle with old method : 0.0007569789886474609 length of segment : 53 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.67656 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.00029730796813964844 nb_pixel_total : 6098 time to create 1 rle with old method : 0.01179957389831543 length of segment : 103 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.10625 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.00029206275939941406 length of segment : 15 time for calcul the mask position with numpy : 0.0001800060272216797 nb_pixel_total : 11477 time to create 1 rle with old method : 0.014142990112304688 length of segment : 91 time for calcul the mask position with numpy : 0.0003681182861328125 nb_pixel_total : 10609 time to create 1 rle with old method : 0.01291656494140625 length of segment : 226 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.0001995563507080078 length of segment : 14 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 754 time to create 1 rle with old method : 0.0010516643524169922 length of segment : 49 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.16641 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 391 time to create 1 rle with old method : 0.0005526542663574219 length of segment : 26 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 755 time to create 1 rle with old method : 0.0010271072387695312 length of segment : 37 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 96 time to create 1 rle with old method : 0.0002665519714355469 length of segment : 11 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 1391 time to create 1 rle with old method : 0.0017631053924560547 length of segment : 78 time for calcul the mask position with numpy : 0.0006773471832275391 nb_pixel_total : 41828 time to create 1 rle with old method : 0.04964733123779297 length of segment : 275 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 758 time to create 1 rle with old method : 0.0011065006256103516 length of segment : 37 Processing 1 images image shape: (400, 400, 3) min: 18.00000 max: 220.00000 molded_images shape: (1, 640, 640, 3) min: -86.47734 max: 91.82109 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.62969 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 0.0001232624053955078 nb_pixel_total : 2849 time to create 1 rle with old method : 0.004858970642089844 length of segment : 55 time for calcul the mask position with numpy : 8.606910705566406e-05 nb_pixel_total : 308 time to create 1 rle with old method : 0.0006558895111083984 length of segment : 16 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 475 time to create 1 rle with old method : 0.0008561611175537109 length of segment : 35 time for calcul the mask position with numpy : 0.0001068115234375 nb_pixel_total : 461 time to create 1 rle with old method : 0.0008971691131591797 length of segment : 25 time for calcul the mask position with numpy : 0.0001125335693359375 nb_pixel_total : 1242 time to create 1 rle with old method : 0.0023260116577148438 length of segment : 33 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 90 time to create 1 rle with old method : 0.00023794174194335938 length of segment : 11 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00025463104248046875 length of segment : 7 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 79 time to create 1 rle with old method : 0.0001976490020751953 length of segment : 11 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.23516 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 0.00014472007751464844 nb_pixel_total : 948 time to create 1 rle with old method : 0.0017695426940917969 length of segment : 40 time for calcul the mask position with numpy : 0.00011301040649414062 nb_pixel_total : 1055 time to create 1 rle with old method : 0.0019626617431640625 length of segment : 30 time for calcul the mask position with numpy : 0.0005109310150146484 nb_pixel_total : 13784 time to create 1 rle with old method : 0.024576663970947266 length of segment : 126 time for calcul the mask position with numpy : 0.00011849403381347656 nb_pixel_total : 115 time to create 1 rle with old method : 0.00024271011352539062 length of segment : 17 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 291 time to create 1 rle with old method : 0.0006089210510253906 length of segment : 23 time for calcul the mask position with numpy : 9.107589721679688e-05 nb_pixel_total : 1534 time to create 1 rle with old method : 0.0021555423736572266 length of segment : 39 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 1254 time to create 1 rle with old method : 0.0016562938690185547 length of segment : 30 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 695 time to create 1 rle with old method : 0.0009968280792236328 length of segment : 33 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 480 time to create 1 rle with old method : 0.0007772445678710938 length of segment : 23 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 306 time to create 1 rle with old method : 0.0004627704620361328 length of segment : 17 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 797 time to create 1 rle with old method : 0.0011241436004638672 length of segment : 40 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.70391 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0008182525634765625 length of segment : 44 time for calcul the mask position with numpy : 0.00010395050048828125 nb_pixel_total : 3371 time to create 1 rle with old method : 0.004745006561279297 length of segment : 73 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 1257 time to create 1 rle with old method : 0.0017361640930175781 length of segment : 39 time for calcul the mask position with numpy : 9.012222290039062e-05 nb_pixel_total : 1274 time to create 1 rle with old method : 0.0017905235290527344 length of segment : 65 time for calcul the mask position with numpy : 0.0008060932159423828 nb_pixel_total : 23687 time to create 1 rle with old method : 0.025893688201904297 length of segment : 332 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 598 time to create 1 rle with old method : 0.0008814334869384766 length of segment : 44 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 447 time to create 1 rle with old method : 0.0007174015045166016 length of segment : 29 time for calcul the mask position with numpy : 0.000102996826171875 nb_pixel_total : 2105 time to create 1 rle with old method : 0.0032563209533691406 length of segment : 105 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00021409988403320312 length of segment : 11 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 709 time to create 1 rle with old method : 0.0009009838104248047 length of segment : 36 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.05312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 1468 time to create 1 rle with old method : 0.0018351078033447266 length of segment : 58 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 783 time to create 1 rle with old method : 0.000997304916381836 length of segment : 44 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0007579326629638672 length of segment : 29 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 101 time to create 1 rle with old method : 0.00017905235290527344 length of segment : 9 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 526 time to create 1 rle with old method : 0.0007126331329345703 length of segment : 53 time for calcul the mask position with numpy : 9.703636169433594e-05 nb_pixel_total : 833 time to create 1 rle with old method : 0.001129150390625 length of segment : 76 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 1611 time to create 1 rle with old method : 0.0019567012786865234 length of segment : 78 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 699 time to create 1 rle with old method : 0.0009200572967529297 length of segment : 63 time for calcul the mask position with numpy : 0.0001537799835205078 nb_pixel_total : 2563 time to create 1 rle with old method : 0.0032393932342529297 length of segment : 184 time for calcul the mask position with numpy : 0.00018072128295898438 nb_pixel_total : 2756 time to create 1 rle with old method : 0.0034744739532470703 length of segment : 206 Processing 1 images image shape: (280, 400, 3) min: 17.00000 max: 196.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 76.56094 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0010755062103271484 nb_pixel_total : 106856 time to create 1 rle with old method : 0.12207913398742676 length of segment : 282 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 208 time to create 1 rle with old method : 0.000400543212890625 length of segment : 15 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 418 time to create 1 rle with old method : 0.0005578994750976562 length of segment : 34 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 498 time to create 1 rle with old method : 0.0007045269012451172 length of segment : 23 time for calcul the mask position with numpy : 9.584426879882812e-05 nb_pixel_total : 1532 time to create 1 rle with old method : 0.003323793411254883 length of segment : 56 time for calcul the mask position with numpy : 0.0002627372741699219 nb_pixel_total : 3786 time to create 1 rle with old method : 0.006955862045288086 length of segment : 84 time for calcul the mask position with numpy : 0.00016689300537109375 nb_pixel_total : 4765 time to create 1 rle with old method : 0.0060596466064453125 length of segment : 130 time for calcul the mask position with numpy : 9.72747802734375e-05 nb_pixel_total : 560 time to create 1 rle with old method : 0.0008256435394287109 length of segment : 43 time for calcul the mask position with numpy : 0.00017023086547851562 nb_pixel_total : 5331 time to create 1 rle with old method : 0.006683349609375 length of segment : 83 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.0003094673156738281 length of segment : 13 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 799 time to create 1 rle with old method : 0.0010123252868652344 length of segment : 48 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 356 time to create 1 rle with old method : 0.0005464553833007812 length of segment : 19 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 893 time to create 1 rle with old method : 0.0011856555938720703 length of segment : 51 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 1739 time to create 1 rle with old method : 0.0022742748260498047 length of segment : 44 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 39 time for calcul the mask position with numpy : 0.00010609626770019531 nb_pixel_total : 343 time to create 1 rle with old method : 0.0006761550903320312 length of segment : 36 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 895 time to create 1 rle with old method : 0.0016293525695800781 length of segment : 39 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 277 time to create 1 rle with old method : 0.0006022453308105469 length of segment : 16 time for calcul the mask position with numpy : 0.0001087188720703125 nb_pixel_total : 656 time to create 1 rle with old method : 0.0011529922485351562 length of segment : 36 time for calcul the mask position with numpy : 0.00011420249938964844 nb_pixel_total : 1592 time to create 1 rle with old method : 0.002839803695678711 length of segment : 52 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 1256 time to create 1 rle with old method : 0.0022749900817871094 length of segment : 46 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 378 time to create 1 rle with old method : 0.0007588863372802734 length of segment : 21 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 274 time to create 1 rle with old method : 0.0005106925964355469 length of segment : 27 time for calcul the mask position with numpy : 0.00012421607971191406 nb_pixel_total : 1722 time to create 1 rle with old method : 0.0032923221588134766 length of segment : 64 time for calcul the mask position with numpy : 9.369850158691406e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0003783702850341797 length of segment : 17 time for calcul the mask position with numpy : 0.0001010894775390625 nb_pixel_total : 1059 time to create 1 rle with old method : 0.0018453598022460938 length of segment : 54 time for calcul the mask position with numpy : 8.845329284667969e-05 nb_pixel_total : 561 time to create 1 rle with old method : 0.0010254383087158203 length of segment : 40 time for calcul the mask position with numpy : 0.00010895729064941406 nb_pixel_total : 1695 time to create 1 rle with old method : 0.0028848648071289062 length of segment : 65 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.000438690185546875 length of segment : 16 time for calcul the mask position with numpy : 0.00010395050048828125 nb_pixel_total : 1590 time to create 1 rle with old method : 0.0026781558990478516 length of segment : 49 time for calcul the mask position with numpy : 9.179115295410156e-05 nb_pixel_total : 849 time to create 1 rle with old method : 0.0015621185302734375 length of segment : 37 time for calcul the mask position with numpy : 9.5367431640625e-05 nb_pixel_total : 816 time to create 1 rle with old method : 0.0014677047729492188 length of segment : 38 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.0004296302795410156 length of segment : 14 time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 685 time to create 1 rle with old method : 0.0012106895446777344 length of segment : 31 time for calcul the mask position with numpy : 7.557868957519531e-05 nb_pixel_total : 459 time to create 1 rle with old method : 0.0008678436279296875 length of segment : 27 time for calcul the mask position with numpy : 9.5367431640625e-05 nb_pixel_total : 494 time to create 1 rle with old method : 0.0014460086822509766 length of segment : 22 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 690 time to create 1 rle with old method : 0.0012438297271728516 length of segment : 37 time for calcul the mask position with numpy : 0.00012350082397460938 nb_pixel_total : 1526 time to create 1 rle with old method : 0.0027501583099365234 length of segment : 50 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 509 time to create 1 rle with old method : 0.0009720325469970703 length of segment : 22 time for calcul the mask position with numpy : 8.749961853027344e-05 nb_pixel_total : 736 time to create 1 rle with old method : 0.0014214515686035156 length of segment : 43 time for calcul the mask position with numpy : 0.0003037452697753906 nb_pixel_total : 2014 time to create 1 rle with old method : 0.005904197692871094 length of segment : 83 time for calcul the mask position with numpy : 0.00014519691467285156 nb_pixel_total : 1648 time to create 1 rle with old method : 0.0028488636016845703 length of segment : 75 time for calcul the mask position with numpy : 0.00011110305786132812 nb_pixel_total : 1338 time to create 1 rle with old method : 0.0023643970489501953 length of segment : 47 time for calcul the mask position with numpy : 6.866455078125e-05 nb_pixel_total : 459 time to create 1 rle with old method : 0.0008969306945800781 length of segment : 33 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.00035309791564941406 length of segment : 28 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.0004298686981201172 length of segment : 14 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.00044035911560058594 length of segment : 32 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 416 time to create 1 rle with old method : 0.0007834434509277344 length of segment : 24 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 233 time to create 1 rle with old method : 0.0004868507385253906 length of segment : 21 time for calcul the mask position with numpy : 0.00010943412780761719 nb_pixel_total : 886 time to create 1 rle with old method : 0.0016677379608154297 length of segment : 38 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.0004506111145019531 length of segment : 18 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.0008301734924316406 length of segment : 24 time for calcul the mask position with numpy : 0.00011301040649414062 nb_pixel_total : 1297 time to create 1 rle with old method : 0.0025734901428222656 length of segment : 47 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 724 time to create 1 rle with old method : 0.0014181137084960938 length of segment : 35 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 20 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 263 time to create 1 rle with old method : 0.0005803108215332031 length of segment : 28 time for calcul the mask position with numpy : 0.00024056434631347656 nb_pixel_total : 10821 time to create 1 rle with old method : 0.01338815689086914 length of segment : 110 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00021147727966308594 length of segment : 15 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 950 time to create 1 rle with old method : 0.0012860298156738281 length of segment : 44 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 319 time to create 1 rle with old method : 0.00044918060302734375 length of segment : 55 time for calcul the mask position with numpy : 7.82012939453125e-05 nb_pixel_total : 1781 time to create 1 rle with old method : 0.002443075180053711 length of segment : 34 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 397 time to create 1 rle with old method : 0.0005733966827392578 length of segment : 23 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.00029015541076660156 length of segment : 23 time for calcul the mask position with numpy : 0.00025343894958496094 nb_pixel_total : 11269 time to create 1 rle with old method : 0.012959957122802734 length of segment : 196 time for calcul the mask position with numpy : 9.131431579589844e-05 nb_pixel_total : 2 time to create 1 rle with old method : 3.4809112548828125e-05 length of segment : 2 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 402 time to create 1 rle with old method : 0.0005025863647460938 length of segment : 32 time for calcul the mask position with numpy : 9.107589721679688e-05 nb_pixel_total : 432 time to create 1 rle with old method : 0.0009396076202392578 length of segment : 25 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 387 time to create 1 rle with old method : 0.0008032321929931641 length of segment : 28 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 275 time to create 1 rle with old method : 0.0003998279571533203 length of segment : 27 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.00022077560424804688 length of segment : 27 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 511 time to create 1 rle with old method : 0.0007445812225341797 length of segment : 18 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 961 time to create 1 rle with old method : 0.0013222694396972656 length of segment : 36 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.00039005279541015625 length of segment : 14 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006580352783203125 length of segment : 34 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 369 time to create 1 rle with old method : 0.0005106925964355469 length of segment : 30 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 132.66250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 607 time to create 1 rle with old method : 0.0008096694946289062 length of segment : 49 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 511 time to create 1 rle with old method : 0.0007333755493164062 length of segment : 52 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 179 time to create 1 rle with old method : 0.0003235340118408203 length of segment : 12 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.0003638267517089844 length of segment : 12 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 88 time to create 1 rle with old method : 0.00018310546875 length of segment : 10 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 73 time to create 1 rle with old method : 0.00014853477478027344 length of segment : 9 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.12188 max: 148.37344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 275 time to create 1 rle with old method : 0.00040268898010253906 length of segment : 38 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 53 time to create 1 rle with old method : 0.00011682510375976562 length of segment : 19 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 239 time to create 1 rle with old method : 0.0004057884216308594 length of segment : 10 time for calcul the mask position with numpy : 0.00031828880310058594 nb_pixel_total : 14009 time to create 1 rle with old method : 0.020610570907592773 length of segment : 210 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 361 time to create 1 rle with old method : 0.0005421638488769531 length of segment : 29 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.00027441978454589844 length of segment : 34 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 147 time to create 1 rle with old method : 0.0002346038818359375 length of segment : 29 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003407001495361328 length of segment : 36 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00012493133544921875 length of segment : 20 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 369 time to create 1 rle with old method : 0.0005180835723876953 length of segment : 36 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -112.79766 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 0.00013566017150878906 nb_pixel_total : 2499 time to create 1 rle with old method : 0.005673885345458984 length of segment : 83 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.0005190372467041016 length of segment : 29 time for calcul the mask position with numpy : 0.0002422332763671875 nb_pixel_total : 3121 time to create 1 rle with old method : 0.006796598434448242 length of segment : 124 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 310 time to create 1 rle with old method : 0.0007266998291015625 length of segment : 11 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 870 time to create 1 rle with old method : 0.0016689300537109375 length of segment : 48 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 94 time to create 1 rle with old method : 0.0002455711364746094 length of segment : 14 time for calcul the mask position with numpy : 0.0001347064971923828 nb_pixel_total : 1092 time to create 1 rle with old method : 0.0022644996643066406 length of segment : 60 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 278 time to create 1 rle with old method : 0.0005552768707275391 length of segment : 42 time for calcul the mask position with numpy : 0.00010418891906738281 nb_pixel_total : 1522 time to create 1 rle with old method : 0.002724170684814453 length of segment : 71 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 28 time to create 1 rle with old method : 9.655952453613281e-05 length of segment : 5 time for calcul the mask position with numpy : 0.00012254714965820312 nb_pixel_total : 2731 time to create 1 rle with old method : 0.0045545101165771484 length of segment : 107 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.99297 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 2 time for calcul the mask position with numpy : 0.00014138221740722656 nb_pixel_total : 3740 time to create 1 rle with old method : 0.006919384002685547 length of segment : 68 time for calcul the mask position with numpy : 0.00013637542724609375 nb_pixel_total : 1724 time to create 1 rle with old method : 0.002872467041015625 length of segment : 37 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.67266 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 0.0005979537963867188 nb_pixel_total : 10554 time to create 1 rle with old method : 0.019583702087402344 length of segment : 218 time for calcul the mask position with numpy : 9.298324584960938e-05 nb_pixel_total : 174 time to create 1 rle with old method : 0.0004341602325439453 length of segment : 15 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 91 time to create 1 rle with old method : 0.00025200843811035156 length of segment : 11 time for calcul the mask position with numpy : 0.0003120899200439453 nb_pixel_total : 11527 time to create 1 rle with old method : 0.02457427978515625 length of segment : 93 time for calcul the mask position with numpy : 9.274482727050781e-05 nb_pixel_total : 711 time to create 1 rle with old method : 0.0017387866973876953 length of segment : 55 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 765 time to create 1 rle with old method : 0.0013027191162109375 length of segment : 43 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 352 time to create 1 rle with old method : 0.0005664825439453125 length of segment : 22 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 337 time to create 1 rle with old method : 0.0005733966827392578 length of segment : 21 time for calcul the mask position with numpy : 0.0002384185791015625 nb_pixel_total : 12975 time to create 1 rle with old method : 0.01621723175048828 length of segment : 93 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.62344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.00042057037353515625 length of segment : 13 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 647 time to create 1 rle with old method : 0.0015420913696289062 length of segment : 33 time for calcul the mask position with numpy : 0.00010418891906738281 nb_pixel_total : 1478 time to create 1 rle with old method : 0.0021657943725585938 length of segment : 177 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 1661 time to create 1 rle with old method : 0.0023255348205566406 length of segment : 84 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 89 time to create 1 rle with old method : 0.00017499923706054688 length of segment : 11 time for calcul the mask position with numpy : 0.0009510517120361328 nb_pixel_total : 49585 time to create 1 rle with old method : 0.0637056827545166 length of segment : 255 Processing 1 images image shape: (400, 400, 3) min: 18.00000 max: 223.00000 molded_images shape: (1, 640, 640, 3) min: -85.20781 max: 95.85000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.0002994537353515625 length of segment : 21 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 1204 time to create 1 rle with old method : 0.001596212387084961 length of segment : 30 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 488 time to create 1 rle with old method : 0.0007088184356689453 length of segment : 23 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 300 time to create 1 rle with old method : 0.00046563148498535156 length of segment : 16 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.00022125244140625 length of segment : 13 time for calcul the mask position with numpy : 0.00010251998901367188 nb_pixel_total : 1774 time to create 1 rle with old method : 0.0029163360595703125 length of segment : 47 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 540 time to create 1 rle with old method : 0.0009424686431884766 length of segment : 23 time for calcul the mask position with numpy : 0.00011301040649414062 nb_pixel_total : 3136 time to create 1 rle with old method : 0.004620790481567383 length of segment : 60 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.68047 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 918 time to create 1 rle with old method : 0.0012369155883789062 length of segment : 40 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0005095005035400391 length of segment : 17 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 1267 time to create 1 rle with old method : 0.0016291141510009766 length of segment : 30 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 1478 time to create 1 rle with old method : 0.0021975040435791016 length of segment : 35 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 983 time to create 1 rle with old method : 0.0014047622680664062 length of segment : 29 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.00017523765563964844 length of segment : 14 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 1140 time to create 1 rle with old method : 0.0016713142395019531 length of segment : 55 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 464 time to create 1 rle with old method : 0.0007998943328857422 length of segment : 25 time for calcul the mask position with numpy : 0.00034236907958984375 nb_pixel_total : 14301 time to create 1 rle with old method : 0.019169092178344727 length of segment : 126 time for calcul the mask position with numpy : 0.00013566017150878906 nb_pixel_total : 87 time to create 1 rle with old method : 0.00024366378784179688 length of segment : 8 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 505 time to create 1 rle with old method : 0.001104116439819336 length of segment : 23 time for calcul the mask position with numpy : 0.0001418590545654297 nb_pixel_total : 1695 time to create 1 rle with old method : 0.0030939579010009766 length of segment : 42 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.53203 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 0.00013756752014160156 nb_pixel_total : 1249 time to create 1 rle with old method : 0.0031938552856445312 length of segment : 39 time for calcul the mask position with numpy : 0.0001881122589111328 nb_pixel_total : 2545 time to create 1 rle with old method : 0.005711078643798828 length of segment : 78 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 106 time to create 1 rle with old method : 0.00028228759765625 length of segment : 10 time for calcul the mask position with numpy : 0.0005958080291748047 nb_pixel_total : 18828 time to create 1 rle with old method : 0.044080495834350586 length of segment : 279 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00019097328186035156 length of segment : 10 time for calcul the mask position with numpy : 0.00011444091796875 nb_pixel_total : 1617 time to create 1 rle with old method : 0.003945827484130859 length of segment : 56 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 431 time to create 1 rle with old method : 0.0008020401000976562 length of segment : 28 time for calcul the mask position with numpy : 0.00018072128295898438 nb_pixel_total : 4162 time to create 1 rle with old method : 0.007321596145629883 length of segment : 128 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 720 time to create 1 rle with old method : 0.001249551773071289 length of segment : 35 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 149.24062 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 1359 time to create 1 rle with old method : 0.002389192581176758 length of segment : 57 time for calcul the mask position with numpy : 9.441375732421875e-05 nb_pixel_total : 892 time to create 1 rle with old method : 0.00159454345703125 length of segment : 48 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.0005581378936767578 length of segment : 32 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 582 time to create 1 rle with old method : 0.0011355876922607422 length of segment : 46 time for calcul the mask position with numpy : 0.00013184547424316406 nb_pixel_total : 766 time to create 1 rle with old method : 0.001413106918334961 length of segment : 70 time for calcul the mask position with numpy : 8.606910705566406e-05 nb_pixel_total : 510 time to create 1 rle with old method : 0.00098419189453125 length of segment : 54 time for calcul the mask position with numpy : 0.0004665851593017578 nb_pixel_total : 2909 time to create 1 rle with old method : 0.004820346832275391 length of segment : 194 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 872 time to create 1 rle with old method : 0.0010404586791992188 length of segment : 62 time for calcul the mask position with numpy : 0.00011610984802246094 nb_pixel_total : 887 time to create 1 rle with old method : 0.0016164779663085938 length of segment : 69 time for calcul the mask position with numpy : 0.00010752677917480469 nb_pixel_total : 1630 time to create 1 rle with old method : 0.0028302669525146484 length of segment : 78 Processing 1 images image shape: (280, 400, 3) min: 14.00000 max: 204.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 86.80703 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0012099742889404297 nb_pixel_total : 106752 time to create 1 rle with old method : 0.18514084815979004 length of segment : 282 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.06484 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 195 time to create 1 rle with old method : 0.0004181861877441406 length of segment : 16 time for calcul the mask position with numpy : 8.392333984375e-05 nb_pixel_total : 1531 time to create 1 rle with old method : 0.002613544464111328 length of segment : 57 time for calcul the mask position with numpy : 8.487701416015625e-05 nb_pixel_total : 644 time to create 1 rle with old method : 0.0014336109161376953 length of segment : 43 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 405 time to create 1 rle with old method : 0.0008196830749511719 length of segment : 29 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00026488304138183594 length of segment : 13 time for calcul the mask position with numpy : 0.0001850128173828125 nb_pixel_total : 4590 time to create 1 rle with old method : 0.008099079132080078 length of segment : 118 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 502 time to create 1 rle with old method : 0.0010485649108886719 length of segment : 22 time for calcul the mask position with numpy : 0.00012993812561035156 nb_pixel_total : 5308 time to create 1 rle with old method : 0.009568452835083008 length of segment : 89 time for calcul the mask position with numpy : 0.00012183189392089844 nb_pixel_total : 4908 time to create 1 rle with old method : 0.005486965179443359 length of segment : 136 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 404 time to create 1 rle with old method : 0.0005431175231933594 length of segment : 30 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00019550323486328125 length of segment : 13 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 922 time to create 1 rle with old method : 0.001161813735961914 length of segment : 55 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 794 time to create 1 rle with old method : 0.0010361671447753906 length of segment : 47 time for calcul the mask position with numpy : 0.00013828277587890625 nb_pixel_total : 7147 time to create 1 rle with old method : 0.008409261703491211 length of segment : 69 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 1704 time to create 1 rle with old method : 0.0022890567779541016 length of segment : 43 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.85391 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 38 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 352 time to create 1 rle with old method : 0.0004956722259521484 length of segment : 36 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 943 time to create 1 rle with old method : 0.0012516975402832031 length of segment : 41 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 894 time to create 1 rle with old method : 0.001176595687866211 length of segment : 58 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 255 time to create 1 rle with old method : 0.0004112720489501953 length of segment : 15 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 730 time to create 1 rle with old method : 0.0010638236999511719 length of segment : 33 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 621 time to create 1 rle with old method : 0.0009484291076660156 length of segment : 31 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.00032973289489746094 length of segment : 23 time for calcul the mask position with numpy : 8.153915405273438e-05 nb_pixel_total : 2187 time to create 1 rle with old method : 0.0028998851776123047 length of segment : 78 time for calcul the mask position with numpy : 0.00010204315185546875 nb_pixel_total : 1553 time to create 1 rle with old method : 0.0032188892364501953 length of segment : 55 time for calcul the mask position with numpy : 7.605552673339844e-05 nb_pixel_total : 1030 time to create 1 rle with old method : 0.0013153553009033203 length of segment : 48 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 1538 time to create 1 rle with old method : 0.0020742416381835938 length of segment : 52 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 200 time to create 1 rle with old method : 0.0003135204315185547 length of segment : 15 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0004892349243164062 length of segment : 22 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 1852 time to create 1 rle with old method : 0.002461671829223633 length of segment : 67 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 1443 time to create 1 rle with old method : 0.0022864341735839844 length of segment : 50 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 364 time to create 1 rle with old method : 0.0007421970367431641 length of segment : 27 time for calcul the mask position with numpy : 7.605552673339844e-05 nb_pixel_total : 595 time to create 1 rle with old method : 0.0008225440979003906 length of segment : 33 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 745 time to create 1 rle with old method : 0.001361846923828125 length of segment : 33 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 1278 time to create 1 rle with old method : 0.0021181106567382812 length of segment : 49 time for calcul the mask position with numpy : 0.0001418590545654297 nb_pixel_total : 2240 time to create 1 rle with old method : 0.0030291080474853516 length of segment : 79 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 1490 time to create 1 rle with old method : 0.0020368099212646484 length of segment : 51 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.00033855438232421875 length of segment : 17 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 442 time to create 1 rle with old method : 0.000774383544921875 length of segment : 19 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 533 time to create 1 rle with old method : 0.0008456707000732422 length of segment : 33 time for calcul the mask position with numpy : 0.00010061264038085938 nb_pixel_total : 1002 time to create 1 rle with old method : 0.0018355846405029297 length of segment : 43 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0008907318115234375 length of segment : 27 time for calcul the mask position with numpy : 0.0001850128173828125 nb_pixel_total : 1766 time to create 1 rle with old method : 0.002989053726196289 length of segment : 59 time for calcul the mask position with numpy : 0.00010013580322265625 nb_pixel_total : 577 time to create 1 rle with old method : 0.0010116100311279297 length of segment : 33 time for calcul the mask position with numpy : 0.000141143798828125 nb_pixel_total : 1740 time to create 1 rle with old method : 0.002615213394165039 length of segment : 85 time for calcul the mask position with numpy : 9.703636169433594e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0008933544158935547 length of segment : 23 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 222 time to create 1 rle with old method : 0.0004608631134033203 length of segment : 21 time for calcul the mask position with numpy : 0.00010657310485839844 nb_pixel_total : 482 time to create 1 rle with old method : 0.0008733272552490234 length of segment : 37 time for calcul the mask position with numpy : 0.00012540817260742188 nb_pixel_total : 1758 time to create 1 rle with old method : 0.002499818801879883 length of segment : 52 time for calcul the mask position with numpy : 0.00013256072998046875 nb_pixel_total : 964 time to create 1 rle with old method : 0.0016810894012451172 length of segment : 42 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 521 time to create 1 rle with old method : 0.0008282661437988281 length of segment : 28 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.0009024143218994141 length of segment : 25 time for calcul the mask position with numpy : 9.655952453613281e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00020503997802734375 length of segment : 25 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 351 time to create 1 rle with old method : 0.00048065185546875 length of segment : 27 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 250 time to create 1 rle with old method : 0.0004398822784423828 length of segment : 29 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1069 time to create 1 rle with old method : 0.0017695426940917969 length of segment : 53 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 359 time to create 1 rle with old method : 0.0005228519439697266 length of segment : 31 time for calcul the mask position with numpy : 0.0001990795135498047 nb_pixel_total : 9276 time to create 1 rle with old method : 0.01175999641418457 length of segment : 117 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 341 time to create 1 rle with old method : 0.0005028247833251953 length of segment : 22 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 132 time to create 1 rle with old method : 0.0001957416534423828 length of segment : 26 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 58 time to create 1 rle with old method : 0.00012350082397460938 length of segment : 9 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00017023086547851562 length of segment : 14 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0008037090301513672 length of segment : 17 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 299 time to create 1 rle with old method : 0.0004096031188964844 length of segment : 22 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 417 time to create 1 rle with old method : 0.0005600452423095703 length of segment : 26 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.0006194114685058594 length of segment : 32 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 163 time to create 1 rle with old method : 0.0002722740173339844 length of segment : 19 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 355 time to create 1 rle with old method : 0.0004968643188476562 length of segment : 25 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 447 time to create 1 rle with old method : 0.0007200241088867188 length of segment : 31 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 130.03750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 628 time to create 1 rle with old method : 0.0009443759918212891 length of segment : 50 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 544 time to create 1 rle with old method : 0.0008845329284667969 length of segment : 53 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.00040984153747558594 length of segment : 12 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 65 time to create 1 rle with old method : 0.00014829635620117188 length of segment : 8 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 147 time to create 1 rle with old method : 0.00041103363037109375 length of segment : 19 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00016832351684570312 length of segment : 9 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.24297 max: 147.72500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 403 time to create 1 rle with old method : 0.0007328987121582031 length of segment : 35 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 59 time to create 1 rle with old method : 0.00012993812561035156 length of segment : 19 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.0004818439483642578 length of segment : 36 time for calcul the mask position with numpy : 0.0003414154052734375 nb_pixel_total : 10163 time to create 1 rle with old method : 0.014591693878173828 length of segment : 221 time for calcul the mask position with numpy : 0.00011754035949707031 nb_pixel_total : 110 time to create 1 rle with old method : 0.0003006458282470703 length of segment : 19 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.0005006790161132812 length of segment : 33 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.0004451274871826172 length of segment : 10 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 138 time to create 1 rle with old method : 0.0003566741943359375 length of segment : 36 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 145 time to create 1 rle with old method : 0.000347137451171875 length of segment : 36 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.0004725456237792969 length of segment : 31 time for calcul the mask position with numpy : 0.00011396408081054688 nb_pixel_total : 68 time to create 1 rle with old method : 0.0003879070281982422 length of segment : 18 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.20391 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 2577 time to create 1 rle with old method : 0.003076791763305664 length of segment : 86 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 2906 time to create 1 rle with old method : 0.0033936500549316406 length of segment : 110 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 316 time to create 1 rle with old method : 0.0004324913024902344 length of segment : 28 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002675056457519531 length of segment : 26 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 898 time to create 1 rle with old method : 0.00112152099609375 length of segment : 48 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 947 time to create 1 rle with old method : 0.0011742115020751953 length of segment : 41 time for calcul the mask position with numpy : 0.00012969970703125 nb_pixel_total : 2258 time to create 1 rle with old method : 0.0026683807373046875 length of segment : 81 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 1306 time to create 1 rle with old method : 0.0016369819641113281 length of segment : 38 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 1298 time to create 1 rle with old method : 0.0017573833465576172 length of segment : 39 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 370 time to create 1 rle with old method : 0.0005424022674560547 length of segment : 42 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.43047 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.000125885009765625 nb_pixel_total : 6213 time to create 1 rle with old method : 0.010404586791992188 length of segment : 107 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.62188 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.00025844573974609375 nb_pixel_total : 11551 time to create 1 rle with old method : 0.024332523345947266 length of segment : 90 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 188 time to create 1 rle with old method : 0.0002722740173339844 length of segment : 16 time for calcul the mask position with numpy : 0.0002760887145996094 nb_pixel_total : 10855 time to create 1 rle with old method : 0.01313471794128418 length of segment : 236 time for calcul the mask position with numpy : 9.393692016601562e-05 nb_pixel_total : 854 time to create 1 rle with old method : 0.00151824951171875 length of segment : 61 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 97 time to create 1 rle with old method : 0.00022220611572265625 length of segment : 13 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.02187 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 170 time to create 1 rle with old method : 0.0002837181091308594 length of segment : 12 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 774 time to create 1 rle with old method : 0.0010459423065185547 length of segment : 39 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00019741058349609375 length of segment : 12 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.000247955322265625 length of segment : 18 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1534 time to create 1 rle with old method : 0.0017681121826171875 length of segment : 74 time for calcul the mask position with numpy : 0.0005350112915039062 nb_pixel_total : 47900 time to create 1 rle with old method : 0.051850080490112305 length of segment : 270 time for calcul the mask position with numpy : 0.0009598731994628906 nb_pixel_total : 58432 time to create 1 rle with old method : 0.06604766845703125 length of segment : 302 Processing 1 images image shape: (400, 400, 3) min: 21.00000 max: 224.00000 molded_images shape: (1, 640, 640, 3) min: -86.83281 max: 97.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.34063 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 0.00012183189392089844 nb_pixel_total : 3124 time to create 1 rle with old method : 0.004515409469604492 length of segment : 58 time for calcul the mask position with numpy : 9.918212890625e-05 nb_pixel_total : 1101 time to create 1 rle with old method : 0.0017614364624023438 length of segment : 34 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00026035308837890625 length of segment : 12 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.00039076805114746094 length of segment : 20 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0003962516784667969 length of segment : 10 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 546 time to create 1 rle with old method : 0.0009715557098388672 length of segment : 20 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0005924701690673828 length of segment : 18 time for calcul the mask position with numpy : 0.00011396408081054688 nb_pixel_total : 1089 time to create 1 rle with old method : 0.00179290771484375 length of segment : 44 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.07500 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 942 time to create 1 rle with old method : 0.0012323856353759766 length of segment : 40 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 1291 time to create 1 rle with old method : 0.0016927719116210938 length of segment : 24 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 1329 time to create 1 rle with old method : 0.001819610595703125 length of segment : 28 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001785755157470703 length of segment : 15 time for calcul the mask position with numpy : 0.0003063678741455078 nb_pixel_total : 13906 time to create 1 rle with old method : 0.01607203483581543 length of segment : 134 time for calcul the mask position with numpy : 9.942054748535156e-05 nb_pixel_total : 1557 time to create 1 rle with old method : 0.0020630359649658203 length of segment : 36 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 307 time to create 1 rle with old method : 0.0005021095275878906 length of segment : 22 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 503 time to create 1 rle with old method : 0.0008008480072021484 length of segment : 26 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 284 time to create 1 rle with old method : 0.00047397613525390625 length of segment : 17 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 450 time to create 1 rle with old method : 0.0007100105285644531 length of segment : 21 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.46172 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 9.822845458984375e-05 nb_pixel_total : 114 time to create 1 rle with old method : 0.00045561790466308594 length of segment : 10 time for calcul the mask position with numpy : 0.00016069412231445312 nb_pixel_total : 1276 time to create 1 rle with old method : 0.0042150020599365234 length of segment : 56 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 322 time to create 1 rle with old method : 0.00045490264892578125 length of segment : 51 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.0001671314239501953 length of segment : 10 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 515 time to create 1 rle with old method : 0.0007610321044921875 length of segment : 25 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 1262 time to create 1 rle with old method : 0.0015795230865478516 length of segment : 41 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 678 time to create 1 rle with old method : 0.0008261203765869141 length of segment : 35 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.30312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 0.00011014938354492188 nb_pixel_total : 1408 time to create 1 rle with old method : 0.001748800277709961 length of segment : 58 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 255 time to create 1 rle with old method : 0.000484466552734375 length of segment : 47 time for calcul the mask position with numpy : 0.00010704994201660156 nb_pixel_total : 965 time to create 1 rle with old method : 0.0016396045684814453 length of segment : 55 time for calcul the mask position with numpy : 0.00010156631469726562 nb_pixel_total : 527 time to create 1 rle with old method : 0.0008168220520019531 length of segment : 54 time for calcul the mask position with numpy : 0.0001537799835205078 nb_pixel_total : 900 time to create 1 rle with old method : 0.001569986343383789 length of segment : 69 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.00031304359436035156 length of segment : 10 time for calcul the mask position with numpy : 0.0003285408020019531 nb_pixel_total : 2674 time to create 1 rle with old method : 0.004262208938598633 length of segment : 186 time for calcul the mask position with numpy : 0.00016641616821289062 nb_pixel_total : 1574 time to create 1 rle with old method : 0.002322673797607422 length of segment : 77 time for calcul the mask position with numpy : 0.0001804828643798828 nb_pixel_total : 955 time to create 1 rle with old method : 0.0016133785247802734 length of segment : 68 time for calcul the mask position with numpy : 0.0003113746643066406 nb_pixel_total : 2265 time to create 1 rle with old method : 0.0049898624420166016 length of segment : 175 Processing 1 images image shape: (280, 400, 3) min: 12.00000 max: 195.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 74.57266 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009522438049316406 nb_pixel_total : 107033 time to create 1 rle with old method : 0.15183258056640625 length of segment : 283 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 205 time to create 1 rle with old method : 0.0004260540008544922 length of segment : 16 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 423 time to create 1 rle with old method : 0.0008177757263183594 length of segment : 33 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 412 time to create 1 rle with old method : 0.0007617473602294922 length of segment : 32 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1513 time to create 1 rle with old method : 0.0024716854095458984 length of segment : 54 time for calcul the mask position with numpy : 0.0001800060272216797 nb_pixel_total : 4970 time to create 1 rle with old method : 0.03685283660888672 length of segment : 135 time for calcul the mask position with numpy : 0.00017380714416503906 nb_pixel_total : 8343 time to create 1 rle with old method : 0.010305166244506836 length of segment : 71 time for calcul the mask position with numpy : 0.0001266002655029297 nb_pixel_total : 307 time to create 1 rle with old method : 0.000476837158203125 length of segment : 26 time for calcul the mask position with numpy : 0.00021982192993164062 nb_pixel_total : 4651 time to create 1 rle with old method : 0.006500959396362305 length of segment : 114 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 96 time to create 1 rle with old method : 0.00021910667419433594 length of segment : 12 time for calcul the mask position with numpy : 0.0002880096435546875 nb_pixel_total : 5074 time to create 1 rle with old method : 0.007706642150878906 length of segment : 141 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 885 time to create 1 rle with old method : 0.0013914108276367188 length of segment : 50 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 1849 time to create 1 rle with old method : 0.0023190975189208984 length of segment : 48 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 452 time to create 1 rle with old method : 0.0007014274597167969 length of segment : 33 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00021839141845703125 length of segment : 11 time for calcul the mask position with numpy : 0.00011968612670898438 nb_pixel_total : 4408 time to create 1 rle with old method : 0.0064852237701416016 length of segment : 122 time for calcul the mask position with numpy : 0.0001842975616455078 nb_pixel_total : 4595 time to create 1 rle with old method : 0.006657600402832031 length of segment : 99 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.81875 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 39 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 625 time to create 1 rle with old method : 0.0011134147644042969 length of segment : 33 time for calcul the mask position with numpy : 0.00010275840759277344 nb_pixel_total : 889 time to create 1 rle with old method : 0.0018589496612548828 length of segment : 38 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 583 time to create 1 rle with old method : 0.0011556148529052734 length of segment : 36 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 274 time to create 1 rle with old method : 0.0006208419799804688 length of segment : 16 time for calcul the mask position with numpy : 9.250640869140625e-05 nb_pixel_total : 1510 time to create 1 rle with old method : 0.0030350685119628906 length of segment : 46 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 632 time to create 1 rle with old method : 0.0012240409851074219 length of segment : 32 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 297 time to create 1 rle with old method : 0.0006096363067626953 length of segment : 23 time for calcul the mask position with numpy : 7.343292236328125e-05 nb_pixel_total : 1219 time to create 1 rle with old method : 0.002218961715698242 length of segment : 56 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.00040078163146972656 length of segment : 14 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 1660 time to create 1 rle with old method : 0.0029239654541015625 length of segment : 54 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.0004889965057373047 length of segment : 40 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 916 time to create 1 rle with old method : 0.0012934207916259766 length of segment : 41 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.0004911422729492188 length of segment : 23 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 655 time to create 1 rle with old method : 0.0008742809295654297 length of segment : 34 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 960 time to create 1 rle with old method : 0.0012738704681396484 length of segment : 40 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 1545 time to create 1 rle with old method : 0.0019991397857666016 length of segment : 49 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004780292510986328 length of segment : 36 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.00047779083251953125 length of segment : 26 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 263 time to create 1 rle with old method : 0.00041484832763671875 length of segment : 32 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 401 time to create 1 rle with old method : 0.0005898475646972656 length of segment : 26 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 327 time to create 1 rle with old method : 0.0004799365997314453 length of segment : 36 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.0002307891845703125 length of segment : 10 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 205 time to create 1 rle with old method : 0.0003161430358886719 length of segment : 16 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 451 time to create 1 rle with old method : 0.0006268024444580078 length of segment : 22 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 1757 time to create 1 rle with old method : 0.0022873878479003906 length of segment : 60 time for calcul the mask position with numpy : 7.557868957519531e-05 nb_pixel_total : 1007 time to create 1 rle with old method : 0.0013194084167480469 length of segment : 46 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.00021195411682128906 length of segment : 16 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 605 time to create 1 rle with old method : 0.0008008480072021484 length of segment : 37 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 676 time to create 1 rle with old method : 0.0009262561798095703 length of segment : 34 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 257 time to create 1 rle with old method : 0.0003883838653564453 length of segment : 34 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 1566 time to create 1 rle with old method : 0.0019979476928710938 length of segment : 52 time for calcul the mask position with numpy : 0.00017023086547851562 nb_pixel_total : 3149 time to create 1 rle with old method : 0.005740165710449219 length of segment : 80 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.00042557716369628906 length of segment : 18 time for calcul the mask position with numpy : 8.797645568847656e-05 nb_pixel_total : 1849 time to create 1 rle with old method : 0.0037946701049804688 length of segment : 81 time for calcul the mask position with numpy : 8.606910705566406e-05 nb_pixel_total : 552 time to create 1 rle with old method : 0.0011472702026367188 length of segment : 26 time for calcul the mask position with numpy : 0.00011396408081054688 nb_pixel_total : 594 time to create 1 rle with old method : 0.0020294189453125 length of segment : 34 time for calcul the mask position with numpy : 0.00013303756713867188 nb_pixel_total : 1417 time to create 1 rle with old method : 0.003081083297729492 length of segment : 47 time for calcul the mask position with numpy : 0.00012946128845214844 nb_pixel_total : 1967 time to create 1 rle with old method : 0.0038509368896484375 length of segment : 89 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 642 time to create 1 rle with old method : 0.0010457038879394531 length of segment : 31 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 23 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 860 time to create 1 rle with old method : 0.0011942386627197266 length of segment : 42 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 237 time to create 1 rle with old method : 0.00032258033752441406 length of segment : 29 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 80 time to create 1 rle with old method : 0.0001742839813232422 length of segment : 21 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 342 time to create 1 rle with old method : 0.0004763603210449219 length of segment : 29 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 937 time to create 1 rle with old method : 0.0011718273162841797 length of segment : 41 time for calcul the mask position with numpy : 0.00018787384033203125 nb_pixel_total : 8820 time to create 1 rle with old method : 0.010245323181152344 length of segment : 163 time for calcul the mask position with numpy : 0.0001354217529296875 nb_pixel_total : 163 time to create 1 rle with old method : 0.00032019615173339844 length of segment : 33 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 64 time to create 1 rle with old method : 0.00012063980102539062 length of segment : 17 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0005786418914794922 length of segment : 55 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 368 time to create 1 rle with old method : 0.0005178451538085938 length of segment : 23 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 1063 time to create 1 rle with old method : 0.0013854503631591797 length of segment : 66 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 512 time to create 1 rle with old method : 0.0006821155548095703 length of segment : 34 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 83 time to create 1 rle with old method : 0.00014448165893554688 length of segment : 12 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 82 time to create 1 rle with old method : 0.00022745132446289062 length of segment : 8 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 150 time to create 1 rle with old method : 0.0003082752227783203 length of segment : 20 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 223 time to create 1 rle with old method : 0.0003147125244140625 length of segment : 23 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 390 time to create 1 rle with old method : 0.0005691051483154297 length of segment : 40 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 455 time to create 1 rle with old method : 0.0006864070892333984 length of segment : 16 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 92 time to create 1 rle with old method : 0.00018525123596191406 length of segment : 26 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.0004680156707763672 length of segment : 25 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 392 time to create 1 rle with old method : 0.0008113384246826172 length of segment : 29 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0006527900695800781 length of segment : 29 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 1585 time to create 1 rle with old method : 0.002038240432739258 length of segment : 111 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 131.91250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 631 time to create 1 rle with old method : 0.0009887218475341797 length of segment : 51 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 528 time to create 1 rle with old method : 0.0007431507110595703 length of segment : 52 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.00043964385986328125 length of segment : 12 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 324 time to create 1 rle with old method : 0.0006704330444335938 length of segment : 38 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 69 time to create 1 rle with old method : 0.00019860267639160156 length of segment : 10 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 73 time to create 1 rle with old method : 0.00014209747314453125 length of segment : 9 time for calcul the mask position with numpy : 9.799003601074219e-05 nb_pixel_total : 1092 time to create 1 rle with old method : 0.0016930103302001953 length of segment : 104 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.80547 max: 147.45547 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 0.0003600120544433594 nb_pixel_total : 20364 time to create 1 rle with old method : 0.026316165924072266 length of segment : 165 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 330 time to create 1 rle with old method : 0.0004897117614746094 length of segment : 29 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 294 time to create 1 rle with old method : 0.0005016326904296875 length of segment : 37 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 54 time to create 1 rle with old method : 0.00011086463928222656 length of segment : 18 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 66 time to create 1 rle with old method : 0.00011229515075683594 length of segment : 20 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.00032067298889160156 length of segment : 35 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 320 time to create 1 rle with old method : 0.0005502700805664062 length of segment : 34 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 157 time to create 1 rle with old method : 0.0002796649932861328 length of segment : 27 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 1 time to create 1 rle with old method : 1.811981201171875e-05 length of segment : 1 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 223 time to create 1 rle with old method : 0.0003304481506347656 length of segment : 31 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.71953 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 2697 time to create 1 rle with old method : 0.0031757354736328125 length of segment : 91 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 2971 time to create 1 rle with old method : 0.0038881301879882812 length of segment : 68 time for calcul the mask position with numpy : 0.00013875961303710938 nb_pixel_total : 2777 time to create 1 rle with old method : 0.0051190853118896484 length of segment : 133 time for calcul the mask position with numpy : 9.369850158691406e-05 nb_pixel_total : 487 time to create 1 rle with old method : 0.0009050369262695312 length of segment : 30 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 1717 time to create 1 rle with old method : 0.002411365509033203 length of segment : 58 time for calcul the mask position with numpy : 8.368492126464844e-05 nb_pixel_total : 1234 time to create 1 rle with old method : 0.0019352436065673828 length of segment : 84 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.00035834312438964844 length of segment : 25 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 19 time to create 1 rle with old method : 5.340576171875e-05 length of segment : 5 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 959 time to create 1 rle with old method : 0.001241445541381836 length of segment : 55 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 327 time to create 1 rle with old method : 0.0006852149963378906 length of segment : 12 time for calcul the mask position with numpy : 0.00014781951904296875 nb_pixel_total : 2321 time to create 1 rle with old method : 0.003918886184692383 length of segment : 83 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.07500 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 3 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 1986 time to create 1 rle with old method : 0.0026464462280273438 length of segment : 41 time for calcul the mask position with numpy : 0.00014591217041015625 nb_pixel_total : 5689 time to create 1 rle with old method : 0.0068395137786865234 length of segment : 110 time for calcul the mask position with numpy : 0.00012874603271484375 nb_pixel_total : 3672 time to create 1 rle with old method : 0.004601240158081055 length of segment : 62 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.10234 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.0001595020294189453 nb_pixel_total : 11044 time to create 1 rle with old method : 0.012749433517456055 length of segment : 90 time for calcul the mask position with numpy : 0.0003769397735595703 nb_pixel_total : 10487 time to create 1 rle with old method : 0.013103008270263672 length of segment : 221 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.00035190582275390625 length of segment : 17 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 96 time to create 1 rle with old method : 0.0002338886260986328 length of segment : 13 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 788 time to create 1 rle with old method : 0.0010688304901123047 length of segment : 55 time for calcul the mask position with numpy : 8.296966552734375e-05 nb_pixel_total : 426 time to create 1 rle with old method : 0.0008387565612792969 length of segment : 26 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 761 time to create 1 rle with old method : 0.0011048316955566406 length of segment : 45 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.14687 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 351 time to create 1 rle with old method : 0.0004742145538330078 length of segment : 24 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00020503997802734375 length of segment : 12 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 747 time to create 1 rle with old method : 0.000986337661743164 length of segment : 39 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 789 time to create 1 rle with old method : 0.001081705093383789 length of segment : 39 time for calcul the mask position with numpy : 0.00014495849609375 nb_pixel_total : 1668 time to create 1 rle with old method : 0.0019996166229248047 length of segment : 174 time for calcul the mask position with numpy : 0.00016927719116210938 nb_pixel_total : 2380 time to create 1 rle with old method : 0.003696918487548828 length of segment : 63 Processing 1 images image shape: (400, 400, 3) min: 21.00000 max: 229.00000 molded_images shape: (1, 640, 640, 3) min: -84.85234 max: 100.50078 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.71953 max: 150.67031 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 311 time to create 1 rle with old method : 0.0011343955993652344 length of segment : 16 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.00038695335388183594 length of segment : 15 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.0003228187561035156 length of segment : 20 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 824 time to create 1 rle with old method : 0.0011043548583984375 length of segment : 36 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 491 time to create 1 rle with old method : 0.0007035732269287109 length of segment : 27 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.51641 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.0003352165222167969 length of segment : 16 time for calcul the mask position with numpy : 0.00034999847412109375 nb_pixel_total : 13350 time to create 1 rle with old method : 0.014943599700927734 length of segment : 119 time for calcul the mask position with numpy : 9.226799011230469e-05 nb_pixel_total : 978 time to create 1 rle with old method : 0.0014641284942626953 length of segment : 42 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.0002841949462890625 length of segment : 15 time for calcul the mask position with numpy : 0.00010538101196289062 nb_pixel_total : 1507 time to create 1 rle with old method : 0.0021131038665771484 length of segment : 39 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 942 time to create 1 rle with old method : 0.0013442039489746094 length of segment : 35 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 828 time to create 1 rle with old method : 0.0011625289916992188 length of segment : 28 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0008139610290527344 length of segment : 24 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 508 time to create 1 rle with old method : 0.0007088184356689453 length of segment : 33 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 316 time to create 1 rle with old method : 0.00043201446533203125 length of segment : 16 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 333 time to create 1 rle with old method : 0.0004909038543701172 length of segment : 25 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 527 time to create 1 rle with old method : 0.0008180141448974609 length of segment : 23 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 243 time to create 1 rle with old method : 0.0003209114074707031 length of segment : 20 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 907 time to create 1 rle with old method : 0.001249074935913086 length of segment : 37 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.64922 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 604 time to create 1 rle with old method : 0.0007593631744384766 length of segment : 46 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00019741058349609375 length of segment : 11 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005695819854736328 length of segment : 26 time for calcul the mask position with numpy : 0.00010848045349121094 nb_pixel_total : 623 time to create 1 rle with old method : 0.0008141994476318359 length of segment : 92 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 1279 time to create 1 rle with old method : 0.0015249252319335938 length of segment : 38 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 1067 time to create 1 rle with old method : 0.0014383792877197266 length of segment : 60 time for calcul the mask position with numpy : 0.0005247592926025391 nb_pixel_total : 31565 time to create 1 rle with old method : 0.04376983642578125 length of segment : 359 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 288 time to create 1 rle with old method : 0.00042319297790527344 length of segment : 21 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 1202 time to create 1 rle with old method : 0.0016591548919677734 length of segment : 35 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.00021576881408691406 length of segment : 17 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 666 time to create 1 rle with old method : 0.0008275508880615234 length of segment : 35 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.11953 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 1388 time to create 1 rle with old method : 0.0016341209411621094 length of segment : 55 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 89 time to create 1 rle with old method : 0.00018167495727539062 length of segment : 27 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 895 time to create 1 rle with old method : 0.001064300537109375 length of segment : 49 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 1576 time to create 1 rle with old method : 0.0019495487213134766 length of segment : 77 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 919 time to create 1 rle with old method : 0.0011043548583984375 length of segment : 64 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 555 time to create 1 rle with old method : 0.0007469654083251953 length of segment : 57 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 1144 time to create 1 rle with old method : 0.0013284683227539062 length of segment : 74 time for calcul the mask position with numpy : 0.0001723766326904297 nb_pixel_total : 3151 time to create 1 rle with old method : 0.0036139488220214844 length of segment : 199 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 111 time to create 1 rle with old method : 0.000209808349609375 length of segment : 9 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 1177 time to create 1 rle with old method : 0.0014851093292236328 length of segment : 47 time for calcul the mask position with numpy : 8.249282836914062e-05 nb_pixel_total : 806 time to create 1 rle with old method : 0.0011475086212158203 length of segment : 64 time for calcul the mask position with numpy : 0.00016355514526367188 nb_pixel_total : 2657 time to create 1 rle with old method : 0.003226041793823242 length of segment : 188 time for calcul the mask position with numpy : 0.0001609325408935547 nb_pixel_total : 2833 time to create 1 rle with old method : 0.0031690597534179688 length of segment : 197 Processing 1 images image shape: (280, 400, 3) min: 3.00000 max: 204.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 76.63125 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0013353824615478516 nb_pixel_total : 106057 time to create 1 rle with old method : 0.1912248134613037 length of segment : 284 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.00028395652770996094 length of segment : 17 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 440 time to create 1 rle with old method : 0.0007386207580566406 length of segment : 37 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 330 time to create 1 rle with old method : 0.0004971027374267578 length of segment : 26 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 449 time to create 1 rle with old method : 0.0006690025329589844 length of segment : 52 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 526 time to create 1 rle with old method : 0.0008463859558105469 length of segment : 25 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.0002186298370361328 length of segment : 12 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 1546 time to create 1 rle with old method : 0.0019609928131103516 length of segment : 56 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00020360946655273438 length of segment : 14 time for calcul the mask position with numpy : 0.00011754035949707031 nb_pixel_total : 2920 time to create 1 rle with old method : 0.003674030303955078 length of segment : 97 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005469322204589844 length of segment : 30 time for calcul the mask position with numpy : 0.00014925003051757812 nb_pixel_total : 7923 time to create 1 rle with old method : 0.009300708770751953 length of segment : 85 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 973 time to create 1 rle with old method : 0.0013422966003417969 length of segment : 47 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 1600 time to create 1 rle with old method : 0.0021126270294189453 length of segment : 56 time for calcul the mask position with numpy : 9.870529174804688e-05 nb_pixel_total : 2946 time to create 1 rle with old method : 0.0038945674896240234 length of segment : 54 time for calcul the mask position with numpy : 0.00010609626770019531 nb_pixel_total : 1735 time to create 1 rle with old method : 0.002286195755004883 length of segment : 53 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.94766 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 39 time for calcul the mask position with numpy : 7.939338684082031e-05 nb_pixel_total : 611 time to create 1 rle with old method : 0.0007956027984619141 length of segment : 34 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 912 time to create 1 rle with old method : 0.0012898445129394531 length of segment : 38 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 240 time to create 1 rle with old method : 0.00035643577575683594 length of segment : 26 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 342 time to create 1 rle with old method : 0.0005035400390625 length of segment : 36 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 1038 time to create 1 rle with old method : 0.0014345645904541016 length of segment : 59 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 261 time to create 1 rle with old method : 0.00043272972106933594 length of segment : 15 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006864070892333984 length of segment : 20 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005338191986083984 length of segment : 29 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 596 time to create 1 rle with old method : 0.0008368492126464844 length of segment : 35 time for calcul the mask position with numpy : 9.322166442871094e-05 nb_pixel_total : 978 time to create 1 rle with old method : 0.0018830299377441406 length of segment : 42 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 432 time to create 1 rle with old method : 0.0006368160247802734 length of segment : 44 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 978 time to create 1 rle with old method : 0.0013339519500732422 length of segment : 39 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 576 time to create 1 rle with old method : 0.0007197856903076172 length of segment : 48 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 617 time to create 1 rle with old method : 0.0007791519165039062 length of segment : 35 time for calcul the mask position with numpy : 0.0003292560577392578 nb_pixel_total : 1564 time to create 1 rle with old method : 0.0028400421142578125 length of segment : 52 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002765655517578125 length of segment : 30 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 317 time to create 1 rle with old method : 0.00044417381286621094 length of segment : 23 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 676 time to create 1 rle with old method : 0.0008742809295654297 length of segment : 32 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 1722 time to create 1 rle with old method : 0.002121448516845703 length of segment : 59 time for calcul the mask position with numpy : 0.00011420249938964844 nb_pixel_total : 2262 time to create 1 rle with old method : 0.0028192996978759766 length of segment : 127 time for calcul the mask position with numpy : 9.179115295410156e-05 nb_pixel_total : 1416 time to create 1 rle with old method : 0.001943826675415039 length of segment : 47 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0006425380706787109 length of segment : 30 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 1135 time to create 1 rle with old method : 0.0014231204986572266 length of segment : 56 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 473 time to create 1 rle with old method : 0.0005822181701660156 length of segment : 44 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00019860267639160156 length of segment : 9 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 959 time to create 1 rle with old method : 0.0012803077697753906 length of segment : 38 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 1548 time to create 1 rle with old method : 0.0018885135650634766 length of segment : 51 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.0002918243408203125 length of segment : 18 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 1381 time to create 1 rle with old method : 0.0017077922821044922 length of segment : 47 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 416 time to create 1 rle with old method : 0.0005514621734619141 length of segment : 30 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 1282 time to create 1 rle with old method : 0.001691579818725586 length of segment : 54 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.0002315044403076172 length of segment : 17 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 692 time to create 1 rle with old method : 0.0008695125579833984 length of segment : 35 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 1621 time to create 1 rle with old method : 0.002059459686279297 length of segment : 77 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 1053 time to create 1 rle with old method : 0.0014147758483886719 length of segment : 38 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 361 time to create 1 rle with old method : 0.0004956722259521484 length of segment : 25 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 1535 time to create 1 rle with old method : 0.0017995834350585938 length of segment : 69 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 1572 time to create 1 rle with old method : 0.001863718032836914 length of segment : 79 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 741 time to create 1 rle with old method : 0.0009291172027587891 length of segment : 35 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 18 time for calcul the mask position with numpy : 0.00012421607971191406 nb_pixel_total : 110 time to create 1 rle with old method : 0.00040531158447265625 length of segment : 22 time for calcul the mask position with numpy : 0.00037479400634765625 nb_pixel_total : 10494 time to create 1 rle with old method : 0.02455306053161621 length of segment : 113 time for calcul the mask position with numpy : 9.441375732421875e-05 nb_pixel_total : 285 time to create 1 rle with old method : 0.0005424022674560547 length of segment : 32 time for calcul the mask position with numpy : 8.559226989746094e-05 nb_pixel_total : 941 time to create 1 rle with old method : 0.0016739368438720703 length of segment : 45 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 641 time to create 1 rle with old method : 0.001127004623413086 length of segment : 54 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 336 time to create 1 rle with old method : 0.0006377696990966797 length of segment : 27 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 354 time to create 1 rle with old method : 0.0006685256958007812 length of segment : 34 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 56 time to create 1 rle with old method : 0.0001633167266845703 length of segment : 19 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 533 time to create 1 rle with old method : 0.000986337661743164 length of segment : 35 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0005781650543212891 length of segment : 21 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 414 time to create 1 rle with old method : 0.00459599494934082 length of segment : 25 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.0005218982696533203 length of segment : 15 time for calcul the mask position with numpy : 0.00011181831359863281 nb_pixel_total : 515 time to create 1 rle with old method : 0.0007584095001220703 length of segment : 65 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 315 time to create 1 rle with old method : 0.0005414485931396484 length of segment : 21 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.0001347064971923828 length of segment : 14 time for calcul the mask position with numpy : 0.00026106834411621094 nb_pixel_total : 7140 time to create 1 rle with old method : 0.00865793228149414 length of segment : 197 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 157 time to create 1 rle with old method : 0.0002665519714355469 length of segment : 22 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 133 time to create 1 rle with old method : 0.0002186298370361328 length of segment : 16 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 134.53750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.0001277923583984375 nb_pixel_total : 591 time to create 1 rle with old method : 0.0017116069793701172 length of segment : 50 time for calcul the mask position with numpy : 0.00010395050048828125 nb_pixel_total : 530 time to create 1 rle with old method : 0.0012733936309814453 length of segment : 53 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 177 time to create 1 rle with old method : 0.0005810260772705078 length of segment : 12 time for calcul the mask position with numpy : 0.00011134147644042969 nb_pixel_total : 476 time to create 1 rle with old method : 0.0014333724975585938 length of segment : 34 time for calcul the mask position with numpy : 0.00015234947204589844 nb_pixel_total : 802 time to create 1 rle with old method : 0.0020754337310791016 length of segment : 87 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 71 time to create 1 rle with old method : 0.0002830028533935547 length of segment : 10 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 83 time to create 1 rle with old method : 0.0002243518829345703 length of segment : 10 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.80156 max: 148.37344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 67 time to create 1 rle with old method : 0.00018525123596191406 length of segment : 20 length of segment : 0 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 419 time to create 1 rle with old method : 0.0008058547973632812 length of segment : 49 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.00045490264892578125 length of segment : 41 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 204 time to create 1 rle with old method : 0.0004305839538574219 length of segment : 26 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.0004999637603759766 length of segment : 33 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 59 time to create 1 rle with old method : 0.00010848045349121094 length of segment : 17 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.0002117156982421875 length of segment : 34 time for calcul the mask position with numpy : 0.00027942657470703125 nb_pixel_total : 14656 time to create 1 rle with old method : 0.01732349395751953 length of segment : 118 time for calcul the mask position with numpy : 0.00012350082397460938 nb_pixel_total : 368 time to create 1 rle with old method : 0.0006906986236572266 length of segment : 37 time for calcul the mask position with numpy : 0.00022029876708984375 nb_pixel_total : 12603 time to create 1 rle with old method : 0.014366865158081055 length of segment : 115 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 1612 time to create 1 rle with old method : 0.002053976058959961 length of segment : 54 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.15703 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 2294 time to create 1 rle with old method : 0.0027163028717041016 length of segment : 85 time for calcul the mask position with numpy : 0.00011038780212402344 nb_pixel_total : 3439 time to create 1 rle with old method : 0.0040187835693359375 length of segment : 114 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 380 time to create 1 rle with old method : 0.0005540847778320312 length of segment : 34 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 28 time to create 1 rle with old method : 6.29425048828125e-05 length of segment : 6 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 964 time to create 1 rle with old method : 0.0011334419250488281 length of segment : 44 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 21 time to create 1 rle with old method : 5.91278076171875e-05 length of segment : 4 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 2068 time to create 1 rle with old method : 0.002614259719848633 length of segment : 35 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 2433 time to create 1 rle with old method : 0.003008127212524414 length of segment : 60 time for calcul the mask position with numpy : 9.870529174804688e-05 nb_pixel_total : 3044 time to create 1 rle with old method : 0.003466367721557617 length of segment : 107 time for calcul the mask position with numpy : 0.0001270771026611328 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0015783309936523438 length of segment : 131 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 838 time to create 1 rle with old method : 0.0010285377502441406 length of segment : 38 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 343 time to create 1 rle with old method : 0.0005698204040527344 length of segment : 13 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 2446 time to create 1 rle with old method : 0.002978086471557617 length of segment : 91 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.07500 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 3 time for calcul the mask position with numpy : 0.00013494491577148438 nb_pixel_total : 4491 time to create 1 rle with old method : 0.00782632827758789 length of segment : 58 time for calcul the mask position with numpy : 0.000102996826171875 nb_pixel_total : 3260 time to create 1 rle with old method : 0.0054891109466552734 length of segment : 102 time for calcul the mask position with numpy : 7.271766662597656e-05 nb_pixel_total : 1618 time to create 1 rle with old method : 0.003078460693359375 length of segment : 39 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -111.98516 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.00022220611572265625 nb_pixel_total : 10680 time to create 1 rle with old method : 0.011956453323364258 length of segment : 215 time for calcul the mask position with numpy : 0.000179290771484375 nb_pixel_total : 11161 time to create 1 rle with old method : 0.01329350471496582 length of segment : 91 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 85 time to create 1 rle with old method : 0.000152587890625 length of segment : 12 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 431 time to create 1 rle with old method : 0.0006837844848632812 length of segment : 27 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.0003199577331542969 length of segment : 18 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.65859 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.0002353191375732422 length of segment : 11 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 249 time to create 1 rle with old method : 0.0004894733428955078 length of segment : 17 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 769 time to create 1 rle with old method : 0.0014111995697021484 length of segment : 38 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.0003707408905029297 length of segment : 23 time for calcul the mask position with numpy : 0.00015926361083984375 nb_pixel_total : 1532 time to create 1 rle with old method : 0.0023648738861083984 length of segment : 179 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 755 time to create 1 rle with old method : 0.0013065338134765625 length of segment : 36 time for calcul the mask position with numpy : 0.0008487701416015625 nb_pixel_total : 41731 time to create 1 rle with old method : 0.06997108459472656 length of segment : 256 time for calcul the mask position with numpy : 0.0011868476867675781 nb_pixel_total : 40356 time to create 1 rle with old method : 0.07901716232299805 length of segment : 255 Processing 1 images image shape: (400, 400, 3) min: 26.00000 max: 229.00000 molded_images shape: (1, 640, 640, 3) min: -82.92266 max: 100.91484 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.05156 max: 150.87344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.0003974437713623047 length of segment : 15 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.000301361083984375 length of segment : 27 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 2949 time to create 1 rle with old method : 0.0036487579345703125 length of segment : 55 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 296 time to create 1 rle with old method : 0.00047206878662109375 length of segment : 15 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 903 time to create 1 rle with old method : 0.0011417865753173828 length of segment : 46 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 466 time to create 1 rle with old method : 0.0007143020629882812 length of segment : 18 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 102 time to create 1 rle with old method : 0.00018405914306640625 length of segment : 12 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 530 time to create 1 rle with old method : 0.0007672309875488281 length of segment : 43 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.0002148151397705078 length of segment : 13 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.08281 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 0.00020241737365722656 nb_pixel_total : 12940 time to create 1 rle with old method : 0.014512777328491211 length of segment : 126 time for calcul the mask position with numpy : 0.00019240379333496094 nb_pixel_total : 949 time to create 1 rle with old method : 0.0018889904022216797 length of segment : 40 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.00047707557678222656 length of segment : 17 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 880 time to create 1 rle with old method : 0.001186370849609375 length of segment : 25 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 466 time to create 1 rle with old method : 0.0007395744323730469 length of segment : 22 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 496 time to create 1 rle with old method : 0.0008070468902587891 length of segment : 25 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 1175 time to create 1 rle with old method : 0.0016362667083740234 length of segment : 28 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 535 time to create 1 rle with old method : 0.0008130073547363281 length of segment : 24 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 530 time to create 1 rle with old method : 0.000682830810546875 length of segment : 27 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 979 time to create 1 rle with old method : 0.0013718605041503906 length of segment : 31 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.19609 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 782 time to create 1 rle with old method : 0.0010089874267578125 length of segment : 51 time for calcul the mask position with numpy : 9.942054748535156e-05 nb_pixel_total : 3603 time to create 1 rle with old method : 0.005031108856201172 length of segment : 75 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 1195 time to create 1 rle with old method : 0.0016300678253173828 length of segment : 43 time for calcul the mask position with numpy : 0.00024127960205078125 nb_pixel_total : 1449 time to create 1 rle with old method : 0.0024259090423583984 length of segment : 166 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 274 time to create 1 rle with old method : 0.00045871734619140625 length of segment : 20 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 903 time to create 1 rle with old method : 0.0014307498931884766 length of segment : 79 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 588 time to create 1 rle with old method : 0.0010204315185546875 length of segment : 27 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 698 time to create 1 rle with old method : 0.0013740062713623047 length of segment : 36 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.0004382133483886719 length of segment : 19 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.0003647804260253906 length of segment : 11 time for calcul the mask position with numpy : 0.00017976760864257812 nb_pixel_total : 3504 time to create 1 rle with old method : 0.006536245346069336 length of segment : 122 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.26016 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 1302 time to create 1 rle with old method : 0.0022590160369873047 length of segment : 54 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 869 time to create 1 rle with old method : 0.0016682147979736328 length of segment : 73 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 569 time to create 1 rle with old method : 0.0009949207305908203 length of segment : 55 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 903 time to create 1 rle with old method : 0.001543283462524414 length of segment : 48 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.0005624294281005859 length of segment : 32 time for calcul the mask position with numpy : 0.00019693374633789062 nb_pixel_total : 3271 time to create 1 rle with old method : 0.0042266845703125 length of segment : 196 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 1167 time to create 1 rle with old method : 0.0015249252319335938 length of segment : 63 time for calcul the mask position with numpy : 9.369850158691406e-05 nb_pixel_total : 1669 time to create 1 rle with old method : 0.003011465072631836 length of segment : 81 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 92 time to create 1 rle with old method : 0.00015592575073242188 length of segment : 16 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 611 time to create 1 rle with old method : 0.0007960796356201172 length of segment : 39 time for calcul the mask position with numpy : 0.00019097328186035156 nb_pixel_total : 2758 time to create 1 rle with old method : 0.004301786422729492 length of segment : 206 Processing 1 images image shape: (280, 400, 3) min: 21.00000 max: 215.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 91.97500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.001026153564453125 nb_pixel_total : 106598 time to create 1 rle with old method : 0.11549663543701172 length of segment : 281 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.00029778480529785156 length of segment : 16 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 510 time to create 1 rle with old method : 0.0007383823394775391 length of segment : 23 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 481 time to create 1 rle with old method : 0.0006310939788818359 length of segment : 36 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 443 time to create 1 rle with old method : 0.0006177425384521484 length of segment : 32 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 527 time to create 1 rle with old method : 0.0007128715515136719 length of segment : 40 time for calcul the mask position with numpy : 8.654594421386719e-05 nb_pixel_total : 1542 time to create 1 rle with old method : 0.0023598670959472656 length of segment : 55 time for calcul the mask position with numpy : 0.0001811981201171875 nb_pixel_total : 5239 time to create 1 rle with old method : 0.006414890289306641 length of segment : 151 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.00032830238342285156 length of segment : 13 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00026035308837890625 length of segment : 13 time for calcul the mask position with numpy : 9.012222290039062e-05 nb_pixel_total : 1245 time to create 1 rle with old method : 0.0019927024841308594 length of segment : 53 time for calcul the mask position with numpy : 0.00015807151794433594 nb_pixel_total : 915 time to create 1 rle with old method : 0.0028679370880126953 length of segment : 53 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 280 time to create 1 rle with old method : 0.0007169246673583984 length of segment : 33 time for calcul the mask position with numpy : 0.0001919269561767578 nb_pixel_total : 5450 time to create 1 rle with old method : 0.008020401000976562 length of segment : 187 time for calcul the mask position with numpy : 0.00019884109497070312 nb_pixel_total : 4647 time to create 1 rle with old method : 0.008250951766967773 length of segment : 97 time for calcul the mask position with numpy : 0.00021982192993164062 nb_pixel_total : 5257 time to create 1 rle with old method : 0.00688934326171875 length of segment : 94 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 50 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.0004680156707763672 length of segment : 24 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 871 time to create 1 rle with old method : 0.0013799667358398438 length of segment : 37 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 291 time to create 1 rle with old method : 0.0005409717559814453 length of segment : 21 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 246 time to create 1 rle with old method : 0.0004792213439941406 length of segment : 15 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 425 time to create 1 rle with old method : 0.0007517337799072266 length of segment : 30 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 115 time to create 1 rle with old method : 0.0002663135528564453 length of segment : 10 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 892 time to create 1 rle with old method : 0.0014023780822753906 length of segment : 53 time for calcul the mask position with numpy : 0.0001666545867919922 nb_pixel_total : 3327 time to create 1 rle with old method : 0.004879474639892578 length of segment : 83 time for calcul the mask position with numpy : 7.605552673339844e-05 nb_pixel_total : 344 time to create 1 rle with old method : 0.0006363391876220703 length of segment : 34 time for calcul the mask position with numpy : 8.487701416015625e-05 nb_pixel_total : 1400 time to create 1 rle with old method : 0.002389192581176758 length of segment : 58 time for calcul the mask position with numpy : 0.0001277923583984375 nb_pixel_total : 657 time to create 1 rle with old method : 0.0018858909606933594 length of segment : 35 time for calcul the mask position with numpy : 0.00011992454528808594 nb_pixel_total : 1151 time to create 1 rle with old method : 0.0025720596313476562 length of segment : 50 time for calcul the mask position with numpy : 7.605552673339844e-05 nb_pixel_total : 1667 time to create 1 rle with old method : 0.002685070037841797 length of segment : 54 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.0004329681396484375 length of segment : 33 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 195 time to create 1 rle with old method : 0.0004246234893798828 length of segment : 18 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 1311 time to create 1 rle with old method : 0.002117156982421875 length of segment : 82 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 738 time to create 1 rle with old method : 0.0009589195251464844 length of segment : 33 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 918 time to create 1 rle with old method : 0.0012791156768798828 length of segment : 40 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.0002384185791015625 length of segment : 14 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.00029468536376953125 length of segment : 17 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 526 time to create 1 rle with old method : 0.0006723403930664062 length of segment : 47 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 204 time to create 1 rle with old method : 0.0002968311309814453 length of segment : 33 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002722740173339844 length of segment : 15 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 1586 time to create 1 rle with old method : 0.0019390583038330078 length of segment : 52 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 342 time to create 1 rle with old method : 0.00047016143798828125 length of segment : 35 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 649 time to create 1 rle with old method : 0.0011374950408935547 length of segment : 30 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 435 time to create 1 rle with old method : 0.0007712841033935547 length of segment : 34 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.0007083415985107422 length of segment : 28 time for calcul the mask position with numpy : 9.870529174804688e-05 nb_pixel_total : 1370 time to create 1 rle with old method : 0.0022766590118408203 length of segment : 69 time for calcul the mask position with numpy : 7.104873657226562e-05 nb_pixel_total : 772 time to create 1 rle with old method : 0.001354217529296875 length of segment : 35 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005421638488769531 length of segment : 20 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 1363 time to create 1 rle with old method : 0.0015718936920166016 length of segment : 67 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 977 time to create 1 rle with old method : 0.00122833251953125 length of segment : 38 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 200 time to create 1 rle with old method : 0.00031638145446777344 length of segment : 13 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 1520 time to create 1 rle with old method : 0.0018143653869628906 length of segment : 48 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 75 time to create 1 rle with old method : 0.0001354217529296875 length of segment : 10 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 359 time to create 1 rle with old method : 0.0005075931549072266 length of segment : 19 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.00037217140197753906 length of segment : 34 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 1585 time to create 1 rle with old method : 0.002085447311401367 length of segment : 49 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.00021457672119140625 length of segment : 15 time for calcul the mask position with numpy : 0.00014519691467285156 nb_pixel_total : 783 time to create 1 rle with old method : 0.0010144710540771484 length of segment : 35 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 472 time to create 1 rle with old method : 0.0007071495056152344 length of segment : 21 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 476 time to create 1 rle with old method : 0.0006499290466308594 length of segment : 22 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 326 time to create 1 rle with old method : 0.00046443939208984375 length of segment : 22 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00020551681518554688 length of segment : 16 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 333 time to create 1 rle with old method : 0.0004477500915527344 length of segment : 24 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 1598 time to create 1 rle with old method : 0.0019538402557373047 length of segment : 56 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0006687641143798828 length of segment : 21 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 1581 time to create 1 rle with old method : 0.001847982406616211 length of segment : 56 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 704 time to create 1 rle with old method : 0.0009226799011230469 length of segment : 34 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 20 time for calcul the mask position with numpy : 0.00020122528076171875 nb_pixel_total : 10775 time to create 1 rle with old method : 0.013957023620605469 length of segment : 117 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 275 time to create 1 rle with old method : 0.0004737377166748047 length of segment : 31 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003991127014160156 length of segment : 27 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 246 time to create 1 rle with old method : 0.00044536590576171875 length of segment : 19 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 907 time to create 1 rle with old method : 0.0013494491577148438 length of segment : 38 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 418 time to create 1 rle with old method : 0.0010569095611572266 length of segment : 16 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 63 time to create 1 rle with old method : 0.0001976490020751953 length of segment : 22 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 58 time to create 1 rle with old method : 0.00021147727966308594 length of segment : 21 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0008766651153564453 length of segment : 23 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 145 time to create 1 rle with old method : 0.0003330707550048828 length of segment : 27 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 312 time to create 1 rle with old method : 0.0006070137023925781 length of segment : 23 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 613 time to create 1 rle with old method : 0.001271963119506836 length of segment : 45 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 519 time to create 1 rle with old method : 0.0011017322540283203 length of segment : 34 time for calcul the mask position with numpy : 9.250640869140625e-05 nb_pixel_total : 1911 time to create 1 rle with old method : 0.0031211376190185547 length of segment : 59 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.000286102294921875 length of segment : 21 time for calcul the mask position with numpy : 9.489059448242188e-05 nb_pixel_total : 359 time to create 1 rle with old method : 0.0005733966827392578 length of segment : 57 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 641 time to create 1 rle with old method : 0.0007929801940917969 length of segment : 56 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 65 time to create 1 rle with old method : 0.0001373291015625 length of segment : 11 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005614757537841797 length of segment : 28 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 444 time to create 1 rle with old method : 0.0006477832794189453 length of segment : 16 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 130.66250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 571 time to create 1 rle with old method : 0.0007305145263671875 length of segment : 49 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 541 time to create 1 rle with old method : 0.0007221698760986328 length of segment : 51 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.0003502368927001953 length of segment : 12 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 63 time to create 1 rle with old method : 0.00013065338134765625 length of segment : 9 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 83 time to create 1 rle with old method : 0.0001800060272216797 length of segment : 10 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 327 time to create 1 rle with old method : 0.0005450248718261719 length of segment : 32 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.29375 max: 148.37344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.0005099773406982422 length of segment : 29 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 39 time to create 1 rle with old method : 8.678436279296875e-05 length of segment : 14 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.00040268898010253906 length of segment : 36 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.00030875205993652344 length of segment : 37 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.00025391578674316406 length of segment : 27 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 1 time to create 1 rle with old method : 1.8835067749023438e-05 length of segment : 1 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 61 time to create 1 rle with old method : 0.00011110305786132812 length of segment : 22 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0003998279571533203 length of segment : 29 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 138 time to create 1 rle with old method : 0.0002675056457519531 length of segment : 30 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.46172 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 2780 time to create 1 rle with old method : 0.0033109188079833984 length of segment : 97 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 24 time to create 1 rle with old method : 6.079673767089844e-05 length of segment : 5 time for calcul the mask position with numpy : 2.7179718017578125e-05 nb_pixel_total : 26 time to create 1 rle with old method : 6.365776062011719e-05 length of segment : 5 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.00018835067749023438 length of segment : 34 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 329 time to create 1 rle with old method : 0.0005748271942138672 length of segment : 11 time for calcul the mask position with numpy : 9.918212890625e-05 nb_pixel_total : 2112 time to create 1 rle with old method : 0.0030236244201660156 length of segment : 121 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 258 time to create 1 rle with old method : 0.00039696693420410156 length of segment : 36 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 786 time to create 1 rle with old method : 0.0012359619140625 length of segment : 51 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 19 time to create 1 rle with old method : 5.91278076171875e-05 length of segment : 9 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.81328 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 0.00010800361633300781 nb_pixel_total : 5103 time to create 1 rle with old method : 0.006783723831176758 length of segment : 61 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 3219 time to create 1 rle with old method : 0.003643035888671875 length of segment : 99 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 2035 time to create 1 rle with old method : 0.002610445022583008 length of segment : 42 time for calcul the mask position with numpy : 0.00015807151794433594 nb_pixel_total : 4377 time to create 1 rle with old method : 0.00694584846496582 length of segment : 78 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -110.34453 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 0.000209808349609375 nb_pixel_total : 11517 time to create 1 rle with old method : 0.0179898738861084 length of segment : 90 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.00026345252990722656 length of segment : 13 time for calcul the mask position with numpy : 0.0002453327178955078 nb_pixel_total : 10430 time to create 1 rle with old method : 0.012294292449951172 length of segment : 223 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.00030875205993652344 length of segment : 17 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.95937 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 102 time to create 1 rle with old method : 0.00019311904907226562 length of segment : 11 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 833 time to create 1 rle with old method : 0.001079559326171875 length of segment : 39 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 1368 time to create 1 rle with old method : 0.0015997886657714844 length of segment : 157 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 158 time to create 1 rle with old method : 0.0002467632293701172 length of segment : 23 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 359 time to create 1 rle with old method : 0.0004863739013671875 length of segment : 24 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.0002086162567138672 length of segment : 13 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 679 time to create 1 rle with old method : 0.0009491443634033203 length of segment : 40 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 1785 time to create 1 rle with old method : 0.002903461456298828 length of segment : 54 time for calcul the mask position with numpy : 0.0005102157592773438 nb_pixel_total : 41389 time to create 1 rle with old method : 0.07186484336853027 length of segment : 227 time for calcul the mask position with numpy : 8.845329284667969e-05 nb_pixel_total : 695 time to create 1 rle with old method : 0.0012464523315429688 length of segment : 43 time for calcul the mask position with numpy : 0.0010194778442382812 nb_pixel_total : 59312 time to create 1 rle with old method : 0.07221841812133789 length of segment : 316 Processing 1 images image shape: (400, 400, 3) min: 27.00000 max: 227.00000 molded_images shape: (1, 640, 640, 3) min: -84.09063 max: 98.70000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -113.67656 max: 150.87344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 263 time to create 1 rle with old method : 0.0004067420959472656 length of segment : 16 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.0003075599670410156 length of segment : 24 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 288 time to create 1 rle with old method : 0.0004432201385498047 length of segment : 14 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 824 time to create 1 rle with old method : 0.0011663436889648438 length of segment : 39 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 481 time to create 1 rle with old method : 0.0007143020629882812 length of segment : 18 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 3341 time to create 1 rle with old method : 0.004069089889526367 length of segment : 60 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1007 time to create 1 rle with old method : 0.0016369819641113281 length of segment : 55 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.0002455711364746094 length of segment : 13 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.59844 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 0.00020265579223632812 nb_pixel_total : 13081 time to create 1 rle with old method : 0.015079021453857422 length of segment : 118 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 928 time to create 1 rle with old method : 0.0012142658233642578 length of segment : 40 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.00019550323486328125 length of segment : 18 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 472 time to create 1 rle with old method : 0.0006282329559326172 length of segment : 22 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 548 time to create 1 rle with old method : 0.0008351802825927734 length of segment : 26 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 1292 time to create 1 rle with old method : 0.001653909683227539 length of segment : 44 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 489 time to create 1 rle with old method : 0.0006434917449951172 length of segment : 23 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 1059 time to create 1 rle with old method : 0.0013928413391113281 length of segment : 43 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 715 time to create 1 rle with old method : 0.0009582042694091797 length of segment : 22 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 494 time to create 1 rle with old method : 0.0006632804870605469 length of segment : 22 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 223 time to create 1 rle with old method : 0.00033736228942871094 length of segment : 14 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.00021910667419433594 length of segment : 19 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 818 time to create 1 rle with old method : 0.001119375228881836 length of segment : 26 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 444 time to create 1 rle with old method : 0.0006160736083984375 length of segment : 23 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -112.80938 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 97 time to create 1 rle with old method : 0.0002789497375488281 length of segment : 14 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 1153 time to create 1 rle with old method : 0.0015950202941894531 length of segment : 73 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 1087 time to create 1 rle with old method : 0.0016758441925048828 length of segment : 74 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 992 time to create 1 rle with old method : 0.0017249584197998047 length of segment : 35 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 1014 time to create 1 rle with old method : 0.0017337799072265625 length of segment : 36 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 787 time to create 1 rle with old method : 0.0009582042694091797 length of segment : 37 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 632 time to create 1 rle with old method : 0.0008127689361572266 length of segment : 67 time for calcul the mask position with numpy : 0.00011920928955078125 nb_pixel_total : 2464 time to create 1 rle with old method : 0.0035181045532226562 length of segment : 97 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 309 time to create 1 rle with old method : 0.0007774829864501953 length of segment : 20 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 123 time to create 1 rle with old method : 0.00034117698669433594 length of segment : 10 time for calcul the mask position with numpy : 9.131431579589844e-05 nb_pixel_total : 690 time to create 1 rle with old method : 0.0039670467376708984 length of segment : 38 time for calcul the mask position with numpy : 0.0002396106719970703 nb_pixel_total : 613 time to create 1 rle with old method : 0.0058133602142333984 length of segment : 27 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.26016 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 1314 time to create 1 rle with old method : 0.0015590190887451172 length of segment : 57 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 12 time to create 1 rle with old method : 7.486343383789062e-05 length of segment : 8 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 912 time to create 1 rle with old method : 0.0012140274047851562 length of segment : 79 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 913 time to create 1 rle with old method : 0.0011668205261230469 length of segment : 48 time for calcul the mask position with numpy : 0.00022220611572265625 nb_pixel_total : 3512 time to create 1 rle with old method : 0.004628658294677734 length of segment : 203 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 102 time to create 1 rle with old method : 0.00020122528076171875 length of segment : 17 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 1063 time to create 1 rle with old method : 0.0015552043914794922 length of segment : 64 time for calcul the mask position with numpy : 0.00010800361633300781 nb_pixel_total : 1667 time to create 1 rle with old method : 0.0037081241607666016 length of segment : 78 time for calcul the mask position with numpy : 6.866455078125e-05 nb_pixel_total : 567 time to create 1 rle with old method : 0.0010602474212646484 length of segment : 56 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 297 time to create 1 rle with old method : 0.0004150867462158203 length of segment : 24 time for calcul the mask position with numpy : 0.00015473365783691406 nb_pixel_total : 3394 time to create 1 rle with old method : 0.00404667854309082 length of segment : 222 Processing 1 images image shape: (280, 400, 3) min: 24.00000 max: 212.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 90.42031 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0010766983032226562 nb_pixel_total : 106524 time to create 1 rle with old method : 0.12220430374145508 length of segment : 280 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.00036215782165527344 length of segment : 15 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 503 time to create 1 rle with old method : 0.0007958412170410156 length of segment : 22 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00019741058349609375 length of segment : 13 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 534 time to create 1 rle with old method : 0.0007529258728027344 length of segment : 44 time for calcul the mask position with numpy : 0.00013136863708496094 nb_pixel_total : 7898 time to create 1 rle with old method : 0.00975346565246582 length of segment : 91 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 487 time to create 1 rle with old method : 0.0008723735809326172 length of segment : 40 time for calcul the mask position with numpy : 0.00019288063049316406 nb_pixel_total : 5040 time to create 1 rle with old method : 0.007308006286621094 length of segment : 142 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 1557 time to create 1 rle with old method : 0.0023336410522460938 length of segment : 56 time for calcul the mask position with numpy : 9.894371032714844e-05 nb_pixel_total : 3099 time to create 1 rle with old method : 0.004600048065185547 length of segment : 46 time for calcul the mask position with numpy : 0.00016307830810546875 nb_pixel_total : 5174 time to create 1 rle with old method : 0.009184598922729492 length of segment : 137 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.0007731914520263672 length of segment : 29 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.0003025531768798828 length of segment : 12 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 1422 time to create 1 rle with old method : 0.002645730972290039 length of segment : 54 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 44 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 857 time to create 1 rle with old method : 0.0011162757873535156 length of segment : 37 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 269 time to create 1 rle with old method : 0.0003752708435058594 length of segment : 24 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 820 time to create 1 rle with old method : 0.001050710678100586 length of segment : 45 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 298 time to create 1 rle with old method : 0.00042724609375 length of segment : 17 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 612 time to create 1 rle with old method : 0.0007853507995605469 length of segment : 33 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 1091 time to create 1 rle with old method : 0.0013463497161865234 length of segment : 47 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 267 time to create 1 rle with old method : 0.0003619194030761719 length of segment : 27 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 358 time to create 1 rle with old method : 0.0004892349243164062 length of segment : 35 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 1954 time to create 1 rle with old method : 0.002338886260986328 length of segment : 76 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 389 time to create 1 rle with old method : 0.0005643367767333984 length of segment : 32 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 1467 time to create 1 rle with old method : 0.001861572265625 length of segment : 50 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 888 time to create 1 rle with old method : 0.0011854171752929688 length of segment : 39 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.0004634857177734375 length of segment : 27 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.00027108192443847656 length of segment : 17 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 1366 time to create 1 rle with old method : 0.0016105175018310547 length of segment : 53 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 1402 time to create 1 rle with old method : 0.0018219947814941406 length of segment : 55 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 241 time to create 1 rle with old method : 0.0003581047058105469 length of segment : 15 time for calcul the mask position with numpy : 9.298324584960938e-05 nb_pixel_total : 899 time to create 1 rle with old method : 0.002177000045776367 length of segment : 38 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.0004215240478515625 length of segment : 18 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.0002970695495605469 length of segment : 15 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 2027 time to create 1 rle with old method : 0.0024750232696533203 length of segment : 57 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 415 time to create 1 rle with old method : 0.000659942626953125 length of segment : 26 time for calcul the mask position with numpy : 8.749961853027344e-05 nb_pixel_total : 1442 time to create 1 rle with old method : 0.0017940998077392578 length of segment : 77 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00021338462829589844 length of segment : 10 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 619 time to create 1 rle with old method : 0.0009019374847412109 length of segment : 30 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 787 time to create 1 rle with old method : 0.0009813308715820312 length of segment : 36 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.00025534629821777344 length of segment : 16 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 828 time to create 1 rle with old method : 0.0011138916015625 length of segment : 35 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 258 time to create 1 rle with old method : 0.0005717277526855469 length of segment : 34 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0009653568267822266 length of segment : 27 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 503 time to create 1 rle with old method : 0.0008182525634765625 length of segment : 22 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 516 time to create 1 rle with old method : 0.0007839202880859375 length of segment : 30 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 1862 time to create 1 rle with old method : 0.00276947021484375 length of segment : 65 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 97 time to create 1 rle with old method : 0.00014495849609375 length of segment : 17 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.00035262107849121094 length of segment : 19 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 496 time to create 1 rle with old method : 0.0010793209075927734 length of segment : 31 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 824 time to create 1 rle with old method : 0.0016312599182128906 length of segment : 38 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 1893 time to create 1 rle with old method : 0.003246307373046875 length of segment : 84 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 300 time to create 1 rle with old method : 0.0004813671112060547 length of segment : 24 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 254 time to create 1 rle with old method : 0.0005872249603271484 length of segment : 17 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 2300 time to create 1 rle with old method : 0.003923177719116211 length of segment : 59 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.0005745887756347656 length of segment : 24 time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 1460 time to create 1 rle with old method : 0.0026633739471435547 length of segment : 71 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 466 time to create 1 rle with old method : 0.0010149478912353516 length of segment : 19 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 23 time for calcul the mask position with numpy : 0.0001621246337890625 nb_pixel_total : 10716 time to create 1 rle with old method : 0.01218414306640625 length of segment : 118 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.00022459030151367188 length of segment : 18 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 425 time to create 1 rle with old method : 0.0006422996520996094 length of segment : 18 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 879 time to create 1 rle with old method : 0.0011005401611328125 length of segment : 40 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 320 time to create 1 rle with old method : 0.0004558563232421875 length of segment : 19 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 273 time to create 1 rle with old method : 0.00037980079650878906 length of segment : 29 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 150 time to create 1 rle with old method : 0.00021219253540039062 length of segment : 28 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 378 time to create 1 rle with old method : 0.0005266666412353516 length of segment : 26 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 53 time to create 1 rle with old method : 0.00011277198791503906 length of segment : 21 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 52 time to create 1 rle with old method : 0.0001316070556640625 length of segment : 20 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 238 time to create 1 rle with old method : 0.00033664703369140625 length of segment : 31 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 529 time to create 1 rle with old method : 0.0006935596466064453 length of segment : 36 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0002567768096923828 length of segment : 19 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 1953 time to create 1 rle with old method : 0.002573728561401367 length of segment : 61 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006892681121826172 length of segment : 38 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 413 time to create 1 rle with old method : 0.0005574226379394531 length of segment : 26 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 839 time to create 1 rle with old method : 0.0010235309600830078 length of segment : 67 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 312 time to create 1 rle with old method : 0.0004189014434814453 length of segment : 23 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 508 time to create 1 rle with old method : 0.0007822513580322266 length of segment : 46 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 978 time to create 1 rle with old method : 0.0011687278747558594 length of segment : 68 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0006182193756103516 length of segment : 27 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 827 time to create 1 rle with old method : 0.0010540485382080078 length of segment : 41 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 355 time to create 1 rle with old method : 0.0005388259887695312 length of segment : 26 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 130.72500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 562 time to create 1 rle with old method : 0.0008592605590820312 length of segment : 44 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 541 time to create 1 rle with old method : 0.0008060932159423828 length of segment : 52 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.0003292560577392578 length of segment : 13 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 294 time to create 1 rle with old method : 0.00039577484130859375 length of segment : 39 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 69 time to create 1 rle with old method : 0.00012969970703125 length of segment : 8 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.000446319580078125 length of segment : 19 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.0001571178436279297 length of segment : 9 NEW PHOTO pour l'instant on ne peut pas sauvegarder la photo dans les tile Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.91875 max: 149.18594 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 51 time to create 1 rle with old method : 0.00011229515075683594 length of segment : 16 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 383 time to create 1 rle with old method : 0.0005095005035400391 length of segment : 27 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.00048351287841796875 length of segment : 28 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003447532653808594 length of segment : 37 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 133 time to create 1 rle with old method : 0.0001952648162841797 length of segment : 27 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1569 time to create 1 rle with old method : 0.002066373825073242 length of segment : 51 time for calcul the mask position with numpy : 6.866455078125e-05 nb_pixel_total : 177 time to create 1 rle with old method : 0.00034880638122558594 length of segment : 10 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.07891 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 0.0001659393310546875 nb_pixel_total : 2772 time to create 1 rle with old method : 0.003401041030883789 length of segment : 94 time for calcul the mask position with numpy : 0.00021886825561523438 nb_pixel_total : 3399 time to create 1 rle with old method : 0.004159450531005859 length of segment : 113 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 23 time to create 1 rle with old method : 7.82012939453125e-05 length of segment : 6 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.0003757476806640625 length of segment : 45 time for calcul the mask position with numpy : 0.00014090538024902344 nb_pixel_total : 808 time to create 1 rle with old method : 0.001148223876953125 length of segment : 27 time for calcul the mask position with numpy : 0.0001571178436279297 nb_pixel_total : 2859 time to create 1 rle with old method : 0.003570556640625 length of segment : 103 length of segment : 0 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 18 time to create 1 rle with old method : 6.651878356933594e-05 length of segment : 5 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 434 time to create 1 rle with old method : 0.0007464885711669922 length of segment : 45 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0005772113800048828 length of segment : 43 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 989 time to create 1 rle with old method : 0.0016818046569824219 length of segment : 44 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 941 time to create 1 rle with old method : 0.0012354850769042969 length of segment : 45 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.78594 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.00010585784912109375 nb_pixel_total : 5964 time to create 1 rle with old method : 0.0067174434661865234 length of segment : 107 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 1902 time to create 1 rle with old method : 0.002318859100341797 length of segment : 56 time for calcul the mask position with numpy : 0.00012946128845214844 nb_pixel_total : 3327 time to create 1 rle with old method : 0.004236459732055664 length of segment : 56 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 1271 time to create 1 rle with old method : 0.0015757083892822266 length of segment : 54 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 3004 time to create 1 rle with old method : 0.0035195350646972656 length of segment : 105 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -113.07500 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 0.00020003318786621094 nb_pixel_total : 11096 time to create 1 rle with old method : 0.015729904174804688 length of segment : 90 time for calcul the mask position with numpy : 0.0003998279571533203 nb_pixel_total : 11327 time to create 1 rle with old method : 0.013404369354248047 length of segment : 241 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.000263214111328125 length of segment : 15 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.0004134178161621094 length of segment : 17 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 930 time to create 1 rle with old method : 0.0014493465423583984 length of segment : 56 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.89297 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 290 time to create 1 rle with old method : 0.00046563148498535156 length of segment : 20 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 433 time to create 1 rle with old method : 0.0006377696990966797 length of segment : 31 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 643 time to create 1 rle with old method : 0.0008952617645263672 length of segment : 31 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00020360946655273438 length of segment : 10 time for calcul the mask position with numpy : 0.00010967254638671875 nb_pixel_total : 1553 time to create 1 rle with old method : 0.0017998218536376953 length of segment : 176 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 743 time to create 1 rle with old method : 0.0009448528289794922 length of segment : 35 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 816 time to create 1 rle with old method : 0.0009930133819580078 length of segment : 52 time for calcul the mask position with numpy : 0.00015211105346679688 nb_pixel_total : 5203 time to create 1 rle with old method : 0.006548881530761719 length of segment : 60 time for calcul the mask position with numpy : 0.0006284713745117188 nb_pixel_total : 49058 time to create 1 rle with old method : 0.05785202980041504 length of segment : 361 Processing 1 images image shape: (400, 400, 3) min: 26.00000 max: 237.00000 molded_images shape: (1, 640, 640, 3) min: -82.31328 max: 108.25078 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 0 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -113.88750 max: 150.87344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003254413604736328 length of segment : 23 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 300 time to create 1 rle with old method : 0.00047397613525390625 length of segment : 16 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00023555755615234375 length of segment : 13 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 607 time to create 1 rle with old method : 0.0009720325469970703 length of segment : 48 time for calcul the mask position with numpy : 0.00011110305786132812 nb_pixel_total : 3287 time to create 1 rle with old method : 0.004795074462890625 length of segment : 59 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 87 time to create 1 rle with old method : 0.0001990795135498047 length of segment : 10 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 234 time to create 1 rle with old method : 0.0004496574401855469 length of segment : 13 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.0007131099700927734 length of segment : 27 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 674 time to create 1 rle with old method : 0.0008785724639892578 length of segment : 32 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 424 time to create 1 rle with old method : 0.0006730556488037109 length of segment : 17 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 308 time to create 1 rle with old method : 0.0004184246063232422 length of segment : 21 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.70391 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 881 time to create 1 rle with old method : 0.0011382102966308594 length of segment : 39 time for calcul the mask position with numpy : 0.00020837783813476562 nb_pixel_total : 10613 time to create 1 rle with old method : 0.012383222579956055 length of segment : 124 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 866 time to create 1 rle with old method : 0.0012187957763671875 length of segment : 27 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 469 time to create 1 rle with old method : 0.0006272792816162109 length of segment : 24 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 533 time to create 1 rle with old method : 0.0008399486541748047 length of segment : 26 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 296 time to create 1 rle with old method : 0.00047850608825683594 length of segment : 21 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 322 time to create 1 rle with old method : 0.0004749298095703125 length of segment : 17 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 447 time to create 1 rle with old method : 0.0005755424499511719 length of segment : 58 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 1100 time to create 1 rle with old method : 0.001478433609008789 length of segment : 32 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.89922 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 1236 time to create 1 rle with old method : 0.0014562606811523438 length of segment : 76 time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 724 time to create 1 rle with old method : 0.001064300537109375 length of segment : 51 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 84 time to create 1 rle with old method : 0.00015354156494140625 length of segment : 10 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 1014 time to create 1 rle with old method : 0.0014181137084960938 length of segment : 37 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.00020003318786621094 length of segment : 11 time for calcul the mask position with numpy : 0.0002586841583251953 nb_pixel_total : 11243 time to create 1 rle with old method : 0.012739419937133789 length of segment : 208 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 942 time to create 1 rle with old method : 0.0012631416320800781 length of segment : 69 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 813 time to create 1 rle with old method : 0.0009465217590332031 length of segment : 39 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 903 time to create 1 rle with old method : 0.0012004375457763672 length of segment : 85 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 298 time to create 1 rle with old method : 0.00039887428283691406 length of segment : 20 time for calcul the mask position with numpy : 0.0001690387725830078 nb_pixel_total : 112 time to create 1 rle with old method : 0.0006656646728515625 length of segment : 12 time for calcul the mask position with numpy : 8.630752563476562e-05 nb_pixel_total : 901 time to create 1 rle with old method : 0.001087188720703125 length of segment : 104 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 748 time to create 1 rle with old method : 0.0008599758148193359 length of segment : 37 Processing 1 images image shape: (400, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 150.26016 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 1313 time to create 1 rle with old method : 0.0021677017211914062 length of segment : 58 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 888 time to create 1 rle with old method : 0.0010323524475097656 length of segment : 48 time for calcul the mask position with numpy : 8.988380432128906e-05 nb_pixel_total : 1746 time to create 1 rle with old method : 0.002259969711303711 length of segment : 115 time for calcul the mask position with numpy : 0.00011277198791503906 nb_pixel_total : 1532 time to create 1 rle with old method : 0.001806020736694336 length of segment : 87 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 617 time to create 1 rle with old method : 0.0007708072662353516 length of segment : 59 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 258 time to create 1 rle with old method : 0.0003981590270996094 length of segment : 37 time for calcul the mask position with numpy : 0.0002524852752685547 nb_pixel_total : 2969 time to create 1 rle with old method : 0.003506898880004883 length of segment : 200 time for calcul the mask position with numpy : 0.00011396408081054688 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0020623207092285156 length of segment : 81 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 678 time to create 1 rle with old method : 0.0008831024169921875 length of segment : 54 Processing 1 images image shape: (280, 400, 3) min: 17.00000 max: 222.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 95.92422 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009484291076660156 nb_pixel_total : 106074 time to create 1 rle with old method : 0.1233363151550293 length of segment : 282 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002689361572265625 length of segment : 16 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 521 time to create 1 rle with old method : 0.0006816387176513672 length of segment : 23 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 375 time to create 1 rle with old method : 0.0004892349243164062 length of segment : 31 time for calcul the mask position with numpy : 2.6941299438476562e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.00015735626220703125 length of segment : 12 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 474 time to create 1 rle with old method : 0.0005662441253662109 length of segment : 40 time for calcul the mask position with numpy : 8.821487426757812e-05 nb_pixel_total : 4035 time to create 1 rle with old method : 0.005165576934814453 length of segment : 81 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 529 time to create 1 rle with old method : 0.0006501674652099609 length of segment : 45 time for calcul the mask position with numpy : 0.00010609626770019531 nb_pixel_total : 7040 time to create 1 rle with old method : 0.007950782775878906 length of segment : 98 time for calcul the mask position with numpy : 0.0002372264862060547 nb_pixel_total : 10386 time to create 1 rle with old method : 0.011253118515014648 length of segment : 259 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 1615 time to create 1 rle with old method : 0.0018715858459472656 length of segment : 40 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 1554 time to create 1 rle with old method : 0.0017535686492919922 length of segment : 54 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 39 time for calcul the mask position with numpy : 0.00011086463928222656 nb_pixel_total : 707 time to create 1 rle with old method : 0.00103759765625 length of segment : 44 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 300 time to create 1 rle with old method : 0.000453948974609375 length of segment : 18 time for calcul the mask position with numpy : 9.751319885253906e-05 nb_pixel_total : 838 time to create 1 rle with old method : 0.0017817020416259766 length of segment : 40 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 2009 time to create 1 rle with old method : 0.0023758411407470703 length of segment : 82 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 123 time to create 1 rle with old method : 0.00018143653869628906 length of segment : 19 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.0002560615539550781 length of segment : 17 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 404 time to create 1 rle with old method : 0.0005574226379394531 length of segment : 29 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 133 time to create 1 rle with old method : 0.0002155303955078125 length of segment : 11 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 379 time to create 1 rle with old method : 0.0004744529724121094 length of segment : 28 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 829 time to create 1 rle with old method : 0.0010666847229003906 length of segment : 35 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 344 time to create 1 rle with old method : 0.0004534721374511719 length of segment : 27 time for calcul the mask position with numpy : 0.0001201629638671875 nb_pixel_total : 2375 time to create 1 rle with old method : 0.003602743148803711 length of segment : 60 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.0006270408630371094 length of segment : 26 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 520 time to create 1 rle with old method : 0.0007109642028808594 length of segment : 28 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 233 time to create 1 rle with old method : 0.0003294944763183594 length of segment : 19 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 861 time to create 1 rle with old method : 0.001104116439819336 length of segment : 39 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 292 time to create 1 rle with old method : 0.00037860870361328125 length of segment : 31 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 620 time to create 1 rle with old method : 0.0007314682006835938 length of segment : 35 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 1176 time to create 1 rle with old method : 0.0014317035675048828 length of segment : 45 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002543926239013672 length of segment : 15 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.0003960132598876953 length of segment : 32 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1484 time to create 1 rle with old method : 0.0017242431640625 length of segment : 56 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 1363 time to create 1 rle with old method : 0.0016269683837890625 length of segment : 59 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 1892 time to create 1 rle with old method : 0.00217437744140625 length of segment : 64 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002703666687011719 length of segment : 31 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.00030517578125 length of segment : 15 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 529 time to create 1 rle with old method : 0.0006554126739501953 length of segment : 42 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.00022864341735839844 length of segment : 12 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 289 time to create 1 rle with old method : 0.00037479400634765625 length of segment : 28 time for calcul the mask position with numpy : 0.00011181831359863281 nb_pixel_total : 2384 time to create 1 rle with old method : 0.0030364990234375 length of segment : 63 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 442 time to create 1 rle with old method : 0.0005719661712646484 length of segment : 24 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 223 time to create 1 rle with old method : 0.00033545494079589844 length of segment : 18 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 843 time to create 1 rle with old method : 0.0011339187622070312 length of segment : 37 time for calcul the mask position with numpy : 8.845329284667969e-05 nb_pixel_total : 2563 time to create 1 rle with old method : 0.0030875205993652344 length of segment : 61 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 408 time to create 1 rle with old method : 0.0005137920379638672 length of segment : 24 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 1284 time to create 1 rle with old method : 0.0016326904296875 length of segment : 48 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 1397 time to create 1 rle with old method : 0.0016713142395019531 length of segment : 50 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 1217 time to create 1 rle with old method : 0.0015177726745605469 length of segment : 64 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1195 time to create 1 rle with old method : 0.0014607906341552734 length of segment : 42 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 23 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 1020 time to create 1 rle with old method : 0.0012500286102294922 length of segment : 55 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 904 time to create 1 rle with old method : 0.0012133121490478516 length of segment : 40 time for calcul the mask position with numpy : 0.0001919269561767578 nb_pixel_total : 10464 time to create 1 rle with old method : 0.012217998504638672 length of segment : 119 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 258 time to create 1 rle with old method : 0.0003514289855957031 length of segment : 31 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 47 time to create 1 rle with old method : 0.00012254714965820312 length of segment : 16 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.0002472400665283203 length of segment : 20 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 306 time to create 1 rle with old method : 0.0004303455352783203 length of segment : 20 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.0002944469451904297 length of segment : 29 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002720355987548828 length of segment : 20 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 45 time to create 1 rle with old method : 0.0001087188720703125 length of segment : 20 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 1135 time to create 1 rle with old method : 0.001516103744506836 length of segment : 56 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.0003027915954589844 length of segment : 11 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 435 time to create 1 rle with old method : 0.0005688667297363281 length of segment : 30 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.00015926361083984375 length of segment : 11 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.00023031234741210938 length of segment : 27 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 405 time to create 1 rle with old method : 0.0005519390106201172 length of segment : 23 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 2032 time to create 1 rle with old method : 0.0026035308837890625 length of segment : 82 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.0003306865692138672 length of segment : 15 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0007050037384033203 length of segment : 17 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 552 time to create 1 rle with old method : 0.0007219314575195312 length of segment : 54 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005385875701904297 length of segment : 28 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 441 time to create 1 rle with old method : 0.0006539821624755859 length of segment : 19 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 517 time to create 1 rle with old method : 0.0007264614105224609 length of segment : 35 Processing 1 images image shape: (280, 320, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 132.03750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 553 time to create 1 rle with old method : 0.0007340908050537109 length of segment : 53 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 626 time to create 1 rle with old method : 0.0008070468902587891 length of segment : 50 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 174 time to create 1 rle with old method : 0.000308990478515625 length of segment : 12 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 670 time to create 1 rle with old method : 0.0009326934814453125 length of segment : 30 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 74 time to create 1 rle with old method : 0.00014352798461914062 length of segment : 9 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00019025802612304688 length of segment : 12 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 76 time to create 1 rle with old method : 0.0001361370086669922 length of segment : 9 Detection mask done ! Trying to reset tf kernel 2209142 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5264 tf kernel not reseted sub process len(results) : 3323 len(list_Values) 3323 None max_time_sub_proc : 3600 parent process len(results) : 0 len(list_Values) 3323 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10553 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 3607 chid ids of type : 4228 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 91346 save missing photos in datou_result : time spend for datou_step_exec : 100.08830952644348 time spend to save output : 6.554924726486206 total time spend for step 1 : 106.64323425292969 step2:brightness Tue Feb 11 10:55:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness toutes les photos sont déjà traitées, on saute les calculs Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 21 time used for this insertion : 0.00973820686340332 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 21 time used for this insertion : 0.01507568359375 save missing photos in datou_result : time spend for datou_step_exec : 0.028644323348999023 time spend to save output : 0.029584169387817383 total time spend for step 2 : 0.058228492736816406 step3:blur_detection Tue Feb 11 10:55:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection toutes les photos sont déjà traitées, on saute les calculs Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 21 time used for this insertion : 0.009567975997924805 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 21 time used for this insertion : 0.011119365692138672 save missing photos in datou_result : time spend for datou_step_exec : 0.017642974853515625 time spend to save output : 0.025403261184692383 total time spend for step 3 : 0.04304623603820801 step4:rle_unique_nms_with_priority Tue Feb 11 10:55:05 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 3607 chid ids of type : 4228 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 160 nb_hashtags : 6 time to prepare the origin masks : 2.338621139526367 time for calcul the mask position with numpy : 0.02853107452392578 nb_pixel_total : 1759597 time to create 1 rle with new method : 0.3568384647369385 time for calcul the mask position with numpy : 0.008042097091674805 nb_pixel_total : 11 time to create 1 rle with old method : 4.982948303222656e-05 time for calcul the mask position with numpy : 0.007874488830566406 nb_pixel_total : 68 time to create 1 rle with old method : 9.775161743164062e-05 time for calcul the mask position with numpy : 0.00798940658569336 nb_pixel_total : 45 time to create 1 rle with old method : 8.106231689453125e-05 time for calcul the mask position with numpy : 0.008212804794311523 nb_pixel_total : 53 time to create 1 rle with old method : 7.128715515136719e-05 time for calcul the mask position with numpy : 0.007971763610839844 nb_pixel_total : 392 time to create 1 rle with old method : 0.0004513263702392578 time for calcul the mask position with numpy : 0.007840871810913086 nb_pixel_total : 229 time to create 1 rle with old method : 0.0002617835998535156 time for calcul the mask position with numpy : 0.007941246032714844 nb_pixel_total : 55 time to create 1 rle with old method : 6.604194641113281e-05 time for calcul the mask position with numpy : 0.007944583892822266 nb_pixel_total : 165 time to create 1 rle with old method : 0.00017786026000976562 time for calcul the mask position with numpy : 0.007966041564941406 nb_pixel_total : 75 time to create 1 rle with old method : 0.00011038780212402344 time for calcul the mask position with numpy : 0.007841348648071289 nb_pixel_total : 33 time to create 1 rle with old method : 4.2438507080078125e-05 time for calcul the mask position with numpy : 0.007993221282958984 nb_pixel_total : 49 time to create 1 rle with old method : 6.842613220214844e-05 time for calcul the mask position with numpy : 0.008079767227172852 nb_pixel_total : 9 time to create 1 rle with old method : 2.193450927734375e-05 time for calcul the mask position with numpy : 0.007940292358398438 nb_pixel_total : 2433 time to create 1 rle with old method : 0.0026302337646484375 time for calcul the mask position with numpy : 0.0078582763671875 nb_pixel_total : 244 time to create 1 rle with old method : 0.00029468536376953125 time for calcul the mask position with numpy : 0.007867097854614258 nb_pixel_total : 30 time to create 1 rle with old method : 3.814697265625e-05 time for calcul the mask position with numpy : 0.00843191146850586 nb_pixel_total : 27498 time to create 1 rle with old method : 0.02837681770324707 time for calcul the mask position with numpy : 0.008153438568115234 nb_pixel_total : 806 time to create 1 rle with old method : 0.0009002685546875 time for calcul the mask position with numpy : 0.007992029190063477 nb_pixel_total : 16800 time to create 1 rle with old method : 0.018816232681274414 time for calcul the mask position with numpy : 0.008195161819458008 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002143383026123047 time for calcul the mask position with numpy : 0.008581399917602539 nb_pixel_total : 2011 time to create 1 rle with old method : 0.002242565155029297 time for calcul the mask position with numpy : 0.00836181640625 nb_pixel_total : 343 time to create 1 rle with old method : 0.000400543212890625 time for calcul the mask position with numpy : 0.008300065994262695 nb_pixel_total : 6323 time to create 1 rle with old method : 0.007142305374145508 time for calcul the mask position with numpy : 0.008335113525390625 nb_pixel_total : 11621 time to create 1 rle with old method : 0.012346506118774414 time for calcul the mask position with numpy : 0.008022308349609375 nb_pixel_total : 395 time to create 1 rle with old method : 0.00048279762268066406 time for calcul the mask position with numpy : 0.008243083953857422 nb_pixel_total : 731 time to create 1 rle with old method : 0.00091552734375 time for calcul the mask position with numpy : 0.008188724517822266 nb_pixel_total : 186 time to create 1 rle with old method : 0.00022745132446289062 time for calcul the mask position with numpy : 0.007901906967163086 nb_pixel_total : 171 time to create 1 rle with old method : 0.0001952648162841797 time for calcul the mask position with numpy : 0.007869958877563477 nb_pixel_total : 941 time to create 1 rle with old method : 0.0010225772857666016 time for calcul the mask position with numpy : 0.007817745208740234 nb_pixel_total : 14252 time to create 1 rle with old method : 0.015486955642700195 time for calcul the mask position with numpy : 0.008158206939697266 nb_pixel_total : 801 time to create 1 rle with old method : 0.0008718967437744141 time for calcul the mask position with numpy : 0.008058309555053711 nb_pixel_total : 190 time to create 1 rle with old method : 0.00021123886108398438 time for calcul the mask position with numpy : 0.008298158645629883 nb_pixel_total : 12 time to create 1 rle with old method : 3.266334533691406e-05 time for calcul the mask position with numpy : 0.008101701736450195 nb_pixel_total : 160 time to create 1 rle with old method : 0.00018215179443359375 time for calcul the mask position with numpy : 0.007871627807617188 nb_pixel_total : 1 time to create 1 rle with old method : 8.344650268554688e-06 time for calcul the mask position with numpy : 0.008770465850830078 nb_pixel_total : 1150 time to create 1 rle with old method : 0.001346588134765625 time for calcul the mask position with numpy : 0.008054256439208984 nb_pixel_total : 896 time to create 1 rle with old method : 0.0009772777557373047 time for calcul the mask position with numpy : 0.00819253921508789 nb_pixel_total : 74 time to create 1 rle with old method : 0.00010347366333007812 time for calcul the mask position with numpy : 0.00798797607421875 nb_pixel_total : 181 time to create 1 rle with old method : 0.0003495216369628906 time for calcul the mask position with numpy : 0.007967710494995117 nb_pixel_total : 13814 time to create 1 rle with old method : 0.014954805374145508 time for calcul the mask position with numpy : 0.008043289184570312 nb_pixel_total : 89 time to create 1 rle with old method : 0.0001456737518310547 time for calcul the mask position with numpy : 0.00821065902709961 nb_pixel_total : 1273 time to create 1 rle with old method : 0.0014433860778808594 time for calcul the mask position with numpy : 0.007853031158447266 nb_pixel_total : 221 time to create 1 rle with old method : 0.0002598762512207031 time for calcul the mask position with numpy : 0.00777435302734375 nb_pixel_total : 2400 time to create 1 rle with old method : 0.0026941299438476562 time for calcul the mask position with numpy : 0.008013010025024414 nb_pixel_total : 49 time to create 1 rle with old method : 9.1552734375e-05 time for calcul the mask position with numpy : 0.007984161376953125 nb_pixel_total : 495 time to create 1 rle with old method : 0.0006260871887207031 time for calcul the mask position with numpy : 0.007852792739868164 nb_pixel_total : 11 time to create 1 rle with old method : 3.838539123535156e-05 time for calcul the mask position with numpy : 0.00775909423828125 nb_pixel_total : 186 time to create 1 rle with old method : 0.00022268295288085938 time for calcul the mask position with numpy : 0.0077702999114990234 nb_pixel_total : 155 time to create 1 rle with old method : 0.00021576881408691406 time for calcul the mask position with numpy : 0.007973432540893555 nb_pixel_total : 25595 time to create 1 rle with old method : 0.026981830596923828 time for calcul the mask position with numpy : 0.008018255233764648 nb_pixel_total : 1074 time to create 1 rle with old method : 0.0013103485107421875 time for calcul the mask position with numpy : 0.008021831512451172 nb_pixel_total : 178 time to create 1 rle with old method : 0.0003981590270996094 time for calcul the mask position with numpy : 0.007957220077514648 nb_pixel_total : 16 time to create 1 rle with old method : 3.4809112548828125e-05 time for calcul the mask position with numpy : 0.007834672927856445 nb_pixel_total : 92 time to create 1 rle with old method : 0.00010943412780761719 time for calcul the mask position with numpy : 0.007819652557373047 nb_pixel_total : 673 time to create 1 rle with old method : 0.0007510185241699219 time for calcul the mask position with numpy : 0.007830142974853516 nb_pixel_total : 261 time to create 1 rle with old method : 0.0003514289855957031 time for calcul the mask position with numpy : 0.008004903793334961 nb_pixel_total : 768 time to create 1 rle with old method : 0.0009102821350097656 time for calcul the mask position with numpy : 0.00800013542175293 nb_pixel_total : 2799 time to create 1 rle with old method : 0.0030422210693359375 time for calcul the mask position with numpy : 0.007899045944213867 nb_pixel_total : 10 time to create 1 rle with old method : 3.457069396972656e-05 time for calcul the mask position with numpy : 0.007776498794555664 nb_pixel_total : 247 time to create 1 rle with old method : 0.0002734661102294922 time for calcul the mask position with numpy : 0.008012056350708008 nb_pixel_total : 1391 time to create 1 rle with old method : 0.0016870498657226562 time for calcul the mask position with numpy : 0.008413314819335938 nb_pixel_total : 1229 time to create 1 rle with old method : 0.0013887882232666016 time for calcul the mask position with numpy : 0.008255243301391602 nb_pixel_total : 109 time to create 1 rle with old method : 0.0001366138458251953 time for calcul the mask position with numpy : 0.008192300796508789 nb_pixel_total : 58 time to create 1 rle with old method : 0.0001704692840576172 time for calcul the mask position with numpy : 0.008003473281860352 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002079010009765625 time for calcul the mask position with numpy : 0.008048295974731445 nb_pixel_total : 747 time to create 1 rle with old method : 0.0008463859558105469 time for calcul the mask position with numpy : 0.007776021957397461 nb_pixel_total : 1499 time to create 1 rle with old method : 0.0016443729400634766 time for calcul the mask position with numpy : 0.007758140563964844 nb_pixel_total : 16 time to create 1 rle with old method : 5.9604644775390625e-05 time for calcul the mask position with numpy : 0.00802755355834961 nb_pixel_total : 30 time to create 1 rle with old method : 7.987022399902344e-05 time for calcul the mask position with numpy : 0.007968902587890625 nb_pixel_total : 17 time to create 1 rle with old method : 0.00010418891906738281 time for calcul the mask position with numpy : 0.007868528366088867 nb_pixel_total : 305 time to create 1 rle with old method : 0.0003490447998046875 time for calcul the mask position with numpy : 0.007880687713623047 nb_pixel_total : 294 time to create 1 rle with old method : 0.0003483295440673828 time for calcul the mask position with numpy : 0.008193492889404297 nb_pixel_total : 19 time to create 1 rle with old method : 5.841255187988281e-05 time for calcul the mask position with numpy : 0.00836491584777832 nb_pixel_total : 15 time to create 1 rle with old method : 3.886222839355469e-05 time for calcul the mask position with numpy : 0.008581399917602539 nb_pixel_total : 365 time to create 1 rle with old method : 0.0004868507385253906 time for calcul the mask position with numpy : 0.008503198623657227 nb_pixel_total : 2 time to create 1 rle with old method : 1.6450881958007812e-05 time for calcul the mask position with numpy : 0.008330345153808594 nb_pixel_total : 195 time to create 1 rle with old method : 0.0002186298370361328 time for calcul the mask position with numpy : 0.007848024368286133 nb_pixel_total : 795 time to create 1 rle with old method : 0.0009324550628662109 time for calcul the mask position with numpy : 0.007849693298339844 nb_pixel_total : 939 time to create 1 rle with old method : 0.0010404586791992188 time for calcul the mask position with numpy : 0.007792949676513672 nb_pixel_total : 450 time to create 1 rle with old method : 0.0005252361297607422 time for calcul the mask position with numpy : 0.007807493209838867 nb_pixel_total : 1 time to create 1 rle with old method : 1.0013580322265625e-05 time for calcul the mask position with numpy : 0.007928133010864258 nb_pixel_total : 322 time to create 1 rle with old method : 0.0003726482391357422 time for calcul the mask position with numpy : 0.008080005645751953 nb_pixel_total : 356 time to create 1 rle with old method : 0.00041365623474121094 time for calcul the mask position with numpy : 0.008050680160522461 nb_pixel_total : 16 time to create 1 rle with old method : 4.601478576660156e-05 time for calcul the mask position with numpy : 0.007977008819580078 nb_pixel_total : 26 time to create 1 rle with old method : 9.226799011230469e-05 time for calcul the mask position with numpy : 0.007915735244750977 nb_pixel_total : 153 time to create 1 rle with old method : 0.00020503997802734375 time for calcul the mask position with numpy : 0.007852792739868164 nb_pixel_total : 1326 time to create 1 rle with old method : 0.0014405250549316406 time for calcul the mask position with numpy : 0.007929801940917969 nb_pixel_total : 110 time to create 1 rle with old method : 0.0001468658447265625 time for calcul the mask position with numpy : 0.008189678192138672 nb_pixel_total : 3 time to create 1 rle with old method : 1.2159347534179688e-05 time for calcul the mask position with numpy : 0.008953332901000977 nb_pixel_total : 106929 time to create 1 rle with old method : 0.10907483100891113 time for calcul the mask position with numpy : 0.007857561111450195 nb_pixel_total : 6405 time to create 1 rle with old method : 0.0071103572845458984 time for calcul the mask position with numpy : 0.007932186126708984 nb_pixel_total : 174 time to create 1 rle with old method : 0.00020313262939453125 time for calcul the mask position with numpy : 0.007908105850219727 nb_pixel_total : 558 time to create 1 rle with old method : 0.0006909370422363281 time for calcul the mask position with numpy : 0.007872581481933594 nb_pixel_total : 81 time to create 1 rle with old method : 0.00010609626770019531 time for calcul the mask position with numpy : 0.008000850677490234 nb_pixel_total : 1368 time to create 1 rle with old method : 0.0017888545989990234 time for calcul the mask position with numpy : 0.007935047149658203 nb_pixel_total : 314 time to create 1 rle with old method : 0.0003380775451660156 time for calcul the mask position with numpy : 0.007823467254638672 nb_pixel_total : 173 time to create 1 rle with old method : 0.00019812583923339844 time for calcul the mask position with numpy : 0.007807254791259766 nb_pixel_total : 1575 time to create 1 rle with old method : 0.0017142295837402344 time for calcul the mask position with numpy : 0.007868289947509766 nb_pixel_total : 193 time to create 1 rle with old method : 0.00022125244140625 time for calcul the mask position with numpy : 0.007794618606567383 nb_pixel_total : 1136 time to create 1 rle with old method : 0.0012402534484863281 time for calcul the mask position with numpy : 0.00830388069152832 nb_pixel_total : 1774 time to create 1 rle with old method : 0.0020368099212646484 time for calcul the mask position with numpy : 0.007751941680908203 nb_pixel_total : 159 time to create 1 rle with old method : 0.00018262863159179688 time for calcul the mask position with numpy : 0.007737159729003906 nb_pixel_total : 668 time to create 1 rle with old method : 0.0007359981536865234 time for calcul the mask position with numpy : 0.007749319076538086 nb_pixel_total : 196 time to create 1 rle with old method : 0.0002257823944091797 time for calcul the mask position with numpy : 0.008212089538574219 nb_pixel_total : 8 time to create 1 rle with old method : 3.266334533691406e-05 time for calcul the mask position with numpy : 0.008338451385498047 nb_pixel_total : 730 time to create 1 rle with old method : 0.0007832050323486328 time for calcul the mask position with numpy : 0.007907390594482422 nb_pixel_total : 203 time to create 1 rle with old method : 0.00023293495178222656 time for calcul the mask position with numpy : 0.007868528366088867 nb_pixel_total : 449 time to create 1 rle with old method : 0.0004889965057373047 time for calcul the mask position with numpy : 0.00794672966003418 nb_pixel_total : 143 time to create 1 rle with old method : 0.0001659393310546875 time for calcul the mask position with numpy : 0.007757902145385742 nb_pixel_total : 252 time to create 1 rle with old method : 0.0002853870391845703 time for calcul the mask position with numpy : 0.00775599479675293 nb_pixel_total : 1 time to create 1 rle with old method : 7.3909759521484375e-06 time for calcul the mask position with numpy : 0.007858514785766602 nb_pixel_total : 1580 time to create 1 rle with old method : 0.0017659664154052734 time for calcul the mask position with numpy : 0.008103370666503906 nb_pixel_total : 290 time to create 1 rle with old method : 0.0003228187561035156 time for calcul the mask position with numpy : 0.008211374282836914 nb_pixel_total : 1000 time to create 1 rle with old method : 0.0011076927185058594 time for calcul the mask position with numpy : 0.008012533187866211 nb_pixel_total : 444 time to create 1 rle with old method : 0.0005066394805908203 time for calcul the mask position with numpy : 0.007855653762817383 nb_pixel_total : 465 time to create 1 rle with old method : 0.0005190372467041016 time for calcul the mask position with numpy : 0.007768154144287109 nb_pixel_total : 2113 time to create 1 rle with old method : 0.002314329147338867 time for calcul the mask position with numpy : 0.007982730865478516 nb_pixel_total : 125 time to create 1 rle with old method : 0.0001742839813232422 time for calcul the mask position with numpy : 0.007966041564941406 nb_pixel_total : 2211 time to create 1 rle with old method : 0.00243377685546875 time for calcul the mask position with numpy : 0.007941007614135742 nb_pixel_total : 513 time to create 1 rle with old method : 0.0005991458892822266 time for calcul the mask position with numpy : 0.008048057556152344 nb_pixel_total : 8 time to create 1 rle with old method : 2.765655517578125e-05 time for calcul the mask position with numpy : 0.007993936538696289 nb_pixel_total : 442 time to create 1 rle with old method : 0.0005278587341308594 time for calcul the mask position with numpy : 0.008076667785644531 nb_pixel_total : 916 time to create 1 rle with old method : 0.001024484634399414 time for calcul the mask position with numpy : 0.00783991813659668 nb_pixel_total : 576 time to create 1 rle with old method : 0.0006504058837890625 time for calcul the mask position with numpy : 0.00776219367980957 nb_pixel_total : 22 time to create 1 rle with old method : 5.793571472167969e-05 time for calcul the mask position with numpy : 0.007988452911376953 nb_pixel_total : 1186 time to create 1 rle with old method : 0.0013380050659179688 time for calcul the mask position with numpy : 0.008261442184448242 nb_pixel_total : 194 time to create 1 rle with old method : 0.00024509429931640625 time for calcul the mask position with numpy : 0.008229970932006836 nb_pixel_total : 1545 time to create 1 rle with old method : 0.0017981529235839844 time for calcul the mask position with numpy : 0.008232355117797852 nb_pixel_total : 4269 time to create 1 rle with old method : 0.004821300506591797 time for calcul the mask position with numpy : 0.00807499885559082 nb_pixel_total : 10 time to create 1 rle with old method : 3.814697265625e-05 time for calcul the mask position with numpy : 0.008064985275268555 nb_pixel_total : 1947 time to create 1 rle with old method : 0.0022122859954833984 time for calcul the mask position with numpy : 0.007934808731079102 nb_pixel_total : 591 time to create 1 rle with old method : 0.0007479190826416016 time for calcul the mask position with numpy : 0.008077144622802734 nb_pixel_total : 11 time to create 1 rle with old method : 6.604194641113281e-05 time for calcul the mask position with numpy : 0.00788426399230957 nb_pixel_total : 70 time to create 1 rle with old method : 0.00011849403381347656 time for calcul the mask position with numpy : 0.007838249206542969 nb_pixel_total : 987 time to create 1 rle with old method : 0.0010945796966552734 time for calcul the mask position with numpy : 0.007771730422973633 nb_pixel_total : 709 time to create 1 rle with old method : 0.0008885860443115234 time for calcul the mask position with numpy : 0.007822990417480469 nb_pixel_total : 521 time to create 1 rle with old method : 0.0005753040313720703 time for calcul the mask position with numpy : 0.007878780364990234 nb_pixel_total : 1942 time to create 1 rle with old method : 0.0021467208862304688 time for calcul the mask position with numpy : 0.007982969284057617 nb_pixel_total : 285 time to create 1 rle with old method : 0.00032138824462890625 time for calcul the mask position with numpy : 0.007999181747436523 nb_pixel_total : 1410 time to create 1 rle with old method : 0.00160980224609375 time for calcul the mask position with numpy : 0.007870912551879883 nb_pixel_total : 72 time to create 1 rle with old method : 8.988380432128906e-05 time for calcul the mask position with numpy : 0.007807731628417969 nb_pixel_total : 347 time to create 1 rle with old method : 0.00037980079650878906 time for calcul the mask position with numpy : 0.007919073104858398 nb_pixel_total : 584 time to create 1 rle with old method : 0.0006766319274902344 time for calcul the mask position with numpy : 0.007927417755126953 nb_pixel_total : 140 time to create 1 rle with old method : 0.0001666545867919922 time for calcul the mask position with numpy : 0.008100509643554688 nb_pixel_total : 716 time to create 1 rle with old method : 0.0007688999176025391 time for calcul the mask position with numpy : 0.007799625396728516 nb_pixel_total : 19 time to create 1 rle with old method : 4.291534423828125e-05 time for calcul the mask position with numpy : 0.007818222045898438 nb_pixel_total : 490 time to create 1 rle with old method : 0.0005354881286621094 time for calcul the mask position with numpy : 0.007800579071044922 nb_pixel_total : 149 time to create 1 rle with old method : 0.00016236305236816406 time for calcul the mask position with numpy : 0.007814884185791016 nb_pixel_total : 278 time to create 1 rle with old method : 0.0003006458282470703 time for calcul the mask position with numpy : 0.008360147476196289 nb_pixel_total : 471 time to create 1 rle with old method : 0.000560760498046875 time for calcul the mask position with numpy : 0.008565902709960938 nb_pixel_total : 1121 time to create 1 rle with old method : 0.0013852119445800781 time for calcul the mask position with numpy : 0.00827932357788086 nb_pixel_total : 462 time to create 1 rle with old method : 0.0005083084106445312 time for calcul the mask position with numpy : 0.008306741714477539 nb_pixel_total : 442 time to create 1 rle with old method : 0.00046372413635253906 time for calcul the mask position with numpy : 0.008018255233764648 nb_pixel_total : 482 time to create 1 rle with old method : 0.0005397796630859375 time for calcul the mask position with numpy : 0.007961511611938477 nb_pixel_total : 201 time to create 1 rle with old method : 0.00022339820861816406 time for calcul the mask position with numpy : 0.00780940055847168 nb_pixel_total : 3632 time to create 1 rle with old method : 0.003857851028442383 time for calcul the mask position with numpy : 0.007802724838256836 nb_pixel_total : 1422 time to create 1 rle with old method : 0.0014965534210205078 time for calcul the mask position with numpy : 0.0077724456787109375 nb_pixel_total : 225 time to create 1 rle with old method : 0.0002396106719970703 time for calcul the mask position with numpy : 0.007800102233886719 nb_pixel_total : 1245 time to create 1 rle with old method : 0.0014052391052246094 time for calcul the mask position with numpy : 0.007916688919067383 nb_pixel_total : 89 time to create 1 rle with old method : 0.00010323524475097656 time for calcul the mask position with numpy : 0.0077555179595947266 nb_pixel_total : 549 time to create 1 rle with old method : 0.0005817413330078125 create new chi : 2.01377272605896 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.008446455001831055 batch 1 Loaded 220 chid ids of type : 4230 Number RLEs to save : 14262 TO DO : save crop sub photo not yet done ! save time : 2.830603837966919 nb_obj : 157 nb_hashtags : 8 time to prepare the origin masks : 1.828446865081787 time for calcul the mask position with numpy : 0.09742975234985352 nb_pixel_total : 1705402 time to create 1 rle with new method : 0.2739250659942627 time for calcul the mask position with numpy : 0.008557796478271484 nb_pixel_total : 776 time to create 1 rle with old method : 0.0009179115295410156 time for calcul the mask position with numpy : 0.00846719741821289 nb_pixel_total : 562 time to create 1 rle with old method : 0.0006656646728515625 time for calcul the mask position with numpy : 0.008500099182128906 nb_pixel_total : 23267 time to create 1 rle with old method : 0.02521514892578125 time for calcul the mask position with numpy : 0.00843667984008789 nb_pixel_total : 467 time to create 1 rle with old method : 0.0005488395690917969 time for calcul the mask position with numpy : 0.008458852767944336 nb_pixel_total : 58519 time to create 1 rle with old method : 0.06285429000854492 time for calcul the mask position with numpy : 0.008386373519897461 nb_pixel_total : 601 time to create 1 rle with old method : 0.0007121562957763672 time for calcul the mask position with numpy : 0.008211851119995117 nb_pixel_total : 64 time to create 1 rle with old method : 8.726119995117188e-05 time for calcul the mask position with numpy : 0.00834798812866211 nb_pixel_total : 68 time to create 1 rle with old method : 9.34600830078125e-05 time for calcul the mask position with numpy : 0.008423805236816406 nb_pixel_total : 277 time to create 1 rle with old method : 0.0003371238708496094 time for calcul the mask position with numpy : 0.008409738540649414 nb_pixel_total : 512 time to create 1 rle with old method : 0.0006036758422851562 time for calcul the mask position with numpy : 0.008413076400756836 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002090930938720703 time for calcul the mask position with numpy : 0.008409976959228516 nb_pixel_total : 193 time to create 1 rle with old method : 0.00022792816162109375 time for calcul the mask position with numpy : 0.008459806442260742 nb_pixel_total : 188 time to create 1 rle with old method : 0.00022912025451660156 time for calcul the mask position with numpy : 0.008408784866333008 nb_pixel_total : 314 time to create 1 rle with old method : 0.0003657341003417969 time for calcul the mask position with numpy : 0.008443117141723633 nb_pixel_total : 8 time to create 1 rle with old method : 2.6941299438476562e-05 time for calcul the mask position with numpy : 0.008398294448852539 nb_pixel_total : 25 time to create 1 rle with old method : 3.647804260253906e-05 time for calcul the mask position with numpy : 0.008279800415039062 nb_pixel_total : 1 time to create 1 rle with old method : 1.1205673217773438e-05 time for calcul the mask position with numpy : 0.008369684219360352 nb_pixel_total : 630 time to create 1 rle with old method : 0.0009984970092773438 time for calcul the mask position with numpy : 0.008320093154907227 nb_pixel_total : 472 time to create 1 rle with old method : 0.0007343292236328125 time for calcul the mask position with numpy : 0.008403301239013672 nb_pixel_total : 530 time to create 1 rle with old method : 0.0006246566772460938 time for calcul the mask position with numpy : 0.008443593978881836 nb_pixel_total : 124 time to create 1 rle with old method : 0.00016307830810546875 time for calcul the mask position with numpy : 0.008429527282714844 nb_pixel_total : 2250 time to create 1 rle with old method : 0.0026166439056396484 time for calcul the mask position with numpy : 0.008439302444458008 nb_pixel_total : 211 time to create 1 rle with old method : 0.0002646446228027344 time for calcul the mask position with numpy : 0.008443355560302734 nb_pixel_total : 609 time to create 1 rle with old method : 0.0007212162017822266 time for calcul the mask position with numpy : 0.008090496063232422 nb_pixel_total : 785 time to create 1 rle with old method : 0.0009477138519287109 time for calcul the mask position with numpy : 0.00805521011352539 nb_pixel_total : 26 time to create 1 rle with old method : 0.00017070770263671875 time for calcul the mask position with numpy : 0.008397817611694336 nb_pixel_total : 15348 time to create 1 rle with old method : 0.01918172836303711 time for calcul the mask position with numpy : 0.008443117141723633 nb_pixel_total : 10 time to create 1 rle with old method : 2.4318695068359375e-05 time for calcul the mask position with numpy : 0.00839853286743164 nb_pixel_total : 652 time to create 1 rle with old method : 0.0007719993591308594 time for calcul the mask position with numpy : 0.008457660675048828 nb_pixel_total : 110 time to create 1 rle with old method : 0.00014257431030273438 time for calcul the mask position with numpy : 0.008380651473999023 nb_pixel_total : 260 time to create 1 rle with old method : 0.00031304359436035156 time for calcul the mask position with numpy : 0.008359432220458984 nb_pixel_total : 10540 time to create 1 rle with old method : 0.01203775405883789 time for calcul the mask position with numpy : 0.00837254524230957 nb_pixel_total : 584 time to create 1 rle with old method : 0.0007295608520507812 time for calcul the mask position with numpy : 0.008421897888183594 nb_pixel_total : 210 time to create 1 rle with old method : 0.0002548694610595703 time for calcul the mask position with numpy : 0.008355140686035156 nb_pixel_total : 2045 time to create 1 rle with old method : 0.002428770065307617 time for calcul the mask position with numpy : 0.008361577987670898 nb_pixel_total : 3788 time to create 1 rle with old method : 0.0044286251068115234 time for calcul the mask position with numpy : 0.008374929428100586 nb_pixel_total : 824 time to create 1 rle with old method : 0.0009593963623046875 time for calcul the mask position with numpy : 0.008252143859863281 nb_pixel_total : 172 time to create 1 rle with old method : 0.00019621849060058594 time for calcul the mask position with numpy : 0.008236408233642578 nb_pixel_total : 308 time to create 1 rle with old method : 0.0003666877746582031 time for calcul the mask position with numpy : 0.008056879043579102 nb_pixel_total : 614 time to create 1 rle with old method : 0.0007839202880859375 time for calcul the mask position with numpy : 0.00841522216796875 nb_pixel_total : 12264 time to create 1 rle with old method : 0.019208908081054688 time for calcul the mask position with numpy : 0.00959324836730957 nb_pixel_total : 238 time to create 1 rle with old method : 0.0004286766052246094 time for calcul the mask position with numpy : 0.009678125381469727 nb_pixel_total : 1 time to create 1 rle with old method : 1.0251998901367188e-05 time for calcul the mask position with numpy : 0.009614706039428711 nb_pixel_total : 897 time to create 1 rle with old method : 0.0015912055969238281 time for calcul the mask position with numpy : 0.009572744369506836 nb_pixel_total : 212 time to create 1 rle with old method : 0.00037479400634765625 time for calcul the mask position with numpy : 0.00956416130065918 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0022025108337402344 time for calcul the mask position with numpy : 0.009317398071289062 nb_pixel_total : 3299 time to create 1 rle with old method : 0.004214286804199219 time for calcul the mask position with numpy : 0.008249521255493164 nb_pixel_total : 158 time to create 1 rle with old method : 0.0002923011779785156 time for calcul the mask position with numpy : 0.008318185806274414 nb_pixel_total : 627 time to create 1 rle with old method : 0.0007681846618652344 time for calcul the mask position with numpy : 0.008342981338500977 nb_pixel_total : 341 time to create 1 rle with old method : 0.0004067420959472656 time for calcul the mask position with numpy : 0.008226633071899414 nb_pixel_total : 743 time to create 1 rle with old method : 0.0008869171142578125 time for calcul the mask position with numpy : 0.008329391479492188 nb_pixel_total : 667 time to create 1 rle with old method : 0.0007765293121337891 time for calcul the mask position with numpy : 0.008147478103637695 nb_pixel_total : 18831 time to create 1 rle with old method : 0.02077627182006836 time for calcul the mask position with numpy : 0.008354663848876953 nb_pixel_total : 1288 time to create 1 rle with old method : 0.0015816688537597656 time for calcul the mask position with numpy : 0.008383750915527344 nb_pixel_total : 925 time to create 1 rle with old method : 0.0011453628540039062 time for calcul the mask position with numpy : 0.00842595100402832 nb_pixel_total : 359 time to create 1 rle with old method : 0.000446319580078125 time for calcul the mask position with numpy : 0.00839543342590332 nb_pixel_total : 12670 time to create 1 rle with old method : 0.014434337615966797 time for calcul the mask position with numpy : 0.008432149887084961 nb_pixel_total : 170 time to create 1 rle with old method : 0.0002524852752685547 time for calcul the mask position with numpy : 0.008009910583496094 nb_pixel_total : 965 time to create 1 rle with old method : 0.0011563301086425781 time for calcul the mask position with numpy : 0.008371114730834961 nb_pixel_total : 2623 time to create 1 rle with old method : 0.0031418800354003906 time for calcul the mask position with numpy : 0.008458614349365234 nb_pixel_total : 198 time to create 1 rle with old method : 0.0002465248107910156 time for calcul the mask position with numpy : 0.008408069610595703 nb_pixel_total : 113 time to create 1 rle with old method : 0.00013947486877441406 time for calcul the mask position with numpy : 0.008164644241333008 nb_pixel_total : 1352 time to create 1 rle with old method : 0.0015950202941894531 time for calcul the mask position with numpy : 0.008303165435791016 nb_pixel_total : 1 time to create 1 rle with old method : 7.867813110351562e-06 time for calcul the mask position with numpy : 0.008287191390991211 nb_pixel_total : 2943 time to create 1 rle with old method : 0.0033953189849853516 time for calcul the mask position with numpy : 0.008413314819335938 nb_pixel_total : 778 time to create 1 rle with old method : 0.0009109973907470703 time for calcul the mask position with numpy : 0.008320808410644531 nb_pixel_total : 1347 time to create 1 rle with old method : 0.0015702247619628906 time for calcul the mask position with numpy : 0.008130788803100586 nb_pixel_total : 1255 time to create 1 rle with old method : 0.0014772415161132812 time for calcul the mask position with numpy : 0.008252143859863281 nb_pixel_total : 413 time to create 1 rle with old method : 0.0005128383636474609 time for calcul the mask position with numpy : 0.008423805236816406 nb_pixel_total : 97 time to create 1 rle with old method : 0.00012683868408203125 time for calcul the mask position with numpy : 0.00840306282043457 nb_pixel_total : 327 time to create 1 rle with old method : 0.0003924369812011719 time for calcul the mask position with numpy : 0.00850677490234375 nb_pixel_total : 7 time to create 1 rle with old method : 5.412101745605469e-05 time for calcul the mask position with numpy : 0.008407115936279297 nb_pixel_total : 381 time to create 1 rle with old method : 0.000469207763671875 time for calcul the mask position with numpy : 0.008584022521972656 nb_pixel_total : 2549 time to create 1 rle with old method : 0.0030329227447509766 time for calcul the mask position with numpy : 0.008522748947143555 nb_pixel_total : 586 time to create 1 rle with old method : 0.0007073879241943359 time for calcul the mask position with numpy : 0.008271932601928711 nb_pixel_total : 137 time to create 1 rle with old method : 0.00017905235290527344 time for calcul the mask position with numpy : 0.008468866348266602 nb_pixel_total : 939 time to create 1 rle with old method : 0.0011172294616699219 time for calcul the mask position with numpy : 0.008473873138427734 nb_pixel_total : 445 time to create 1 rle with old method : 0.0007390975952148438 time for calcul the mask position with numpy : 0.008520364761352539 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0013840198516845703 time for calcul the mask position with numpy : 0.008469343185424805 nb_pixel_total : 581 time to create 1 rle with old method : 0.0007147789001464844 time for calcul the mask position with numpy : 0.008472681045532227 nb_pixel_total : 280 time to create 1 rle with old method : 0.00034427642822265625 time for calcul the mask position with numpy : 0.00870966911315918 nb_pixel_total : 106909 time to create 1 rle with old method : 0.11679267883300781 time for calcul the mask position with numpy : 0.00841069221496582 nb_pixel_total : 41 time to create 1 rle with old method : 0.0003628730773925781 time for calcul the mask position with numpy : 0.008411169052124023 nb_pixel_total : 61 time to create 1 rle with old method : 9.202957153320312e-05 time for calcul the mask position with numpy : 0.008409261703491211 nb_pixel_total : 10375 time to create 1 rle with old method : 0.012343406677246094 time for calcul the mask position with numpy : 0.008426189422607422 nb_pixel_total : 82 time to create 1 rle with old method : 0.00012040138244628906 time for calcul the mask position with numpy : 0.008419036865234375 nb_pixel_total : 266 time to create 1 rle with old method : 0.0003299713134765625 time for calcul the mask position with numpy : 0.008411407470703125 nb_pixel_total : 33 time to create 1 rle with old method : 0.00013446807861328125 time for calcul the mask position with numpy : 0.008409500122070312 nb_pixel_total : 802 time to create 1 rle with old method : 0.0009806156158447266 time for calcul the mask position with numpy : 0.008480072021484375 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002315044403076172 time for calcul the mask position with numpy : 0.008447647094726562 nb_pixel_total : 88 time to create 1 rle with old method : 0.0002522468566894531 time for calcul the mask position with numpy : 0.008437871932983398 nb_pixel_total : 1240 time to create 1 rle with old method : 0.0014171600341796875 time for calcul the mask position with numpy : 0.008392095565795898 nb_pixel_total : 209 time to create 1 rle with old method : 0.00026297569274902344 time for calcul the mask position with numpy : 0.008432388305664062 nb_pixel_total : 148 time to create 1 rle with old method : 0.0001995563507080078 time for calcul the mask position with numpy : 0.008408308029174805 nb_pixel_total : 2265 time to create 1 rle with old method : 0.0026040077209472656 time for calcul the mask position with numpy : 0.008439779281616211 nb_pixel_total : 8 time to create 1 rle with old method : 2.3603439331054688e-05 time for calcul the mask position with numpy : 0.008369207382202148 nb_pixel_total : 220 time to create 1 rle with old method : 0.0002682209014892578 time for calcul the mask position with numpy : 0.008358955383300781 nb_pixel_total : 546 time to create 1 rle with old method : 0.0006556510925292969 time for calcul the mask position with numpy : 0.010167121887207031 nb_pixel_total : 8 time to create 1 rle with old method : 4.792213439941406e-05 time for calcul the mask position with numpy : 0.008694648742675781 nb_pixel_total : 756 time to create 1 rle with old method : 0.0008828639984130859 time for calcul the mask position with numpy : 0.008420705795288086 nb_pixel_total : 236 time to create 1 rle with old method : 0.0002834796905517578 time for calcul the mask position with numpy : 0.008244991302490234 nb_pixel_total : 88 time to create 1 rle with old method : 0.00012063980102539062 time for calcul the mask position with numpy : 0.00844430923461914 nb_pixel_total : 7108 time to create 1 rle with old method : 0.007818222045898438 time for calcul the mask position with numpy : 0.008440971374511719 nb_pixel_total : 287 time to create 1 rle with old method : 0.0003364086151123047 time for calcul the mask position with numpy : 0.008359909057617188 nb_pixel_total : 124 time to create 1 rle with old method : 0.00014281272888183594 time for calcul the mask position with numpy : 0.008130788803100586 nb_pixel_total : 911 time to create 1 rle with old method : 0.0010836124420166016 time for calcul the mask position with numpy : 0.008259773254394531 nb_pixel_total : 1940 time to create 1 rle with old method : 0.0021789073944091797 time for calcul the mask position with numpy : 0.008309125900268555 nb_pixel_total : 535 time to create 1 rle with old method : 0.0006310939788818359 time for calcul the mask position with numpy : 0.008419036865234375 nb_pixel_total : 238 time to create 1 rle with old method : 0.00028228759765625 time for calcul the mask position with numpy : 0.008439302444458008 nb_pixel_total : 18 time to create 1 rle with old method : 3.5762786865234375e-05 time for calcul the mask position with numpy : 0.008939504623413086 nb_pixel_total : 338 time to create 1 rle with old method : 0.0006837844848632812 time for calcul the mask position with numpy : 0.008426666259765625 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002491474151611328 time for calcul the mask position with numpy : 0.008388996124267578 nb_pixel_total : 1544 time to create 1 rle with old method : 0.0017743110656738281 time for calcul the mask position with numpy : 0.008399486541748047 nb_pixel_total : 22 time to create 1 rle with old method : 5.9604644775390625e-05 time for calcul the mask position with numpy : 0.008251428604125977 nb_pixel_total : 17 time to create 1 rle with old method : 5.340576171875e-05 time for calcul the mask position with numpy : 0.00825047492980957 nb_pixel_total : 21 time to create 1 rle with old method : 6.508827209472656e-05 time for calcul the mask position with numpy : 0.008365869522094727 nb_pixel_total : 928 time to create 1 rle with old method : 0.0010886192321777344 time for calcul the mask position with numpy : 0.00970005989074707 nb_pixel_total : 185 time to create 1 rle with old method : 0.0003407001495361328 time for calcul the mask position with numpy : 0.009670495986938477 nb_pixel_total : 725 time to create 1 rle with old method : 0.001245260238647461 time for calcul the mask position with numpy : 0.009650468826293945 nb_pixel_total : 1288 time to create 1 rle with old method : 0.002146482467651367 time for calcul the mask position with numpy : 0.00964498519897461 nb_pixel_total : 439 time to create 1 rle with old method : 0.0007574558258056641 time for calcul the mask position with numpy : 0.00965428352355957 nb_pixel_total : 308 time to create 1 rle with old method : 0.0005362033843994141 time for calcul the mask position with numpy : 0.009654760360717773 nb_pixel_total : 90 time to create 1 rle with old method : 0.0001857280731201172 time for calcul the mask position with numpy : 0.009607553482055664 nb_pixel_total : 5053 time to create 1 rle with old method : 0.006951808929443359 time for calcul the mask position with numpy : 0.008148908615112305 nb_pixel_total : 1344 time to create 1 rle with old method : 0.0015482902526855469 time for calcul the mask position with numpy : 0.008418560028076172 nb_pixel_total : 499 time to create 1 rle with old method : 0.000598907470703125 time for calcul the mask position with numpy : 0.008414983749389648 nb_pixel_total : 6 time to create 1 rle with old method : 2.3126602172851562e-05 time for calcul the mask position with numpy : 0.008450984954833984 nb_pixel_total : 30 time to create 1 rle with old method : 9.632110595703125e-05 time for calcul the mask position with numpy : 0.008426904678344727 nb_pixel_total : 4419 time to create 1 rle with old method : 0.005024433135986328 time for calcul the mask position with numpy : 0.008394241333007812 nb_pixel_total : 25 time to create 1 rle with old method : 0.00010943412780761719 time for calcul the mask position with numpy : 0.008400440216064453 nb_pixel_total : 1567 time to create 1 rle with old method : 0.0017955303192138672 time for calcul the mask position with numpy : 0.008396625518798828 nb_pixel_total : 922 time to create 1 rle with old method : 0.0010585784912109375 time for calcul the mask position with numpy : 0.00838780403137207 nb_pixel_total : 101 time to create 1 rle with old method : 0.00012922286987304688 time for calcul the mask position with numpy : 0.008295059204101562 nb_pixel_total : 45 time to create 1 rle with old method : 7.581710815429688e-05 time for calcul the mask position with numpy : 0.00838780403137207 nb_pixel_total : 973 time to create 1 rle with old method : 0.001138925552368164 time for calcul the mask position with numpy : 0.009653568267822266 nb_pixel_total : 583 time to create 1 rle with old method : 0.0006973743438720703 time for calcul the mask position with numpy : 0.008233070373535156 nb_pixel_total : 268 time to create 1 rle with old method : 0.0003228187561035156 time for calcul the mask position with numpy : 0.008353471755981445 nb_pixel_total : 8 time to create 1 rle with old method : 0.00020456314086914062 time for calcul the mask position with numpy : 0.00831294059753418 nb_pixel_total : 537 time to create 1 rle with old method : 0.0006246566772460938 time for calcul the mask position with numpy : 0.008401632308959961 nb_pixel_total : 469 time to create 1 rle with old method : 0.0005285739898681641 time for calcul the mask position with numpy : 0.008356332778930664 nb_pixel_total : 27 time to create 1 rle with old method : 5.8650970458984375e-05 time for calcul the mask position with numpy : 0.008476734161376953 nb_pixel_total : 67 time to create 1 rle with old method : 8.749961853027344e-05 time for calcul the mask position with numpy : 0.00832819938659668 nb_pixel_total : 1570 time to create 1 rle with old method : 0.0018815994262695312 time for calcul the mask position with numpy : 0.008372306823730469 nb_pixel_total : 118 time to create 1 rle with old method : 0.000152587890625 time for calcul the mask position with numpy : 0.008415699005126953 nb_pixel_total : 11 time to create 1 rle with old method : 3.337860107421875e-05 time for calcul the mask position with numpy : 0.00834202766418457 nb_pixel_total : 347 time to create 1 rle with old method : 0.000396728515625 time for calcul the mask position with numpy : 0.008311986923217773 nb_pixel_total : 236 time to create 1 rle with old method : 0.0002803802490234375 time for calcul the mask position with numpy : 0.008450984954833984 nb_pixel_total : 440 time to create 1 rle with old method : 0.0005311965942382812 time for calcul the mask position with numpy : 0.008413553237915039 nb_pixel_total : 134 time to create 1 rle with old method : 0.00016379356384277344 time for calcul the mask position with numpy : 0.008417129516601562 nb_pixel_total : 168 time to create 1 rle with old method : 0.0002162456512451172 time for calcul the mask position with numpy : 0.008840084075927734 nb_pixel_total : 1116 time to create 1 rle with old method : 0.001634836196899414 time for calcul the mask position with numpy : 0.008521080017089844 nb_pixel_total : 508 time to create 1 rle with old method : 0.0006074905395507812 time for calcul the mask position with numpy : 0.008414983749389648 nb_pixel_total : 428 time to create 1 rle with old method : 0.0005042552947998047 time for calcul the mask position with numpy : 0.008422374725341797 nb_pixel_total : 508 time to create 1 rle with old method : 0.0006022453308105469 time for calcul the mask position with numpy : 0.008360147476196289 nb_pixel_total : 247 time to create 1 rle with old method : 0.00029921531677246094 time for calcul the mask position with numpy : 0.008374929428100586 nb_pixel_total : 1475 time to create 1 rle with old method : 0.0016603469848632812 time for calcul the mask position with numpy : 0.008388996124267578 nb_pixel_total : 605 time to create 1 rle with old method : 0.000701904296875 create new chi : 2.1473774909973145 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0030689239501953125 batch 1 Loaded 180 chid ids of type : 4230 Number RLEs to save : 16174 TO DO : save crop sub photo not yet done ! save time : 1.2966806888580322 nb_obj : 186 nb_hashtags : 8 time to prepare the origin masks : 4.028622627258301 time for calcul the mask position with numpy : 1.1480152606964111 nb_pixel_total : 1709766 time to create 1 rle with new method : 0.14548707008361816 time for calcul the mask position with numpy : 0.010226726531982422 nb_pixel_total : 1891 time to create 1 rle with old method : 0.0022263526916503906 time for calcul the mask position with numpy : 0.009783267974853516 nb_pixel_total : 78 time to create 1 rle with old method : 0.00013065338134765625 time for calcul the mask position with numpy : 0.010137081146240234 nb_pixel_total : 132 time to create 1 rle with old method : 0.00018405914306640625 time for calcul the mask position with numpy : 0.007899284362792969 nb_pixel_total : 460 time to create 1 rle with old method : 0.0006968975067138672 time for calcul the mask position with numpy : 0.008381843566894531 nb_pixel_total : 5425 time to create 1 rle with old method : 0.007908105850219727 time for calcul the mask position with numpy : 0.007973432540893555 nb_pixel_total : 611 time to create 1 rle with old method : 0.0009779930114746094 time for calcul the mask position with numpy : 0.008734464645385742 nb_pixel_total : 523 time to create 1 rle with old method : 0.0010683536529541016 time for calcul the mask position with numpy : 0.009132146835327148 nb_pixel_total : 17215 time to create 1 rle with old method : 0.04141354560852051 time for calcul the mask position with numpy : 0.015167713165283203 nb_pixel_total : 219 time to create 1 rle with old method : 0.0008373260498046875 time for calcul the mask position with numpy : 0.014945030212402344 nb_pixel_total : 196 time to create 1 rle with old method : 0.00029540061950683594 time for calcul the mask position with numpy : 0.013077735900878906 nb_pixel_total : 2 time to create 1 rle with old method : 3.0279159545898438e-05 time for calcul the mask position with numpy : 0.011975526809692383 nb_pixel_total : 434 time to create 1 rle with old method : 0.0007002353668212891 time for calcul the mask position with numpy : 0.011618852615356445 nb_pixel_total : 10 time to create 1 rle with old method : 4.5299530029296875e-05 time for calcul the mask position with numpy : 0.01078176498413086 nb_pixel_total : 73 time to create 1 rle with old method : 0.00013637542724609375 time for calcul the mask position with numpy : 0.012235164642333984 nb_pixel_total : 75 time to create 1 rle with old method : 0.00010943412780761719 time for calcul the mask position with numpy : 0.010797500610351562 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002524852752685547 time for calcul the mask position with numpy : 0.006274700164794922 nb_pixel_total : 1557 time to create 1 rle with old method : 0.0018897056579589844 time for calcul the mask position with numpy : 0.006812334060668945 nb_pixel_total : 227 time to create 1 rle with old method : 0.00030493736267089844 time for calcul the mask position with numpy : 0.007369279861450195 nb_pixel_total : 121 time to create 1 rle with old method : 0.0001709461212158203 time for calcul the mask position with numpy : 0.0073931217193603516 nb_pixel_total : 303 time to create 1 rle with old method : 0.00041222572326660156 time for calcul the mask position with numpy : 0.006815195083618164 nb_pixel_total : 290 time to create 1 rle with old method : 0.00037598609924316406 time for calcul the mask position with numpy : 0.006701469421386719 nb_pixel_total : 265 time to create 1 rle with old method : 0.00034117698669433594 time for calcul the mask position with numpy : 0.0061757564544677734 nb_pixel_total : 59 time to create 1 rle with old method : 9.989738464355469e-05 time for calcul the mask position with numpy : 0.006434440612792969 nb_pixel_total : 35 time to create 1 rle with old method : 7.009506225585938e-05 time for calcul the mask position with numpy : 0.006552219390869141 nb_pixel_total : 45658 time to create 1 rle with old method : 0.06315493583679199 time for calcul the mask position with numpy : 0.011016368865966797 nb_pixel_total : 45 time to create 1 rle with old method : 7.510185241699219e-05 time for calcul the mask position with numpy : 0.010818243026733398 nb_pixel_total : 63 time to create 1 rle with old method : 0.00011754035949707031 time for calcul the mask position with numpy : 0.01078343391418457 nb_pixel_total : 24 time to create 1 rle with old method : 5.507469177246094e-05 time for calcul the mask position with numpy : 0.010926246643066406 nb_pixel_total : 475 time to create 1 rle with old method : 0.0005867481231689453 time for calcul the mask position with numpy : 0.010690927505493164 nb_pixel_total : 10 time to create 1 rle with old method : 3.814697265625e-05 time for calcul the mask position with numpy : 0.010683774948120117 nb_pixel_total : 2395 time to create 1 rle with old method : 0.002810955047607422 time for calcul the mask position with numpy : 0.010480403900146484 nb_pixel_total : 28 time to create 1 rle with old method : 5.459785461425781e-05 time for calcul the mask position with numpy : 0.010459661483764648 nb_pixel_total : 25 time to create 1 rle with old method : 5.3882598876953125e-05 time for calcul the mask position with numpy : 0.010451316833496094 nb_pixel_total : 35 time to create 1 rle with old method : 6.270408630371094e-05 time for calcul the mask position with numpy : 0.01122736930847168 nb_pixel_total : 783 time to create 1 rle with old method : 0.0009241104125976562 time for calcul the mask position with numpy : 0.011713266372680664 nb_pixel_total : 1219 time to create 1 rle with old method : 0.0017533302307128906 time for calcul the mask position with numpy : 0.011280298233032227 nb_pixel_total : 1011 time to create 1 rle with old method : 0.0012531280517578125 time for calcul the mask position with numpy : 0.010875225067138672 nb_pixel_total : 9566 time to create 1 rle with old method : 0.011741161346435547 time for calcul the mask position with numpy : 0.01170969009399414 nb_pixel_total : 536 time to create 1 rle with old method : 0.0006413459777832031 time for calcul the mask position with numpy : 0.010528087615966797 nb_pixel_total : 507 time to create 1 rle with old method : 0.0006120204925537109 time for calcul the mask position with numpy : 0.010657548904418945 nb_pixel_total : 194 time to create 1 rle with old method : 0.00025010108947753906 time for calcul the mask position with numpy : 0.014526128768920898 nb_pixel_total : 163 time to create 1 rle with old method : 0.00021409988403320312 time for calcul the mask position with numpy : 0.010213851928710938 nb_pixel_total : 3374 time to create 1 rle with old method : 0.003952741622924805 time for calcul the mask position with numpy : 0.010716676712036133 nb_pixel_total : 3998 time to create 1 rle with old method : 0.0046880245208740234 time for calcul the mask position with numpy : 0.01060938835144043 nb_pixel_total : 803 time to create 1 rle with old method : 0.0009889602661132812 time for calcul the mask position with numpy : 0.010341882705688477 nb_pixel_total : 112 time to create 1 rle with old method : 0.00016307830810546875 time for calcul the mask position with numpy : 0.010261297225952148 nb_pixel_total : 3 time to create 1 rle with old method : 6.67572021484375e-05 time for calcul the mask position with numpy : 0.010396003723144531 nb_pixel_total : 334 time to create 1 rle with old method : 0.0004265308380126953 time for calcul the mask position with numpy : 0.01017308235168457 nb_pixel_total : 5964 time to create 1 rle with old method : 0.0069391727447509766 time for calcul the mask position with numpy : 0.015271186828613281 nb_pixel_total : 10934 time to create 1 rle with old method : 0.012312889099121094 time for calcul the mask position with numpy : 0.010665178298950195 nb_pixel_total : 168 time to create 1 rle with old method : 0.00023055076599121094 time for calcul the mask position with numpy : 0.011159181594848633 nb_pixel_total : 155 time to create 1 rle with old method : 0.0002567768096923828 time for calcul the mask position with numpy : 0.010684728622436523 nb_pixel_total : 657 time to create 1 rle with old method : 0.0007979869842529297 time for calcul the mask position with numpy : 0.010508298873901367 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002219676971435547 time for calcul the mask position with numpy : 0.011253595352172852 nb_pixel_total : 169 time to create 1 rle with old method : 0.00022363662719726562 time for calcul the mask position with numpy : 0.011971712112426758 nb_pixel_total : 1245 time to create 1 rle with old method : 0.0019299983978271484 time for calcul the mask position with numpy : 0.011001348495483398 nb_pixel_total : 773 time to create 1 rle with old method : 0.0009441375732421875 time for calcul the mask position with numpy : 0.010885238647460938 nb_pixel_total : 9 time to create 1 rle with old method : 4.220008850097656e-05 time for calcul the mask position with numpy : 0.012310266494750977 nb_pixel_total : 225 time to create 1 rle with old method : 0.0003333091735839844 time for calcul the mask position with numpy : 0.012044191360473633 nb_pixel_total : 642 time to create 1 rle with old method : 0.0008199214935302734 time for calcul the mask position with numpy : 0.010533571243286133 nb_pixel_total : 237 time to create 1 rle with old method : 0.0003142356872558594 time for calcul the mask position with numpy : 0.01111602783203125 nb_pixel_total : 1285 time to create 1 rle with old method : 0.0018870830535888672 time for calcul the mask position with numpy : 0.011137247085571289 nb_pixel_total : 966 time to create 1 rle with old method : 0.001148223876953125 time for calcul the mask position with numpy : 0.010721445083618164 nb_pixel_total : 326 time to create 1 rle with old method : 0.0004208087921142578 time for calcul the mask position with numpy : 0.010829925537109375 nb_pixel_total : 13843 time to create 1 rle with old method : 0.016115188598632812 time for calcul the mask position with numpy : 0.010791301727294922 nb_pixel_total : 8722 time to create 1 rle with old method : 0.009886503219604492 time for calcul the mask position with numpy : 0.015094995498657227 nb_pixel_total : 605 time to create 1 rle with old method : 0.0007476806640625 time for calcul the mask position with numpy : 0.009933710098266602 nb_pixel_total : 619 time to create 1 rle with old method : 0.0008218288421630859 time for calcul the mask position with numpy : 0.01024484634399414 nb_pixel_total : 333 time to create 1 rle with old method : 0.00044989585876464844 time for calcul the mask position with numpy : 0.010134458541870117 nb_pixel_total : 2887 time to create 1 rle with old method : 0.0035202503204345703 time for calcul the mask position with numpy : 0.010325193405151367 nb_pixel_total : 128 time to create 1 rle with old method : 0.00023627281188964844 time for calcul the mask position with numpy : 0.010234594345092773 nb_pixel_total : 873 time to create 1 rle with old method : 0.001065969467163086 time for calcul the mask position with numpy : 0.010449409484863281 nb_pixel_total : 198 time to create 1 rle with old method : 0.0002582073211669922 time for calcul the mask position with numpy : 0.010286808013916016 nb_pixel_total : 1369 time to create 1 rle with old method : 0.001634836196899414 time for calcul the mask position with numpy : 0.010223150253295898 nb_pixel_total : 149 time to create 1 rle with old method : 0.0002186298370361328 time for calcul the mask position with numpy : 0.010559558868408203 nb_pixel_total : 741 time to create 1 rle with old method : 0.0008993148803710938 time for calcul the mask position with numpy : 0.010563135147094727 nb_pixel_total : 1964 time to create 1 rle with old method : 0.0024225711822509766 time for calcul the mask position with numpy : 0.011036157608032227 nb_pixel_total : 597 time to create 1 rle with old method : 0.0007312297821044922 time for calcul the mask position with numpy : 0.010526895523071289 nb_pixel_total : 1514 time to create 1 rle with old method : 0.0019063949584960938 time for calcul the mask position with numpy : 0.010198831558227539 nb_pixel_total : 110 time to create 1 rle with old method : 0.0001552104949951172 time for calcul the mask position with numpy : 0.011409759521484375 nb_pixel_total : 1507 time to create 1 rle with old method : 0.0017955303192138672 time for calcul the mask position with numpy : 0.010565996170043945 nb_pixel_total : 81 time to create 1 rle with old method : 0.00015974044799804688 time for calcul the mask position with numpy : 0.010691404342651367 nb_pixel_total : 528 time to create 1 rle with old method : 0.0006461143493652344 time for calcul the mask position with numpy : 0.01072239875793457 nb_pixel_total : 341 time to create 1 rle with old method : 0.0004401206970214844 time for calcul the mask position with numpy : 0.012559175491333008 nb_pixel_total : 346 time to create 1 rle with old method : 0.0004646778106689453 time for calcul the mask position with numpy : 0.011419057846069336 nb_pixel_total : 366 time to create 1 rle with old method : 0.0004665851593017578 time for calcul the mask position with numpy : 0.01522517204284668 nb_pixel_total : 274 time to create 1 rle with old method : 0.0005185604095458984 time for calcul the mask position with numpy : 0.01188969612121582 nb_pixel_total : 4655 time to create 1 rle with old method : 0.007359027862548828 time for calcul the mask position with numpy : 0.011972904205322266 nb_pixel_total : 2249 time to create 1 rle with old method : 0.003615140914916992 time for calcul the mask position with numpy : 0.012499094009399414 nb_pixel_total : 425 time to create 1 rle with old method : 0.0007271766662597656 time for calcul the mask position with numpy : 0.01184225082397461 nb_pixel_total : 180 time to create 1 rle with old method : 0.0003292560577392578 time for calcul the mask position with numpy : 0.011522531509399414 nb_pixel_total : 100 time to create 1 rle with old method : 0.0002009868621826172 time for calcul the mask position with numpy : 0.011829137802124023 nb_pixel_total : 918 time to create 1 rle with old method : 0.0016281604766845703 time for calcul the mask position with numpy : 0.012093305587768555 nb_pixel_total : 180 time to create 1 rle with old method : 0.00034999847412109375 time for calcul the mask position with numpy : 0.012651443481445312 nb_pixel_total : 1511 time to create 1 rle with old method : 0.0027430057525634766 time for calcul the mask position with numpy : 0.012212038040161133 nb_pixel_total : 372 time to create 1 rle with old method : 0.0006361007690429688 time for calcul the mask position with numpy : 0.012346267700195312 nb_pixel_total : 126 time to create 1 rle with old method : 0.0002434253692626953 time for calcul the mask position with numpy : 0.011793375015258789 nb_pixel_total : 35 time to create 1 rle with old method : 0.00017189979553222656 time for calcul the mask position with numpy : 0.013439416885375977 nb_pixel_total : 106943 time to create 1 rle with old method : 0.18993234634399414 time for calcul the mask position with numpy : 0.015154123306274414 nb_pixel_total : 156 time to create 1 rle with old method : 0.00034046173095703125 time for calcul the mask position with numpy : 0.01338505744934082 nb_pixel_total : 63 time to create 1 rle with old method : 0.00017261505126953125 time for calcul the mask position with numpy : 0.012952327728271484 nb_pixel_total : 214 time to create 1 rle with old method : 0.0004680156707763672 time for calcul the mask position with numpy : 0.012787580490112305 nb_pixel_total : 2290 time to create 1 rle with old method : 0.002713441848754883 time for calcul the mask position with numpy : 0.011574506759643555 nb_pixel_total : 10673 time to create 1 rle with old method : 0.014105796813964844 time for calcul the mask position with numpy : 0.01172780990600586 nb_pixel_total : 31 time to create 1 rle with old method : 7.724761962890625e-05 time for calcul the mask position with numpy : 0.015910863876342773 nb_pixel_total : 1571 time to create 1 rle with old method : 0.0023183822631835938 time for calcul the mask position with numpy : 0.013046503067016602 nb_pixel_total : 69 time to create 1 rle with old method : 0.0001163482666015625 time for calcul the mask position with numpy : 0.01143646240234375 nb_pixel_total : 628 time to create 1 rle with old method : 0.0007739067077636719 time for calcul the mask position with numpy : 0.011567831039428711 nb_pixel_total : 260 time to create 1 rle with old method : 0.0003342628479003906 time for calcul the mask position with numpy : 0.01120901107788086 nb_pixel_total : 218 time to create 1 rle with old method : 0.0003113746643066406 time for calcul the mask position with numpy : 0.011206388473510742 nb_pixel_total : 24 time to create 1 rle with old method : 6.818771362304688e-05 time for calcul the mask position with numpy : 0.012809514999389648 nb_pixel_total : 1391 time to create 1 rle with old method : 0.002044200897216797 time for calcul the mask position with numpy : 0.012007951736450195 nb_pixel_total : 2176 time to create 1 rle with old method : 0.002553701400756836 time for calcul the mask position with numpy : 0.012240171432495117 nb_pixel_total : 895 time to create 1 rle with old method : 0.0010859966278076172 time for calcul the mask position with numpy : 0.011510133743286133 nb_pixel_total : 667 time to create 1 rle with old method : 0.0008561611175537109 time for calcul the mask position with numpy : 0.011376142501831055 nb_pixel_total : 148 time to create 1 rle with old method : 0.000202178955078125 time for calcul the mask position with numpy : 0.011249542236328125 nb_pixel_total : 6842 time to create 1 rle with old method : 0.007712364196777344 time for calcul the mask position with numpy : 0.011888980865478516 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002613067626953125 time for calcul the mask position with numpy : 0.012424707412719727 nb_pixel_total : 484 time to create 1 rle with old method : 0.0006108283996582031 time for calcul the mask position with numpy : 0.012079954147338867 nb_pixel_total : 6 time to create 1 rle with old method : 4.792213439941406e-05 time for calcul the mask position with numpy : 0.013499736785888672 nb_pixel_total : 2185 time to create 1 rle with old method : 0.003182649612426758 time for calcul the mask position with numpy : 0.012961864471435547 nb_pixel_total : 198 time to create 1 rle with old method : 0.00034332275390625 time for calcul the mask position with numpy : 0.011591672897338867 nb_pixel_total : 232 time to create 1 rle with old method : 0.00029206275939941406 time for calcul the mask position with numpy : 0.012003898620605469 nb_pixel_total : 1197 time to create 1 rle with old method : 0.0014481544494628906 time for calcul the mask position with numpy : 0.011975765228271484 nb_pixel_total : 356 time to create 1 rle with old method : 0.00045299530029296875 time for calcul the mask position with numpy : 0.01237940788269043 nb_pixel_total : 169 time to create 1 rle with old method : 0.00021648406982421875 time for calcul the mask position with numpy : 0.012351751327514648 nb_pixel_total : 2 time to create 1 rle with old method : 3.0994415283203125e-05 time for calcul the mask position with numpy : 0.012534618377685547 nb_pixel_total : 523 time to create 1 rle with old method : 0.0006320476531982422 time for calcul the mask position with numpy : 0.012365579605102539 nb_pixel_total : 12 time to create 1 rle with old method : 4.792213439941406e-05 time for calcul the mask position with numpy : 0.014220237731933594 nb_pixel_total : 1273 time to create 1 rle with old method : 0.0029296875 time for calcul the mask position with numpy : 0.011649370193481445 nb_pixel_total : 457 time to create 1 rle with old method : 0.000560760498046875 time for calcul the mask position with numpy : 0.011420965194702148 nb_pixel_total : 411 time to create 1 rle with old method : 0.0005197525024414062 time for calcul the mask position with numpy : 0.011683225631713867 nb_pixel_total : 226 time to create 1 rle with old method : 0.0003020763397216797 time for calcul the mask position with numpy : 0.012002706527709961 nb_pixel_total : 587 time to create 1 rle with old method : 0.0007336139678955078 time for calcul the mask position with numpy : 0.012096166610717773 nb_pixel_total : 1655 time to create 1 rle with old method : 0.0020418167114257812 time for calcul the mask position with numpy : 0.011800289154052734 nb_pixel_total : 4 time to create 1 rle with old method : 5.3882598876953125e-05 time for calcul the mask position with numpy : 0.011952400207519531 nb_pixel_total : 877 time to create 1 rle with old method : 0.0010492801666259766 time for calcul the mask position with numpy : 0.011799812316894531 nb_pixel_total : 150 time to create 1 rle with old method : 0.0002987384796142578 time for calcul the mask position with numpy : 0.013748407363891602 nb_pixel_total : 1354 time to create 1 rle with old method : 0.0022051334381103516 time for calcul the mask position with numpy : 0.013425350189208984 nb_pixel_total : 172 time to create 1 rle with old method : 0.0003407001495361328 time for calcul the mask position with numpy : 0.019805908203125 nb_pixel_total : 34 time to create 1 rle with old method : 0.0002646446228027344 time for calcul the mask position with numpy : 0.013305425643920898 nb_pixel_total : 40 time to create 1 rle with old method : 0.00011277198791503906 time for calcul the mask position with numpy : 0.01287078857421875 nb_pixel_total : 3070 time to create 1 rle with old method : 0.005204677581787109 time for calcul the mask position with numpy : 0.012739419937133789 nb_pixel_total : 299 time to create 1 rle with old method : 0.0005557537078857422 time for calcul the mask position with numpy : 0.019620656967163086 nb_pixel_total : 1437 time to create 1 rle with old method : 0.0021615028381347656 time for calcul the mask position with numpy : 0.012292623519897461 nb_pixel_total : 28 time to create 1 rle with old method : 9.655952453613281e-05 time for calcul the mask position with numpy : 0.011766195297241211 nb_pixel_total : 40 time to create 1 rle with old method : 8.869171142578125e-05 time for calcul the mask position with numpy : 0.011556863784790039 nb_pixel_total : 498 time to create 1 rle with old method : 0.0006692409515380859 time for calcul the mask position with numpy : 0.011703968048095703 nb_pixel_total : 109 time to create 1 rle with old method : 0.0002079010009765625 time for calcul the mask position with numpy : 0.011359930038452148 nb_pixel_total : 1580 time to create 1 rle with old method : 0.0018148422241210938 time for calcul the mask position with numpy : 0.011968612670898438 nb_pixel_total : 144 time to create 1 rle with old method : 0.00025773048400878906 time for calcul the mask position with numpy : 0.012778282165527344 nb_pixel_total : 13208 time to create 1 rle with old method : 0.014973163604736328 time for calcul the mask position with numpy : 0.010609865188598633 nb_pixel_total : 1233 time to create 1 rle with old method : 0.0014772415161132812 time for calcul the mask position with numpy : 0.01087045669555664 nb_pixel_total : 4 time to create 1 rle with old method : 5.173683166503906e-05 time for calcul the mask position with numpy : 0.010662555694580078 nb_pixel_total : 415 time to create 1 rle with old method : 0.0006260871887207031 time for calcul the mask position with numpy : 0.010548591613769531 nb_pixel_total : 95 time to create 1 rle with old method : 0.00014281272888183594 time for calcul the mask position with numpy : 0.010364770889282227 nb_pixel_total : 198 time to create 1 rle with old method : 0.00027680397033691406 time for calcul the mask position with numpy : 0.008661508560180664 nb_pixel_total : 968 time to create 1 rle with old method : 0.0011239051818847656 time for calcul the mask position with numpy : 0.008635282516479492 nb_pixel_total : 620 time to create 1 rle with old method : 0.00077056884765625 time for calcul the mask position with numpy : 0.008585214614868164 nb_pixel_total : 307 time to create 1 rle with old method : 0.00038623809814453125 time for calcul the mask position with numpy : 0.008677482604980469 nb_pixel_total : 2206 time to create 1 rle with old method : 0.002588987350463867 time for calcul the mask position with numpy : 0.008657693862915039 nb_pixel_total : 1354 time to create 1 rle with old method : 0.0015306472778320312 time for calcul the mask position with numpy : 0.008614778518676758 nb_pixel_total : 493 time to create 1 rle with old method : 0.0006434917449951172 time for calcul the mask position with numpy : 0.008640766143798828 nb_pixel_total : 514 time to create 1 rle with old method : 0.0007557868957519531 time for calcul the mask position with numpy : 0.010284185409545898 nb_pixel_total : 1740 time to create 1 rle with old method : 0.0021543502807617188 time for calcul the mask position with numpy : 0.008657217025756836 nb_pixel_total : 76 time to create 1 rle with old method : 0.00011014938354492188 time for calcul the mask position with numpy : 0.008709907531738281 nb_pixel_total : 349 time to create 1 rle with old method : 0.0004897117614746094 time for calcul the mask position with numpy : 0.008945703506469727 nb_pixel_total : 19 time to create 1 rle with old method : 0.00010156631469726562 time for calcul the mask position with numpy : 0.008912801742553711 nb_pixel_total : 9 time to create 1 rle with old method : 5.125999450683594e-05 time for calcul the mask position with numpy : 0.008671283721923828 nb_pixel_total : 573 time to create 1 rle with old method : 0.0006952285766601562 time for calcul the mask position with numpy : 0.008676528930664062 nb_pixel_total : 2 time to create 1 rle with old method : 3.24249267578125e-05 time for calcul the mask position with numpy : 0.008555889129638672 nb_pixel_total : 6 time to create 1 rle with old method : 3.9577484130859375e-05 time for calcul the mask position with numpy : 0.008586883544921875 nb_pixel_total : 381 time to create 1 rle with old method : 0.0004813671112060547 time for calcul the mask position with numpy : 0.008623600006103516 nb_pixel_total : 272 time to create 1 rle with old method : 0.0003650188446044922 time for calcul the mask position with numpy : 0.012763738632202148 nb_pixel_total : 1086 time to create 1 rle with old method : 0.0012612342834472656 time for calcul the mask position with numpy : 0.008697509765625 nb_pixel_total : 452 time to create 1 rle with old method : 0.0005958080291748047 time for calcul the mask position with numpy : 0.008539438247680664 nb_pixel_total : 43 time to create 1 rle with old method : 0.0001125335693359375 time for calcul the mask position with numpy : 0.010106563568115234 nb_pixel_total : 523 time to create 1 rle with old method : 0.0006358623504638672 time for calcul the mask position with numpy : 0.008531332015991211 nb_pixel_total : 510 time to create 1 rle with old method : 0.0006840229034423828 time for calcul the mask position with numpy : 0.008516788482666016 nb_pixel_total : 308 time to create 1 rle with old method : 0.00039577484130859375 time for calcul the mask position with numpy : 0.00855398178100586 nb_pixel_total : 3573 time to create 1 rle with old method : 0.00414276123046875 time for calcul the mask position with numpy : 0.008553743362426758 nb_pixel_total : 230 time to create 1 rle with old method : 0.0002846717834472656 time for calcul the mask position with numpy : 0.008529186248779297 nb_pixel_total : 1495 time to create 1 rle with old method : 0.0016503334045410156 time for calcul the mask position with numpy : 0.008566141128540039 nb_pixel_total : 7 time to create 1 rle with old method : 3.314018249511719e-05 time for calcul the mask position with numpy : 0.00856328010559082 nb_pixel_total : 661 time to create 1 rle with old method : 0.0008165836334228516 time for calcul the mask position with numpy : 0.008538246154785156 nb_pixel_total : 119 time to create 1 rle with old method : 0.00014638900756835938 create new chi : 3.901012420654297 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.007822990417480469 batch 1 Loaded 200 chid ids of type : 4230 Number RLEs to save : 16852 TO DO : save crop sub photo not yet done ! save time : 1.0976135730743408 nb_obj : 174 nb_hashtags : 7 time to prepare the origin masks : 2.8999531269073486 time for calcul the mask position with numpy : 0.02890324592590332 nb_pixel_total : 1626395 time to create 1 rle with new method : 0.0598301887512207 time for calcul the mask position with numpy : 0.007386207580566406 nb_pixel_total : 143 time to create 1 rle with old method : 0.00023436546325683594 time for calcul the mask position with numpy : 0.0071904659271240234 nb_pixel_total : 83 time to create 1 rle with old method : 0.00017976760864257812 time for calcul the mask position with numpy : 0.011044979095458984 nb_pixel_total : 12 time to create 1 rle with old method : 5.2928924560546875e-05 time for calcul the mask position with numpy : 0.011760234832763672 nb_pixel_total : 1965 time to create 1 rle with old method : 0.0022516250610351562 time for calcul the mask position with numpy : 0.010824203491210938 nb_pixel_total : 1359 time to create 1 rle with old method : 0.0016088485717773438 time for calcul the mask position with numpy : 0.01060795783996582 nb_pixel_total : 4540 time to create 1 rle with old method : 0.0052869319915771484 time for calcul the mask position with numpy : 0.010851621627807617 nb_pixel_total : 46 time to create 1 rle with old method : 0.00023865699768066406 time for calcul the mask position with numpy : 0.010367631912231445 nb_pixel_total : 3645 time to create 1 rle with old method : 0.004304409027099609 time for calcul the mask position with numpy : 0.010506629943847656 nb_pixel_total : 5 time to create 1 rle with old method : 3.7670135498046875e-05 time for calcul the mask position with numpy : 0.010342121124267578 nb_pixel_total : 130 time to create 1 rle with old method : 0.0001876354217529297 time for calcul the mask position with numpy : 0.011386871337890625 nb_pixel_total : 934 time to create 1 rle with old method : 0.001093149185180664 time for calcul the mask position with numpy : 0.0064814090728759766 nb_pixel_total : 331 time to create 1 rle with old method : 0.00043511390686035156 time for calcul the mask position with numpy : 0.006158113479614258 nb_pixel_total : 206 time to create 1 rle with old method : 0.00027441978454589844 time for calcul the mask position with numpy : 0.0062139034271240234 nb_pixel_total : 971 time to create 1 rle with old method : 0.001132965087890625 time for calcul the mask position with numpy : 0.0075185298919677734 nb_pixel_total : 44 time to create 1 rle with old method : 7.605552673339844e-05 time for calcul the mask position with numpy : 0.0103302001953125 nb_pixel_total : 58 time to create 1 rle with old method : 9.989738464355469e-05 time for calcul the mask position with numpy : 0.014268636703491211 nb_pixel_total : 222 time to create 1 rle with old method : 0.0002677440643310547 time for calcul the mask position with numpy : 0.010107755661010742 nb_pixel_total : 183 time to create 1 rle with old method : 0.00023746490478515625 time for calcul the mask position with numpy : 0.01015782356262207 nb_pixel_total : 85 time to create 1 rle with old method : 0.0001513957977294922 time for calcul the mask position with numpy : 0.006410360336303711 nb_pixel_total : 1448 time to create 1 rle with old method : 0.0017845630645751953 time for calcul the mask position with numpy : 0.006309986114501953 nb_pixel_total : 245 time to create 1 rle with old method : 0.0003135204315185547 time for calcul the mask position with numpy : 0.006390094757080078 nb_pixel_total : 28 time to create 1 rle with old method : 6.318092346191406e-05 time for calcul the mask position with numpy : 0.00637507438659668 nb_pixel_total : 70 time to create 1 rle with old method : 0.00010323524475097656 time for calcul the mask position with numpy : 0.006432056427001953 nb_pixel_total : 71 time to create 1 rle with old method : 0.00017547607421875 time for calcul the mask position with numpy : 0.006931781768798828 nb_pixel_total : 25 time to create 1 rle with old method : 4.982948303222656e-05 time for calcul the mask position with numpy : 0.006041049957275391 nb_pixel_total : 3 time to create 1 rle with old method : 2.765655517578125e-05 time for calcul the mask position with numpy : 0.0060884952545166016 nb_pixel_total : 2267 time to create 1 rle with old method : 0.0026247501373291016 time for calcul the mask position with numpy : 0.0072553157806396484 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001785755157470703 time for calcul the mask position with numpy : 0.006281852722167969 nb_pixel_total : 30 time to create 1 rle with old method : 5.91278076171875e-05 time for calcul the mask position with numpy : 0.0064504146575927734 nb_pixel_total : 980 time to create 1 rle with old method : 0.0011761188507080078 time for calcul the mask position with numpy : 0.006354093551635742 nb_pixel_total : 28 time to create 1 rle with old method : 7.510185241699219e-05 time for calcul the mask position with numpy : 0.006340503692626953 nb_pixel_total : 281 time to create 1 rle with old method : 0.00033855438232421875 time for calcul the mask position with numpy : 0.006231546401977539 nb_pixel_total : 1677 time to create 1 rle with old method : 0.0021364688873291016 time for calcul the mask position with numpy : 0.0064427852630615234 nb_pixel_total : 4093 time to create 1 rle with old method : 0.004754781723022461 time for calcul the mask position with numpy : 0.006183147430419922 nb_pixel_total : 10036 time to create 1 rle with old method : 0.011147022247314453 time for calcul the mask position with numpy : 0.006205558776855469 nb_pixel_total : 194 time to create 1 rle with old method : 0.0002512931823730469 time for calcul the mask position with numpy : 0.006326913833618164 nb_pixel_total : 649 time to create 1 rle with old method : 0.0008661746978759766 time for calcul the mask position with numpy : 0.0065538883209228516 nb_pixel_total : 2346 time to create 1 rle with old method : 0.002792835235595703 time for calcul the mask position with numpy : 0.006506681442260742 nb_pixel_total : 112 time to create 1 rle with old method : 0.00015020370483398438 time for calcul the mask position with numpy : 0.006081581115722656 nb_pixel_total : 715 time to create 1 rle with old method : 0.0008656978607177734 time for calcul the mask position with numpy : 0.006147861480712891 nb_pixel_total : 2707 time to create 1 rle with old method : 0.0032112598419189453 time for calcul the mask position with numpy : 0.011008977890014648 nb_pixel_total : 9 time to create 1 rle with old method : 6.866455078125e-05 time for calcul the mask position with numpy : 0.006194114685058594 nb_pixel_total : 78 time to create 1 rle with old method : 0.0002300739288330078 time for calcul the mask position with numpy : 0.006422519683837891 nb_pixel_total : 5829 time to create 1 rle with old method : 0.006543397903442383 time for calcul the mask position with numpy : 0.006346940994262695 nb_pixel_total : 1196 time to create 1 rle with old method : 0.0016295909881591797 time for calcul the mask position with numpy : 0.008517265319824219 nb_pixel_total : 6209 time to create 1 rle with old method : 0.007245779037475586 time for calcul the mask position with numpy : 0.006098508834838867 nb_pixel_total : 11493 time to create 1 rle with old method : 0.012542247772216797 time for calcul the mask position with numpy : 0.006499528884887695 nb_pixel_total : 58 time to create 1 rle with old method : 0.00012922286987304688 time for calcul the mask position with numpy : 0.006339311599731445 nb_pixel_total : 830 time to create 1 rle with old method : 0.0010089874267578125 time for calcul the mask position with numpy : 0.006383180618286133 nb_pixel_total : 117 time to create 1 rle with old method : 0.00016689300537109375 time for calcul the mask position with numpy : 0.0062024593353271484 nb_pixel_total : 172 time to create 1 rle with old method : 0.000217437744140625 time for calcul the mask position with numpy : 0.007135629653930664 nb_pixel_total : 154032 time to create 1 rle with new method : 0.029222965240478516 time for calcul the mask position with numpy : 0.010951042175292969 nb_pixel_total : 532 time to create 1 rle with old method : 0.0006787776947021484 time for calcul the mask position with numpy : 0.01043558120727539 nb_pixel_total : 1000 time to create 1 rle with old method : 0.00119781494140625 time for calcul the mask position with numpy : 0.010289192199707031 nb_pixel_total : 647 time to create 1 rle with old method : 0.0007731914520263672 time for calcul the mask position with numpy : 0.010349035263061523 nb_pixel_total : 27 time to create 1 rle with old method : 7.200241088867188e-05 time for calcul the mask position with numpy : 0.01038813591003418 nb_pixel_total : 1275 time to create 1 rle with old method : 0.0014429092407226562 time for calcul the mask position with numpy : 0.010514259338378906 nb_pixel_total : 914 time to create 1 rle with old method : 0.0010924339294433594 time for calcul the mask position with numpy : 0.011078596115112305 nb_pixel_total : 16273 time to create 1 rle with old method : 0.018942832946777344 time for calcul the mask position with numpy : 0.010329961776733398 nb_pixel_total : 14915 time to create 1 rle with old method : 0.0168459415435791 time for calcul the mask position with numpy : 0.010738849639892578 nb_pixel_total : 3024 time to create 1 rle with old method : 0.0035886764526367188 time for calcul the mask position with numpy : 0.010404348373413086 nb_pixel_total : 244 time to create 1 rle with old method : 0.0003464221954345703 time for calcul the mask position with numpy : 0.010299921035766602 nb_pixel_total : 158 time to create 1 rle with old method : 0.0002720355987548828 time for calcul the mask position with numpy : 0.010439872741699219 nb_pixel_total : 104 time to create 1 rle with old method : 0.00014710426330566406 time for calcul the mask position with numpy : 0.010280370712280273 nb_pixel_total : 197 time to create 1 rle with old method : 0.000263214111328125 time for calcul the mask position with numpy : 0.01138615608215332 nb_pixel_total : 368 time to create 1 rle with old method : 0.0004639625549316406 time for calcul the mask position with numpy : 0.007714271545410156 nb_pixel_total : 1859 time to create 1 rle with old method : 0.0022249221801757812 time for calcul the mask position with numpy : 0.01034402847290039 nb_pixel_total : 148 time to create 1 rle with old method : 0.0002014636993408203 time for calcul the mask position with numpy : 0.011642217636108398 nb_pixel_total : 145 time to create 1 rle with old method : 0.00028228759765625 time for calcul the mask position with numpy : 0.010866165161132812 nb_pixel_total : 1411 time to create 1 rle with old method : 0.0018732547760009766 time for calcul the mask position with numpy : 0.010370254516601562 nb_pixel_total : 731 time to create 1 rle with old method : 0.0008959770202636719 time for calcul the mask position with numpy : 0.013458251953125 nb_pixel_total : 18 time to create 1 rle with old method : 0.00010466575622558594 time for calcul the mask position with numpy : 0.01022028923034668 nb_pixel_total : 2417 time to create 1 rle with old method : 0.0028252601623535156 time for calcul the mask position with numpy : 0.012113094329833984 nb_pixel_total : 225 time to create 1 rle with old method : 0.00030112266540527344 time for calcul the mask position with numpy : 0.010358333587646484 nb_pixel_total : 1322 time to create 1 rle with old method : 0.0015993118286132812 time for calcul the mask position with numpy : 0.010357856750488281 nb_pixel_total : 1327 time to create 1 rle with old method : 0.0016274452209472656 time for calcul the mask position with numpy : 0.01029205322265625 nb_pixel_total : 46 time to create 1 rle with old method : 9.632110595703125e-05 time for calcul the mask position with numpy : 0.010460615158081055 nb_pixel_total : 108 time to create 1 rle with old method : 0.0001494884490966797 time for calcul the mask position with numpy : 0.010063648223876953 nb_pixel_total : 542 time to create 1 rle with old method : 0.0006711483001708984 time for calcul the mask position with numpy : 0.009950637817382812 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005071163177490234 time for calcul the mask position with numpy : 0.01074981689453125 nb_pixel_total : 740 time to create 1 rle with old method : 0.0008695125579833984 time for calcul the mask position with numpy : 0.010374784469604492 nb_pixel_total : 23 time to create 1 rle with old method : 9.989738464355469e-05 time for calcul the mask position with numpy : 0.01103067398071289 nb_pixel_total : 313 time to create 1 rle with old method : 0.0004930496215820312 time for calcul the mask position with numpy : 0.010387659072875977 nb_pixel_total : 14 time to create 1 rle with old method : 5.078315734863281e-05 time for calcul the mask position with numpy : 0.011369466781616211 nb_pixel_total : 315 time to create 1 rle with old method : 0.000438690185546875 time for calcul the mask position with numpy : 0.010348796844482422 nb_pixel_total : 4 time to create 1 rle with old method : 3.743171691894531e-05 time for calcul the mask position with numpy : 0.010214567184448242 nb_pixel_total : 2436 time to create 1 rle with old method : 0.0030198097229003906 time for calcul the mask position with numpy : 0.012956619262695312 nb_pixel_total : 1110 time to create 1 rle with old method : 0.0013453960418701172 time for calcul the mask position with numpy : 0.009997844696044922 nb_pixel_total : 906 time to create 1 rle with old method : 0.0013623237609863281 time for calcul the mask position with numpy : 0.010048866271972656 nb_pixel_total : 278 time to create 1 rle with old method : 0.00038814544677734375 time for calcul the mask position with numpy : 0.0100555419921875 nb_pixel_total : 318 time to create 1 rle with old method : 0.0004100799560546875 time for calcul the mask position with numpy : 0.010620355606079102 nb_pixel_total : 274 time to create 1 rle with old method : 0.0003561973571777344 time for calcul the mask position with numpy : 0.010172605514526367 nb_pixel_total : 1400 time to create 1 rle with old method : 0.0016260147094726562 time for calcul the mask position with numpy : 0.010085105895996094 nb_pixel_total : 127 time to create 1 rle with old method : 0.00018215179443359375 time for calcul the mask position with numpy : 0.010059356689453125 nb_pixel_total : 16 time to create 1 rle with old method : 5.054473876953125e-05 time for calcul the mask position with numpy : 0.011055946350097656 nb_pixel_total : 106694 time to create 1 rle with old method : 0.119537353515625 time for calcul the mask position with numpy : 0.01087045669555664 nb_pixel_total : 145 time to create 1 rle with old method : 0.00022864341735839844 time for calcul the mask position with numpy : 0.01075434684753418 nb_pixel_total : 10415 time to create 1 rle with old method : 0.012021064758300781 time for calcul the mask position with numpy : 0.012133359909057617 nb_pixel_total : 156 time to create 1 rle with old method : 0.00027751922607421875 time for calcul the mask position with numpy : 0.010395288467407227 nb_pixel_total : 1656 time to create 1 rle with old method : 0.0021038055419921875 time for calcul the mask position with numpy : 0.011223316192626953 nb_pixel_total : 28 time to create 1 rle with old method : 9.059906005859375e-05 time for calcul the mask position with numpy : 0.012493133544921875 nb_pixel_total : 123 time to create 1 rle with old method : 0.0002422332763671875 time for calcul the mask position with numpy : 0.01221323013305664 nb_pixel_total : 55 time to create 1 rle with old method : 0.0001342296600341797 time for calcul the mask position with numpy : 0.012696266174316406 nb_pixel_total : 287 time to create 1 rle with old method : 0.0004105567932128906 time for calcul the mask position with numpy : 0.013330936431884766 nb_pixel_total : 898 time to create 1 rle with old method : 0.0014820098876953125 time for calcul the mask position with numpy : 0.013098955154418945 nb_pixel_total : 49 time to create 1 rle with old method : 0.00013208389282226562 time for calcul the mask position with numpy : 0.012540578842163086 nb_pixel_total : 191 time to create 1 rle with old method : 0.0003437995910644531 time for calcul the mask position with numpy : 0.01911306381225586 nb_pixel_total : 473 time to create 1 rle with old method : 0.0006880760192871094 time for calcul the mask position with numpy : 0.012625455856323242 nb_pixel_total : 15 time to create 1 rle with old method : 7.724761962890625e-05 time for calcul the mask position with numpy : 0.012393951416015625 nb_pixel_total : 14 time to create 1 rle with old method : 7.581710815429688e-05 time for calcul the mask position with numpy : 0.013952016830444336 nb_pixel_total : 226 time to create 1 rle with old method : 0.00040531158447265625 time for calcul the mask position with numpy : 0.012353658676147461 nb_pixel_total : 183 time to create 1 rle with old method : 0.0003142356872558594 time for calcul the mask position with numpy : 0.012267589569091797 nb_pixel_total : 273 time to create 1 rle with old method : 0.0005156993865966797 time for calcul the mask position with numpy : 0.012718439102172852 nb_pixel_total : 150 time to create 1 rle with old method : 0.00026535987854003906 time for calcul the mask position with numpy : 0.012743711471557617 nb_pixel_total : 685 time to create 1 rle with old method : 0.0010960102081298828 time for calcul the mask position with numpy : 0.012681007385253906 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002818107604980469 time for calcul the mask position with numpy : 0.013236045837402344 nb_pixel_total : 816 time to create 1 rle with old method : 0.0012121200561523438 time for calcul the mask position with numpy : 0.007611513137817383 nb_pixel_total : 1 time to create 1 rle with old method : 2.1219253540039062e-05 time for calcul the mask position with numpy : 0.007654905319213867 nb_pixel_total : 357 time to create 1 rle with old method : 0.0005428791046142578 time for calcul the mask position with numpy : 0.010303497314453125 nb_pixel_total : 195 time to create 1 rle with old method : 0.0003478527069091797 time for calcul the mask position with numpy : 0.007714271545410156 nb_pixel_total : 579 time to create 1 rle with old method : 0.0008263587951660156 time for calcul the mask position with numpy : 0.0075778961181640625 nb_pixel_total : 529 time to create 1 rle with old method : 0.0007958412170410156 time for calcul the mask position with numpy : 0.007624387741088867 nb_pixel_total : 1449 time to create 1 rle with old method : 0.001737833023071289 time for calcul the mask position with numpy : 0.008594274520874023 nb_pixel_total : 492 time to create 1 rle with old method : 0.0006070137023925781 time for calcul the mask position with numpy : 0.006536722183227539 nb_pixel_total : 1089 time to create 1 rle with old method : 0.0013251304626464844 time for calcul the mask position with numpy : 0.006724357604980469 nb_pixel_total : 459 time to create 1 rle with old method : 0.0006191730499267578 time for calcul the mask position with numpy : 0.006582498550415039 nb_pixel_total : 1076 time to create 1 rle with old method : 0.0012824535369873047 time for calcul the mask position with numpy : 0.006906747817993164 nb_pixel_total : 1787 time to create 1 rle with old method : 0.0020215511322021484 time for calcul the mask position with numpy : 0.006530046463012695 nb_pixel_total : 324 time to create 1 rle with old method : 0.0004105567932128906 time for calcul the mask position with numpy : 0.006944417953491211 nb_pixel_total : 866 time to create 1 rle with old method : 0.0010559558868408203 time for calcul the mask position with numpy : 0.0075876712799072266 nb_pixel_total : 488 time to create 1 rle with old method : 0.0005972385406494141 time for calcul the mask position with numpy : 0.006484270095825195 nb_pixel_total : 440 time to create 1 rle with old method : 0.0005195140838623047 time for calcul the mask position with numpy : 0.006665945053100586 nb_pixel_total : 1594 time to create 1 rle with old method : 0.0018858909606933594 time for calcul the mask position with numpy : 0.006968259811401367 nb_pixel_total : 212 time to create 1 rle with old method : 0.00028014183044433594 time for calcul the mask position with numpy : 0.006914854049682617 nb_pixel_total : 497 time to create 1 rle with old method : 0.0007092952728271484 time for calcul the mask position with numpy : 0.007588624954223633 nb_pixel_total : 432 time to create 1 rle with old method : 0.0006313323974609375 time for calcul the mask position with numpy : 0.007700920104980469 nb_pixel_total : 1384 time to create 1 rle with old method : 0.0018925666809082031 time for calcul the mask position with numpy : 0.007940292358398438 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002930164337158203 time for calcul the mask position with numpy : 0.007398843765258789 nb_pixel_total : 186 time to create 1 rle with old method : 0.0003170967102050781 time for calcul the mask position with numpy : 0.007078647613525391 nb_pixel_total : 892 time to create 1 rle with old method : 0.001424551010131836 time for calcul the mask position with numpy : 0.007437229156494141 nb_pixel_total : 36 time to create 1 rle with old method : 0.00011134147644042969 time for calcul the mask position with numpy : 0.007460594177246094 nb_pixel_total : 420 time to create 1 rle with old method : 0.0006051063537597656 time for calcul the mask position with numpy : 0.007172107696533203 nb_pixel_total : 95 time to create 1 rle with old method : 0.0001742839813232422 time for calcul the mask position with numpy : 0.007308006286621094 nb_pixel_total : 136 time to create 1 rle with old method : 0.0002753734588623047 time for calcul the mask position with numpy : 0.007374286651611328 nb_pixel_total : 1374 time to create 1 rle with old method : 0.0018401145935058594 time for calcul the mask position with numpy : 0.007294893264770508 nb_pixel_total : 1565 time to create 1 rle with old method : 0.0019698143005371094 time for calcul the mask position with numpy : 0.007201194763183594 nb_pixel_total : 814 time to create 1 rle with old method : 0.0010983943939208984 time for calcul the mask position with numpy : 0.007041454315185547 nb_pixel_total : 1110 time to create 1 rle with old method : 0.0014309883117675781 time for calcul the mask position with numpy : 0.007094144821166992 nb_pixel_total : 38 time to create 1 rle with old method : 9.965896606445312e-05 time for calcul the mask position with numpy : 0.007001399993896484 nb_pixel_total : 197 time to create 1 rle with old method : 0.0003020763397216797 time for calcul the mask position with numpy : 0.007357597351074219 nb_pixel_total : 604 time to create 1 rle with old method : 0.0008985996246337891 time for calcul the mask position with numpy : 0.0073473453521728516 nb_pixel_total : 3139 time to create 1 rle with old method : 0.0040738582611083984 time for calcul the mask position with numpy : 0.0075685977935791016 nb_pixel_total : 2701 time to create 1 rle with old method : 0.0035088062286376953 time for calcul the mask position with numpy : 0.007331132888793945 nb_pixel_total : 270 time to create 1 rle with old method : 0.00044226646423339844 time for calcul the mask position with numpy : 0.007577657699584961 nb_pixel_total : 13 time to create 1 rle with old method : 7.605552673339844e-05 time for calcul the mask position with numpy : 0.009005308151245117 nb_pixel_total : 1520 time to create 1 rle with old method : 0.0023713111877441406 time for calcul the mask position with numpy : 0.0077207088470458984 nb_pixel_total : 545 time to create 1 rle with old method : 0.0007379055023193359 time for calcul the mask position with numpy : 0.007898807525634766 nb_pixel_total : 74 time to create 1 rle with old method : 0.00014591217041015625 time for calcul the mask position with numpy : 0.008400917053222656 nb_pixel_total : 15 time to create 1 rle with old method : 6.628036499023438e-05 time for calcul the mask position with numpy : 0.008774518966674805 nb_pixel_total : 352 time to create 1 rle with old method : 0.0005688667297363281 time for calcul the mask position with numpy : 0.007985115051269531 nb_pixel_total : 678 time to create 1 rle with old method : 0.0011477470397949219 time for calcul the mask position with numpy : 0.008334875106811523 nb_pixel_total : 125 time to create 1 rle with old method : 0.00021219253540039062 time for calcul the mask position with numpy : 0.008024215698242188 nb_pixel_total : 245 time to create 1 rle with old method : 0.0003323554992675781 time for calcul the mask position with numpy : 0.00796055793762207 nb_pixel_total : 424 time to create 1 rle with old method : 0.0006434917449951172 time for calcul the mask position with numpy : 0.008300542831420898 nb_pixel_total : 1577 time to create 1 rle with old method : 0.002247333526611328 time for calcul the mask position with numpy : 0.013916969299316406 nb_pixel_total : 624 time to create 1 rle with old method : 0.0009310245513916016 time for calcul the mask position with numpy : 0.007624149322509766 nb_pixel_total : 517 time to create 1 rle with old method : 0.0006067752838134766 time for calcul the mask position with numpy : 0.006578683853149414 nb_pixel_total : 304 time to create 1 rle with old method : 0.00037789344787597656 time for calcul the mask position with numpy : 0.006930828094482422 nb_pixel_total : 3208 time to create 1 rle with old method : 0.0037469863891601562 time for calcul the mask position with numpy : 0.006654500961303711 nb_pixel_total : 1217 time to create 1 rle with old method : 0.0014994144439697266 time for calcul the mask position with numpy : 0.0062694549560546875 nb_pixel_total : 24 time to create 1 rle with old method : 7.224082946777344e-05 time for calcul the mask position with numpy : 0.006304264068603516 nb_pixel_total : 218 time to create 1 rle with old method : 0.00028204917907714844 time for calcul the mask position with numpy : 0.006380319595336914 nb_pixel_total : 1453 time to create 1 rle with old method : 0.0016858577728271484 time for calcul the mask position with numpy : 0.0063703060150146484 nb_pixel_total : 579 time to create 1 rle with old method : 0.0006988048553466797 create new chi : 2.0385868549346924 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.006074666976928711 batch 1 Loaded 186 chid ids of type : 4230 Number RLEs to save : 15628 TO DO : save crop sub photo not yet done ! save time : 0.9374847412109375 nb_obj : 181 nb_hashtags : 7 time to prepare the origin masks : 2.976947069168091 time for calcul the mask position with numpy : 0.057157039642333984 nb_pixel_total : 1563210 time to create 1 rle with new method : 0.1683180332183838 time for calcul the mask position with numpy : 0.012755870819091797 nb_pixel_total : 91 time to create 1 rle with old method : 0.0001971721649169922 time for calcul the mask position with numpy : 0.012513160705566406 nb_pixel_total : 146 time to create 1 rle with old method : 0.0003337860107421875 time for calcul the mask position with numpy : 0.0072841644287109375 nb_pixel_total : 72 time to create 1 rle with old method : 0.00011396408081054688 time for calcul the mask position with numpy : 0.006850004196166992 nb_pixel_total : 1868 time to create 1 rle with old method : 0.0021889209747314453 time for calcul the mask position with numpy : 0.011467218399047852 nb_pixel_total : 1151 time to create 1 rle with old method : 0.0013670921325683594 time for calcul the mask position with numpy : 0.011171340942382812 nb_pixel_total : 1286 time to create 1 rle with old method : 0.0016148090362548828 time for calcul the mask position with numpy : 0.010881185531616211 nb_pixel_total : 2614 time to create 1 rle with old method : 0.0033066272735595703 time for calcul the mask position with numpy : 0.010542631149291992 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001499652862548828 time for calcul the mask position with numpy : 0.010431289672851562 nb_pixel_total : 662 time to create 1 rle with old method : 0.0007958412170410156 time for calcul the mask position with numpy : 0.01045680046081543 nb_pixel_total : 63 time to create 1 rle with old method : 9.655952453613281e-05 time for calcul the mask position with numpy : 0.01006937026977539 nb_pixel_total : 39 time to create 1 rle with old method : 7.843971252441406e-05 time for calcul the mask position with numpy : 0.01039433479309082 nb_pixel_total : 32 time to create 1 rle with old method : 6.651878356933594e-05 time for calcul the mask position with numpy : 0.010347843170166016 nb_pixel_total : 63 time to create 1 rle with old method : 0.00010633468627929688 time for calcul the mask position with numpy : 0.010374069213867188 nb_pixel_total : 43 time to create 1 rle with old method : 8.177757263183594e-05 time for calcul the mask position with numpy : 0.010349750518798828 nb_pixel_total : 32 time to create 1 rle with old method : 7.700920104980469e-05 time for calcul the mask position with numpy : 0.010741233825683594 nb_pixel_total : 77 time to create 1 rle with old method : 0.00012373924255371094 time for calcul the mask position with numpy : 0.011197090148925781 nb_pixel_total : 105 time to create 1 rle with old method : 0.0004506111145019531 time for calcul the mask position with numpy : 0.011383295059204102 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003113746643066406 time for calcul the mask position with numpy : 0.010132312774658203 nb_pixel_total : 272 time to create 1 rle with old method : 0.00034499168395996094 time for calcul the mask position with numpy : 0.010892391204833984 nb_pixel_total : 120 time to create 1 rle with old method : 0.0001971721649169922 time for calcul the mask position with numpy : 0.010123014450073242 nb_pixel_total : 68 time to create 1 rle with old method : 0.00010180473327636719 time for calcul the mask position with numpy : 0.010246038436889648 nb_pixel_total : 176 time to create 1 rle with old method : 0.000255584716796875 time for calcul the mask position with numpy : 0.010289192199707031 nb_pixel_total : 21 time to create 1 rle with old method : 5.14984130859375e-05 time for calcul the mask position with numpy : 0.010188579559326172 nb_pixel_total : 54 time to create 1 rle with old method : 0.00010180473327636719 time for calcul the mask position with numpy : 0.010214567184448242 nb_pixel_total : 83 time to create 1 rle with old method : 0.00015354156494140625 time for calcul the mask position with numpy : 0.011519193649291992 nb_pixel_total : 28 time to create 1 rle with old method : 7.224082946777344e-05 time for calcul the mask position with numpy : 0.011826515197753906 nb_pixel_total : 16074 time to create 1 rle with old method : 0.021519899368286133 time for calcul the mask position with numpy : 0.011412858963012695 nb_pixel_total : 2509 time to create 1 rle with old method : 0.003075122833251953 time for calcul the mask position with numpy : 0.01150965690612793 nb_pixel_total : 27 time to create 1 rle with old method : 5.53131103515625e-05 time for calcul the mask position with numpy : 0.011037826538085938 nb_pixel_total : 31 time to create 1 rle with old method : 7.677078247070312e-05 time for calcul the mask position with numpy : 0.006471395492553711 nb_pixel_total : 900 time to create 1 rle with old method : 0.0010576248168945312 time for calcul the mask position with numpy : 0.00683283805847168 nb_pixel_total : 35011 time to create 1 rle with old method : 0.044014930725097656 time for calcul the mask position with numpy : 0.006701946258544922 nb_pixel_total : 15 time to create 1 rle with old method : 0.0003795623779296875 time for calcul the mask position with numpy : 0.006688356399536133 nb_pixel_total : 3520 time to create 1 rle with old method : 0.012248754501342773 time for calcul the mask position with numpy : 0.011847734451293945 nb_pixel_total : 246 time to create 1 rle with old method : 0.0006594657897949219 time for calcul the mask position with numpy : 0.011863231658935547 nb_pixel_total : 11792 time to create 1 rle with old method : 0.016356945037841797 time for calcul the mask position with numpy : 0.010565757751464844 nb_pixel_total : 1069 time to create 1 rle with old method : 0.0013279914855957031 time for calcul the mask position with numpy : 0.010926961898803711 nb_pixel_total : 193 time to create 1 rle with old method : 0.00033783912658691406 time for calcul the mask position with numpy : 0.010893821716308594 nb_pixel_total : 860 time to create 1 rle with old method : 0.0010638236999511719 time for calcul the mask position with numpy : 0.010848283767700195 nb_pixel_total : 847 time to create 1 rle with old method : 0.0010640621185302734 time for calcul the mask position with numpy : 0.01076364517211914 nb_pixel_total : 3078 time to create 1 rle with old method : 0.0036797523498535156 time for calcul the mask position with numpy : 0.006719350814819336 nb_pixel_total : 389 time to create 1 rle with old method : 0.0005130767822265625 time for calcul the mask position with numpy : 0.0064318180084228516 nb_pixel_total : 7323 time to create 1 rle with old method : 0.008715629577636719 time for calcul the mask position with numpy : 0.00632023811340332 nb_pixel_total : 339 time to create 1 rle with old method : 0.0005357265472412109 time for calcul the mask position with numpy : 0.006362199783325195 nb_pixel_total : 862 time to create 1 rle with old method : 0.0011265277862548828 time for calcul the mask position with numpy : 0.006392955780029297 nb_pixel_total : 29 time to create 1 rle with old method : 9.512901306152344e-05 time for calcul the mask position with numpy : 0.006247758865356445 nb_pixel_total : 6221 time to create 1 rle with old method : 0.0073015689849853516 time for calcul the mask position with numpy : 0.0063135623931884766 nb_pixel_total : 11597 time to create 1 rle with old method : 0.013153791427612305 time for calcul the mask position with numpy : 0.006514072418212891 nb_pixel_total : 105 time to create 1 rle with old method : 0.00016021728515625 time for calcul the mask position with numpy : 0.006455183029174805 nb_pixel_total : 2 time to create 1 rle with old method : 3.910064697265625e-05 time for calcul the mask position with numpy : 0.006279468536376953 nb_pixel_total : 159 time to create 1 rle with old method : 0.00031065940856933594 time for calcul the mask position with numpy : 0.006216287612915039 nb_pixel_total : 11 time to create 1 rle with old method : 6.246566772460938e-05 time for calcul the mask position with numpy : 0.006245851516723633 nb_pixel_total : 808 time to create 1 rle with old method : 0.0009965896606445312 time for calcul the mask position with numpy : 0.011687278747558594 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004241466522216797 time for calcul the mask position with numpy : 0.01034688949584961 nb_pixel_total : 101 time to create 1 rle with old method : 0.00015926361083984375 time for calcul the mask position with numpy : 0.012078523635864258 nb_pixel_total : 152761 time to create 1 rle with new method : 0.13663959503173828 time for calcul the mask position with numpy : 0.01050710678100586 nb_pixel_total : 1239 time to create 1 rle with old method : 0.001544952392578125 time for calcul the mask position with numpy : 0.010279178619384766 nb_pixel_total : 839 time to create 1 rle with old method : 0.0010647773742675781 time for calcul the mask position with numpy : 0.010843515396118164 nb_pixel_total : 9769 time to create 1 rle with old method : 0.01188349723815918 time for calcul the mask position with numpy : 0.01136922836303711 nb_pixel_total : 993 time to create 1 rle with old method : 0.0018091201782226562 time for calcul the mask position with numpy : 0.013230323791503906 nb_pixel_total : 78 time to create 1 rle with old method : 0.00045228004455566406 time for calcul the mask position with numpy : 0.012676715850830078 nb_pixel_total : 99 time to create 1 rle with old method : 0.00020647048950195312 time for calcul the mask position with numpy : 0.012616872787475586 nb_pixel_total : 823 time to create 1 rle with old method : 0.0015993118286132812 time for calcul the mask position with numpy : 0.012820005416870117 nb_pixel_total : 2 time to create 1 rle with old method : 3.4332275390625e-05 time for calcul the mask position with numpy : 0.012675285339355469 nb_pixel_total : 244 time to create 1 rle with old method : 0.0004620552062988281 time for calcul the mask position with numpy : 0.012015342712402344 nb_pixel_total : 23 time to create 1 rle with old method : 9.560585021972656e-05 time for calcul the mask position with numpy : 0.010894060134887695 nb_pixel_total : 28 time to create 1 rle with old method : 9.369850158691406e-05 time for calcul the mask position with numpy : 0.010782480239868164 nb_pixel_total : 1288 time to create 1 rle with old method : 0.001550912857055664 time for calcul the mask position with numpy : 0.010826587677001953 nb_pixel_total : 961 time to create 1 rle with old method : 0.0011293888092041016 time for calcul the mask position with numpy : 0.010857105255126953 nb_pixel_total : 1799 time to create 1 rle with old method : 0.004199504852294922 time for calcul the mask position with numpy : 0.012157917022705078 nb_pixel_total : 19113 time to create 1 rle with old method : 0.023257732391357422 time for calcul the mask position with numpy : 0.01153707504272461 nb_pixel_total : 109 time to create 1 rle with old method : 0.00031948089599609375 time for calcul the mask position with numpy : 0.010847806930541992 nb_pixel_total : 13967 time to create 1 rle with old method : 0.015491724014282227 time for calcul the mask position with numpy : 0.011039495468139648 nb_pixel_total : 2322 time to create 1 rle with old method : 0.007296562194824219 time for calcul the mask position with numpy : 0.011101484298706055 nb_pixel_total : 136 time to create 1 rle with old method : 0.0002868175506591797 time for calcul the mask position with numpy : 0.010983705520629883 nb_pixel_total : 719 time to create 1 rle with old method : 0.0009429454803466797 time for calcul the mask position with numpy : 0.010948657989501953 nb_pixel_total : 23 time to create 1 rle with old method : 0.00012946128845214844 time for calcul the mask position with numpy : 0.011153459548950195 nb_pixel_total : 15 time to create 1 rle with old method : 8.487701416015625e-05 time for calcul the mask position with numpy : 0.012204170227050781 nb_pixel_total : 18 time to create 1 rle with old method : 5.340576171875e-05 time for calcul the mask position with numpy : 0.011293888092041016 nb_pixel_total : 1867 time to create 1 rle with old method : 0.0023262500762939453 time for calcul the mask position with numpy : 0.010964632034301758 nb_pixel_total : 85 time to create 1 rle with old method : 0.00014090538024902344 time for calcul the mask position with numpy : 0.011307716369628906 nb_pixel_total : 258 time to create 1 rle with old method : 0.00040078163146972656 time for calcul the mask position with numpy : 0.011522769927978516 nb_pixel_total : 277 time to create 1 rle with old method : 0.00037670135498046875 time for calcul the mask position with numpy : 0.010648488998413086 nb_pixel_total : 63 time to create 1 rle with old method : 0.00012063980102539062 time for calcul the mask position with numpy : 0.010081052780151367 nb_pixel_total : 1375 time to create 1 rle with old method : 0.0018062591552734375 time for calcul the mask position with numpy : 0.011718034744262695 nb_pixel_total : 1632 time to create 1 rle with old method : 0.0021529197692871094 time for calcul the mask position with numpy : 0.010962963104248047 nb_pixel_total : 380 time to create 1 rle with old method : 0.0004813671112060547 time for calcul the mask position with numpy : 0.011165618896484375 nb_pixel_total : 84 time to create 1 rle with old method : 0.00012826919555664062 time for calcul the mask position with numpy : 0.010552406311035156 nb_pixel_total : 3 time to create 1 rle with old method : 4.267692565917969e-05 time for calcul the mask position with numpy : 0.010216236114501953 nb_pixel_total : 286 time to create 1 rle with old method : 0.0003845691680908203 time for calcul the mask position with numpy : 0.010150432586669922 nb_pixel_total : 333 time to create 1 rle with old method : 0.0004181861877441406 time for calcul the mask position with numpy : 0.01014852523803711 nb_pixel_total : 305 time to create 1 rle with old method : 0.0003898143768310547 time for calcul the mask position with numpy : 0.010360479354858398 nb_pixel_total : 550 time to create 1 rle with old method : 0.0006656646728515625 time for calcul the mask position with numpy : 0.010276556015014648 nb_pixel_total : 1695 time to create 1 rle with old method : 0.0020749568939208984 time for calcul the mask position with numpy : 0.010651826858520508 nb_pixel_total : 447 time to create 1 rle with old method : 0.0005655288696289062 time for calcul the mask position with numpy : 0.010771036148071289 nb_pixel_total : 681 time to create 1 rle with old method : 0.0008804798126220703 time for calcul the mask position with numpy : 0.011318683624267578 nb_pixel_total : 909 time to create 1 rle with old method : 0.0010967254638671875 time for calcul the mask position with numpy : 0.010677814483642578 nb_pixel_total : 1091 time to create 1 rle with old method : 0.001344919204711914 time for calcul the mask position with numpy : 0.011393308639526367 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004150867462158203 time for calcul the mask position with numpy : 0.012619495391845703 nb_pixel_total : 138 time to create 1 rle with old method : 0.0002396106719970703 time for calcul the mask position with numpy : 0.011128425598144531 nb_pixel_total : 1329 time to create 1 rle with old method : 0.0015544891357421875 time for calcul the mask position with numpy : 0.010788917541503906 nb_pixel_total : 146 time to create 1 rle with old method : 0.00019550323486328125 time for calcul the mask position with numpy : 0.015298128128051758 nb_pixel_total : 106904 time to create 1 rle with old method : 0.11816644668579102 time for calcul the mask position with numpy : 0.011561155319213867 nb_pixel_total : 11080 time to create 1 rle with old method : 0.012113332748413086 time for calcul the mask position with numpy : 0.011291742324829102 nb_pixel_total : 26 time to create 1 rle with old method : 5.841255187988281e-05 time for calcul the mask position with numpy : 0.007759571075439453 nb_pixel_total : 278 time to create 1 rle with old method : 0.0005538463592529297 time for calcul the mask position with numpy : 0.007225990295410156 nb_pixel_total : 754 time to create 1 rle with old method : 0.0009310245513916016 time for calcul the mask position with numpy : 0.006562709808349609 nb_pixel_total : 189 time to create 1 rle with old method : 0.00024819374084472656 time for calcul the mask position with numpy : 0.006448030471801758 nb_pixel_total : 332 time to create 1 rle with old method : 0.0004489421844482422 time for calcul the mask position with numpy : 0.0071964263916015625 nb_pixel_total : 294 time to create 1 rle with old method : 0.00040268898010253906 time for calcul the mask position with numpy : 0.01149296760559082 nb_pixel_total : 155 time to create 1 rle with old method : 0.00023245811462402344 time for calcul the mask position with numpy : 0.010971784591674805 nb_pixel_total : 127 time to create 1 rle with old method : 0.0001804828643798828 time for calcul the mask position with numpy : 0.010576725006103516 nb_pixel_total : 173 time to create 1 rle with old method : 0.00022292137145996094 time for calcul the mask position with numpy : 0.010589361190795898 nb_pixel_total : 673 time to create 1 rle with old method : 0.001056671142578125 time for calcul the mask position with numpy : 0.01074528694152832 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002982616424560547 time for calcul the mask position with numpy : 0.010718822479248047 nb_pixel_total : 9 time to create 1 rle with old method : 5.9604644775390625e-05 time for calcul the mask position with numpy : 0.011057615280151367 nb_pixel_total : 791 time to create 1 rle with old method : 0.000957489013671875 time for calcul the mask position with numpy : 0.011104583740234375 nb_pixel_total : 10100 time to create 1 rle with old method : 0.012339115142822266 time for calcul the mask position with numpy : 0.0077250003814697266 nb_pixel_total : 571 time to create 1 rle with old method : 0.0008230209350585938 time for calcul the mask position with numpy : 0.008112192153930664 nb_pixel_total : 23 time to create 1 rle with old method : 7.62939453125e-05 time for calcul the mask position with numpy : 0.006576061248779297 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006151199340820312 time for calcul the mask position with numpy : 0.00640869140625 nb_pixel_total : 208 time to create 1 rle with old method : 0.00028014183044433594 time for calcul the mask position with numpy : 0.006485462188720703 nb_pixel_total : 37 time to create 1 rle with old method : 9.012222290039062e-05 time for calcul the mask position with numpy : 0.0064961910247802734 nb_pixel_total : 431 time to create 1 rle with old method : 0.0005323886871337891 time for calcul the mask position with numpy : 0.006532430648803711 nb_pixel_total : 4 time to create 1 rle with old method : 3.266334533691406e-05 time for calcul the mask position with numpy : 0.006734132766723633 nb_pixel_total : 998 time to create 1 rle with old method : 0.001210927963256836 time for calcul the mask position with numpy : 0.006474733352661133 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005891323089599609 time for calcul the mask position with numpy : 0.006326436996459961 nb_pixel_total : 996 time to create 1 rle with old method : 0.0011138916015625 time for calcul the mask position with numpy : 0.0065729618072509766 nb_pixel_total : 710 time to create 1 rle with old method : 0.0008482933044433594 time for calcul the mask position with numpy : 0.007481098175048828 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005426406860351562 time for calcul the mask position with numpy : 0.007949113845825195 nb_pixel_total : 2179 time to create 1 rle with old method : 0.002548694610595703 time for calcul the mask position with numpy : 0.007333517074584961 nb_pixel_total : 494 time to create 1 rle with old method : 0.0006041526794433594 time for calcul the mask position with numpy : 0.0068781375885009766 nb_pixel_total : 362 time to create 1 rle with old method : 0.00045371055603027344 time for calcul the mask position with numpy : 0.006561279296875 nb_pixel_total : 2003 time to create 1 rle with old method : 0.0023398399353027344 time for calcul the mask position with numpy : 0.006520748138427734 nb_pixel_total : 19 time to create 1 rle with old method : 7.05718994140625e-05 time for calcul the mask position with numpy : 0.006472110748291016 nb_pixel_total : 1647 time to create 1 rle with old method : 0.001947641372680664 time for calcul the mask position with numpy : 0.00656890869140625 nb_pixel_total : 29 time to create 1 rle with old method : 0.00017642974853515625 time for calcul the mask position with numpy : 0.007164955139160156 nb_pixel_total : 146 time to create 1 rle with old method : 0.0002765655517578125 time for calcul the mask position with numpy : 0.007823944091796875 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002694129943847656 time for calcul the mask position with numpy : 0.00701904296875 nb_pixel_total : 803 time to create 1 rle with old method : 0.001020193099975586 time for calcul the mask position with numpy : 0.008460760116577148 nb_pixel_total : 1108 time to create 1 rle with old method : 0.0017695426940917969 time for calcul the mask position with numpy : 0.007032632827758789 nb_pixel_total : 101 time to create 1 rle with old method : 0.00019121170043945312 time for calcul the mask position with numpy : 0.006738185882568359 nb_pixel_total : 56 time to create 1 rle with old method : 0.00010180473327636719 time for calcul the mask position with numpy : 0.00652623176574707 nb_pixel_total : 86 time to create 1 rle with old method : 0.0001289844512939453 time for calcul the mask position with numpy : 0.006747007369995117 nb_pixel_total : 1332 time to create 1 rle with old method : 0.0015912055969238281 time for calcul the mask position with numpy : 0.006513118743896484 nb_pixel_total : 317 time to create 1 rle with old method : 0.0004057884216308594 time for calcul the mask position with numpy : 0.006808280944824219 nb_pixel_total : 109 time to create 1 rle with old method : 0.00016355514526367188 time for calcul the mask position with numpy : 0.0068738460540771484 nb_pixel_total : 466 time to create 1 rle with old method : 0.0005838871002197266 time for calcul the mask position with numpy : 0.006838798522949219 nb_pixel_total : 33 time to create 1 rle with old method : 0.00010967254638671875 time for calcul the mask position with numpy : 0.01133275032043457 nb_pixel_total : 127 time to create 1 rle with old method : 0.0003001689910888672 time for calcul the mask position with numpy : 0.006495952606201172 nb_pixel_total : 1332 time to create 1 rle with old method : 0.0015816688537597656 time for calcul the mask position with numpy : 0.006561994552612305 nb_pixel_total : 861 time to create 1 rle with old method : 0.001041412353515625 time for calcul the mask position with numpy : 0.0066225528717041016 nb_pixel_total : 9114 time to create 1 rle with old method : 0.01003122329711914 time for calcul the mask position with numpy : 0.006356954574584961 nb_pixel_total : 96 time to create 1 rle with old method : 0.00014257431030273438 time for calcul the mask position with numpy : 0.0062713623046875 nb_pixel_total : 144 time to create 1 rle with old method : 0.000217437744140625 time for calcul the mask position with numpy : 0.006373882293701172 nb_pixel_total : 934 time to create 1 rle with old method : 0.0011162757873535156 time for calcul the mask position with numpy : 0.006464242935180664 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007107257843017578 time for calcul the mask position with numpy : 0.006544828414916992 nb_pixel_total : 317 time to create 1 rle with old method : 0.00037217140197753906 time for calcul the mask position with numpy : 0.0063457489013671875 nb_pixel_total : 521 time to create 1 rle with old method : 0.0006356239318847656 time for calcul the mask position with numpy : 0.0065457820892333984 nb_pixel_total : 541 time to create 1 rle with old method : 0.0006985664367675781 time for calcul the mask position with numpy : 0.006301403045654297 nb_pixel_total : 9 time to create 1 rle with old method : 4.673004150390625e-05 time for calcul the mask position with numpy : 0.008437395095825195 nb_pixel_total : 68 time to create 1 rle with old method : 8.940696716308594e-05 time for calcul the mask position with numpy : 0.008491754531860352 nb_pixel_total : 25 time to create 1 rle with old method : 0.00011110305786132812 time for calcul the mask position with numpy : 0.008570671081542969 nb_pixel_total : 362 time to create 1 rle with old method : 0.0004138946533203125 time for calcul the mask position with numpy : 0.0084228515625 nb_pixel_total : 2 time to create 1 rle with old method : 1.0967254638671875e-05 time for calcul the mask position with numpy : 0.008412361145019531 nb_pixel_total : 5 time to create 1 rle with old method : 3.147125244140625e-05 time for calcul the mask position with numpy : 0.008418798446655273 nb_pixel_total : 694 time to create 1 rle with old method : 0.0007791519165039062 time for calcul the mask position with numpy : 0.008434057235717773 nb_pixel_total : 21 time to create 1 rle with old method : 4.267692565917969e-05 time for calcul the mask position with numpy : 0.008493423461914062 nb_pixel_total : 48 time to create 1 rle with old method : 7.939338684082031e-05 time for calcul the mask position with numpy : 0.00870370864868164 nb_pixel_total : 167 time to create 1 rle with old method : 0.00020933151245117188 time for calcul the mask position with numpy : 0.008526086807250977 nb_pixel_total : 954 time to create 1 rle with old method : 0.0011293888092041016 time for calcul the mask position with numpy : 0.008527278900146484 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0011746883392333984 time for calcul the mask position with numpy : 0.008558273315429688 nb_pixel_total : 789 time to create 1 rle with old method : 0.0009443759918212891 time for calcul the mask position with numpy : 0.008506536483764648 nb_pixel_total : 457 time to create 1 rle with old method : 0.0005471706390380859 time for calcul the mask position with numpy : 0.00847935676574707 nb_pixel_total : 21 time to create 1 rle with old method : 5.841255187988281e-05 time for calcul the mask position with numpy : 0.008465766906738281 nb_pixel_total : 380 time to create 1 rle with old method : 0.00045299530029296875 time for calcul the mask position with numpy : 0.008516073226928711 nb_pixel_total : 5226 time to create 1 rle with old method : 0.0060155391693115234 time for calcul the mask position with numpy : 0.0083770751953125 nb_pixel_total : 128 time to create 1 rle with old method : 0.00015306472778320312 time for calcul the mask position with numpy : 0.008424758911132812 nb_pixel_total : 279 time to create 1 rle with old method : 0.000335693359375 time for calcul the mask position with numpy : 0.008428335189819336 nb_pixel_total : 72 time to create 1 rle with old method : 9.512901306152344e-05 time for calcul the mask position with numpy : 0.008557796478271484 nb_pixel_total : 604 time to create 1 rle with old method : 0.0007259845733642578 create new chi : 2.5041377544403076 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.005220651626586914 batch 1 Loaded 198 chid ids of type : 4230 Number RLEs to save : 17452 TO DO : save crop sub photo not yet done ! save time : 1.1169004440307617 nb_obj : 187 nb_hashtags : 8 time to prepare the origin masks : 3.4330286979675293 time for calcul the mask position with numpy : 0.026847124099731445 nb_pixel_total : 1748413 time to create 1 rle with new method : 0.05803108215332031 time for calcul the mask position with numpy : 0.011478662490844727 nb_pixel_total : 1770 time to create 1 rle with old method : 0.0020515918731689453 time for calcul the mask position with numpy : 0.010813236236572266 nb_pixel_total : 139 time to create 1 rle with old method : 0.0002391338348388672 time for calcul the mask position with numpy : 0.010846138000488281 nb_pixel_total : 38 time to create 1 rle with old method : 7.295608520507812e-05 time for calcul the mask position with numpy : 0.010404586791992188 nb_pixel_total : 329 time to create 1 rle with old method : 0.0004138946533203125 time for calcul the mask position with numpy : 0.011065959930419922 nb_pixel_total : 583 time to create 1 rle with old method : 0.0008108615875244141 time for calcul the mask position with numpy : 0.0066988468170166016 nb_pixel_total : 438 time to create 1 rle with old method : 0.0006058216094970703 time for calcul the mask position with numpy : 0.010860681533813477 nb_pixel_total : 32 time to create 1 rle with old method : 8.916854858398438e-05 time for calcul the mask position with numpy : 0.010669708251953125 nb_pixel_total : 215 time to create 1 rle with old method : 0.0002963542938232422 time for calcul the mask position with numpy : 0.010922670364379883 nb_pixel_total : 71 time to create 1 rle with old method : 0.0001163482666015625 time for calcul the mask position with numpy : 0.010350942611694336 nb_pixel_total : 1717 time to create 1 rle with old method : 0.002177000045776367 time for calcul the mask position with numpy : 0.01058650016784668 nb_pixel_total : 32 time to create 1 rle with old method : 7.677078247070312e-05 time for calcul the mask position with numpy : 0.01046442985534668 nb_pixel_total : 229 time to create 1 rle with old method : 0.00031280517578125 time for calcul the mask position with numpy : 0.01076960563659668 nb_pixel_total : 81 time to create 1 rle with old method : 0.0001266002655029297 time for calcul the mask position with numpy : 0.01012563705444336 nb_pixel_total : 405 time to create 1 rle with old method : 0.0004994869232177734 time for calcul the mask position with numpy : 0.010286331176757812 nb_pixel_total : 175 time to create 1 rle with old method : 0.00024390220642089844 time for calcul the mask position with numpy : 0.010510683059692383 nb_pixel_total : 48 time to create 1 rle with old method : 8.678436279296875e-05 time for calcul the mask position with numpy : 0.010223627090454102 nb_pixel_total : 56 time to create 1 rle with old method : 0.00010585784912109375 time for calcul the mask position with numpy : 0.010256290435791016 nb_pixel_total : 21 time to create 1 rle with old method : 5.054473876953125e-05 time for calcul the mask position with numpy : 0.012016057968139648 nb_pixel_total : 51885 time to create 1 rle with old method : 0.056014060974121094 time for calcul the mask position with numpy : 0.010514974594116211 nb_pixel_total : 16 time to create 1 rle with old method : 4.887580871582031e-05 time for calcul the mask position with numpy : 0.010318756103515625 nb_pixel_total : 2437 time to create 1 rle with old method : 0.0028100013732910156 time for calcul the mask position with numpy : 0.010103225708007812 nb_pixel_total : 25 time to create 1 rle with old method : 5.53131103515625e-05 time for calcul the mask position with numpy : 0.01033926010131836 nb_pixel_total : 786 time to create 1 rle with old method : 0.0009534358978271484 time for calcul the mask position with numpy : 0.010259866714477539 nb_pixel_total : 940 time to create 1 rle with old method : 0.001096487045288086 time for calcul the mask position with numpy : 0.010307788848876953 nb_pixel_total : 11331 time to create 1 rle with old method : 0.012348175048828125 time for calcul the mask position with numpy : 0.01038217544555664 nb_pixel_total : 194 time to create 1 rle with old method : 0.0002493858337402344 time for calcul the mask position with numpy : 0.010144233703613281 nb_pixel_total : 4498 time to create 1 rle with old method : 0.005192279815673828 time for calcul the mask position with numpy : 0.009960412979125977 nb_pixel_total : 58 time to create 1 rle with old method : 0.000118255615234375 time for calcul the mask position with numpy : 0.009948253631591797 nb_pixel_total : 875 time to create 1 rle with old method : 0.0010385513305664062 time for calcul the mask position with numpy : 0.010138511657714844 nb_pixel_total : 373 time to create 1 rle with old method : 0.0004963874816894531 time for calcul the mask position with numpy : 0.01445627212524414 nb_pixel_total : 225 time to create 1 rle with old method : 0.00029778480529785156 time for calcul the mask position with numpy : 0.009885549545288086 nb_pixel_total : 399 time to create 1 rle with old method : 0.00045800209045410156 time for calcul the mask position with numpy : 0.009799957275390625 nb_pixel_total : 11352 time to create 1 rle with old method : 0.012912273406982422 time for calcul the mask position with numpy : 0.010051727294921875 nb_pixel_total : 65 time to create 1 rle with old method : 0.00014281272888183594 time for calcul the mask position with numpy : 0.010178089141845703 nb_pixel_total : 733 time to create 1 rle with old method : 0.0008933544158935547 time for calcul the mask position with numpy : 0.01042485237121582 nb_pixel_total : 20 time to create 1 rle with old method : 7.224082946777344e-05 time for calcul the mask position with numpy : 0.010228872299194336 nb_pixel_total : 141 time to create 1 rle with old method : 0.0002052783966064453 time for calcul the mask position with numpy : 0.010905742645263672 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002524852752685547 time for calcul the mask position with numpy : 0.010809659957885742 nb_pixel_total : 1222 time to create 1 rle with old method : 0.0014715194702148438 time for calcul the mask position with numpy : 0.010298013687133789 nb_pixel_total : 1013 time to create 1 rle with old method : 0.001241445541381836 time for calcul the mask position with numpy : 0.010294198989868164 nb_pixel_total : 1178 time to create 1 rle with old method : 0.0015020370483398438 time for calcul the mask position with numpy : 0.010378599166870117 nb_pixel_total : 65 time to create 1 rle with old method : 0.00010752677917480469 time for calcul the mask position with numpy : 0.010428667068481445 nb_pixel_total : 604 time to create 1 rle with old method : 0.0007712841033935547 time for calcul the mask position with numpy : 0.006338596343994141 nb_pixel_total : 32 time to create 1 rle with old method : 5.9604644775390625e-05 time for calcul the mask position with numpy : 0.006144523620605469 nb_pixel_total : 221 time to create 1 rle with old method : 0.00028228759765625 time for calcul the mask position with numpy : 0.006326436996459961 nb_pixel_total : 778 time to create 1 rle with old method : 0.0009527206420898438 time for calcul the mask position with numpy : 0.006299734115600586 nb_pixel_total : 1243 time to create 1 rle with old method : 0.0014045238494873047 time for calcul the mask position with numpy : 0.006293058395385742 nb_pixel_total : 870 time to create 1 rle with old method : 0.001070261001586914 time for calcul the mask position with numpy : 0.006163120269775391 nb_pixel_total : 312 time to create 1 rle with old method : 0.0004379749298095703 time for calcul the mask position with numpy : 0.009728670120239258 nb_pixel_total : 13768 time to create 1 rle with old method : 0.01647353172302246 time for calcul the mask position with numpy : 0.010092020034790039 nb_pixel_total : 616 time to create 1 rle with old method : 0.0007481575012207031 time for calcul the mask position with numpy : 0.010151863098144531 nb_pixel_total : 3077 time to create 1 rle with old method : 0.003466367721557617 time for calcul the mask position with numpy : 0.00999140739440918 nb_pixel_total : 291 time to create 1 rle with old method : 0.0003886222839355469 time for calcul the mask position with numpy : 0.010026931762695312 nb_pixel_total : 196 time to create 1 rle with old method : 0.0002689361572265625 time for calcul the mask position with numpy : 0.010050535202026367 nb_pixel_total : 611 time to create 1 rle with old method : 0.0007832050323486328 time for calcul the mask position with numpy : 0.010023117065429688 nb_pixel_total : 427 time to create 1 rle with old method : 0.0005538463592529297 time for calcul the mask position with numpy : 0.009881973266601562 nb_pixel_total : 948 time to create 1 rle with old method : 0.0010876655578613281 time for calcul the mask position with numpy : 0.009840011596679688 nb_pixel_total : 25 time to create 1 rle with old method : 9.512901306152344e-05 time for calcul the mask position with numpy : 0.009908914566040039 nb_pixel_total : 104 time to create 1 rle with old method : 0.00014162063598632812 time for calcul the mask position with numpy : 0.010717391967773438 nb_pixel_total : 769 time to create 1 rle with old method : 0.0010833740234375 time for calcul the mask position with numpy : 0.011514425277709961 nb_pixel_total : 18734 time to create 1 rle with old method : 0.021203279495239258 time for calcul the mask position with numpy : 0.010805845260620117 nb_pixel_total : 2845 time to create 1 rle with old method : 0.0032966136932373047 time for calcul the mask position with numpy : 0.010378837585449219 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001590251922607422 time for calcul the mask position with numpy : 0.00643157958984375 nb_pixel_total : 1334 time to create 1 rle with old method : 0.0016167163848876953 time for calcul the mask position with numpy : 0.006092548370361328 nb_pixel_total : 817 time to create 1 rle with old method : 0.0010352134704589844 time for calcul the mask position with numpy : 0.006107807159423828 nb_pixel_total : 117 time to create 1 rle with old method : 0.00016307830810546875 time for calcul the mask position with numpy : 0.006209373474121094 nb_pixel_total : 1539 time to create 1 rle with old method : 0.0018801689147949219 time for calcul the mask position with numpy : 0.006195783615112305 nb_pixel_total : 110 time to create 1 rle with old method : 0.00016546249389648438 time for calcul the mask position with numpy : 0.006319999694824219 nb_pixel_total : 878 time to create 1 rle with old method : 0.0010499954223632812 time for calcul the mask position with numpy : 0.006766080856323242 nb_pixel_total : 431 time to create 1 rle with old method : 0.0005767345428466797 time for calcul the mask position with numpy : 0.006296396255493164 nb_pixel_total : 136 time to create 1 rle with old method : 0.00020456314086914062 time for calcul the mask position with numpy : 0.006304264068603516 nb_pixel_total : 334 time to create 1 rle with old method : 0.00045371055603027344 time for calcul the mask position with numpy : 0.0064465999603271484 nb_pixel_total : 311 time to create 1 rle with old method : 0.00040340423583984375 time for calcul the mask position with numpy : 0.0062198638916015625 nb_pixel_total : 308 time to create 1 rle with old method : 0.0004010200500488281 time for calcul the mask position with numpy : 0.006166696548461914 nb_pixel_total : 2624 time to create 1 rle with old method : 0.003125429153442383 time for calcul the mask position with numpy : 0.006278038024902344 nb_pixel_total : 393 time to create 1 rle with old method : 0.000518798828125 time for calcul the mask position with numpy : 0.006248950958251953 nb_pixel_total : 9 time to create 1 rle with old method : 4.935264587402344e-05 time for calcul the mask position with numpy : 0.006204366683959961 nb_pixel_total : 719 time to create 1 rle with old method : 0.0008821487426757812 time for calcul the mask position with numpy : 0.006842374801635742 nb_pixel_total : 892 time to create 1 rle with old method : 0.0010867118835449219 time for calcul the mask position with numpy : 0.006268978118896484 nb_pixel_total : 673 time to create 1 rle with old method : 0.0008053779602050781 time for calcul the mask position with numpy : 0.006311655044555664 nb_pixel_total : 249 time to create 1 rle with old method : 0.0003211498260498047 time for calcul the mask position with numpy : 0.005999326705932617 nb_pixel_total : 305 time to create 1 rle with old method : 0.00036454200744628906 time for calcul the mask position with numpy : 0.005968570709228516 nb_pixel_total : 120 time to create 1 rle with old method : 0.0001628398895263672 time for calcul the mask position with numpy : 0.006090879440307617 nb_pixel_total : 1313 time to create 1 rle with old method : 0.0015647411346435547 time for calcul the mask position with numpy : 0.009799003601074219 nb_pixel_total : 283 time to create 1 rle with old method : 0.00035119056701660156 time for calcul the mask position with numpy : 0.006801605224609375 nb_pixel_total : 106984 time to create 1 rle with old method : 0.11432266235351562 time for calcul the mask position with numpy : 0.006655693054199219 nb_pixel_total : 140 time to create 1 rle with old method : 0.0002067089080810547 time for calcul the mask position with numpy : 0.006015777587890625 nb_pixel_total : 105 time to create 1 rle with old method : 0.00015878677368164062 time for calcul the mask position with numpy : 0.006052255630493164 nb_pixel_total : 8087 time to create 1 rle with old method : 0.009033203125 time for calcul the mask position with numpy : 0.005789041519165039 nb_pixel_total : 1309 time to create 1 rle with old method : 0.0014672279357910156 time for calcul the mask position with numpy : 0.005913496017456055 nb_pixel_total : 301 time to create 1 rle with old method : 0.0003712177276611328 time for calcul the mask position with numpy : 0.005859851837158203 nb_pixel_total : 1546 time to create 1 rle with old method : 0.0017747879028320312 time for calcul the mask position with numpy : 0.0058209896087646484 nb_pixel_total : 191 time to create 1 rle with old method : 0.0002396106719970703 time for calcul the mask position with numpy : 0.005845546722412109 nb_pixel_total : 218 time to create 1 rle with old method : 0.00028514862060546875 time for calcul the mask position with numpy : 0.006104946136474609 nb_pixel_total : 399 time to create 1 rle with old method : 0.0005013942718505859 time for calcul the mask position with numpy : 0.006133556365966797 nb_pixel_total : 101 time to create 1 rle with old method : 0.00016427040100097656 time for calcul the mask position with numpy : 0.006321907043457031 nb_pixel_total : 239 time to create 1 rle with old method : 0.000347137451171875 time for calcul the mask position with numpy : 0.006157875061035156 nb_pixel_total : 2445 time to create 1 rle with old method : 0.0028171539306640625 time for calcul the mask position with numpy : 0.006398677825927734 nb_pixel_total : 10 time to create 1 rle with old method : 5.8650970458984375e-05 time for calcul the mask position with numpy : 0.006112098693847656 nb_pixel_total : 656 time to create 1 rle with old method : 0.0007917881011962891 time for calcul the mask position with numpy : 0.0064203739166259766 nb_pixel_total : 146 time to create 1 rle with old method : 0.00021409988403320312 time for calcul the mask position with numpy : 0.006145000457763672 nb_pixel_total : 602 time to create 1 rle with old method : 0.0008091926574707031 time for calcul the mask position with numpy : 0.006256818771362305 nb_pixel_total : 737 time to create 1 rle with old method : 0.0009102821350097656 time for calcul the mask position with numpy : 0.006209611892700195 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003180503845214844 time for calcul the mask position with numpy : 0.006528377532958984 nb_pixel_total : 350 time to create 1 rle with old method : 0.0005605220794677734 time for calcul the mask position with numpy : 0.008258581161499023 nb_pixel_total : 21 time to create 1 rle with old method : 0.00013709068298339844 time for calcul the mask position with numpy : 0.0070154666900634766 nb_pixel_total : 563 time to create 1 rle with old method : 0.0007102489471435547 time for calcul the mask position with numpy : 0.006439924240112305 nb_pixel_total : 540 time to create 1 rle with old method : 0.0007660388946533203 time for calcul the mask position with numpy : 0.0068035125732421875 nb_pixel_total : 202 time to create 1 rle with old method : 0.00029015541076660156 time for calcul the mask position with numpy : 0.006422281265258789 nb_pixel_total : 6 time to create 1 rle with old method : 6.771087646484375e-05 time for calcul the mask position with numpy : 0.007537364959716797 nb_pixel_total : 307 time to create 1 rle with old method : 0.00043010711669921875 time for calcul the mask position with numpy : 0.006806850433349609 nb_pixel_total : 437 time to create 1 rle with old method : 0.0005211830139160156 time for calcul the mask position with numpy : 0.006181001663208008 nb_pixel_total : 8 time to create 1 rle with old method : 4.291534423828125e-05 time for calcul the mask position with numpy : 0.006036996841430664 nb_pixel_total : 13 time to create 1 rle with old method : 5.078315734863281e-05 time for calcul the mask position with numpy : 0.006216764450073242 nb_pixel_total : 249 time to create 1 rle with old method : 0.00038051605224609375 time for calcul the mask position with numpy : 0.00649714469909668 nb_pixel_total : 1519 time to create 1 rle with old method : 0.0017817020416259766 time for calcul the mask position with numpy : 0.006192922592163086 nb_pixel_total : 7 time to create 1 rle with old method : 3.361701965332031e-05 time for calcul the mask position with numpy : 0.006453275680541992 nb_pixel_total : 465 time to create 1 rle with old method : 0.0005869865417480469 time for calcul the mask position with numpy : 0.006315469741821289 nb_pixel_total : 1028 time to create 1 rle with old method : 0.0012443065643310547 time for calcul the mask position with numpy : 0.0061337947845458984 nb_pixel_total : 17 time to create 1 rle with old method : 7.295608520507812e-05 time for calcul the mask position with numpy : 0.006328582763671875 nb_pixel_total : 420 time to create 1 rle with old method : 0.0005135536193847656 time for calcul the mask position with numpy : 0.006102085113525391 nb_pixel_total : 555 time to create 1 rle with old method : 0.0006890296936035156 time for calcul the mask position with numpy : 0.006009578704833984 nb_pixel_total : 219 time to create 1 rle with old method : 0.00029349327087402344 time for calcul the mask position with numpy : 0.006196022033691406 nb_pixel_total : 62 time to create 1 rle with old method : 0.00016379356384277344 time for calcul the mask position with numpy : 0.005953550338745117 nb_pixel_total : 56 time to create 1 rle with old method : 0.00015306472778320312 time for calcul the mask position with numpy : 0.009657144546508789 nb_pixel_total : 426 time to create 1 rle with old method : 0.0005342960357666016 time for calcul the mask position with numpy : 0.006186008453369141 nb_pixel_total : 18 time to create 1 rle with old method : 9.083747863769531e-05 time for calcul the mask position with numpy : 0.006375789642333984 nb_pixel_total : 4520 time to create 1 rle with old method : 0.005429983139038086 time for calcul the mask position with numpy : 0.00623011589050293 nb_pixel_total : 43 time to create 1 rle with old method : 0.00011086463928222656 time for calcul the mask position with numpy : 0.006103992462158203 nb_pixel_total : 177 time to create 1 rle with old method : 0.00029468536376953125 time for calcul the mask position with numpy : 0.006648540496826172 nb_pixel_total : 189 time to create 1 rle with old method : 0.0003840923309326172 time for calcul the mask position with numpy : 0.006674051284790039 nb_pixel_total : 889 time to create 1 rle with old method : 0.0015866756439208984 time for calcul the mask position with numpy : 0.006776571273803711 nb_pixel_total : 1151 time to create 1 rle with old method : 0.0019371509552001953 time for calcul the mask position with numpy : 0.006551265716552734 nb_pixel_total : 194 time to create 1 rle with old method : 0.0003657341003417969 time for calcul the mask position with numpy : 0.006647586822509766 nb_pixel_total : 14 time to create 1 rle with old method : 9.417533874511719e-05 time for calcul the mask position with numpy : 0.006625175476074219 nb_pixel_total : 978 time to create 1 rle with old method : 0.001661539077758789 time for calcul the mask position with numpy : 0.006642580032348633 nb_pixel_total : 105 time to create 1 rle with old method : 0.00028443336486816406 time for calcul the mask position with numpy : 0.006852388381958008 nb_pixel_total : 319 time to create 1 rle with old method : 0.0005688667297363281 time for calcul the mask position with numpy : 0.006728649139404297 nb_pixel_total : 15 time to create 1 rle with old method : 8.893013000488281e-05 time for calcul the mask position with numpy : 0.007047414779663086 nb_pixel_total : 1319 time to create 1 rle with old method : 0.002208709716796875 time for calcul the mask position with numpy : 0.0070307254791259766 nb_pixel_total : 483 time to create 1 rle with old method : 0.0008540153503417969 time for calcul the mask position with numpy : 0.0068111419677734375 nb_pixel_total : 9 time to create 1 rle with old method : 7.581710815429688e-05 time for calcul the mask position with numpy : 0.007006168365478516 nb_pixel_total : 1624 time to create 1 rle with old method : 0.0027348995208740234 time for calcul the mask position with numpy : 0.007592201232910156 nb_pixel_total : 11 time to create 1 rle with old method : 5.888938903808594e-05 time for calcul the mask position with numpy : 0.007578372955322266 nb_pixel_total : 800 time to create 1 rle with old method : 0.0014188289642333984 time for calcul the mask position with numpy : 0.007333278656005859 nb_pixel_total : 4456 time to create 1 rle with old method : 0.007342100143432617 time for calcul the mask position with numpy : 0.008500099182128906 nb_pixel_total : 59 time to create 1 rle with old method : 0.00024247169494628906 time for calcul the mask position with numpy : 0.007173061370849609 nb_pixel_total : 15 time to create 1 rle with old method : 8.940696716308594e-05 time for calcul the mask position with numpy : 0.0065996646881103516 nb_pixel_total : 4 time to create 1 rle with old method : 4.76837158203125e-05 time for calcul the mask position with numpy : 0.006333589553833008 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002682209014892578 time for calcul the mask position with numpy : 0.006144046783447266 nb_pixel_total : 18 time to create 1 rle with old method : 6.437301635742188e-05 time for calcul the mask position with numpy : 0.006369113922119141 nb_pixel_total : 972 time to create 1 rle with old method : 0.0011143684387207031 time for calcul the mask position with numpy : 0.006705284118652344 nb_pixel_total : 26 time to create 1 rle with old method : 6.723403930664062e-05 time for calcul the mask position with numpy : 0.0061798095703125 nb_pixel_total : 664 time to create 1 rle with old method : 0.0007588863372802734 time for calcul the mask position with numpy : 0.0062062740325927734 nb_pixel_total : 286 time to create 1 rle with old method : 0.0003592967987060547 time for calcul the mask position with numpy : 0.006830930709838867 nb_pixel_total : 769 time to create 1 rle with old method : 0.0009176731109619141 time for calcul the mask position with numpy : 0.006168842315673828 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006275177001953125 time for calcul the mask position with numpy : 0.0060999393463134766 nb_pixel_total : 12 time to create 1 rle with old method : 5.5789947509765625e-05 time for calcul the mask position with numpy : 0.006124258041381836 nb_pixel_total : 496 time to create 1 rle with old method : 0.0005927085876464844 time for calcul the mask position with numpy : 0.0061419010162353516 nb_pixel_total : 72 time to create 1 rle with old method : 0.00012946128845214844 time for calcul the mask position with numpy : 0.006399869918823242 nb_pixel_total : 383 time to create 1 rle with old method : 0.0005183219909667969 time for calcul the mask position with numpy : 0.00675654411315918 nb_pixel_total : 24 time to create 1 rle with old method : 6.914138793945312e-05 time for calcul the mask position with numpy : 0.006533384323120117 nb_pixel_total : 692 time to create 1 rle with old method : 0.0008399486541748047 time for calcul the mask position with numpy : 0.006520986557006836 nb_pixel_total : 2 time to create 1 rle with old method : 2.1696090698242188e-05 time for calcul the mask position with numpy : 0.006515979766845703 nb_pixel_total : 121 time to create 1 rle with old method : 0.00022912025451660156 time for calcul the mask position with numpy : 0.0065975189208984375 nb_pixel_total : 438 time to create 1 rle with old method : 0.0005459785461425781 time for calcul the mask position with numpy : 0.006297111511230469 nb_pixel_total : 196 time to create 1 rle with old method : 0.00026035308837890625 time for calcul the mask position with numpy : 0.0065495967864990234 nb_pixel_total : 6 time to create 1 rle with old method : 3.838539123535156e-05 time for calcul the mask position with numpy : 0.0061588287353515625 nb_pixel_total : 1843 time to create 1 rle with old method : 0.002136707305908203 time for calcul the mask position with numpy : 0.0062160491943359375 nb_pixel_total : 14 time to create 1 rle with old method : 7.557868957519531e-05 time for calcul the mask position with numpy : 0.0061511993408203125 nb_pixel_total : 996 time to create 1 rle with old method : 0.001138925552368164 time for calcul the mask position with numpy : 0.006129264831542969 nb_pixel_total : 9 time to create 1 rle with old method : 6.771087646484375e-05 time for calcul the mask position with numpy : 0.006215810775756836 nb_pixel_total : 436 time to create 1 rle with old method : 0.0005991458892822266 time for calcul the mask position with numpy : 0.006256818771362305 nb_pixel_total : 26 time to create 1 rle with old method : 0.00010967254638671875 time for calcul the mask position with numpy : 0.006173372268676758 nb_pixel_total : 496 time to create 1 rle with old method : 0.0006177425384521484 time for calcul the mask position with numpy : 0.0062673091888427734 nb_pixel_total : 207 time to create 1 rle with old method : 0.00030422210693359375 time for calcul the mask position with numpy : 0.008075952529907227 nb_pixel_total : 483 time to create 1 rle with old method : 0.0005950927734375 time for calcul the mask position with numpy : 0.008065462112426758 nb_pixel_total : 3813 time to create 1 rle with old method : 0.0047359466552734375 time for calcul the mask position with numpy : 0.006991386413574219 nb_pixel_total : 496 time to create 1 rle with old method : 0.0006136894226074219 time for calcul the mask position with numpy : 0.010793209075927734 nb_pixel_total : 117 time to create 1 rle with old method : 0.00017070770263671875 time for calcul the mask position with numpy : 0.006217241287231445 nb_pixel_total : 1301 time to create 1 rle with old method : 0.0014574527740478516 time for calcul the mask position with numpy : 0.0061643123626708984 nb_pixel_total : 281 time to create 1 rle with old method : 0.00036406517028808594 time for calcul the mask position with numpy : 0.006348133087158203 nb_pixel_total : 1499 time to create 1 rle with old method : 0.0017330646514892578 time for calcul the mask position with numpy : 0.006146669387817383 nb_pixel_total : 421 time to create 1 rle with old method : 0.0005736351013183594 time for calcul the mask position with numpy : 0.008432865142822266 nb_pixel_total : 3 time to create 1 rle with old method : 4.7206878662109375e-05 time for calcul the mask position with numpy : 0.008394002914428711 nb_pixel_total : 672 time to create 1 rle with old method : 0.0007710456848144531 time for calcul the mask position with numpy : 0.008393526077270508 nb_pixel_total : 5 time to create 1 rle with old method : 3.457069396972656e-05 create new chi : 1.920365333557129 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.004276275634765625 batch 1 Loaded 210 chid ids of type : 4230 Number RLEs to save : 14774 TO DO : save crop sub photo not yet done ! save time : 0.8737952709197998 nb_obj : 172 nb_hashtags : 7 time to prepare the origin masks : 2.5859131813049316 time for calcul the mask position with numpy : 0.03520774841308594 nb_pixel_total : 1775994 time to create 1 rle with new method : 0.0639500617980957 time for calcul the mask position with numpy : 0.015688657760620117 nb_pixel_total : 1603 time to create 1 rle with old method : 0.0032727718353271484 time for calcul the mask position with numpy : 0.014686107635498047 nb_pixel_total : 1198 time to create 1 rle with old method : 0.00251007080078125 time for calcul the mask position with numpy : 0.014148235321044922 nb_pixel_total : 2025 time to create 1 rle with old method : 0.004124641418457031 time for calcul the mask position with numpy : 0.014309883117675781 nb_pixel_total : 87 time to create 1 rle with old method : 0.00028705596923828125 time for calcul the mask position with numpy : 0.014139652252197266 nb_pixel_total : 777 time to create 1 rle with old method : 0.0015766620635986328 time for calcul the mask position with numpy : 0.013756990432739258 nb_pixel_total : 322 time to create 1 rle with old method : 0.0004928112030029297 time for calcul the mask position with numpy : 0.012737751007080078 nb_pixel_total : 228 time to create 1 rle with old method : 0.0004146099090576172 time for calcul the mask position with numpy : 0.01368403434753418 nb_pixel_total : 401 time to create 1 rle with old method : 0.0005669593811035156 time for calcul the mask position with numpy : 0.012195587158203125 nb_pixel_total : 9 time to create 1 rle with old method : 6.961822509765625e-05 time for calcul the mask position with numpy : 0.01660943031311035 nb_pixel_total : 476 time to create 1 rle with old method : 0.0005848407745361328 time for calcul the mask position with numpy : 0.010376930236816406 nb_pixel_total : 63 time to create 1 rle with old method : 0.00010085105895996094 time for calcul the mask position with numpy : 0.01043391227722168 nb_pixel_total : 274 time to create 1 rle with old method : 0.00034332275390625 time for calcul the mask position with numpy : 0.012035369873046875 nb_pixel_total : 68 time to create 1 rle with old method : 0.00011348724365234375 time for calcul the mask position with numpy : 0.010629415512084961 nb_pixel_total : 29 time to create 1 rle with old method : 6.866455078125e-05 time for calcul the mask position with numpy : 0.011065959930419922 nb_pixel_total : 27 time to create 1 rle with old method : 7.104873657226562e-05 time for calcul the mask position with numpy : 0.00635981559753418 nb_pixel_total : 101 time to create 1 rle with old method : 0.00015664100646972656 time for calcul the mask position with numpy : 0.006232023239135742 nb_pixel_total : 24 time to create 1 rle with old method : 5.4836273193359375e-05 time for calcul the mask position with numpy : 0.006551980972290039 nb_pixel_total : 2673 time to create 1 rle with old method : 0.0031311511993408203 time for calcul the mask position with numpy : 0.006292104721069336 nb_pixel_total : 51 time to create 1 rle with old method : 0.0001380443572998047 time for calcul the mask position with numpy : 0.0062067508697509766 nb_pixel_total : 16 time to create 1 rle with old method : 4.2438507080078125e-05 time for calcul the mask position with numpy : 0.006218910217285156 nb_pixel_total : 22 time to create 1 rle with old method : 0.00010466575622558594 time for calcul the mask position with numpy : 0.006358146667480469 nb_pixel_total : 17332 time to create 1 rle with old method : 0.019047021865844727 time for calcul the mask position with numpy : 0.006249427795410156 nb_pixel_total : 920 time to create 1 rle with old method : 0.0010983943939208984 time for calcul the mask position with numpy : 0.010686159133911133 nb_pixel_total : 9723 time to create 1 rle with old method : 0.01107645034790039 time for calcul the mask position with numpy : 0.010739564895629883 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002460479736328125 time for calcul the mask position with numpy : 0.01058816909790039 nb_pixel_total : 2157 time to create 1 rle with old method : 0.002698183059692383 time for calcul the mask position with numpy : 0.010713338851928711 nb_pixel_total : 835 time to create 1 rle with old method : 0.0010068416595458984 time for calcul the mask position with numpy : 0.010619401931762695 nb_pixel_total : 377 time to create 1 rle with old method : 0.0005943775177001953 time for calcul the mask position with numpy : 0.010496377944946289 nb_pixel_total : 3499 time to create 1 rle with old method : 0.0040738582611083984 time for calcul the mask position with numpy : 0.010301589965820312 nb_pixel_total : 4 time to create 1 rle with old method : 4.1484832763671875e-05 time for calcul the mask position with numpy : 0.010537862777709961 nb_pixel_total : 415 time to create 1 rle with old method : 0.0005538463592529297 time for calcul the mask position with numpy : 0.010683774948120117 nb_pixel_total : 6314 time to create 1 rle with old method : 0.007881879806518555 time for calcul the mask position with numpy : 0.014447689056396484 nb_pixel_total : 11050 time to create 1 rle with old method : 0.013550043106079102 time for calcul the mask position with numpy : 0.011690616607666016 nb_pixel_total : 44 time to create 1 rle with old method : 0.00013899803161621094 time for calcul the mask position with numpy : 0.011725664138793945 nb_pixel_total : 900 time to create 1 rle with old method : 0.0011506080627441406 time for calcul the mask position with numpy : 0.011978864669799805 nb_pixel_total : 303 time to create 1 rle with old method : 0.0004055500030517578 time for calcul the mask position with numpy : 0.01528024673461914 nb_pixel_total : 1202 time to create 1 rle with old method : 0.0017044544219970703 time for calcul the mask position with numpy : 0.011616945266723633 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006096363067626953 time for calcul the mask position with numpy : 0.010787248611450195 nb_pixel_total : 83 time to create 1 rle with old method : 0.000171661376953125 time for calcul the mask position with numpy : 0.011276960372924805 nb_pixel_total : 731 time to create 1 rle with old method : 0.0009796619415283203 time for calcul the mask position with numpy : 0.011601448059082031 nb_pixel_total : 186 time to create 1 rle with old method : 0.0003008842468261719 time for calcul the mask position with numpy : 0.011449098587036133 nb_pixel_total : 1262 time to create 1 rle with old method : 0.0018270015716552734 time for calcul the mask position with numpy : 0.011675119400024414 nb_pixel_total : 964 time to create 1 rle with old method : 0.0013463497161865234 time for calcul the mask position with numpy : 0.01220393180847168 nb_pixel_total : 13020 time to create 1 rle with old method : 0.017049789428710938 time for calcul the mask position with numpy : 0.011303186416625977 nb_pixel_total : 562 time to create 1 rle with old method : 0.0007843971252441406 time for calcul the mask position with numpy : 0.01186370849609375 nb_pixel_total : 369 time to create 1 rle with old method : 0.0005831718444824219 time for calcul the mask position with numpy : 0.010983705520629883 nb_pixel_total : 469 time to create 1 rle with old method : 0.0005979537963867188 time for calcul the mask position with numpy : 0.011494159698486328 nb_pixel_total : 13 time to create 1 rle with old method : 6.532669067382812e-05 time for calcul the mask position with numpy : 0.011777639389038086 nb_pixel_total : 14433 time to create 1 rle with old method : 0.017093420028686523 time for calcul the mask position with numpy : 0.01140737533569336 nb_pixel_total : 2205 time to create 1 rle with old method : 0.002883434295654297 time for calcul the mask position with numpy : 0.012344121932983398 nb_pixel_total : 507 time to create 1 rle with old method : 0.0009112358093261719 time for calcul the mask position with numpy : 0.012999296188354492 nb_pixel_total : 27 time to create 1 rle with old method : 0.00012683868408203125 time for calcul the mask position with numpy : 0.013041973114013672 nb_pixel_total : 534 time to create 1 rle with old method : 0.0008246898651123047 time for calcul the mask position with numpy : 0.012471914291381836 nb_pixel_total : 483 time to create 1 rle with old method : 0.0007970333099365234 time for calcul the mask position with numpy : 0.012211084365844727 nb_pixel_total : 26 time to create 1 rle with old method : 0.00011396408081054688 time for calcul the mask position with numpy : 0.012766599655151367 nb_pixel_total : 935 time to create 1 rle with old method : 0.0014109611511230469 time for calcul the mask position with numpy : 0.012274026870727539 nb_pixel_total : 114 time to create 1 rle with old method : 0.00020623207092285156 time for calcul the mask position with numpy : 0.012224912643432617 nb_pixel_total : 1002 time to create 1 rle with old method : 0.0014884471893310547 time for calcul the mask position with numpy : 0.012580633163452148 nb_pixel_total : 707 time to create 1 rle with old method : 0.000980377197265625 time for calcul the mask position with numpy : 0.01683807373046875 nb_pixel_total : 219 time to create 1 rle with old method : 0.0003349781036376953 time for calcul the mask position with numpy : 0.011578559875488281 nb_pixel_total : 1912 time to create 1 rle with old method : 0.002339601516723633 time for calcul the mask position with numpy : 0.012116193771362305 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003540515899658203 time for calcul the mask position with numpy : 0.011851310729980469 nb_pixel_total : 56 time to create 1 rle with old method : 0.00011205673217773438 time for calcul the mask position with numpy : 0.011596918106079102 nb_pixel_total : 1 time to create 1 rle with old method : 3.552436828613281e-05 time for calcul the mask position with numpy : 0.012449026107788086 nb_pixel_total : 1682 time to create 1 rle with old method : 0.0025708675384521484 time for calcul the mask position with numpy : 0.011545181274414062 nb_pixel_total : 708 time to create 1 rle with old method : 0.0009016990661621094 time for calcul the mask position with numpy : 0.011942386627197266 nb_pixel_total : 1273 time to create 1 rle with old method : 0.001901388168334961 time for calcul the mask position with numpy : 0.0115509033203125 nb_pixel_total : 758 time to create 1 rle with old method : 0.0009286403656005859 time for calcul the mask position with numpy : 0.012282848358154297 nb_pixel_total : 198 time to create 1 rle with old method : 0.00036978721618652344 time for calcul the mask position with numpy : 0.011256217956542969 nb_pixel_total : 1452 time to create 1 rle with old method : 0.0017209053039550781 time for calcul the mask position with numpy : 0.010873556137084961 nb_pixel_total : 115 time to create 1 rle with old method : 0.00018978118896484375 time for calcul the mask position with numpy : 0.011433124542236328 nb_pixel_total : 796 time to create 1 rle with old method : 0.0010890960693359375 time for calcul the mask position with numpy : 0.011795997619628906 nb_pixel_total : 490 time to create 1 rle with old method : 0.0007145404815673828 time for calcul the mask position with numpy : 0.01060628890991211 nb_pixel_total : 1010 time to create 1 rle with old method : 0.0014386177062988281 time for calcul the mask position with numpy : 0.01105642318725586 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005664825439453125 time for calcul the mask position with numpy : 0.010383367538452148 nb_pixel_total : 3753 time to create 1 rle with old method : 0.0045015811920166016 time for calcul the mask position with numpy : 0.010489940643310547 nb_pixel_total : 3068 time to create 1 rle with old method : 0.0037279129028320312 time for calcul the mask position with numpy : 0.010826349258422852 nb_pixel_total : 33 time to create 1 rle with old method : 0.0001800060272216797 time for calcul the mask position with numpy : 0.011519432067871094 nb_pixel_total : 433 time to create 1 rle with old method : 0.0006022453308105469 time for calcul the mask position with numpy : 0.011008977890014648 nb_pixel_total : 701 time to create 1 rle with old method : 0.0009081363677978516 time for calcul the mask position with numpy : 0.010350465774536133 nb_pixel_total : 122 time to create 1 rle with old method : 0.0001857280731201172 time for calcul the mask position with numpy : 0.010528326034545898 nb_pixel_total : 915 time to create 1 rle with old method : 0.001107931137084961 time for calcul the mask position with numpy : 0.010245084762573242 nb_pixel_total : 26 time to create 1 rle with old method : 9.465217590332031e-05 time for calcul the mask position with numpy : 0.010165691375732422 nb_pixel_total : 295 time to create 1 rle with old method : 0.00037598609924316406 time for calcul the mask position with numpy : 0.010384321212768555 nb_pixel_total : 192 time to create 1 rle with old method : 0.00029468536376953125 time for calcul the mask position with numpy : 0.010349273681640625 nb_pixel_total : 1123 time to create 1 rle with old method : 0.0012810230255126953 time for calcul the mask position with numpy : 0.01519775390625 nb_pixel_total : 106786 time to create 1 rle with old method : 0.11671018600463867 time for calcul the mask position with numpy : 0.0114898681640625 nb_pixel_total : 107 time to create 1 rle with old method : 0.0005130767822265625 time for calcul the mask position with numpy : 0.010829687118530273 nb_pixel_total : 140 time to create 1 rle with old method : 0.00019478797912597656 time for calcul the mask position with numpy : 0.010701179504394531 nb_pixel_total : 1712 time to create 1 rle with old method : 0.0020270347595214844 time for calcul the mask position with numpy : 0.010648727416992188 nb_pixel_total : 1696 time to create 1 rle with old method : 0.00203704833984375 time for calcul the mask position with numpy : 0.010440587997436523 nb_pixel_total : 87 time to create 1 rle with old method : 0.00013494491577148438 time for calcul the mask position with numpy : 0.010356426239013672 nb_pixel_total : 77 time to create 1 rle with old method : 0.00013780593872070312 time for calcul the mask position with numpy : 0.010543107986450195 nb_pixel_total : 9849 time to create 1 rle with old method : 0.015355825424194336 time for calcul the mask position with numpy : 0.01259756088256836 nb_pixel_total : 337 time to create 1 rle with old method : 0.0005695819854736328 time for calcul the mask position with numpy : 0.013146162033081055 nb_pixel_total : 420 time to create 1 rle with old method : 0.000774383544921875 time for calcul the mask position with numpy : 0.012228012084960938 nb_pixel_total : 216 time to create 1 rle with old method : 0.00029850006103515625 time for calcul the mask position with numpy : 0.010839462280273438 nb_pixel_total : 1513 time to create 1 rle with old method : 0.0018303394317626953 time for calcul the mask position with numpy : 0.01084136962890625 nb_pixel_total : 293 time to create 1 rle with old method : 0.0003886222839355469 time for calcul the mask position with numpy : 0.01035165786743164 nb_pixel_total : 365 time to create 1 rle with old method : 0.0004963874816894531 time for calcul the mask position with numpy : 0.01062631607055664 nb_pixel_total : 258 time to create 1 rle with old method : 0.0003764629364013672 time for calcul the mask position with numpy : 0.01060938835144043 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007441043853759766 time for calcul the mask position with numpy : 0.010381698608398438 nb_pixel_total : 167 time to create 1 rle with old method : 0.0002391338348388672 time for calcul the mask position with numpy : 0.01020503044128418 nb_pixel_total : 799 time to create 1 rle with old method : 0.0009839534759521484 time for calcul the mask position with numpy : 0.010285139083862305 nb_pixel_total : 733 time to create 1 rle with old method : 0.0009567737579345703 time for calcul the mask position with numpy : 0.010563850402832031 nb_pixel_total : 2634 time to create 1 rle with old method : 0.0031659603118896484 time for calcul the mask position with numpy : 0.010652542114257812 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005490779876708984 time for calcul the mask position with numpy : 0.010609865188598633 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003147125244140625 time for calcul the mask position with numpy : 0.010735034942626953 nb_pixel_total : 193 time to create 1 rle with old method : 0.0002689361572265625 time for calcul the mask position with numpy : 0.01063990592956543 nb_pixel_total : 594 time to create 1 rle with old method : 0.0007529258728027344 time for calcul the mask position with numpy : 0.010613203048706055 nb_pixel_total : 189 time to create 1 rle with old method : 0.0002639293670654297 time for calcul the mask position with numpy : 0.010851144790649414 nb_pixel_total : 497 time to create 1 rle with old method : 0.0006632804870605469 time for calcul the mask position with numpy : 0.010712623596191406 nb_pixel_total : 269 time to create 1 rle with old method : 0.0003600120544433594 time for calcul the mask position with numpy : 0.010859966278076172 nb_pixel_total : 271 time to create 1 rle with old method : 0.0003821849822998047 time for calcul the mask position with numpy : 0.01049041748046875 nb_pixel_total : 991 time to create 1 rle with old method : 0.0012319087982177734 time for calcul the mask position with numpy : 0.010409355163574219 nb_pixel_total : 985 time to create 1 rle with old method : 0.0013031959533691406 time for calcul the mask position with numpy : 0.012187719345092773 nb_pixel_total : 2 time to create 1 rle with old method : 6.0558319091796875e-05 time for calcul the mask position with numpy : 0.011152029037475586 nb_pixel_total : 969 time to create 1 rle with old method : 0.0012576580047607422 time for calcul the mask position with numpy : 0.010975837707519531 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005388259887695312 time for calcul the mask position with numpy : 0.010555028915405273 nb_pixel_total : 341 time to create 1 rle with old method : 0.0004286766052246094 time for calcul the mask position with numpy : 0.01000356674194336 nb_pixel_total : 2544 time to create 1 rle with old method : 0.002972126007080078 time for calcul the mask position with numpy : 0.013657331466674805 nb_pixel_total : 1786 time to create 1 rle with old method : 0.002050638198852539 time for calcul the mask position with numpy : 0.010133981704711914 nb_pixel_total : 241 time to create 1 rle with old method : 0.0003008842468261719 time for calcul the mask position with numpy : 0.010279178619384766 nb_pixel_total : 602 time to create 1 rle with old method : 0.0007526874542236328 time for calcul the mask position with numpy : 0.010250091552734375 nb_pixel_total : 145 time to create 1 rle with old method : 0.0002446174621582031 time for calcul the mask position with numpy : 0.01237797737121582 nb_pixel_total : 683 time to create 1 rle with old method : 0.0010151863098144531 time for calcul the mask position with numpy : 0.011056661605834961 nb_pixel_total : 930 time to create 1 rle with old method : 0.0011093616485595703 time for calcul the mask position with numpy : 0.010200977325439453 nb_pixel_total : 165 time to create 1 rle with old method : 0.00022673606872558594 time for calcul the mask position with numpy : 0.011785507202148438 nb_pixel_total : 107 time to create 1 rle with old method : 0.0001850128173828125 time for calcul the mask position with numpy : 0.011472702026367188 nb_pixel_total : 43 time to create 1 rle with old method : 8.20159912109375e-05 time for calcul the mask position with numpy : 0.010557889938354492 nb_pixel_total : 13 time to create 1 rle with old method : 4.57763671875e-05 time for calcul the mask position with numpy : 0.010802984237670898 nb_pixel_total : 137 time to create 1 rle with old method : 0.00018668174743652344 time for calcul the mask position with numpy : 0.010367870330810547 nb_pixel_total : 314 time to create 1 rle with old method : 0.00039458274841308594 time for calcul the mask position with numpy : 0.010421037673950195 nb_pixel_total : 22 time to create 1 rle with old method : 7.939338684082031e-05 time for calcul the mask position with numpy : 0.010383129119873047 nb_pixel_total : 1424 time to create 1 rle with old method : 0.0017740726470947266 time for calcul the mask position with numpy : 0.010253667831420898 nb_pixel_total : 438 time to create 1 rle with old method : 0.0005400180816650391 time for calcul the mask position with numpy : 0.010248661041259766 nb_pixel_total : 37 time to create 1 rle with old method : 0.000152587890625 time for calcul the mask position with numpy : 0.010049104690551758 nb_pixel_total : 55 time to create 1 rle with old method : 9.5367431640625e-05 time for calcul the mask position with numpy : 0.01001286506652832 nb_pixel_total : 4939 time to create 1 rle with old method : 0.005811452865600586 time for calcul the mask position with numpy : 0.014015674591064453 nb_pixel_total : 1610 time to create 1 rle with old method : 0.0019073486328125 time for calcul the mask position with numpy : 0.010114669799804688 nb_pixel_total : 268 time to create 1 rle with old method : 0.000324249267578125 time for calcul the mask position with numpy : 0.01009821891784668 nb_pixel_total : 731 time to create 1 rle with old method : 0.0008821487426757812 time for calcul the mask position with numpy : 0.010250568389892578 nb_pixel_total : 868 time to create 1 rle with old method : 0.0010273456573486328 time for calcul the mask position with numpy : 0.010110139846801758 nb_pixel_total : 1439 time to create 1 rle with old method : 0.0017151832580566406 time for calcul the mask position with numpy : 0.011165857315063477 nb_pixel_total : 4 time to create 1 rle with old method : 5.269050598144531e-05 time for calcul the mask position with numpy : 0.011974334716796875 nb_pixel_total : 591 time to create 1 rle with old method : 0.0007317066192626953 time for calcul the mask position with numpy : 0.010953426361083984 nb_pixel_total : 254 time to create 1 rle with old method : 0.0003247261047363281 time for calcul the mask position with numpy : 0.009937763214111328 nb_pixel_total : 12 time to create 1 rle with old method : 5.53131103515625e-05 time for calcul the mask position with numpy : 0.00985407829284668 nb_pixel_total : 481 time to create 1 rle with old method : 0.0005838871002197266 time for calcul the mask position with numpy : 0.009860515594482422 nb_pixel_total : 538 time to create 1 rle with old method : 0.00064849853515625 time for calcul the mask position with numpy : 0.009864091873168945 nb_pixel_total : 77 time to create 1 rle with old method : 0.00011706352233886719 time for calcul the mask position with numpy : 0.009834051132202148 nb_pixel_total : 369 time to create 1 rle with old method : 0.0004563331604003906 time for calcul the mask position with numpy : 0.01000833511352539 nb_pixel_total : 2 time to create 1 rle with old method : 2.5510787963867188e-05 time for calcul the mask position with numpy : 0.010342121124267578 nb_pixel_total : 636 time to create 1 rle with old method : 0.0007579326629638672 time for calcul the mask position with numpy : 0.011041641235351562 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002872943878173828 time for calcul the mask position with numpy : 0.01158905029296875 nb_pixel_total : 400 time to create 1 rle with old method : 0.0006823539733886719 time for calcul the mask position with numpy : 0.011497735977172852 nb_pixel_total : 195 time to create 1 rle with old method : 0.0003514289855957031 time for calcul the mask position with numpy : 0.018448352813720703 nb_pixel_total : 1485 time to create 1 rle with old method : 0.0024449825286865234 time for calcul the mask position with numpy : 0.011474847793579102 nb_pixel_total : 79 time to create 1 rle with old method : 0.00022864341735839844 time for calcul the mask position with numpy : 0.011451959609985352 nb_pixel_total : 1031 time to create 1 rle with old method : 0.0014052391052246094 time for calcul the mask position with numpy : 0.01023554801940918 nb_pixel_total : 460 time to create 1 rle with old method : 0.0005753040313720703 time for calcul the mask position with numpy : 0.010146141052246094 nb_pixel_total : 196 time to create 1 rle with old method : 0.0002582073211669922 time for calcul the mask position with numpy : 0.0102996826171875 nb_pixel_total : 486 time to create 1 rle with old method : 0.0006022453308105469 time for calcul the mask position with numpy : 0.010492801666259766 nb_pixel_total : 5031 time to create 1 rle with old method : 0.0056972503662109375 time for calcul the mask position with numpy : 0.010347843170166016 nb_pixel_total : 117 time to create 1 rle with old method : 0.00018358230590820312 time for calcul the mask position with numpy : 0.01010751724243164 nb_pixel_total : 357 time to create 1 rle with old method : 0.00044274330139160156 time for calcul the mask position with numpy : 0.010133504867553711 nb_pixel_total : 127 time to create 1 rle with old method : 0.000179290771484375 time for calcul the mask position with numpy : 0.010325431823730469 nb_pixel_total : 297 time to create 1 rle with old method : 0.0003662109375 time for calcul the mask position with numpy : 0.010232925415039062 nb_pixel_total : 1608 time to create 1 rle with old method : 0.0018656253814697266 time for calcul the mask position with numpy : 0.01051020622253418 nb_pixel_total : 613 time to create 1 rle with old method : 0.0007724761962890625 time for calcul the mask position with numpy : 0.010292291641235352 nb_pixel_total : 328 time to create 1 rle with old method : 0.0004525184631347656 time for calcul the mask position with numpy : 0.01049184799194336 nb_pixel_total : 114 time to create 1 rle with old method : 0.00015044212341308594 create new chi : 2.3841817378997803 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0031213760375976562 batch 1 Loaded 183 chid ids of type : 4230 Number RLEs to save : 15543 TO DO : save crop sub photo not yet done ! save time : 0.9209868907928467 nb_obj : 151 nb_hashtags : 7 time to prepare the origin masks : 1.9821431636810303 time for calcul the mask position with numpy : 0.0176541805267334 nb_pixel_total : 1602487 time to create 1 rle with new method : 0.038576364517211914 time for calcul the mask position with numpy : 0.010024547576904297 nb_pixel_total : 1892 time to create 1 rle with old method : 0.002068758010864258 time for calcul the mask position with numpy : 0.005924224853515625 nb_pixel_total : 160 time to create 1 rle with old method : 0.00020551681518554688 time for calcul the mask position with numpy : 0.005909442901611328 nb_pixel_total : 237 time to create 1 rle with old method : 0.00030159950256347656 time for calcul the mask position with numpy : 0.009895086288452148 nb_pixel_total : 365 time to create 1 rle with old method : 0.00039958953857421875 time for calcul the mask position with numpy : 0.008712053298950195 nb_pixel_total : 291 time to create 1 rle with old method : 0.0003383159637451172 time for calcul the mask position with numpy : 0.0060122013092041016 nb_pixel_total : 750 time to create 1 rle with old method : 0.00092315673828125 time for calcul the mask position with numpy : 0.006009817123413086 nb_pixel_total : 520 time to create 1 rle with old method : 0.0006053447723388672 time for calcul the mask position with numpy : 0.00606226921081543 nb_pixel_total : 15 time to create 1 rle with old method : 4.57763671875e-05 time for calcul the mask position with numpy : 0.01006174087524414 nb_pixel_total : 173 time to create 1 rle with old method : 0.00029754638671875 time for calcul the mask position with numpy : 0.010185718536376953 nb_pixel_total : 35 time to create 1 rle with old method : 6.842613220214844e-05 time for calcul the mask position with numpy : 0.012705564498901367 nb_pixel_total : 380 time to create 1 rle with old method : 0.0007045269012451172 time for calcul the mask position with numpy : 0.012567281723022461 nb_pixel_total : 1642 time to create 1 rle with old method : 0.0027081966400146484 time for calcul the mask position with numpy : 0.013007402420043945 nb_pixel_total : 221 time to create 1 rle with old method : 0.0003924369812011719 time for calcul the mask position with numpy : 0.01153421401977539 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002357959747314453 time for calcul the mask position with numpy : 0.010409832000732422 nb_pixel_total : 26 time to create 1 rle with old method : 5.340576171875e-05 time for calcul the mask position with numpy : 0.010413646697998047 nb_pixel_total : 2346 time to create 1 rle with old method : 0.0027496814727783203 time for calcul the mask position with numpy : 0.010474443435668945 nb_pixel_total : 237 time to create 1 rle with old method : 0.0003464221954345703 time for calcul the mask position with numpy : 0.010598182678222656 nb_pixel_total : 29 time to create 1 rle with old method : 5.817413330078125e-05 time for calcul the mask position with numpy : 0.010909557342529297 nb_pixel_total : 19135 time to create 1 rle with old method : 0.027497053146362305 time for calcul the mask position with numpy : 0.012198686599731445 nb_pixel_total : 2607 time to create 1 rle with old method : 0.00446629524230957 time for calcul the mask position with numpy : 0.015328645706176758 nb_pixel_total : 10019 time to create 1 rle with old method : 0.0182802677154541 time for calcul the mask position with numpy : 0.013368368148803711 nb_pixel_total : 188 time to create 1 rle with old method : 0.00039887428283691406 time for calcul the mask position with numpy : 0.01750922203063965 nb_pixel_total : 349 time to create 1 rle with old method : 0.0007841587066650391 time for calcul the mask position with numpy : 0.013796329498291016 nb_pixel_total : 275 time to create 1 rle with old method : 0.0003955364227294922 time for calcul the mask position with numpy : 0.012770891189575195 nb_pixel_total : 6003 time to create 1 rle with old method : 0.007038116455078125 time for calcul the mask position with numpy : 0.013497352600097656 nb_pixel_total : 11224 time to create 1 rle with old method : 0.01538538932800293 time for calcul the mask position with numpy : 0.012017250061035156 nb_pixel_total : 95 time to create 1 rle with old method : 0.0001704692840576172 time for calcul the mask position with numpy : 0.011209964752197266 nb_pixel_total : 803 time to create 1 rle with old method : 0.0009891986846923828 time for calcul the mask position with numpy : 0.00751495361328125 nb_pixel_total : 165 time to create 1 rle with old method : 0.0002956390380859375 time for calcul the mask position with numpy : 0.010221242904663086 nb_pixel_total : 151996 time to create 1 rle with new method : 0.03302478790283203 time for calcul the mask position with numpy : 0.006456851959228516 nb_pixel_total : 1363 time to create 1 rle with old method : 0.0016407966613769531 time for calcul the mask position with numpy : 0.0064127445220947266 nb_pixel_total : 1134 time to create 1 rle with old method : 0.0013740062713623047 time for calcul the mask position with numpy : 0.010213613510131836 nb_pixel_total : 822 time to create 1 rle with old method : 0.0010535717010498047 time for calcul the mask position with numpy : 0.0060617923736572266 nb_pixel_total : 82 time to create 1 rle with old method : 0.00012946128845214844 time for calcul the mask position with numpy : 0.006096839904785156 nb_pixel_total : 725 time to create 1 rle with old method : 0.0008845329284667969 time for calcul the mask position with numpy : 0.006299734115600586 nb_pixel_total : 206 time to create 1 rle with old method : 0.00027441978454589844 time for calcul the mask position with numpy : 0.00632476806640625 nb_pixel_total : 751 time to create 1 rle with old method : 0.0009224414825439453 time for calcul the mask position with numpy : 0.00626683235168457 nb_pixel_total : 371 time to create 1 rle with old method : 0.0004940032958984375 time for calcul the mask position with numpy : 0.006307363510131836 nb_pixel_total : 1306 time to create 1 rle with old method : 0.001556396484375 time for calcul the mask position with numpy : 0.0074002742767333984 nb_pixel_total : 969 time to create 1 rle with old method : 0.001491546630859375 time for calcul the mask position with numpy : 0.007550954818725586 nb_pixel_total : 19718 time to create 1 rle with old method : 0.022144079208374023 time for calcul the mask position with numpy : 0.006415843963623047 nb_pixel_total : 14018 time to create 1 rle with old method : 0.01570868492126465 time for calcul the mask position with numpy : 0.0067937374114990234 nb_pixel_total : 383 time to create 1 rle with old method : 0.0007314682006835938 time for calcul the mask position with numpy : 0.006735324859619141 nb_pixel_total : 108 time to create 1 rle with old method : 0.0002713203430175781 time for calcul the mask position with numpy : 0.006716728210449219 nb_pixel_total : 1277 time to create 1 rle with old method : 0.0023331642150878906 time for calcul the mask position with numpy : 0.00658726692199707 nb_pixel_total : 182 time to create 1 rle with old method : 0.00041556358337402344 time for calcul the mask position with numpy : 0.006922006607055664 nb_pixel_total : 1103 time to create 1 rle with old method : 0.002064943313598633 time for calcul the mask position with numpy : 0.0065991878509521484 nb_pixel_total : 167 time to create 1 rle with old method : 0.0004324913024902344 time for calcul the mask position with numpy : 0.006635189056396484 nb_pixel_total : 3178 time to create 1 rle with old method : 0.003768444061279297 time for calcul the mask position with numpy : 0.006872892379760742 nb_pixel_total : 995 time to create 1 rle with old method : 0.0012254714965820312 time for calcul the mask position with numpy : 0.006852388381958008 nb_pixel_total : 451 time to create 1 rle with old method : 0.0005998611450195312 time for calcul the mask position with numpy : 0.006791591644287109 nb_pixel_total : 24772 time to create 1 rle with old method : 0.027267932891845703 time for calcul the mask position with numpy : 0.006248950958251953 nb_pixel_total : 908 time to create 1 rle with old method : 0.001115560531616211 time for calcul the mask position with numpy : 0.006217479705810547 nb_pixel_total : 114 time to create 1 rle with old method : 0.0001697540283203125 time for calcul the mask position with numpy : 0.006348609924316406 nb_pixel_total : 796 time to create 1 rle with old method : 0.0009922981262207031 time for calcul the mask position with numpy : 0.006258726119995117 nb_pixel_total : 503 time to create 1 rle with old method : 0.0006515979766845703 time for calcul the mask position with numpy : 0.006178379058837891 nb_pixel_total : 1419 time to create 1 rle with old method : 0.0016970634460449219 time for calcul the mask position with numpy : 0.006184577941894531 nb_pixel_total : 99 time to create 1 rle with old method : 0.00015044212341308594 time for calcul the mask position with numpy : 0.0062160491943359375 nb_pixel_total : 206 time to create 1 rle with old method : 0.00027871131896972656 time for calcul the mask position with numpy : 0.006093502044677734 nb_pixel_total : 272 time to create 1 rle with old method : 0.0003418922424316406 time for calcul the mask position with numpy : 0.0061686038970947266 nb_pixel_total : 1501 time to create 1 rle with old method : 0.0018544197082519531 time for calcul the mask position with numpy : 0.007531404495239258 nb_pixel_total : 120 time to create 1 rle with old method : 0.0003044605255126953 time for calcul the mask position with numpy : 0.006397247314453125 nb_pixel_total : 426 time to create 1 rle with old method : 0.0005650520324707031 time for calcul the mask position with numpy : 0.006366252899169922 nb_pixel_total : 60 time to create 1 rle with old method : 0.00015306472778320312 time for calcul the mask position with numpy : 0.0063474178314208984 nb_pixel_total : 650 time to create 1 rle with old method : 0.0008189678192138672 time for calcul the mask position with numpy : 0.006316423416137695 nb_pixel_total : 294 time to create 1 rle with old method : 0.0003783702850341797 time for calcul the mask position with numpy : 0.006432533264160156 nb_pixel_total : 4109 time to create 1 rle with old method : 0.00497889518737793 time for calcul the mask position with numpy : 0.0074803829193115234 nb_pixel_total : 372 time to create 1 rle with old method : 0.0006237030029296875 time for calcul the mask position with numpy : 0.0075452327728271484 nb_pixel_total : 419 time to create 1 rle with old method : 0.0006556510925292969 time for calcul the mask position with numpy : 0.007518768310546875 nb_pixel_total : 17 time to create 1 rle with old method : 6.4849853515625e-05 time for calcul the mask position with numpy : 0.007289886474609375 nb_pixel_total : 913 time to create 1 rle with old method : 0.0011224746704101562 time for calcul the mask position with numpy : 0.01009368896484375 nb_pixel_total : 364 time to create 1 rle with old method : 0.00045371055603027344 time for calcul the mask position with numpy : 0.006228446960449219 nb_pixel_total : 222 time to create 1 rle with old method : 0.00034618377685546875 time for calcul the mask position with numpy : 0.00619959831237793 nb_pixel_total : 958 time to create 1 rle with old method : 0.0011887550354003906 time for calcul the mask position with numpy : 0.006186485290527344 nb_pixel_total : 252 time to create 1 rle with old method : 0.0003235340118408203 time for calcul the mask position with numpy : 0.007091045379638672 nb_pixel_total : 106903 time to create 1 rle with old method : 0.12634992599487305 time for calcul the mask position with numpy : 0.006426811218261719 nb_pixel_total : 360 time to create 1 rle with old method : 0.00048279762268066406 time for calcul the mask position with numpy : 0.010131359100341797 nb_pixel_total : 68 time to create 1 rle with old method : 0.00011420249938964844 time for calcul the mask position with numpy : 0.006148338317871094 nb_pixel_total : 212 time to create 1 rle with old method : 0.0002868175506591797 time for calcul the mask position with numpy : 0.0061872005462646484 nb_pixel_total : 10851 time to create 1 rle with old method : 0.013070344924926758 time for calcul the mask position with numpy : 0.006721973419189453 nb_pixel_total : 1718 time to create 1 rle with old method : 0.0019249916076660156 time for calcul the mask position with numpy : 0.006181955337524414 nb_pixel_total : 105 time to create 1 rle with old method : 0.00015974044799804688 time for calcul the mask position with numpy : 0.006118297576904297 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004000663757324219 time for calcul the mask position with numpy : 0.006103515625 nb_pixel_total : 1284 time to create 1 rle with old method : 0.0016064643859863281 time for calcul the mask position with numpy : 0.00587010383605957 nb_pixel_total : 189 time to create 1 rle with old method : 0.0002505779266357422 time for calcul the mask position with numpy : 0.00604557991027832 nb_pixel_total : 237 time to create 1 rle with old method : 0.00032782554626464844 time for calcul the mask position with numpy : 0.006062746047973633 nb_pixel_total : 176 time to create 1 rle with old method : 0.000217437744140625 time for calcul the mask position with numpy : 0.005888223648071289 nb_pixel_total : 1880 time to create 1 rle with old method : 0.002246856689453125 time for calcul the mask position with numpy : 0.00579380989074707 nb_pixel_total : 537 time to create 1 rle with old method : 0.0006802082061767578 time for calcul the mask position with numpy : 0.005888700485229492 nb_pixel_total : 777 time to create 1 rle with old method : 0.0009071826934814453 time for calcul the mask position with numpy : 0.005873918533325195 nb_pixel_total : 253 time to create 1 rle with old method : 0.0003349781036376953 time for calcul the mask position with numpy : 0.005868673324584961 nb_pixel_total : 528 time to create 1 rle with old method : 0.0006067752838134766 time for calcul the mask position with numpy : 0.006456613540649414 nb_pixel_total : 436 time to create 1 rle with old method : 0.0005440711975097656 time for calcul the mask position with numpy : 0.006083488464355469 nb_pixel_total : 143 time to create 1 rle with old method : 0.00021505355834960938 time for calcul the mask position with numpy : 0.005877017974853516 nb_pixel_total : 29 time to create 1 rle with old method : 9.965896606445312e-05 time for calcul the mask position with numpy : 0.00599360466003418 nb_pixel_total : 2235 time to create 1 rle with old method : 0.0025179386138916016 time for calcul the mask position with numpy : 0.00587010383605957 nb_pixel_total : 419 time to create 1 rle with old method : 0.0005145072937011719 time for calcul the mask position with numpy : 0.005843400955200195 nb_pixel_total : 277 time to create 1 rle with old method : 0.00036454200744628906 time for calcul the mask position with numpy : 0.0059261322021484375 nb_pixel_total : 910 time to create 1 rle with old method : 0.0011396408081054688 time for calcul the mask position with numpy : 0.006218910217285156 nb_pixel_total : 62 time to create 1 rle with old method : 0.00010991096496582031 time for calcul the mask position with numpy : 0.006208896636962891 nb_pixel_total : 435 time to create 1 rle with old method : 0.0005872249603271484 time for calcul the mask position with numpy : 0.006252765655517578 nb_pixel_total : 192 time to create 1 rle with old method : 0.00023484230041503906 time for calcul the mask position with numpy : 0.00624537467956543 nb_pixel_total : 946 time to create 1 rle with old method : 0.0011534690856933594 time for calcul the mask position with numpy : 0.005995273590087891 nb_pixel_total : 420 time to create 1 rle with old method : 0.0005018711090087891 time for calcul the mask position with numpy : 0.0064084529876708984 nb_pixel_total : 529 time to create 1 rle with old method : 0.0006778240203857422 time for calcul the mask position with numpy : 0.005932331085205078 nb_pixel_total : 1174 time to create 1 rle with old method : 0.001325845718383789 time for calcul the mask position with numpy : 0.0059051513671875 nb_pixel_total : 410 time to create 1 rle with old method : 0.000530242919921875 time for calcul the mask position with numpy : 0.0061151981353759766 nb_pixel_total : 1814 time to create 1 rle with old method : 0.0023174285888671875 time for calcul the mask position with numpy : 0.0062444210052490234 nb_pixel_total : 3 time to create 1 rle with old method : 2.384185791015625e-05 time for calcul the mask position with numpy : 0.006218910217285156 nb_pixel_total : 752 time to create 1 rle with old method : 0.0009474754333496094 time for calcul the mask position with numpy : 0.006252288818359375 nb_pixel_total : 836 time to create 1 rle with old method : 0.001461029052734375 time for calcul the mask position with numpy : 0.011411428451538086 nb_pixel_total : 195 time to create 1 rle with old method : 0.000370025634765625 time for calcul the mask position with numpy : 0.012175798416137695 nb_pixel_total : 416 time to create 1 rle with old method : 0.0007021427154541016 time for calcul the mask position with numpy : 0.01142740249633789 nb_pixel_total : 161 time to create 1 rle with old method : 0.00028228759765625 time for calcul the mask position with numpy : 0.011460542678833008 nb_pixel_total : 1371 time to create 1 rle with old method : 0.0022313594818115234 time for calcul the mask position with numpy : 0.01152944564819336 nb_pixel_total : 400 time to create 1 rle with old method : 0.0007121562957763672 time for calcul the mask position with numpy : 0.011297464370727539 nb_pixel_total : 110 time to create 1 rle with old method : 0.0002753734588623047 time for calcul the mask position with numpy : 0.01141667366027832 nb_pixel_total : 1739 time to create 1 rle with old method : 0.0019903182983398438 time for calcul the mask position with numpy : 0.010177135467529297 nb_pixel_total : 456 time to create 1 rle with old method : 0.0005643367767333984 time for calcul the mask position with numpy : 0.01009368896484375 nb_pixel_total : 536 time to create 1 rle with old method : 0.0006387233734130859 time for calcul the mask position with numpy : 0.010736942291259766 nb_pixel_total : 2 time to create 1 rle with old method : 3.409385681152344e-05 time for calcul the mask position with numpy : 0.011534452438354492 nb_pixel_total : 5126 time to create 1 rle with old method : 0.005690574645996094 time for calcul the mask position with numpy : 0.010111570358276367 nb_pixel_total : 871 time to create 1 rle with old method : 0.00110626220703125 time for calcul the mask position with numpy : 0.010307550430297852 nb_pixel_total : 295 time to create 1 rle with old method : 0.00037097930908203125 time for calcul the mask position with numpy : 0.014066696166992188 nb_pixel_total : 569 time to create 1 rle with old method : 0.0006887912750244141 time for calcul the mask position with numpy : 0.010502099990844727 nb_pixel_total : 3092 time to create 1 rle with old method : 0.003612995147705078 time for calcul the mask position with numpy : 0.010138273239135742 nb_pixel_total : 492 time to create 1 rle with old method : 0.000579833984375 time for calcul the mask position with numpy : 0.010283231735229492 nb_pixel_total : 514 time to create 1 rle with old method : 0.000614166259765625 time for calcul the mask position with numpy : 0.009810924530029297 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006256103515625 time for calcul the mask position with numpy : 0.009796380996704102 nb_pixel_total : 75 time to create 1 rle with old method : 0.00010013580322265625 time for calcul the mask position with numpy : 0.01044321060180664 nb_pixel_total : 357 time to create 1 rle with old method : 0.0004458427429199219 time for calcul the mask position with numpy : 0.010205984115600586 nb_pixel_total : 320 time to create 1 rle with old method : 0.00039839744567871094 time for calcul the mask position with numpy : 0.010010480880737305 nb_pixel_total : 18 time to create 1 rle with old method : 7.05718994140625e-05 time for calcul the mask position with numpy : 0.00989079475402832 nb_pixel_total : 751 time to create 1 rle with old method : 0.0009074211120605469 time for calcul the mask position with numpy : 0.009912967681884766 nb_pixel_total : 467 time to create 1 rle with old method : 0.0005686283111572266 time for calcul the mask position with numpy : 0.009905099868774414 nb_pixel_total : 363 time to create 1 rle with old method : 0.0004584789276123047 time for calcul the mask position with numpy : 0.009847879409790039 nb_pixel_total : 154 time to create 1 rle with old method : 0.00019693374633789062 time for calcul the mask position with numpy : 0.010201454162597656 nb_pixel_total : 1377 time to create 1 rle with old method : 0.0016078948974609375 time for calcul the mask position with numpy : 0.010246038436889648 nb_pixel_total : 30 time to create 1 rle with old method : 8.606910705566406e-05 time for calcul the mask position with numpy : 0.010095357894897461 nb_pixel_total : 1066 time to create 1 rle with old method : 0.0012559890747070312 time for calcul the mask position with numpy : 0.01012563705444336 nb_pixel_total : 579 time to create 1 rle with old method : 0.0007054805755615234 time for calcul the mask position with numpy : 0.010083198547363281 nb_pixel_total : 608 time to create 1 rle with old method : 0.0007419586181640625 time for calcul the mask position with numpy : 0.010162353515625 nb_pixel_total : 377 time to create 1 rle with old method : 0.0005712509155273438 time for calcul the mask position with numpy : 0.010468721389770508 nb_pixel_total : 333 time to create 1 rle with old method : 0.00041294097900390625 time for calcul the mask position with numpy : 0.010370254516601562 nb_pixel_total : 4586 time to create 1 rle with old method : 0.005276679992675781 time for calcul the mask position with numpy : 0.009952068328857422 nb_pixel_total : 303 time to create 1 rle with old method : 0.0003714561462402344 time for calcul the mask position with numpy : 0.010036230087280273 nb_pixel_total : 145 time to create 1 rle with old method : 0.0002925395965576172 time for calcul the mask position with numpy : 0.010056257247924805 nb_pixel_total : 152 time to create 1 rle with old method : 0.00018668174743652344 time for calcul the mask position with numpy : 0.010041236877441406 nb_pixel_total : 1501 time to create 1 rle with old method : 0.0017468929290771484 time for calcul the mask position with numpy : 0.010229110717773438 nb_pixel_total : 222 time to create 1 rle with old method : 0.0002796649932861328 time for calcul the mask position with numpy : 0.010113000869750977 nb_pixel_total : 605 time to create 1 rle with old method : 0.0007224082946777344 create new chi : 1.762258768081665 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0028891563415527344 batch 1 Loaded 160 chid ids of type : 4230 Number RLEs to save : 15236 TO DO : save crop sub photo not yet done ! save time : 0.8579349517822266 nb_obj : 157 nb_hashtags : 8 time to prepare the origin masks : 1.8898873329162598 time for calcul the mask position with numpy : 0.017891645431518555 nb_pixel_total : 1792747 time to create 1 rle with new method : 0.04190516471862793 time for calcul the mask position with numpy : 0.006526947021484375 nb_pixel_total : 1634 time to create 1 rle with old method : 0.001786947250366211 time for calcul the mask position with numpy : 0.00614619255065918 nb_pixel_total : 241 time to create 1 rle with old method : 0.0003604888916015625 time for calcul the mask position with numpy : 0.0064542293548583984 nb_pixel_total : 570 time to create 1 rle with old method : 0.0008068084716796875 time for calcul the mask position with numpy : 0.006102085113525391 nb_pixel_total : 334 time to create 1 rle with old method : 0.00039458274841308594 time for calcul the mask position with numpy : 0.006066322326660156 nb_pixel_total : 156 time to create 1 rle with old method : 0.0002224445343017578 time for calcul the mask position with numpy : 0.006045341491699219 nb_pixel_total : 561 time to create 1 rle with old method : 0.0006959438323974609 time for calcul the mask position with numpy : 0.0060577392578125 nb_pixel_total : 55 time to create 1 rle with old method : 8.535385131835938e-05 time for calcul the mask position with numpy : 0.006139516830444336 nb_pixel_total : 89 time to create 1 rle with old method : 0.00012445449829101562 time for calcul the mask position with numpy : 0.006589412689208984 nb_pixel_total : 244 time to create 1 rle with old method : 0.00038814544677734375 time for calcul the mask position with numpy : 0.008105993270874023 nb_pixel_total : 193 time to create 1 rle with old method : 0.0004029273986816406 time for calcul the mask position with numpy : 0.005995988845825195 nb_pixel_total : 56 time to create 1 rle with old method : 8.225440979003906e-05 time for calcul the mask position with numpy : 0.006025552749633789 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002522468566894531 time for calcul the mask position with numpy : 0.005972385406494141 nb_pixel_total : 346 time to create 1 rle with old method : 0.0003972053527832031 time for calcul the mask position with numpy : 0.006025552749633789 nb_pixel_total : 34 time to create 1 rle with old method : 6.270408630371094e-05 time for calcul the mask position with numpy : 0.0060040950775146484 nb_pixel_total : 91 time to create 1 rle with old method : 0.00014257431030273438 time for calcul the mask position with numpy : 0.005978584289550781 nb_pixel_total : 2401 time to create 1 rle with old method : 0.0025200843811035156 time for calcul the mask position with numpy : 0.005939006805419922 nb_pixel_total : 10 time to create 1 rle with old method : 6.389617919921875e-05 time for calcul the mask position with numpy : 0.005999565124511719 nb_pixel_total : 12608 time to create 1 rle with old method : 0.013724327087402344 time for calcul the mask position with numpy : 0.005814075469970703 nb_pixel_total : 7 time to create 1 rle with old method : 3.790855407714844e-05 time for calcul the mask position with numpy : 0.006028890609741211 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002193450927734375 time for calcul the mask position with numpy : 0.005929231643676758 nb_pixel_total : 4017 time to create 1 rle with old method : 0.004677534103393555 time for calcul the mask position with numpy : 0.0059506893157958984 nb_pixel_total : 361 time to create 1 rle with old method : 0.0004534721374511719 time for calcul the mask position with numpy : 0.0058743953704833984 nb_pixel_total : 5776 time to create 1 rle with old method : 0.006479501724243164 time for calcul the mask position with numpy : 0.005812406539916992 nb_pixel_total : 29 time to create 1 rle with old method : 0.00010085105895996094 time for calcul the mask position with numpy : 0.005865335464477539 nb_pixel_total : 11883 time to create 1 rle with old method : 0.012906789779663086 time for calcul the mask position with numpy : 0.005911588668823242 nb_pixel_total : 904 time to create 1 rle with old method : 0.0010371208190917969 time for calcul the mask position with numpy : 0.006244182586669922 nb_pixel_total : 98 time to create 1 rle with old method : 0.00013971328735351562 time for calcul the mask position with numpy : 0.0058095455169677734 nb_pixel_total : 728 time to create 1 rle with old method : 0.0009031295776367188 time for calcul the mask position with numpy : 0.005844831466674805 nb_pixel_total : 1044 time to create 1 rle with old method : 0.0012934207916259766 time for calcul the mask position with numpy : 0.005964517593383789 nb_pixel_total : 1640 time to create 1 rle with old method : 0.0017313957214355469 time for calcul the mask position with numpy : 0.005900859832763672 nb_pixel_total : 1075 time to create 1 rle with old method : 0.0012547969818115234 time for calcul the mask position with numpy : 0.005930185317993164 nb_pixel_total : 39 time to create 1 rle with old method : 6.556510925292969e-05 time for calcul the mask position with numpy : 0.005864381790161133 nb_pixel_total : 1862 time to create 1 rle with old method : 0.002178192138671875 time for calcul the mask position with numpy : 0.005967378616333008 nb_pixel_total : 23096 time to create 1 rle with old method : 0.024317264556884766 time for calcul the mask position with numpy : 0.005970954895019531 nb_pixel_total : 944 time to create 1 rle with old method : 0.0010714530944824219 time for calcul the mask position with numpy : 0.006130695343017578 nb_pixel_total : 704 time to create 1 rle with old method : 0.0008318424224853516 time for calcul the mask position with numpy : 0.006029605865478516 nb_pixel_total : 187 time to create 1 rle with old method : 0.00022649765014648438 time for calcul the mask position with numpy : 0.0061037540435791016 nb_pixel_total : 20 time to create 1 rle with old method : 8.940696716308594e-05 time for calcul the mask position with numpy : 0.006246805191040039 nb_pixel_total : 1296 time to create 1 rle with old method : 0.0015063285827636719 time for calcul the mask position with numpy : 0.0061283111572265625 nb_pixel_total : 879 time to create 1 rle with old method : 0.001050710678100586 time for calcul the mask position with numpy : 0.006214141845703125 nb_pixel_total : 13488 time to create 1 rle with old method : 0.01744222640991211 time for calcul the mask position with numpy : 0.006333112716674805 nb_pixel_total : 664 time to create 1 rle with old method : 0.0008451938629150391 time for calcul the mask position with numpy : 0.006114482879638672 nb_pixel_total : 614 time to create 1 rle with old method : 0.0007441043853759766 time for calcul the mask position with numpy : 0.0061719417572021484 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003151893615722656 time for calcul the mask position with numpy : 0.0060884952545166016 nb_pixel_total : 2163 time to create 1 rle with old method : 0.0027201175689697266 time for calcul the mask position with numpy : 0.006298542022705078 nb_pixel_total : 7 time to create 1 rle with old method : 4.9591064453125e-05 time for calcul the mask position with numpy : 0.0064258575439453125 nb_pixel_total : 449 time to create 1 rle with old method : 0.0005593299865722656 time for calcul the mask position with numpy : 0.006513118743896484 nb_pixel_total : 560 time to create 1 rle with old method : 0.0008802413940429688 time for calcul the mask position with numpy : 0.006760120391845703 nb_pixel_total : 322 time to create 1 rle with old method : 0.0004363059997558594 time for calcul the mask position with numpy : 0.006609916687011719 nb_pixel_total : 405 time to create 1 rle with old method : 0.0006337165832519531 time for calcul the mask position with numpy : 0.007990360260009766 nb_pixel_total : 1407 time to create 1 rle with old method : 0.0018787384033203125 time for calcul the mask position with numpy : 0.007609128952026367 nb_pixel_total : 1168 time to create 1 rle with old method : 0.002074718475341797 time for calcul the mask position with numpy : 0.008394479751586914 nb_pixel_total : 3 time to create 1 rle with old method : 5.936622619628906e-05 time for calcul the mask position with numpy : 0.008068323135375977 nb_pixel_total : 134 time to create 1 rle with old method : 0.0001971721649169922 time for calcul the mask position with numpy : 0.006870269775390625 nb_pixel_total : 2723 time to create 1 rle with old method : 0.0032346248626708984 time for calcul the mask position with numpy : 0.008046388626098633 nb_pixel_total : 812 time to create 1 rle with old method : 0.0013918876647949219 time for calcul the mask position with numpy : 0.008140802383422852 nb_pixel_total : 766 time to create 1 rle with old method : 0.0012238025665283203 time for calcul the mask position with numpy : 0.007337331771850586 nb_pixel_total : 606 time to create 1 rle with old method : 0.0008223056793212891 time for calcul the mask position with numpy : 0.007966279983520508 nb_pixel_total : 1305 time to create 1 rle with old method : 0.002081632614135742 time for calcul the mask position with numpy : 0.00671076774597168 nb_pixel_total : 395 time to create 1 rle with old method : 0.000522613525390625 time for calcul the mask position with numpy : 0.006500244140625 nb_pixel_total : 1615 time to create 1 rle with old method : 0.0018949508666992188 time for calcul the mask position with numpy : 0.0061681270599365234 nb_pixel_total : 119 time to create 1 rle with old method : 0.00017333030700683594 time for calcul the mask position with numpy : 0.006208181381225586 nb_pixel_total : 58 time to create 1 rle with old method : 0.00013971328735351562 time for calcul the mask position with numpy : 0.006279468536376953 nb_pixel_total : 490 time to create 1 rle with old method : 0.0006153583526611328 time for calcul the mask position with numpy : 0.0061779022216796875 nb_pixel_total : 3599 time to create 1 rle with old method : 0.004243135452270508 time for calcul the mask position with numpy : 0.006279706954956055 nb_pixel_total : 479 time to create 1 rle with old method : 0.0005834102630615234 time for calcul the mask position with numpy : 0.0060040950775146484 nb_pixel_total : 347 time to create 1 rle with old method : 0.0004405975341796875 time for calcul the mask position with numpy : 0.00600433349609375 nb_pixel_total : 326 time to create 1 rle with old method : 0.000396728515625 time for calcul the mask position with numpy : 0.006295442581176758 nb_pixel_total : 444 time to create 1 rle with old method : 0.00064849853515625 time for calcul the mask position with numpy : 0.006586790084838867 nb_pixel_total : 905 time to create 1 rle with old method : 0.0011165142059326172 time for calcul the mask position with numpy : 0.006479978561401367 nb_pixel_total : 330 time to create 1 rle with old method : 0.0004582405090332031 time for calcul the mask position with numpy : 0.006690263748168945 nb_pixel_total : 134 time to create 1 rle with old method : 0.00020551681518554688 time for calcul the mask position with numpy : 0.0073163509368896484 nb_pixel_total : 1114 time to create 1 rle with old method : 0.0015583038330078125 time for calcul the mask position with numpy : 0.007062196731567383 nb_pixel_total : 60 time to create 1 rle with old method : 0.00010585784912109375 time for calcul the mask position with numpy : 0.007297515869140625 nb_pixel_total : 84 time to create 1 rle with old method : 0.0002601146697998047 time for calcul the mask position with numpy : 0.008439779281616211 nb_pixel_total : 202 time to create 1 rle with old method : 0.00037598609924316406 time for calcul the mask position with numpy : 0.007772207260131836 nb_pixel_total : 106521 time to create 1 rle with old method : 0.11519193649291992 time for calcul the mask position with numpy : 0.0058765411376953125 nb_pixel_total : 10745 time to create 1 rle with old method : 0.012152910232543945 time for calcul the mask position with numpy : 0.006266355514526367 nb_pixel_total : 1607 time to create 1 rle with old method : 0.0020716190338134766 time for calcul the mask position with numpy : 0.005921363830566406 nb_pixel_total : 1497 time to create 1 rle with old method : 0.0017771720886230469 time for calcul the mask position with numpy : 0.0058362483978271484 nb_pixel_total : 402 time to create 1 rle with old method : 0.0005173683166503906 time for calcul the mask position with numpy : 0.0057833194732666016 nb_pixel_total : 186 time to create 1 rle with old method : 0.00027823448181152344 time for calcul the mask position with numpy : 0.005963325500488281 nb_pixel_total : 570 time to create 1 rle with old method : 0.0007166862487792969 time for calcul the mask position with numpy : 0.006300926208496094 nb_pixel_total : 1361 time to create 1 rle with old method : 0.0016629695892333984 time for calcul the mask position with numpy : 0.006010532379150391 nb_pixel_total : 1303 time to create 1 rle with old method : 0.0015327930450439453 time for calcul the mask position with numpy : 0.005967378616333008 nb_pixel_total : 623 time to create 1 rle with old method : 0.0007648468017578125 time for calcul the mask position with numpy : 0.005941152572631836 nb_pixel_total : 151 time to create 1 rle with old method : 0.0001957416534423828 time for calcul the mask position with numpy : 0.0059320926666259766 nb_pixel_total : 474 time to create 1 rle with old method : 0.0005433559417724609 time for calcul the mask position with numpy : 0.005987882614135742 nb_pixel_total : 239 time to create 1 rle with old method : 0.0003132820129394531 time for calcul the mask position with numpy : 0.005967617034912109 nb_pixel_total : 606 time to create 1 rle with old method : 0.0007574558258056641 time for calcul the mask position with numpy : 0.006047248840332031 nb_pixel_total : 218 time to create 1 rle with old method : 0.0002715587615966797 time for calcul the mask position with numpy : 0.006168842315673828 nb_pixel_total : 189 time to create 1 rle with old method : 0.00023865699768066406 time for calcul the mask position with numpy : 0.006465911865234375 nb_pixel_total : 472 time to create 1 rle with old method : 0.0008172988891601562 time for calcul the mask position with numpy : 0.0062770843505859375 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003275871276855469 time for calcul the mask position with numpy : 0.0064411163330078125 nb_pixel_total : 1173 time to create 1 rle with old method : 0.0013964176177978516 time for calcul the mask position with numpy : 0.0064563751220703125 nb_pixel_total : 1472 time to create 1 rle with old method : 0.0020868778228759766 time for calcul the mask position with numpy : 0.007579803466796875 nb_pixel_total : 122 time to create 1 rle with old method : 0.0002951622009277344 time for calcul the mask position with numpy : 0.006591320037841797 nb_pixel_total : 351 time to create 1 rle with old method : 0.00044465065002441406 time for calcul the mask position with numpy : 0.007227897644042969 nb_pixel_total : 290 time to create 1 rle with old method : 0.0004591941833496094 time for calcul the mask position with numpy : 0.007338523864746094 nb_pixel_total : 431 time to create 1 rle with old method : 0.0005300045013427734 time for calcul the mask position with numpy : 0.006138324737548828 nb_pixel_total : 923 time to create 1 rle with old method : 0.0015053749084472656 time for calcul the mask position with numpy : 0.008790969848632812 nb_pixel_total : 131 time to create 1 rle with old method : 0.00018262863159179688 time for calcul the mask position with numpy : 0.0064983367919921875 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005404949188232422 time for calcul the mask position with numpy : 0.007314920425415039 nb_pixel_total : 60 time to create 1 rle with old method : 0.00010228157043457031 time for calcul the mask position with numpy : 0.006473064422607422 nb_pixel_total : 420 time to create 1 rle with old method : 0.0005230903625488281 time for calcul the mask position with numpy : 0.007065773010253906 nb_pixel_total : 2 time to create 1 rle with old method : 4.00543212890625e-05 time for calcul the mask position with numpy : 0.00853872299194336 nb_pixel_total : 31 time to create 1 rle with old method : 0.00013399124145507812 time for calcul the mask position with numpy : 0.009225845336914062 nb_pixel_total : 111 time to create 1 rle with old method : 0.0002892017364501953 time for calcul the mask position with numpy : 0.0089874267578125 nb_pixel_total : 171 time to create 1 rle with old method : 0.0003428459167480469 time for calcul the mask position with numpy : 0.011573076248168945 nb_pixel_total : 815 time to create 1 rle with old method : 0.0026280879974365234 time for calcul the mask position with numpy : 0.011606454849243164 nb_pixel_total : 1547 time to create 1 rle with old method : 0.003344297409057617 time for calcul the mask position with numpy : 0.011722803115844727 nb_pixel_total : 56 time to create 1 rle with old method : 0.0002124309539794922 time for calcul the mask position with numpy : 0.01796126365661621 nb_pixel_total : 156 time to create 1 rle with old method : 0.0007281303405761719 time for calcul the mask position with numpy : 0.015569925308227539 nb_pixel_total : 5289 time to create 1 rle with old method : 0.010282516479492188 time for calcul the mask position with numpy : 0.013453960418701172 nb_pixel_total : 884 time to create 1 rle with old method : 0.0017244815826416016 time for calcul the mask position with numpy : 0.007237434387207031 nb_pixel_total : 484 time to create 1 rle with old method : 0.0007190704345703125 time for calcul the mask position with numpy : 0.007276296615600586 nb_pixel_total : 190 time to create 1 rle with old method : 0.0003829002380371094 time for calcul the mask position with numpy : 0.009669065475463867 nb_pixel_total : 28 time to create 1 rle with old method : 0.0001442432403564453 time for calcul the mask position with numpy : 0.009133100509643555 nb_pixel_total : 450 time to create 1 rle with old method : 0.0010342597961425781 time for calcul the mask position with numpy : 0.009255170822143555 nb_pixel_total : 121 time to create 1 rle with old method : 0.0002636909484863281 time for calcul the mask position with numpy : 0.009512186050415039 nb_pixel_total : 1272 time to create 1 rle with old method : 0.0026721954345703125 time for calcul the mask position with numpy : 0.009185314178466797 nb_pixel_total : 132 time to create 1 rle with old method : 0.0003743171691894531 time for calcul the mask position with numpy : 0.009755373001098633 nb_pixel_total : 381 time to create 1 rle with old method : 0.0008118152618408203 time for calcul the mask position with numpy : 0.009290695190429688 nb_pixel_total : 1643 time to create 1 rle with old method : 0.0030982494354248047 time for calcul the mask position with numpy : 0.009371042251586914 nb_pixel_total : 633 time to create 1 rle with old method : 0.0017199516296386719 time for calcul the mask position with numpy : 0.008168697357177734 nb_pixel_total : 4215 time to create 1 rle with old method : 0.005384206771850586 time for calcul the mask position with numpy : 0.00772857666015625 nb_pixel_total : 17 time to create 1 rle with old method : 8.940696716308594e-05 time for calcul the mask position with numpy : 0.006898403167724609 nb_pixel_total : 895 time to create 1 rle with old method : 0.0013172626495361328 time for calcul the mask position with numpy : 0.0077364444732666016 nb_pixel_total : 600 time to create 1 rle with old method : 0.00074005126953125 time for calcul the mask position with numpy : 0.006701231002807617 nb_pixel_total : 268 time to create 1 rle with old method : 0.0003497600555419922 time for calcul the mask position with numpy : 0.006511688232421875 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004684925079345703 time for calcul the mask position with numpy : 0.006846427917480469 nb_pixel_total : 506 time to create 1 rle with old method : 0.0007545948028564453 time for calcul the mask position with numpy : 0.00734400749206543 nb_pixel_total : 528 time to create 1 rle with old method : 0.0006806850433349609 time for calcul the mask position with numpy : 0.00690770149230957 nb_pixel_total : 72 time to create 1 rle with old method : 0.00011157989501953125 time for calcul the mask position with numpy : 0.006401538848876953 nb_pixel_total : 26 time to create 1 rle with old method : 6.794929504394531e-05 time for calcul the mask position with numpy : 0.006498575210571289 nb_pixel_total : 369 time to create 1 rle with old method : 0.0004467964172363281 time for calcul the mask position with numpy : 0.006642341613769531 nb_pixel_total : 17 time to create 1 rle with old method : 8.487701416015625e-05 time for calcul the mask position with numpy : 0.00724339485168457 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005221366882324219 time for calcul the mask position with numpy : 0.006781578063964844 nb_pixel_total : 623 time to create 1 rle with old method : 0.0007786750793457031 time for calcul the mask position with numpy : 0.006249904632568359 nb_pixel_total : 11 time to create 1 rle with old method : 6.127357482910156e-05 time for calcul the mask position with numpy : 0.006342887878417969 nb_pixel_total : 726 time to create 1 rle with old method : 0.0008511543273925781 time for calcul the mask position with numpy : 0.0066432952880859375 nb_pixel_total : 28 time to create 1 rle with old method : 0.00010704994201660156 time for calcul the mask position with numpy : 0.0077571868896484375 nb_pixel_total : 267 time to create 1 rle with old method : 0.0004868507385253906 time for calcul the mask position with numpy : 0.008311033248901367 nb_pixel_total : 117 time to create 1 rle with old method : 0.00026535987854003906 time for calcul the mask position with numpy : 0.006771087646484375 nb_pixel_total : 1333 time to create 1 rle with old method : 0.0015344619750976562 time for calcul the mask position with numpy : 0.006875514984130859 nb_pixel_total : 1317 time to create 1 rle with old method : 0.001489400863647461 time for calcul the mask position with numpy : 0.0062105655670166016 nb_pixel_total : 1294 time to create 1 rle with old method : 0.001512289047241211 time for calcul the mask position with numpy : 0.0067446231842041016 nb_pixel_total : 14 time to create 1 rle with old method : 5.7697296142578125e-05 time for calcul the mask position with numpy : 0.0067784786224365234 nb_pixel_total : 16 time to create 1 rle with old method : 5.745887756347656e-05 time for calcul the mask position with numpy : 0.006042003631591797 nb_pixel_total : 416 time to create 1 rle with old method : 0.00047779083251953125 time for calcul the mask position with numpy : 0.0059587955474853516 nb_pixel_total : 495 time to create 1 rle with old method : 0.0005953311920166016 time for calcul the mask position with numpy : 0.005963802337646484 nb_pixel_total : 705 time to create 1 rle with old method : 0.0008018016815185547 time for calcul the mask position with numpy : 0.00598454475402832 nb_pixel_total : 3 time to create 1 rle with old method : 3.0994415283203125e-05 time for calcul the mask position with numpy : 0.005875349044799805 nb_pixel_total : 301 time to create 1 rle with old method : 0.000347137451171875 time for calcul the mask position with numpy : 0.005992412567138672 nb_pixel_total : 1561 time to create 1 rle with old method : 0.0016713142395019531 time for calcul the mask position with numpy : 0.005974769592285156 nb_pixel_total : 624 time to create 1 rle with old method : 0.0007491111755371094 time for calcul the mask position with numpy : 0.005877017974853516 nb_pixel_total : 1 time to create 1 rle with old method : 2.2649765014648438e-05 create new chi : 1.502417802810669 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.002678394317626953 batch 1 Loaded 173 chid ids of type : 4230 Number RLEs to save : 14291 TO DO : save crop sub photo not yet done ! save time : 0.8365399837493896 nb_obj : 183 nb_hashtags : 8 time to prepare the origin masks : 2.0550537109375 time for calcul the mask position with numpy : 0.05505514144897461 nb_pixel_total : 1782338 time to create 1 rle with new method : 0.4773073196411133 time for calcul the mask position with numpy : 0.005965232849121094 nb_pixel_total : 66 time to create 1 rle with old method : 0.0001163482666015625 time for calcul the mask position with numpy : 0.006218910217285156 nb_pixel_total : 1688 time to create 1 rle with old method : 0.0019221305847167969 time for calcul the mask position with numpy : 0.009365320205688477 nb_pixel_total : 311 time to create 1 rle with old method : 0.00038933753967285156 time for calcul the mask position with numpy : 0.00957489013671875 nb_pixel_total : 10 time to create 1 rle with old method : 4.863739013671875e-05 time for calcul the mask position with numpy : 0.009162425994873047 nb_pixel_total : 835 time to create 1 rle with old method : 0.0009801387786865234 time for calcul the mask position with numpy : 0.005895137786865234 nb_pixel_total : 75 time to create 1 rle with old method : 0.00013208389282226562 time for calcul the mask position with numpy : 0.005795478820800781 nb_pixel_total : 235 time to create 1 rle with old method : 0.0002913475036621094 time for calcul the mask position with numpy : 0.005991458892822266 nb_pixel_total : 51 time to create 1 rle with old method : 8.654594421386719e-05 time for calcul the mask position with numpy : 0.005937814712524414 nb_pixel_total : 1031 time to create 1 rle with old method : 0.0011820793151855469 time for calcul the mask position with numpy : 0.005741119384765625 nb_pixel_total : 189 time to create 1 rle with old method : 0.0002536773681640625 time for calcul the mask position with numpy : 0.005972146987915039 nb_pixel_total : 283 time to create 1 rle with old method : 0.0003256797790527344 time for calcul the mask position with numpy : 0.005896091461181641 nb_pixel_total : 13 time to create 1 rle with old method : 3.695487976074219e-05 time for calcul the mask position with numpy : 0.0057604312896728516 nb_pixel_total : 214 time to create 1 rle with old method : 0.0002875328063964844 time for calcul the mask position with numpy : 0.005861043930053711 nb_pixel_total : 63 time to create 1 rle with old method : 0.00010538101196289062 time for calcul the mask position with numpy : 0.00579380989074707 nb_pixel_total : 27 time to create 1 rle with old method : 6.580352783203125e-05 time for calcul the mask position with numpy : 0.005895376205444336 nb_pixel_total : 23 time to create 1 rle with old method : 4.506111145019531e-05 time for calcul the mask position with numpy : 0.005972623825073242 nb_pixel_total : 1635 time to create 1 rle with old method : 0.001903533935546875 time for calcul the mask position with numpy : 0.010057687759399414 nb_pixel_total : 646 time to create 1 rle with old method : 0.0008122920989990234 time for calcul the mask position with numpy : 0.010025978088378906 nb_pixel_total : 45 time to create 1 rle with old method : 7.295608520507812e-05 time for calcul the mask position with numpy : 0.009738922119140625 nb_pixel_total : 1074 time to create 1 rle with old method : 0.0012631416320800781 time for calcul the mask position with numpy : 0.00889730453491211 nb_pixel_total : 446 time to create 1 rle with old method : 0.0005755424499511719 time for calcul the mask position with numpy : 0.009683370590209961 nb_pixel_total : 4402 time to create 1 rle with old method : 0.0049228668212890625 time for calcul the mask position with numpy : 0.00943136215209961 nb_pixel_total : 2331 time to create 1 rle with old method : 0.002962827682495117 time for calcul the mask position with numpy : 0.009955167770385742 nb_pixel_total : 12995 time to create 1 rle with old method : 0.014743566513061523 time for calcul the mask position with numpy : 0.00969839096069336 nb_pixel_total : 190 time to create 1 rle with old method : 0.0003643035888671875 time for calcul the mask position with numpy : 0.009630441665649414 nb_pixel_total : 1001 time to create 1 rle with old method : 0.0012447834014892578 time for calcul the mask position with numpy : 0.005938291549682617 nb_pixel_total : 604 time to create 1 rle with old method : 0.0008847713470458984 time for calcul the mask position with numpy : 0.006777286529541016 nb_pixel_total : 2268 time to create 1 rle with old method : 0.003003358840942383 time for calcul the mask position with numpy : 0.006295919418334961 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002646446228027344 time for calcul the mask position with numpy : 0.006378889083862305 nb_pixel_total : 763 time to create 1 rle with old method : 0.0009350776672363281 time for calcul the mask position with numpy : 0.006053924560546875 nb_pixel_total : 992 time to create 1 rle with old method : 0.0012233257293701172 time for calcul the mask position with numpy : 0.006021976470947266 nb_pixel_total : 440 time to create 1 rle with old method : 0.0005764961242675781 time for calcul the mask position with numpy : 0.005991697311401367 nb_pixel_total : 1806 time to create 1 rle with old method : 0.0020668506622314453 time for calcul the mask position with numpy : 0.005972146987915039 nb_pixel_total : 60 time to create 1 rle with old method : 0.00021791458129882812 time for calcul the mask position with numpy : 0.0059185028076171875 nb_pixel_total : 4022 time to create 1 rle with old method : 0.004861116409301758 time for calcul the mask position with numpy : 0.006253480911254883 nb_pixel_total : 768 time to create 1 rle with old method : 0.0010030269622802734 time for calcul the mask position with numpy : 0.006161212921142578 nb_pixel_total : 5861 time to create 1 rle with old method : 0.006841182708740234 time for calcul the mask position with numpy : 0.006219387054443359 nb_pixel_total : 11467 time to create 1 rle with old method : 0.013162851333618164 time for calcul the mask position with numpy : 0.006139516830444336 nb_pixel_total : 103 time to create 1 rle with old method : 0.00015544891357421875 time for calcul the mask position with numpy : 0.0060040950775146484 nb_pixel_total : 40 time to create 1 rle with old method : 0.00010132789611816406 time for calcul the mask position with numpy : 0.006094694137573242 nb_pixel_total : 238 time to create 1 rle with old method : 0.00031948089599609375 time for calcul the mask position with numpy : 0.006120204925537109 nb_pixel_total : 29 time to create 1 rle with old method : 0.0001163482666015625 time for calcul the mask position with numpy : 0.0061147212982177734 nb_pixel_total : 854 time to create 1 rle with old method : 0.0010645389556884766 time for calcul the mask position with numpy : 0.006276369094848633 nb_pixel_total : 94 time to create 1 rle with old method : 0.000141143798828125 time for calcul the mask position with numpy : 0.005877017974853516 nb_pixel_total : 180 time to create 1 rle with old method : 0.00025963783264160156 time for calcul the mask position with numpy : 0.005933284759521484 nb_pixel_total : 12 time to create 1 rle with old method : 5.507469177246094e-05 time for calcul the mask position with numpy : 0.005986452102661133 nb_pixel_total : 1303 time to create 1 rle with old method : 0.0016553401947021484 time for calcul the mask position with numpy : 0.005804300308227539 nb_pixel_total : 14 time to create 1 rle with old method : 8.273124694824219e-05 time for calcul the mask position with numpy : 0.005850791931152344 nb_pixel_total : 977 time to create 1 rle with old method : 0.0012044906616210938 time for calcul the mask position with numpy : 0.005944252014160156 nb_pixel_total : 62 time to create 1 rle with old method : 0.00011396408081054688 time for calcul the mask position with numpy : 0.006295442581176758 nb_pixel_total : 697 time to create 1 rle with old method : 0.0008394718170166016 time for calcul the mask position with numpy : 0.005971193313598633 nb_pixel_total : 228 time to create 1 rle with old method : 0.0002799034118652344 time for calcul the mask position with numpy : 0.006476163864135742 nb_pixel_total : 1269 time to create 1 rle with old method : 0.0014498233795166016 time for calcul the mask position with numpy : 0.005770206451416016 nb_pixel_total : 907 time to create 1 rle with old method : 0.0010745525360107422 time for calcul the mask position with numpy : 0.005940914154052734 nb_pixel_total : 13184 time to create 1 rle with old method : 0.014102935791015625 time for calcul the mask position with numpy : 0.005774259567260742 nb_pixel_total : 20340 time to create 1 rle with old method : 0.022083282470703125 time for calcul the mask position with numpy : 0.006151676177978516 nb_pixel_total : 2132 time to create 1 rle with old method : 0.002852916717529297 time for calcul the mask position with numpy : 0.006030559539794922 nb_pixel_total : 8 time to create 1 rle with old method : 5.793571472167969e-05 time for calcul the mask position with numpy : 0.006051301956176758 nb_pixel_total : 340 time to create 1 rle with old method : 0.00044155120849609375 time for calcul the mask position with numpy : 0.005781412124633789 nb_pixel_total : 16 time to create 1 rle with old method : 7.176399230957031e-05 time for calcul the mask position with numpy : 0.005888462066650391 nb_pixel_total : 491 time to create 1 rle with old method : 0.0006551742553710938 time for calcul the mask position with numpy : 0.005793094635009766 nb_pixel_total : 41 time to create 1 rle with old method : 0.00011086463928222656 time for calcul the mask position with numpy : 0.005944252014160156 nb_pixel_total : 2959 time to create 1 rle with old method : 0.003448963165283203 time for calcul the mask position with numpy : 0.005950212478637695 nb_pixel_total : 13 time to create 1 rle with old method : 6.127357482910156e-05 time for calcul the mask position with numpy : 0.00588226318359375 nb_pixel_total : 62 time to create 1 rle with old method : 0.00014352798461914062 time for calcul the mask position with numpy : 0.005830287933349609 nb_pixel_total : 536 time to create 1 rle with old method : 0.0006568431854248047 time for calcul the mask position with numpy : 0.00579833984375 nb_pixel_total : 354 time to create 1 rle with old method : 0.0004611015319824219 time for calcul the mask position with numpy : 0.0057451725006103516 nb_pixel_total : 1089 time to create 1 rle with old method : 0.0012729167938232422 time for calcul the mask position with numpy : 0.005850076675415039 nb_pixel_total : 118 time to create 1 rle with old method : 0.0001609325408935547 time for calcul the mask position with numpy : 0.00574803352355957 nb_pixel_total : 3 time to create 1 rle with old method : 2.47955322265625e-05 time for calcul the mask position with numpy : 0.005825519561767578 nb_pixel_total : 1326 time to create 1 rle with old method : 0.0015671253204345703 time for calcul the mask position with numpy : 0.0058138370513916016 nb_pixel_total : 5 time to create 1 rle with old method : 4.1484832763671875e-05 time for calcul the mask position with numpy : 0.005896329879760742 nb_pixel_total : 108 time to create 1 rle with old method : 0.00015425682067871094 time for calcul the mask position with numpy : 0.005815744400024414 nb_pixel_total : 109 time to create 1 rle with old method : 0.00017118453979492188 time for calcul the mask position with numpy : 0.005928516387939453 nb_pixel_total : 363 time to create 1 rle with old method : 0.0004410743713378906 time for calcul the mask position with numpy : 0.0057582855224609375 nb_pixel_total : 3 time to create 1 rle with old method : 3.2901763916015625e-05 time for calcul the mask position with numpy : 0.005702972412109375 nb_pixel_total : 1650 time to create 1 rle with old method : 0.0019228458404541016 time for calcul the mask position with numpy : 0.005850076675415039 nb_pixel_total : 123 time to create 1 rle with old method : 0.00017571449279785156 time for calcul the mask position with numpy : 0.005752086639404297 nb_pixel_total : 920 time to create 1 rle with old method : 0.0013384819030761719 time for calcul the mask position with numpy : 0.005755424499511719 nb_pixel_total : 24 time to create 1 rle with old method : 8.606910705566406e-05 time for calcul the mask position with numpy : 0.005835533142089844 nb_pixel_total : 409 time to create 1 rle with old method : 0.0005481243133544922 time for calcul the mask position with numpy : 0.005823612213134766 nb_pixel_total : 71 time to create 1 rle with old method : 0.0001590251922607422 time for calcul the mask position with numpy : 0.005864143371582031 nb_pixel_total : 441 time to create 1 rle with old method : 0.0005881786346435547 time for calcul the mask position with numpy : 0.005884647369384766 nb_pixel_total : 271 time to create 1 rle with old method : 0.0003943443298339844 time for calcul the mask position with numpy : 0.0059049129486083984 nb_pixel_total : 1 time to create 1 rle with old method : 2.6226043701171875e-05 time for calcul the mask position with numpy : 0.005800724029541016 nb_pixel_total : 284 time to create 1 rle with old method : 0.00033545494079589844 time for calcul the mask position with numpy : 0.005900382995605469 nb_pixel_total : 3796 time to create 1 rle with old method : 0.00432133674621582 time for calcul the mask position with numpy : 0.00592494010925293 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005486011505126953 time for calcul the mask position with numpy : 0.005857229232788086 nb_pixel_total : 200 time to create 1 rle with old method : 0.000324249267578125 time for calcul the mask position with numpy : 0.005847454071044922 nb_pixel_total : 473 time to create 1 rle with old method : 0.0005681514739990234 time for calcul the mask position with numpy : 0.005843639373779297 nb_pixel_total : 104 time to create 1 rle with old method : 0.0001690387725830078 time for calcul the mask position with numpy : 0.005837202072143555 nb_pixel_total : 930 time to create 1 rle with old method : 0.0011391639709472656 time for calcul the mask position with numpy : 0.005835533142089844 nb_pixel_total : 10 time to create 1 rle with old method : 7.796287536621094e-05 time for calcul the mask position with numpy : 0.0057909488677978516 nb_pixel_total : 289 time to create 1 rle with old method : 0.00035953521728515625 time for calcul the mask position with numpy : 0.005865812301635742 nb_pixel_total : 162 time to create 1 rle with old method : 0.00024199485778808594 time for calcul the mask position with numpy : 0.005802631378173828 nb_pixel_total : 380 time to create 1 rle with old method : 0.0005164146423339844 time for calcul the mask position with numpy : 0.005919694900512695 nb_pixel_total : 1327 time to create 1 rle with old method : 0.0015985965728759766 time for calcul the mask position with numpy : 0.00586247444152832 nb_pixel_total : 23 time to create 1 rle with old method : 0.00010776519775390625 time for calcul the mask position with numpy : 0.005767822265625 nb_pixel_total : 3 time to create 1 rle with old method : 2.5510787963867188e-05 time for calcul the mask position with numpy : 0.005847930908203125 nb_pixel_total : 7 time to create 1 rle with old method : 8.177757263183594e-05 time for calcul the mask position with numpy : 0.005795955657958984 nb_pixel_total : 129 time to create 1 rle with old method : 0.00018858909606933594 time for calcul the mask position with numpy : 0.006230354309082031 nb_pixel_total : 106089 time to create 1 rle with old method : 0.11473703384399414 time for calcul the mask position with numpy : 0.006216526031494141 nb_pixel_total : 10429 time to create 1 rle with old method : 0.012270212173461914 time for calcul the mask position with numpy : 0.006056785583496094 nb_pixel_total : 110 time to create 1 rle with old method : 0.0001704692840576172 time for calcul the mask position with numpy : 0.006128072738647461 nb_pixel_total : 1718 time to create 1 rle with old method : 0.0020074844360351562 time for calcul the mask position with numpy : 0.005948305130004883 nb_pixel_total : 67 time to create 1 rle with old method : 0.00011301040649414062 time for calcul the mask position with numpy : 0.006032228469848633 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005235671997070312 time for calcul the mask position with numpy : 0.0059185028076171875 nb_pixel_total : 124 time to create 1 rle with old method : 0.00017976760864257812 time for calcul the mask position with numpy : 0.005914926528930664 nb_pixel_total : 266 time to create 1 rle with old method : 0.00033736228942871094 time for calcul the mask position with numpy : 0.006188631057739258 nb_pixel_total : 3440 time to create 1 rle with old method : 0.004078865051269531 time for calcul the mask position with numpy : 0.005951404571533203 nb_pixel_total : 61 time to create 1 rle with old method : 0.00015616416931152344 time for calcul the mask position with numpy : 0.00607609748840332 nb_pixel_total : 930 time to create 1 rle with old method : 0.0011518001556396484 time for calcul the mask position with numpy : 0.006282806396484375 nb_pixel_total : 204 time to create 1 rle with old method : 0.0002582073211669922 time for calcul the mask position with numpy : 0.006373405456542969 nb_pixel_total : 449 time to create 1 rle with old method : 0.0005900859832763672 time for calcul the mask position with numpy : 0.006027936935424805 nb_pixel_total : 85 time to create 1 rle with old method : 0.0001652240753173828 time for calcul the mask position with numpy : 0.00603485107421875 nb_pixel_total : 128 time to create 1 rle with old method : 0.0001952648162841797 time for calcul the mask position with numpy : 0.005960941314697266 nb_pixel_total : 1046 time to create 1 rle with old method : 0.001287698745727539 time for calcul the mask position with numpy : 0.0059506893157958984 nb_pixel_total : 458 time to create 1 rle with old method : 0.0005960464477539062 time for calcul the mask position with numpy : 0.0059545040130615234 nb_pixel_total : 572 time to create 1 rle with old method : 0.0006434917449951172 time for calcul the mask position with numpy : 0.006005525588989258 nb_pixel_total : 136 time to create 1 rle with old method : 0.00021386146545410156 time for calcul the mask position with numpy : 0.005997896194458008 nb_pixel_total : 219 time to create 1 rle with old method : 0.0002799034118652344 time for calcul the mask position with numpy : 0.006302833557128906 nb_pixel_total : 282 time to create 1 rle with old method : 0.00037169456481933594 time for calcul the mask position with numpy : 0.006174325942993164 nb_pixel_total : 70 time to create 1 rle with old method : 0.00012040138244628906 time for calcul the mask position with numpy : 0.006316423416137695 nb_pixel_total : 504 time to create 1 rle with old method : 0.00060272216796875 time for calcul the mask position with numpy : 0.006062746047973633 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003161430358886719 time for calcul the mask position with numpy : 0.006316184997558594 nb_pixel_total : 15 time to create 1 rle with old method : 5.841255187988281e-05 time for calcul the mask position with numpy : 0.006188631057739258 nb_pixel_total : 980 time to create 1 rle with old method : 0.0011799335479736328 time for calcul the mask position with numpy : 0.006117343902587891 nb_pixel_total : 482 time to create 1 rle with old method : 0.0006046295166015625 time for calcul the mask position with numpy : 0.0060923099517822266 nb_pixel_total : 187 time to create 1 rle with old method : 0.00025844573974609375 time for calcul the mask position with numpy : 0.005967855453491211 nb_pixel_total : 886 time to create 1 rle with old method : 0.001050710678100586 time for calcul the mask position with numpy : 0.006163597106933594 nb_pixel_total : 566 time to create 1 rle with old method : 0.000713348388671875 time for calcul the mask position with numpy : 0.0062944889068603516 nb_pixel_total : 261 time to create 1 rle with old method : 0.0003516674041748047 time for calcul the mask position with numpy : 0.006054878234863281 nb_pixel_total : 1202 time to create 1 rle with old method : 0.0015063285827636719 time for calcul the mask position with numpy : 0.0062046051025390625 nb_pixel_total : 15 time to create 1 rle with old method : 5.173683166503906e-05 time for calcul the mask position with numpy : 0.006258964538574219 nb_pixel_total : 4568 time to create 1 rle with old method : 0.005215644836425781 time for calcul the mask position with numpy : 0.0062067508697509766 nb_pixel_total : 1987 time to create 1 rle with old method : 0.0023317337036132812 time for calcul the mask position with numpy : 0.005941867828369141 nb_pixel_total : 58 time to create 1 rle with old method : 9.131431579589844e-05 time for calcul the mask position with numpy : 0.005936145782470703 nb_pixel_total : 160 time to create 1 rle with old method : 0.00019168853759765625 time for calcul the mask position with numpy : 0.0060634613037109375 nb_pixel_total : 760 time to create 1 rle with old method : 0.0009028911590576172 time for calcul the mask position with numpy : 0.008376836776733398 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006077289581298828 time for calcul the mask position with numpy : 0.008315086364746094 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002243518829345703 time for calcul the mask position with numpy : 0.008353948593139648 nb_pixel_total : 153 time to create 1 rle with old method : 0.00018930435180664062 time for calcul the mask position with numpy : 0.00831747055053711 nb_pixel_total : 1321 time to create 1 rle with old method : 0.0014755725860595703 time for calcul the mask position with numpy : 0.008291482925415039 nb_pixel_total : 394 time to create 1 rle with old method : 0.0004699230194091797 time for calcul the mask position with numpy : 0.008406639099121094 nb_pixel_total : 32 time to create 1 rle with old method : 5.793571472167969e-05 time for calcul the mask position with numpy : 0.008432865142822266 nb_pixel_total : 13 time to create 1 rle with old method : 3.600120544433594e-05 time for calcul the mask position with numpy : 0.00838470458984375 nb_pixel_total : 999 time to create 1 rle with old method : 0.0011789798736572266 time for calcul the mask position with numpy : 0.012483358383178711 nb_pixel_total : 6793 time to create 1 rle with old method : 0.007852792739868164 time for calcul the mask position with numpy : 0.008473873138427734 nb_pixel_total : 1601 time to create 1 rle with old method : 0.0018606185913085938 time for calcul the mask position with numpy : 0.008561134338378906 nb_pixel_total : 970 time to create 1 rle with old method : 0.001149892807006836 time for calcul the mask position with numpy : 0.008621454238891602 nb_pixel_total : 576 time to create 1 rle with old method : 0.0007064342498779297 time for calcul the mask position with numpy : 0.008743524551391602 nb_pixel_total : 256 time to create 1 rle with old method : 0.0003268718719482422 time for calcul the mask position with numpy : 0.008343696594238281 nb_pixel_total : 454 time to create 1 rle with old method : 0.0005409717559814453 time for calcul the mask position with numpy : 0.008347034454345703 nb_pixel_total : 481 time to create 1 rle with old method : 0.0005743503570556641 time for calcul the mask position with numpy : 0.008385896682739258 nb_pixel_total : 539 time to create 1 rle with old method : 0.0006365776062011719 time for calcul the mask position with numpy : 0.0082244873046875 nb_pixel_total : 1149 time to create 1 rle with old method : 0.0013370513916015625 time for calcul the mask position with numpy : 0.008266210556030273 nb_pixel_total : 6 time to create 1 rle with old method : 2.002716064453125e-05 time for calcul the mask position with numpy : 0.008305788040161133 nb_pixel_total : 68 time to create 1 rle with old method : 8.940696716308594e-05 time for calcul the mask position with numpy : 0.008242130279541016 nb_pixel_total : 8 time to create 1 rle with old method : 2.8133392333984375e-05 time for calcul the mask position with numpy : 0.008379220962524414 nb_pixel_total : 362 time to create 1 rle with old method : 0.00045108795166015625 time for calcul the mask position with numpy : 0.008341312408447266 nb_pixel_total : 63 time to create 1 rle with old method : 9.846687316894531e-05 time for calcul the mask position with numpy : 0.00827932357788086 nb_pixel_total : 616 time to create 1 rle with old method : 0.00070953369140625 time for calcul the mask position with numpy : 0.008140802383422852 nb_pixel_total : 154 time to create 1 rle with old method : 0.0001881122589111328 time for calcul the mask position with numpy : 0.008204936981201172 nb_pixel_total : 22 time to create 1 rle with old method : 9.465217590332031e-05 time for calcul the mask position with numpy : 0.00830531120300293 nb_pixel_total : 422 time to create 1 rle with old method : 0.00044655799865722656 time for calcul the mask position with numpy : 0.008178234100341797 nb_pixel_total : 281 time to create 1 rle with old method : 0.00031685829162597656 time for calcul the mask position with numpy : 0.008139610290527344 nb_pixel_total : 1254 time to create 1 rle with old method : 0.0013849735260009766 time for calcul the mask position with numpy : 0.008143424987792969 nb_pixel_total : 548 time to create 1 rle with old method : 0.0006527900695800781 time for calcul the mask position with numpy : 0.008259773254394531 nb_pixel_total : 198 time to create 1 rle with old method : 0.00022864341735839844 time for calcul the mask position with numpy : 0.0081787109375 nb_pixel_total : 501 time to create 1 rle with old method : 0.0006017684936523438 time for calcul the mask position with numpy : 0.008064985275268555 nb_pixel_total : 79 time to create 1 rle with old method : 0.00013136863708496094 time for calcul the mask position with numpy : 0.008079290390014648 nb_pixel_total : 11 time to create 1 rle with old method : 3.0040740966796875e-05 time for calcul the mask position with numpy : 0.008095026016235352 nb_pixel_total : 412 time to create 1 rle with old method : 0.0004582405090332031 time for calcul the mask position with numpy : 0.008060216903686523 nb_pixel_total : 3656 time to create 1 rle with old method : 0.004055023193359375 time for calcul the mask position with numpy : 0.00823068618774414 nb_pixel_total : 638 time to create 1 rle with old method : 0.0006926059722900391 time for calcul the mask position with numpy : 0.008309602737426758 nb_pixel_total : 147 time to create 1 rle with old method : 0.0001823902130126953 time for calcul the mask position with numpy : 0.008098602294921875 nb_pixel_total : 1558 time to create 1 rle with old method : 0.0017864704132080078 time for calcul the mask position with numpy : 0.008169412612915039 nb_pixel_total : 229 time to create 1 rle with old method : 0.00027060508728027344 time for calcul the mask position with numpy : 0.00828695297241211 nb_pixel_total : 29 time to create 1 rle with old method : 6.318092346191406e-05 time for calcul the mask position with numpy : 0.008357048034667969 nb_pixel_total : 477 time to create 1 rle with old method : 0.0005819797515869141 time for calcul the mask position with numpy : 0.008128166198730469 nb_pixel_total : 515 time to create 1 rle with old method : 0.0006349086761474609 time for calcul the mask position with numpy : 0.0081329345703125 nb_pixel_total : 107 time to create 1 rle with old method : 0.0001373291015625 time for calcul the mask position with numpy : 0.008218526840209961 nb_pixel_total : 120 time to create 1 rle with old method : 0.0001342296600341797 create new chi : 2.1262869834899902 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0031156539916992188 batch 1 Loaded 205 chid ids of type : 4230 Number RLEs to save : 15381 TO DO : save crop sub photo not yet done ! save time : 0.9268887042999268 nb_obj : 155 nb_hashtags : 7 time to prepare the origin masks : 2.2326791286468506 time for calcul the mask position with numpy : 0.02508234977722168 nb_pixel_total : 1780802 time to create 1 rle with new method : 0.04965949058532715 time for calcul the mask position with numpy : 0.006542682647705078 nb_pixel_total : 1632 time to create 1 rle with old method : 0.0019207000732421875 time for calcul the mask position with numpy : 0.006324052810668945 nb_pixel_total : 199 time to create 1 rle with old method : 0.0002689361572265625 time for calcul the mask position with numpy : 0.006300926208496094 nb_pixel_total : 630 time to create 1 rle with old method : 0.0007874965667724609 time for calcul the mask position with numpy : 0.005785942077636719 nb_pixel_total : 1049 time to create 1 rle with old method : 0.0012803077697753906 time for calcul the mask position with numpy : 0.005888700485229492 nb_pixel_total : 72 time to create 1 rle with old method : 0.00014257431030273438 time for calcul the mask position with numpy : 0.005864143371582031 nb_pixel_total : 166 time to create 1 rle with old method : 0.00021409988403320312 time for calcul the mask position with numpy : 0.005773782730102539 nb_pixel_total : 176 time to create 1 rle with old method : 0.0002200603485107422 time for calcul the mask position with numpy : 0.005897998809814453 nb_pixel_total : 157 time to create 1 rle with old method : 0.0001971721649169922 time for calcul the mask position with numpy : 0.005815029144287109 nb_pixel_total : 214 time to create 1 rle with old method : 0.0002741813659667969 time for calcul the mask position with numpy : 0.0060193538665771484 nb_pixel_total : 168 time to create 1 rle with old method : 0.0002238750457763672 time for calcul the mask position with numpy : 0.005984067916870117 nb_pixel_total : 26 time to create 1 rle with old method : 4.982948303222656e-05 time for calcul the mask position with numpy : 0.006157875061035156 nb_pixel_total : 31 time to create 1 rle with old method : 5.459785461425781e-05 time for calcul the mask position with numpy : 0.0060427188873291016 nb_pixel_total : 1520 time to create 1 rle with old method : 0.0017037391662597656 time for calcul the mask position with numpy : 0.006008148193359375 nb_pixel_total : 221 time to create 1 rle with old method : 0.00028324127197265625 time for calcul the mask position with numpy : 0.006302356719970703 nb_pixel_total : 357 time to create 1 rle with old method : 0.0004239082336425781 time for calcul the mask position with numpy : 0.0061533451080322266 nb_pixel_total : 714 time to create 1 rle with old method : 0.0008511543273925781 time for calcul the mask position with numpy : 0.0060117244720458984 nb_pixel_total : 2882 time to create 1 rle with old method : 0.003395557403564453 time for calcul the mask position with numpy : 0.005826711654663086 nb_pixel_total : 77 time to create 1 rle with old method : 0.00022149085998535156 time for calcul the mask position with numpy : 0.006009817123413086 nb_pixel_total : 1101 time to create 1 rle with old method : 0.0013036727905273438 time for calcul the mask position with numpy : 0.005968570709228516 nb_pixel_total : 1755 time to create 1 rle with old method : 0.002148151397705078 time for calcul the mask position with numpy : 0.005883693695068359 nb_pixel_total : 2352 time to create 1 rle with old method : 0.0027666091918945312 time for calcul the mask position with numpy : 0.005924701690673828 nb_pixel_total : 1563 time to create 1 rle with old method : 0.0018863677978515625 time for calcul the mask position with numpy : 0.00600743293762207 nb_pixel_total : 13922 time to create 1 rle with old method : 0.015266180038452148 time for calcul the mask position with numpy : 0.005941152572631836 nb_pixel_total : 1025 time to create 1 rle with old method : 0.001163482666015625 time for calcul the mask position with numpy : 0.0057065486907958984 nb_pixel_total : 186 time to create 1 rle with old method : 0.0002205371856689453 time for calcul the mask position with numpy : 0.005808115005493164 nb_pixel_total : 774 time to create 1 rle with old method : 0.0008702278137207031 time for calcul the mask position with numpy : 0.005818843841552734 nb_pixel_total : 1418 time to create 1 rle with old method : 0.0016589164733886719 time for calcul the mask position with numpy : 0.005787372589111328 nb_pixel_total : 2225 time to create 1 rle with old method : 0.0025255680084228516 time for calcul the mask position with numpy : 0.00568079948425293 nb_pixel_total : 121 time to create 1 rle with old method : 0.00022101402282714844 time for calcul the mask position with numpy : 0.005628347396850586 nb_pixel_total : 11956 time to create 1 rle with old method : 0.012453317642211914 time for calcul the mask position with numpy : 0.005889892578125 nb_pixel_total : 723 time to create 1 rle with old method : 0.0008134841918945312 time for calcul the mask position with numpy : 0.0058095455169677734 nb_pixel_total : 881 time to create 1 rle with old method : 0.0010020732879638672 time for calcul the mask position with numpy : 0.005842447280883789 nb_pixel_total : 43 time to create 1 rle with old method : 0.00011587142944335938 time for calcul the mask position with numpy : 0.005872964859008789 nb_pixel_total : 153 time to create 1 rle with old method : 0.00021028518676757812 time for calcul the mask position with numpy : 0.006302833557128906 nb_pixel_total : 87 time to create 1 rle with old method : 0.00011920928955078125 time for calcul the mask position with numpy : 0.006247520446777344 nb_pixel_total : 165 time to create 1 rle with old method : 0.00020766258239746094 time for calcul the mask position with numpy : 0.006117105484008789 nb_pixel_total : 1365 time to create 1 rle with old method : 0.0016515254974365234 time for calcul the mask position with numpy : 0.005880594253540039 nb_pixel_total : 11 time to create 1 rle with old method : 4.029273986816406e-05 time for calcul the mask position with numpy : 0.006132841110229492 nb_pixel_total : 71 time to create 1 rle with old method : 0.00010538101196289062 time for calcul the mask position with numpy : 0.005918741226196289 nb_pixel_total : 447 time to create 1 rle with old method : 0.0005342960357666016 time for calcul the mask position with numpy : 0.005949258804321289 nb_pixel_total : 700 time to create 1 rle with old method : 0.000789642333984375 time for calcul the mask position with numpy : 0.006017923355102539 nb_pixel_total : 255 time to create 1 rle with old method : 0.0003135204315185547 time for calcul the mask position with numpy : 0.005935192108154297 nb_pixel_total : 1259 time to create 1 rle with old method : 0.001489877700805664 time for calcul the mask position with numpy : 0.005948543548583984 nb_pixel_total : 15 time to create 1 rle with old method : 5.555152893066406e-05 time for calcul the mask position with numpy : 0.00587916374206543 nb_pixel_total : 915 time to create 1 rle with old method : 0.0011506080627441406 time for calcul the mask position with numpy : 0.00593256950378418 nb_pixel_total : 13814 time to create 1 rle with old method : 0.0159914493560791 time for calcul the mask position with numpy : 0.006068229675292969 nb_pixel_total : 878 time to create 1 rle with old method : 0.0011491775512695312 time for calcul the mask position with numpy : 0.0059125423431396484 nb_pixel_total : 268 time to create 1 rle with old method : 0.00035119056701660156 time for calcul the mask position with numpy : 0.005942344665527344 nb_pixel_total : 1009 time to create 1 rle with old method : 0.0010960102081298828 time for calcul the mask position with numpy : 0.006089687347412109 nb_pixel_total : 21016 time to create 1 rle with old method : 0.02242136001586914 time for calcul the mask position with numpy : 0.00594639778137207 nb_pixel_total : 643 time to create 1 rle with old method : 0.0007073879241943359 time for calcul the mask position with numpy : 0.005876064300537109 nb_pixel_total : 123 time to create 1 rle with old method : 0.0003368854522705078 time for calcul the mask position with numpy : 0.005822658538818359 nb_pixel_total : 267 time to create 1 rle with old method : 0.0003571510314941406 time for calcul the mask position with numpy : 0.005846738815307617 nb_pixel_total : 423 time to create 1 rle with old method : 0.0005140304565429688 time for calcul the mask position with numpy : 0.005944490432739258 nb_pixel_total : 1939 time to create 1 rle with old method : 0.00231170654296875 time for calcul the mask position with numpy : 0.0060520172119140625 nb_pixel_total : 564 time to create 1 rle with old method : 0.0006988048553466797 time for calcul the mask position with numpy : 0.00623011589050293 nb_pixel_total : 35 time to create 1 rle with old method : 6.723403930664062e-05 time for calcul the mask position with numpy : 0.006245136260986328 nb_pixel_total : 869 time to create 1 rle with old method : 0.0009779930114746094 time for calcul the mask position with numpy : 0.006051301956176758 nb_pixel_total : 118 time to create 1 rle with old method : 0.00016069412231445312 time for calcul the mask position with numpy : 0.00608372688293457 nb_pixel_total : 924 time to create 1 rle with old method : 0.0011637210845947266 time for calcul the mask position with numpy : 0.006213188171386719 nb_pixel_total : 3241 time to create 1 rle with old method : 0.0037994384765625 time for calcul the mask position with numpy : 0.005974769592285156 nb_pixel_total : 184 time to create 1 rle with old method : 0.00024008750915527344 time for calcul the mask position with numpy : 0.006074190139770508 nb_pixel_total : 1495 time to create 1 rle with old method : 0.0016472339630126953 time for calcul the mask position with numpy : 0.005864143371582031 nb_pixel_total : 1532 time to create 1 rle with old method : 0.0018811225891113281 time for calcul the mask position with numpy : 0.006656646728515625 nb_pixel_total : 112 time to create 1 rle with old method : 0.00015592575073242188 time for calcul the mask position with numpy : 0.006441354751586914 nb_pixel_total : 470 time to create 1 rle with old method : 0.0005922317504882812 time for calcul the mask position with numpy : 0.0063610076904296875 nb_pixel_total : 296 time to create 1 rle with old method : 0.0003814697265625 time for calcul the mask position with numpy : 0.006049394607543945 nb_pixel_total : 2446 time to create 1 rle with old method : 0.0029273033142089844 time for calcul the mask position with numpy : 0.005759716033935547 nb_pixel_total : 375 time to create 1 rle with old method : 0.0004222393035888672 time for calcul the mask position with numpy : 0.005967140197753906 nb_pixel_total : 254 time to create 1 rle with old method : 0.0002994537353515625 time for calcul the mask position with numpy : 0.00613856315612793 nb_pixel_total : 277 time to create 1 rle with old method : 0.0003464221954345703 time for calcul the mask position with numpy : 0.005888700485229492 nb_pixel_total : 896 time to create 1 rle with old method : 0.0010533332824707031 time for calcul the mask position with numpy : 0.006285190582275391 nb_pixel_total : 5 time to create 1 rle with old method : 3.7670135498046875e-05 time for calcul the mask position with numpy : 0.005906343460083008 nb_pixel_total : 23 time to create 1 rle with old method : 6.127357482910156e-05 time for calcul the mask position with numpy : 0.0060882568359375 nb_pixel_total : 298 time to create 1 rle with old method : 0.0003757476806640625 time for calcul the mask position with numpy : 0.005948305130004883 nb_pixel_total : 108 time to create 1 rle with old method : 0.0001881122589111328 time for calcul the mask position with numpy : 0.005833864212036133 nb_pixel_total : 1263 time to create 1 rle with old method : 0.0014781951904296875 time for calcul the mask position with numpy : 0.005871295928955078 nb_pixel_total : 11 time to create 1 rle with old method : 7.2479248046875e-05 time for calcul the mask position with numpy : 0.005951881408691406 nb_pixel_total : 18 time to create 1 rle with old method : 4.38690185546875e-05 time for calcul the mask position with numpy : 0.006448984146118164 nb_pixel_total : 106314 time to create 1 rle with old method : 0.11130642890930176 time for calcul the mask position with numpy : 0.006127119064331055 nb_pixel_total : 127 time to create 1 rle with old method : 0.0001761913299560547 time for calcul the mask position with numpy : 0.005948066711425781 nb_pixel_total : 27 time to create 1 rle with old method : 6.175041198730469e-05 time for calcul the mask position with numpy : 0.005858182907104492 nb_pixel_total : 1812 time to create 1 rle with old method : 0.002074003219604492 time for calcul the mask position with numpy : 0.0058956146240234375 nb_pixel_total : 10775 time to create 1 rle with old method : 0.01215052604675293 time for calcul the mask position with numpy : 0.0058858394622802734 nb_pixel_total : 283 time to create 1 rle with old method : 0.00035858154296875 time for calcul the mask position with numpy : 0.009874582290649414 nb_pixel_total : 117 time to create 1 rle with old method : 0.00016117095947265625 time for calcul the mask position with numpy : 0.005728960037231445 nb_pixel_total : 257 time to create 1 rle with old method : 0.00032973289489746094 time for calcul the mask position with numpy : 0.005662441253662109 nb_pixel_total : 1 time to create 1 rle with old method : 1.811981201171875e-05 time for calcul the mask position with numpy : 0.005648136138916016 nb_pixel_total : 189 time to create 1 rle with old method : 0.00022339820861816406 time for calcul the mask position with numpy : 0.0057108402252197266 nb_pixel_total : 399 time to create 1 rle with old method : 0.0004730224609375 time for calcul the mask position with numpy : 0.005860805511474609 nb_pixel_total : 1425 time to create 1 rle with old method : 0.0016634464263916016 time for calcul the mask position with numpy : 0.005786418914794922 nb_pixel_total : 261 time to create 1 rle with old method : 0.0003170967102050781 time for calcul the mask position with numpy : 0.00639033317565918 nb_pixel_total : 155 time to create 1 rle with old method : 0.00021719932556152344 time for calcul the mask position with numpy : 0.005863666534423828 nb_pixel_total : 160 time to create 1 rle with old method : 0.00019812583923339844 time for calcul the mask position with numpy : 0.0059053897857666016 nb_pixel_total : 442 time to create 1 rle with old method : 0.0005600452423095703 time for calcul the mask position with numpy : 0.005988359451293945 nb_pixel_total : 520 time to create 1 rle with old method : 0.0007002353668212891 time for calcul the mask position with numpy : 0.006001472473144531 nb_pixel_total : 10323 time to create 1 rle with old method : 0.011382579803466797 time for calcul the mask position with numpy : 0.0060460567474365234 nb_pixel_total : 551 time to create 1 rle with old method : 0.0007011890411376953 time for calcul the mask position with numpy : 0.00584101676940918 nb_pixel_total : 245 time to create 1 rle with old method : 0.0002932548522949219 time for calcul the mask position with numpy : 0.0060236454010009766 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005323886871337891 time for calcul the mask position with numpy : 0.005814790725708008 nb_pixel_total : 239 time to create 1 rle with old method : 0.0003039836883544922 time for calcul the mask position with numpy : 0.005924224853515625 nb_pixel_total : 1667 time to create 1 rle with old method : 0.001917123794555664 time for calcul the mask position with numpy : 0.0059850215911865234 nb_pixel_total : 1407 time to create 1 rle with old method : 0.0016319751739501953 time for calcul the mask position with numpy : 0.00599217414855957 nb_pixel_total : 254 time to create 1 rle with old method : 0.0004100799560546875 time for calcul the mask position with numpy : 0.0059185028076171875 nb_pixel_total : 943 time to create 1 rle with old method : 0.0010933876037597656 time for calcul the mask position with numpy : 0.005914926528930664 nb_pixel_total : 275 time to create 1 rle with old method : 0.00034546852111816406 time for calcul the mask position with numpy : 0.0059356689453125 nb_pixel_total : 455 time to create 1 rle with old method : 0.0005526542663574219 time for calcul the mask position with numpy : 0.006036043167114258 nb_pixel_total : 874 time to create 1 rle with old method : 0.0012297630310058594 time for calcul the mask position with numpy : 0.006011247634887695 nb_pixel_total : 486 time to create 1 rle with old method : 0.0005941390991210938 time for calcul the mask position with numpy : 0.006035327911376953 nb_pixel_total : 1889 time to create 1 rle with old method : 0.0021712779998779297 time for calcul the mask position with numpy : 0.00593113899230957 nb_pixel_total : 184 time to create 1 rle with old method : 0.00023627281188964844 time for calcul the mask position with numpy : 0.005828142166137695 nb_pixel_total : 912 time to create 1 rle with old method : 0.0009953975677490234 time for calcul the mask position with numpy : 0.0059299468994140625 nb_pixel_total : 187 time to create 1 rle with old method : 0.00024819374084472656 time for calcul the mask position with numpy : 0.005908489227294922 nb_pixel_total : 4512 time to create 1 rle with old method : 0.00499725341796875 time for calcul the mask position with numpy : 0.00575709342956543 nb_pixel_total : 395 time to create 1 rle with old method : 0.00046563148498535156 time for calcul the mask position with numpy : 0.005645036697387695 nb_pixel_total : 13 time to create 1 rle with old method : 5.984306335449219e-05 time for calcul the mask position with numpy : 0.0058231353759765625 nb_pixel_total : 57 time to create 1 rle with old method : 0.00011157989501953125 time for calcul the mask position with numpy : 0.00557708740234375 nb_pixel_total : 438 time to create 1 rle with old method : 0.00047135353088378906 time for calcul the mask position with numpy : 0.005739450454711914 nb_pixel_total : 1599 time to create 1 rle with old method : 0.0017442703247070312 time for calcul the mask position with numpy : 0.005724191665649414 nb_pixel_total : 324 time to create 1 rle with old method : 0.0003628730773925781 time for calcul the mask position with numpy : 0.0056917667388916016 nb_pixel_total : 20 time to create 1 rle with old method : 7.200241088867188e-05 time for calcul the mask position with numpy : 0.005847930908203125 nb_pixel_total : 1623 time to create 1 rle with old method : 0.0017666816711425781 time for calcul the mask position with numpy : 0.005850791931152344 nb_pixel_total : 814 time to create 1 rle with old method : 0.0009317398071289062 time for calcul the mask position with numpy : 0.006021022796630859 nb_pixel_total : 4537 time to create 1 rle with old method : 0.005084514617919922 time for calcul the mask position with numpy : 0.00601506233215332 nb_pixel_total : 151 time to create 1 rle with old method : 0.00020432472229003906 time for calcul the mask position with numpy : 0.0059430599212646484 nb_pixel_total : 896 time to create 1 rle with old method : 0.0009374618530273438 time for calcul the mask position with numpy : 0.005929231643676758 nb_pixel_total : 537 time to create 1 rle with old method : 0.0005950927734375 time for calcul the mask position with numpy : 0.00599360466003418 nb_pixel_total : 247 time to create 1 rle with old method : 0.00030875205993652344 time for calcul the mask position with numpy : 0.0058634281158447266 nb_pixel_total : 507 time to create 1 rle with old method : 0.000560760498046875 time for calcul the mask position with numpy : 0.005936384201049805 nb_pixel_total : 504 time to create 1 rle with old method : 0.0005621910095214844 time for calcul the mask position with numpy : 0.0059506893157958984 nb_pixel_total : 1303 time to create 1 rle with old method : 0.0015070438385009766 time for calcul the mask position with numpy : 0.005823850631713867 nb_pixel_total : 74 time to create 1 rle with old method : 9.417533874511719e-05 time for calcul the mask position with numpy : 0.0058345794677734375 nb_pixel_total : 345 time to create 1 rle with old method : 0.0003879070281982422 time for calcul the mask position with numpy : 0.005678653717041016 nb_pixel_total : 679 time to create 1 rle with old method : 0.000762939453125 time for calcul the mask position with numpy : 0.005822420120239258 nb_pixel_total : 752 time to create 1 rle with old method : 0.0008275508880615234 time for calcul the mask position with numpy : 0.0058171749114990234 nb_pixel_total : 131 time to create 1 rle with old method : 0.0001685619354248047 time for calcul the mask position with numpy : 0.005916595458984375 nb_pixel_total : 470 time to create 1 rle with old method : 0.00054168701171875 time for calcul the mask position with numpy : 0.00607752799987793 nb_pixel_total : 231 time to create 1 rle with old method : 0.00028634071350097656 time for calcul the mask position with numpy : 0.005934238433837891 nb_pixel_total : 1517 time to create 1 rle with old method : 0.0016999244689941406 time for calcul the mask position with numpy : 0.005895853042602539 nb_pixel_total : 1170 time to create 1 rle with old method : 0.0015006065368652344 time for calcul the mask position with numpy : 0.005955934524536133 nb_pixel_total : 143 time to create 1 rle with old method : 0.0002205371856689453 time for calcul the mask position with numpy : 0.0059757232666015625 nb_pixel_total : 741 time to create 1 rle with old method : 0.0008838176727294922 time for calcul the mask position with numpy : 0.005968809127807617 nb_pixel_total : 225 time to create 1 rle with old method : 0.000270843505859375 time for calcul the mask position with numpy : 0.0060923099517822266 nb_pixel_total : 503 time to create 1 rle with old method : 0.0005795955657958984 time for calcul the mask position with numpy : 0.006052494049072266 nb_pixel_total : 470 time to create 1 rle with old method : 0.0005640983581542969 time for calcul the mask position with numpy : 0.006099700927734375 nb_pixel_total : 5 time to create 1 rle with old method : 5.14984130859375e-05 time for calcul the mask position with numpy : 0.005904674530029297 nb_pixel_total : 2419 time to create 1 rle with old method : 0.0026013851165771484 time for calcul the mask position with numpy : 0.006090402603149414 nb_pixel_total : 3782 time to create 1 rle with old method : 0.004389762878417969 time for calcul the mask position with numpy : 0.0060024261474609375 nb_pixel_total : 642 time to create 1 rle with old method : 0.0007231235504150391 time for calcul the mask position with numpy : 0.005855083465576172 nb_pixel_total : 3 time to create 1 rle with old method : 2.09808349609375e-05 time for calcul the mask position with numpy : 0.006020784378051758 nb_pixel_total : 1554 time to create 1 rle with old method : 0.0016853809356689453 time for calcul the mask position with numpy : 0.005967378616333008 nb_pixel_total : 426 time to create 1 rle with old method : 0.0005097389221191406 time for calcul the mask position with numpy : 0.006075859069824219 nb_pixel_total : 825 time to create 1 rle with old method : 0.0009644031524658203 time for calcul the mask position with numpy : 0.005961894989013672 nb_pixel_total : 561 time to create 1 rle with old method : 0.0006098747253417969 time for calcul the mask position with numpy : 0.00600433349609375 nb_pixel_total : 86 time to create 1 rle with old method : 0.00011944770812988281 create new chi : 1.3322017192840576 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.002682209014892578 batch 1 Loaded 169 chid ids of type : 4230 Number RLEs to save : 14825 TO DO : save crop sub photo not yet done ! save time : 0.9617271423339844 nb_obj : 163 nb_hashtags : 7 time to prepare the origin masks : 1.5181667804718018 time for calcul the mask position with numpy : 0.024378299713134766 nb_pixel_total : 1732812 time to create 1 rle with new method : 0.05058884620666504 time for calcul the mask position with numpy : 0.010065317153930664 nb_pixel_total : 1360 time to create 1 rle with old method : 0.001575469970703125 time for calcul the mask position with numpy : 0.009534120559692383 nb_pixel_total : 3276 time to create 1 rle with old method : 0.0036940574645996094 time for calcul the mask position with numpy : 0.0059168338775634766 nb_pixel_total : 2774 time to create 1 rle with old method : 0.0031876564025878906 time for calcul the mask position with numpy : 0.0058748722076416016 nb_pixel_total : 1351 time to create 1 rle with old method : 0.0016160011291503906 time for calcul the mask position with numpy : 0.005826234817504883 nb_pixel_total : 2416 time to create 1 rle with old method : 0.002724885940551758 time for calcul the mask position with numpy : 0.005873918533325195 nb_pixel_total : 252 time to create 1 rle with old method : 0.0003108978271484375 time for calcul the mask position with numpy : 0.005934238433837891 nb_pixel_total : 39 time to create 1 rle with old method : 7.104873657226562e-05 time for calcul the mask position with numpy : 0.009784460067749023 nb_pixel_total : 79 time to create 1 rle with old method : 0.00012636184692382812 time for calcul the mask position with numpy : 0.009522199630737305 nb_pixel_total : 16 time to create 1 rle with old method : 4.1961669921875e-05 time for calcul the mask position with numpy : 0.009761810302734375 nb_pixel_total : 1119 time to create 1 rle with old method : 0.0013651847839355469 time for calcul the mask position with numpy : 0.007544040679931641 nb_pixel_total : 71 time to create 1 rle with old method : 0.00010371208190917969 time for calcul the mask position with numpy : 0.005965471267700195 nb_pixel_total : 185 time to create 1 rle with old method : 0.00023984909057617188 time for calcul the mask position with numpy : 0.005949735641479492 nb_pixel_total : 322 time to create 1 rle with old method : 0.00038909912109375 time for calcul the mask position with numpy : 0.006467580795288086 nb_pixel_total : 154 time to create 1 rle with old method : 0.00020360946655273438 time for calcul the mask position with numpy : 0.009795188903808594 nb_pixel_total : 1053 time to create 1 rle with old method : 0.0012218952178955078 time for calcul the mask position with numpy : 0.00987100601196289 nb_pixel_total : 28 time to create 1 rle with old method : 5.459785461425781e-05 time for calcul the mask position with numpy : 0.010548114776611328 nb_pixel_total : 48975 time to create 1 rle with old method : 0.051885128021240234 time for calcul the mask position with numpy : 0.009811162948608398 nb_pixel_total : 15 time to create 1 rle with old method : 3.695487976074219e-05 time for calcul the mask position with numpy : 0.009750843048095703 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003299713134765625 time for calcul the mask position with numpy : 0.010021686553955078 nb_pixel_total : 66 time to create 1 rle with old method : 0.000125885009765625 time for calcul the mask position with numpy : 0.01011967658996582 nb_pixel_total : 720 time to create 1 rle with old method : 0.000919342041015625 time for calcul the mask position with numpy : 0.0062062740325927734 nb_pixel_total : 10725 time to create 1 rle with old method : 0.012427330017089844 time for calcul the mask position with numpy : 0.01107645034790039 nb_pixel_total : 184 time to create 1 rle with old method : 0.00024628639221191406 time for calcul the mask position with numpy : 0.010244369506835938 nb_pixel_total : 2483 time to create 1 rle with old method : 0.0029268264770507812 time for calcul the mask position with numpy : 0.009952783584594727 nb_pixel_total : 362 time to create 1 rle with old method : 0.0004286766052246094 time for calcul the mask position with numpy : 0.009657859802246094 nb_pixel_total : 40 time to create 1 rle with old method : 0.00010013580322265625 time for calcul the mask position with numpy : 0.009611129760742188 nb_pixel_total : 1515 time to create 1 rle with old method : 0.0017807483673095703 time for calcul the mask position with numpy : 0.009768009185791016 nb_pixel_total : 5859 time to create 1 rle with old method : 0.0070116519927978516 time for calcul the mask position with numpy : 0.009936094284057617 nb_pixel_total : 11726 time to create 1 rle with old method : 0.01339864730834961 time for calcul the mask position with numpy : 0.008240461349487305 nb_pixel_total : 116 time to create 1 rle with old method : 0.00014662742614746094 time for calcul the mask position with numpy : 0.0058782100677490234 nb_pixel_total : 821 time to create 1 rle with old method : 0.0008847713470458984 time for calcul the mask position with numpy : 0.0058972835540771484 nb_pixel_total : 91 time to create 1 rle with old method : 0.00012445449829101562 time for calcul the mask position with numpy : 0.005820751190185547 nb_pixel_total : 172 time to create 1 rle with old method : 0.00022459030151367188 time for calcul the mask position with numpy : 0.005890846252441406 nb_pixel_total : 1049 time to create 1 rle with old method : 0.0012097358703613281 time for calcul the mask position with numpy : 0.00601959228515625 nb_pixel_total : 1069 time to create 1 rle with old method : 0.001287221908569336 time for calcul the mask position with numpy : 0.0063266754150390625 nb_pixel_total : 80 time to create 1 rle with old method : 0.00011539459228515625 time for calcul the mask position with numpy : 0.006294965744018555 nb_pixel_total : 159 time to create 1 rle with old method : 0.00022792816162109375 time for calcul the mask position with numpy : 0.006276130676269531 nb_pixel_total : 712 time to create 1 rle with old method : 0.0008389949798583984 time for calcul the mask position with numpy : 0.006066083908081055 nb_pixel_total : 220 time to create 1 rle with old method : 0.0002620220184326172 time for calcul the mask position with numpy : 0.006224155426025391 nb_pixel_total : 16130 time to create 1 rle with old method : 0.018175601959228516 time for calcul the mask position with numpy : 0.0067942142486572266 nb_pixel_total : 1259 time to create 1 rle with old method : 0.0014863014221191406 time for calcul the mask position with numpy : 0.006422281265258789 nb_pixel_total : 1014 time to create 1 rle with old method : 0.001264333724975586 time for calcul the mask position with numpy : 0.006181001663208008 nb_pixel_total : 17640 time to create 1 rle with old method : 0.019861698150634766 time for calcul the mask position with numpy : 0.0064640045166015625 nb_pixel_total : 351 time to create 1 rle with old method : 0.0004436969757080078 time for calcul the mask position with numpy : 0.006094694137573242 nb_pixel_total : 1309 time to create 1 rle with old method : 0.0015265941619873047 time for calcul the mask position with numpy : 0.006051540374755859 nb_pixel_total : 71 time to create 1 rle with old method : 0.00011682510375976562 time for calcul the mask position with numpy : 0.0061435699462890625 nb_pixel_total : 270 time to create 1 rle with old method : 0.00038695335388183594 time for calcul the mask position with numpy : 0.00650787353515625 nb_pixel_total : 51 time to create 1 rle with old method : 0.00011181831359863281 time for calcul the mask position with numpy : 0.006510019302368164 nb_pixel_total : 2035 time to create 1 rle with old method : 0.0024437904357910156 time for calcul the mask position with numpy : 0.006047487258911133 nb_pixel_total : 13 time to create 1 rle with old method : 7.534027099609375e-05 time for calcul the mask position with numpy : 0.0060269832611083984 nb_pixel_total : 229 time to create 1 rle with old method : 0.0004143714904785156 time for calcul the mask position with numpy : 0.005899190902709961 nb_pixel_total : 548 time to create 1 rle with old method : 0.0006780624389648438 time for calcul the mask position with numpy : 0.0059926509857177734 nb_pixel_total : 11 time to create 1 rle with old method : 4.863739013671875e-05 time for calcul the mask position with numpy : 0.006092548370361328 nb_pixel_total : 872 time to create 1 rle with old method : 0.0010333061218261719 time for calcul the mask position with numpy : 0.0059740543365478516 nb_pixel_total : 476 time to create 1 rle with old method : 0.0005886554718017578 time for calcul the mask position with numpy : 0.005888938903808594 nb_pixel_total : 139 time to create 1 rle with old method : 0.0001785755157470703 time for calcul the mask position with numpy : 0.006054878234863281 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016069412231445312 time for calcul the mask position with numpy : 0.0059680938720703125 nb_pixel_total : 756 time to create 1 rle with old method : 0.0008749961853027344 time for calcul the mask position with numpy : 0.0058710575103759766 nb_pixel_total : 839 time to create 1 rle with old method : 0.0010161399841308594 time for calcul the mask position with numpy : 0.005876779556274414 nb_pixel_total : 3501 time to create 1 rle with old method : 0.00400543212890625 time for calcul the mask position with numpy : 0.00584721565246582 nb_pixel_total : 5 time to create 1 rle with old method : 3.4332275390625e-05 time for calcul the mask position with numpy : 0.005822896957397461 nb_pixel_total : 10 time to create 1 rle with old method : 4.410743713378906e-05 time for calcul the mask position with numpy : 0.005812644958496094 nb_pixel_total : 1328 time to create 1 rle with old method : 0.0016257762908935547 time for calcul the mask position with numpy : 0.005860090255737305 nb_pixel_total : 1630 time to create 1 rle with old method : 0.001991748809814453 time for calcul the mask position with numpy : 0.005825996398925781 nb_pixel_total : 118 time to create 1 rle with old method : 0.00017213821411132812 time for calcul the mask position with numpy : 0.0059168338775634766 nb_pixel_total : 483 time to create 1 rle with old method : 0.00058746337890625 time for calcul the mask position with numpy : 0.0061457157135009766 nb_pixel_total : 57 time to create 1 rle with old method : 0.00012350082397460938 time for calcul the mask position with numpy : 0.005898237228393555 nb_pixel_total : 344 time to create 1 rle with old method : 0.000457763671875 time for calcul the mask position with numpy : 0.0058553218841552734 nb_pixel_total : 2998 time to create 1 rle with old method : 0.0036296844482421875 time for calcul the mask position with numpy : 0.005983829498291016 nb_pixel_total : 2284 time to create 1 rle with old method : 0.0026869773864746094 time for calcul the mask position with numpy : 0.005850791931152344 nb_pixel_total : 948 time to create 1 rle with old method : 0.0011272430419921875 time for calcul the mask position with numpy : 0.005952596664428711 nb_pixel_total : 350 time to create 1 rle with old method : 0.0004558563232421875 time for calcul the mask position with numpy : 0.006186962127685547 nb_pixel_total : 718 time to create 1 rle with old method : 0.0007719993591308594 time for calcul the mask position with numpy : 0.005992889404296875 nb_pixel_total : 295 time to create 1 rle with old method : 0.0003707408905029297 time for calcul the mask position with numpy : 0.00586700439453125 nb_pixel_total : 82 time to create 1 rle with old method : 0.00015115737915039062 time for calcul the mask position with numpy : 0.0060520172119140625 nb_pixel_total : 1284 time to create 1 rle with old method : 0.0015244483947753906 time for calcul the mask position with numpy : 0.005959033966064453 nb_pixel_total : 265 time to create 1 rle with old method : 0.0003299713134765625 time for calcul the mask position with numpy : 0.005743503570556641 nb_pixel_total : 1 time to create 1 rle with old method : 1.8835067749023438e-05 time for calcul the mask position with numpy : 0.006656646728515625 nb_pixel_total : 106684 time to create 1 rle with old method : 0.11331057548522949 time for calcul the mask position with numpy : 0.0060274600982666016 nb_pixel_total : 7098 time to create 1 rle with old method : 0.008270740509033203 time for calcul the mask position with numpy : 0.00588679313659668 nb_pixel_total : 881 time to create 1 rle with old method : 0.001092672348022461 time for calcul the mask position with numpy : 0.005810737609863281 nb_pixel_total : 1914 time to create 1 rle with old method : 0.0021071434020996094 time for calcul the mask position with numpy : 0.006036281585693359 nb_pixel_total : 1752 time to create 1 rle with old method : 0.002034425735473633 time for calcul the mask position with numpy : 0.005970478057861328 nb_pixel_total : 271 time to create 1 rle with old method : 0.0003426074981689453 time for calcul the mask position with numpy : 0.0060427188873291016 nb_pixel_total : 108 time to create 1 rle with old method : 0.0001499652862548828 time for calcul the mask position with numpy : 0.005860328674316406 nb_pixel_total : 258 time to create 1 rle with old method : 0.0004010200500488281 time for calcul the mask position with numpy : 0.005789518356323242 nb_pixel_total : 309 time to create 1 rle with old method : 0.00036597251892089844 time for calcul the mask position with numpy : 0.005731821060180664 nb_pixel_total : 443 time to create 1 rle with old method : 0.0005917549133300781 time for calcul the mask position with numpy : 0.005951404571533203 nb_pixel_total : 781 time to create 1 rle with old method : 0.0009584426879882812 time for calcul the mask position with numpy : 0.005814790725708008 nb_pixel_total : 516 time to create 1 rle with old method : 0.0005984306335449219 time for calcul the mask position with numpy : 0.005873441696166992 nb_pixel_total : 198 time to create 1 rle with old method : 0.00025963783264160156 time for calcul the mask position with numpy : 0.005971670150756836 nb_pixel_total : 96 time to create 1 rle with old method : 0.0001513957977294922 time for calcul the mask position with numpy : 0.0062713623046875 nb_pixel_total : 51 time to create 1 rle with old method : 0.00010132789611816406 time for calcul the mask position with numpy : 0.006057024002075195 nb_pixel_total : 438 time to create 1 rle with old method : 0.0005159378051757812 time for calcul the mask position with numpy : 0.005961894989013672 nb_pixel_total : 1059 time to create 1 rle with old method : 0.0012793540954589844 time for calcul the mask position with numpy : 0.005806684494018555 nb_pixel_total : 169 time to create 1 rle with old method : 0.00021696090698242188 time for calcul the mask position with numpy : 0.0068035125732421875 nb_pixel_total : 163 time to create 1 rle with old method : 0.0001995563507080078 time for calcul the mask position with numpy : 0.005810976028442383 nb_pixel_total : 215 time to create 1 rle with old method : 0.000278472900390625 time for calcul the mask position with numpy : 0.005872488021850586 nb_pixel_total : 101 time to create 1 rle with old method : 0.00016260147094726562 time for calcul the mask position with numpy : 0.006011247634887695 nb_pixel_total : 594 time to create 1 rle with old method : 0.0007214546203613281 time for calcul the mask position with numpy : 0.0059359073638916016 nb_pixel_total : 527 time to create 1 rle with old method : 0.0006444454193115234 time for calcul the mask position with numpy : 0.005963802337646484 nb_pixel_total : 292 time to create 1 rle with old method : 0.00035309791564941406 time for calcul the mask position with numpy : 0.006064414978027344 nb_pixel_total : 7295 time to create 1 rle with old method : 0.008129119873046875 time for calcul the mask position with numpy : 0.00594329833984375 nb_pixel_total : 466 time to create 1 rle with old method : 0.0005307197570800781 time for calcul the mask position with numpy : 0.0059356689453125 nb_pixel_total : 244 time to create 1 rle with old method : 0.0003120899200439453 time for calcul the mask position with numpy : 0.005827665328979492 nb_pixel_total : 1458 time to create 1 rle with old method : 0.0017511844635009766 time for calcul the mask position with numpy : 0.006063699722290039 nb_pixel_total : 1833 time to create 1 rle with old method : 0.0020210742950439453 time for calcul the mask position with numpy : 0.005918741226196289 nb_pixel_total : 16 time to create 1 rle with old method : 5.054473876953125e-05 time for calcul the mask position with numpy : 0.006158113479614258 nb_pixel_total : 964 time to create 1 rle with old method : 0.0011341571807861328 time for calcul the mask position with numpy : 0.00996708869934082 nb_pixel_total : 916 time to create 1 rle with old method : 0.0010669231414794922 time for calcul the mask position with numpy : 0.0059468746185302734 nb_pixel_total : 337 time to create 1 rle with old method : 0.00040411949157714844 time for calcul the mask position with numpy : 0.005964040756225586 nb_pixel_total : 27 time to create 1 rle with old method : 4.696846008300781e-05 time for calcul the mask position with numpy : 0.005781888961791992 nb_pixel_total : 190 time to create 1 rle with old method : 0.00023746490478515625 time for calcul the mask position with numpy : 0.0057566165924072266 nb_pixel_total : 513 time to create 1 rle with old method : 0.0005807876586914062 time for calcul the mask position with numpy : 0.005864143371582031 nb_pixel_total : 536 time to create 1 rle with old method : 0.00058746337890625 time for calcul the mask position with numpy : 0.005785942077636719 nb_pixel_total : 386 time to create 1 rle with old method : 0.0004627704620361328 time for calcul the mask position with numpy : 0.0060613155364990234 nb_pixel_total : 5005 time to create 1 rle with old method : 0.00548553466796875 time for calcul the mask position with numpy : 0.0060803890228271484 nb_pixel_total : 1847 time to create 1 rle with old method : 0.002176523208618164 time for calcul the mask position with numpy : 0.0060617923736572266 nb_pixel_total : 1397 time to create 1 rle with old method : 0.0016674995422363281 time for calcul the mask position with numpy : 0.006063699722290039 nb_pixel_total : 833 time to create 1 rle with old method : 0.0009646415710449219 time for calcul the mask position with numpy : 0.006015777587890625 nb_pixel_total : 28 time to create 1 rle with old method : 8.034706115722656e-05 time for calcul the mask position with numpy : 0.005733013153076172 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002453327178955078 time for calcul the mask position with numpy : 0.006012439727783203 nb_pixel_total : 489 time to create 1 rle with old method : 0.0006034374237060547 time for calcul the mask position with numpy : 0.0059146881103515625 nb_pixel_total : 29 time to create 1 rle with old method : 8.082389831542969e-05 time for calcul the mask position with numpy : 0.005890846252441406 nb_pixel_total : 128 time to create 1 rle with old method : 0.00016450881958007812 time for calcul the mask position with numpy : 0.0057141780853271484 nb_pixel_total : 1634 time to create 1 rle with old method : 0.0018227100372314453 time for calcul the mask position with numpy : 0.005788326263427734 nb_pixel_total : 159 time to create 1 rle with old method : 0.00020885467529296875 time for calcul the mask position with numpy : 0.00602269172668457 nb_pixel_total : 52 time to create 1 rle with old method : 9.34600830078125e-05 time for calcul the mask position with numpy : 0.006090641021728516 nb_pixel_total : 1673 time to create 1 rle with old method : 0.0020568370819091797 time for calcul the mask position with numpy : 0.006082773208618164 nb_pixel_total : 588 time to create 1 rle with old method : 0.0007436275482177734 time for calcul the mask position with numpy : 0.006119728088378906 nb_pixel_total : 1004 time to create 1 rle with old method : 0.0011858940124511719 time for calcul the mask position with numpy : 0.006002664566040039 nb_pixel_total : 7 time to create 1 rle with old method : 4.4345855712890625e-05 time for calcul the mask position with numpy : 0.005737781524658203 nb_pixel_total : 4994 time to create 1 rle with old method : 0.005805253982543945 time for calcul the mask position with numpy : 0.0060884952545166016 nb_pixel_total : 2 time to create 1 rle with old method : 3.218650817871094e-05 time for calcul the mask position with numpy : 0.006236553192138672 nb_pixel_total : 856 time to create 1 rle with old method : 0.0009944438934326172 time for calcul the mask position with numpy : 0.005777597427368164 nb_pixel_total : 1516 time to create 1 rle with old method : 0.0016942024230957031 time for calcul the mask position with numpy : 0.005902767181396484 nb_pixel_total : 641 time to create 1 rle with old method : 0.0008149147033691406 time for calcul the mask position with numpy : 0.006178617477416992 nb_pixel_total : 255 time to create 1 rle with old method : 0.00035309791564941406 time for calcul the mask position with numpy : 0.006150007247924805 nb_pixel_total : 11 time to create 1 rle with old method : 3.4809112548828125e-05 time for calcul the mask position with numpy : 0.0060193538665771484 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006241798400878906 time for calcul the mask position with numpy : 0.0061299800872802734 nb_pixel_total : 542 time to create 1 rle with old method : 0.0006575584411621094 time for calcul the mask position with numpy : 0.006145477294921875 nb_pixel_total : 74 time to create 1 rle with old method : 0.00010466575622558594 time for calcul the mask position with numpy : 0.005962371826171875 nb_pixel_total : 2 time to create 1 rle with old method : 2.1457672119140625e-05 time for calcul the mask position with numpy : 0.005973100662231445 nb_pixel_total : 8 time to create 1 rle with old method : 4.0531158447265625e-05 time for calcul the mask position with numpy : 0.005973100662231445 nb_pixel_total : 308 time to create 1 rle with old method : 0.0003638267517089844 time for calcul the mask position with numpy : 0.006120443344116211 nb_pixel_total : 671 time to create 1 rle with old method : 0.0007910728454589844 time for calcul the mask position with numpy : 0.0057332515716552734 nb_pixel_total : 113 time to create 1 rle with old method : 0.00015974044799804688 time for calcul the mask position with numpy : 0.005955696105957031 nb_pixel_total : 260 time to create 1 rle with old method : 0.00032210350036621094 time for calcul the mask position with numpy : 0.006038188934326172 nb_pixel_total : 46 time to create 1 rle with old method : 7.939338684082031e-05 time for calcul the mask position with numpy : 0.006098270416259766 nb_pixel_total : 1395 time to create 1 rle with old method : 0.0016469955444335938 time for calcul the mask position with numpy : 0.005949258804321289 nb_pixel_total : 954 time to create 1 rle with old method : 0.0010154247283935547 time for calcul the mask position with numpy : 0.006124258041381836 nb_pixel_total : 662 time to create 1 rle with old method : 0.0007846355438232422 time for calcul the mask position with numpy : 0.006110191345214844 nb_pixel_total : 488 time to create 1 rle with old method : 0.0005977153778076172 time for calcul the mask position with numpy : 0.005825996398925781 nb_pixel_total : 49 time to create 1 rle with old method : 8.153915405273438e-05 time for calcul the mask position with numpy : 0.0058939456939697266 nb_pixel_total : 339 time to create 1 rle with old method : 0.00041413307189941406 time for calcul the mask position with numpy : 0.005780458450317383 nb_pixel_total : 76 time to create 1 rle with old method : 0.00019121170043945312 time for calcul the mask position with numpy : 0.006067991256713867 nb_pixel_total : 4226 time to create 1 rle with old method : 0.0051441192626953125 time for calcul the mask position with numpy : 0.006217479705810547 nb_pixel_total : 675 time to create 1 rle with old method : 0.0008018016815185547 time for calcul the mask position with numpy : 0.00612187385559082 nb_pixel_total : 592 time to create 1 rle with old method : 0.00070953369140625 time for calcul the mask position with numpy : 0.005812168121337891 nb_pixel_total : 371 time to create 1 rle with old method : 0.00041174888610839844 time for calcul the mask position with numpy : 0.005951881408691406 nb_pixel_total : 1490 time to create 1 rle with old method : 0.0017092227935791016 time for calcul the mask position with numpy : 0.00883626937866211 nb_pixel_total : 617 time to create 1 rle with old method : 0.0007617473602294922 time for calcul the mask position with numpy : 0.005993843078613281 nb_pixel_total : 125 time to create 1 rle with old method : 0.00016379356384277344 create new chi : 1.526639461517334 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0028443336486816406 batch 1 Loaded 166 chid ids of type : 4230 Number RLEs to save : 15793 TO DO : save crop sub photo not yet done ! save time : 0.9119517803192139 nb_obj : 154 nb_hashtags : 8 time to prepare the origin masks : 1.651597261428833 time for calcul the mask position with numpy : 0.2390427589416504 nb_pixel_total : 1728575 time to create 1 rle with new method : 0.14112162590026855 time for calcul the mask position with numpy : 0.009899616241455078 nb_pixel_total : 3242 time to create 1 rle with old method : 0.003787994384765625 time for calcul the mask position with numpy : 0.0060422420501708984 nb_pixel_total : 8 time to create 1 rle with old method : 7.486343383789062e-05 time for calcul the mask position with numpy : 0.005957841873168945 nb_pixel_total : 1538 time to create 1 rle with old method : 0.0018224716186523438 time for calcul the mask position with numpy : 0.0062389373779296875 nb_pixel_total : 3032 time to create 1 rle with old method : 0.003511190414428711 time for calcul the mask position with numpy : 0.009850263595581055 nb_pixel_total : 66 time to create 1 rle with old method : 0.00011920928955078125 time for calcul the mask position with numpy : 0.009741067886352539 nb_pixel_total : 51 time to create 1 rle with old method : 0.00010275840759277344 time for calcul the mask position with numpy : 0.010106563568115234 nb_pixel_total : 59 time to create 1 rle with old method : 0.00011205673217773438 time for calcul the mask position with numpy : 0.00620269775390625 nb_pixel_total : 1252 time to create 1 rle with old method : 0.0015501976013183594 time for calcul the mask position with numpy : 0.006038665771484375 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003314018249511719 time for calcul the mask position with numpy : 0.0060923099517822266 nb_pixel_total : 2059 time to create 1 rle with old method : 0.002432107925415039 time for calcul the mask position with numpy : 0.0060846805572509766 nb_pixel_total : 50 time to create 1 rle with old method : 8.749961853027344e-05 time for calcul the mask position with numpy : 0.006033182144165039 nb_pixel_total : 346 time to create 1 rle with old method : 0.0005741119384765625 time for calcul the mask position with numpy : 0.006574153900146484 nb_pixel_total : 218 time to create 1 rle with old method : 0.00028395652770996094 time for calcul the mask position with numpy : 0.0060579776763916016 nb_pixel_total : 233 time to create 1 rle with old method : 0.00031495094299316406 time for calcul the mask position with numpy : 0.006198883056640625 nb_pixel_total : 199 time to create 1 rle with old method : 0.00027108192443847656 time for calcul the mask position with numpy : 0.005899190902709961 nb_pixel_total : 19 time to create 1 rle with old method : 5.2928924560546875e-05 time for calcul the mask position with numpy : 0.006009817123413086 nb_pixel_total : 385 time to create 1 rle with old method : 0.0006701946258544922 time for calcul the mask position with numpy : 0.006500959396362305 nb_pixel_total : 41458 time to create 1 rle with old method : 0.04445242881774902 time for calcul the mask position with numpy : 0.006063699722290039 nb_pixel_total : 22 time to create 1 rle with old method : 4.792213439941406e-05 time for calcul the mask position with numpy : 0.006472349166870117 nb_pixel_total : 1374 time to create 1 rle with old method : 0.0020551681518554688 time for calcul the mask position with numpy : 0.007081270217895508 nb_pixel_total : 657 time to create 1 rle with old method : 0.0009293556213378906 time for calcul the mask position with numpy : 0.00641942024230957 nb_pixel_total : 244 time to create 1 rle with old method : 0.00032830238342285156 time for calcul the mask position with numpy : 0.010087013244628906 nb_pixel_total : 3858 time to create 1 rle with old method : 0.004453182220458984 time for calcul the mask position with numpy : 0.01022648811340332 nb_pixel_total : 824 time to create 1 rle with old method : 0.0010085105895996094 time for calcul the mask position with numpy : 0.010088920593261719 nb_pixel_total : 726 time to create 1 rle with old method : 0.0008788108825683594 time for calcul the mask position with numpy : 0.009902238845825195 nb_pixel_total : 13391 time to create 1 rle with old method : 0.014911651611328125 time for calcul the mask position with numpy : 0.009892463684082031 nb_pixel_total : 203 time to create 1 rle with old method : 0.00028228759765625 time for calcul the mask position with numpy : 0.009970903396606445 nb_pixel_total : 1661 time to create 1 rle with old method : 0.002001047134399414 time for calcul the mask position with numpy : 0.013946771621704102 nb_pixel_total : 2560 time to create 1 rle with old method : 0.0030565261840820312 time for calcul the mask position with numpy : 0.010178327560424805 nb_pixel_total : 6148 time to create 1 rle with old method : 0.007231235504150391 time for calcul the mask position with numpy : 0.010139942169189453 nb_pixel_total : 11363 time to create 1 rle with old method : 0.013029098510742188 time for calcul the mask position with numpy : 0.010190248489379883 nb_pixel_total : 110 time to create 1 rle with old method : 0.0001666545867919922 time for calcul the mask position with numpy : 0.010116338729858398 nb_pixel_total : 693 time to create 1 rle with old method : 0.0009250640869140625 time for calcul the mask position with numpy : 0.010215282440185547 nb_pixel_total : 103 time to create 1 rle with old method : 0.00015735626220703125 time for calcul the mask position with numpy : 0.0060329437255859375 nb_pixel_total : 170 time to create 1 rle with old method : 0.00024318695068359375 time for calcul the mask position with numpy : 0.005887746810913086 nb_pixel_total : 973 time to create 1 rle with old method : 0.0011310577392578125 time for calcul the mask position with numpy : 0.006052494049072266 nb_pixel_total : 78 time to create 1 rle with old method : 0.00012302398681640625 time for calcul the mask position with numpy : 0.006113767623901367 nb_pixel_total : 17 time to create 1 rle with old method : 5.888938903808594e-05 time for calcul the mask position with numpy : 0.005907535552978516 nb_pixel_total : 476 time to create 1 rle with old method : 0.0006067752838134766 time for calcul the mask position with numpy : 0.0059621334075927734 nb_pixel_total : 667 time to create 1 rle with old method : 0.0008132457733154297 time for calcul the mask position with numpy : 0.005742788314819336 nb_pixel_total : 206 time to create 1 rle with old method : 0.0002460479736328125 time for calcul the mask position with numpy : 0.005995750427246094 nb_pixel_total : 1273 time to create 1 rle with old method : 0.0015265941619873047 time for calcul the mask position with numpy : 0.006117343902587891 nb_pixel_total : 883 time to create 1 rle with old method : 0.0011053085327148438 time for calcul the mask position with numpy : 0.006211519241333008 nb_pixel_total : 19185 time to create 1 rle with old method : 0.021632909774780273 time for calcul the mask position with numpy : 0.006330966949462891 nb_pixel_total : 12867 time to create 1 rle with old method : 0.014674901962280273 time for calcul the mask position with numpy : 0.006059169769287109 nb_pixel_total : 565 time to create 1 rle with old method : 0.0007162094116210938 time for calcul the mask position with numpy : 0.005966663360595703 nb_pixel_total : 126 time to create 1 rle with old method : 0.0001857280731201172 time for calcul the mask position with numpy : 0.005980253219604492 nb_pixel_total : 2250 time to create 1 rle with old method : 0.006852149963378906 time for calcul the mask position with numpy : 0.010106086730957031 nb_pixel_total : 571 time to create 1 rle with old method : 0.0007412433624267578 time for calcul the mask position with numpy : 0.010108709335327148 nb_pixel_total : 1457 time to create 1 rle with old method : 0.0017409324645996094 time for calcul the mask position with numpy : 0.010049581527709961 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005342960357666016 time for calcul the mask position with numpy : 0.010075807571411133 nb_pixel_total : 104 time to create 1 rle with old method : 0.00016069412231445312 time for calcul the mask position with numpy : 0.00980377197265625 nb_pixel_total : 894 time to create 1 rle with old method : 0.001123666763305664 time for calcul the mask position with numpy : 0.009655475616455078 nb_pixel_total : 2784 time to create 1 rle with old method : 0.003246784210205078 time for calcul the mask position with numpy : 0.010030746459960938 nb_pixel_total : 1365 time to create 1 rle with old method : 0.0016243457794189453 time for calcul the mask position with numpy : 0.010125875473022461 nb_pixel_total : 109 time to create 1 rle with old method : 0.00015664100646972656 time for calcul the mask position with numpy : 0.011465787887573242 nb_pixel_total : 1577 time to create 1 rle with old method : 0.0019311904907226562 time for calcul the mask position with numpy : 0.010355234146118164 nb_pixel_total : 111 time to create 1 rle with old method : 0.00018286705017089844 time for calcul the mask position with numpy : 0.009966611862182617 nb_pixel_total : 976 time to create 1 rle with old method : 0.001176595687866211 time for calcul the mask position with numpy : 0.010179996490478516 nb_pixel_total : 456 time to create 1 rle with old method : 0.0006306171417236328 time for calcul the mask position with numpy : 0.010090351104736328 nb_pixel_total : 3421 time to create 1 rle with old method : 0.004128694534301758 time for calcul the mask position with numpy : 0.010261774063110352 nb_pixel_total : 272 time to create 1 rle with old method : 0.00035953521728515625 time for calcul the mask position with numpy : 0.010390758514404297 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002453327178955078 time for calcul the mask position with numpy : 0.010394811630249023 nb_pixel_total : 1301 time to create 1 rle with old method : 0.0016341209411621094 time for calcul the mask position with numpy : 0.010128974914550781 nb_pixel_total : 916 time to create 1 rle with old method : 0.0011510848999023438 time for calcul the mask position with numpy : 0.010313272476196289 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004818439483642578 time for calcul the mask position with numpy : 0.010142087936401367 nb_pixel_total : 367 time to create 1 rle with old method : 0.0004601478576660156 time for calcul the mask position with numpy : 0.010064125061035156 nb_pixel_total : 238 time to create 1 rle with old method : 0.0003476142883300781 time for calcul the mask position with numpy : 0.010398149490356445 nb_pixel_total : 986 time to create 1 rle with old method : 0.0011873245239257812 time for calcul the mask position with numpy : 0.010504484176635742 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002853870391845703 time for calcul the mask position with numpy : 0.011046886444091797 nb_pixel_total : 106845 time to create 1 rle with old method : 0.11961197853088379 time for calcul the mask position with numpy : 0.010273933410644531 nb_pixel_total : 68 time to create 1 rle with old method : 0.00012350082397460938 time for calcul the mask position with numpy : 0.010459184646606445 nb_pixel_total : 11066 time to create 1 rle with old method : 0.012815237045288086 time for calcul the mask position with numpy : 0.010087251663208008 nb_pixel_total : 31 time to create 1 rle with old method : 7.200241088867188e-05 time for calcul the mask position with numpy : 0.010039091110229492 nb_pixel_total : 132 time to create 1 rle with old method : 0.00019669532775878906 time for calcul the mask position with numpy : 0.009796142578125 nb_pixel_total : 72 time to create 1 rle with old method : 0.00011658668518066406 time for calcul the mask position with numpy : 0.009954214096069336 nb_pixel_total : 117 time to create 1 rle with old method : 0.00017499923706054688 time for calcul the mask position with numpy : 0.009718179702758789 nb_pixel_total : 719 time to create 1 rle with old method : 0.0009005069732666016 time for calcul the mask position with numpy : 0.009865760803222656 nb_pixel_total : 224 time to create 1 rle with old method : 0.0003104209899902344 time for calcul the mask position with numpy : 0.010660648345947266 nb_pixel_total : 225 time to create 1 rle with old method : 0.0003097057342529297 time for calcul the mask position with numpy : 0.010112762451171875 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002498626708984375 time for calcul the mask position with numpy : 0.010110855102539062 nb_pixel_total : 455 time to create 1 rle with old method : 0.0006048679351806641 time for calcul the mask position with numpy : 0.010006427764892578 nb_pixel_total : 295 time to create 1 rle with old method : 0.0003838539123535156 time for calcul the mask position with numpy : 0.009998083114624023 nb_pixel_total : 1212 time to create 1 rle with old method : 0.0014176368713378906 time for calcul the mask position with numpy : 0.010061979293823242 nb_pixel_total : 1872 time to create 1 rle with old method : 0.002132415771484375 time for calcul the mask position with numpy : 0.010089635848999023 nb_pixel_total : 174 time to create 1 rle with old method : 0.0002353191375732422 time for calcul the mask position with numpy : 0.010105609893798828 nb_pixel_total : 525 time to create 1 rle with old method : 0.0006806850433349609 time for calcul the mask position with numpy : 0.010188102722167969 nb_pixel_total : 19 time to create 1 rle with old method : 6.842613220214844e-05 time for calcul the mask position with numpy : 0.010259151458740234 nb_pixel_total : 9388 time to create 1 rle with old method : 0.010870695114135742 time for calcul the mask position with numpy : 0.010158777236938477 nb_pixel_total : 594 time to create 1 rle with old method : 0.0007469654083251953 time for calcul the mask position with numpy : 0.01011347770690918 nb_pixel_total : 336 time to create 1 rle with old method : 0.0004107952117919922 time for calcul the mask position with numpy : 0.009652376174926758 nb_pixel_total : 495 time to create 1 rle with old method : 0.0006377696990966797 time for calcul the mask position with numpy : 0.01012563705444336 nb_pixel_total : 572 time to create 1 rle with old method : 0.000720977783203125 time for calcul the mask position with numpy : 0.013068437576293945 nb_pixel_total : 458 time to create 1 rle with old method : 0.0006124973297119141 time for calcul the mask position with numpy : 0.01015782356262207 nb_pixel_total : 1295 time to create 1 rle with old method : 0.0015816688537597656 time for calcul the mask position with numpy : 0.013833284378051758 nb_pixel_total : 367 time to create 1 rle with old method : 0.0004725456237792969 time for calcul the mask position with numpy : 0.010096073150634766 nb_pixel_total : 219 time to create 1 rle with old method : 0.0002956390380859375 time for calcul the mask position with numpy : 0.010118722915649414 nb_pixel_total : 1844 time to create 1 rle with old method : 0.002134561538696289 time for calcul the mask position with numpy : 0.009988546371459961 nb_pixel_total : 157 time to create 1 rle with old method : 0.00028777122497558594 time for calcul the mask position with numpy : 0.010174036026000977 nb_pixel_total : 10 time to create 1 rle with old method : 4.3392181396484375e-05 time for calcul the mask position with numpy : 0.010154962539672852 nb_pixel_total : 298 time to create 1 rle with old method : 0.00039768218994140625 time for calcul the mask position with numpy : 0.010032176971435547 nb_pixel_total : 250 time to create 1 rle with old method : 0.0003426074981689453 time for calcul the mask position with numpy : 0.010325193405151367 nb_pixel_total : 48 time to create 1 rle with old method : 0.00013208389282226562 time for calcul the mask position with numpy : 0.010468721389770508 nb_pixel_total : 477 time to create 1 rle with old method : 0.0006477832794189453 time for calcul the mask position with numpy : 0.010106325149536133 nb_pixel_total : 815 time to create 1 rle with old method : 0.0009856224060058594 time for calcul the mask position with numpy : 0.012980461120605469 nb_pixel_total : 2033 time to create 1 rle with old method : 0.003935575485229492 time for calcul the mask position with numpy : 0.012137651443481445 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002727508544921875 time for calcul the mask position with numpy : 0.010327339172363281 nb_pixel_total : 427 time to create 1 rle with old method : 0.0007040500640869141 time for calcul the mask position with numpy : 0.010744094848632812 nb_pixel_total : 1262 time to create 1 rle with old method : 0.0015518665313720703 time for calcul the mask position with numpy : 0.012861251831054688 nb_pixel_total : 50 time to create 1 rle with old method : 0.00014090538024902344 time for calcul the mask position with numpy : 0.010309219360351562 nb_pixel_total : 801 time to create 1 rle with old method : 0.0010366439819335938 time for calcul the mask position with numpy : 0.01099705696105957 nb_pixel_total : 29 time to create 1 rle with old method : 0.00013947486877441406 time for calcul the mask position with numpy : 0.010679960250854492 nb_pixel_total : 242 time to create 1 rle with old method : 0.0003256797790527344 time for calcul the mask position with numpy : 0.014636993408203125 nb_pixel_total : 173 time to create 1 rle with old method : 0.0002613067626953125 time for calcul the mask position with numpy : 0.010691642761230469 nb_pixel_total : 4238 time to create 1 rle with old method : 0.004992485046386719 time for calcul the mask position with numpy : 0.01008915901184082 nb_pixel_total : 393 time to create 1 rle with old method : 0.0004904270172119141 time for calcul the mask position with numpy : 0.009926795959472656 nb_pixel_total : 120 time to create 1 rle with old method : 0.00017642974853515625 time for calcul the mask position with numpy : 0.010460138320922852 nb_pixel_total : 1553 time to create 1 rle with old method : 0.0017309188842773438 time for calcul the mask position with numpy : 0.009702682495117188 nb_pixel_total : 465 time to create 1 rle with old method : 0.000568389892578125 time for calcul the mask position with numpy : 0.009972333908081055 nb_pixel_total : 35 time to create 1 rle with old method : 7.414817810058594e-05 time for calcul the mask position with numpy : 0.01001596450805664 nb_pixel_total : 14 time to create 1 rle with old method : 5.6743621826171875e-05 time for calcul the mask position with numpy : 0.009998559951782227 nb_pixel_total : 45 time to create 1 rle with old method : 0.00010538101196289062 time for calcul the mask position with numpy : 0.010158538818359375 nb_pixel_total : 1655 time to create 1 rle with old method : 0.0019457340240478516 time for calcul the mask position with numpy : 0.010050773620605469 nb_pixel_total : 341 time to create 1 rle with old method : 0.0003902912139892578 time for calcul the mask position with numpy : 0.010269403457641602 nb_pixel_total : 761 time to create 1 rle with old method : 0.0009062290191650391 time for calcul the mask position with numpy : 0.00998544692993164 nb_pixel_total : 12160 time to create 1 rle with old method : 0.013840675354003906 time for calcul the mask position with numpy : 0.01202392578125 nb_pixel_total : 216 time to create 1 rle with old method : 0.0004184246063232422 time for calcul the mask position with numpy : 0.010872364044189453 nb_pixel_total : 852 time to create 1 rle with old method : 0.001001119613647461 time for calcul the mask position with numpy : 0.010108232498168945 nb_pixel_total : 584 time to create 1 rle with old method : 0.000720977783203125 time for calcul the mask position with numpy : 0.008410930633544922 nb_pixel_total : 261 time to create 1 rle with old method : 0.0003154277801513672 time for calcul the mask position with numpy : 0.012510299682617188 nb_pixel_total : 769 time to create 1 rle with old method : 0.0009217262268066406 time for calcul the mask position with numpy : 0.009268760681152344 nb_pixel_total : 525 time to create 1 rle with old method : 0.0010366439819335938 time for calcul the mask position with numpy : 0.008764028549194336 nb_pixel_total : 74 time to create 1 rle with old method : 0.00010251998901367188 time for calcul the mask position with numpy : 0.008463144302368164 nb_pixel_total : 347 time to create 1 rle with old method : 0.0004062652587890625 time for calcul the mask position with numpy : 0.008575916290283203 nb_pixel_total : 135 time to create 1 rle with old method : 0.0001823902130126953 time for calcul the mask position with numpy : 0.008478879928588867 nb_pixel_total : 615 time to create 1 rle with old method : 0.0007243156433105469 time for calcul the mask position with numpy : 0.00846099853515625 nb_pixel_total : 120 time to create 1 rle with old method : 0.00015616416931152344 time for calcul the mask position with numpy : 0.008486032485961914 nb_pixel_total : 29 time to create 1 rle with old method : 5.888938903808594e-05 time for calcul the mask position with numpy : 0.00850987434387207 nb_pixel_total : 140 time to create 1 rle with old method : 0.00017762184143066406 time for calcul the mask position with numpy : 0.008469104766845703 nb_pixel_total : 1940 time to create 1 rle with old method : 0.0021512508392333984 time for calcul the mask position with numpy : 0.008383035659790039 nb_pixel_total : 1221 time to create 1 rle with old method : 0.0013537406921386719 time for calcul the mask position with numpy : 0.008413553237915039 nb_pixel_total : 557 time to create 1 rle with old method : 0.0006978511810302734 time for calcul the mask position with numpy : 0.008518457412719727 nb_pixel_total : 5 time to create 1 rle with old method : 3.600120544433594e-05 time for calcul the mask position with numpy : 0.00848078727722168 nb_pixel_total : 400 time to create 1 rle with old method : 0.00048661231994628906 time for calcul the mask position with numpy : 0.008484840393066406 nb_pixel_total : 497 time to create 1 rle with old method : 0.0005946159362792969 time for calcul the mask position with numpy : 0.008373737335205078 nb_pixel_total : 4657 time to create 1 rle with old method : 0.005541801452636719 time for calcul the mask position with numpy : 0.008403539657592773 nb_pixel_total : 734 time to create 1 rle with old method : 0.0009829998016357422 time for calcul the mask position with numpy : 0.008442163467407227 nb_pixel_total : 1307 time to create 1 rle with old method : 0.001438140869140625 time for calcul the mask position with numpy : 0.008147001266479492 nb_pixel_total : 1573 time to create 1 rle with old method : 0.0017762184143066406 time for calcul the mask position with numpy : 0.00848388671875 nb_pixel_total : 272 time to create 1 rle with old method : 0.0003314018249511719 time for calcul the mask position with numpy : 0.008211612701416016 nb_pixel_total : 178 time to create 1 rle with old method : 0.0002391338348388672 time for calcul the mask position with numpy : 0.008421182632446289 nb_pixel_total : 460 time to create 1 rle with old method : 0.0005576610565185547 time for calcul the mask position with numpy : 0.008384466171264648 nb_pixel_total : 21 time to create 1 rle with old method : 3.933906555175781e-05 time for calcul the mask position with numpy : 0.008393049240112305 nb_pixel_total : 626 time to create 1 rle with old method : 0.0007393360137939453 create new chi : 2.2275631427764893 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0030221939086914062 batch 1 Loaded 156 chid ids of type : 4230 Number RLEs to save : 15302 TO DO : save crop sub photo not yet done ! save time : 0.9341773986816406 nb_obj : 194 nb_hashtags : 7 time to prepare the origin masks : 2.0374538898468018 time for calcul the mask position with numpy : 0.08683919906616211 nb_pixel_total : 1729035 time to create 1 rle with new method : 0.6777889728546143 time for calcul the mask position with numpy : 0.006528615951538086 nb_pixel_total : 105 time to create 1 rle with old method : 0.00017404556274414062 time for calcul the mask position with numpy : 0.0061686038970947266 nb_pixel_total : 3253 time to create 1 rle with old method : 0.0036728382110595703 time for calcul the mask position with numpy : 0.0061492919921875 nb_pixel_total : 988 time to create 1 rle with old method : 0.0012485980987548828 time for calcul the mask position with numpy : 0.0063054561614990234 nb_pixel_total : 1839 time to create 1 rle with old method : 0.0020761489868164062 time for calcul the mask position with numpy : 0.006170988082885742 nb_pixel_total : 1523 time to create 1 rle with old method : 0.0018506050109863281 time for calcul the mask position with numpy : 0.006487131118774414 nb_pixel_total : 770 time to create 1 rle with old method : 0.0009584426879882812 time for calcul the mask position with numpy : 0.006589412689208984 nb_pixel_total : 2528 time to create 1 rle with old method : 0.003664731979370117 time for calcul the mask position with numpy : 0.006612539291381836 nb_pixel_total : 144 time to create 1 rle with old method : 0.0002484321594238281 time for calcul the mask position with numpy : 0.006928682327270508 nb_pixel_total : 370 time to create 1 rle with old method : 0.0006155967712402344 time for calcul the mask position with numpy : 0.006788969039916992 nb_pixel_total : 1433 time to create 1 rle with old method : 0.001733541488647461 time for calcul the mask position with numpy : 0.006483793258666992 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003464221954345703 time for calcul the mask position with numpy : 0.006087779998779297 nb_pixel_total : 42 time to create 1 rle with old method : 7.05718994140625e-05 time for calcul the mask position with numpy : 0.006331443786621094 nb_pixel_total : 53 time to create 1 rle with old method : 9.679794311523438e-05 time for calcul the mask position with numpy : 0.006727933883666992 nb_pixel_total : 113 time to create 1 rle with old method : 0.00017786026000976562 time for calcul the mask position with numpy : 0.006673097610473633 nb_pixel_total : 244 time to create 1 rle with old method : 0.00032782554626464844 time for calcul the mask position with numpy : 0.006815195083618164 nb_pixel_total : 35 time to create 1 rle with old method : 7.748603820800781e-05 time for calcul the mask position with numpy : 0.006613254547119141 nb_pixel_total : 146 time to create 1 rle with old method : 0.0002186298370361328 time for calcul the mask position with numpy : 0.007773399353027344 nb_pixel_total : 4365 time to create 1 rle with old method : 0.0061283111572265625 time for calcul the mask position with numpy : 0.0065500736236572266 nb_pixel_total : 134 time to create 1 rle with old method : 0.00020074844360351562 time for calcul the mask position with numpy : 0.006761312484741211 nb_pixel_total : 1960 time to create 1 rle with old method : 0.0029036998748779297 time for calcul the mask position with numpy : 0.008073091506958008 nb_pixel_total : 39592 time to create 1 rle with old method : 0.04515957832336426 time for calcul the mask position with numpy : 0.006833314895629883 nb_pixel_total : 1326 time to create 1 rle with old method : 0.0016646385192871094 time for calcul the mask position with numpy : 0.006629228591918945 nb_pixel_total : 753 time to create 1 rle with old method : 0.0010585784912109375 time for calcul the mask position with numpy : 0.006433963775634766 nb_pixel_total : 9 time to create 1 rle with old method : 5.459785461425781e-05 time for calcul the mask position with numpy : 0.006426811218261719 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002830028533935547 time for calcul the mask position with numpy : 0.007108449935913086 nb_pixel_total : 77 time to create 1 rle with old method : 0.00022482872009277344 time for calcul the mask position with numpy : 0.006846427917480469 nb_pixel_total : 6523 time to create 1 rle with old method : 0.007848501205444336 time for calcul the mask position with numpy : 0.006945133209228516 nb_pixel_total : 707 time to create 1 rle with old method : 0.0009071826934814453 time for calcul the mask position with numpy : 0.0065882205963134766 nb_pixel_total : 869 time to create 1 rle with old method : 0.0011222362518310547 time for calcul the mask position with numpy : 0.006571054458618164 nb_pixel_total : 11240 time to create 1 rle with old method : 0.013309001922607422 time for calcul the mask position with numpy : 0.006886959075927734 nb_pixel_total : 590 time to create 1 rle with old method : 0.0008959770202636719 time for calcul the mask position with numpy : 0.0066680908203125 nb_pixel_total : 23 time to create 1 rle with old method : 0.00011110305786132812 time for calcul the mask position with numpy : 0.006588459014892578 nb_pixel_total : 197 time to create 1 rle with old method : 0.00048613548278808594 time for calcul the mask position with numpy : 0.00629734992980957 nb_pixel_total : 1 time to create 1 rle with old method : 2.2411346435546875e-05 time for calcul the mask position with numpy : 0.006203651428222656 nb_pixel_total : 1753 time to create 1 rle with old method : 0.0020291805267333984 time for calcul the mask position with numpy : 0.006666898727416992 nb_pixel_total : 881 time to create 1 rle with old method : 0.0011920928955078125 time for calcul the mask position with numpy : 0.007005214691162109 nb_pixel_total : 2094 time to create 1 rle with old method : 0.0037648677825927734 time for calcul the mask position with numpy : 0.007081270217895508 nb_pixel_total : 4006 time to create 1 rle with old method : 0.0069582462310791016 time for calcul the mask position with numpy : 0.006838798522949219 nb_pixel_total : 360 time to create 1 rle with old method : 0.0007436275482177734 time for calcul the mask position with numpy : 0.006766080856323242 nb_pixel_total : 545 time to create 1 rle with old method : 0.0010335445404052734 time for calcul the mask position with numpy : 0.006703615188598633 nb_pixel_total : 100 time to create 1 rle with old method : 0.00033473968505859375 time for calcul the mask position with numpy : 0.012575626373291016 nb_pixel_total : 6147 time to create 1 rle with old method : 0.007871150970458984 time for calcul the mask position with numpy : 0.006386518478393555 nb_pixel_total : 709 time to create 1 rle with old method : 0.001285552978515625 time for calcul the mask position with numpy : 0.012053966522216797 nb_pixel_total : 12156 time to create 1 rle with old method : 0.014204025268554688 time for calcul the mask position with numpy : 0.01016855239868164 nb_pixel_total : 3 time to create 1 rle with old method : 3.218650817871094e-05 time for calcul the mask position with numpy : 0.010181903839111328 nb_pixel_total : 93 time to create 1 rle with old method : 0.00014352798461914062 time for calcul the mask position with numpy : 0.010207891464233398 nb_pixel_total : 31 time to create 1 rle with old method : 0.00010275840759277344 time for calcul the mask position with numpy : 0.009454011917114258 nb_pixel_total : 906 time to create 1 rle with old method : 0.0011916160583496094 time for calcul the mask position with numpy : 0.00622868537902832 nb_pixel_total : 132 time to create 1 rle with old method : 0.0001976490020751953 time for calcul the mask position with numpy : 0.006289958953857422 nb_pixel_total : 157 time to create 1 rle with old method : 0.00022411346435546875 time for calcul the mask position with numpy : 0.006540536880493164 nb_pixel_total : 29 time to create 1 rle with old method : 6.29425048828125e-05 time for calcul the mask position with numpy : 0.006417989730834961 nb_pixel_total : 6 time to create 1 rle with old method : 4.1961669921875e-05 time for calcul the mask position with numpy : 0.006342411041259766 nb_pixel_total : 84 time to create 1 rle with old method : 0.00012111663818359375 time for calcul the mask position with numpy : 0.00634765625 nb_pixel_total : 4 time to create 1 rle with old method : 3.337860107421875e-05 time for calcul the mask position with numpy : 0.006334066390991211 nb_pixel_total : 168 time to create 1 rle with old method : 0.000225067138671875 time for calcul the mask position with numpy : 0.006595134735107422 nb_pixel_total : 12 time to create 1 rle with old method : 3.981590270996094e-05 time for calcul the mask position with numpy : 0.006704568862915039 nb_pixel_total : 1056 time to create 1 rle with old method : 0.001302480697631836 time for calcul the mask position with numpy : 0.006787538528442383 nb_pixel_total : 1337 time to create 1 rle with old method : 0.0016181468963623047 time for calcul the mask position with numpy : 0.00644993782043457 nb_pixel_total : 1079 time to create 1 rle with old method : 0.0012776851654052734 time for calcul the mask position with numpy : 0.006499767303466797 nb_pixel_total : 76 time to create 1 rle with old method : 0.00011777877807617188 time for calcul the mask position with numpy : 0.006592750549316406 nb_pixel_total : 406 time to create 1 rle with old method : 0.0005059242248535156 time for calcul the mask position with numpy : 0.007188558578491211 nb_pixel_total : 675 time to create 1 rle with old method : 0.0008461475372314453 time for calcul the mask position with numpy : 0.00744938850402832 nb_pixel_total : 206 time to create 1 rle with old method : 0.00027561187744140625 time for calcul the mask position with numpy : 0.007093191146850586 nb_pixel_total : 16204 time to create 1 rle with old method : 0.018052101135253906 time for calcul the mask position with numpy : 0.007020711898803711 nb_pixel_total : 1280 time to create 1 rle with old method : 0.0015330314636230469 time for calcul the mask position with numpy : 0.006684064865112305 nb_pixel_total : 909 time to create 1 rle with old method : 0.0011126995086669922 time for calcul the mask position with numpy : 0.006848335266113281 nb_pixel_total : 316 time to create 1 rle with old method : 0.00041937828063964844 time for calcul the mask position with numpy : 0.006783485412597656 nb_pixel_total : 13644 time to create 1 rle with old method : 0.01591634750366211 time for calcul the mask position with numpy : 0.006951332092285156 nb_pixel_total : 1261 time to create 1 rle with old method : 0.0016026496887207031 time for calcul the mask position with numpy : 0.007238626480102539 nb_pixel_total : 15 time to create 1 rle with old method : 0.00017070770263671875 time for calcul the mask position with numpy : 0.007143974304199219 nb_pixel_total : 513 time to create 1 rle with old method : 0.0006618499755859375 time for calcul the mask position with numpy : 0.007021427154541016 nb_pixel_total : 47 time to create 1 rle with old method : 0.00011992454528808594 time for calcul the mask position with numpy : 0.007653951644897461 nb_pixel_total : 4006 time to create 1 rle with old method : 0.005297422409057617 time for calcul the mask position with numpy : 0.007293701171875 nb_pixel_total : 121 time to create 1 rle with old method : 0.000362396240234375 time for calcul the mask position with numpy : 0.00774383544921875 nb_pixel_total : 2081 time to create 1 rle with old method : 0.0035448074340820312 time for calcul the mask position with numpy : 0.008226871490478516 nb_pixel_total : 76 time to create 1 rle with old method : 0.0001621246337890625 time for calcul the mask position with numpy : 0.008056163787841797 nb_pixel_total : 545 time to create 1 rle with old method : 0.0010571479797363281 time for calcul the mask position with numpy : 0.008093833923339844 nb_pixel_total : 653 time to create 1 rle with old method : 0.0011746883392333984 time for calcul the mask position with numpy : 0.008503437042236328 nb_pixel_total : 565 time to create 1 rle with old method : 0.0009562969207763672 time for calcul the mask position with numpy : 0.007178544998168945 nb_pixel_total : 857 time to create 1 rle with old method : 0.001691579818725586 time for calcul the mask position with numpy : 0.007322788238525391 nb_pixel_total : 696 time to create 1 rle with old method : 0.0013713836669921875 time for calcul the mask position with numpy : 0.007136821746826172 nb_pixel_total : 101 time to create 1 rle with old method : 0.00018930435180664062 time for calcul the mask position with numpy : 0.007301807403564453 nb_pixel_total : 3125 time to create 1 rle with old method : 0.0036704540252685547 time for calcul the mask position with numpy : 0.006860494613647461 nb_pixel_total : 1098 time to create 1 rle with old method : 0.001340627670288086 time for calcul the mask position with numpy : 0.006925106048583984 nb_pixel_total : 1485 time to create 1 rle with old method : 0.00180816650390625 time for calcul the mask position with numpy : 0.010422706604003906 nb_pixel_total : 1680 time to create 1 rle with old method : 0.0020225048065185547 time for calcul the mask position with numpy : 0.010281801223754883 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0013322830200195312 time for calcul the mask position with numpy : 0.010315418243408203 nb_pixel_total : 120 time to create 1 rle with old method : 0.00016999244689941406 time for calcul the mask position with numpy : 0.010149717330932617 nb_pixel_total : 79 time to create 1 rle with old method : 0.0001742839813232422 time for calcul the mask position with numpy : 0.010423421859741211 nb_pixel_total : 440 time to create 1 rle with old method : 0.0005652904510498047 time for calcul the mask position with numpy : 0.010263681411743164 nb_pixel_total : 417 time to create 1 rle with old method : 0.0005269050598144531 time for calcul the mask position with numpy : 0.010283708572387695 nb_pixel_total : 288 time to create 1 rle with old method : 0.00038123130798339844 time for calcul the mask position with numpy : 0.01033926010131836 nb_pixel_total : 475 time to create 1 rle with old method : 0.0005891323089599609 time for calcul the mask position with numpy : 0.010303020477294922 nb_pixel_total : 495 time to create 1 rle with old method : 0.0006155967712402344 time for calcul the mask position with numpy : 0.01019287109375 nb_pixel_total : 249 time to create 1 rle with old method : 0.00035500526428222656 time for calcul the mask position with numpy : 0.014454841613769531 nb_pixel_total : 201 time to create 1 rle with old method : 0.0002791881561279297 time for calcul the mask position with numpy : 0.010460615158081055 nb_pixel_total : 1920 time to create 1 rle with old method : 0.002386808395385742 time for calcul the mask position with numpy : 0.010276317596435547 nb_pixel_total : 891 time to create 1 rle with old method : 0.0010879039764404297 time for calcul the mask position with numpy : 0.010207653045654297 nb_pixel_total : 355 time to create 1 rle with old method : 0.00044655799865722656 time for calcul the mask position with numpy : 0.010070323944091797 nb_pixel_total : 181 time to create 1 rle with old method : 0.0002636909484863281 time for calcul the mask position with numpy : 0.010214805603027344 nb_pixel_total : 127 time to create 1 rle with old method : 0.00018095970153808594 time for calcul the mask position with numpy : 0.010113239288330078 nb_pixel_total : 1144 time to create 1 rle with old method : 0.0013909339904785156 time for calcul the mask position with numpy : 0.010450124740600586 nb_pixel_total : 74 time to create 1 rle with old method : 0.00011920928955078125 time for calcul the mask position with numpy : 0.011184215545654297 nb_pixel_total : 106967 time to create 1 rle with old method : 0.1444261074066162 time for calcul the mask position with numpy : 0.01042318344116211 nb_pixel_total : 345 time to create 1 rle with old method : 0.0004506111145019531 time for calcul the mask position with numpy : 0.01005864143371582 nb_pixel_total : 9613 time to create 1 rle with old method : 0.010560989379882812 time for calcul the mask position with numpy : 0.010369300842285156 nb_pixel_total : 51 time to create 1 rle with old method : 8.869171142578125e-05 time for calcul the mask position with numpy : 0.010162353515625 nb_pixel_total : 114 time to create 1 rle with old method : 0.0001583099365234375 time for calcul the mask position with numpy : 0.01042938232421875 nb_pixel_total : 273 time to create 1 rle with old method : 0.00033402442932128906 time for calcul the mask position with numpy : 0.010230302810668945 nb_pixel_total : 1533 time to create 1 rle with old method : 0.0016474723815917969 time for calcul the mask position with numpy : 0.010361671447753906 nb_pixel_total : 557 time to create 1 rle with old method : 0.0006906986236572266 time for calcul the mask position with numpy : 0.010112524032592773 nb_pixel_total : 190 time to create 1 rle with old method : 0.00024199485778808594 time for calcul the mask position with numpy : 0.010072708129882812 nb_pixel_total : 518 time to create 1 rle with old method : 0.0006084442138671875 time for calcul the mask position with numpy : 0.010014057159423828 nb_pixel_total : 299 time to create 1 rle with old method : 0.0003788471221923828 time for calcul the mask position with numpy : 0.010050058364868164 nb_pixel_total : 37 time to create 1 rle with old method : 7.343292236328125e-05 time for calcul the mask position with numpy : 0.012066364288330078 nb_pixel_total : 201 time to create 1 rle with old method : 0.0002636909484863281 time for calcul the mask position with numpy : 0.0069122314453125 nb_pixel_total : 478 time to create 1 rle with old method : 0.0006034374237060547 time for calcul the mask position with numpy : 0.006283283233642578 nb_pixel_total : 1793 time to create 1 rle with old method : 0.0021209716796875 time for calcul the mask position with numpy : 0.006444692611694336 nb_pixel_total : 629 time to create 1 rle with old method : 0.0007336139678955078 time for calcul the mask position with numpy : 0.006222963333129883 nb_pixel_total : 383 time to create 1 rle with old method : 0.0005104541778564453 time for calcul the mask position with numpy : 0.006281852722167969 nb_pixel_total : 431 time to create 1 rle with old method : 0.0005290508270263672 time for calcul the mask position with numpy : 0.006512880325317383 nb_pixel_total : 282 time to create 1 rle with old method : 0.0003662109375 time for calcul the mask position with numpy : 0.0063457489013671875 nb_pixel_total : 1618 time to create 1 rle with old method : 0.0019147396087646484 time for calcul the mask position with numpy : 0.006215572357177734 nb_pixel_total : 287 time to create 1 rle with old method : 0.0003514289855957031 time for calcul the mask position with numpy : 0.0062444210052490234 nb_pixel_total : 254 time to create 1 rle with old method : 0.0003192424774169922 time for calcul the mask position with numpy : 0.006238698959350586 nb_pixel_total : 13 time to create 1 rle with old method : 4.863739013671875e-05 time for calcul the mask position with numpy : 0.006700038909912109 nb_pixel_total : 1493 time to create 1 rle with old method : 0.0018398761749267578 time for calcul the mask position with numpy : 0.006478071212768555 nb_pixel_total : 268 time to create 1 rle with old method : 0.000347137451171875 time for calcul the mask position with numpy : 0.007616996765136719 nb_pixel_total : 893 time to create 1 rle with old method : 0.0013337135314941406 time for calcul the mask position with numpy : 0.007127523422241211 nb_pixel_total : 357 time to create 1 rle with old method : 0.0004398822784423828 time for calcul the mask position with numpy : 0.0062868595123291016 nb_pixel_total : 6 time to create 1 rle with old method : 5.4836273193359375e-05 time for calcul the mask position with numpy : 0.0066449642181396484 nb_pixel_total : 467 time to create 1 rle with old method : 0.0005769729614257812 time for calcul the mask position with numpy : 0.006456851959228516 nb_pixel_total : 22 time to create 1 rle with old method : 7.104873657226562e-05 time for calcul the mask position with numpy : 0.00638270378112793 nb_pixel_total : 401 time to create 1 rle with old method : 0.00048613548278808594 time for calcul the mask position with numpy : 0.0060884952545166016 nb_pixel_total : 6 time to create 1 rle with old method : 3.6716461181640625e-05 time for calcul the mask position with numpy : 0.00597381591796875 nb_pixel_total : 147 time to create 1 rle with old method : 0.00020074844360351562 time for calcul the mask position with numpy : 0.009755611419677734 nb_pixel_total : 1739 time to create 1 rle with old method : 0.0020089149475097656 time for calcul the mask position with numpy : 0.006030559539794922 nb_pixel_total : 27 time to create 1 rle with old method : 6.580352783203125e-05 time for calcul the mask position with numpy : 0.005789041519165039 nb_pixel_total : 182 time to create 1 rle with old method : 0.00022602081298828125 time for calcul the mask position with numpy : 0.006002902984619141 nb_pixel_total : 372 time to create 1 rle with old method : 0.0004627704620361328 time for calcul the mask position with numpy : 0.005919694900512695 nb_pixel_total : 59 time to create 1 rle with old method : 8.678436279296875e-05 time for calcul the mask position with numpy : 0.005939960479736328 nb_pixel_total : 12 time to create 1 rle with old method : 7.557868957519531e-05 time for calcul the mask position with numpy : 0.005857229232788086 nb_pixel_total : 994 time to create 1 rle with old method : 0.0012748241424560547 time for calcul the mask position with numpy : 0.005969047546386719 nb_pixel_total : 23 time to create 1 rle with old method : 8.130073547363281e-05 time for calcul the mask position with numpy : 0.006389617919921875 nb_pixel_total : 184 time to create 1 rle with old method : 0.0002446174621582031 time for calcul the mask position with numpy : 0.006105899810791016 nb_pixel_total : 887 time to create 1 rle with old method : 0.001085042953491211 time for calcul the mask position with numpy : 0.005864620208740234 nb_pixel_total : 372 time to create 1 rle with old method : 0.0004706382751464844 time for calcul the mask position with numpy : 0.00601649284362793 nb_pixel_total : 134 time to create 1 rle with old method : 0.00017261505126953125 time for calcul the mask position with numpy : 0.005883932113647461 nb_pixel_total : 437 time to create 1 rle with old method : 0.0005438327789306641 time for calcul the mask position with numpy : 0.006003856658935547 nb_pixel_total : 145 time to create 1 rle with old method : 0.0001964569091796875 time for calcul the mask position with numpy : 0.005981922149658203 nb_pixel_total : 21 time to create 1 rle with old method : 6.246566772460938e-05 time for calcul the mask position with numpy : 0.005974531173706055 nb_pixel_total : 3188 time to create 1 rle with old method : 0.004359006881713867 time for calcul the mask position with numpy : 0.006079196929931641 nb_pixel_total : 1602 time to create 1 rle with old method : 0.001844167709350586 time for calcul the mask position with numpy : 0.005888223648071289 nb_pixel_total : 10 time to create 1 rle with old method : 5.555152893066406e-05 time for calcul the mask position with numpy : 0.006026268005371094 nb_pixel_total : 479 time to create 1 rle with old method : 0.00061798095703125 time for calcul the mask position with numpy : 0.008452177047729492 nb_pixel_total : 71 time to create 1 rle with old method : 0.00012731552124023438 time for calcul the mask position with numpy : 0.008385896682739258 nb_pixel_total : 311 time to create 1 rle with old method : 0.00040030479431152344 time for calcul the mask position with numpy : 0.008261680603027344 nb_pixel_total : 476 time to create 1 rle with old method : 0.000568389892578125 time for calcul the mask position with numpy : 0.00844717025756836 nb_pixel_total : 1603 time to create 1 rle with old method : 0.0019137859344482422 time for calcul the mask position with numpy : 0.00857090950012207 nb_pixel_total : 23 time to create 1 rle with old method : 6.842613220214844e-05 time for calcul the mask position with numpy : 0.00850057601928711 nb_pixel_total : 877 time to create 1 rle with old method : 0.0010509490966796875 time for calcul the mask position with numpy : 0.009332895278930664 nb_pixel_total : 4327 time to create 1 rle with old method : 0.005248546600341797 time for calcul the mask position with numpy : 0.008591413497924805 nb_pixel_total : 31 time to create 1 rle with old method : 7.343292236328125e-05 time for calcul the mask position with numpy : 0.008710622787475586 nb_pixel_total : 926 time to create 1 rle with old method : 0.001493215560913086 time for calcul the mask position with numpy : 0.01127767562866211 nb_pixel_total : 7 time to create 1 rle with old method : 5.340576171875e-05 time for calcul the mask position with numpy : 0.011230230331420898 nb_pixel_total : 3 time to create 1 rle with old method : 4.124641418457031e-05 time for calcul the mask position with numpy : 0.010406494140625 nb_pixel_total : 594 time to create 1 rle with old method : 0.0010516643524169922 time for calcul the mask position with numpy : 0.010467052459716797 nb_pixel_total : 260 time to create 1 rle with old method : 0.00048661231994628906 time for calcul the mask position with numpy : 0.010604143142700195 nb_pixel_total : 26 time to create 1 rle with old method : 0.00010585784912109375 time for calcul the mask position with numpy : 0.010675191879272461 nb_pixel_total : 508 time to create 1 rle with old method : 0.0008943080902099609 time for calcul the mask position with numpy : 0.010636568069458008 nb_pixel_total : 554 time to create 1 rle with old method : 0.0010123252868652344 time for calcul the mask position with numpy : 0.010730743408203125 nb_pixel_total : 75 time to create 1 rle with old method : 0.00017213821411132812 time for calcul the mask position with numpy : 0.0108642578125 nb_pixel_total : 339 time to create 1 rle with old method : 0.0006687641143798828 time for calcul the mask position with numpy : 0.010847806930541992 nb_pixel_total : 25 time to create 1 rle with old method : 0.0001842975616455078 time for calcul the mask position with numpy : 0.011448860168457031 nb_pixel_total : 580 time to create 1 rle with old method : 0.0010552406311035156 time for calcul the mask position with numpy : 0.010947227478027344 nb_pixel_total : 430 time to create 1 rle with old method : 0.0008597373962402344 time for calcul the mask position with numpy : 0.009854793548583984 nb_pixel_total : 176 time to create 1 rle with old method : 0.0003528594970703125 time for calcul the mask position with numpy : 0.009800434112548828 nb_pixel_total : 1850 time to create 1 rle with old method : 0.0030450820922851562 time for calcul the mask position with numpy : 0.010954141616821289 nb_pixel_total : 20 time to create 1 rle with old method : 0.0002155303955078125 time for calcul the mask position with numpy : 0.010670900344848633 nb_pixel_total : 247 time to create 1 rle with old method : 0.0004763603210449219 time for calcul the mask position with numpy : 0.01603531837463379 nb_pixel_total : 104 time to create 1 rle with old method : 0.0002014636993408203 time for calcul the mask position with numpy : 0.010729074478149414 nb_pixel_total : 1444 time to create 1 rle with old method : 0.002274036407470703 time for calcul the mask position with numpy : 0.010826826095581055 nb_pixel_total : 17 time to create 1 rle with old method : 6.794929504394531e-05 time for calcul the mask position with numpy : 0.011013507843017578 nb_pixel_total : 9 time to create 1 rle with old method : 6.318092346191406e-05 time for calcul the mask position with numpy : 0.011077165603637695 nb_pixel_total : 68 time to create 1 rle with old method : 0.0001995563507080078 time for calcul the mask position with numpy : 0.010586023330688477 nb_pixel_total : 1169 time to create 1 rle with old method : 0.0018742084503173828 time for calcul the mask position with numpy : 0.010641098022460938 nb_pixel_total : 473 time to create 1 rle with old method : 0.0008127689361572266 time for calcul the mask position with numpy : 0.010787248611450195 nb_pixel_total : 4441 time to create 1 rle with old method : 0.007219076156616211 time for calcul the mask position with numpy : 0.011661767959594727 nb_pixel_total : 679 time to create 1 rle with old method : 0.0011591911315917969 time for calcul the mask position with numpy : 0.008909463882446289 nb_pixel_total : 207 time to create 1 rle with old method : 0.00047516822814941406 time for calcul the mask position with numpy : 0.00931692123413086 nb_pixel_total : 1435 time to create 1 rle with old method : 0.0017695426940917969 time for calcul the mask position with numpy : 0.008895158767700195 nb_pixel_total : 263 time to create 1 rle with old method : 0.0005018711090087891 time for calcul the mask position with numpy : 0.008903026580810547 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001919269561767578 time for calcul the mask position with numpy : 0.008764028549194336 nb_pixel_total : 644 time to create 1 rle with old method : 0.0008456707000732422 create new chi : 2.8050498962402344 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0038843154907226562 batch 1 Loaded 204 chid ids of type : 4230 Number RLEs to save : 16746 TO DO : save crop sub photo not yet done ! save time : 1.8278985023498535 nb_obj : 160 nb_hashtags : 7 time to prepare the origin masks : 2.1312811374664307 time for calcul the mask position with numpy : 0.021796226501464844 nb_pixel_total : 1714469 time to create 1 rle with new method : 0.04438591003417969 time for calcul the mask position with numpy : 0.010989189147949219 nb_pixel_total : 309 time to create 1 rle with old method : 0.0004494190216064453 time for calcul the mask position with numpy : 0.01212000846862793 nb_pixel_total : 3239 time to create 1 rle with old method : 0.00397038459777832 time for calcul the mask position with numpy : 0.011336803436279297 nb_pixel_total : 2873 time to create 1 rle with old method : 0.003471851348876953 time for calcul the mask position with numpy : 0.0067975521087646484 nb_pixel_total : 1787 time to create 1 rle with old method : 0.0021927356719970703 time for calcul the mask position with numpy : 0.007007598876953125 nb_pixel_total : 48 time to create 1 rle with old method : 0.00010466575622558594 time for calcul the mask position with numpy : 0.007080793380737305 nb_pixel_total : 244 time to create 1 rle with old method : 0.0003361701965332031 time for calcul the mask position with numpy : 0.006982088088989258 nb_pixel_total : 346 time to create 1 rle with old method : 0.00044536590576171875 time for calcul the mask position with numpy : 0.006993293762207031 nb_pixel_total : 1871 time to create 1 rle with old method : 0.0023193359375 time for calcul the mask position with numpy : 0.006960391998291016 nb_pixel_total : 33 time to create 1 rle with old method : 8.7738037109375e-05 time for calcul the mask position with numpy : 0.010206937789916992 nb_pixel_total : 237 time to create 1 rle with old method : 0.00041961669921875 time for calcul the mask position with numpy : 0.007239580154418945 nb_pixel_total : 323 time to create 1 rle with old method : 0.0004146099090576172 time for calcul the mask position with numpy : 0.007234334945678711 nb_pixel_total : 250 time to create 1 rle with old method : 0.0003495216369628906 time for calcul the mask position with numpy : 0.007292032241821289 nb_pixel_total : 47 time to create 1 rle with old method : 0.00011587142944335938 time for calcul the mask position with numpy : 0.007199287414550781 nb_pixel_total : 21 time to create 1 rle with old method : 5.1975250244140625e-05 time for calcul the mask position with numpy : 0.007524251937866211 nb_pixel_total : 5026 time to create 1 rle with old method : 0.0065898895263671875 time for calcul the mask position with numpy : 0.006849050521850586 nb_pixel_total : 32 time to create 1 rle with old method : 6.794929504394531e-05 time for calcul the mask position with numpy : 0.0074558258056640625 nb_pixel_total : 42895 time to create 1 rle with old method : 0.056345224380493164 time for calcul the mask position with numpy : 0.008241653442382812 nb_pixel_total : 683 time to create 1 rle with old method : 0.0010364055633544922 time for calcul the mask position with numpy : 0.00733637809753418 nb_pixel_total : 13317 time to create 1 rle with old method : 0.016820430755615234 time for calcul the mask position with numpy : 0.006966590881347656 nb_pixel_total : 10609 time to create 1 rle with old method : 0.013212442398071289 time for calcul the mask position with numpy : 0.007181406021118164 nb_pixel_total : 185 time to create 1 rle with old method : 0.00027680397033691406 time for calcul the mask position with numpy : 0.006991386413574219 nb_pixel_total : 867 time to create 1 rle with old method : 0.001102447509765625 time for calcul the mask position with numpy : 0.006921529769897461 nb_pixel_total : 732 time to create 1 rle with old method : 0.0009477138519287109 time for calcul the mask position with numpy : 0.006792545318603516 nb_pixel_total : 1391 time to create 1 rle with old method : 0.0021436214447021484 time for calcul the mask position with numpy : 0.00874638557434082 nb_pixel_total : 6096 time to create 1 rle with old method : 0.008047819137573242 time for calcul the mask position with numpy : 0.007330894470214844 nb_pixel_total : 13 time to create 1 rle with old method : 5.364418029785156e-05 time for calcul the mask position with numpy : 0.0072519779205322266 nb_pixel_total : 11478 time to create 1 rle with old method : 0.01454305648803711 time for calcul the mask position with numpy : 0.0069158077239990234 nb_pixel_total : 96 time to create 1 rle with old method : 0.00016164779663085938 time for calcul the mask position with numpy : 0.007843971252441406 nb_pixel_total : 19 time to create 1 rle with old method : 8.630752563476562e-05 time for calcul the mask position with numpy : 0.006961345672607422 nb_pixel_total : 755 time to create 1 rle with old method : 0.0011134147644042969 time for calcul the mask position with numpy : 0.007691860198974609 nb_pixel_total : 390 time to create 1 rle with old method : 0.0005981922149658203 time for calcul the mask position with numpy : 0.007098197937011719 nb_pixel_total : 127 time to create 1 rle with old method : 0.00018739700317382812 time for calcul the mask position with numpy : 0.0071392059326171875 nb_pixel_total : 1535 time to create 1 rle with old method : 0.0021314620971679688 time for calcul the mask position with numpy : 0.008120536804199219 nb_pixel_total : 5 time to create 1 rle with old method : 9.298324584960938e-05 time for calcul the mask position with numpy : 0.012265920639038086 nb_pixel_total : 833 time to create 1 rle with old method : 0.0011861324310302734 time for calcul the mask position with numpy : 0.0074841976165771484 nb_pixel_total : 90 time to create 1 rle with old method : 0.00017070770263671875 time for calcul the mask position with numpy : 0.0075871944427490234 nb_pixel_total : 475 time to create 1 rle with old method : 0.0007395744323730469 time for calcul the mask position with numpy : 0.006971120834350586 nb_pixel_total : 130 time to create 1 rle with old method : 0.0002834796905517578 time for calcul the mask position with numpy : 0.0067522525787353516 nb_pixel_total : 16 time to create 1 rle with old method : 7.772445678710938e-05 time for calcul the mask position with numpy : 0.0066089630126953125 nb_pixel_total : 709 time to create 1 rle with old method : 0.0012772083282470703 time for calcul the mask position with numpy : 0.006669759750366211 nb_pixel_total : 2489 time to create 1 rle with old method : 0.0049402713775634766 time for calcul the mask position with numpy : 0.006777524948120117 nb_pixel_total : 23687 time to create 1 rle with old method : 0.03966355323791504 time for calcul the mask position with numpy : 0.0062999725341796875 nb_pixel_total : 1257 time to create 1 rle with old method : 0.0015115737915039062 time for calcul the mask position with numpy : 0.006195783615112305 nb_pixel_total : 783 time to create 1 rle with old method : 0.0009608268737792969 time for calcul the mask position with numpy : 0.006186723709106445 nb_pixel_total : 599 time to create 1 rle with old method : 0.0008664131164550781 time for calcul the mask position with numpy : 0.007383584976196289 nb_pixel_total : 290 time to create 1 rle with old method : 0.0004756450653076172 time for calcul the mask position with numpy : 0.0062024593353271484 nb_pixel_total : 13784 time to create 1 rle with old method : 0.015545368194580078 time for calcul the mask position with numpy : 0.00621342658996582 nb_pixel_total : 510 time to create 1 rle with old method : 0.0006673336029052734 time for calcul the mask position with numpy : 0.006086111068725586 nb_pixel_total : 330 time to create 1 rle with old method : 0.0005176067352294922 time for calcul the mask position with numpy : 0.0061724185943603516 nb_pixel_total : 1939 time to create 1 rle with old method : 0.0024323463439941406 time for calcul the mask position with numpy : 0.0061953067779541016 nb_pixel_total : 6 time to create 1 rle with old method : 5.53131103515625e-05 time for calcul the mask position with numpy : 0.006176471710205078 nb_pixel_total : 526 time to create 1 rle with old method : 0.0007307529449462891 time for calcul the mask position with numpy : 0.006093502044677734 nb_pixel_total : 1242 time to create 1 rle with old method : 0.0016238689422607422 time for calcul the mask position with numpy : 0.006088972091674805 nb_pixel_total : 6 time to create 1 rle with old method : 4.100799560546875e-05 time for calcul the mask position with numpy : 0.0064775943756103516 nb_pixel_total : 119 time to create 1 rle with old method : 0.00017142295837402344 time for calcul the mask position with numpy : 0.006276607513427734 nb_pixel_total : 695 time to create 1 rle with old method : 0.0008590221405029297 time for calcul the mask position with numpy : 0.0059926509857177734 nb_pixel_total : 2853 time to create 1 rle with old method : 0.003204822540283203 time for calcul the mask position with numpy : 0.00618290901184082 nb_pixel_total : 698 time to create 1 rle with old method : 0.0008831024169921875 time for calcul the mask position with numpy : 0.0062255859375 nb_pixel_total : 2 time to create 1 rle with old method : 2.4557113647460938e-05 time for calcul the mask position with numpy : 0.006188154220581055 nb_pixel_total : 1468 time to create 1 rle with old method : 0.001758575439453125 time for calcul the mask position with numpy : 0.006125688552856445 nb_pixel_total : 101 time to create 1 rle with old method : 0.00015115737915039062 time for calcul the mask position with numpy : 0.006117582321166992 nb_pixel_total : 1610 time to create 1 rle with old method : 0.0019178390502929688 time for calcul the mask position with numpy : 0.006060361862182617 nb_pixel_total : 479 time to create 1 rle with old method : 0.0005962848663330078 time for calcul the mask position with numpy : 0.006073951721191406 nb_pixel_total : 488 time to create 1 rle with old method : 0.0006527900695800781 time for calcul the mask position with numpy : 0.00603485107421875 nb_pixel_total : 308 time to create 1 rle with old method : 0.0003795623779296875 time for calcul the mask position with numpy : 0.006435871124267578 nb_pixel_total : 3371 time to create 1 rle with old method : 0.003977537155151367 time for calcul the mask position with numpy : 0.00604701042175293 nb_pixel_total : 146 time to create 1 rle with old method : 0.00020456314086914062 time for calcul the mask position with numpy : 0.006215333938598633 nb_pixel_total : 1047 time to create 1 rle with old method : 0.0013709068298339844 time for calcul the mask position with numpy : 0.006081581115722656 nb_pixel_total : 461 time to create 1 rle with old method : 0.0005817413330078125 time for calcul the mask position with numpy : 0.006330251693725586 nb_pixel_total : 1248 time to create 1 rle with old method : 0.0015511512756347656 time for calcul the mask position with numpy : 0.005905628204345703 nb_pixel_total : 949 time to create 1 rle with old method : 0.0011320114135742188 time for calcul the mask position with numpy : 0.005898952484130859 nb_pixel_total : 79 time to create 1 rle with old method : 0.00011897087097167969 time for calcul the mask position with numpy : 0.005909442901611328 nb_pixel_total : 536 time to create 1 rle with old method : 0.0006785392761230469 time for calcul the mask position with numpy : 0.006184101104736328 nb_pixel_total : 306 time to create 1 rle with old method : 0.0003876686096191406 time for calcul the mask position with numpy : 0.006088972091674805 nb_pixel_total : 223 time to create 1 rle with old method : 0.0003237724304199219 time for calcul the mask position with numpy : 0.0066678524017333984 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0012519359588623047 time for calcul the mask position with numpy : 0.0063817501068115234 nb_pixel_total : 16 time to create 1 rle with old method : 4.8160552978515625e-05 time for calcul the mask position with numpy : 0.006215333938598633 nb_pixel_total : 99 time to create 1 rle with old method : 0.00013494491577148438 time for calcul the mask position with numpy : 0.0070688724517822266 nb_pixel_total : 106892 time to create 1 rle with old method : 0.11650729179382324 time for calcul the mask position with numpy : 0.0061299800872802734 nb_pixel_total : 356 time to create 1 rle with old method : 0.00045609474182128906 time for calcul the mask position with numpy : 0.006214141845703125 nb_pixel_total : 10821 time to create 1 rle with old method : 0.01313328742980957 time for calcul the mask position with numpy : 0.006533384323120117 nb_pixel_total : 1722 time to create 1 rle with old method : 0.0021271705627441406 time for calcul the mask position with numpy : 0.006411552429199219 nb_pixel_total : 1781 time to create 1 rle with old method : 0.002155780792236328 time for calcul the mask position with numpy : 0.0063550472259521484 nb_pixel_total : 88 time to create 1 rle with old method : 0.00019741058349609375 time for calcul the mask position with numpy : 0.006428956985473633 nb_pixel_total : 274 time to create 1 rle with old method : 0.0003745555877685547 time for calcul the mask position with numpy : 0.006517171859741211 nb_pixel_total : 961 time to create 1 rle with old method : 0.0011942386627197266 time for calcul the mask position with numpy : 0.0064051151275634766 nb_pixel_total : 208 time to create 1 rle with old method : 0.0002741813659667969 time for calcul the mask position with numpy : 0.006249427795410156 nb_pixel_total : 432 time to create 1 rle with old method : 0.000560760498046875 time for calcul the mask position with numpy : 0.006768703460693359 nb_pixel_total : 187 time to create 1 rle with old method : 0.00028395652770996094 time for calcul the mask position with numpy : 0.006216287612915039 nb_pixel_total : 560 time to create 1 rle with old method : 0.0007033348083496094 time for calcul the mask position with numpy : 0.006346225738525391 nb_pixel_total : 690 time to create 1 rle with old method : 0.0008504390716552734 time for calcul the mask position with numpy : 0.01032567024230957 nb_pixel_total : 1739 time to create 1 rle with old method : 0.002061128616333008 time for calcul the mask position with numpy : 0.010646581649780273 nb_pixel_total : 10745 time to create 1 rle with old method : 0.016444683074951172 time for calcul the mask position with numpy : 0.01071310043334961 nb_pixel_total : 736 time to create 1 rle with old method : 0.0009224414825439453 time for calcul the mask position with numpy : 0.010793685913085938 nb_pixel_total : 511 time to create 1 rle with old method : 0.0007219314575195312 time for calcul the mask position with numpy : 0.006804943084716797 nb_pixel_total : 560 time to create 1 rle with old method : 0.0007309913635253906 time for calcul the mask position with numpy : 0.006830692291259766 nb_pixel_total : 40 time to create 1 rle with old method : 8.559226989746094e-05 time for calcul the mask position with numpy : 0.006620645523071289 nb_pixel_total : 171 time to create 1 rle with old method : 0.0002598762512207031 time for calcul the mask position with numpy : 0.0067234039306640625 nb_pixel_total : 1621 time to create 1 rle with old method : 0.0019283294677734375 time for calcul the mask position with numpy : 0.006490230560302734 nb_pixel_total : 275 time to create 1 rle with old method : 0.00036072731018066406 time for calcul the mask position with numpy : 0.006314992904663086 nb_pixel_total : 1527 time to create 1 rle with old method : 0.0018267631530761719 time for calcul the mask position with numpy : 0.0063800811767578125 nb_pixel_total : 218 time to create 1 rle with old method : 0.0002853870391845703 time for calcul the mask position with numpy : 0.0060901641845703125 nb_pixel_total : 73 time to create 1 rle with old method : 0.0001392364501953125 time for calcul the mask position with numpy : 0.006479740142822266 nb_pixel_total : 949 time to create 1 rle with old method : 0.0011458396911621094 time for calcul the mask position with numpy : 0.006337881088256836 nb_pixel_total : 459 time to create 1 rle with old method : 0.0005762577056884766 time for calcul the mask position with numpy : 0.006038665771484375 nb_pixel_total : 206 time to create 1 rle with old method : 0.00027298927307128906 time for calcul the mask position with numpy : 0.0061948299407958984 nb_pixel_total : 98 time to create 1 rle with old method : 0.00021409988403320312 time for calcul the mask position with numpy : 0.0064013004302978516 nb_pixel_total : 377 time to create 1 rle with old method : 0.0004930496215820312 time for calcul the mask position with numpy : 0.006585121154785156 nb_pixel_total : 799 time to create 1 rle with old method : 0.000997304916381836 time for calcul the mask position with numpy : 0.006564140319824219 nb_pixel_total : 418 time to create 1 rle with old method : 0.0005726814270019531 time for calcul the mask position with numpy : 0.006532192230224609 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002720355987548828 time for calcul the mask position with numpy : 0.009901285171508789 nb_pixel_total : 2014 time to create 1 rle with old method : 0.002390623092651367 time for calcul the mask position with numpy : 0.010434150695800781 nb_pixel_total : 1694 time to create 1 rle with old method : 0.002087116241455078 time for calcul the mask position with numpy : 0.00974583625793457 nb_pixel_total : 4766 time to create 1 rle with old method : 0.005548000335693359 time for calcul the mask position with numpy : 0.00990438461303711 nb_pixel_total : 29 time to create 1 rle with old method : 9.369850158691406e-05 time for calcul the mask position with numpy : 0.009041070938110352 nb_pixel_total : 179 time to create 1 rle with old method : 0.00023627281188964844 time for calcul the mask position with numpy : 0.009824752807617188 nb_pixel_total : 847 time to create 1 rle with old method : 0.0010106563568115234 time for calcul the mask position with numpy : 0.00963592529296875 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002548694610595703 time for calcul the mask position with numpy : 0.009822368621826172 nb_pixel_total : 161 time to create 1 rle with old method : 0.0002079010009765625 time for calcul the mask position with numpy : 0.00677180290222168 nb_pixel_total : 45 time to create 1 rle with old method : 0.00011324882507324219 time for calcul the mask position with numpy : 0.006730794906616211 nb_pixel_total : 32 time to create 1 rle with old method : 6.341934204101562e-05 time for calcul the mask position with numpy : 0.0065937042236328125 nb_pixel_total : 124 time to create 1 rle with old method : 0.00019693374633789062 time for calcul the mask position with numpy : 0.0067594051361083984 nb_pixel_total : 30 time to create 1 rle with old method : 7.62939453125e-05 time for calcul the mask position with numpy : 0.006680965423583984 nb_pixel_total : 1338 time to create 1 rle with old method : 0.0016074180603027344 time for calcul the mask position with numpy : 0.006701231002807617 nb_pixel_total : 23 time to create 1 rle with old method : 7.271766662597656e-05 time for calcul the mask position with numpy : 0.006694793701171875 nb_pixel_total : 494 time to create 1 rle with old method : 0.0006127357482910156 time for calcul the mask position with numpy : 0.0069828033447265625 nb_pixel_total : 1060 time to create 1 rle with old method : 0.0013530254364013672 time for calcul the mask position with numpy : 0.007995843887329102 nb_pixel_total : 3784 time to create 1 rle with old method : 0.00464320182800293 time for calcul the mask position with numpy : 0.008440732955932617 nb_pixel_total : 1592 time to create 1 rle with old method : 0.0019326210021972656 time for calcul the mask position with numpy : 0.00877690315246582 nb_pixel_total : 895 time to create 1 rle with old method : 0.001165151596069336 time for calcul the mask position with numpy : 0.00814366340637207 nb_pixel_total : 13 time to create 1 rle with old method : 4.506111145019531e-05 time for calcul the mask position with numpy : 0.008419513702392578 nb_pixel_total : 599 time to create 1 rle with old method : 0.0007503032684326172 time for calcul the mask position with numpy : 0.00793147087097168 nb_pixel_total : 277 time to create 1 rle with old method : 0.00035452842712402344 time for calcul the mask position with numpy : 0.008453369140625 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006477832794189453 time for calcul the mask position with numpy : 0.008163928985595703 nb_pixel_total : 511 time to create 1 rle with old method : 0.0006570816040039062 time for calcul the mask position with numpy : 0.00811314582824707 nb_pixel_total : 73 time to create 1 rle with old method : 0.00011563301086425781 time for calcul the mask position with numpy : 0.008562088012695312 nb_pixel_total : 343 time to create 1 rle with old method : 0.00045013427734375 time for calcul the mask position with numpy : 0.008364677429199219 nb_pixel_total : 49 time to create 1 rle with old method : 9.179115295410156e-05 time for calcul the mask position with numpy : 0.008112430572509766 nb_pixel_total : 685 time to create 1 rle with old method : 0.0008604526519775391 time for calcul the mask position with numpy : 0.007416486740112305 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005373954772949219 time for calcul the mask position with numpy : 0.00946497917175293 nb_pixel_total : 152 time to create 1 rle with old method : 0.00022149085998535156 time for calcul the mask position with numpy : 0.010258197784423828 nb_pixel_total : 1590 time to create 1 rle with old method : 0.0019199848175048828 time for calcul the mask position with numpy : 0.00917363166809082 nb_pixel_total : 1254 time to create 1 rle with old method : 0.0015187263488769531 time for calcul the mask position with numpy : 0.009581804275512695 nb_pixel_total : 16 time to create 1 rle with old method : 9.560585021972656e-05 time for calcul the mask position with numpy : 0.010962486267089844 nb_pixel_total : 182 time to create 1 rle with old method : 0.00026917457580566406 time for calcul the mask position with numpy : 0.010316848754882812 nb_pixel_total : 458 time to create 1 rle with old method : 0.0005717277526855469 time for calcul the mask position with numpy : 0.009002447128295898 nb_pixel_total : 498 time to create 1 rle with old method : 0.0006799697875976562 time for calcul the mask position with numpy : 0.008963584899902344 nb_pixel_total : 388 time to create 1 rle with old method : 0.00047397613525390625 time for calcul the mask position with numpy : 0.0062410831451416016 nb_pixel_total : 5332 time to create 1 rle with old method : 0.005966901779174805 time for calcul the mask position with numpy : 0.006180524826049805 nb_pixel_total : 397 time to create 1 rle with old method : 0.0005369186401367188 time for calcul the mask position with numpy : 0.006131887435913086 nb_pixel_total : 64 time to create 1 rle with old method : 0.00010752677917480469 time for calcul the mask position with numpy : 0.006169557571411133 nb_pixel_total : 164 time to create 1 rle with old method : 0.000217437744140625 time for calcul the mask position with numpy : 0.006127357482910156 nb_pixel_total : 1532 time to create 1 rle with old method : 0.0017914772033691406 time for calcul the mask position with numpy : 0.0061070919036865234 nb_pixel_total : 263 time to create 1 rle with old method : 0.0003647804260253906 time for calcul the mask position with numpy : 0.006204843521118164 nb_pixel_total : 14 time to create 1 rle with old method : 5.245208740234375e-05 time for calcul the mask position with numpy : 0.006175518035888672 nb_pixel_total : 416 time to create 1 rle with old method : 0.0005707740783691406 time for calcul the mask position with numpy : 0.006052494049072266 nb_pixel_total : 10 time to create 1 rle with old method : 3.409385681152344e-05 time for calcul the mask position with numpy : 0.00675201416015625 nb_pixel_total : 2 time to create 1 rle with old method : 3.528594970703125e-05 time for calcul the mask position with numpy : 0.005975961685180664 nb_pixel_total : 369 time to create 1 rle with old method : 0.00045800209045410156 time for calcul the mask position with numpy : 0.005877256393432617 nb_pixel_total : 656 time to create 1 rle with old method : 0.0007333755493164062 create new chi : 1.690734624862671 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.004393100738525391 batch 1 Loaded 165 chid ids of type : 4230 Number RLEs to save : 15122 TO DO : save crop sub photo not yet done ! save time : 1.0226562023162842 nb_obj : 176 nb_hashtags : 7 time to prepare the origin masks : 1.7843856811523438 time for calcul the mask position with numpy : 0.06308531761169434 nb_pixel_total : 1722152 time to create 1 rle with new method : 0.29109764099121094 time for calcul the mask position with numpy : 0.007353544235229492 nb_pixel_total : 310 time to create 1 rle with old method : 0.0004038810729980469 time for calcul the mask position with numpy : 0.006406545639038086 nb_pixel_total : 1522 time to create 1 rle with old method : 0.0018572807312011719 time for calcul the mask position with numpy : 0.006609916687011719 nb_pixel_total : 3122 time to create 1 rle with old method : 0.005175352096557617 time for calcul the mask position with numpy : 0.0062944889068603516 nb_pixel_total : 1478 time to create 1 rle with old method : 0.0016880035400390625 time for calcul the mask position with numpy : 0.010029315948486328 nb_pixel_total : 68 time to create 1 rle with old method : 0.00010466575622558594 time for calcul the mask position with numpy : 0.010164499282836914 nb_pixel_total : 42 time to create 1 rle with old method : 9.1552734375e-05 time for calcul the mask position with numpy : 0.010219097137451172 nb_pixel_total : 278 time to create 1 rle with old method : 0.00035190582275390625 time for calcul the mask position with numpy : 0.010386943817138672 nb_pixel_total : 23 time to create 1 rle with old method : 5.5789947509765625e-05 time for calcul the mask position with numpy : 0.01060795783996582 nb_pixel_total : 45 time to create 1 rle with old method : 8.654594421386719e-05 time for calcul the mask position with numpy : 0.006317138671875 nb_pixel_total : 64 time to create 1 rle with old method : 0.00010013580322265625 time for calcul the mask position with numpy : 0.006296396255493164 nb_pixel_total : 361 time to create 1 rle with old method : 0.0004405975341796875 time for calcul the mask position with numpy : 0.006444215774536133 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002617835998535156 time for calcul the mask position with numpy : 0.0068323612213134766 nb_pixel_total : 53 time to create 1 rle with old method : 8.535385131835938e-05 time for calcul the mask position with numpy : 0.0062868595123291016 nb_pixel_total : 94 time to create 1 rle with old method : 0.00013136863708496094 time for calcul the mask position with numpy : 0.00741124153137207 nb_pixel_total : 2499 time to create 1 rle with old method : 0.00348663330078125 time for calcul the mask position with numpy : 0.006420135498046875 nb_pixel_total : 275 time to create 1 rle with old method : 0.0003402233123779297 time for calcul the mask position with numpy : 0.006183624267578125 nb_pixel_total : 176 time to create 1 rle with old method : 0.00022983551025390625 time for calcul the mask position with numpy : 0.006240367889404297 nb_pixel_total : 43 time to create 1 rle with old method : 7.724761962890625e-05 time for calcul the mask position with numpy : 0.006632566452026367 nb_pixel_total : 28 time to create 1 rle with old method : 5.412101745605469e-05 time for calcul the mask position with numpy : 0.006735563278198242 nb_pixel_total : 48831 time to create 1 rle with old method : 0.05730271339416504 time for calcul the mask position with numpy : 0.00984334945678711 nb_pixel_total : 771 time to create 1 rle with old method : 0.0013034343719482422 time for calcul the mask position with numpy : 0.010907411575317383 nb_pixel_total : 1093 time to create 1 rle with old method : 0.001338958740234375 time for calcul the mask position with numpy : 0.010868072509765625 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003120899200439453 time for calcul the mask position with numpy : 0.010947465896606445 nb_pixel_total : 637 time to create 1 rle with old method : 0.0007913112640380859 time for calcul the mask position with numpy : 0.010958671569824219 nb_pixel_total : 36 time to create 1 rle with old method : 8.225440979003906e-05 time for calcul the mask position with numpy : 0.010985612869262695 nb_pixel_total : 14619 time to create 1 rle with old method : 0.02152085304260254 time for calcul the mask position with numpy : 0.010556459426879883 nb_pixel_total : 870 time to create 1 rle with old method : 0.0009984970092773438 time for calcul the mask position with numpy : 0.010854005813598633 nb_pixel_total : 1104 time to create 1 rle with old method : 0.0016515254974365234 time for calcul the mask position with numpy : 0.011060476303100586 nb_pixel_total : 10554 time to create 1 rle with old method : 0.012425899505615234 time for calcul the mask position with numpy : 0.011555194854736328 nb_pixel_total : 174 time to create 1 rle with old method : 0.0002384185791015625 time for calcul the mask position with numpy : 0.010793924331665039 nb_pixel_total : 22 time to create 1 rle with old method : 6.29425048828125e-05 time for calcul the mask position with numpy : 0.012481212615966797 nb_pixel_total : 1725 time to create 1 rle with old method : 0.001992464065551758 time for calcul the mask position with numpy : 0.011121273040771484 nb_pixel_total : 3757 time to create 1 rle with old method : 0.0049250125885009766 time for calcul the mask position with numpy : 0.010669946670532227 nb_pixel_total : 22 time to create 1 rle with old method : 0.0001087188720703125 time for calcul the mask position with numpy : 0.010790824890136719 nb_pixel_total : 352 time to create 1 rle with old method : 0.0004665851593017578 time for calcul the mask position with numpy : 0.012399911880493164 nb_pixel_total : 222 time to create 1 rle with old method : 0.0003409385681152344 time for calcul the mask position with numpy : 0.006995439529418945 nb_pixel_total : 34 time to create 1 rle with old method : 9.894371032714844e-05 time for calcul the mask position with numpy : 0.007096529006958008 nb_pixel_total : 288 time to create 1 rle with old method : 0.0004143714904785156 time for calcul the mask position with numpy : 0.006947755813598633 nb_pixel_total : 765 time to create 1 rle with old method : 0.0009601116180419922 time for calcul the mask position with numpy : 0.007125139236450195 nb_pixel_total : 1634 time to create 1 rle with old method : 0.0020270347595214844 time for calcul the mask position with numpy : 0.007027149200439453 nb_pixel_total : 641 time to create 1 rle with old method : 0.0009846687316894531 time for calcul the mask position with numpy : 0.007439374923706055 nb_pixel_total : 12354 time to create 1 rle with old method : 0.013781070709228516 time for calcul the mask position with numpy : 0.007323503494262695 nb_pixel_total : 75 time to create 1 rle with old method : 0.0002231597900390625 time for calcul the mask position with numpy : 0.010256052017211914 nb_pixel_total : 320 time to create 1 rle with old method : 0.001384735107421875 time for calcul the mask position with numpy : 0.010307550430297852 nb_pixel_total : 88 time to create 1 rle with old method : 0.00019860267639160156 time for calcul the mask position with numpy : 0.007387399673461914 nb_pixel_total : 643 time to create 1 rle with old method : 0.0011129379272460938 time for calcul the mask position with numpy : 0.007505655288696289 nb_pixel_total : 12 time to create 1 rle with old method : 5.626678466796875e-05 time for calcul the mask position with numpy : 0.007440090179443359 nb_pixel_total : 91 time to create 1 rle with old method : 0.00018215179443359375 time for calcul the mask position with numpy : 0.0074388980865478516 nb_pixel_total : 169 time to create 1 rle with old method : 0.00031065940856933594 time for calcul the mask position with numpy : 0.007380962371826172 nb_pixel_total : 1477 time to create 1 rle with old method : 0.0024871826171875 time for calcul the mask position with numpy : 0.007366180419921875 nb_pixel_total : 875 time to create 1 rle with old method : 0.0014646053314208984 time for calcul the mask position with numpy : 0.007225513458251953 nb_pixel_total : 775 time to create 1 rle with old method : 0.0012974739074707031 time for calcul the mask position with numpy : 0.006848573684692383 nb_pixel_total : 2 time to create 1 rle with old method : 2.8371810913085938e-05 time for calcul the mask position with numpy : 0.006787300109863281 nb_pixel_total : 8 time to create 1 rle with old method : 4.410743713378906e-05 time for calcul the mask position with numpy : 0.007259845733642578 nb_pixel_total : 720 time to create 1 rle with old method : 0.0009391307830810547 time for calcul the mask position with numpy : 0.00663304328918457 nb_pixel_total : 213 time to create 1 rle with old method : 0.0002741813659667969 time for calcul the mask position with numpy : 0.006680965423583984 nb_pixel_total : 18815 time to create 1 rle with old method : 0.021616458892822266 time for calcul the mask position with numpy : 0.006444215774536133 nb_pixel_total : 1249 time to create 1 rle with old method : 0.0014600753784179688 time for calcul the mask position with numpy : 0.006268978118896484 nb_pixel_total : 892 time to create 1 rle with old method : 0.0010945796966552734 time for calcul the mask position with numpy : 0.006722450256347656 nb_pixel_total : 13168 time to create 1 rle with old method : 0.014569759368896484 time for calcul the mask position with numpy : 0.0064356327056884766 nb_pixel_total : 582 time to create 1 rle with old method : 0.000713348388671875 time for calcul the mask position with numpy : 0.006718635559082031 nb_pixel_total : 220 time to create 1 rle with old method : 0.0003046989440917969 time for calcul the mask position with numpy : 0.010428190231323242 nb_pixel_total : 2909 time to create 1 rle with old method : 0.0036613941192626953 time for calcul the mask position with numpy : 0.006426572799682617 nb_pixel_total : 75 time to create 1 rle with old method : 0.00016450881958007812 time for calcul the mask position with numpy : 0.00635528564453125 nb_pixel_total : 510 time to create 1 rle with old method : 0.0006096363067626953 time for calcul the mask position with numpy : 0.006251335144042969 nb_pixel_total : 1132 time to create 1 rle with old method : 0.0013861656188964844 time for calcul the mask position with numpy : 0.006345510482788086 nb_pixel_total : 12 time to create 1 rle with old method : 6.127357482910156e-05 time for calcul the mask position with numpy : 0.006207704544067383 nb_pixel_total : 539 time to create 1 rle with old method : 0.00066375732421875 time for calcul the mask position with numpy : 0.006270885467529297 nb_pixel_total : 1242 time to create 1 rle with old method : 0.0014851093292236328 time for calcul the mask position with numpy : 0.006407022476196289 nb_pixel_total : 107 time to create 1 rle with old method : 0.00015163421630859375 time for calcul the mask position with numpy : 0.006788730621337891 nb_pixel_total : 57 time to create 1 rle with old method : 0.00018978118896484375 time for calcul the mask position with numpy : 0.007373332977294922 nb_pixel_total : 3080 time to create 1 rle with old method : 0.003599882125854492 time for calcul the mask position with numpy : 0.006623744964599609 nb_pixel_total : 887 time to create 1 rle with old method : 0.0010821819305419922 time for calcul the mask position with numpy : 0.006445884704589844 nb_pixel_total : 2 time to create 1 rle with old method : 4.3392181396484375e-05 time for calcul the mask position with numpy : 0.0063898563385009766 nb_pixel_total : 12 time to create 1 rle with old method : 5.1975250244140625e-05 time for calcul the mask position with numpy : 0.006695985794067383 nb_pixel_total : 1837 time to create 1 rle with old method : 0.0021905899047851562 time for calcul the mask position with numpy : 0.006538867950439453 nb_pixel_total : 1359 time to create 1 rle with old method : 0.0016198158264160156 time for calcul the mask position with numpy : 0.0062847137451171875 nb_pixel_total : 21 time to create 1 rle with old method : 6.794929504394531e-05 time for calcul the mask position with numpy : 0.0061719417572021484 nb_pixel_total : 891 time to create 1 rle with old method : 0.001081705093383789 time for calcul the mask position with numpy : 0.006465911865234375 nb_pixel_total : 129 time to create 1 rle with old method : 0.00018095970153808594 time for calcul the mask position with numpy : 0.006506681442260742 nb_pixel_total : 68 time to create 1 rle with old method : 0.0001430511474609375 time for calcul the mask position with numpy : 0.006518840789794922 nb_pixel_total : 464 time to create 1 rle with old method : 0.0005996227264404297 time for calcul the mask position with numpy : 0.0065381526947021484 nb_pixel_total : 1 time to create 1 rle with old method : 1.71661376953125e-05 time for calcul the mask position with numpy : 0.00641179084777832 nb_pixel_total : 487 time to create 1 rle with old method : 0.0005960464477539062 time for calcul the mask position with numpy : 0.006224393844604492 nb_pixel_total : 2736 time to create 1 rle with old method : 0.0032460689544677734 time for calcul the mask position with numpy : 0.006245136260986328 nb_pixel_total : 301 time to create 1 rle with old method : 0.0003809928894042969 time for calcul the mask position with numpy : 0.006272792816162109 nb_pixel_total : 15 time to create 1 rle with old method : 7.534027099609375e-05 time for calcul the mask position with numpy : 0.006459474563598633 nb_pixel_total : 1636 time to create 1 rle with old method : 0.0019948482513427734 time for calcul the mask position with numpy : 0.006409168243408203 nb_pixel_total : 421 time to create 1 rle with old method : 0.0004937648773193359 time for calcul the mask position with numpy : 0.006215810775756836 nb_pixel_total : 2546 time to create 1 rle with old method : 0.0030753612518310547 time for calcul the mask position with numpy : 0.006097078323364258 nb_pixel_total : 1695 time to create 1 rle with old method : 0.0020089149475097656 time for calcul the mask position with numpy : 0.006238460540771484 nb_pixel_total : 918 time to create 1 rle with old method : 0.0011026859283447266 time for calcul the mask position with numpy : 0.006164073944091797 nb_pixel_total : 74 time to create 1 rle with old method : 0.0001819133758544922 time for calcul the mask position with numpy : 0.010476112365722656 nb_pixel_total : 363 time to create 1 rle with old method : 0.00045180320739746094 time for calcul the mask position with numpy : 0.006343364715576172 nb_pixel_total : 1267 time to create 1 rle with old method : 0.0014810562133789062 time for calcul the mask position with numpy : 0.006224632263183594 nb_pixel_total : 106 time to create 1 rle with old method : 0.00014972686767578125 time for calcul the mask position with numpy : 0.00622868537902832 nb_pixel_total : 70 time to create 1 rle with old method : 0.00010895729064941406 time for calcul the mask position with numpy : 0.006244182586669922 nb_pixel_total : 38 time to create 1 rle with old method : 6.532669067382812e-05 time for calcul the mask position with numpy : 0.00684666633605957 nb_pixel_total : 106842 time to create 1 rle with old method : 0.11529660224914551 time for calcul the mask position with numpy : 0.0059506893157958984 nb_pixel_total : 87 time to create 1 rle with old method : 0.0004055500030517578 time for calcul the mask position with numpy : 0.005852937698364258 nb_pixel_total : 1852 time to create 1 rle with old method : 0.0019969940185546875 time for calcul the mask position with numpy : 0.005945444107055664 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016379356384277344 time for calcul the mask position with numpy : 0.005830287933349609 nb_pixel_total : 9279 time to create 1 rle with old method : 0.009913206100463867 time for calcul the mask position with numpy : 0.006009101867675781 nb_pixel_total : 226 time to create 1 rle with old method : 0.000286102294921875 time for calcul the mask position with numpy : 0.0059015750885009766 nb_pixel_total : 195 time to create 1 rle with old method : 0.00023174285888671875 time for calcul the mask position with numpy : 0.006102800369262695 nb_pixel_total : 448 time to create 1 rle with old method : 0.0005633831024169922 time for calcul the mask position with numpy : 0.00596308708190918 nb_pixel_total : 149 time to create 1 rle with old method : 0.00020885467529296875 time for calcul the mask position with numpy : 0.006015777587890625 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002307891845703125 time for calcul the mask position with numpy : 0.005991458892822266 nb_pixel_total : 621 time to create 1 rle with old method : 0.0007455348968505859 time for calcul the mask position with numpy : 0.006030082702636719 nb_pixel_total : 745 time to create 1 rle with old method : 0.0008785724639892578 time for calcul the mask position with numpy : 0.009851217269897461 nb_pixel_total : 1704 time to create 1 rle with old method : 0.0019843578338623047 time for calcul the mask position with numpy : 0.006157398223876953 nb_pixel_total : 533 time to create 1 rle with old method : 0.0006208419799804688 time for calcul the mask position with numpy : 0.00607609748840332 nb_pixel_total : 1070 time to create 1 rle with old method : 0.0011870861053466797 time for calcul the mask position with numpy : 0.005911588668823242 nb_pixel_total : 404 time to create 1 rle with old method : 0.0004851818084716797 time for calcul the mask position with numpy : 0.006091594696044922 nb_pixel_total : 536 time to create 1 rle with old method : 0.000637054443359375 time for calcul the mask position with numpy : 0.006109952926635742 nb_pixel_total : 283 time to create 1 rle with old method : 0.0003464221954345703 time for calcul the mask position with numpy : 0.006031513214111328 nb_pixel_total : 1759 time to create 1 rle with old method : 0.001993417739868164 time for calcul the mask position with numpy : 0.005982637405395508 nb_pixel_total : 200 time to create 1 rle with old method : 0.0002486705780029297 time for calcul the mask position with numpy : 0.010117530822753906 nb_pixel_total : 1537 time to create 1 rle with old method : 0.0017850399017333984 time for calcul the mask position with numpy : 0.010040044784545898 nb_pixel_total : 222 time to create 1 rle with old method : 0.0002808570861816406 time for calcul the mask position with numpy : 0.010042905807495117 nb_pixel_total : 922 time to create 1 rle with old method : 0.0010788440704345703 time for calcul the mask position with numpy : 0.010059833526611328 nb_pixel_total : 364 time to create 1 rle with old method : 0.0004367828369140625 time for calcul the mask position with numpy : 0.013922929763793945 nb_pixel_total : 644 time to create 1 rle with old method : 0.0007674694061279297 time for calcul the mask position with numpy : 0.010111570358276367 nb_pixel_total : 14 time to create 1 rle with old method : 5.4836273193359375e-05 time for calcul the mask position with numpy : 0.010379314422607422 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006170272827148438 time for calcul the mask position with numpy : 0.010120391845703125 nb_pixel_total : 2187 time to create 1 rle with old method : 0.002515077590942383 time for calcul the mask position with numpy : 0.010035514831542969 nb_pixel_total : 90 time to create 1 rle with old method : 0.0001418590545654297 time for calcul the mask position with numpy : 0.010607004165649414 nb_pixel_total : 58 time to create 1 rle with old method : 9.417533874511719e-05 time for calcul the mask position with numpy : 0.010247468948364258 nb_pixel_total : 1741 time to create 1 rle with old method : 0.00201416015625 time for calcul the mask position with numpy : 0.010102510452270508 nb_pixel_total : 163 time to create 1 rle with old method : 0.00022840499877929688 time for calcul the mask position with numpy : 0.010335922241210938 nb_pixel_total : 23 time to create 1 rle with old method : 7.081031799316406e-05 time for calcul the mask position with numpy : 0.010499000549316406 nb_pixel_total : 1027 time to create 1 rle with old method : 0.0012128353118896484 time for calcul the mask position with numpy : 0.014199495315551758 nb_pixel_total : 181 time to create 1 rle with old method : 0.0002505779266357422 time for calcul the mask position with numpy : 0.010171651840209961 nb_pixel_total : 4803 time to create 1 rle with old method : 0.0053250789642333984 time for calcul the mask position with numpy : 0.01020669937133789 nb_pixel_total : 7 time to create 1 rle with old method : 3.1948089599609375e-05 time for calcul the mask position with numpy : 0.010099172592163086 nb_pixel_total : 31 time to create 1 rle with old method : 9.703636169433594e-05 time for calcul the mask position with numpy : 0.00988626480102539 nb_pixel_total : 24 time to create 1 rle with old method : 7.534027099609375e-05 time for calcul the mask position with numpy : 0.010149717330932617 nb_pixel_total : 351 time to create 1 rle with old method : 0.0004658699035644531 time for calcul the mask position with numpy : 0.010101795196533203 nb_pixel_total : 109 time to create 1 rle with old method : 0.00014710426330566406 time for calcul the mask position with numpy : 0.009887218475341797 nb_pixel_total : 1442 time to create 1 rle with old method : 0.0016460418701171875 time for calcul the mask position with numpy : 0.009967803955078125 nb_pixel_total : 442 time to create 1 rle with old method : 0.0005884170532226562 time for calcul the mask position with numpy : 0.010041475296020508 nb_pixel_total : 1553 time to create 1 rle with old method : 0.0017695426940917969 time for calcul the mask position with numpy : 0.0099945068359375 nb_pixel_total : 894 time to create 1 rle with old method : 0.0010793209075927734 time for calcul the mask position with numpy : 0.010140180587768555 nb_pixel_total : 61 time to create 1 rle with old method : 0.00012564659118652344 time for calcul the mask position with numpy : 0.010188579559326172 nb_pixel_total : 87 time to create 1 rle with old method : 0.00022125244140625 time for calcul the mask position with numpy : 0.010289430618286133 nb_pixel_total : 4590 time to create 1 rle with old method : 0.005278825759887695 time for calcul the mask position with numpy : 0.010103225708007812 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001385211944580078 time for calcul the mask position with numpy : 0.010128259658813477 nb_pixel_total : 70 time to create 1 rle with old method : 0.0001068115234375 time for calcul the mask position with numpy : 0.01013803482055664 nb_pixel_total : 943 time to create 1 rle with old method : 0.0011560916900634766 time for calcul the mask position with numpy : 0.009964227676391602 nb_pixel_total : 628 time to create 1 rle with old method : 0.0007159709930419922 time for calcul the mask position with numpy : 0.009993791580200195 nb_pixel_total : 255 time to create 1 rle with old method : 0.00032520294189453125 time for calcul the mask position with numpy : 0.01015615463256836 nb_pixel_total : 479 time to create 1 rle with old method : 0.0005948543548583984 time for calcul the mask position with numpy : 0.010154247283935547 nb_pixel_total : 544 time to create 1 rle with old method : 0.0006501674652099609 time for calcul the mask position with numpy : 0.01466679573059082 nb_pixel_total : 6 time to create 1 rle with old method : 3.4809112548828125e-05 time for calcul the mask position with numpy : 0.009968042373657227 nb_pixel_total : 65 time to create 1 rle with old method : 9.393692016601562e-05 time for calcul the mask position with numpy : 0.00835871696472168 nb_pixel_total : 352 time to create 1 rle with old method : 0.0004239082336425781 time for calcul the mask position with numpy : 0.008027315139770508 nb_pixel_total : 730 time to create 1 rle with old method : 0.0008947849273681641 time for calcul the mask position with numpy : 0.008231163024902344 nb_pixel_total : 23 time to create 1 rle with old method : 4.2438507080078125e-05 time for calcul the mask position with numpy : 0.008217334747314453 nb_pixel_total : 359 time to create 1 rle with old method : 0.00042057037353515625 time for calcul the mask position with numpy : 0.008574724197387695 nb_pixel_total : 132 time to create 1 rle with old method : 0.0001575946807861328 time for calcul the mask position with numpy : 0.008210182189941406 nb_pixel_total : 404 time to create 1 rle with old method : 0.00043892860412597656 time for calcul the mask position with numpy : 0.008080482482910156 nb_pixel_total : 1754 time to create 1 rle with old method : 0.0019004344940185547 time for calcul the mask position with numpy : 0.008232355117797852 nb_pixel_total : 1278 time to create 1 rle with old method : 0.0014507770538330078 time for calcul the mask position with numpy : 0.00801539421081543 nb_pixel_total : 520 time to create 1 rle with old method : 0.0005846023559570312 time for calcul the mask position with numpy : 0.008133649826049805 nb_pixel_total : 482 time to create 1 rle with old method : 0.0005741119384765625 time for calcul the mask position with numpy : 0.008402347564697266 nb_pixel_total : 502 time to create 1 rle with old method : 0.0006086826324462891 time for calcul the mask position with numpy : 0.008425712585449219 nb_pixel_total : 417 time to create 1 rle with old method : 0.0005059242248535156 time for calcul the mask position with numpy : 0.008434772491455078 nb_pixel_total : 1905 time to create 1 rle with old method : 0.002241373062133789 time for calcul the mask position with numpy : 0.010121345520019531 nb_pixel_total : 5338 time to create 1 rle with old method : 0.006074428558349609 time for calcul the mask position with numpy : 0.008401870727539062 nb_pixel_total : 341 time to create 1 rle with old method : 0.0004088878631591797 time for calcul the mask position with numpy : 0.008502006530761719 nb_pixel_total : 1533 time to create 1 rle with old method : 0.0017910003662109375 time for calcul the mask position with numpy : 0.008556842803955078 nb_pixel_total : 250 time to create 1 rle with old method : 0.0003180503845214844 time for calcul the mask position with numpy : 0.008831977844238281 nb_pixel_total : 36 time to create 1 rle with old method : 0.00013518333435058594 time for calcul the mask position with numpy : 0.0105438232421875 nb_pixel_total : 299 time to create 1 rle with old method : 0.00036787986755371094 time for calcul the mask position with numpy : 0.008553743362426758 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007245540618896484 time for calcul the mask position with numpy : 0.011177778244018555 nb_pixel_total : 13 time to create 1 rle with old method : 6.580352783203125e-05 create new chi : 2.231940507888794 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.003998517990112305 batch 1 Loaded 188 chid ids of type : 4230 Number RLEs to save : 16254 TO DO : save crop sub photo not yet done ! save time : 1.5764861106872559 nb_obj : 161 nb_hashtags : 8 time to prepare the origin masks : 2.2331039905548096 time for calcul the mask position with numpy : 0.025643587112426758 nb_pixel_total : 1743138 time to create 1 rle with new method : 0.05260205268859863 time for calcul the mask position with numpy : 0.006196498870849609 nb_pixel_total : 2906 time to create 1 rle with old method : 0.0033829212188720703 time for calcul the mask position with numpy : 0.006117343902587891 nb_pixel_total : 136 time to create 1 rle with old method : 0.00019979476928710938 time for calcul the mask position with numpy : 0.010163068771362305 nb_pixel_total : 12364 time to create 1 rle with old method : 0.013986349105834961 time for calcul the mask position with numpy : 0.014524698257446289 nb_pixel_total : 47719 time to create 1 rle with old method : 0.05222201347351074 time for calcul the mask position with numpy : 0.009983301162719727 nb_pixel_total : 2578 time to create 1 rle with old method : 0.002809286117553711 time for calcul the mask position with numpy : 0.010283231735229492 nb_pixel_total : 31 time to create 1 rle with old method : 5.984306335449219e-05 time for calcul the mask position with numpy : 0.010111570358276367 nb_pixel_total : 21 time to create 1 rle with old method : 5.7220458984375e-05 time for calcul the mask position with numpy : 0.011612415313720703 nb_pixel_total : 260 time to create 1 rle with old method : 0.0008459091186523438 time for calcul the mask position with numpy : 0.014595985412597656 nb_pixel_total : 370 time to create 1 rle with old method : 0.0004401206970214844 time for calcul the mask position with numpy : 0.009945154190063477 nb_pixel_total : 316 time to create 1 rle with old method : 0.00037932395935058594 time for calcul the mask position with numpy : 0.010351419448852539 nb_pixel_total : 164 time to create 1 rle with old method : 0.0004565715789794922 time for calcul the mask position with numpy : 0.0062656402587890625 nb_pixel_total : 403 time to create 1 rle with old method : 0.0005860328674316406 time for calcul the mask position with numpy : 0.00621342658996582 nb_pixel_total : 59 time to create 1 rle with old method : 0.00010061264038085938 time for calcul the mask position with numpy : 0.006008625030517578 nb_pixel_total : 20 time to create 1 rle with old method : 5.245208740234375e-05 time for calcul the mask position with numpy : 0.006043434143066406 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002429485321044922 time for calcul the mask position with numpy : 0.005897045135498047 nb_pixel_total : 116 time to create 1 rle with old method : 0.0001780986785888672 time for calcul the mask position with numpy : 0.0060577392578125 nb_pixel_total : 17 time to create 1 rle with old method : 5.412101745605469e-05 time for calcul the mask position with numpy : 0.006741046905517578 nb_pixel_total : 110 time to create 1 rle with old method : 0.00019669532775878906 time for calcul the mask position with numpy : 0.006531715393066406 nb_pixel_total : 2 time to create 1 rle with old method : 2.5987625122070312e-05 time for calcul the mask position with numpy : 0.006014823913574219 nb_pixel_total : 41 time to create 1 rle with old method : 0.00010180473327636719 time for calcul the mask position with numpy : 0.013702630996704102 nb_pixel_total : 1305 time to create 1 rle with old method : 0.001524209976196289 time for calcul the mask position with numpy : 0.01003718376159668 nb_pixel_total : 167 time to create 1 rle with old method : 0.00024437904357910156 time for calcul the mask position with numpy : 0.010221004486083984 nb_pixel_total : 781 time to create 1 rle with old method : 0.0009424686431884766 time for calcul the mask position with numpy : 0.010107278823852539 nb_pixel_total : 10145 time to create 1 rle with old method : 0.011276006698608398 time for calcul the mask position with numpy : 0.010152339935302734 nb_pixel_total : 898 time to create 1 rle with old method : 0.001077890396118164 time for calcul the mask position with numpy : 0.010373592376708984 nb_pixel_total : 10858 time to create 1 rle with old method : 0.012249469757080078 time for calcul the mask position with numpy : 0.010164737701416016 nb_pixel_total : 188 time to create 1 rle with old method : 0.0002486705780029297 time for calcul the mask position with numpy : 0.010211944580078125 nb_pixel_total : 947 time to create 1 rle with old method : 0.0010592937469482422 time for calcul the mask position with numpy : 0.010107278823852539 nb_pixel_total : 1534 time to create 1 rle with old method : 0.0017120838165283203 time for calcul the mask position with numpy : 0.010107994079589844 nb_pixel_total : 6214 time to create 1 rle with old method : 0.007009029388427734 time for calcul the mask position with numpy : 0.009994983673095703 nb_pixel_total : 11550 time to create 1 rle with old method : 0.013498544692993164 time for calcul the mask position with numpy : 0.009930849075317383 nb_pixel_total : 104 time to create 1 rle with old method : 0.0001506805419921875 time for calcul the mask position with numpy : 0.009954452514648438 nb_pixel_total : 774 time to create 1 rle with old method : 0.0009751319885253906 time for calcul the mask position with numpy : 0.011303186416625977 nb_pixel_total : 126 time to create 1 rle with old method : 0.00019216537475585938 time for calcul the mask position with numpy : 0.010102510452270508 nb_pixel_total : 97 time to create 1 rle with old method : 0.00013899803161621094 time for calcul the mask position with numpy : 0.01402902603149414 nb_pixel_total : 170 time to create 1 rle with old method : 0.000240325927734375 time for calcul the mask position with numpy : 0.0059969425201416016 nb_pixel_total : 1557 time to create 1 rle with old method : 0.0018372535705566406 time for calcul the mask position with numpy : 0.006014108657836914 nb_pixel_total : 955 time to create 1 rle with old method : 0.001096963882446289 time for calcul the mask position with numpy : 0.005837917327880859 nb_pixel_total : 678 time to create 1 rle with old method : 0.0008320808410644531 time for calcul the mask position with numpy : 0.005954742431640625 nb_pixel_total : 191 time to create 1 rle with old method : 0.00023889541625976562 time for calcul the mask position with numpy : 0.005898475646972656 nb_pixel_total : 965 time to create 1 rle with old method : 0.0011630058288574219 time for calcul the mask position with numpy : 0.006078481674194336 nb_pixel_total : 1262 time to create 1 rle with old method : 0.0014653205871582031 time for calcul the mask position with numpy : 0.005892515182495117 nb_pixel_total : 307 time to create 1 rle with old method : 0.00038504600524902344 time for calcul the mask position with numpy : 0.005987644195556641 nb_pixel_total : 13910 time to create 1 rle with old method : 0.015113115310668945 time for calcul the mask position with numpy : 0.0061492919921875 nb_pixel_total : 255 time to create 1 rle with old method : 0.00036835670471191406 time for calcul the mask position with numpy : 0.0060694217681884766 nb_pixel_total : 119 time to create 1 rle with old method : 0.0002465248107910156 time for calcul the mask position with numpy : 0.005924701690673828 nb_pixel_total : 2674 time to create 1 rle with old method : 0.0031964778900146484 time for calcul the mask position with numpy : 0.006135463714599609 nb_pixel_total : 126 time to create 1 rle with old method : 0.0003235340118408203 time for calcul the mask position with numpy : 0.005899190902709961 nb_pixel_total : 526 time to create 1 rle with old method : 0.0006349086761474609 time for calcul the mask position with numpy : 0.005946636199951172 nb_pixel_total : 546 time to create 1 rle with old method : 0.0006806850433349609 time for calcul the mask position with numpy : 0.005952596664428711 nb_pixel_total : 1101 time to create 1 rle with old method : 0.0013229846954345703 time for calcul the mask position with numpy : 0.005893230438232422 nb_pixel_total : 103 time to create 1 rle with old method : 0.00015425682067871094 time for calcul the mask position with numpy : 0.005986690521240234 nb_pixel_total : 900 time to create 1 rle with old method : 0.0011086463928222656 time for calcul the mask position with numpy : 0.006228446960449219 nb_pixel_total : 3124 time to create 1 rle with old method : 0.0036962032318115234 time for calcul the mask position with numpy : 0.006294727325439453 nb_pixel_total : 200 time to create 1 rle with old method : 0.0002727508544921875 time for calcul the mask position with numpy : 0.010577201843261719 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002677440643310547 time for calcul the mask position with numpy : 0.010628938674926758 nb_pixel_total : 1408 time to create 1 rle with old method : 0.0016961097717285156 time for calcul the mask position with numpy : 0.010277986526489258 nb_pixel_total : 110 time to create 1 rle with old method : 0.00016880035400390625 time for calcul the mask position with numpy : 0.010167121887207031 nb_pixel_total : 975 time to create 1 rle with old method : 0.0012478828430175781 time for calcul the mask position with numpy : 0.010245561599731445 nb_pixel_total : 1089 time to create 1 rle with old method : 0.0013184547424316406 time for calcul the mask position with numpy : 0.01012277603149414 nb_pixel_total : 118 time to create 1 rle with old method : 0.00017189979553222656 time for calcul the mask position with numpy : 0.01013326644897461 nb_pixel_total : 514 time to create 1 rle with old method : 0.0007412433624267578 time for calcul the mask position with numpy : 0.010017871856689453 nb_pixel_total : 40 time to create 1 rle with old method : 0.00011444091796875 time for calcul the mask position with numpy : 0.009831905364990234 nb_pixel_total : 503 time to create 1 rle with old method : 0.0006985664367675781 time for calcul the mask position with numpy : 0.009827136993408203 nb_pixel_total : 1275 time to create 1 rle with old method : 0.0015430450439453125 time for calcul the mask position with numpy : 0.010033607482910156 nb_pixel_total : 942 time to create 1 rle with old method : 0.0012798309326171875 time for calcul the mask position with numpy : 0.00999140739440918 nb_pixel_total : 322 time to create 1 rle with old method : 0.00043392181396484375 time for calcul the mask position with numpy : 0.009781599044799805 nb_pixel_total : 283 time to create 1 rle with old method : 0.0003707408905029297 time for calcul the mask position with numpy : 0.010097265243530273 nb_pixel_total : 114 time to create 1 rle with old method : 0.00015354156494140625 time for calcul the mask position with numpy : 0.009909629821777344 nb_pixel_total : 117 time to create 1 rle with old method : 0.00018405914306640625 time for calcul the mask position with numpy : 0.010074377059936523 nb_pixel_total : 1291 time to create 1 rle with old method : 0.0015201568603515625 time for calcul the mask position with numpy : 0.01419973373413086 nb_pixel_total : 71 time to create 1 rle with old method : 0.00010800361633300781 time for calcul the mask position with numpy : 0.0108642578125 nb_pixel_total : 107035 time to create 1 rle with old method : 0.11896419525146484 time for calcul the mask position with numpy : 0.010450601577758789 nb_pixel_total : 92 time to create 1 rle with old method : 0.00015616416931152344 time for calcul the mask position with numpy : 0.010693788528442383 nb_pixel_total : 8787 time to create 1 rle with old method : 0.010236501693725586 time for calcul the mask position with numpy : 0.010440826416015625 nb_pixel_total : 69 time to create 1 rle with old method : 0.00012803077697753906 time for calcul the mask position with numpy : 0.006519794464111328 nb_pixel_total : 1096 time to create 1 rle with old method : 0.0015141963958740234 time for calcul the mask position with numpy : 0.006421566009521484 nb_pixel_total : 1757 time to create 1 rle with old method : 0.002094745635986328 time for calcul the mask position with numpy : 0.006968498229980469 nb_pixel_total : 301 time to create 1 rle with old method : 0.00048232078552246094 time for calcul the mask position with numpy : 0.0062448978424072266 nb_pixel_total : 109 time to create 1 rle with old method : 0.00017070770263671875 time for calcul the mask position with numpy : 0.006279706954956055 nb_pixel_total : 297 time to create 1 rle with old method : 0.0004420280456542969 time for calcul the mask position with numpy : 0.006113767623901367 nb_pixel_total : 3149 time to create 1 rle with old method : 0.004965782165527344 time for calcul the mask position with numpy : 0.00691986083984375 nb_pixel_total : 125 time to create 1 rle with old method : 0.0001838207244873047 time for calcul the mask position with numpy : 0.006172895431518555 nb_pixel_total : 205 time to create 1 rle with old method : 0.00027251243591308594 time for calcul the mask position with numpy : 0.006376981735229492 nb_pixel_total : 55 time to create 1 rle with old method : 0.0001316070556640625 time for calcul the mask position with numpy : 0.006183624267578125 nb_pixel_total : 390 time to create 1 rle with old method : 0.0005085468292236328 time for calcul the mask position with numpy : 0.006196260452270508 nb_pixel_total : 386 time to create 1 rle with old method : 0.0004963874816894531 time for calcul the mask position with numpy : 0.006121397018432617 nb_pixel_total : 324 time to create 1 rle with old method : 0.00045680999755859375 time for calcul the mask position with numpy : 0.009366273880004883 nb_pixel_total : 1850 time to create 1 rle with old method : 0.003602743148803711 time for calcul the mask position with numpy : 0.008891105651855469 nb_pixel_total : 1030 time to create 1 rle with old method : 0.002106904983520508 time for calcul the mask position with numpy : 0.011387825012207031 nb_pixel_total : 632 time to create 1 rle with old method : 0.0010981559753417969 time for calcul the mask position with numpy : 0.006325244903564453 nb_pixel_total : 655 time to create 1 rle with old method : 0.0008418560028076172 time for calcul the mask position with numpy : 0.006612539291381836 nb_pixel_total : 452 time to create 1 rle with old method : 0.0005905628204345703 time for calcul the mask position with numpy : 0.006674051284790039 nb_pixel_total : 455 time to create 1 rle with old method : 0.0005638599395751953 time for calcul the mask position with numpy : 0.01158761978149414 nb_pixel_total : 205 time to create 1 rle with old method : 0.0002980232238769531 time for calcul the mask position with numpy : 0.006799459457397461 nb_pixel_total : 263 time to create 1 rle with old method : 0.00037407875061035156 time for calcul the mask position with numpy : 0.007030963897705078 nb_pixel_total : 460 time to create 1 rle with old method : 0.0006322860717773438 time for calcul the mask position with numpy : 0.007120370864868164 nb_pixel_total : 186 time to create 1 rle with old method : 0.0002605915069580078 time for calcul the mask position with numpy : 0.006937980651855469 nb_pixel_total : 80 time to create 1 rle with old method : 0.00014734268188476562 time for calcul the mask position with numpy : 0.007019758224487305 nb_pixel_total : 937 time to create 1 rle with old method : 0.0012395381927490234 time for calcul the mask position with numpy : 0.007112741470336914 nb_pixel_total : 191 time to create 1 rle with old method : 0.0002899169921875 time for calcul the mask position with numpy : 0.006822347640991211 nb_pixel_total : 347 time to create 1 rle with old method : 0.0006144046783447266 time for calcul the mask position with numpy : 0.00676417350769043 nb_pixel_total : 885 time to create 1 rle with old method : 0.0014376640319824219 time for calcul the mask position with numpy : 0.007441520690917969 nb_pixel_total : 552 time to create 1 rle with old method : 0.000705718994140625 time for calcul the mask position with numpy : 0.006974220275878906 nb_pixel_total : 1540 time to create 1 rle with old method : 0.0019297599792480469 time for calcul the mask position with numpy : 0.011612653732299805 nb_pixel_total : 423 time to create 1 rle with old method : 0.0005762577056884766 time for calcul the mask position with numpy : 0.011708974838256836 nb_pixel_total : 450 time to create 1 rle with old method : 0.0007419586181640625 time for calcul the mask position with numpy : 0.01263427734375 nb_pixel_total : 1847 time to create 1 rle with old method : 0.002271413803100586 time for calcul the mask position with numpy : 0.01209712028503418 nb_pixel_total : 150 time to create 1 rle with old method : 0.00022792816162109375 time for calcul the mask position with numpy : 0.012092351913452148 nb_pixel_total : 82 time to create 1 rle with old method : 0.000141143798828125 time for calcul the mask position with numpy : 0.011113882064819336 nb_pixel_total : 4970 time to create 1 rle with old method : 0.006034374237060547 time for calcul the mask position with numpy : 0.011813640594482422 nb_pixel_total : 916 time to create 1 rle with old method : 0.0017905235290527344 time for calcul the mask position with numpy : 0.011074304580688477 nb_pixel_total : 67 time to create 1 rle with old method : 0.0001513957977294922 time for calcul the mask position with numpy : 0.010559320449829102 nb_pixel_total : 1218 time to create 1 rle with old method : 0.0014753341674804688 time for calcul the mask position with numpy : 0.010656595230102539 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002663135528564453 time for calcul the mask position with numpy : 0.010424375534057617 nb_pixel_total : 140 time to create 1 rle with old method : 0.00023818016052246094 time for calcul the mask position with numpy : 0.01043701171875 nb_pixel_total : 83 time to create 1 rle with old method : 0.00013566017150878906 time for calcul the mask position with numpy : 0.010618448257446289 nb_pixel_total : 401 time to create 1 rle with old method : 0.0005340576171875 time for calcul the mask position with numpy : 0.010676145553588867 nb_pixel_total : 1546 time to create 1 rle with old method : 0.0018525123596191406 time for calcul the mask position with numpy : 0.010472297668457031 nb_pixel_total : 451 time to create 1 rle with old method : 0.0005948543548583984 time for calcul the mask position with numpy : 0.010710477828979492 nb_pixel_total : 307 time to create 1 rle with old method : 0.0004887580871582031 time for calcul the mask position with numpy : 0.01077723503112793 nb_pixel_total : 63 time to create 1 rle with old method : 0.00010824203491210938 time for calcul the mask position with numpy : 0.013130664825439453 nb_pixel_total : 47 time to create 1 rle with old method : 0.0001773834228515625 time for calcul the mask position with numpy : 0.010293245315551758 nb_pixel_total : 4627 time to create 1 rle with old method : 0.005501270294189453 time for calcul the mask position with numpy : 0.010417461395263672 nb_pixel_total : 583 time to create 1 rle with old method : 0.0007026195526123047 time for calcul the mask position with numpy : 0.014773368835449219 nb_pixel_total : 1510 time to create 1 rle with old method : 0.0017788410186767578 time for calcul the mask position with numpy : 0.010791301727294922 nb_pixel_total : 3 time to create 1 rle with old method : 3.24249267578125e-05 time for calcul the mask position with numpy : 0.010751485824584961 nb_pixel_total : 889 time to create 1 rle with old method : 0.0011096000671386719 time for calcul the mask position with numpy : 0.01094198226928711 nb_pixel_total : 208 time to create 1 rle with old method : 0.0003795623779296875 time for calcul the mask position with numpy : 0.01064443588256836 nb_pixel_total : 631 time to create 1 rle with old method : 0.001064300537109375 time for calcul the mask position with numpy : 0.010710477828979492 nb_pixel_total : 274 time to create 1 rle with old method : 0.0003380775451660156 time for calcul the mask position with numpy : 0.010480642318725586 nb_pixel_total : 512 time to create 1 rle with old method : 0.0006308555603027344 time for calcul the mask position with numpy : 0.010805130004882812 nb_pixel_total : 528 time to create 1 rle with old method : 0.0006449222564697266 time for calcul the mask position with numpy : 0.010805606842041016 nb_pixel_total : 73 time to create 1 rle with old method : 0.00011205673217773438 time for calcul the mask position with numpy : 0.01191258430480957 nb_pixel_total : 339 time to create 1 rle with old method : 0.00041556358337402344 time for calcul the mask position with numpy : 0.012039899826049805 nb_pixel_total : 135 time to create 1 rle with old method : 0.0002574920654296875 time for calcul the mask position with numpy : 0.012441158294677734 nb_pixel_total : 676 time to create 1 rle with old method : 0.0010907649993896484 time for calcul the mask position with numpy : 0.013144493103027344 nb_pixel_total : 10 time to create 1 rle with old method : 7.033348083496094e-05 time for calcul the mask position with numpy : 0.012748956680297852 nb_pixel_total : 5 time to create 1 rle with old method : 5.1021575927734375e-05 time for calcul the mask position with numpy : 0.00710296630859375 nb_pixel_total : 412 time to create 1 rle with old method : 0.0005195140838623047 time for calcul the mask position with numpy : 0.006575345993041992 nb_pixel_total : 48 time to create 1 rle with old method : 0.00010037422180175781 time for calcul the mask position with numpy : 0.006818532943725586 nb_pixel_total : 223 time to create 1 rle with old method : 0.0003952980041503906 time for calcul the mask position with numpy : 0.006712913513183594 nb_pixel_total : 1566 time to create 1 rle with old method : 0.0018665790557861328 time for calcul the mask position with numpy : 0.006695270538330078 nb_pixel_total : 26 time to create 1 rle with old method : 0.0001251697540283203 time for calcul the mask position with numpy : 0.0065860748291015625 nb_pixel_total : 1007 time to create 1 rle with old method : 0.0011942386627197266 time for calcul the mask position with numpy : 0.006728410720825195 nb_pixel_total : 163 time to create 1 rle with old method : 0.0002410411834716797 time for calcul the mask position with numpy : 0.00652003288269043 nb_pixel_total : 8 time to create 1 rle with old method : 5.8650970458984375e-05 time for calcul the mask position with numpy : 0.006631135940551758 nb_pixel_total : 605 time to create 1 rle with old method : 0.0007410049438476562 time for calcul the mask position with numpy : 0.006716012954711914 nb_pixel_total : 342 time to create 1 rle with old method : 0.0005750656127929688 time for calcul the mask position with numpy : 0.006835222244262695 nb_pixel_total : 123 time to create 1 rle with old method : 0.00025916099548339844 time for calcul the mask position with numpy : 0.0068895816802978516 nb_pixel_total : 8343 time to create 1 rle with old method : 0.009376049041748047 time for calcul the mask position with numpy : 0.0068209171295166016 nb_pixel_total : 368 time to create 1 rle with old method : 0.00045752525329589844 time for calcul the mask position with numpy : 0.007490634918212891 nb_pixel_total : 37 time to create 1 rle with old method : 8.869171142578125e-05 time for calcul the mask position with numpy : 0.008043527603149414 nb_pixel_total : 245 time to create 1 rle with old method : 0.0005311965942382812 time for calcul the mask position with numpy : 0.009126424789428711 nb_pixel_total : 258 time to create 1 rle with old method : 0.0004172325134277344 time for calcul the mask position with numpy : 0.0070950984954833984 nb_pixel_total : 237 time to create 1 rle with old method : 0.00033211708068847656 time for calcul the mask position with numpy : 0.006537675857543945 nb_pixel_total : 1473 time to create 1 rle with old method : 0.0017442703247070312 time for calcul the mask position with numpy : 0.006550312042236328 nb_pixel_total : 41 time to create 1 rle with old method : 7.605552673339844e-05 time for calcul the mask position with numpy : 0.0064983367919921875 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0019168853759765625 time for calcul the mask position with numpy : 0.007149934768676758 nb_pixel_total : 860 time to create 1 rle with old method : 0.001172780990600586 time for calcul the mask position with numpy : 0.006941080093383789 nb_pixel_total : 625 time to create 1 rle with old method : 0.0007808208465576172 create new chi : 1.8932828903198242 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.007186174392700195 batch 1 Loaded 164 chid ids of type : 4230 Number RLEs to save : 14867 TO DO : save crop sub photo not yet done ! save time : 1.18581223487854 nb_obj : 174 nb_hashtags : 8 time to prepare the origin masks : 1.7715866565704346 time for calcul the mask position with numpy : 0.07811856269836426 nb_pixel_total : 1745229 time to create 1 rle with new method : 0.10135197639465332 time for calcul the mask position with numpy : 0.0071315765380859375 nb_pixel_total : 327 time to create 1 rle with old method : 0.0004239082336425781 time for calcul the mask position with numpy : 0.006749868392944336 nb_pixel_total : 33 time to create 1 rle with old method : 0.00010347366333007812 time for calcul the mask position with numpy : 0.0066454410552978516 nb_pixel_total : 2972 time to create 1 rle with old method : 0.003481149673461914 time for calcul the mask position with numpy : 0.0069773197174072266 nb_pixel_total : 2778 time to create 1 rle with old method : 0.003258228302001953 time for calcul the mask position with numpy : 0.006848812103271484 nb_pixel_total : 197 time to create 1 rle with old method : 0.0002624988555908203 time for calcul the mask position with numpy : 0.011113882064819336 nb_pixel_total : 1668 time to create 1 rle with old method : 0.0019745826721191406 time for calcul the mask position with numpy : 0.011072874069213867 nb_pixel_total : 2697 time to create 1 rle with old method : 0.0031807422637939453 time for calcul the mask position with numpy : 0.012109518051147461 nb_pixel_total : 54 time to create 1 rle with old method : 0.000118255615234375 time for calcul the mask position with numpy : 0.011394023895263672 nb_pixel_total : 53 time to create 1 rle with old method : 0.00010132789611816406 time for calcul the mask position with numpy : 0.011883974075317383 nb_pixel_total : 212 time to create 1 rle with old method : 0.0003962516784667969 time for calcul the mask position with numpy : 0.01220703125 nb_pixel_total : 294 time to create 1 rle with old method : 0.0003924369812011719 time for calcul the mask position with numpy : 0.011910438537597656 nb_pixel_total : 199 time to create 1 rle with old method : 0.00027060508728027344 time for calcul the mask position with numpy : 0.011518478393554688 nb_pixel_total : 487 time to create 1 rle with old method : 0.0006287097930908203 time for calcul the mask position with numpy : 0.01108551025390625 nb_pixel_total : 20 time to create 1 rle with old method : 4.458427429199219e-05 time for calcul the mask position with numpy : 0.007371187210083008 nb_pixel_total : 330 time to create 1 rle with old method : 0.00044035911560058594 time for calcul the mask position with numpy : 0.006804943084716797 nb_pixel_total : 45 time to create 1 rle with old method : 9.202957153320312e-05 time for calcul the mask position with numpy : 0.006868124008178711 nb_pixel_total : 35 time to create 1 rle with old method : 6.890296936035156e-05 time for calcul the mask position with numpy : 0.0068585872650146484 nb_pixel_total : 1 time to create 1 rle with old method : 2.3126602172851562e-05 time for calcul the mask position with numpy : 0.007085323333740234 nb_pixel_total : 19 time to create 1 rle with old method : 4.8160552978515625e-05 time for calcul the mask position with numpy : 0.007052183151245117 nb_pixel_total : 1235 time to create 1 rle with old method : 0.0015037059783935547 time for calcul the mask position with numpy : 0.00690913200378418 nb_pixel_total : 121 time to create 1 rle with old method : 0.0003628730773925781 time for calcul the mask position with numpy : 0.006755352020263672 nb_pixel_total : 20364 time to create 1 rle with old method : 0.0222930908203125 time for calcul the mask position with numpy : 0.006294727325439453 nb_pixel_total : 707 time to create 1 rle with old method : 0.0008471012115478516 time for calcul the mask position with numpy : 0.006426334381103516 nb_pixel_total : 39 time to create 1 rle with old method : 0.0001163482666015625 time for calcul the mask position with numpy : 0.00793910026550293 nb_pixel_total : 958 time to create 1 rle with old method : 0.0011234283447265625 time for calcul the mask position with numpy : 0.006516456604003906 nb_pixel_total : 8300 time to create 1 rle with old method : 0.011659383773803711 time for calcul the mask position with numpy : 0.0061833858489990234 nb_pixel_total : 10487 time to create 1 rle with old method : 0.013397216796875 time for calcul the mask position with numpy : 0.006215333938598633 nb_pixel_total : 196 time to create 1 rle with old method : 0.00026154518127441406 time for calcul the mask position with numpy : 0.005979776382446289 nb_pixel_total : 1986 time to create 1 rle with old method : 0.0023047924041748047 time for calcul the mask position with numpy : 0.00640106201171875 nb_pixel_total : 3673 time to create 1 rle with old method : 0.004927873611450195 time for calcul the mask position with numpy : 0.006039857864379883 nb_pixel_total : 426 time to create 1 rle with old method : 0.0005383491516113281 time for calcul the mask position with numpy : 0.006304025650024414 nb_pixel_total : 2374 time to create 1 rle with old method : 0.0028100013732910156 time for calcul the mask position with numpy : 0.006173372268676758 nb_pixel_total : 761 time to create 1 rle with old method : 0.0009560585021972656 time for calcul the mask position with numpy : 0.006429195404052734 nb_pixel_total : 5689 time to create 1 rle with old method : 0.006452798843383789 time for calcul the mask position with numpy : 0.006461381912231445 nb_pixel_total : 11044 time to create 1 rle with old method : 0.01732659339904785 time for calcul the mask position with numpy : 0.0063207149505615234 nb_pixel_total : 177 time to create 1 rle with old method : 0.00033855438232421875 time for calcul the mask position with numpy : 0.006641387939453125 nb_pixel_total : 109 time to create 1 rle with old method : 0.000152587890625 time for calcul the mask position with numpy : 0.006560087203979492 nb_pixel_total : 74 time to create 1 rle with old method : 0.00022530555725097656 time for calcul the mask position with numpy : 0.006562232971191406 nb_pixel_total : 747 time to create 1 rle with old method : 0.0008761882781982422 time for calcul the mask position with numpy : 0.0063931941986083984 nb_pixel_total : 98 time to create 1 rle with old method : 0.0001819133758544922 time for calcul the mask position with numpy : 0.0072367191314697266 nb_pixel_total : 351 time to create 1 rle with old method : 0.0005426406860351562 time for calcul the mask position with numpy : 0.007936716079711914 nb_pixel_total : 21 time to create 1 rle with old method : 5.030632019042969e-05 time for calcul the mask position with numpy : 0.006699800491333008 nb_pixel_total : 1507 time to create 1 rle with old method : 0.0018222332000732422 time for calcul the mask position with numpy : 0.007356405258178711 nb_pixel_total : 919 time to create 1 rle with old method : 0.0011353492736816406 time for calcul the mask position with numpy : 0.006574392318725586 nb_pixel_total : 1143 time to create 1 rle with old method : 0.0014011859893798828 time for calcul the mask position with numpy : 0.0070416927337646484 nb_pixel_total : 31529 time to create 1 rle with old method : 0.03532600402832031 time for calcul the mask position with numpy : 0.0070858001708984375 nb_pixel_total : 623 time to create 1 rle with old method : 0.0007882118225097656 time for calcul the mask position with numpy : 0.006917238235473633 nb_pixel_total : 666 time to create 1 rle with old method : 0.0007975101470947266 time for calcul the mask position with numpy : 0.007060527801513672 nb_pixel_total : 171 time to create 1 rle with old method : 0.00023031234741210938 time for calcul the mask position with numpy : 0.0063402652740478516 nb_pixel_total : 895 time to create 1 rle with old method : 0.0010309219360351562 time for calcul the mask position with numpy : 0.006760358810424805 nb_pixel_total : 1279 time to create 1 rle with old method : 0.0015101432800292969 time for calcul the mask position with numpy : 0.0067081451416015625 nb_pixel_total : 13357 time to create 1 rle with old method : 0.015076875686645508 time for calcul the mask position with numpy : 0.006607770919799805 nb_pixel_total : 333 time to create 1 rle with old method : 0.000431060791015625 time for calcul the mask position with numpy : 0.0061130523681640625 nb_pixel_total : 22 time to create 1 rle with old method : 7.05718994140625e-05 time for calcul the mask position with numpy : 0.006080150604248047 nb_pixel_total : 88 time to create 1 rle with old method : 0.00014019012451171875 time for calcul the mask position with numpy : 0.00598597526550293 nb_pixel_total : 255 time to create 1 rle with old method : 0.0003960132598876953 time for calcul the mask position with numpy : 0.0061130523681640625 nb_pixel_total : 2477 time to create 1 rle with old method : 0.0028924942016601562 time for calcul the mask position with numpy : 0.0062236785888671875 nb_pixel_total : 56 time to create 1 rle with old method : 0.0001227855682373047 time for calcul the mask position with numpy : 0.006056308746337891 nb_pixel_total : 555 time to create 1 rle with old method : 0.0006949901580810547 time for calcul the mask position with numpy : 0.006429433822631836 nb_pixel_total : 7 time to create 1 rle with old method : 4.6253204345703125e-05 time for calcul the mask position with numpy : 0.00641632080078125 nb_pixel_total : 463 time to create 1 rle with old method : 0.0006234645843505859 time for calcul the mask position with numpy : 0.006198883056640625 nb_pixel_total : 1177 time to create 1 rle with old method : 0.0014276504516601562 time for calcul the mask position with numpy : 0.0062482357025146484 nb_pixel_total : 98 time to create 1 rle with old method : 0.0001862049102783203 time for calcul the mask position with numpy : 0.007052898406982422 nb_pixel_total : 824 time to create 1 rle with old method : 0.0010101795196533203 time for calcul the mask position with numpy : 0.00628352165222168 nb_pixel_total : 110 time to create 1 rle with old method : 0.00015234947204589844 time for calcul the mask position with numpy : 0.006165504455566406 nb_pixel_total : 1 time to create 1 rle with old method : 2.5033950805664062e-05 time for calcul the mask position with numpy : 0.006470441818237305 nb_pixel_total : 794 time to create 1 rle with old method : 0.0009624958038330078 time for calcul the mask position with numpy : 0.006218671798706055 nb_pixel_total : 256 time to create 1 rle with old method : 0.00032806396484375 time for calcul the mask position with numpy : 0.006495237350463867 nb_pixel_total : 1387 time to create 1 rle with old method : 0.0016832351684570312 time for calcul the mask position with numpy : 0.006327152252197266 nb_pixel_total : 112 time to create 1 rle with old method : 0.00015497207641601562 time for calcul the mask position with numpy : 0.006244659423828125 nb_pixel_total : 83 time to create 1 rle with old method : 0.00012922286987304688 time for calcul the mask position with numpy : 0.006196498870849609 nb_pixel_total : 1576 time to create 1 rle with old method : 0.0018169879913330078 time for calcul the mask position with numpy : 0.006236076354980469 nb_pixel_total : 1202 time to create 1 rle with old method : 0.0014691352844238281 time for calcul the mask position with numpy : 0.0061800479888916016 nb_pixel_total : 508 time to create 1 rle with old method : 0.0007221698760986328 time for calcul the mask position with numpy : 0.0062105655670166016 nb_pixel_total : 74 time to create 1 rle with old method : 0.00015425682067871094 time for calcul the mask position with numpy : 0.006303071975708008 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006287097930908203 time for calcul the mask position with numpy : 0.006346940994262695 nb_pixel_total : 491 time to create 1 rle with old method : 0.0006041526794433594 time for calcul the mask position with numpy : 0.010515213012695312 nb_pixel_total : 311 time to create 1 rle with old method : 0.0004000663757324219 time for calcul the mask position with numpy : 0.010394573211669922 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005326271057128906 time for calcul the mask position with numpy : 0.010773897171020508 nb_pixel_total : 218 time to create 1 rle with old method : 0.00028824806213378906 time for calcul the mask position with numpy : 0.008310079574584961 nb_pixel_total : 605 time to create 1 rle with old method : 0.0007166862487792969 time for calcul the mask position with numpy : 0.006879091262817383 nb_pixel_total : 243 time to create 1 rle with old method : 0.0003185272216796875 time for calcul the mask position with numpy : 0.0064144134521484375 nb_pixel_total : 1036 time to create 1 rle with old method : 0.0012090206146240234 time for calcul the mask position with numpy : 0.006399631500244141 nb_pixel_total : 976 time to create 1 rle with old method : 0.001171112060546875 time for calcul the mask position with numpy : 0.00634765625 nb_pixel_total : 244 time to create 1 rle with old method : 0.0003311634063720703 time for calcul the mask position with numpy : 0.007192373275756836 nb_pixel_total : 316 time to create 1 rle with old method : 0.0003952980041503906 time for calcul the mask position with numpy : 0.006868839263916016 nb_pixel_total : 118 time to create 1 rle with old method : 0.00015807151794433594 time for calcul the mask position with numpy : 0.006378173828125 nb_pixel_total : 167 time to create 1 rle with old method : 0.00024890899658203125 time for calcul the mask position with numpy : 0.006540536880493164 nb_pixel_total : 829 time to create 1 rle with old method : 0.0009636878967285156 time for calcul the mask position with numpy : 0.007483482360839844 nb_pixel_total : 106058 time to create 1 rle with old method : 0.12326240539550781 time for calcul the mask position with numpy : 0.010729312896728516 nb_pixel_total : 1416 time to create 1 rle with old method : 0.001798868179321289 time for calcul the mask position with numpy : 0.011866092681884766 nb_pixel_total : 83 time to create 1 rle with old method : 0.00013494491577148438 time for calcul the mask position with numpy : 0.01086878776550293 nb_pixel_total : 10384 time to create 1 rle with old method : 0.011746644973754883 time for calcul the mask position with numpy : 0.011737585067749023 nb_pixel_total : 125 time to create 1 rle with old method : 0.00019288063049316406 time for calcul the mask position with numpy : 0.012381553649902344 nb_pixel_total : 317 time to create 1 rle with old method : 0.0005784034729003906 time for calcul the mask position with numpy : 0.010143518447875977 nb_pixel_total : 802 time to create 1 rle with old method : 0.0013797283172607422 time for calcul the mask position with numpy : 0.006913423538208008 nb_pixel_total : 134 time to create 1 rle with old method : 0.000255584716796875 time for calcul the mask position with numpy : 0.006842374801635742 nb_pixel_total : 190 time to create 1 rle with old method : 0.00034165382385253906 time for calcul the mask position with numpy : 0.007050752639770508 nb_pixel_total : 314 time to create 1 rle with old method : 0.0005576610565185547 time for calcul the mask position with numpy : 0.0069277286529541016 nb_pixel_total : 2264 time to create 1 rle with old method : 0.0037946701049804688 time for calcul the mask position with numpy : 0.006787538528442383 nb_pixel_total : 475 time to create 1 rle with old method : 0.0008411407470703125 time for calcul the mask position with numpy : 0.006866455078125 nb_pixel_total : 432 time to create 1 rle with old method : 0.0007495880126953125 time for calcul the mask position with numpy : 0.006773471832275391 nb_pixel_total : 617 time to create 1 rle with old method : 0.0012881755828857422 time for calcul the mask position with numpy : 0.006690025329589844 nb_pixel_total : 1733 time to create 1 rle with old method : 0.002131223678588867 time for calcul the mask position with numpy : 0.0064623355865478516 nb_pixel_total : 449 time to create 1 rle with old method : 0.0005567073822021484 time for calcul the mask position with numpy : 0.006505489349365234 nb_pixel_total : 115 time to create 1 rle with old method : 0.00015783309936523438 time for calcul the mask position with numpy : 0.006664276123046875 nb_pixel_total : 7077 time to create 1 rle with old method : 0.008089065551757812 time for calcul the mask position with numpy : 0.0068073272705078125 nb_pixel_total : 301 time to create 1 rle with old method : 0.00038123130798339844 time for calcul the mask position with numpy : 0.00643157958984375 nb_pixel_total : 641 time to create 1 rle with old method : 0.0007483959197998047 time for calcul the mask position with numpy : 0.0064699649810791016 nb_pixel_total : 575 time to create 1 rle with old method : 0.0006818771362304688 time for calcul the mask position with numpy : 0.006405353546142578 nb_pixel_total : 110 time to create 1 rle with old method : 0.00015926361083984375 time for calcul the mask position with numpy : 0.006779909133911133 nb_pixel_total : 941 time to create 1 rle with old method : 0.0011134147644042969 time for calcul the mask position with numpy : 0.006390094757080078 nb_pixel_total : 1283 time to create 1 rle with old method : 0.0014400482177734375 time for calcul the mask position with numpy : 0.006364345550537109 nb_pixel_total : 393 time to create 1 rle with old method : 0.0004649162292480469 time for calcul the mask position with numpy : 0.007958412170410156 nb_pixel_total : 440 time to create 1 rle with old method : 0.00058746337890625 time for calcul the mask position with numpy : 0.007386445999145508 nb_pixel_total : 62 time to create 1 rle with old method : 0.00010251998901367188 time for calcul the mask position with numpy : 0.007104635238647461 nb_pixel_total : 157 time to create 1 rle with old method : 0.000202178955078125 time for calcul the mask position with numpy : 0.006873130798339844 nb_pixel_total : 1621 time to create 1 rle with old method : 0.001909494400024414 time for calcul the mask position with numpy : 0.0064961910247802734 nb_pixel_total : 12 time to create 1 rle with old method : 0.00011444091796875 time for calcul the mask position with numpy : 0.007008790969848633 nb_pixel_total : 978 time to create 1 rle with old method : 0.0011992454528808594 time for calcul the mask position with numpy : 0.006278514862060547 nb_pixel_total : 41 time to create 1 rle with old method : 0.00010085105895996094 time for calcul the mask position with numpy : 0.0063419342041015625 nb_pixel_total : 286 time to create 1 rle with old method : 0.00036025047302246094 time for calcul the mask position with numpy : 0.006256580352783203 nb_pixel_total : 177 time to create 1 rle with old method : 0.00023603439331054688 time for calcul the mask position with numpy : 0.0062770843505859375 nb_pixel_total : 94 time to create 1 rle with old method : 0.0001327991485595703 time for calcul the mask position with numpy : 0.0061261653900146484 nb_pixel_total : 361 time to create 1 rle with old method : 0.00041294097900390625 time for calcul the mask position with numpy : 0.006075382232666016 nb_pixel_total : 1400 time to create 1 rle with old method : 0.0016248226165771484 time for calcul the mask position with numpy : 0.006329536437988281 nb_pixel_total : 330 time to create 1 rle with old method : 0.00039649009704589844 time for calcul the mask position with numpy : 0.006120443344116211 nb_pixel_total : 471 time to create 1 rle with old method : 0.0005679130554199219 time for calcul the mask position with numpy : 0.0061190128326416016 nb_pixel_total : 20 time to create 1 rle with old method : 6.079673767089844e-05 time for calcul the mask position with numpy : 0.006148338317871094 nb_pixel_total : 8 time to create 1 rle with old method : 3.409385681152344e-05 time for calcul the mask position with numpy : 0.008625268936157227 nb_pixel_total : 1564 time to create 1 rle with old method : 0.001814126968383789 time for calcul the mask position with numpy : 0.010659217834472656 nb_pixel_total : 1037 time to create 1 rle with old method : 0.0012345314025878906 time for calcul the mask position with numpy : 0.010344505310058594 nb_pixel_total : 56 time to create 1 rle with old method : 8.916854858398438e-05 time for calcul the mask position with numpy : 0.01092076301574707 nb_pixel_total : 118 time to create 1 rle with old method : 0.00022602081298828125 time for calcul the mask position with numpy : 0.011864662170410156 nb_pixel_total : 912 time to create 1 rle with old method : 0.0014657974243164062 time for calcul the mask position with numpy : 0.011771678924560547 nb_pixel_total : 24 time to create 1 rle with old method : 0.00011205673217773438 time for calcul the mask position with numpy : 0.011723041534423828 nb_pixel_total : 591 time to create 1 rle with old method : 0.0009810924530029297 time for calcul the mask position with numpy : 0.012125253677368164 nb_pixel_total : 3 time to create 1 rle with old method : 4.1484832763671875e-05 time for calcul the mask position with numpy : 0.011589288711547852 nb_pixel_total : 2905 time to create 1 rle with old method : 0.00473475456237793 time for calcul the mask position with numpy : 0.011821508407592773 nb_pixel_total : 261 time to create 1 rle with old method : 0.00033545494079589844 time for calcul the mask position with numpy : 0.011368513107299805 nb_pixel_total : 533 time to create 1 rle with old method : 0.004656553268432617 time for calcul the mask position with numpy : 0.010332345962524414 nb_pixel_total : 530 time to create 1 rle with old method : 0.0006265640258789062 time for calcul the mask position with numpy : 0.010519266128540039 nb_pixel_total : 71 time to create 1 rle with old method : 0.00010776519775390625 time for calcul the mask position with numpy : 0.010764598846435547 nb_pixel_total : 342 time to create 1 rle with old method : 0.00042629241943359375 time for calcul the mask position with numpy : 0.011459112167358398 nb_pixel_total : 118 time to create 1 rle with old method : 0.0001575946807861328 time for calcul the mask position with numpy : 0.010462760925292969 nb_pixel_total : 16 time to create 1 rle with old method : 5.936622619628906e-05 time for calcul the mask position with numpy : 0.010521173477172852 nb_pixel_total : 73 time to create 1 rle with old method : 0.00011014938354492188 time for calcul the mask position with numpy : 0.010685920715332031 nb_pixel_total : 676 time to create 1 rle with old method : 0.0007970333099365234 time for calcul the mask position with numpy : 0.010419368743896484 nb_pixel_total : 354 time to create 1 rle with old method : 0.00044155120849609375 time for calcul the mask position with numpy : 0.010164737701416016 nb_pixel_total : 37 time to create 1 rle with old method : 6.890296936035156e-05 time for calcul the mask position with numpy : 0.010528802871704102 nb_pixel_total : 394 time to create 1 rle with old method : 0.0004792213439941406 time for calcul the mask position with numpy : 0.010323524475097656 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006194114685058594 time for calcul the mask position with numpy : 0.010444164276123047 nb_pixel_total : 1722 time to create 1 rle with old method : 0.0020678043365478516 time for calcul the mask position with numpy : 0.010614871978759766 nb_pixel_total : 979 time to create 1 rle with old method : 0.0012874603271484375 time for calcul the mask position with numpy : 0.010986804962158203 nb_pixel_total : 461 time to create 1 rle with old method : 0.0005853176116943359 time for calcul the mask position with numpy : 0.01110529899597168 nb_pixel_total : 972 time to create 1 rle with old method : 0.0017275810241699219 time for calcul the mask position with numpy : 0.012115478515625 nb_pixel_total : 596 time to create 1 rle with old method : 0.0009949207305908203 time for calcul the mask position with numpy : 0.009790658950805664 nb_pixel_total : 526 time to create 1 rle with old method : 0.00089263916015625 time for calcul the mask position with numpy : 0.009747028350830078 nb_pixel_total : 336 time to create 1 rle with old method : 0.0005609989166259766 time for calcul the mask position with numpy : 0.009821653366088867 nb_pixel_total : 7926 time to create 1 rle with old method : 0.012637138366699219 time for calcul the mask position with numpy : 0.009833097457885742 nb_pixel_total : 414 time to create 1 rle with old method : 0.0007426738739013672 time for calcul the mask position with numpy : 0.009420156478881836 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003039836883544922 time for calcul the mask position with numpy : 0.008936882019042969 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002906322479248047 time for calcul the mask position with numpy : 0.008844852447509766 nb_pixel_total : 285 time to create 1 rle with old method : 0.00035500526428222656 time for calcul the mask position with numpy : 0.008562088012695312 nb_pixel_total : 64 time to create 1 rle with old method : 0.00014472007751464844 time for calcul the mask position with numpy : 0.008519411087036133 nb_pixel_total : 1546 time to create 1 rle with old method : 0.0017969608306884766 time for calcul the mask position with numpy : 0.008543252944946289 nb_pixel_total : 1053 time to create 1 rle with old method : 0.0012359619140625 time for calcul the mask position with numpy : 0.008477210998535156 nb_pixel_total : 7 time to create 1 rle with old method : 3.075599670410156e-05 time for calcul the mask position with numpy : 0.008468389511108398 nb_pixel_total : 1136 time to create 1 rle with old method : 0.0013599395751953125 time for calcul the mask position with numpy : 0.008591175079345703 nb_pixel_total : 459 time to create 1 rle with old method : 0.0005593299865722656 time for calcul the mask position with numpy : 0.008573055267333984 nb_pixel_total : 611 time to create 1 rle with old method : 0.0008380413055419922 time for calcul the mask position with numpy : 0.008496761322021484 nb_pixel_total : 69 time to create 1 rle with old method : 9.870529174804688e-05 time for calcul the mask position with numpy : 0.008448123931884766 nb_pixel_total : 416 time to create 1 rle with old method : 0.0004889965057373047 time for calcul the mask position with numpy : 0.008609533309936523 nb_pixel_total : 214 time to create 1 rle with old method : 0.0003135204315185547 create new chi : 1.993032455444336 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0033130645751953125 batch 1 Loaded 187 chid ids of type : 4230 Number RLEs to save : 16279 TO DO : save crop sub photo not yet done ! save time : 1.0345418453216553 nb_obj : 185 nb_hashtags : 8 time to prepare the origin masks : 1.8332531452178955 time for calcul the mask position with numpy : 0.5336158275604248 nb_pixel_total : 1743468 time to create 1 rle with new method : 0.10222387313842773 time for calcul the mask position with numpy : 0.006654262542724609 nb_pixel_total : 343 time to create 1 rle with old method : 0.00047135353088378906 time for calcul the mask position with numpy : 0.006422758102416992 nb_pixel_total : 2433 time to create 1 rle with old method : 0.002827167510986328 time for calcul the mask position with numpy : 0.006691455841064453 nb_pixel_total : 1531 time to create 1 rle with old method : 0.0018315315246582031 time for calcul the mask position with numpy : 0.0065250396728515625 nb_pixel_total : 3441 time to create 1 rle with old method : 0.003935098648071289 time for calcul the mask position with numpy : 0.006352424621582031 nb_pixel_total : 20 time to create 1 rle with old method : 7.62939453125e-05 time for calcul the mask position with numpy : 0.006697416305541992 nb_pixel_total : 1 time to create 1 rle with old method : 2.956390380859375e-05 time for calcul the mask position with numpy : 0.006670475006103516 nb_pixel_total : 513 time to create 1 rle with old method : 0.000705718994140625 time for calcul the mask position with numpy : 0.00655055046081543 nb_pixel_total : 2294 time to create 1 rle with old method : 0.002593517303466797 time for calcul the mask position with numpy : 0.006798982620239258 nb_pixel_total : 5 time to create 1 rle with old method : 2.6941299438476562e-05 time for calcul the mask position with numpy : 0.006403684616088867 nb_pixel_total : 47 time to create 1 rle with old method : 8.893013000488281e-05 time for calcul the mask position with numpy : 0.006512641906738281 nb_pixel_total : 204 time to create 1 rle with old method : 0.00026488304138183594 time for calcul the mask position with numpy : 0.0076372623443603516 nb_pixel_total : 226 time to create 1 rle with old method : 0.0003578662872314453 time for calcul the mask position with numpy : 0.0068132877349853516 nb_pixel_total : 1612 time to create 1 rle with old method : 0.001897573471069336 time for calcul the mask position with numpy : 0.006445646286010742 nb_pixel_total : 13 time to create 1 rle with old method : 5.5789947509765625e-05 time for calcul the mask position with numpy : 0.006471872329711914 nb_pixel_total : 67 time to create 1 rle with old method : 0.00010371208190917969 time for calcul the mask position with numpy : 0.006414175033569336 nb_pixel_total : 419 time to create 1 rle with old method : 0.0005166530609130859 time for calcul the mask position with numpy : 0.00649714469909668 nb_pixel_total : 381 time to create 1 rle with old method : 0.0004799365997314453 time for calcul the mask position with numpy : 0.008130788803100586 nb_pixel_total : 41739 time to create 1 rle with old method : 0.05229020118713379 time for calcul the mask position with numpy : 0.006726264953613281 nb_pixel_total : 21 time to create 1 rle with old method : 5.14984130859375e-05 time for calcul the mask position with numpy : 0.006786823272705078 nb_pixel_total : 7 time to create 1 rle with old method : 3.4332275390625e-05 time for calcul the mask position with numpy : 0.006589174270629883 nb_pixel_total : 28 time to create 1 rle with old method : 5.91278076171875e-05 time for calcul the mask position with numpy : 0.007148027420043945 nb_pixel_total : 4551 time to create 1 rle with old method : 0.005729198455810547 time for calcul the mask position with numpy : 0.006846427917480469 nb_pixel_total : 3260 time to create 1 rle with old method : 0.00384521484375 time for calcul the mask position with numpy : 0.006937980651855469 nb_pixel_total : 14648 time to create 1 rle with old method : 0.01684737205505371 time for calcul the mask position with numpy : 0.0069200992584228516 nb_pixel_total : 2069 time to create 1 rle with old method : 0.002499818801879883 time for calcul the mask position with numpy : 0.0068607330322265625 nb_pixel_total : 109 time to create 1 rle with old method : 0.00032401084899902344 time for calcul the mask position with numpy : 0.006565093994140625 nb_pixel_total : 55 time to create 1 rle with old method : 0.000194549560546875 time for calcul the mask position with numpy : 0.006561994552612305 nb_pixel_total : 10680 time to create 1 rle with old method : 0.012546539306640625 time for calcul the mask position with numpy : 0.0067882537841796875 nb_pixel_total : 90 time to create 1 rle with old method : 0.0002949237823486328 time for calcul the mask position with numpy : 0.011675357818603516 nb_pixel_total : 184 time to create 1 rle with old method : 0.00039958953857421875 time for calcul the mask position with numpy : 0.011285781860351562 nb_pixel_total : 226 time to create 1 rle with old method : 0.00030994415283203125 time for calcul the mask position with numpy : 0.011594772338867188 nb_pixel_total : 962 time to create 1 rle with old method : 0.0012104511260986328 time for calcul the mask position with numpy : 0.01195836067199707 nb_pixel_total : 1619 time to create 1 rle with old method : 0.0019690990447998047 time for calcul the mask position with numpy : 0.01114797592163086 nb_pixel_total : 4493 time to create 1 rle with old method : 0.00680088996887207 time for calcul the mask position with numpy : 0.011146068572998047 nb_pixel_total : 22 time to create 1 rle with old method : 8.20159912109375e-05 time for calcul the mask position with numpy : 0.01006937026977539 nb_pixel_total : 418 time to create 1 rle with old method : 0.0005433559417724609 time for calcul the mask position with numpy : 0.00869297981262207 nb_pixel_total : 1102 time to create 1 rle with old method : 0.0013697147369384766 time for calcul the mask position with numpy : 0.010167121887207031 nb_pixel_total : 2 time to create 1 rle with old method : 2.47955322265625e-05 time for calcul the mask position with numpy : 0.010450363159179688 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016760826110839844 time for calcul the mask position with numpy : 0.010186195373535156 nb_pixel_total : 11238 time to create 1 rle with old method : 0.01302480697631836 time for calcul the mask position with numpy : 0.009964942932128906 nb_pixel_total : 103 time to create 1 rle with old method : 0.00014400482177734375 time for calcul the mask position with numpy : 0.006112337112426758 nb_pixel_total : 19 time to create 1 rle with old method : 5.626678466796875e-05 time for calcul the mask position with numpy : 0.006136178970336914 nb_pixel_total : 769 time to create 1 rle with old method : 0.000993967056274414 time for calcul the mask position with numpy : 0.0059795379638671875 nb_pixel_total : 249 time to create 1 rle with old method : 0.00029397010803222656 time for calcul the mask position with numpy : 0.006103992462158203 nb_pixel_total : 85 time to create 1 rle with old method : 0.000125885009765625 time for calcul the mask position with numpy : 0.006023883819580078 nb_pixel_total : 1167 time to create 1 rle with old method : 0.0014824867248535156 time for calcul the mask position with numpy : 0.006263017654418945 nb_pixel_total : 466 time to create 1 rle with old method : 0.0005717277526855469 time for calcul the mask position with numpy : 0.0060176849365234375 nb_pixel_total : 530 time to create 1 rle with old method : 0.000640869140625 time for calcul the mask position with numpy : 0.006434917449951172 nb_pixel_total : 1454 time to create 1 rle with old method : 0.0018148422241210938 time for calcul the mask position with numpy : 0.006498575210571289 nb_pixel_total : 698 time to create 1 rle with old method : 0.0008370876312255859 time for calcul the mask position with numpy : 0.006273508071899414 nb_pixel_total : 218 time to create 1 rle with old method : 0.0004048347473144531 time for calcul the mask position with numpy : 0.006441831588745117 nb_pixel_total : 904 time to create 1 rle with old method : 0.0015993118286132812 time for calcul the mask position with numpy : 0.0065195560455322266 nb_pixel_total : 12939 time to create 1 rle with old method : 0.021674633026123047 time for calcul the mask position with numpy : 0.006471872329711914 nb_pixel_total : 156 time to create 1 rle with old method : 0.0003001689910888672 time for calcul the mask position with numpy : 0.0064983367919921875 nb_pixel_total : 611 time to create 1 rle with old method : 0.0010578632354736328 time for calcul the mask position with numpy : 0.006518363952636719 nb_pixel_total : 162 time to create 1 rle with old method : 0.00034499168395996094 time for calcul the mask position with numpy : 0.007142305374145508 nb_pixel_total : 3282 time to create 1 rle with old method : 0.005880117416381836 time for calcul the mask position with numpy : 0.0066106319427490234 nb_pixel_total : 17 time to create 1 rle with old method : 9.226799011230469e-05 time for calcul the mask position with numpy : 0.005928993225097656 nb_pixel_total : 92 time to create 1 rle with old method : 0.0001392364501953125 time for calcul the mask position with numpy : 0.005942344665527344 nb_pixel_total : 569 time to create 1 rle with old method : 0.0006690025329589844 time for calcul the mask position with numpy : 0.006023406982421875 nb_pixel_total : 80 time to create 1 rle with old method : 0.0001666545867919922 time for calcul the mask position with numpy : 0.00716090202331543 nb_pixel_total : 463 time to create 1 rle with old method : 0.0005996227264404297 time for calcul the mask position with numpy : 0.005883216857910156 nb_pixel_total : 869 time to create 1 rle with old method : 0.0010225772857666016 time for calcul the mask position with numpy : 0.006019115447998047 nb_pixel_total : 903 time to create 1 rle with old method : 0.0010449886322021484 time for calcul the mask position with numpy : 0.00582432746887207 nb_pixel_total : 2949 time to create 1 rle with old method : 0.0033249855041503906 time for calcul the mask position with numpy : 0.005891084671020508 nb_pixel_total : 256 time to create 1 rle with old method : 0.00032901763916015625 time for calcul the mask position with numpy : 0.005828142166137695 nb_pixel_total : 1302 time to create 1 rle with old method : 0.001508951187133789 time for calcul the mask position with numpy : 0.005950927734375 nb_pixel_total : 996 time to create 1 rle with old method : 0.0012154579162597656 time for calcul the mask position with numpy : 0.0058422088623046875 nb_pixel_total : 118 time to create 1 rle with old method : 0.000179290771484375 time for calcul the mask position with numpy : 0.005869150161743164 nb_pixel_total : 588 time to create 1 rle with old method : 0.0007548332214355469 time for calcul the mask position with numpy : 0.005790233612060547 nb_pixel_total : 93 time to create 1 rle with old method : 0.0003466606140136719 time for calcul the mask position with numpy : 0.0059964656829833984 nb_pixel_total : 496 time to create 1 rle with old method : 0.000659942626953125 time for calcul the mask position with numpy : 0.00597834587097168 nb_pixel_total : 1835 time to create 1 rle with old method : 0.0024023056030273438 time for calcul the mask position with numpy : 0.006024837493896484 nb_pixel_total : 1195 time to create 1 rle with old method : 0.0014884471893310547 time for calcul the mask position with numpy : 0.005957841873168945 nb_pixel_total : 296 time to create 1 rle with old method : 0.0003943443298339844 time for calcul the mask position with numpy : 0.006018400192260742 nb_pixel_total : 3603 time to create 1 rle with old method : 0.004300355911254883 time for calcul the mask position with numpy : 0.005875825881958008 nb_pixel_total : 782 time to create 1 rle with old method : 0.0009834766387939453 time for calcul the mask position with numpy : 0.006187915802001953 nb_pixel_total : 949 time to create 1 rle with old method : 0.0011603832244873047 time for calcul the mask position with numpy : 0.006076335906982422 nb_pixel_total : 102 time to create 1 rle with old method : 0.0001575946807861328 time for calcul the mask position with numpy : 0.0060503482818603516 nb_pixel_total : 903 time to create 1 rle with old method : 0.0011706352233886719 time for calcul the mask position with numpy : 0.0061168670654296875 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004405975341796875 time for calcul the mask position with numpy : 0.0071773529052734375 nb_pixel_total : 530 time to create 1 rle with old method : 0.0006515979766845703 time for calcul the mask position with numpy : 0.00594329833984375 nb_pixel_total : 298 time to create 1 rle with old method : 0.0004012584686279297 time for calcul the mask position with numpy : 0.006014823913574219 nb_pixel_total : 19 time to create 1 rle with old method : 8.058547973632812e-05 time for calcul the mask position with numpy : 0.006104707717895508 nb_pixel_total : 880 time to create 1 rle with old method : 0.0010638236999511719 time for calcul the mask position with numpy : 0.005934238433837891 nb_pixel_total : 274 time to create 1 rle with old method : 0.00039386749267578125 time for calcul the mask position with numpy : 0.005937099456787109 nb_pixel_total : 27 time to create 1 rle with old method : 6.771087646484375e-05 time for calcul the mask position with numpy : 0.006734609603881836 nb_pixel_total : 106599 time to create 1 rle with old method : 0.11509442329406738 time for calcul the mask position with numpy : 0.006273508071899414 nb_pixel_total : 10776 time to create 1 rle with old method : 0.012080669403076172 time for calcul the mask position with numpy : 0.006209611892700195 nb_pixel_total : 83 time to create 1 rle with old method : 0.0001373291015625 time for calcul the mask position with numpy : 0.006117582321166992 nb_pixel_total : 127 time to create 1 rle with old method : 0.0001735687255859375 time for calcul the mask position with numpy : 0.006173133850097656 nb_pixel_total : 291 time to create 1 rle with old method : 0.0003840923309326172 time for calcul the mask position with numpy : 0.006231784820556641 nb_pixel_total : 3328 time to create 1 rle with old method : 0.004065752029418945 time for calcul the mask position with numpy : 0.006104469299316406 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002579689025878906 time for calcul the mask position with numpy : 0.006480216979980469 nb_pixel_total : 246 time to create 1 rle with old method : 0.00033545494079589844 time for calcul the mask position with numpy : 0.0061991214752197266 nb_pixel_total : 327 time to create 1 rle with old method : 0.00045037269592285156 time for calcul the mask position with numpy : 0.006359100341796875 nb_pixel_total : 195 time to create 1 rle with old method : 0.0002512931823730469 time for calcul the mask position with numpy : 0.006217241287231445 nb_pixel_total : 435 time to create 1 rle with old method : 0.0006222724914550781 time for calcul the mask position with numpy : 0.006223917007446289 nb_pixel_total : 527 time to create 1 rle with old method : 0.0006754398345947266 time for calcul the mask position with numpy : 0.010074853897094727 nb_pixel_total : 40 time to create 1 rle with old method : 0.00011086463928222656 time for calcul the mask position with numpy : 0.010687112808227539 nb_pixel_total : 418 time to create 1 rle with old method : 0.0005526542663574219 time for calcul the mask position with numpy : 0.010298013687133789 nb_pixel_total : 74 time to create 1 rle with old method : 0.00014138221740722656 time for calcul the mask position with numpy : 0.010051727294921875 nb_pixel_total : 1310 time to create 1 rle with old method : 0.001684427261352539 time for calcul the mask position with numpy : 0.010294437408447266 nb_pixel_total : 641 time to create 1 rle with old method : 0.0008547306060791016 time for calcul the mask position with numpy : 0.010263681411743164 nb_pixel_total : 495 time to create 1 rle with old method : 0.0006289482116699219 time for calcul the mask position with numpy : 0.01027679443359375 nb_pixel_total : 907 time to create 1 rle with old method : 0.0011141300201416016 time for calcul the mask position with numpy : 0.010265111923217773 nb_pixel_total : 200 time to create 1 rle with old method : 0.00027942657470703125 time for calcul the mask position with numpy : 0.010747194290161133 nb_pixel_total : 5 time to create 1 rle with old method : 3.695487976074219e-05 time for calcul the mask position with numpy : 0.010060548782348633 nb_pixel_total : 12 time to create 1 rle with old method : 5.459785461425781e-05 time for calcul the mask position with numpy : 0.010088920593261719 nb_pixel_total : 125 time to create 1 rle with old method : 0.0001926422119140625 time for calcul the mask position with numpy : 0.010372638702392578 nb_pixel_total : 914 time to create 1 rle with old method : 0.0011708736419677734 time for calcul the mask position with numpy : 0.010277748107910156 nb_pixel_total : 281 time to create 1 rle with old method : 0.0003924369812011719 time for calcul the mask position with numpy : 0.007607460021972656 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005028247833251953 time for calcul the mask position with numpy : 0.00686335563659668 nb_pixel_total : 348 time to create 1 rle with old method : 0.0004553794860839844 time for calcul the mask position with numpy : 0.008888721466064453 nb_pixel_total : 11 time to create 1 rle with old method : 5.316734313964844e-05 time for calcul the mask position with numpy : 0.0073451995849609375 nb_pixel_total : 481 time to create 1 rle with old method : 0.0006091594696044922 time for calcul the mask position with numpy : 0.007384777069091797 nb_pixel_total : 5242 time to create 1 rle with old method : 0.006265878677368164 time for calcul the mask position with numpy : 0.007086992263793945 nb_pixel_total : 1372 time to create 1 rle with old method : 0.0015959739685058594 time for calcul the mask position with numpy : 0.007307291030883789 nb_pixel_total : 156 time to create 1 rle with old method : 0.0002071857452392578 time for calcul the mask position with numpy : 0.008641481399536133 nb_pixel_total : 65 time to create 1 rle with old method : 0.00011134147644042969 time for calcul the mask position with numpy : 0.009320497512817383 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002219676971435547 time for calcul the mask position with numpy : 0.008381843566894531 nb_pixel_total : 613 time to create 1 rle with old method : 0.0007488727569580078 time for calcul the mask position with numpy : 0.007957220077514648 nb_pixel_total : 26 time to create 1 rle with old method : 6.604194641113281e-05 time for calcul the mask position with numpy : 0.006917715072631836 nb_pixel_total : 918 time to create 1 rle with old method : 0.0010476112365722656 time for calcul the mask position with numpy : 0.0073392391204833984 nb_pixel_total : 84 time to create 1 rle with old method : 0.0001571178436279297 time for calcul the mask position with numpy : 0.007481098175048828 nb_pixel_total : 185 time to create 1 rle with old method : 0.00023674964904785156 time for calcul the mask position with numpy : 0.006974697113037109 nb_pixel_total : 1400 time to create 1 rle with old method : 0.0016362667083740234 time for calcul the mask position with numpy : 0.008744001388549805 nb_pixel_total : 75 time to create 1 rle with old method : 0.00011610984802246094 time for calcul the mask position with numpy : 0.008035659790039062 nb_pixel_total : 317 time to create 1 rle with old method : 0.000354766845703125 time for calcul the mask position with numpy : 0.007594585418701172 nb_pixel_total : 168 time to create 1 rle with old method : 0.0002110004425048828 time for calcul the mask position with numpy : 0.009624958038330078 nb_pixel_total : 25 time to create 1 rle with old method : 6.008148193359375e-05 time for calcul the mask position with numpy : 0.0059163570404052734 nb_pixel_total : 10 time to create 1 rle with old method : 3.790855407714844e-05 time for calcul the mask position with numpy : 0.005930900573730469 nb_pixel_total : 88 time to create 1 rle with old method : 0.0001537799835205078 time for calcul the mask position with numpy : 0.005873441696166992 nb_pixel_total : 12 time to create 1 rle with old method : 5.841255187988281e-05 time for calcul the mask position with numpy : 0.005976200103759766 nb_pixel_total : 1521 time to create 1 rle with old method : 0.0017228126525878906 time for calcul the mask position with numpy : 0.005994081497192383 nb_pixel_total : 472 time to create 1 rle with old method : 0.0005548000335693359 time for calcul the mask position with numpy : 0.005889177322387695 nb_pixel_total : 98 time to create 1 rle with old method : 0.0002269744873046875 time for calcul the mask position with numpy : 0.005884885787963867 nb_pixel_total : 1586 time to create 1 rle with old method : 0.0017595291137695312 time for calcul the mask position with numpy : 0.0058977603912353516 nb_pixel_total : 5 time to create 1 rle with old method : 3.4809112548828125e-05 time for calcul the mask position with numpy : 0.005831718444824219 nb_pixel_total : 893 time to create 1 rle with old method : 0.0010256767272949219 time for calcul the mask position with numpy : 0.005822420120239258 nb_pixel_total : 63 time to create 1 rle with old method : 0.00010251998901367188 time for calcul the mask position with numpy : 0.005820035934448242 nb_pixel_total : 5126 time to create 1 rle with old method : 0.005880117416381836 time for calcul the mask position with numpy : 0.005879402160644531 nb_pixel_total : 169 time to create 1 rle with old method : 0.00023937225341796875 time for calcul the mask position with numpy : 0.005967140197753906 nb_pixel_total : 871 time to create 1 rle with old method : 0.0009667873382568359 time for calcul the mask position with numpy : 0.005850553512573242 nb_pixel_total : 571 time to create 1 rle with old method : 0.0006613731384277344 time for calcul the mask position with numpy : 0.005982160568237305 nb_pixel_total : 246 time to create 1 rle with old method : 0.00032067298889160156 time for calcul the mask position with numpy : 0.00594019889831543 nb_pixel_total : 519 time to create 1 rle with old method : 0.0006260871887207031 time for calcul the mask position with numpy : 0.00603795051574707 nb_pixel_total : 541 time to create 1 rle with old method : 0.0006563663482666016 time for calcul the mask position with numpy : 0.005964756011962891 nb_pixel_total : 63 time to create 1 rle with old method : 0.00010347366333007812 time for calcul the mask position with numpy : 0.006024837493896484 nb_pixel_total : 115 time to create 1 rle with old method : 0.00016999244689941406 time for calcul the mask position with numpy : 0.005990743637084961 nb_pixel_total : 9 time to create 1 rle with old method : 4.172325134277344e-05 time for calcul the mask position with numpy : 0.0060329437255859375 nb_pixel_total : 344 time to create 1 rle with old method : 0.00043201446533203125 time for calcul the mask position with numpy : 0.0060040950775146484 nb_pixel_total : 96 time to create 1 rle with old method : 0.0001556873321533203 time for calcul the mask position with numpy : 0.005992889404296875 nb_pixel_total : 649 time to create 1 rle with old method : 0.0008656978607177734 time for calcul the mask position with numpy : 0.006032466888427734 nb_pixel_total : 1 time to create 1 rle with old method : 1.9311904907226562e-05 time for calcul the mask position with numpy : 0.006019115447998047 nb_pixel_total : 167 time to create 1 rle with old method : 0.0002224445343017578 time for calcul the mask position with numpy : 0.006008148193359375 nb_pixel_total : 50 time to create 1 rle with old method : 0.00012159347534179688 time for calcul the mask position with numpy : 0.00613713264465332 nb_pixel_total : 342 time to create 1 rle with old method : 0.0004649162292480469 time for calcul the mask position with numpy : 0.00622105598449707 nb_pixel_total : 3 time to create 1 rle with old method : 2.765655517578125e-05 time for calcul the mask position with numpy : 0.0062503814697265625 nb_pixel_total : 444 time to create 1 rle with old method : 0.0005488395690917969 time for calcul the mask position with numpy : 0.006146907806396484 nb_pixel_total : 236 time to create 1 rle with old method : 0.0003039836883544922 time for calcul the mask position with numpy : 0.006043672561645508 nb_pixel_total : 55 time to create 1 rle with old method : 0.00010848045349121094 time for calcul the mask position with numpy : 0.005994558334350586 nb_pixel_total : 1667 time to create 1 rle with old method : 0.0018627643585205078 time for calcul the mask position with numpy : 0.006258726119995117 nb_pixel_total : 738 time to create 1 rle with old method : 0.00130462646484375 time for calcul the mask position with numpy : 0.006451845169067383 nb_pixel_total : 16 time to create 1 rle with old method : 9.775161743164062e-05 time for calcul the mask position with numpy : 0.00647282600402832 nb_pixel_total : 3 time to create 1 rle with old method : 4.124641418457031e-05 time for calcul the mask position with numpy : 0.006439685821533203 nb_pixel_total : 424 time to create 1 rle with old method : 0.0007703304290771484 time for calcul the mask position with numpy : 0.006483316421508789 nb_pixel_total : 104 time to create 1 rle with old method : 0.00022172927856445312 time for calcul the mask position with numpy : 0.0064313411712646484 nb_pixel_total : 505 time to create 1 rle with old method : 0.0008814334869384766 time for calcul the mask position with numpy : 0.0064737796783447266 nb_pixel_total : 312 time to create 1 rle with old method : 0.0005674362182617188 time for calcul the mask position with numpy : 0.0064203739166259766 nb_pixel_total : 539 time to create 1 rle with old method : 0.0011250972747802734 time for calcul the mask position with numpy : 0.006480693817138672 nb_pixel_total : 4513 time to create 1 rle with old method : 0.007459402084350586 time for calcul the mask position with numpy : 0.0063974857330322266 nb_pixel_total : 363 time to create 1 rle with old method : 0.0006508827209472656 time for calcul the mask position with numpy : 0.006318569183349609 nb_pixel_total : 869 time to create 1 rle with old method : 0.0011148452758789062 time for calcul the mask position with numpy : 0.006075382232666016 nb_pixel_total : 59 time to create 1 rle with old method : 0.00011491775512695312 time for calcul the mask position with numpy : 0.008444786071777344 nb_pixel_total : 204 time to create 1 rle with old method : 0.0002543926239013672 time for calcul the mask position with numpy : 0.008436203002929688 nb_pixel_total : 245 time to create 1 rle with old method : 0.0002899169921875 time for calcul the mask position with numpy : 0.008426904678344727 nb_pixel_total : 1850 time to create 1 rle with old method : 0.002048015594482422 time for calcul the mask position with numpy : 0.008435726165771484 nb_pixel_total : 26 time to create 1 rle with old method : 6.461143493652344e-05 time for calcul the mask position with numpy : 0.008453845977783203 nb_pixel_total : 1244 time to create 1 rle with old method : 0.0014312267303466797 time for calcul the mask position with numpy : 0.00842738151550293 nb_pixel_total : 275 time to create 1 rle with old method : 0.0003235340118408203 time for calcul the mask position with numpy : 0.008441686630249023 nb_pixel_total : 1542 time to create 1 rle with old method : 0.001760721206665039 time for calcul the mask position with numpy : 0.008452892303466797 nb_pixel_total : 1151 time to create 1 rle with old method : 0.0013303756713867188 time for calcul the mask position with numpy : 0.008469820022583008 nb_pixel_total : 657 time to create 1 rle with old method : 0.0007524490356445312 time for calcul the mask position with numpy : 0.008454322814941406 nb_pixel_total : 54 time to create 1 rle with old method : 0.0001494884490966797 create new chi : 2.3740198612213135 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.005762577056884766 batch 1 Loaded 189 chid ids of type : 4230 Number RLEs to save : 15968 TO DO : save crop sub photo not yet done ! save time : 0.9423837661743164 nb_obj : 175 nb_hashtags : 8 time to prepare the origin masks : 1.7381720542907715 time for calcul the mask position with numpy : 0.030939817428588867 nb_pixel_total : 1755575 time to create 1 rle with new method : 0.03852987289428711 time for calcul the mask position with numpy : 0.010420560836791992 nb_pixel_total : 329 time to create 1 rle with old method : 0.000423431396484375 time for calcul the mask position with numpy : 0.006193637847900391 nb_pixel_total : 2115 time to create 1 rle with old method : 0.002433300018310547 time for calcul the mask position with numpy : 0.00632023811340332 nb_pixel_total : 1367 time to create 1 rle with old method : 0.0015876293182373047 time for calcul the mask position with numpy : 0.006344795227050781 nb_pixel_total : 258 time to create 1 rle with old method : 0.0003178119659423828 time for calcul the mask position with numpy : 0.006395578384399414 nb_pixel_total : 17659 time to create 1 rle with old method : 0.019567012786865234 time for calcul the mask position with numpy : 0.006604909896850586 nb_pixel_total : 41382 time to create 1 rle with old method : 0.045714616775512695 time for calcul the mask position with numpy : 0.00698542594909668 nb_pixel_total : 2780 time to create 1 rle with old method : 0.0032711029052734375 time for calcul the mask position with numpy : 0.006696462631225586 nb_pixel_total : 283 time to create 1 rle with old method : 0.000339508056640625 time for calcul the mask position with numpy : 0.00652003288269043 nb_pixel_total : 61 time to create 1 rle with old method : 9.989738464355469e-05 time for calcul the mask position with numpy : 0.006465435028076172 nb_pixel_total : 34 time to create 1 rle with old method : 0.00019860267639160156 time for calcul the mask position with numpy : 0.00713348388671875 nb_pixel_total : 211 time to create 1 rle with old method : 0.000396728515625 time for calcul the mask position with numpy : 0.006949424743652344 nb_pixel_total : 357 time to create 1 rle with old method : 0.0012593269348144531 time for calcul the mask position with numpy : 0.00667572021484375 nb_pixel_total : 39 time to create 1 rle with old method : 9.703636169433594e-05 time for calcul the mask position with numpy : 0.006929874420166016 nb_pixel_total : 103 time to create 1 rle with old method : 0.00021409988403320312 time for calcul the mask position with numpy : 0.006710052490234375 nb_pixel_total : 19 time to create 1 rle with old method : 5.602836608886719e-05 time for calcul the mask position with numpy : 0.008274078369140625 nb_pixel_total : 26 time to create 1 rle with old method : 6.794929504394531e-05 time for calcul the mask position with numpy : 0.006441593170166016 nb_pixel_total : 23 time to create 1 rle with old method : 5.173683166503906e-05 time for calcul the mask position with numpy : 0.006161928176879883 nb_pixel_total : 1 time to create 1 rle with old method : 2.0265579223632812e-05 time for calcul the mask position with numpy : 0.006317615509033203 nb_pixel_total : 2 time to create 1 rle with old method : 2.1219253540039062e-05 time for calcul the mask position with numpy : 0.0063169002532958984 nb_pixel_total : 24 time to create 1 rle with old method : 4.696846008300781e-05 time for calcul the mask position with numpy : 0.0063860416412353516 nb_pixel_total : 3219 time to create 1 rle with old method : 0.0037376880645751953 time for calcul the mask position with numpy : 0.006417989730834961 nb_pixel_total : 786 time to create 1 rle with old method : 0.0008852481842041016 time for calcul the mask position with numpy : 0.006658792495727539 nb_pixel_total : 10430 time to create 1 rle with old method : 0.011542081832885742 time for calcul the mask position with numpy : 0.0064885616302490234 nb_pixel_total : 24 time to create 1 rle with old method : 0.0001125335693359375 time for calcul the mask position with numpy : 0.0067729949951171875 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002574920654296875 time for calcul the mask position with numpy : 0.006613492965698242 nb_pixel_total : 5102 time to create 1 rle with old method : 0.005650997161865234 time for calcul the mask position with numpy : 0.0069997310638427734 nb_pixel_total : 2043 time to create 1 rle with old method : 0.002498626708984375 time for calcul the mask position with numpy : 0.006559133529663086 nb_pixel_total : 853 time to create 1 rle with old method : 0.0011212825775146484 time for calcul the mask position with numpy : 0.00639033317565918 nb_pixel_total : 1790 time to create 1 rle with old method : 0.0020875930786132812 time for calcul the mask position with numpy : 0.00648808479309082 nb_pixel_total : 679 time to create 1 rle with old method : 0.0008246898651123047 time for calcul the mask position with numpy : 0.006478309631347656 nb_pixel_total : 93 time to create 1 rle with old method : 0.00014925003051757812 time for calcul the mask position with numpy : 0.006459474563598633 nb_pixel_total : 93 time to create 1 rle with old method : 0.00014543533325195312 time for calcul the mask position with numpy : 0.006997823715209961 nb_pixel_total : 11517 time to create 1 rle with old method : 0.0127410888671875 time for calcul the mask position with numpy : 0.006951093673706055 nb_pixel_total : 102 time to create 1 rle with old method : 0.0002048015594482422 time for calcul the mask position with numpy : 0.0067729949951171875 nb_pixel_total : 833 time to create 1 rle with old method : 0.0014739036560058594 time for calcul the mask position with numpy : 0.006983518600463867 nb_pixel_total : 102 time to create 1 rle with old method : 0.00019884109497070312 time for calcul the mask position with numpy : 0.007976770401000977 nb_pixel_total : 359 time to create 1 rle with old method : 0.000667572021484375 time for calcul the mask position with numpy : 0.0069196224212646484 nb_pixel_total : 125 time to create 1 rle with old method : 0.00023746490478515625 time for calcul the mask position with numpy : 0.006708860397338867 nb_pixel_total : 1292 time to create 1 rle with old method : 0.002275228500366211 time for calcul the mask position with numpy : 0.006842374801635742 nb_pixel_total : 1063 time to create 1 rle with old method : 0.0018515586853027344 time for calcul the mask position with numpy : 0.006794452667236328 nb_pixel_total : 612 time to create 1 rle with old method : 0.002889871597290039 time for calcul the mask position with numpy : 0.006783723831176758 nb_pixel_total : 787 time to create 1 rle with old method : 0.00136566162109375 time for calcul the mask position with numpy : 0.00699925422668457 nb_pixel_total : 18 time to create 1 rle with old method : 6.29425048828125e-05 time for calcul the mask position with numpy : 0.007485151290893555 nb_pixel_total : 218 time to create 1 rle with old method : 0.00037860870361328125 time for calcul the mask position with numpy : 0.007595539093017578 nb_pixel_total : 913 time to create 1 rle with old method : 0.0015213489532470703 time for calcul the mask position with numpy : 0.00751042366027832 nb_pixel_total : 13080 time to create 1 rle with old method : 0.01468038558959961 time for calcul the mask position with numpy : 0.008173704147338867 nb_pixel_total : 59 time to create 1 rle with old method : 0.00020885467529296875 time for calcul the mask position with numpy : 0.007331371307373047 nb_pixel_total : 3511 time to create 1 rle with old method : 0.006671905517578125 time for calcul the mask position with numpy : 0.007603168487548828 nb_pixel_total : 11 time to create 1 rle with old method : 7.2479248046875e-05 time for calcul the mask position with numpy : 0.008416414260864258 nb_pixel_total : 102 time to create 1 rle with old method : 0.0002498626708984375 time for calcul the mask position with numpy : 0.008306264877319336 nb_pixel_total : 567 time to create 1 rle with old method : 0.0007560253143310547 time for calcul the mask position with numpy : 0.007135629653930664 nb_pixel_total : 1153 time to create 1 rle with old method : 0.0014088153839111328 time for calcul the mask position with numpy : 0.006838321685791016 nb_pixel_total : 12 time to create 1 rle with old method : 5.9604644775390625e-05 time for calcul the mask position with numpy : 0.007092952728271484 nb_pixel_total : 297 time to create 1 rle with old method : 0.0004019737243652344 time for calcul the mask position with numpy : 0.011599302291870117 nb_pixel_total : 481 time to create 1 rle with old method : 0.0011904239654541016 time for calcul the mask position with numpy : 0.009775638580322266 nb_pixel_total : 824 time to create 1 rle with old method : 0.001245737075805664 time for calcul the mask position with numpy : 0.007837772369384766 nb_pixel_total : 22 time to create 1 rle with old method : 6.508827209472656e-05 time for calcul the mask position with numpy : 0.008186817169189453 nb_pixel_total : 912 time to create 1 rle with old method : 0.0012054443359375 time for calcul the mask position with numpy : 0.007567882537841797 nb_pixel_total : 135 time to create 1 rle with old method : 0.000202178955078125 time for calcul the mask position with numpy : 0.007860660552978516 nb_pixel_total : 871 time to create 1 rle with old method : 0.0011661052703857422 time for calcul the mask position with numpy : 0.0072994232177734375 nb_pixel_total : 3342 time to create 1 rle with old method : 0.0039463043212890625 time for calcul the mask position with numpy : 0.0069692134857177734 nb_pixel_total : 263 time to create 1 rle with old method : 0.00035071372985839844 time for calcul the mask position with numpy : 0.007364034652709961 nb_pixel_total : 97 time to create 1 rle with old method : 0.00015878677368164062 time for calcul the mask position with numpy : 0.006823062896728516 nb_pixel_total : 1314 time to create 1 rle with old method : 0.0016682147979736328 time for calcul the mask position with numpy : 0.006824493408203125 nb_pixel_total : 613 time to create 1 rle with old method : 0.0007886886596679688 time for calcul the mask position with numpy : 0.006479024887084961 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001666545867919922 time for calcul the mask position with numpy : 0.006519317626953125 nb_pixel_total : 1567 time to create 1 rle with old method : 0.001859426498413086 time for calcul the mask position with numpy : 0.006655454635620117 nb_pixel_total : 548 time to create 1 rle with old method : 0.0006926059722900391 time for calcul the mask position with numpy : 0.006262063980102539 nb_pixel_total : 992 time to create 1 rle with old method : 0.001226663589477539 time for calcul the mask position with numpy : 0.006330966949462891 nb_pixel_total : 42 time to create 1 rle with old method : 0.00014400482177734375 time for calcul the mask position with numpy : 0.006392955780029297 nb_pixel_total : 25 time to create 1 rle with old method : 0.0001277923583984375 time for calcul the mask position with numpy : 0.006932497024536133 nb_pixel_total : 288 time to create 1 rle with old method : 0.0003745555877685547 time for calcul the mask position with numpy : 0.006328582763671875 nb_pixel_total : 818 time to create 1 rle with old method : 0.0010149478912353516 time for calcul the mask position with numpy : 0.006384134292602539 nb_pixel_total : 28 time to create 1 rle with old method : 8.845329284667969e-05 time for calcul the mask position with numpy : 0.006526470184326172 nb_pixel_total : 472 time to create 1 rle with old method : 0.00058746337890625 time for calcul the mask position with numpy : 0.006886005401611328 nb_pixel_total : 632 time to create 1 rle with old method : 0.0014650821685791016 time for calcul the mask position with numpy : 0.006535530090332031 nb_pixel_total : 223 time to create 1 rle with old method : 0.0002899169921875 time for calcul the mask position with numpy : 0.006378650665283203 nb_pixel_total : 928 time to create 1 rle with old method : 0.0011603832244873047 time for calcul the mask position with numpy : 0.006337642669677734 nb_pixel_total : 16 time to create 1 rle with old method : 5.2928924560546875e-05 time for calcul the mask position with numpy : 0.006231546401977539 nb_pixel_total : 309 time to create 1 rle with old method : 0.0003757476806640625 time for calcul the mask position with numpy : 0.00664830207824707 nb_pixel_total : 489 time to create 1 rle with old method : 0.0005824565887451172 time for calcul the mask position with numpy : 0.006319522857666016 nb_pixel_total : 123 time to create 1 rle with old method : 0.0001747608184814453 time for calcul the mask position with numpy : 0.006543397903442383 nb_pixel_total : 1059 time to create 1 rle with old method : 0.00128936767578125 time for calcul the mask position with numpy : 0.006622314453125 nb_pixel_total : 3 time to create 1 rle with old method : 2.7418136596679688e-05 time for calcul the mask position with numpy : 0.007094144821166992 nb_pixel_total : 106524 time to create 1 rle with old method : 0.1190941333770752 time for calcul the mask position with numpy : 0.007137775421142578 nb_pixel_total : 10716 time to create 1 rle with old method : 0.012498140335083008 time for calcul the mask position with numpy : 0.006234407424926758 nb_pixel_total : 14 time to create 1 rle with old method : 0.0003566741943359375 time for calcul the mask position with numpy : 0.0071489810943603516 nb_pixel_total : 1863 time to create 1 rle with old method : 0.0024111270904541016 time for calcul the mask position with numpy : 0.006363630294799805 nb_pixel_total : 70 time to create 1 rle with old method : 0.0001125335693359375 time for calcul the mask position with numpy : 0.007288694381713867 nb_pixel_total : 119 time to create 1 rle with old method : 0.0002779960632324219 time for calcul the mask position with numpy : 0.008475303649902344 nb_pixel_total : 269 time to create 1 rle with old method : 0.0003476142883300781 time for calcul the mask position with numpy : 0.0067005157470703125 nb_pixel_total : 618 time to create 1 rle with old method : 0.0007722377777099609 time for calcul the mask position with numpy : 0.006940603256225586 nb_pixel_total : 336 time to create 1 rle with old method : 0.0004413127899169922 time for calcul the mask position with numpy : 0.006723642349243164 nb_pixel_total : 186 time to create 1 rle with old method : 0.00024271011352539062 time for calcul the mask position with numpy : 0.006431102752685547 nb_pixel_total : 320 time to create 1 rle with old method : 0.0003962516784667969 time for calcul the mask position with numpy : 0.006380796432495117 nb_pixel_total : 214 time to create 1 rle with old method : 0.00027871131896972656 time for calcul the mask position with numpy : 0.00797128677368164 nb_pixel_total : 180 time to create 1 rle with old method : 0.0002353191375732422 time for calcul the mask position with numpy : 0.006263256072998047 nb_pixel_total : 817 time to create 1 rle with old method : 0.0010085105895996094 time for calcul the mask position with numpy : 0.0063326358795166016 nb_pixel_total : 43 time to create 1 rle with old method : 8.130073547363281e-05 time for calcul the mask position with numpy : 0.0063114166259765625 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006422996520996094 time for calcul the mask position with numpy : 0.00642848014831543 nb_pixel_total : 425 time to create 1 rle with old method : 0.0005328655242919922 time for calcul the mask position with numpy : 0.006392478942871094 nb_pixel_total : 185 time to create 1 rle with old method : 0.00026416778564453125 time for calcul the mask position with numpy : 0.006463527679443359 nb_pixel_total : 487 time to create 1 rle with old method : 0.0006175041198730469 time for calcul the mask position with numpy : 0.006495952606201172 nb_pixel_total : 191 time to create 1 rle with old method : 0.0002779960632324219 time for calcul the mask position with numpy : 0.006574153900146484 nb_pixel_total : 1950 time to create 1 rle with old method : 0.0023145675659179688 time for calcul the mask position with numpy : 0.006340742111206055 nb_pixel_total : 155 time to create 1 rle with old method : 0.00019097328186035156 time for calcul the mask position with numpy : 0.00623631477355957 nb_pixel_total : 1426 time to create 1 rle with old method : 0.0017228126525878906 time for calcul the mask position with numpy : 0.006500720977783203 nb_pixel_total : 264 time to create 1 rle with old method : 0.000347137451171875 time for calcul the mask position with numpy : 0.006261587142944336 nb_pixel_total : 5 time to create 1 rle with old method : 3.600120544433594e-05 time for calcul the mask position with numpy : 0.00613713264465332 nb_pixel_total : 103 time to create 1 rle with old method : 0.00022459030151367188 time for calcul the mask position with numpy : 0.009287118911743164 nb_pixel_total : 879 time to create 1 rle with old method : 0.0010492801666259766 time for calcul the mask position with numpy : 0.010880708694458008 nb_pixel_total : 59 time to create 1 rle with old method : 0.00012445449829101562 time for calcul the mask position with numpy : 0.010635614395141602 nb_pixel_total : 415 time to create 1 rle with old method : 0.0005097389221191406 time for calcul the mask position with numpy : 0.01042628288269043 nb_pixel_total : 7 time to create 1 rle with old method : 3.0994415283203125e-05 time for calcul the mask position with numpy : 0.010568618774414062 nb_pixel_total : 2 time to create 1 rle with old method : 3.0517578125e-05 time for calcul the mask position with numpy : 0.011073827743530273 nb_pixel_total : 350 time to create 1 rle with old method : 0.00043511390686035156 time for calcul the mask position with numpy : 0.011296749114990234 nb_pixel_total : 828 time to create 1 rle with old method : 0.000993967056274414 time for calcul the mask position with numpy : 0.010474443435668945 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006356239318847656 time for calcul the mask position with numpy : 0.010487794876098633 nb_pixel_total : 409 time to create 1 rle with old method : 0.0005838871002197266 time for calcul the mask position with numpy : 0.010760784149169922 nb_pixel_total : 534 time to create 1 rle with old method : 0.0006473064422607422 time for calcul the mask position with numpy : 0.010803461074829102 nb_pixel_total : 4922 time to create 1 rle with old method : 0.005698204040527344 time for calcul the mask position with numpy : 0.010405540466308594 nb_pixel_total : 973 time to create 1 rle with old method : 0.0015535354614257812 time for calcul the mask position with numpy : 0.01042485237121582 nb_pixel_total : 1893 time to create 1 rle with old method : 0.0022194385528564453 time for calcul the mask position with numpy : 0.010312795639038086 nb_pixel_total : 154 time to create 1 rle with old method : 0.00020837783813476562 time for calcul the mask position with numpy : 0.01036214828491211 nb_pixel_total : 69 time to create 1 rle with old method : 0.00013875961303710938 time for calcul the mask position with numpy : 0.010342597961425781 nb_pixel_total : 888 time to create 1 rle with old method : 0.0012581348419189453 time for calcul the mask position with numpy : 0.010389566421508789 nb_pixel_total : 1091 time to create 1 rle with old method : 0.001318216323852539 time for calcul the mask position with numpy : 0.010442495346069336 nb_pixel_total : 181 time to create 1 rle with old method : 0.00024700164794921875 time for calcul the mask position with numpy : 0.010294437408447266 nb_pixel_total : 9 time to create 1 rle with old method : 3.62396240234375e-05 time for calcul the mask position with numpy : 0.010186195373535156 nb_pixel_total : 300 time to create 1 rle with old method : 0.00038170814514160156 time for calcul the mask position with numpy : 0.010719537734985352 nb_pixel_total : 196 time to create 1 rle with old method : 0.0002665519714355469 time for calcul the mask position with numpy : 0.010875940322875977 nb_pixel_total : 1467 time to create 1 rle with old method : 0.001739501953125 time for calcul the mask position with numpy : 0.010393142700195312 nb_pixel_total : 503 time to create 1 rle with old method : 0.0006239414215087891 time for calcul the mask position with numpy : 0.010216951370239258 nb_pixel_total : 827 time to create 1 rle with old method : 0.0010082721710205078 time for calcul the mask position with numpy : 0.010318994522094727 nb_pixel_total : 9 time to create 1 rle with old method : 3.7670135498046875e-05 time for calcul the mask position with numpy : 0.011381387710571289 nb_pixel_total : 305 time to create 1 rle with old method : 0.00039577484130859375 time for calcul the mask position with numpy : 0.012333393096923828 nb_pixel_total : 59 time to create 1 rle with old method : 0.00013184547424316406 time for calcul the mask position with numpy : 0.010585546493530273 nb_pixel_total : 1366 time to create 1 rle with old method : 0.0016217231750488281 time for calcul the mask position with numpy : 0.010517120361328125 nb_pixel_total : 820 time to create 1 rle with old method : 0.000993490219116211 time for calcul the mask position with numpy : 0.010541200637817383 nb_pixel_total : 52 time to create 1 rle with old method : 9.584426879882812e-05 time for calcul the mask position with numpy : 0.010658502578735352 nb_pixel_total : 4 time to create 1 rle with old method : 6.127357482910156e-05 time for calcul the mask position with numpy : 0.010723590850830078 nb_pixel_total : 117 time to create 1 rle with old method : 0.000156402587890625 time for calcul the mask position with numpy : 0.009063720703125 nb_pixel_total : 857 time to create 1 rle with old method : 0.0010013580322265625 time for calcul the mask position with numpy : 0.006616830825805664 nb_pixel_total : 578 time to create 1 rle with old method : 0.0007302761077880859 time for calcul the mask position with numpy : 0.0064449310302734375 nb_pixel_total : 298 time to create 1 rle with old method : 0.0003731250762939453 time for calcul the mask position with numpy : 0.006374359130859375 nb_pixel_total : 12 time to create 1 rle with old method : 5.435943603515625e-05 time for calcul the mask position with numpy : 0.0064487457275390625 nb_pixel_total : 309 time to create 1 rle with old method : 0.0003809928894042969 time for calcul the mask position with numpy : 0.006247758865356445 nb_pixel_total : 1953 time to create 1 rle with old method : 0.002279043197631836 time for calcul the mask position with numpy : 0.006379365921020508 nb_pixel_total : 529 time to create 1 rle with old method : 0.0006291866302490234 time for calcul the mask position with numpy : 0.006358146667480469 nb_pixel_total : 551 time to create 1 rle with old method : 0.0006344318389892578 time for calcul the mask position with numpy : 0.006376504898071289 nb_pixel_total : 69 time to create 1 rle with old method : 9.72747802734375e-05 time for calcul the mask position with numpy : 0.00645136833190918 nb_pixel_total : 119 time to create 1 rle with old method : 0.0001556873321533203 time for calcul the mask position with numpy : 0.006134033203125 nb_pixel_total : 354 time to create 1 rle with old method : 0.0004322528839111328 time for calcul the mask position with numpy : 0.006147146224975586 nb_pixel_total : 787 time to create 1 rle with old method : 0.0008780956268310547 time for calcul the mask position with numpy : 0.006068706512451172 nb_pixel_total : 150 time to create 1 rle with old method : 0.00019049644470214844 time for calcul the mask position with numpy : 0.006026029586791992 nb_pixel_total : 825 time to create 1 rle with old method : 0.0009257793426513672 time for calcul the mask position with numpy : 0.0061566829681396484 nb_pixel_total : 239 time to create 1 rle with old method : 0.0002913475036621094 time for calcul the mask position with numpy : 0.006348371505737305 nb_pixel_total : 2039 time to create 1 rle with old method : 0.002302408218383789 time for calcul the mask position with numpy : 0.006341457366943359 nb_pixel_total : 3 time to create 1 rle with old method : 2.6702880859375e-05 time for calcul the mask position with numpy : 0.0064945220947265625 nb_pixel_total : 293 time to create 1 rle with old method : 0.0003669261932373047 time for calcul the mask position with numpy : 0.006150245666503906 nb_pixel_total : 389 time to create 1 rle with old method : 0.0004737377166748047 time for calcul the mask position with numpy : 0.00644230842590332 nb_pixel_total : 278 time to create 1 rle with old method : 0.00045490264892578125 time for calcul the mask position with numpy : 0.006486654281616211 nb_pixel_total : 503 time to create 1 rle with old method : 0.0006196498870849609 time for calcul the mask position with numpy : 0.00674891471862793 nb_pixel_total : 378 time to create 1 rle with old method : 0.00045013427734375 time for calcul the mask position with numpy : 0.006846904754638672 nb_pixel_total : 12 time to create 1 rle with old method : 5.53131103515625e-05 time for calcul the mask position with numpy : 0.006655693054199219 nb_pixel_total : 412 time to create 1 rle with old method : 0.00047206878662109375 time for calcul the mask position with numpy : 0.006722211837768555 nb_pixel_total : 7888 time to create 1 rle with old method : 0.008851051330566406 time for calcul the mask position with numpy : 0.00657200813293457 nb_pixel_total : 241 time to create 1 rle with old method : 0.00030231475830078125 time for calcul the mask position with numpy : 0.0066852569580078125 nb_pixel_total : 267 time to create 1 rle with old method : 0.0003120899200439453 time for calcul the mask position with numpy : 0.006407022476196289 nb_pixel_total : 273 time to create 1 rle with old method : 0.0003380775451660156 time for calcul the mask position with numpy : 0.006386756896972656 nb_pixel_total : 42 time to create 1 rle with old method : 9.679794311523438e-05 time for calcul the mask position with numpy : 0.006729841232299805 nb_pixel_total : 1557 time to create 1 rle with old method : 0.001829385757446289 time for calcul the mask position with numpy : 0.00632023811340332 nb_pixel_total : 612 time to create 1 rle with old method : 0.0007107257843017578 time for calcul the mask position with numpy : 0.0065517425537109375 nb_pixel_total : 97 time to create 1 rle with old method : 0.0001366138458251953 time for calcul the mask position with numpy : 0.006415367126464844 nb_pixel_total : 231 time to create 1 rle with old method : 0.00034308433532714844 create new chi : 1.7682218551635742 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0032520294189453125 batch 1 Loaded 179 chid ids of type : 4230 Number RLEs to save : 14579 TO DO : save crop sub photo not yet done ! save time : 0.9720578193664551 nb_obj : 170 nb_hashtags : 7 time to prepare the origin masks : 1.5744102001190186 time for calcul the mask position with numpy : 0.02493906021118164 nb_pixel_total : 1744223 time to create 1 rle with new method : 0.051261186599731445 time for calcul the mask position with numpy : 0.006279945373535156 nb_pixel_total : 433 time to create 1 rle with old method : 0.0005557537078857422 time for calcul the mask position with numpy : 0.006021022796630859 nb_pixel_total : 27 time to create 1 rle with old method : 7.104873657226562e-05 time for calcul the mask position with numpy : 0.005909442901611328 nb_pixel_total : 3399 time to create 1 rle with old method : 0.0039560794830322266 time for calcul the mask position with numpy : 0.0062825679779052734 nb_pixel_total : 1552 time to create 1 rle with old method : 0.0018241405487060547 time for calcul the mask position with numpy : 0.0061876773834228516 nb_pixel_total : 809 time to create 1 rle with old method : 0.0009665489196777344 time for calcul the mask position with numpy : 0.006211280822753906 nb_pixel_total : 48786 time to create 1 rle with old method : 0.05563664436340332 time for calcul the mask position with numpy : 0.0070917606353759766 nb_pixel_total : 2772 time to create 1 rle with old method : 0.003332376480102539 time for calcul the mask position with numpy : 0.0059757232666015625 nb_pixel_total : 104 time to create 1 rle with old method : 0.00015687942504882812 time for calcul the mask position with numpy : 0.005999088287353516 nb_pixel_total : 51 time to create 1 rle with old method : 8.034706115722656e-05 time for calcul the mask position with numpy : 0.0064733028411865234 nb_pixel_total : 334 time to create 1 rle with old method : 0.0006766319274902344 time for calcul the mask position with numpy : 0.010048389434814453 nb_pixel_total : 234 time to create 1 rle with old method : 0.0002880096435546875 time for calcul the mask position with numpy : 0.005906105041503906 nb_pixel_total : 1569 time to create 1 rle with old method : 0.0018379688262939453 time for calcul the mask position with numpy : 0.006328582763671875 nb_pixel_total : 383 time to create 1 rle with old method : 0.0007278919219970703 time for calcul the mask position with numpy : 0.006380796432495117 nb_pixel_total : 256 time to create 1 rle with old method : 0.0003294944763183594 time for calcul the mask position with numpy : 0.005877256393432617 nb_pixel_total : 18 time to create 1 rle with old method : 4.1484832763671875e-05 time for calcul the mask position with numpy : 0.0060694217681884766 nb_pixel_total : 5 time to create 1 rle with old method : 2.5033950805664062e-05 time for calcul the mask position with numpy : 0.006543874740600586 nb_pixel_total : 23 time to create 1 rle with old method : 7.05718994140625e-05 time for calcul the mask position with numpy : 0.006235599517822266 nb_pixel_total : 177 time to create 1 rle with old method : 0.0002391338348388672 time for calcul the mask position with numpy : 0.005960941314697266 nb_pixel_total : 815 time to create 1 rle with old method : 0.0009629726409912109 time for calcul the mask position with numpy : 0.005843400955200195 nb_pixel_total : 3004 time to create 1 rle with old method : 0.005621910095214844 time for calcul the mask position with numpy : 0.0062334537506103516 nb_pixel_total : 11319 time to create 1 rle with old method : 0.01293039321899414 time for calcul the mask position with numpy : 0.006227016448974609 nb_pixel_total : 211 time to create 1 rle with old method : 0.0002593994140625 time for calcul the mask position with numpy : 0.006178140640258789 nb_pixel_total : 1907 time to create 1 rle with old method : 0.0021772384643554688 time for calcul the mask position with numpy : 0.006195783615112305 nb_pixel_total : 989 time to create 1 rle with old method : 0.0011706352233886719 time for calcul the mask position with numpy : 0.0060977935791015625 nb_pixel_total : 7 time to create 1 rle with old method : 3.528594970703125e-05 time for calcul the mask position with numpy : 0.0062520503997802734 nb_pixel_total : 3320 time to create 1 rle with old method : 0.003896951675415039 time for calcul the mask position with numpy : 0.006098747253417969 nb_pixel_total : 5216 time to create 1 rle with old method : 0.006070375442504883 time for calcul the mask position with numpy : 0.006066560745239258 nb_pixel_total : 11 time to create 1 rle with old method : 9.989738464355469e-05 time for calcul the mask position with numpy : 0.006722927093505859 nb_pixel_total : 5963 time to create 1 rle with old method : 0.009655237197875977 time for calcul the mask position with numpy : 0.006489992141723633 nb_pixel_total : 743 time to create 1 rle with old method : 0.001230478286743164 time for calcul the mask position with numpy : 0.006494760513305664 nb_pixel_total : 433 time to create 1 rle with old method : 0.0007395744323730469 time for calcul the mask position with numpy : 0.0064847469329833984 nb_pixel_total : 11096 time to create 1 rle with old method : 0.01776719093322754 time for calcul the mask position with numpy : 0.0066530704498291016 nb_pixel_total : 18 time to create 1 rle with old method : 0.00010442733764648438 time for calcul the mask position with numpy : 0.006390810012817383 nb_pixel_total : 1263 time to create 1 rle with old method : 0.0021271705627441406 time for calcul the mask position with numpy : 0.006554365158081055 nb_pixel_total : 12 time to create 1 rle with old method : 4.76837158203125e-05 time for calcul the mask position with numpy : 0.006464242935180664 nb_pixel_total : 643 time to create 1 rle with old method : 0.0010876655578613281 time for calcul the mask position with numpy : 0.005890607833862305 nb_pixel_total : 816 time to create 1 rle with old method : 0.0009756088256835938 time for calcul the mask position with numpy : 0.0059359073638916016 nb_pixel_total : 131 time to create 1 rle with old method : 0.0001685619354248047 time for calcul the mask position with numpy : 0.006102085113525391 nb_pixel_total : 290 time to create 1 rle with old method : 0.0003635883331298828 time for calcul the mask position with numpy : 0.006087541580200195 nb_pixel_total : 111 time to create 1 rle with old method : 0.00024318695068359375 time for calcul the mask position with numpy : 0.006031036376953125 nb_pixel_total : 1531 time to create 1 rle with old method : 0.0017631053924560547 time for calcul the mask position with numpy : 0.006233692169189453 nb_pixel_total : 11260 time to create 1 rle with old method : 0.012807130813598633 time for calcul the mask position with numpy : 0.006071567535400391 nb_pixel_total : 813 time to create 1 rle with old method : 0.000896453857421875 time for calcul the mask position with numpy : 0.0058667659759521484 nb_pixel_total : 229 time to create 1 rle with old method : 0.0002899169921875 time for calcul the mask position with numpy : 0.006082296371459961 nb_pixel_total : 167 time to create 1 rle with old method : 0.00030875205993652344 time for calcul the mask position with numpy : 0.005970954895019531 nb_pixel_total : 4 time to create 1 rle with old method : 2.2411346435546875e-05 time for calcul the mask position with numpy : 0.005911350250244141 nb_pixel_total : 888 time to create 1 rle with old method : 0.0010309219360351562 time for calcul the mask position with numpy : 0.005984306335449219 nb_pixel_total : 903 time to create 1 rle with old method : 0.0010526180267333984 time for calcul the mask position with numpy : 0.0061113834381103516 nb_pixel_total : 296 time to create 1 rle with old method : 0.00037932395935058594 time for calcul the mask position with numpy : 0.0059201717376708984 nb_pixel_total : 10629 time to create 1 rle with old method : 0.011568307876586914 time for calcul the mask position with numpy : 0.006228923797607422 nb_pixel_total : 1236 time to create 1 rle with old method : 0.0014760494232177734 time for calcul the mask position with numpy : 0.006033420562744141 nb_pixel_total : 258 time to create 1 rle with old method : 0.00035381317138671875 time for calcul the mask position with numpy : 0.0059051513671875 nb_pixel_total : 17 time to create 1 rle with old method : 6.604194641113281e-05 time for calcul the mask position with numpy : 0.005896806716918945 nb_pixel_total : 2973 time to create 1 rle with old method : 0.0035190582275390625 time for calcul the mask position with numpy : 0.006126880645751953 nb_pixel_total : 617 time to create 1 rle with old method : 0.0008211135864257812 time for calcul the mask position with numpy : 0.0060269832611083984 nb_pixel_total : 448 time to create 1 rle with old method : 0.0005366802215576172 time for calcul the mask position with numpy : 0.005953311920166016 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005388259887695312 time for calcul the mask position with numpy : 0.006106853485107422 nb_pixel_total : 1746 time to create 1 rle with old method : 0.0020895004272460938 time for calcul the mask position with numpy : 0.0059814453125 nb_pixel_total : 607 time to create 1 rle with old method : 0.0007662773132324219 time for calcul the mask position with numpy : 0.0060007572174072266 nb_pixel_total : 32 time to create 1 rle with old method : 0.00010538101196289062 time for calcul the mask position with numpy : 0.0059642791748046875 nb_pixel_total : 84 time to create 1 rle with old method : 0.00011777877807617188 time for calcul the mask position with numpy : 0.009943485260009766 nb_pixel_total : 24 time to create 1 rle with old method : 0.00013875961303710938 time for calcul the mask position with numpy : 0.010071039199829102 nb_pixel_total : 674 time to create 1 rle with old method : 0.0008084774017333984 time for calcul the mask position with numpy : 0.009950637817382812 nb_pixel_total : 3287 time to create 1 rle with old method : 0.003908872604370117 time for calcul the mask position with numpy : 0.010213136672973633 nb_pixel_total : 233 time to create 1 rle with old method : 0.00030303001403808594 time for calcul the mask position with numpy : 0.010133504867553711 nb_pixel_total : 1348 time to create 1 rle with old method : 0.0015926361083984375 time for calcul the mask position with numpy : 0.010018348693847656 nb_pixel_total : 1085 time to create 1 rle with old method : 0.001363992691040039 time for calcul the mask position with numpy : 0.010061502456665039 nb_pixel_total : 3 time to create 1 rle with old method : 4.6253204345703125e-05 time for calcul the mask position with numpy : 0.010143518447875977 nb_pixel_total : 117 time to create 1 rle with old method : 0.0001685619354248047 time for calcul the mask position with numpy : 0.010018348693847656 nb_pixel_total : 533 time to create 1 rle with old method : 0.0006873607635498047 time for calcul the mask position with numpy : 0.009974002838134766 nb_pixel_total : 1014 time to create 1 rle with old method : 0.0013539791107177734 time for calcul the mask position with numpy : 0.010359048843383789 nb_pixel_total : 307 time to create 1 rle with old method : 0.00038433074951171875 time for calcul the mask position with numpy : 0.010121822357177734 nb_pixel_total : 300 time to create 1 rle with old method : 0.00038433074951171875 time for calcul the mask position with numpy : 0.009977579116821289 nb_pixel_total : 469 time to create 1 rle with old method : 0.0005755424499511719 time for calcul the mask position with numpy : 0.010078668594360352 nb_pixel_total : 942 time to create 1 rle with old method : 0.0013375282287597656 time for calcul the mask position with numpy : 0.009732246398925781 nb_pixel_total : 478 time to create 1 rle with old method : 0.00058746337890625 time for calcul the mask position with numpy : 0.006860494613647461 nb_pixel_total : 881 time to create 1 rle with old method : 0.0010755062103271484 time for calcul the mask position with numpy : 0.006757020950317383 nb_pixel_total : 87 time to create 1 rle with old method : 0.0001270771026611328 time for calcul the mask position with numpy : 0.0060825347900390625 nb_pixel_total : 723 time to create 1 rle with old method : 0.0008795261383056641 time for calcul the mask position with numpy : 0.005967855453491211 nb_pixel_total : 76 time to create 1 rle with old method : 0.0001342296600341797 time for calcul the mask position with numpy : 0.0062868595123291016 nb_pixel_total : 322 time to create 1 rle with old method : 0.0005929470062255859 time for calcul the mask position with numpy : 0.006948947906494141 nb_pixel_total : 129 time to create 1 rle with old method : 0.00017380714416503906 time for calcul the mask position with numpy : 0.0061054229736328125 nb_pixel_total : 305 time to create 1 rle with old method : 0.00040411949157714844 time for calcul the mask position with numpy : 0.005969524383544922 nb_pixel_total : 866 time to create 1 rle with old method : 0.0010271072387695312 time for calcul the mask position with numpy : 0.0062711238861083984 nb_pixel_total : 1752 time to create 1 rle with old method : 0.002377033233642578 time for calcul the mask position with numpy : 0.0066204071044921875 nb_pixel_total : 104371 time to create 1 rle with old method : 0.11952447891235352 time for calcul the mask position with numpy : 0.006187915802001953 nb_pixel_total : 191 time to create 1 rle with old method : 0.00025963783264160156 time for calcul the mask position with numpy : 0.006037235260009766 nb_pixel_total : 10464 time to create 1 rle with old method : 0.011919498443603516 time for calcul the mask position with numpy : 0.01019287109375 nb_pixel_total : 96 time to create 1 rle with old method : 0.00013017654418945312 time for calcul the mask position with numpy : 0.006658315658569336 nb_pixel_total : 99 time to create 1 rle with old method : 0.00021886825561523438 time for calcul the mask position with numpy : 0.006949901580810547 nb_pixel_total : 1892 time to create 1 rle with old method : 0.0026082992553710938 time for calcul the mask position with numpy : 0.0063130855560302734 nb_pixel_total : 347 time to create 1 rle with old method : 0.0004363059997558594 time for calcul the mask position with numpy : 0.006256103515625 nb_pixel_total : 914 time to create 1 rle with old method : 0.0012388229370117188 time for calcul the mask position with numpy : 0.006261348724365234 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002453327178955078 time for calcul the mask position with numpy : 0.0064051151275634766 nb_pixel_total : 307 time to create 1 rle with old method : 0.00042057037353515625 time for calcul the mask position with numpy : 0.006242275238037109 nb_pixel_total : 670 time to create 1 rle with old method : 0.0009610652923583984 time for calcul the mask position with numpy : 0.006230354309082031 nb_pixel_total : 171 time to create 1 rle with old method : 0.00022840499877929688 time for calcul the mask position with numpy : 0.006222248077392578 nb_pixel_total : 404 time to create 1 rle with old method : 0.0005218982696533203 time for calcul the mask position with numpy : 0.0061986446380615234 nb_pixel_total : 1615 time to create 1 rle with old method : 0.002012491226196289 time for calcul the mask position with numpy : 0.006365060806274414 nb_pixel_total : 289 time to create 1 rle with old method : 0.00041866302490234375 time for calcul the mask position with numpy : 0.006365299224853516 nb_pixel_total : 528 time to create 1 rle with old method : 0.0007226467132568359 time for calcul the mask position with numpy : 0.006326913833618164 nb_pixel_total : 15 time to create 1 rle with old method : 6.961822509765625e-05 time for calcul the mask position with numpy : 0.006638288497924805 nb_pixel_total : 5 time to create 1 rle with old method : 3.2901763916015625e-05 time for calcul the mask position with numpy : 0.007370471954345703 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006039142608642578 time for calcul the mask position with numpy : 0.006331920623779297 nb_pixel_total : 233 time to create 1 rle with old method : 0.00032591819763183594 time for calcul the mask position with numpy : 0.006242513656616211 nb_pixel_total : 178 time to create 1 rle with old method : 0.00023293495178222656 time for calcul the mask position with numpy : 0.006180763244628906 nb_pixel_total : 137 time to create 1 rle with old method : 0.0001780986785888672 time for calcul the mask position with numpy : 0.00621485710144043 nb_pixel_total : 1215 time to create 1 rle with old method : 0.0014607906341552734 time for calcul the mask position with numpy : 0.006395578384399414 nb_pixel_total : 9 time to create 1 rle with old method : 5.459785461425781e-05 time for calcul the mask position with numpy : 0.006609916687011719 nb_pixel_total : 520 time to create 1 rle with old method : 0.0006499290466308594 time for calcul the mask position with numpy : 0.006373167037963867 nb_pixel_total : 915 time to create 1 rle with old method : 0.0010962486267089844 time for calcul the mask position with numpy : 0.0063550472259521484 nb_pixel_total : 13 time to create 1 rle with old method : 5.4836273193359375e-05 time for calcul the mask position with numpy : 0.0063304901123046875 nb_pixel_total : 525 time to create 1 rle with old method : 0.0010223388671875 time for calcul the mask position with numpy : 0.008831977844238281 nb_pixel_total : 141 time to create 1 rle with old method : 0.00029850006103515625 time for calcul the mask position with numpy : 0.007581949234008789 nb_pixel_total : 538 time to create 1 rle with old method : 0.0006670951843261719 time for calcul the mask position with numpy : 0.0069925785064697266 nb_pixel_total : 379 time to create 1 rle with old method : 0.0005042552947998047 time for calcul the mask position with numpy : 0.006611824035644531 nb_pixel_total : 474 time to create 1 rle with old method : 0.0006241798400878906 time for calcul the mask position with numpy : 0.006549835205078125 nb_pixel_total : 2009 time to create 1 rle with old method : 0.0023698806762695312 time for calcul the mask position with numpy : 0.006944894790649414 nb_pixel_total : 175 time to create 1 rle with old method : 0.00023055076599121094 time for calcul the mask position with numpy : 0.0065119266510009766 nb_pixel_total : 584 time to create 1 rle with old method : 0.0007317066192626953 time for calcul the mask position with numpy : 0.006554365158081055 nb_pixel_total : 1176 time to create 1 rle with old method : 0.0013058185577392578 time for calcul the mask position with numpy : 0.006643056869506836 nb_pixel_total : 71 time to create 1 rle with old method : 0.00013566017150878906 time for calcul the mask position with numpy : 0.006869316101074219 nb_pixel_total : 830 time to create 1 rle with old method : 0.0010297298431396484 time for calcul the mask position with numpy : 0.0065729618072509766 nb_pixel_total : 174 time to create 1 rle with old method : 0.0002307891845703125 time for calcul the mask position with numpy : 0.0065155029296875 nb_pixel_total : 1284 time to create 1 rle with old method : 0.0015251636505126953 time for calcul the mask position with numpy : 0.0069293975830078125 nb_pixel_total : 214 time to create 1 rle with old method : 0.0002715587615966797 time for calcul the mask position with numpy : 0.006755828857421875 nb_pixel_total : 4 time to create 1 rle with old method : 4.887580871582031e-05 time for calcul the mask position with numpy : 0.007832765579223633 nb_pixel_total : 11 time to create 1 rle with old method : 3.790855407714844e-05 time for calcul the mask position with numpy : 0.006998300552368164 nb_pixel_total : 15 time to create 1 rle with old method : 4.38690185546875e-05 time for calcul the mask position with numpy : 0.0065839290618896484 nb_pixel_total : 344 time to create 1 rle with old method : 0.0004227161407470703 time for calcul the mask position with numpy : 0.006701469421386719 nb_pixel_total : 1484 time to create 1 rle with old method : 0.0016422271728515625 time for calcul the mask position with numpy : 0.006730318069458008 nb_pixel_total : 706 time to create 1 rle with old method : 0.0008358955383300781 time for calcul the mask position with numpy : 0.0065648555755615234 nb_pixel_total : 52 time to create 1 rle with old method : 8.797645568847656e-05 time for calcul the mask position with numpy : 0.006639957427978516 nb_pixel_total : 21 time to create 1 rle with old method : 7.05718994140625e-05 time for calcul the mask position with numpy : 0.006844520568847656 nb_pixel_total : 18 time to create 1 rle with old method : 6.4849853515625e-05 time for calcul the mask position with numpy : 0.0066525936126708984 nb_pixel_total : 2031 time to create 1 rle with old method : 0.002347230911254883 time for calcul the mask position with numpy : 0.006466388702392578 nb_pixel_total : 9716 time to create 1 rle with old method : 0.011211156845092773 time for calcul the mask position with numpy : 0.006471157073974609 nb_pixel_total : 172 time to create 1 rle with old method : 0.00025916099548339844 time for calcul the mask position with numpy : 0.006719350814819336 nb_pixel_total : 98 time to create 1 rle with old method : 0.0001404285430908203 time for calcul the mask position with numpy : 0.006283760070800781 nb_pixel_total : 838 time to create 1 rle with old method : 0.001007080078125 time for calcul the mask position with numpy : 0.006364583969116211 nb_pixel_total : 627 time to create 1 rle with old method : 0.0007395744323730469 time for calcul the mask position with numpy : 0.006379604339599609 nb_pixel_total : 300 time to create 1 rle with old method : 0.0003757476806640625 time for calcul the mask position with numpy : 0.006418704986572266 nb_pixel_total : 1020 time to create 1 rle with old method : 0.0011811256408691406 time for calcul the mask position with numpy : 0.00687098503112793 nb_pixel_total : 517 time to create 1 rle with old method : 0.0005867481231689453 time for calcul the mask position with numpy : 0.006342649459838867 nb_pixel_total : 553 time to create 1 rle with old method : 0.0006508827209472656 time for calcul the mask position with numpy : 0.006173372268676758 nb_pixel_total : 74 time to create 1 rle with old method : 0.00011134147644042969 time for calcul the mask position with numpy : 0.006005764007568359 nb_pixel_total : 133 time to create 1 rle with old method : 0.0001747608184814453 time for calcul the mask position with numpy : 0.006119251251220703 nb_pixel_total : 292 time to create 1 rle with old method : 0.0003616809844970703 time for calcul the mask position with numpy : 0.0060803890228271484 nb_pixel_total : 26 time to create 1 rle with old method : 5.9604644775390625e-05 time for calcul the mask position with numpy : 0.006155967712402344 nb_pixel_total : 5 time to create 1 rle with old method : 2.765655517578125e-05 time for calcul the mask position with numpy : 0.006443977355957031 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002703666687011719 time for calcul the mask position with numpy : 0.006175994873046875 nb_pixel_total : 378 time to create 1 rle with old method : 0.0004696846008300781 time for calcul the mask position with numpy : 0.0066754817962646484 nb_pixel_total : 212 time to create 1 rle with old method : 0.0002665519714355469 time for calcul the mask position with numpy : 0.0062448978424072266 nb_pixel_total : 64 time to create 1 rle with old method : 0.0001277923583984375 time for calcul the mask position with numpy : 0.0063402652740478516 nb_pixel_total : 5 time to create 1 rle with old method : 5.14984130859375e-05 time for calcul the mask position with numpy : 0.0066986083984375 nb_pixel_total : 2406 time to create 1 rle with old method : 0.002793550491333008 time for calcul the mask position with numpy : 0.007006645202636719 nb_pixel_total : 2 time to create 1 rle with old method : 5.221366882324219e-05 time for calcul the mask position with numpy : 0.007947206497192383 nb_pixel_total : 140 time to create 1 rle with old method : 0.00028133392333984375 time for calcul the mask position with numpy : 0.007433891296386719 nb_pixel_total : 530 time to create 1 rle with old method : 0.0007238388061523438 time for calcul the mask position with numpy : 0.006936073303222656 nb_pixel_total : 435 time to create 1 rle with old method : 0.0005753040313720703 time for calcul the mask position with numpy : 0.007158041000366211 nb_pixel_total : 11 time to create 1 rle with old method : 6.866455078125e-05 time for calcul the mask position with numpy : 0.007456064224243164 nb_pixel_total : 4049 time to create 1 rle with old method : 0.0049054622650146484 time for calcul the mask position with numpy : 0.00735783576965332 nb_pixel_total : 405 time to create 1 rle with old method : 0.0005338191986083984 time for calcul the mask position with numpy : 0.008089542388916016 nb_pixel_total : 3148 time to create 1 rle with old method : 0.004040718078613281 time for calcul the mask position with numpy : 0.0072019100189208984 nb_pixel_total : 212 time to create 1 rle with old method : 0.000301361083984375 time for calcul the mask position with numpy : 0.0072345733642578125 nb_pixel_total : 123 time to create 1 rle with old method : 0.00022983551025390625 time for calcul the mask position with numpy : 0.007613182067871094 nb_pixel_total : 258 time to create 1 rle with old method : 0.0003216266632080078 time for calcul the mask position with numpy : 0.0071964263916015625 nb_pixel_total : 1554 time to create 1 rle with old method : 0.002214670181274414 time for calcul the mask position with numpy : 0.0072174072265625 nb_pixel_total : 408 time to create 1 rle with old method : 0.0005841255187988281 time for calcul the mask position with numpy : 0.006520986557006836 nb_pixel_total : 620 time to create 1 rle with old method : 0.0007658004760742188 create new chi : 1.6402521133422852 after preparing all the mask , begin to delete the rle from the crop_hashtag_id => VR 28-11-20 : il faut déplacer cela apres le save_crop_hashtag_ids_obj de la ligne 9514 ! we have 0 chi objets contains the rles time to delete rle : 0.0033292770385742188 batch 1 Loaded 175 chid ids of type : 4230 Number RLEs to save : 15538 TO DO : save crop sub photo not yet done ! save time : 0.9026796817779541 map_output_result : {1332934252: (0.0, 'Should be the crop_list due to order', 0.0), 1332934245: (0.0, 'Should be the crop_list due to order', 0.0), 1332934219: (0.0, 'Should be the crop_list due to order', 0.0), 1332934207: (0.0, 'Should be the crop_list due to order', 0.0), 1332934203: (0.0, 'Should be the crop_list due to order', 0.0), 1332934198: (0.0, 'Should be the crop_list due to order', 0.0), 1332934177: (0.0, 'Should be the crop_list due to order', 0.0), 1332934128: (0.0, 'Should be the crop_list due to order', 0.0), 1332934123: (0.0, 'Should be the crop_list due to order', 0.0), 1332934118: (0.0, 'Should be the crop_list due to order', 0.0), 1332934112: (0.0, 'Should be the crop_list due to order', 0.0), 1332934055: (0.0, 'Should be the crop_list due to order', 0.0), 1332934051: (0.0, 'Should be the crop_list due to order', 0.0), 1332933775: (0.0, 'Should be the crop_list due to order', 0.0), 1332933772: (0.0, 'Should be the crop_list due to order', 0.0), 1332933762: (0.0, 'Should be the crop_list due to order', 0.0), 1332933747: (0.0, 'Should be the crop_list due to order', 0.0), 1332933425: (0.0, 'Should be the crop_list due to order', 0.0), 1332933124: (0.0, 'Should be the crop_list due to order', 0.0), 1332933033: (0.0, 'Should be the crop_list due to order', 0.0), 1332933026: (0.0, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1332934252, 1332934245, 1332934219, 1332934207, 1332934203, 1332934198, 1332934177, 1332934128, 1332934123, 1332934118, 1332934112, 1332934055, 1332934051, 1332933775, 1332933772, 1332933762, 1332933747, 1332933425, 1332933124, 1332933033, 1332933026] Looping around the photos to save general results len do output : 21 /1332934252.Didn't retrieve data . /1332934245.Didn't retrieve data . /1332934219.Didn't retrieve data . /1332934207.Didn't retrieve data . /1332934203.Didn't retrieve data . /1332934198.Didn't retrieve data . /1332934177.Didn't retrieve data . /1332934128.Didn't retrieve data . /1332934123.Didn't retrieve data . /1332934118.Didn't retrieve data . /1332934112.Didn't retrieve data . /1332934055.Didn't retrieve data . /1332934051.Didn't retrieve data . /1332933775.Didn't retrieve data . /1332933772.Didn't retrieve data . /1332933762.Didn't retrieve data . /1332933747.Didn't retrieve data . /1332933425.Didn't retrieve data . /1332933124.Didn't retrieve data . /1332933033.Didn't retrieve data . /1332933026.Didn't retrieve data . before output type Used above Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934252', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934245', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934219', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934207', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934203', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934198', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934177', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934128', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934123', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934118', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934112', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934055', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934051', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933775', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933772', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933762', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933747', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933425', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933124', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933033', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933026', None, None, None, None, None, '2529344') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 63 time used for this insertion : 0.02839183807373047 save_final save missing photos in datou_result : time spend for datou_step_exec : 116.39989876747131 time spend to save output : 0.02945733070373535 total time spend for step 4 : 116.42935609817505 step5:crop_condition Tue Feb 11 10:57:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure some photos are not treated, begin crop_condition Loading chi in step crop with photo_hashtag_type : 4230 Loading chi in step crop for list_pids : 21 ! batch 1 Loaded 3857 chid ids of type : 4230 begin to crop the class : papier param for this class : {'min_score': 0.6} filtre for class : papier hashtag_id of this class : 492668766 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! 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Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 1361 About to insert : list_path_to_insert length 1343 new photo from crops ! About to upload 1343 photos upload in portfolio : 4869462 init cache_photo without model_param we have 1343 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739267843_2208836 we have uploaded 1343 photos in the portfolio 4869462 time of upload the photos Elapsed time : 290.30793619155884 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.6} filtre for class : carton hashtag_id of this class : 492774966 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 357 About to insert : list_path_to_insert length 355 new photo from crops ! About to upload 355 photos upload in portfolio : 4869462 init cache_photo without model_param we have 355 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739268139_2208836 we have uploaded 355 photos in the portfolio 4869462 time of upload the photos Elapsed time : 76.22295117378235 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.6} filtre for class : metal hashtag_id of this class : 492628673 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 4869462 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739268216_2208836 we have uploaded 6 photos in the portfolio 4869462 time of upload the photos Elapsed time : 1.506784200668335 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.6} filtre for class : pet_clair hashtag_id of this class : 2107755846 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 306 About to insert : list_path_to_insert length 298 new photo from crops ! About to upload 298 photos upload in portfolio : 4869462 init cache_photo without model_param we have 298 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739268223_2208836 we have uploaded 298 photos in the portfolio 4869462 time of upload the photos Elapsed time : 63.33044385910034 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.6} filtre for class : autre hashtag_id of this class : 494826614 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 421 About to insert : list_path_to_insert length 420 new photo from crops ! About to upload 420 photos upload in portfolio : 4869462 init cache_photo without model_param we have 420 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739268292_2208836 we have uploaded 420 photos in the portfolio 4869462 time of upload the photos Elapsed time : 90.56399941444397 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.6} filtre for class : pehd hashtag_id of this class : 628944319 Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 4869462 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739268384_2208836 we have uploaded 4 photos in the portfolio 4869462 time of upload the photos Elapsed time : 3.3288025856018066 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.6} filtre for class : pet_fonce hashtag_id of this class : 2107755900 Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 9 About to insert : list_path_to_insert length 9 new photo from crops ! About to upload 9 photos upload in portfolio : 4869462 init cache_photo without model_param we have 9 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739268388_2208836 we have uploaded 9 photos in the portfolio 4869462 time of upload the photos Elapsed time : 2.1531481742858887 we have finished the crop for the class : pet_fonce delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1332934252, 1332934245, 1332934219, 1332934207, 1332934203, 1332934198, 1332934177, 1332934128, 1332934123, 1332934118, 1332934112, 1332934055, 1332934051, 1332933775, 1332933772, 1332933762, 1332933747, 1332933425, 1332933124, 1332933033, 1332933026] Looping around the photos to save general results len do output : 2464 /1336784280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784281Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784286Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336546251Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784305Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784307Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336546256Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784309Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784311Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784314Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784315Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784318Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784320Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784321Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784323Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784324Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784325Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784326Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784327Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784328Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784329Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784330Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784331Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784332Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784334Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336546284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336784345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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data .Didn't retrieve data . /1336787366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336787370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336787373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1336787377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934252', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934245', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934219', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934207', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934203', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934198', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934177', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934128', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934123', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934118', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934112', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934055', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934051', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933775', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933772', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933762', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933747', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933425', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933124', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933033', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933026', None, None, None, None, None, '2529344') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 7413 time used for this insertion : 0.6904606819152832 save_final save missing photos in datou_result : time spend for datou_step_exec : 570.890426158905 time spend to save output : 0.7443211078643799 total time spend for step 5 : 571.6347472667694 step6:thcl Tue Feb 11 11:06:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step Thcl ! we are using the classfication for only one thcl 3237 time to import caffe and check if the image exist : 0.1852283477783203 time to convert the images to numpy array : 0.7665462493896484 time to import caffe and check if the image exist : 0.1974351406097412 time to convert the images to numpy array : 0.7909374237060547 time to import caffe and check if the image exist : 0.22818708419799805 time to convert the images to numpy array : 0.7765977382659912 time to import caffe and check if the image exist : 0.20322084426879883 time to convert the images to numpy array : 0.8114323616027832 time to import caffe and check if the image exist : 0.19770121574401855 time to convert the images to numpy array : 0.8217763900756836 time to import caffe and check if the image exist : 0.21861004829406738 time to convert the images to numpy array : 0.8131575584411621 time to import caffe and check if the image exist : 0.1805577278137207 time to convert the images to numpy array : 0.8560316562652588 time to import caffe and check if the image exist : 0.2559351921081543 time to convert the images to numpy array : 0.7828705310821533 time to import caffe and check if the image exist : 0.17563509941101074 time to convert the images to numpy array : 0.8657181262969971 time to import caffe and check if the image exist : 0.2180624008178711 time to convert the images to numpy array : 0.8317475318908691 total time to convert the images to numpy array : 1.3137972354888916 list photo_ids error: [] list photo_ids correct : [1336784531, 1336784532, 1336784533, 1336784534, 1336546471, 1336784535, 1336784536, 1336784537, 1336784538, 1336784539, 1336784540, 1336784543, 1336784544, 1336784545, 1336784546, 1336546479, 1336784547, 1336784548, 1336784549, 1336784550, 1336784551, 1336784552, 1336784553, 1336784555, 1336784556, 1336784557, 1336784558, 1336784560, 1336784561, 1336784562, 1336784563, 1336784564, 1336784565, 1336784566, 1336784567, 1336784568, 1336784569, 1336784570, 1336784571, 1336784572, 1336784573, 1336784574, 1336784575, 1336784576, 1336784577, 1336784578, 1336784579, 1336784580, 1336784581, 1336784582, 1336784583, 1336784584, 1336784585, 1336784586, 1336784587, 1336784588, 1336546517, 1336784589, 1336784590, 1336784591, 1336784592, 1336784593, 1336784594, 1336784595, 1336784596, 1336784597, 1336784598, 1336784599, 1336784600, 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1336785234, 1336785235, 1336785236, 1336785237, 1336785238, 1336785239, 1336785240, 1336785241, 1336785242, 1336785243, 1336785244, 1336785245, 1336785246, 1336785247, 1336785248, 1336785249, 1336785250, 1336785251, 1336785252, 1336785253, 1336785254, 1336785255, 1336785256, 1336785257, 1336785259, 1336785260, 1336785261, 1336785262, 1336785263, 1336785264, 1336785265, 1336785266, 1336785267, 1336785268, 1336785269, 1336785270, 1336785271, 1336785272, 1336785273, 1336785275, 1336785276, 1336785277, 1336785278, 1336785279, 1336785280, 1336785281, 1336785282, 1336785283, 1336785284, 1336785285, 1336785286, 1336785287, 1336785288, 1336785289, 1336785290, 1336785291, 1336785292, 1336785293, 1336785294, 1336785296, 1336785297, 1336785298, 1336785299, 1336785300, 1336785301, 1336785302, 1336785303, 1336785304, 1336785305, 1336785306, 1336785307] number of photos to traite : 2464 try to delete the photos incorrect in DB tagging for thcl : 3237 To do loadFromThcl(), then load ParamDescType : thcl3237 thcls : [{'id': 3237, 'mtr_user_id': 31, 'name': 'learn_rubbia_refus_2500', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Carton,Film_plastique,PEHD,PET_clair,PET_fonce,Papier,Tetrapak,flou,mal_croppe,metal,refus', 'svm_portfolios_learning': '4865689,4865690,4865686,4865684,4865685,4865688,4865691,4865693,4865692,4865687,4865683', 'photo_hashtag_type': 4158, 'photo_desc_type': 5561, 'type_classification': 'caffe', 'hashtag_id_list': '492774966,2107756122,628944319,2107755846,2107755900,492668766,609991870,492777938,2107755527,492628673,538914404'}] thcl {'id': 3237, 'mtr_user_id': 31, 'name': 'learn_rubbia_refus_2500', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Carton,Film_plastique,PEHD,PET_clair,PET_fonce,Papier,Tetrapak,flou,mal_croppe,metal,refus', 'svm_portfolios_learning': '4865689,4865690,4865686,4865684,4865685,4865688,4865691,4865693,4865692,4865687,4865683', 'photo_hashtag_type': 4158, 'photo_desc_type': 5561, 'type_classification': 'caffe', 'hashtag_id_list': '492774966,2107756122,628944319,2107755846,2107755900,492668766,609991870,492777938,2107755527,492628673,538914404'} Update svm_hashtag_type_desc : 5561 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5561, 'learn_rubbia_refus_2500', 2048, 2048, 'learn_rubbia_refus_2500', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2021, 12, 2, 19, 10, 8), datetime.datetime(2021, 12, 2, 19, 10, 8)) To loadFromThcl() : net_5561 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 9754 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5561, 'learn_rubbia_refus_2500', 2048, 2048, 'learn_rubbia_refus_2500', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2021, 12, 2, 19, 10, 8), datetime.datetime(2021, 12, 2, 19, 10, 8)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_rubbia_refus_2500 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_rubbia_refus_2500 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_rubbia_refus_2500 /data/models_weight/learn_rubbia_refus_2500/caffemodel size_local : 94358479 size in s3 : 94358479 create time local : 2021-12-03 18:29:39 create time in s3 : 2021-12-02 17:49:16 caffemodel already exist and didn't need to update /data/models_weight/learn_rubbia_refus_2500/deploy.prototxt size_local : 32544 size in s3 : 32544 create time local : 2021-12-03 18:29:39 create time in s3 : 2021-12-02 17:49:15 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_rubbia_refus_2500/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-12-03 18:29:40 create time in s3 : 2021-12-02 18:09:52 mean.npy already exist and didn't need to update /data/models_weight/learn_rubbia_refus_2500/synset_words.txt size_local : 334 size in s3 : 334 create time local : 2021-12-03 18:29:40 create time in s3 : 2021-12-02 18:10:06 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/:/home/admin/workarea/git/apy/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_rubbia_refus_2500/deploy.prototxt caffemodel_filename : /data/models_weight/learn_rubbia_refus_2500/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 9535 max_wait_temp : 1 max_wait : 0 dict_keys(['pool5', 'prob']) time used to do the prepocess of the images : 11.81894302368164 time used to do the prediction : 8.822516441345215 save descriptor for thcl : 3237 time to traite the descriptors : 14.256380558013916 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 3 To insert : 1336784531 To insert : 1336784532 To insert : 1336784533 To insert : 1336784534 To insert : 1336546471 To insert : 1336784535 To insert : 1336784536 To insert : 1336784537 To insert : 1336784538 To insert : 1336784539 To insert : 1336784540 To insert : 1336784543 To insert : 1336784544 To insert : 1336784545 To insert : 1336784546 To insert : 1336546479 To insert : 1336784547 To insert : 1336784548 To insert : 1336784549 To insert : 1336784550 To insert : 1336784551 To insert : 1336784552 To insert : 1336784553 To insert : 1336784555 To insert : 1336784556 To insert : 1336784557 To insert : 1336784558 To insert : 1336784560 To insert : 1336784561 To insert : 1336784562 To insert : 1336784563 To insert : 1336784564 To insert : 1336784565 To insert : 1336784566 To insert : 1336784567 To insert : 1336784568 To insert : 1336784569 To insert : 1336784570 To insert : 1336784571 To insert : 1336784572 To insert : 1336784573 To insert : 1336784574 To insert : 1336784575 To insert : 1336784576 To insert : 1336784577 To insert : 1336784578 To insert : 1336784579 To insert : 1336784580 To insert : 1336784581 To insert : 1336784582 To insert : 1336784583 To insert : 1336784584 To insert : 1336784585 To insert : 1336784586 To insert : 1336784587 To insert : 1336784588 To insert : 1336546517 To insert : 1336784589 To insert : 1336784590 To insert : 1336784591 To insert : 1336784592 To insert : 1336784593 To insert : 1336784594 To insert : 1336784595 To insert : 1336784596 To insert : 1336784597 To insert : 1336784598 To insert : 1336784599 To insert : 1336784600 To insert : 1336784601 To insert : 1336784602 To insert : 1336784604 To insert : 1336784605 To insert : 1336784606 To insert : 1336784607 To insert : 1336784608 To insert : 1336784609 To insert : 1336784610 To insert : 1336784611 To insert : 1336784612 To insert : 1336784613 To insert : 1336784614 To insert : 1336784615 To insert : 1336784616 To insert : 1336784617 To insert : 1336784618 To insert : 1336784619 To insert : 1336784620 To insert : 1336784621 To insert : 1336784622 To insert : 1336784623 To insert : 1336784624 To insert : 1336784625 To insert : 1336784626 To insert : 1336784627 To insert : 1336546554 To insert : 1336784628 To insert : 1336784629 To insert : 1336784630 To insert : 1336784631 To insert : 1336784633 To insert : 1336784634 To insert : 1336784635 To insert : 1336784636 To insert : 1336784637 To insert : 1336784638 To insert : 1336784639 To insert : 1336784640 To insert : 1336784641 To insert : 1336784642 To insert : 1336784643 To insert : 1336784644 To insert : 1336784645 To insert : 1336784646 To insert : 1336784647 To insert : 1336784648 To insert : 1336784649 To insert : 1336784650 To insert : 1336784651 To insert : 1336784652 To insert : 1336784653 To insert : 1336784654 To insert : 1336784655 To insert : 1336784656 To insert : 1336784657 To insert : 1336784658 To insert : 1336784659 To insert : 1336784660 To insert : 1336784661 To insert : 1336784662 To insert : 1336784664 To insert : 1336784666 To insert : 1336784667 To insert : 1336784668 To insert : 1336784669 To insert : 1336784670 To insert : 1336784671 To insert : 1336784672 To insert : 1336784673 To insert : 1336784675 To insert : 1336784676 To insert : 1336784677 To insert : 1336784678 To insert : 1336784679 To insert : 1336784680 To insert : 1336784681 To insert : 1336784682 To insert : 1336784683 To insert : 1336784684 To insert : 1336784685 To insert : 1336784686 To insert : 1336784687 To insert : 1336784688 To insert : 1336784689 To insert : 1336784690 To insert : 1336784691 To insert : 1336784692 To insert : 1336784693 To insert : 1336784694 To insert : 1336784695 To insert : 1336784696 To insert : 1336784697 To insert : 1336784698 To insert : 1336784699 To insert : 1336784700 To insert : 1336784701 To insert : 1336784702 To insert : 1336784703 To insert : 1336784704 To insert : 1336784705 To insert : 1336784706 To insert : 1336784707 To insert : 1336784708 To insert : 1336784709 To insert : 1336784710 To insert : 1336784711 To insert : 1336784712 To insert : 1336784713 To insert : 1336784714 To insert : 1336784715 To insert : 1336784716 To insert : 1336784717 To insert : 1336784718 To insert : 1336784719 To insert : 1336784720 To insert : 1336784721 To insert : 1336784722 To insert : 1336784723 To insert : 1336784724 To insert : 1336784725 To insert : 1336784726 To insert : 1336784727 To insert : 1336784728 To insert : 1336784729 To insert : 1336546645 To insert : 1336784730 To insert : 1336784731 To insert : 1336784732 To insert : 1336784733 To insert : 1336784734 To insert : 1336784735 To insert : 1336784736 To insert : 1336784737 To insert : 1336784738 To insert : 1336784739 To insert : 1336784740 To insert : 1336784741 To insert : 1336784742 To insert : 1336784743 To insert : 1336784744 To insert : 1336784745 To insert : 1336784746 To insert : 1336784747 To insert : 1336784748 To insert : 1336784749 To insert : 1336784750 To insert : 1336784751 To insert : 1336784752 To insert : 1336784753 To insert : 1336784754 To insert : 1336784755 To insert : 1336784756 To insert : 1336784758 To insert : 1336784759 To insert : 1336784760 To insert : 1336784761 To insert : 1336784762 To insert : 1336784763 To insert : 1336784764 To insert : 1336784765 To insert : 1336784766 To insert : 1336784767 To insert : 1336784768 To insert : 1336784769 To insert : 1336784771 To insert : 1336784772 To insert : 1336784773 To insert : 1336784774 To insert : 1336784775 To insert : 1336784776 To insert : 1336784777 To insert : 1336784778 To insert : 1336784779 To insert : 1336784780 To insert : 1336784781 To insert : 1336784782 To insert : 1336784783 To insert : 1336784280 To insert : 1336784281 To insert : 1336784282 To insert : 1336784283 To insert : 1336784284 To insert : 1336784285 To insert : 1336784286 To insert : 1336784287 To insert : 1336784288 To insert : 1336784289 To insert : 1336784290 To insert : 1336784291 To insert : 1336784292 To insert : 1336784294 To insert : 1336784295 To insert : 1336784296 To insert : 1336784297 To insert : 1336784298 To insert : 1336784300 To insert : 1336784301 To insert : 1336784302 To insert : 1336784303 To insert : 1336784304 To insert : 1336546251 To insert : 1336784305 To insert : 1336784306 To insert : 1336784307 To insert : 1336784308 To insert : 1336546256 To insert : 1336784309 To insert : 1336784310 To insert : 1336784311 To insert : 1336784312 To insert : 1336784313 To insert : 1336784314 To insert : 1336784315 To insert : 1336784316 To insert : 1336784317 To insert : 1336784318 To insert : 1336784319 To insert : 1336784320 To insert : 1336784321 To insert : 1336784322 To insert : 1336784323 To insert : 1336784324 To insert : 1336784325 To insert : 1336784326 To insert : 1336784327 To insert : 1336784328 To insert : 1336784329 To insert : 1336784330 To insert : 1336784331 To insert : 1336784332 To insert : 1336784333 To insert : 1336784334 To insert : 1336784335 To insert : 1336784336 To insert : 1336784337 To insert : 1336784338 To insert : 1336784339 To insert : 1336784340 To insert : 1336546284 To insert : 1336784342 To insert : 1336784343 To insert : 1336784344 To insert : 1336784345 To insert : 1336784346 To insert : 1336784347 To insert : 1336784348 To insert : 1336784349 To insert : 1336784350 To insert : 1336784351 To insert : 1336784352 To insert : 1336784353 To insert : 1336784354 To insert : 1336784355 To insert : 1336784356 To insert : 1336784357 To insert : 1336784358 To insert : 1336784359 To insert : 1336784360 To insert : 1336784361 To insert : 1336784362 To insert : 1336784363 To insert : 1336784364 To insert : 1336784365 To insert : 1336784366 To insert : 1336784367 To insert : 1336784368 To insert : 1336784369 To insert : 1336784370 To insert : 1336784371 To insert : 1336784372 To insert : 1336784373 To insert : 1336784374 To insert : 1336784375 To insert : 1336784376 To insert : 1336784377 To insert : 1336784378 To insert : 1336784379 To insert : 1336784381 To insert : 1336784382 To insert : 1336784383 To insert : 1336784384 To insert : 1336784385 To insert : 1336784386 To insert : 1336784387 To insert : 1336784388 To insert : 1336784390 To insert : 1336784391 To insert : 1336784392 To insert : 1336784393 To insert : 1336784394 To insert : 1336784395 To insert : 1336784396 To insert : 1336784397 To insert : 1336784398 To insert : 1336784399 To insert : 1336784400 To insert : 1336784401 To insert : 1336784402 To insert : 1336784403 To insert : 1336784404 To insert : 1336784405 To insert : 1336784406 To insert : 1336784407 To insert : 1336784408 To insert : 1336784409 To insert : 1336784410 To insert : 1336784411 To insert : 1336784412 To insert : 1336784413 To insert : 1336784414 To insert : 1336784415 To insert : 1336784416 To insert : 1336784417 To insert : 1336784418 To insert : 1336784419 To insert : 1336784420 To insert : 1336784421 To insert : 1336784422 To insert : 1336784423 To insert : 1336784424 To insert : 1336784426 To insert : 1336784427 To insert : 1336784428 To insert : 1336546367 To insert : 1336784429 To insert : 1336784430 To insert : 1336546369 To insert : 1336784431 To insert : 1336784432 To insert : 1336784433 To insert : 1336784434 To insert : 1336784435 To insert : 1336784436 To insert : 1336784437 To insert : 1336784438 To insert : 1336784439 To insert : 1336784440 To insert : 1336784441 To insert : 1336784442 To insert : 1336784443 To insert : 1336784444 To insert : 1336784445 To insert : 1336784446 To insert : 1336784447 To insert : 1336784448 To insert : 1336784449 To insert : 1336784450 To insert : 1336784452 To insert : 1336784453 To insert : 1336784454 To insert : 1336784455 To insert : 1336784456 To insert : 1336784457 To insert : 1336784458 To insert : 1336784459 To insert : 1336784460 To insert : 1336784461 To insert : 1336784462 To insert : 1336784463 To insert : 1336784464 To insert : 1336784465 To insert : 1336784466 To insert : 1336784467 To insert : 1336784469 To insert : 1336784470 To insert : 1336784471 To insert : 1336784472 To insert : 1336784473 To insert : 1336784474 To insert : 1336784475 To insert : 1336784476 To insert : 1336784477 To insert : 1336784478 To insert : 1336784479 To insert : 1336784480 To insert : 1336784481 To insert : 1336784482 To insert : 1336784483 To insert : 1336784484 To insert : 1336784485 To insert : 1336784486 To insert : 1336784487 To insert : 1336784488 To insert : 1336784489 To insert : 1336784490 To insert : 1336784491 To insert : 1336784492 To insert : 1336784493 To insert : 1336784494 To insert : 1336784495 To insert : 1336784496 To insert : 1336784497 To insert : 1336784498 To insert : 1336784499 To insert : 1336784500 To insert : 1336784501 To insert : 1336784502 To insert : 1336784503 To insert : 1336784504 To insert : 1336784505 To insert : 1336784506 To insert : 1336784507 To insert : 1336784508 To insert : 1336784509 To insert : 1336784510 To insert : 1336784511 To insert : 1336784512 To insert : 1336784514 To insert : 1336784515 To insert : 1336784516 To insert : 1336784517 To insert : 1336784518 To insert : 1336784519 To insert : 1336784520 To insert : 1336784521 To insert : 1336784522 To insert : 1336784523 To insert : 1336784524 To insert : 1336784525 To insert : 1336784526 To insert : 1336784527 To insert : 1336784528 To insert : 1336784529 To insert : 1336784530 To insert : 1336786491 To insert : 1336786492 To insert : 1336786494 To insert : 1336786495 To insert : 1336786496 To insert : 1336786497 To insert : 1336786498 To insert : 1336786499 To insert : 1336786500 To insert : 1336786501 To insert : 1336786502 To insert : 1336786503 To insert : 1336786504 To insert : 1336786505 To insert : 1336786506 To insert : 1336786507 To insert : 1336786508 To insert : 1336786509 To insert : 1336786510 To insert : 1336786511 To insert : 1336786512 To insert : 1336786513 To insert : 1336786514 To insert : 1336786516 To insert : 1336786517 To insert : 1336786518 To insert : 1336786519 To insert : 1336786520 To insert : 1336786522 To insert : 1336786523 To insert : 1336786524 To insert : 1336786525 To insert : 1336786526 To insert : 1336786527 To insert : 1336786528 To insert : 1336786529 To insert : 1336786530 To insert : 1336786531 To insert : 1336786532 To insert : 1336786533 To insert : 1336786534 To insert : 1336786535 To insert : 1336786536 To insert : 1336786537 To insert : 1336786538 To insert : 1336786539 To insert : 1336786540 To insert : 1336786541 To insert : 1336786542 To insert : 1336786543 To insert : 1336786544 To insert : 1336786545 To insert : 1336786546 To insert : 1336786547 To insert : 1336786609 To insert : 1336786610 To insert : 1336786611 To insert : 1336786612 To insert : 1336786613 To insert : 1336786614 To insert : 1336786615 To insert : 1336786616 To insert : 1336786617 To insert : 1336786618 To insert : 1336786619 To insert : 1336786620 To insert : 1336786621 To insert : 1336786622 To insert : 1336786623 To insert : 1336786624 To insert : 1336786625 To insert : 1336786626 To insert : 1336786627 To insert : 1336786628 To insert : 1336786629 To insert : 1336786630 To insert : 1336786631 To insert : 1336786632 To insert : 1336786633 To insert : 1336786634 To insert : 1336786635 To insert : 1336786636 To insert : 1336786637 To insert : 1336786638 To insert : 1336786639 To insert : 1336786640 To insert : 1336786641 To insert : 1336786642 To insert : 1336786643 To insert : 1336786644 To insert : 1336786645 To insert : 1336786646 To insert : 1336786647 To insert : 1336786648 To insert : 1336786649 To insert : 1336786650 To insert : 1336786651 To insert : 1336786652 To insert : 1336786653 To insert : 1336786654 To insert : 1336786655 To insert : 1336786656 To insert : 1336786657 To insert : 1336786658 To insert : 1336786659 To insert : 1336786660 To insert : 1336786661 To insert : 1336786662 To insert : 1336786663 To insert : 1336786664 To insert : 1336786665 To insert : 1336786666 To insert : 1336786667 To insert : 1336786668 To insert : 1336786669 To insert : 1336786670 To insert : 1336786671 To insert : 1336786672 To insert : 1336786673 To insert : 1336786674 To insert : 1336786675 To insert : 1336786676 To insert : 1336786677 To insert : 1336786678 To insert : 1336786679 To insert : 1336786680 To insert : 1336786681 To insert : 1336786682 To insert : 1336786683 To insert : 1336786684 To insert : 1336786685 To insert : 1336786686 To insert : 1336786687 To insert : 1336786688 To insert : 1336786689 To insert : 1336786690 To insert : 1336786691 To insert : 1336786692 To insert : 1336786693 To insert : 1336786694 To insert : 1336786695 To insert : 1336786696 To insert : 1336786697 To insert : 1336786698 To insert : 1336786699 To insert : 1336786700 To insert : 1336786701 To insert : 1336786702 To insert : 1336786703 To insert : 1336786704 To insert : 1336786705 To insert : 1336786706 To insert : 1336786707 To insert : 1336786708 To insert : 1336786709 To insert : 1336786710 To insert : 1336786711 To insert : 1336786712 To insert : 1336786713 To insert : 1336786714 To insert : 1336786715 To insert : 1336786716 To insert : 1336786717 To insert : 1336786718 To insert : 1336786719 To insert : 1336786720 To insert : 1336786721 To insert : 1336786722 To insert : 1336786723 To insert : 1336786724 To insert : 1336786725 To insert : 1336786726 To insert : 1336786727 To insert : 1336786728 To insert : 1336786729 To insert : 1336786730 To insert : 1336786731 To insert : 1336786732 To insert : 1336786733 To insert : 1336786734 To insert : 1336786735 To insert : 1336786736 To insert : 1336786737 To insert : 1336786738 To insert : 1336786739 To insert : 1336786740 To insert : 1336786741 To insert : 1336786742 To insert : 1336786743 To insert : 1336786744 To insert : 1336786745 To insert : 1336786746 To insert : 1336786747 To insert : 1336786748 To insert : 1336786749 To insert : 1336786750 To insert : 1336786751 To insert : 1336786752 To insert : 1336786753 To insert : 1336786754 To insert : 1336786755 To insert : 1336786756 To insert : 1336786757 To insert : 1336786758 To insert : 1336786759 To insert : 1336786760 To insert : 1336786761 To insert : 1336786762 To insert : 1336786763 To insert : 1336786765 To insert : 1336786767 To insert : 1336786769 To insert : 1336786771 To insert : 1336786773 To insert : 1336786775 To insert : 1336786777 To insert : 1336786779 To insert : 1336786781 To insert : 1336786783 To insert : 1336786785 To insert : 1336786787 To insert : 1336786789 To insert : 1336786791 To insert : 1336786793 To insert : 1336786795 To insert : 1336786797 To insert : 1336786799 To insert : 1336786801 To insert : 1336786803 To insert : 1336786804 To insert : 1336786805 To insert : 1336786806 To insert : 1336786807 To insert : 1336786808 To insert : 1336786809 To insert : 1336786810 To insert : 1336786811 To insert : 1336786812 To insert : 1336786813 To insert : 1336786814 To insert : 1336786815 To insert : 1336786816 To insert : 1336786817 To insert : 1336786819 To insert : 1336786820 To insert : 1336786821 To insert : 1336786822 To insert : 1336786823 To insert : 1336786824 To insert : 1336786825 To insert : 1336786826 To insert : 1336786827 To insert : 1336786828 To insert : 1336786829 To insert : 1336786830 To insert : 1336786831 To insert : 1336786832 To insert : 1336786833 To insert : 1336786834 To insert : 1336548957 To insert : 1336786835 To insert : 1336786836 To insert : 1336786837 To insert : 1336786838 To insert : 1336786839 To insert : 1336786840 To insert : 1336786841 To insert : 1336786842 To insert : 1336786843 To insert : 1336786844 To insert : 1336786845 To insert : 1336786846 To insert : 1336786849 To insert : 1336786853 To insert : 1336786857 To insert : 1336786861 To insert : 1336786865 To insert : 1336786868 To insert : 1336786871 To insert : 1336786872 To insert : 1336786873 To insert : 1336786874 To insert : 1336786875 To insert : 1336786876 To insert : 1336786877 To insert : 1336786878 To insert : 1336786879 To insert : 1336786880 To insert : 1336786881 To insert : 1336786882 To insert : 1336786883 To insert : 1336786884 To insert : 1336786885 To insert : 1336786886 To insert : 1336786887 To insert : 1336786888 To insert : 1336786889 To insert : 1336786890 To insert : 1336786891 To insert : 1336786892 To insert : 1336786893 To insert : 1336786894 To insert : 1336786895 To insert : 1336786896 To insert : 1336786897 To insert : 1336786898 To insert : 1336786899 To insert : 1336786900 To insert : 1336786901 To insert : 1336786902 To insert : 1336786903 To insert : 1336786904 To insert : 1336786905 To insert : 1336786906 To insert : 1336786907 To insert : 1336786909 To insert : 1336786910 To insert : 1336786911 To insert : 1336786912 To insert : 1336786913 To insert : 1336786914 To insert : 1336786916 To insert : 1336786917 To insert : 1336786918 To insert : 1336786919 To insert : 1336786920 To insert : 1336786921 To insert : 1336786922 To insert : 1336786923 To insert : 1336786924 To insert : 1336786925 To insert : 1336786926 To insert : 1336786927 To insert : 1336786928 To insert : 1336786929 To insert : 1336786930 To insert : 1336786931 To insert : 1336786932 To insert : 1336786933 To insert : 1336786934 To insert : 1336786935 To insert : 1336786936 To insert : 1336786937 To insert : 1336786938 To insert : 1336786939 To insert : 1336786940 To insert : 1336786941 To insert : 1336786942 To insert : 1336786943 To insert : 1336786944 To insert : 1336786945 To insert : 1336786946 To insert : 1336786947 To insert : 1336786948 To insert : 1336786949 To insert : 1336786950 To insert : 1336786951 To insert : 1336786952 To insert : 1336786953 To insert : 1336786954 To insert : 1336786955 To insert : 1336786956 To insert : 1336786957 To insert : 1336786958 To insert : 1336786959 To insert : 1336786960 To insert : 1336786961 To insert : 1336786962 To insert : 1336786963 To insert : 1336786964 To insert : 1336786965 To insert : 1336786966 To insert : 1336786967 To insert : 1336786968 To insert : 1336786969 To insert : 1336786970 To insert : 1336786971 To insert : 1336786972 To insert : 1336786973 To insert : 1336786974 To insert : 1336786975 To insert : 1336786976 To insert : 1336786977 To insert : 1336786978 To insert : 1336786979 To insert : 1336786980 To insert : 1336786981 To insert : 1336786982 To insert : 1336786983 To insert : 1336786984 To insert : 1336786985 To insert : 1336786986 To insert : 1336786987 To insert : 1336786988 To insert : 1336786990 To insert : 1336786991 To insert : 1336786992 To insert : 1336786993 To insert : 1336786994 To insert : 1336786995 To insert : 1336786996 To insert : 1336786997 To insert : 1336786998 To insert : 1336786999 To insert : 1336787001 To insert : 1336787002 To insert : 1336787003 To insert : 1336787004 To insert : 1336787005 To insert : 1336787006 To insert : 1336787007 To insert : 1336787008 To insert : 1336787009 To insert : 1336787010 To insert : 1336787011 To insert : 1336787012 To insert : 1336787013 To insert : 1336787014 To insert : 1336787015 To insert : 1336787016 To insert : 1336787017 To insert : 1336787018 To insert : 1336787019 To insert : 1336787020 To insert : 1336787021 To insert : 1336787022 To insert : 1336787023 To insert : 1336787024 To insert : 1336787025 To insert : 1336787026 To insert : 1336787027 To insert : 1336787028 To insert : 1336787029 To insert : 1336787030 To insert : 1336787031 To insert : 1336787032 To insert : 1336787033 To insert : 1336787034 To insert : 1336787036 To insert : 1336787037 To insert : 1336787038 To insert : 1336787039 To insert : 1336787040 To insert : 1336787041 To insert : 1336787042 To insert : 1336787043 To insert : 1336787044 To insert : 1336787045 To insert : 1336787046 To insert : 1336787047 To insert : 1336787048 To insert : 1336787049 To insert : 1336787050 To insert : 1336787051 To insert : 1336787052 To insert : 1336787053 To insert : 1336787054 To insert : 1336787055 To insert : 1336787056 To insert : 1336787057 To insert : 1336787058 To insert : 1336787059 To insert : 1336787060 To insert : 1336787061 To insert : 1336787062 To insert : 1336787063 To insert : 1336787064 To insert : 1336787065 To insert : 1336787066 To insert : 1336787067 To insert : 1336787068 To insert : 1336787069 To insert : 1336787070 To insert : 1336787071 To insert : 1336787072 To insert : 1336787171 To insert : 1336787175 To insert : 1336787178 To insert : 1336787182 To insert : 1336787347 To insert : 1336787351 To insert : 1336787355 To insert : 1336787359 To insert : 1336787362 To insert : 1336787366 To insert : 1336787370 To insert : 1336787373 To insert : 1336787377 To insert : 1336785952 To insert : 1336785953 To insert : 1336785954 To insert : 1336785955 To insert : 1336785956 To insert : 1336785957 To insert : 1336785958 To insert : 1336785959 To insert : 1336785960 To insert : 1336785961 To insert : 1336785962 To insert : 1336785963 To insert : 1336785964 To insert : 1336785965 To insert : 1336785966 To insert : 1336785967 To insert : 1336785968 To insert : 1336785969 To insert : 1336785970 To insert : 1336785971 To insert : 1336785972 To insert : 1336785973 To insert : 1336785974 To insert : 1336785975 To insert : 1336785976 To insert : 1336785977 To insert : 1336785978 To insert : 1336785979 To insert : 1336785980 To insert : 1336785981 To insert : 1336785982 To insert : 1336785983 To insert : 1336785984 To insert : 1336785985 To insert : 1336785986 To insert : 1336785987 To insert : 1336785988 To insert : 1336785989 To insert : 1336785990 To insert : 1336785991 To insert : 1336785992 To insert : 1336785993 To insert : 1336785995 To insert : 1336785996 To insert : 1336785997 To insert : 1336785998 To insert : 1336785999 To insert : 1336786000 To insert : 1336786002 To insert : 1336786003 To insert : 1336786004 To insert : 1336786005 To insert : 1336786006 To insert : 1336786007 To insert : 1336786008 To insert : 1336786009 To insert : 1336786010 To insert : 1336786011 To insert : 1336786012 To insert : 1336786014 To insert : 1336786016 To insert : 1336786017 To insert : 1336786018 To insert : 1336786019 To insert : 1336786020 To insert : 1336786021 To insert : 1336786022 To insert : 1336786023 To insert : 1336786024 To insert : 1336786025 To insert : 1336786026 To insert : 1336786027 To insert : 1336786028 To insert : 1336786029 To insert : 1336786030 To insert : 1336786031 To insert : 1336786032 To insert : 1336786034 To insert : 1336786035 To insert : 1336786036 To insert : 1336786037 To insert : 1336786038 To insert : 1336786039 To insert : 1336786040 To insert : 1336786041 To insert : 1336786042 To insert : 1336786043 To insert : 1336786044 To insert : 1336786045 To insert : 1336786046 To insert : 1336786047 To insert : 1336786048 To insert : 1336786049 To insert : 1336786050 To insert : 1336786051 To insert : 1336786052 To insert : 1336786053 To insert : 1336786054 To insert : 1336786055 To insert : 1336786056 To insert : 1336786057 To insert : 1336786058 To insert : 1336786059 To insert : 1336786060 To insert : 1336786061 To insert : 1336786062 To insert : 1336786063 To insert : 1336786064 To insert : 1336786065 To insert : 1336786066 To insert : 1336786067 To insert : 1336786068 To insert : 1336786069 To insert : 1336786070 To insert : 1336786071 To insert : 1336786072 To insert : 1336786073 To insert : 1336786074 To insert : 1336786075 To insert : 1336786076 To insert : 1336786077 To insert : 1336786078 To insert : 1336786079 To insert : 1336786080 To insert : 1336786081 To insert : 1336786082 To insert : 1336786083 To insert : 1336786084 To insert : 1336786085 To insert : 1336786086 To insert : 1336786087 To insert : 1336786088 To insert : 1336786089 To insert : 1336786090 To insert : 1336786091 To insert : 1336786093 To insert : 1336786094 To insert : 1336786095 To insert : 1336786096 To insert : 1336786097 To insert : 1336786098 To insert : 1336786099 To insert : 1336786100 To insert : 1336786101 To insert : 1336786102 To insert : 1336786103 To insert : 1336786104 To insert : 1336786105 To insert : 1336786106 To insert : 1336786107 To insert : 1336786108 To insert : 1336786110 To insert : 1336786111 To insert : 1336786112 To insert : 1336786113 To insert : 1336786114 To insert : 1336786115 To insert : 1336786116 To insert : 1336786117 To insert : 1336786118 To insert : 1336786119 To insert : 1336786120 To insert : 1336786121 To insert : 1336786122 To insert : 1336786123 To insert : 1336786124 To insert : 1336786125 To insert : 1336786126 To insert : 1336786127 To insert : 1336786129 To insert : 1336786130 To insert : 1336786131 To insert : 1336786132 To insert : 1336786133 To insert : 1336786134 To insert : 1336786135 To insert : 1336786136 To insert : 1336786137 To insert : 1336786138 To insert : 1336786139 To insert : 1336786140 To insert : 1336786141 To insert : 1336786142 To insert : 1336786143 To insert : 1336786144 To insert : 1336786145 To insert : 1336786146 To insert : 1336786147 To insert : 1336786148 To insert : 1336786149 To insert : 1336786150 To insert : 1336786151 To insert : 1336786152 To insert : 1336786153 To insert : 1336786154 To insert : 1336786155 To insert : 1336786156 To insert : 1336786157 To insert : 1336786158 To insert : 1336786159 To insert : 1336786160 To insert : 1336786161 To insert : 1336786162 To insert : 1336786163 To insert : 1336786164 To insert : 1336786165 To insert : 1336786166 To insert : 1336786167 To insert : 1336786168 To insert : 1336786169 To insert : 1336786170 To insert : 1336786171 To insert : 1336786172 To insert : 1336786173 To insert : 1336786174 To insert : 1336786175 To insert : 1336786176 To insert : 1336786177 To insert : 1336786178 To insert : 1336786179 To insert : 1336786180 To insert : 1336786181 To insert : 1336786182 To insert : 1336786183 To insert : 1336786184 To insert : 1336786185 To insert : 1336786186 To insert : 1336786188 To insert : 1336786189 To insert : 1336786190 To insert : 1336786191 To insert : 1336786192 To insert : 1336786193 To insert : 1336786194 To insert : 1336786195 To insert : 1336786196 To insert : 1336786200 To insert : 1336786201 To insert : 1336786202 To insert : 1336786203 To insert : 1336786204 To insert : 1336786205 To insert : 1336786237 To insert : 1336786238 To insert : 1336548220 To insert : 1336786239 To insert : 1336786240 To insert : 1336785569 To insert : 1336785570 To insert : 1336785571 To insert : 1336785572 To insert : 1336785573 To insert : 1336785574 To insert : 1336785575 To insert : 1336785576 To insert : 1336785577 To insert : 1336785578 To insert : 1336785579 To insert : 1336785580 To insert : 1336785581 To insert : 1336785582 To insert : 1336785583 To insert : 1336785584 To insert : 1336785585 To insert : 1336785586 To insert : 1336785587 To insert : 1336785588 To insert : 1336785589 To insert : 1336785590 To insert : 1336785591 To insert : 1336785592 To insert : 1336785593 To insert : 1336785594 To insert : 1336785595 To insert : 1336785596 To insert : 1336785597 To insert : 1336785598 To insert : 1336785599 To insert : 1336785600 To insert : 1336785601 To insert : 1336785602 To insert : 1336785603 To insert : 1336785604 To insert : 1336785605 To insert : 1336785606 To insert : 1336785607 To insert : 1336785608 To insert : 1336785609 To insert : 1336785610 To insert : 1336785611 To insert : 1336785612 To insert : 1336785613 To insert : 1336785614 To insert : 1336785615 To insert : 1336785616 To insert : 1336785617 To insert : 1336785618 To insert : 1336785619 To insert : 1336785620 To insert : 1336785621 To insert : 1336785622 To insert : 1336785623 To insert : 1336785624 To insert : 1336785625 To insert : 1336785626 To insert : 1336785627 To insert : 1336785628 To insert : 1336785629 To insert : 1336785630 To insert : 1336785631 To insert : 1336785632 To insert : 1336785633 To insert : 1336785634 To insert : 1336785635 To insert : 1336785636 To insert : 1336785637 To insert : 1336785638 To insert : 1336785639 To insert : 1336785640 To insert : 1336785641 To insert : 1336785642 To insert : 1336785643 To insert : 1336785644 To insert : 1336785645 To insert : 1336785646 To insert : 1336785647 To insert : 1336785648 To insert : 1336785649 To insert : 1336785650 To insert : 1336785651 To insert : 1336785652 To insert : 1336785653 To insert : 1336785654 To insert : 1336785656 To insert : 1336785657 To insert : 1336785658 To insert : 1336785659 To insert : 1336785660 To insert : 1336785661 To insert : 1336785662 To insert : 1336785663 To insert : 1336785664 To insert : 1336785665 To insert : 1336785666 To insert : 1336785670 To insert : 1336785674 To insert : 1336785675 To insert : 1336785676 To insert : 1336785677 To insert : 1336785678 To insert : 1336785679 To insert : 1336785680 To insert : 1336785681 To insert : 1336785682 To insert : 1336785683 To insert : 1336785684 To insert : 1336785685 To insert : 1336785686 To insert : 1336785687 To insert : 1336785688 To insert : 1336785689 To insert : 1336785690 To insert : 1336785691 To insert : 1336785692 To insert : 1336785693 To insert : 1336785694 To insert : 1336785695 To insert : 1336785696 To insert : 1336785697 To insert : 1336785698 To insert : 1336785699 To insert : 1336785700 To insert : 1336785701 To insert : 1336785828 To insert : 1336547720 To insert : 1336785829 To insert : 1336785830 To insert : 1336785831 To insert : 1336785832 To insert : 1336785833 To insert : 1336785834 To insert : 1336785835 To insert : 1336785836 To insert : 1336785837 To insert : 1336785838 To insert : 1336785839 To insert : 1336785840 To insert : 1336785841 To insert : 1336785842 To insert : 1336785843 To insert : 1336785844 To insert : 1336785845 To insert : 1336785846 To insert : 1336785847 To insert : 1336785848 To insert : 1336785849 To insert : 1336785850 To insert : 1336785851 To insert : 1336785852 To insert : 1336785853 To insert : 1336785854 To insert : 1336785855 To insert : 1336785856 To insert : 1336785858 To insert : 1336785860 To insert : 1336785861 To insert : 1336785862 To insert : 1336785863 To insert : 1336785864 To insert : 1336785865 To insert : 1336785866 To insert : 1336785867 To insert : 1336785868 To insert : 1336785869 To insert : 1336785870 To insert : 1336785871 To insert : 1336785872 To insert : 1336785873 To insert : 1336785874 To insert : 1336785875 To insert : 1336785876 To insert : 1336785877 To insert : 1336785878 To insert : 1336785879 To insert : 1336785880 To insert : 1336785881 To insert : 1336785882 To insert : 1336785883 To insert : 1336785884 To insert : 1336785885 To insert : 1336547770 To insert : 1336785886 To insert : 1336785888 To insert : 1336785889 To insert : 1336785890 To insert : 1336785891 To insert : 1336785892 To insert : 1336785893 To insert : 1336785894 To insert : 1336785895 To insert : 1336785896 To insert : 1336785897 To insert : 1336785898 To insert : 1336785899 To insert : 1336785901 To insert : 1336785902 To insert : 1336785903 To insert : 1336785904 To insert : 1336785905 To insert : 1336785906 To insert : 1336785907 To insert : 1336785908 To insert : 1336785909 To insert : 1336785910 To insert : 1336785911 To insert : 1336785912 To insert : 1336785913 To insert : 1336785914 To insert : 1336785915 To insert : 1336785916 To insert : 1336785917 To insert : 1336785918 To insert : 1336785919 To insert : 1336785920 To insert : 1336785921 To insert : 1336785922 To insert : 1336785923 To insert : 1336785924 To insert : 1336785925 To insert : 1336785926 To insert : 1336785927 To insert : 1336785928 To insert : 1336785929 To insert : 1336785930 To insert : 1336785931 To insert : 1336785932 To insert : 1336785933 To insert : 1336785934 To insert : 1336785935 To insert : 1336785936 To insert : 1336785937 To insert : 1336785938 To insert : 1336785939 To insert : 1336785940 To insert : 1336785941 To insert : 1336785942 To insert : 1336785943 To insert : 1336785945 To insert : 1336785946 To insert : 1336785947 To insert : 1336785948 To insert : 1336785949 To insert : 1336785950 To insert : 1336785951 To insert : 1336784784 To insert : 1336784785 To insert : 1336784786 To insert : 1336784787 To insert : 1336784788 To insert : 1336784789 To insert : 1336784790 To insert : 1336784791 To insert : 1336784792 To insert : 1336784793 To insert : 1336784794 To insert : 1336784795 To insert : 1336784796 To insert : 1336784797 To insert : 1336784799 To insert : 1336784800 To insert : 1336784801 To insert : 1336784802 To insert : 1336784803 To insert : 1336784804 To insert : 1336784805 To insert : 1336784806 To insert : 1336784807 To insert : 1336784808 To insert : 1336784809 To insert : 1336784810 To insert : 1336784811 To insert : 1336784812 To insert : 1336784813 To insert : 1336784814 To insert : 1336784815 To insert : 1336784816 To insert : 1336784817 To insert : 1336784818 To insert : 1336784819 To insert : 1336784820 To insert : 1336784821 To insert : 1336784822 To insert : 1336784823 To insert : 1336784824 To insert : 1336784825 To insert : 1336784826 To insert : 1336784827 To insert : 1336784828 To insert : 1336784829 To insert : 1336546740 To insert : 1336784830 To insert : 1336784831 To insert : 1336784832 To insert : 1336784833 To insert : 1336784834 To insert : 1336784835 To insert : 1336784836 To insert : 1336784837 To insert : 1336784838 To insert : 1336784839 To insert : 1336784840 To insert : 1336784841 To insert : 1336784842 To insert : 1336784843 To insert : 1336784844 To insert : 1336784845 To insert : 1336784846 To insert : 1336784847 To insert : 1336784849 To insert : 1336784850 To insert : 1336784851 To insert : 1336784852 To insert : 1336784853 To insert : 1336784854 To insert : 1336784855 To insert : 1336784856 To insert : 1336784858 To insert : 1336784859 To insert : 1336784860 To insert : 1336784861 To insert : 1336784862 To insert : 1336784863 To insert : 1336784864 To insert : 1336784865 To insert : 1336784866 To insert : 1336784867 To insert : 1336784868 To insert : 1336784869 To insert : 1336784870 To insert : 1336784871 To insert : 1336784872 To insert : 1336784873 To insert : 1336784874 To insert : 1336784875 To insert : 1336784876 To insert : 1336784878 To insert : 1336784879 To insert : 1336784880 To insert : 1336784881 To insert : 1336784882 To insert : 1336784884 To insert : 1336784885 To insert : 1336784886 To insert : 1336784887 To insert : 1336784888 To insert : 1336784889 To insert : 1336784890 To insert : 1336784891 To insert : 1336784892 To insert : 1336784893 To insert : 1336546800 To insert : 1336784894 To insert : 1336546802 To insert : 1336784895 To insert : 1336784897 To insert : 1336784898 To insert : 1336784899 To insert : 1336784900 To insert : 1336784901 To insert : 1336784902 To insert : 1336784903 To insert : 1336784904 To insert : 1336784905 To insert : 1336784906 To insert : 1336784907 To insert : 1336784908 To insert : 1336784909 To insert : 1336784910 To insert : 1336784911 To insert : 1336784912 To insert : 1336784913 To insert : 1336784914 To insert : 1336784915 To insert : 1336784916 To insert : 1336784917 To insert : 1336784918 To insert : 1336784919 To insert : 1336784921 To insert : 1336784922 To insert : 1336784923 To insert : 1336784924 To insert : 1336784925 To insert : 1336784926 To insert : 1336784927 To insert : 1336784928 To insert : 1336784929 To insert : 1336784930 To insert : 1336784931 To insert : 1336784932 To insert : 1336784933 To insert : 1336784934 To insert : 1336784935 To insert : 1336784936 To insert : 1336784937 To insert : 1336784938 To insert : 1336784939 To insert : 1336784940 To insert : 1336784941 To insert : 1336784942 To insert : 1336784943 To insert : 1336784944 To insert : 1336784946 To insert : 1336784950 To insert : 1336784952 To insert : 1336784953 To insert : 1336784954 To insert : 1336784955 To insert : 1336784956 To insert : 1336784957 To insert : 1336784958 To insert : 1336784959 To insert : 1336784960 To insert : 1336784961 To insert : 1336784962 To insert : 1336784963 To insert : 1336784964 To insert : 1336784965 To insert : 1336546866 To insert : 1336784966 To insert : 1336784967 To insert : 1336784968 To insert : 1336546870 To insert : 1336784969 To insert : 1336784971 To insert : 1336784972 To insert : 1336784973 To insert : 1336784974 To insert : 1336784975 To insert : 1336784976 To insert : 1336784977 To insert : 1336784978 To insert : 1336784979 To insert : 1336784980 To insert : 1336784981 To insert : 1336784983 To insert : 1336784985 To insert : 1336784986 To insert : 1336784988 To insert : 1336784989 To insert : 1336784990 To insert : 1336784991 To insert : 1336784992 To insert : 1336784993 To insert : 1336784994 To insert : 1336784995 To insert : 1336784996 To insert : 1336784997 To insert : 1336784998 To insert : 1336784999 To insert : 1336785000 To insert : 1336785001 To insert : 1336785003 To insert : 1336785004 To insert : 1336785005 To insert : 1336785006 To insert : 1336785007 To insert : 1336785008 To insert : 1336785009 To insert : 1336785010 To insert : 1336785011 To insert : 1336785012 To insert : 1336785013 To insert : 1336785014 To insert : 1336785015 To insert : 1336785016 To insert : 1336785017 To insert : 1336785018 To insert : 1336785019 To insert : 1336785020 To insert : 1336785021 To insert : 1336785022 To insert : 1336785023 To insert : 1336785024 To insert : 1336785025 To insert : 1336785026 To insert : 1336785027 To insert : 1336785028 To insert : 1336785029 To insert : 1336785030 To insert : 1336785031 To insert : 1336785032 To insert : 1336785033 To insert : 1336785034 To insert : 1336546928 To insert : 1336785035 To insert : 1336785036 To insert : 1336785037 To insert : 1336785038 To insert : 1336785039 To insert : 1336546933 To insert : 1336785040 To insert : 1336785308 To insert : 1336785309 To insert : 1336785310 To insert : 1336785311 To insert : 1336785312 To insert : 1336785313 To insert : 1336785314 To insert : 1336785315 To insert : 1336785316 To insert : 1336785317 To insert : 1336785318 To insert : 1336785319 To insert : 1336785320 To insert : 1336785321 To insert : 1336785322 To insert : 1336785323 To insert : 1336785324 To insert : 1336785325 To insert : 1336785326 To insert : 1336785327 To insert : 1336785328 To insert : 1336785329 To insert : 1336785330 To insert : 1336785331 To insert : 1336785332 To insert : 1336785333 To insert : 1336785334 To insert : 1336785335 To insert : 1336785336 To insert : 1336785337 To insert : 1336785338 To insert : 1336785339 To insert : 1336785340 To insert : 1336785341 To insert : 1336785342 To insert : 1336785343 To insert : 1336785344 To insert : 1336785345 To insert : 1336785346 To insert : 1336785347 To insert : 1336785348 To insert : 1336785349 To insert : 1336785350 To insert : 1336785351 To insert : 1336785352 To insert : 1336785353 To insert : 1336785354 To insert : 1336785355 To insert : 1336785356 To insert : 1336785357 To insert : 1336785358 To insert : 1336785359 To insert : 1336785360 To insert : 1336785361 To insert : 1336785362 To insert : 1336785365 To insert : 1336785366 To insert : 1336785367 To insert : 1336785368 To insert : 1336785369 To insert : 1336785370 To insert : 1336785371 To insert : 1336785372 To insert : 1336785373 To insert : 1336785374 To insert : 1336785375 To insert : 1336785376 To insert : 1336785377 To insert : 1336785378 To insert : 1336785379 To insert : 1336785380 To insert : 1336785381 To insert : 1336785382 To insert : 1336785383 To insert : 1336785384 To insert : 1336785386 To insert : 1336785387 To insert : 1336785388 To insert : 1336785389 To insert : 1336785390 To insert : 1336785391 To insert : 1336785392 To insert : 1336785393 To insert : 1336785394 To insert : 1336785395 To insert : 1336785396 To insert : 1336785397 To insert : 1336785398 To insert : 1336785399 To insert : 1336785400 To insert : 1336785401 To insert : 1336785402 To insert : 1336785403 To insert : 1336785404 To insert : 1336785405 To insert : 1336785406 To insert : 1336785407 To insert : 1336785408 To insert : 1336785409 To insert : 1336785410 To insert : 1336785411 To insert : 1336785412 To insert : 1336785413 To insert : 1336785414 To insert : 1336785415 To insert : 1336785416 To insert : 1336785417 To insert : 1336785418 To insert : 1336785419 To insert : 1336785420 To insert : 1336785421 To insert : 1336785422 To insert : 1336785423 To insert : 1336785424 To insert : 1336785425 To insert : 1336785426 To insert : 1336785427 To insert : 1336785428 To insert : 1336785429 To insert : 1336785430 To insert : 1336785431 To insert : 1336785432 To insert : 1336785433 To insert : 1336785434 To insert : 1336785435 To insert : 1336785436 To insert : 1336785437 To insert : 1336785438 To insert : 1336785439 To insert : 1336785440 To insert : 1336785441 To insert : 1336785442 To insert : 1336785443 To insert : 1336785444 To insert : 1336785445 To insert : 1336785446 To insert : 1336785447 To insert : 1336785448 To insert : 1336785449 To insert : 1336785450 To insert : 1336785451 To insert : 1336785452 To insert : 1336785453 To insert : 1336785454 To insert : 1336785455 To insert : 1336785456 To insert : 1336785457 To insert : 1336785458 To insert : 1336785459 To insert : 1336785460 To insert : 1336785461 To insert : 1336785462 To insert : 1336785463 To insert : 1336785464 To insert : 1336785465 To insert : 1336785466 To insert : 1336785467 To insert : 1336785468 To insert : 1336785469 To insert : 1336785470 To insert : 1336785471 To insert : 1336785472 To insert : 1336785473 To insert : 1336785474 To insert : 1336785475 To insert : 1336785476 To insert : 1336785477 To insert : 1336785478 To insert : 1336785479 To insert : 1336785480 To insert : 1336785481 To insert : 1336785482 To insert : 1336785483 To insert : 1336785484 To insert : 1336785485 To insert : 1336785486 To insert : 1336785487 To insert : 1336785488 To insert : 1336785489 To insert : 1336785491 To insert : 1336785493 To insert : 1336785495 To insert : 1336785497 To insert : 1336785499 To insert : 1336785501 To insert : 1336785503 To insert : 1336785505 To insert : 1336785507 To insert : 1336785509 To insert : 1336785511 To insert : 1336785512 To insert : 1336785513 To insert : 1336785514 To insert : 1336785515 To insert : 1336785516 To insert : 1336785517 To insert : 1336785518 To insert : 1336785519 To insert : 1336785520 To insert : 1336785521 To insert : 1336785522 To insert : 1336785523 To insert : 1336785524 To insert : 1336785525 To insert : 1336785526 To insert : 1336785527 To insert : 1336785528 To insert : 1336785529 To insert : 1336785530 To insert : 1336785531 To insert : 1336785532 To insert : 1336785533 To insert : 1336785534 To insert : 1336785535 To insert : 1336785536 To insert : 1336785537 To insert : 1336785538 To insert : 1336785539 To insert : 1336785540 To insert : 1336785541 To insert : 1336785542 To insert : 1336785543 To insert : 1336785544 To insert : 1336785545 To insert : 1336785546 To insert : 1336785547 To insert : 1336785548 To insert : 1336785549 To insert : 1336785550 To insert : 1336785551 To insert : 1336785552 To insert : 1336785553 To insert : 1336785554 To insert : 1336785555 To insert : 1336785556 To insert : 1336785557 To insert : 1336785558 To insert : 1336785559 To insert : 1336785560 To insert : 1336785561 To insert : 1336785562 To insert : 1336785563 To insert : 1336785564 To insert : 1336785565 To insert : 1336785566 To insert : 1336785567 To insert : 1336785568 To insert : 1336786241 To insert : 1336786242 To insert : 1336786243 To insert : 1336786244 To insert : 1336786245 To insert : 1336786246 To insert : 1336786247 To insert : 1336548229 To insert : 1336786248 To insert : 1336786249 To insert : 1336786250 To insert : 1336786251 To insert : 1336786252 To insert : 1336786253 To insert : 1336786254 To insert : 1336786255 To insert : 1336786256 To insert : 1336786257 To insert : 1336786258 To insert : 1336786259 To insert : 1336548241 To insert : 1336786260 To insert : 1336786261 To insert : 1336786262 To insert : 1336786263 To insert : 1336786264 To insert : 1336786265 To insert : 1336786266 To insert : 1336786267 To insert : 1336786268 To insert : 1336786269 To insert : 1336786270 To insert : 1336786271 To insert : 1336786272 To insert : 1336548253 To insert : 1336786273 To insert : 1336786274 To insert : 1336786275 To insert : 1336786276 To insert : 1336786277 To insert : 1336786278 To insert : 1336786279 To insert : 1336786280 To insert : 1336786281 To insert : 1336786282 To insert : 1336786283 To insert : 1336548265 To insert : 1336786284 To insert : 1336786285 To insert : 1336786286 To insert : 1336786287 To insert : 1336786288 To insert : 1336786289 To insert : 1336786290 To insert : 1336786291 To insert : 1336786292 To insert : 1336786293 To insert : 1336786294 To insert : 1336786295 To insert : 1336786296 To insert : 1336786297 To insert : 1336786298 To insert : 1336786299 To insert : 1336786300 To insert : 1336548282 To insert : 1336786301 To insert : 1336786302 To insert : 1336786303 To insert : 1336786304 To insert : 1336548287 To insert : 1336786305 To insert : 1336786307 To insert : 1336786309 To insert : 1336786310 To insert : 1336786311 To insert : 1336786313 To insert : 1336786314 To insert : 1336786315 To insert : 1336786316 To insert : 1336786317 To insert : 1336786318 To insert : 1336786319 To insert : 1336786320 To insert : 1336786321 To insert : 1336786322 To insert : 1336786323 To insert : 1336786324 To insert : 1336786325 To insert : 1336786326 To insert : 1336786327 To insert : 1336786328 To insert : 1336786329 To insert : 1336786330 To insert : 1336786332 To insert : 1336786334 To insert : 1336786335 To insert : 1336786336 To insert : 1336786337 To insert : 1336786338 To insert : 1336786339 To insert : 1336786340 To insert : 1336786341 To insert : 1336786342 To insert : 1336786343 To insert : 1336786344 To insert : 1336786345 To insert : 1336786346 To insert : 1336786347 To insert : 1336786348 To insert : 1336786349 To insert : 1336786350 To insert : 1336786351 To insert : 1336786352 To insert : 1336786353 To insert : 1336786354 To insert : 1336786355 To insert : 1336786356 To insert : 1336786357 To insert : 1336786358 To insert : 1336786359 To insert : 1336786360 To insert : 1336786361 To insert : 1336786362 To insert : 1336786363 To insert : 1336786364 To insert : 1336786365 To insert : 1336786366 To insert : 1336786367 To insert : 1336786368 To insert : 1336786369 To insert : 1336786370 To insert : 1336786371 To insert : 1336786372 To insert : 1336786373 To insert : 1336786374 To insert : 1336786375 To insert : 1336786376 To insert : 1336786377 To insert : 1336786378 To insert : 1336786379 To insert : 1336786380 To insert : 1336786381 To insert : 1336786382 To insert : 1336548438 To insert : 1336786383 To insert : 1336786384 To insert : 1336786385 To insert : 1336786386 To insert : 1336786387 To insert : 1336786388 To insert : 1336786389 To insert : 1336786390 To insert : 1336786391 To insert : 1336786392 To insert : 1336786393 To insert : 1336786394 To insert : 1336786395 To insert : 1336786396 To insert : 1336786397 To insert : 1336786398 To insert : 1336786399 To insert : 1336786400 To insert : 1336786401 To insert : 1336786403 To insert : 1336786404 To insert : 1336786405 To insert : 1336786406 To insert : 1336786407 To insert : 1336786408 To insert : 1336786409 To insert : 1336786410 To insert : 1336786411 To insert : 1336786412 To insert : 1336786413 To insert : 1336786414 To insert : 1336786415 To insert : 1336786416 To insert : 1336786417 To insert : 1336786418 To insert : 1336786420 To insert : 1336786421 To insert : 1336786422 To insert : 1336786423 To insert : 1336786424 To insert : 1336786426 To insert : 1336786429 To insert : 1336786430 To insert : 1336786431 To insert : 1336786432 To insert : 1336786433 To insert : 1336786434 To insert : 1336786435 To insert : 1336786436 To insert : 1336786437 To insert : 1336786438 To insert : 1336786439 To insert : 1336786440 To insert : 1336786441 To insert : 1336786442 To insert : 1336786443 To insert : 1336786444 To insert : 1336786445 To insert : 1336786446 To insert : 1336786447 To insert : 1336786448 To insert : 1336786449 To insert : 1336786450 To insert : 1336786451 To insert : 1336786452 To insert : 1336786453 To insert : 1336786454 To insert : 1336786455 To insert : 1336786456 To insert : 1336786457 To insert : 1336786458 To insert : 1336786459 To insert : 1336786460 To insert : 1336786461 To insert : 1336786462 To insert : 1336786463 To insert : 1336786464 To insert : 1336786465 To insert : 1336786466 To insert : 1336786467 To insert : 1336786468 To insert : 1336786469 To insert : 1336786470 To insert : 1336786471 To insert : 1336786472 To insert : 1336786473 To insert : 1336786474 To insert : 1336786475 To insert : 1336786476 To insert : 1336786477 To insert : 1336786478 To insert : 1336786479 To insert : 1336786480 To insert : 1336786481 To insert : 1336786482 To insert : 1336786483 To insert : 1336786484 To insert : 1336786485 To insert : 1336786486 To insert : 1336786487 To insert : 1336786488 To insert : 1336786489 To insert : 1336786490 To insert : 1336785041 To insert : 1336785042 To insert : 1336785043 To insert : 1336546938 To insert : 1336785044 To insert : 1336785045 To insert : 1336785046 To insert : 1336785047 To insert : 1336785048 To insert : 1336785049 To insert : 1336785050 To insert : 1336785051 To insert : 1336785052 To insert : 1336785053 To insert : 1336785054 To insert : 1336785055 To insert : 1336785056 To insert : 1336785057 To insert : 1336785058 To insert : 1336785059 To insert : 1336785060 To insert : 1336785061 To insert : 1336785062 To insert : 1336785063 To insert : 1336785064 To insert : 1336785065 To insert : 1336785066 To insert : 1336785067 To insert : 1336785068 To insert : 1336785069 To insert : 1336785070 To insert : 1336785071 To insert : 1336785072 To insert : 1336785073 To insert : 1336785074 To insert : 1336785075 To insert : 1336785076 To insert : 1336785077 To insert : 1336785078 To insert : 1336785079 To insert : 1336785080 To insert : 1336785082 To insert : 1336785083 To insert : 1336785084 To insert : 1336785085 To insert : 1336785086 To insert : 1336785088 To insert : 1336785089 To insert : 1336785090 To insert : 1336785091 To insert : 1336785092 To insert : 1336785093 To insert : 1336785094 To insert : 1336785095 To insert : 1336785096 To insert : 1336785098 To insert : 1336785101 To insert : 1336785102 To insert : 1336785104 To insert : 1336785105 To insert : 1336785106 To insert : 1336785107 To insert : 1336785108 To insert : 1336785109 To insert : 1336785110 To insert : 1336785111 To insert : 1336785112 To insert : 1336785113 To insert : 1336785114 To insert : 1336785115 To insert : 1336785116 To insert : 1336785117 To insert : 1336785118 To insert : 1336785119 To insert : 1336785120 To insert : 1336785121 To insert : 1336785122 To insert : 1336785123 To insert : 1336785124 To insert : 1336785125 To insert : 1336785126 To insert : 1336785127 To insert : 1336785128 To insert : 1336785129 To insert : 1336785130 To insert : 1336785131 To insert : 1336785132 To insert : 1336785133 To insert : 1336785134 To insert : 1336785135 To insert : 1336785136 To insert : 1336785137 To insert : 1336785138 To insert : 1336785139 To insert : 1336785140 To insert : 1336785141 To insert : 1336785142 To insert : 1336785143 To insert : 1336785144 To insert : 1336785145 To insert : 1336785146 To insert : 1336785147 To insert : 1336785148 To insert : 1336785149 To insert : 1336785150 To insert : 1336785151 To insert : 1336785152 To insert : 1336785153 To insert : 1336785154 To insert : 1336785155 To insert : 1336785159 To insert : 1336785163 To insert : 1336785165 To insert : 1336785166 To insert : 1336785167 To insert : 1336785168 To insert : 1336785170 To insert : 1336785171 To insert : 1336785172 To insert : 1336785173 To insert : 1336785174 To insert : 1336785175 To insert : 1336785176 To insert : 1336785177 To insert : 1336785178 To insert : 1336785179 To insert : 1336785180 To insert : 1336785181 To insert : 1336785182 To insert : 1336785183 To insert : 1336785184 To insert : 1336785185 To insert : 1336785186 To insert : 1336785187 To insert : 1336785188 To insert : 1336785190 To insert : 1336785191 To insert : 1336785192 To insert : 1336785194 To insert : 1336785195 To insert : 1336785196 To insert : 1336785197 To insert : 1336785198 To insert : 1336785199 To insert : 1336785201 To insert : 1336785202 To insert : 1336785204 To insert : 1336785205 To insert : 1336785206 To insert : 1336785207 To insert : 1336785208 To insert : 1336785209 To insert : 1336785210 To insert : 1336785211 To insert : 1336785212 To insert : 1336785213 To insert : 1336785214 To insert : 1336785215 To insert : 1336785216 To insert : 1336785217 To insert : 1336785218 To insert : 1336785219 To insert : 1336785220 To insert : 1336785221 To insert : 1336785222 To insert : 1336785223 To insert : 1336785224 To insert : 1336785225 To insert : 1336785226 To insert : 1336785227 To insert : 1336785228 To insert : 1336785229 To insert : 1336785230 To insert : 1336785231 To insert : 1336785232 To insert : 1336785233 To insert : 1336785234 To insert : 1336785235 To insert : 1336785236 To insert : 1336785237 To insert : 1336785238 To insert : 1336785239 To insert : 1336785240 To insert : 1336785241 To insert : 1336785242 To insert : 1336785243 To insert : 1336785244 To insert : 1336785245 To insert : 1336785246 To insert : 1336785247 To insert : 1336785248 To insert : 1336785249 To insert : 1336785250 To insert : 1336785251 To insert : 1336785252 To insert : 1336785253 To insert : 1336785254 To insert : 1336785255 To insert : 1336785256 To insert : 1336785257 To insert : 1336785259 To insert : 1336785260 To insert : 1336785261 To insert : 1336785262 To insert : 1336785263 To insert : 1336785264 To insert : 1336785265 To insert : 1336785266 To insert : 1336785267 To insert : 1336785268 To insert : 1336785269 To insert : 1336785270 To insert : 1336785271 To insert : 1336785272 To insert : 1336785273 To insert : 1336785275 To insert : 1336785276 To insert : 1336785277 To insert : 1336785278 To insert : 1336785279 To insert : 1336785280 To insert : 1336785281 To insert : 1336785282 To insert : 1336785283 To insert : 1336785284 To insert : 1336785285 To insert : 1336785286 To insert : 1336785287 To insert : 1336785288 To insert : 1336785289 To insert : 1336785290 To insert : 1336785291 To insert : 1336785292 To insert : 1336785293 To insert : 1336785294 To insert : 1336785296 To insert : 1336785297 To insert : 1336785298 To insert : 1336785299 To insert : 1336785300 To insert : 1336785301 To insert : 1336785302 To insert : 1336785303 To insert : 1336785304 To insert : 1336785305 To insert : 1336785306 To insert : 1336785307 time to insert the descriptors : 445.06973910331726 Inside saveOutput : final : False verbose : 0 time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3824 time used for this insertion : 0.2582864761352539 save missing photos in datou_result : time spend for datou_step_exec : 485.71094131469727 time spend to save output : 0.37978553771972656 total time spend for step 6 : 486.090726852417 step7:ventilate_hashtags_in_portfolio Tue Feb 11 11:14:39 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 20068969 get user id for portfolio 20068969 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20068969 AND mptpi.`type`=4230 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_fonce','Carton','refus','flou','mal_croppe','carton','autre','PET_clair','Tetrapak','Film_plastique','papier','pehd','environnement','pet_clair','PEHD','Papier','PET_fonce','metal')) AND mptpi.`min_score`=0.6 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20068969 AND mptpi.`type`=4231 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_fonce','Carton','refus','flou','mal_croppe','carton','autre','PET_clair','Tetrapak','Film_plastique','papier','pehd','environnement','pet_clair','PEHD','Papier','PET_fonce','metal')) AND mptpi.`min_score`=0.6 To do lien utilise dans velours : https://www.fotonower.com/velours/20160427,20160434,20160429,20160441,20160431,20160432,20160433,20160435,20160437,20160438,20160439,20160442,20160443?tags=pet_clair,papier,mal_croppe,pet_fonce,metal,autre,refus,tetrapak,carton,flou,film_plastique,pehd,environnement&datou_id_consolidate=4235&port_consolidate=20068969 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1332934252, 1332934245, 1332934219, 1332934207, 1332934203, 1332934198, 1332934177, 1332934128, 1332934123, 1332934118, 1332934112, 1332934055, 1332934051, 1332933775, 1332933772, 1332933762, 1332933747, 1332933425, 1332933124, 1332933033, 1332933026] Looping around the photos to save general results len do output : 1 /20068969. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934252', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934245', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934219', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934207', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934203', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934198', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934177', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934128', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934123', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934118', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934112', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934055', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934051', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933775', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933772', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933762', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933747', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933425', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933124', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933033', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933026', None, None, None, None, None, '2529344') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 22 time used for this insertion : 0.014623641967773438 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.4702303409576416 time spend to save output : 0.014994382858276367 total time spend for step 7 : 2.485224723815918 step8:final Tue Feb 11 11:14:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1332934252: ('0.2396665527597547',), 1332934245: ('0.2396665527597547',), 1332934219: ('0.2396665527597547',), 1332934207: ('0.2396665527597547',), 1332934203: ('0.2396665527597547',), 1332934198: ('0.2396665527597547',), 1332934177: ('0.2396665527597547',), 1332934128: ('0.2396665527597547',), 1332934123: ('0.2396665527597547',), 1332934118: ('0.2396665527597547',), 1332934112: ('0.2396665527597547',), 1332934055: ('0.2396665527597547',), 1332934051: ('0.2396665527597547',), 1332933775: ('0.2396665527597547',), 1332933772: ('0.2396665527597547',), 1332933762: ('0.2396665527597547',), 1332933747: ('0.2396665527597547',), 1332933425: ('0.2396665527597547',), 1332933124: ('0.2396665527597547',), 1332933033: ('0.2396665527597547',), 1332933026: ('0.2396665527597547',)} new output for save of step final : {1332934252: ('0.2396665527597547',), 1332934245: ('0.2396665527597547',), 1332934219: ('0.2396665527597547',), 1332934207: ('0.2396665527597547',), 1332934203: ('0.2396665527597547',), 1332934198: ('0.2396665527597547',), 1332934177: ('0.2396665527597547',), 1332934128: ('0.2396665527597547',), 1332934123: ('0.2396665527597547',), 1332934118: ('0.2396665527597547',), 1332934112: ('0.2396665527597547',), 1332934055: ('0.2396665527597547',), 1332934051: ('0.2396665527597547',), 1332933775: ('0.2396665527597547',), 1332933772: ('0.2396665527597547',), 1332933762: ('0.2396665527597547',), 1332933747: ('0.2396665527597547',), 1332933425: ('0.2396665527597547',), 1332933124: ('0.2396665527597547',), 1332933033: ('0.2396665527597547',), 1332933026: ('0.2396665527597547',)} [1332934252, 1332934245, 1332934219, 1332934207, 1332934203, 1332934198, 1332934177, 1332934128, 1332934123, 1332934118, 1332934112, 1332934055, 1332934051, 1332933775, 1332933772, 1332933762, 1332933747, 1332933425, 1332933124, 1332933033, 1332933026] Looping around the photos to save general results len do output : 21 /1332934252.Didn't retrieve data . /1332934245.Didn't retrieve data . /1332934219.Didn't retrieve data . /1332934207.Didn't retrieve data . /1332934203.Didn't retrieve data . /1332934198.Didn't retrieve data . /1332934177.Didn't retrieve data . /1332934128.Didn't retrieve data . /1332934123.Didn't retrieve data . /1332934118.Didn't retrieve data . /1332934112.Didn't retrieve data . /1332934055.Didn't retrieve data . /1332934051.Didn't retrieve data . /1332933775.Didn't retrieve data . /1332933772.Didn't retrieve data . /1332933762.Didn't retrieve data . /1332933747.Didn't retrieve data . /1332933425.Didn't retrieve data . /1332933124.Didn't retrieve data . /1332933033.Didn't retrieve data . /1332933026.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934252', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934245', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934219', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934207', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934203', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934198', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934177', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934128', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934123', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934118', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934112', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934055', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934051', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933775', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933772', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933762', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933747', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933425', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933124', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933033', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933026', None, None, None, None, None, '2529344') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 63 time used for this insertion : 0.015184879302978516 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.2018725872039795 time spend to save output : 0.01610398292541504 total time spend for step 8 : 0.21797657012939453 step9:velours_tree Tue Feb 11 11:14:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure list_portfolios : 20160427,20160434,20160429,20160441,20160431,20160432,20160433,20160435,20160437,20160438,20160439,20160442,20160443 photo desc type : 5561 - Retrieving photos to tag... query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160427 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160434 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160429 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160441 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160431 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160432 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160433 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160435 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160437 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160438 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160439 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160442 ORDER BY ph.size desc query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20160443 ORDER BY ph.size desc - Loading descriptors... Size : 2048 len(descriptors) : 5000 Compute structured hierarchical clustering... ward : AgglomerativeClustering(n_clusters=6) ward.labels_ : [2 2 2 ... 5 5 0] Elapsed time: 21.969085454940796 graph_id used : 77407 - Beta version, working pretty good on 11-5-16 ! too many photos (8973 more than 5000) Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 493.4738609790802 time spend to save output : 0.02164936065673828 total time spend for step 9 : 493.49551033973694 step10:send_mail_cod Tue Feb 11 11:22:55 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin in order to get the selector url, please entre the license of selector results_Qualipapia_P20068969_11-02-2025_11_22_55.pdf 20160409 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette201604091739269375 20160416 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette201604161739269378 20160411 imagette201604111739269380 20160423 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette201604231739269380 20160413 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette201604131739269381 20160414 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette201604141739269383 20160415 imagette201604151739269385 20160417 imagette201604171739269385 20160419 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette201604191739269385 20160420 imagette201604201739269387 20160421 imagette201604211739269387 20160424 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette201604241739269387 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20068969 and hashtag_type = 4230 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20160427,20160434,20160429,20160441,20160431,20160432,20160433,20160435,20160437,20160438,20160439,20160442,20160443?tags=pet_clair,papier,mal_croppe,pet_fonce,metal,autre,refus,tetrapak,carton,flou,film_plastique,pehd,environnement&datou_id_consolidate=4235&port_consolidate=20068969 your option no_mail is active, we will not send the real mail to your client args[1332934252] : ((1332934252, -7.921776221026144, 492609224), (1332934252, -0.27195946695167933, 496442774), '0.2396665527597547') apple ((1332934252, -7.921776221026144, 492609224), (1332934252, -0.27195946695167933, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934245] : ((1332934245, -7.949489636259299, 492609224), (1332934245, -0.36604780056263014, 496442774), '0.2396665527597547') apple ((1332934245, -7.949489636259299, 492609224), (1332934245, -0.36604780056263014, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934219] : ((1332934219, -8.00962149570279, 492609224), (1332934219, -0.24620395614497523, 496442774), '0.2396665527597547') apple ((1332934219, -8.00962149570279, 492609224), (1332934219, -0.24620395614497523, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934207] : ((1332934207, -8.01842602755296, 492609224), (1332934207, -0.22458447241766613, 496442774), '0.2396665527597547') apple ((1332934207, -8.01842602755296, 492609224), (1332934207, -0.22458447241766613, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934203] : ((1332934203, -8.000417001251849, 492609224), (1332934203, -0.22761372289483675, 496442774), '0.2396665527597547') apple ((1332934203, -8.000417001251849, 492609224), (1332934203, -0.22761372289483675, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934198] : ((1332934198, -8.002095500631716, 492609224), (1332934198, -0.23518453621179994, 496442774), '0.2396665527597547') apple ((1332934198, -8.002095500631716, 492609224), (1332934198, -0.23518453621179994, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934177] : ((1332934177, -8.005308393562304, 492609224), (1332934177, -0.23492709634987383, 496442774), '0.2396665527597547') apple ((1332934177, -8.005308393562304, 492609224), (1332934177, -0.23492709634987383, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934128] : ((1332934128, -7.998198579216449, 492609224), (1332934128, -0.220888374401201, 496442774), '0.2396665527597547') apple ((1332934128, -7.998198579216449, 492609224), (1332934128, -0.220888374401201, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934123] : ((1332934123, -7.9973276083061, 492609224), (1332934123, -0.2319665899298088, 496442774), '0.2396665527597547') apple ((1332934123, -7.9973276083061, 492609224), (1332934123, -0.2319665899298088, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934118] : ((1332934118, -8.001898251417668, 492609224), (1332934118, -0.21116719098752884, 496442774), '0.2396665527597547') apple ((1332934118, -8.001898251417668, 492609224), (1332934118, -0.21116719098752884, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934112] : ((1332934112, -7.993317876020503, 492609224), (1332934112, -0.21581812892138025, 496442774), '0.2396665527597547') apple ((1332934112, -7.993317876020503, 492609224), (1332934112, -0.21581812892138025, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934055] : ((1332934055, -8.078622229083994, 492609224), (1332934055, -0.2400827142430947, 496442774), '0.2396665527597547') apple ((1332934055, -8.078622229083994, 492609224), (1332934055, -0.2400827142430947, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332934051] : ((1332934051, -8.072011486223639, 492609224), (1332934051, -0.24520168016765864, 496442774), '0.2396665527597547') apple ((1332934051, -8.072011486223639, 492609224), (1332934051, -0.24520168016765864, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332933775] : ((1332933775, -8.07413886329395, 492609224), (1332933775, -0.2482600084231453, 496442774), '0.2396665527597547') apple ((1332933775, -8.07413886329395, 492609224), (1332933775, -0.2482600084231453, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332933772] : ((1332933772, -8.068065806329585, 492609224), (1332933772, -0.2603739269944097, 496442774), '0.2396665527597547') apple ((1332933772, -8.068065806329585, 492609224), (1332933772, -0.2603739269944097, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332933762] : ((1332933762, -8.067237051463003, 492609224), (1332933762, -0.26393996674160103, 496442774), '0.2396665527597547') apple ((1332933762, -8.067237051463003, 492609224), (1332933762, -0.26393996674160103, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332933747] : ((1332933747, -8.062097686984714, 492609224), (1332933747, -0.2659396360923911, 496442774), '0.2396665527597547') apple ((1332933747, -8.062097686984714, 492609224), (1332933747, -0.2659396360923911, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332933425] : ((1332933425, -8.112263777411986, 492609224), (1332933425, -0.2247235863664335, 496442774), '0.2396665527597547') apple ((1332933425, -8.112263777411986, 492609224), (1332933425, -0.2247235863664335, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332933124] : ((1332933124, -8.073509934732753, 492609224), (1332933124, -0.09156903456551056, 496442774), '0.2396665527597547') apple ((1332933124, -8.073509934732753, 492609224), (1332933124, -0.09156903456551056, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332933033] : ((1332933033, -8.06712797012791, 492609224), (1332933033, -0.09691078230197386, 496442774), '0.2396665527597547') apple ((1332933033, -8.06712797012791, 492609224), (1332933033, -0.09691078230197386, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com args[1332933026] : ((1332933026, -8.088843762926816, 492609224), (1332933026, -0.07672116472163132, 496442774), '0.2396665527597547') apple ((1332933026, -8.088843762926816, 492609224), (1332933026, -0.07672116472163132, 496442774), '0.2396665527597547') We are sending mail with results at cod@fotonower.com refus_total : 0.2396665527597547 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=20068969 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20068969_11-02-2025_11_22_55.pdf results_Qualipapia_P20068969_11-02-2025_11_22_55.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20068969_11-02-2025_11_22_55.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('4234','20068969','results_Qualipapia_P20068969_11-02-2025_11_22_55.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20068969_11-02-2025_11_22_55.pdf','pdf','','0.37','0.2396665527597547') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1332934252, 1332934245, 1332934219, 1332934207, 1332934203, 1332934198, 1332934177, 1332934128, 1332934123, 1332934118, 1332934112, 1332934055, 1332934051, 1332933775, 1332933772, 1332933762, 1332933747, 1332933425, 1332933124, 1332933033, 1332933026] Looping around the photos to save general results len do output : 0 before output type Used above Managing all output in save final without adding information in the mtr_datou_result ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934252', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934245', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934219', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934207', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934203', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934198', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934177', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934128', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934123', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934118', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934112', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934055', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934051', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933775', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933772', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933762', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933747', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933425', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933124', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933033', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933026', None, None, None, None, None, '2529344') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.9132733345031738 save_final save missing photos in datou_result : time spend for datou_step_exec : 14.240325689315796 time spend to save output : 0.9136831760406494 total time spend for step 10 : 15.154008865356445 step11:split_time_score Tue Feb 11 11:23:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 3442, 'mtr_user_id': 31, 'name': 'classifieur_2camions_valcor_021122_v1', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'deux_camions,camion_droite,camion_gauche,pas_de_camion', 'svm_portfolios_learning': '7659379,7659034,7657685,7657114', 'photo_hashtag_type': 4458, 'photo_desc_type': 5723, 'type_classification': 'tf_classification2', 'hashtag_id_list': '2107760533,2107760534,2107760535,2107760536'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('09', 64),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 30012025 20068969 Nombre de photos uploadées : 64 / 23040 (0%) 30012025 20068969 Nombre de photos taguées (types de déchets): 64 / 64 (100%) 30012025 20068969 Nombre de photos taguées (volume) : 0 / 64 (0%) elapsed_time : load_data_split_time_score 3.0994415283203125e-06 elapsed_time : order_list_meta_photo_and_scores 9.059906005859375e-06 ???????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0017151832580566406 elapsed_time : insert_dashboard_record_day_entry 0.021862268447875977 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.22693876157835563 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20063504_30-01-2025_07_48_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20063504 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20063504 AND mptpi.`type`=4230 To do Qualite : 0.2335510206581774 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20068969_11-02-2025_11_22_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20068969 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20068969 AND mptpi.`type`=4230 To do Qualite : 0.2448722109157829 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20068970_02-02-2025_20_48_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20068970 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20068970 AND mptpi.`type`=4230 To do Qualite : 0.23699455881366807 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20068971_30-01-2025_10_36_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20068971 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20068971 AND mptpi.`type`=4230 To do Qualite : 0.23903533915716346 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20070508_09-02-2025_22_56_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20070508 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20070508 AND mptpi.`type`=4230 To do Qualite : 0.2267289860685958 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20070511_02-02-2025_21_21_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20070511 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20070511 AND mptpi.`type`=4230 To do Qualite : 0.24958994343336438 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20070512_02-02-2025_21_13_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20070512 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20070512 AND mptpi.`type`=4230 To do Qualite : 0.16845548133972718 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20070513_02-02-2025_21_18_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20070513 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20070513 AND mptpi.`type`=4230 To do Qualite : 0.217794962606474 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20070515_02-02-2025_21_37_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20070515 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20070515 AND mptpi.`type`=4230 To do Qualite : 0.21820137750312157 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20071883_09-02-2025_23_09_23.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20071883 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20071883 AND mptpi.`type`=4230 To do Qualite : 0.20819616112697062 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20071886_02-02-2025_22_04_23.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20071886 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20071886 AND mptpi.`type`=4230 To do Qualite : 0.17062984678858323 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072229_09-02-2025_20_46_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072229 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072229 AND mptpi.`type`=4230 To do Qualite : 0.19677363902651848 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072251_06-02-2025_08_38_59.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072251 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072251 AND mptpi.`type`=4230 To do Qualite : 0.1746484565689831 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072253_02-02-2025_22_29_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072253 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072253 AND mptpi.`type`=4230 To do Qualite : 0.17303092258060765 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072255_02-02-2025_22_36_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072255 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072255 AND mptpi.`type`=4230 To do Qualite : 0.20371445878214417 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072256_30-01-2025_12_36_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072256 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072256 AND mptpi.`type`=4230 To do Qualite : 0.1764359712811868 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072257_02-02-2025_22_32_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072257 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072257 AND mptpi.`type`=4230 To do Qualite : 0.20935969687329123 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072753_09-02-2025_20_44_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072753 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072753 AND mptpi.`type`=4230 To do Qualite : 0.19951198749905955 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072755_30-01-2025_12_49_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072755 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072755 AND mptpi.`type`=4230 To do Qualite : 0.20818347326433534 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072756_02-02-2025_23_10_16.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072756 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072756 AND mptpi.`type`=4230 To do Qualite : 0.19996280124350127 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20072757_30-01-2025_12_35_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20072757 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20072757 AND mptpi.`type`=4230 To do Qualite : 0.15953416140961033 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073335_02-02-2025_23_13_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073335 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073335 AND mptpi.`type`=4230 To do Qualite : 0.16136739676099449 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073336_02-02-2025_23_21_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073336 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073336 AND mptpi.`type`=4230 To do Qualite : 0.19201780499810753 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073337_02-02-2025_23_23_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073337 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073337 AND mptpi.`type`=4230 To do Qualite : 0.2073794185799003 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073338_02-02-2025_23_28_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073338 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073338 AND mptpi.`type`=4230 To do Qualite : 0.18518688869154812 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073339_02-02-2025_23_36_24.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073339 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073339 AND mptpi.`type`=4230 To do Qualite : 0.1711898814978626 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073340_02-02-2025_23_37_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073340 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073340 AND mptpi.`type`=4230 To do Qualite : 0.20138338396477048 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073341_02-02-2025_23_44_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073341 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073341 AND mptpi.`type`=4230 To do Qualite : 0.22483519086015635 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073342_02-02-2025_23_49_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073342 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073342 AND mptpi.`type`=4230 To do Qualite : 0.15094555581768238 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073343_02-02-2025_23_52_52.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073343 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073343 AND mptpi.`type`=4230 To do Qualite : 0.1908776405748835 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073344_30-01-2025_13_32_54.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073344 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073344 AND mptpi.`type`=4230 To do Qualite : 0.16890669474980996 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073346_30-01-2025_13_17_39.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073346 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073346 AND mptpi.`type`=4230 To do Qualite : 0.27278225085972063 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073784_03-02-2025_00_12_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073784 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073784 AND mptpi.`type`=4230 To do Qualite : 0.19930008030200935 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073786_03-02-2025_00_20_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073786 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073786 AND mptpi.`type`=4230 To do Qualite : 0.20177491655639912 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073787_03-02-2025_00_24_18.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073787 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073787 AND mptpi.`type`=4230 To do Qualite : 0.20761010880778516 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073788_03-02-2025_00_27_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073788 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073788 AND mptpi.`type`=4230 To do Qualite : 0.2621322063192155 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073789_03-02-2025_00_32_56.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073789 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073789 AND mptpi.`type`=4230 To do Qualite : 0.16185644840569585 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073790_03-02-2025_00_38_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073790 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073790 AND mptpi.`type`=4230 To do Qualite : 0.20518428083670448 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073791_03-02-2025_00_43_00.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073791 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073791 AND mptpi.`type`=4230 To do Qualite : 0.17516102269045353 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073792_03-02-2025_00_47_56.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073792 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073792 AND mptpi.`type`=4230 To do Qualite : 0.22904050894269737 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073793_03-02-2025_00_55_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073793 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073793 AND mptpi.`type`=4230 To do Qualite : 0.2327448981337093 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073794_03-02-2025_00_59_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073794 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073794 AND mptpi.`type`=4230 To do Qualite : 0.2519866549186122 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073795_03-02-2025_01_04_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073795 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073795 AND mptpi.`type`=4230 To do Qualite : 0.27279521077132673 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073796_03-02-2025_01_09_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073796 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073796 AND mptpi.`type`=4230 To do Qualite : 0.22476525587641968 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073797_03-02-2025_01_14_50.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073797 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073797 AND mptpi.`type`=4230 To do Qualite : 0.2046162764923528 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073798_03-02-2025_01_20_17.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073798 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073798 AND mptpi.`type`=4230 To do Qualite : 0.18692244292717872 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073799_03-02-2025_01_41_30.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073799 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073799 AND mptpi.`type`=4230 To do Qualite : 0.17672003395817876 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073800_03-02-2025_01_28_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073800 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073800 AND mptpi.`type`=4230 To do Qualite : 0.1741707833607706 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073801_30-01-2025_13_49_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073801 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073801 AND mptpi.`type`=4230 To do Qualite : 0.1726835503437969 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20073802_30-01-2025_13_35_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20073802 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20073802 AND mptpi.`type`=4230 To do Qualite : 0.1741737165746781 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074211_03-02-2025_01_32_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074211 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074211 AND mptpi.`type`=4230 To do Qualite : 0.16924346386378786 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074213_03-02-2025_01_38_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074213 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074213 AND mptpi.`type`=4230 To do Qualite : 0.20713976309539786 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074214_03-02-2025_01_42_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074214 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074214 AND mptpi.`type`=4230 To do Qualite : 0.13130871295469465 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074215_03-02-2025_01_47_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074215 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074215 AND mptpi.`type`=4230 To do Qualite : 0.13511382076228984 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074216_03-02-2025_01_55_19.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074216 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074216 AND mptpi.`type`=4230 To do Qualite : 0.17973086031645555 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074217_03-02-2025_02_14_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074217 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074217 AND mptpi.`type`=4230 To do Qualite : 0.1574519934880975 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074218_03-02-2025_02_05_26.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074218 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074218 AND mptpi.`type`=4230 To do Qualite : 0.2026825551583433 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074219_30-01-2025_14_35_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074219 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074219 AND mptpi.`type`=4230 To do Qualite : 0.1514165166709212 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074220_30-01-2025_14_11_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074220 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074220 AND mptpi.`type`=4230 To do Qualite : 0.2656827380981514 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074221_30-01-2025_14_17_24.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074221 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074221 AND mptpi.`type`=4230 To do Qualite : 0.20452066455707454 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074682_03-02-2025_02_13_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074682 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074682 AND mptpi.`type`=4230 To do Qualite : 0.22279539657662634 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074683_03-02-2025_02_19_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074683 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074683 AND mptpi.`type`=4230 To do Qualite : 0.23991866636874898 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074684_03-02-2025_02_28_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074684 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074684 AND mptpi.`type`=4230 To do Qualite : 0.22206551848919698 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074685_03-02-2025_02_29_14.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074685 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074685 AND mptpi.`type`=4230 To do Qualite : 0.18486581442936356 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074700_30-01-2025_14_37_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074700 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074700 AND mptpi.`type`=4230 To do Qualite : 0.21611591323646565 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20074714_30-01-2025_14_47_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20074714 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20074714 AND mptpi.`type`=4230 To do Qualite : 0.18057694218619433 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20075391_03-02-2025_02_33_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20075391 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20075391 AND mptpi.`type`=4230 To do Qualite : 0.1767237921103319 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20075392_03-02-2025_02_39_39.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20075392 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20075392 AND mptpi.`type`=4230 To do Qualite : 0.21994658301357212 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20075393_03-02-2025_02_43_35.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20075393 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20075393 AND mptpi.`type`=4230 To do Qualite : 0.23909673500165332 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20075394_03-02-2025_04_34_05.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20075394 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20075394 AND mptpi.`type`=4230 To do Qualite : 0.2334190851354611 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076069_03-02-2025_03_09_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076069 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076069 AND mptpi.`type`=4230 To do Qualite : 0.21719269091310342 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076072_03-02-2025_02_59_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076072 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076072 AND mptpi.`type`=4230 To do Qualite : 0.23924951240082318 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076073_03-02-2025_03_04_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076073 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076073 AND mptpi.`type`=4230 To do Qualite : 0.19584072674229094 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076074_03-02-2025_03_12_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076074 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076074 AND mptpi.`type`=4230 To do Qualite : 0.1898720408617791 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076075_03-02-2025_03_14_10.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076075 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076075 AND mptpi.`type`=4230 To do Qualite : 0.21095538047110773 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076077_03-02-2025_03_17_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076077 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076077 AND mptpi.`type`=4230 To do Qualite : 0.21870706740527399 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076079_03-02-2025_03_24_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076079 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076079 AND mptpi.`type`=4230 To do Qualite : 0.22468279836525576 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076081_03-02-2025_03_28_36.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076081 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076081 AND mptpi.`type`=4230 To do Qualite : 0.23484919820707623 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076083_03-02-2025_03_32_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076083 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076083 AND mptpi.`type`=4230 To do Qualite : 0.19532813261553927 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076085_03-02-2025_03_42_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076085 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076085 AND mptpi.`type`=4230 To do Qualite : 0.19189450051973977 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076087_03-02-2025_03_46_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076087 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076087 AND mptpi.`type`=4230 To do Qualite : 0.17988553039582883 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076089_06-02-2025_08_41_23.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076089 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076089 AND mptpi.`type`=4230 To do Qualite : 0.18714803099215696 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076090_03-02-2025_04_02_39.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076090 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076090 AND mptpi.`type`=4230 To do Qualite : 0.16940153073075123 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076091_03-02-2025_03_57_41.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076091 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076091 AND mptpi.`type`=4230 To do Qualite : 0.1417931745480291 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076092_30-01-2025_15_48_36.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076092 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076092 AND mptpi.`type`=4230 To do Qualite : 0.17256460295585596 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076774_03-02-2025_04_42_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076774 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076774 AND mptpi.`type`=4230 To do Qualite : 0.13630451642610808 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076776_03-02-2025_04_42_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076776 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076776 AND mptpi.`type`=4230 To do Qualite : 0.15368291674681434 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076778_03-02-2025_04_50_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076778 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076778 AND mptpi.`type`=4230 To do Qualite : 0.13734702128058643 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076779_03-02-2025_04_52_20.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076779 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076779 AND mptpi.`type`=4230 To do Qualite : 0.13398305970171231 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076780_03-02-2025_04_57_30.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076780 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076780 AND mptpi.`type`=4230 To do Qualite : 0.12489845700959583 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076782_03-02-2025_05_03_19.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076782 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076782 AND mptpi.`type`=4230 To do Qualite : 0.15549279508528238 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076783_03-02-2025_05_07_35.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076783 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076783 AND mptpi.`type`=4230 To do Qualite : 0.12056754142321051 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076785_03-02-2025_05_12_25.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076785 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076785 AND mptpi.`type`=4230 To do Qualite : 0.20408722455386583 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076787_03-02-2025_05_17_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076787 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076787 AND mptpi.`type`=4230 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076789 order by id desc limit 1 Qualite : 0.18805375953681405 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076791_03-02-2025_05_28_52.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076791 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076791 AND mptpi.`type`=4230 To do Qualite : 0.1535338926608827 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076793_03-02-2025_05_38_19.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076793 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076793 AND mptpi.`type`=4230 To do Qualite : 0.16144475040911127 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20076795_03-02-2025_06_16_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20076795 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20076795 AND mptpi.`type`=4230 To do Qualite : 0.15235773768992275 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20077969_03-02-2025_06_01_50.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20077969 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20077969 AND mptpi.`type`=4230 To do Qualite : 0.1399318479409724 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20079007_09-02-2025_23_51_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20079007 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20079007 AND mptpi.`type`=4230 To do Qualite : 0.17830060147208618 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20079008_03-02-2025_06_39_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20079008 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20079008 AND mptpi.`type`=4230 To do Qualite : 0.18880818591367152 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20079009_03-02-2025_06_39_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20079009 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20079009 AND mptpi.`type`=4230 To do Qualite : 0.20071619005594835 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20079011_09-02-2025_23_47_25.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20079011 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20079011 AND mptpi.`type`=4230 To do Qualite : 0.21211216233363775 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20079013_06-02-2025_10_23_02.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20079013 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20079013 AND mptpi.`type`=4230 To do Qualite : 0.20031261377056875 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20079015_06-02-2025_10_20_30.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20079015 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20079015 AND mptpi.`type`=4230 To do Qualite : 0.19203296352652885 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080180_10-02-2025_01_00_05.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080180 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080180 AND mptpi.`type`=4230 To do Qualite : 0.20659158330668348 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080182_03-02-2025_07_40_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080182 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080182 AND mptpi.`type`=4230 To do Qualite : 0.18942749045222543 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080183_03-02-2025_07_45_36.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080183 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080183 AND mptpi.`type`=4230 To do Qualite : 0.21088080831119854 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080184_10-02-2025_00_24_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080184 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080184 AND mptpi.`type`=4230 To do Qualite : 0.17948984429838863 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080186_03-02-2025_07_52_41.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080186 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080186 AND mptpi.`type`=4230 To do Qualite : 0.19491561619773923 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080187_03-02-2025_07_57_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080187 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080187 AND mptpi.`type`=4230 To do Qualite : 0.18638218950960506 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080189_03-02-2025_08_05_10.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080189 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080189 AND mptpi.`type`=4230 To do Qualite : 0.20148600743580142 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080191_03-02-2025_08_07_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080191 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080191 AND mptpi.`type`=4230 To do Qualite : 0.21160946767734545 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080193_03-02-2025_08_12_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080193 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080193 AND mptpi.`type`=4230 To do Qualite : 0.258998663198811 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080195_03-02-2025_08_18_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080195 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080195 AND mptpi.`type`=4230 To do Qualite : 0.1912781088207734 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080197_11-02-2025_11_07_30.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080197 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080197 AND mptpi.`type`=4230 To do Qualite : 0.19253200921513683 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080199_03-02-2025_08_32_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080199 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080199 AND mptpi.`type`=4230 To do Qualite : 0.22795659309183963 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080200_30-01-2025_17_48_23.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080200 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080200 AND mptpi.`type`=4230 To do Qualite : 0.23427551845336614 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080880_03-02-2025_08_48_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080880 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080880 AND mptpi.`type`=4230 To do Qualite : 0.23710703679424758 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080881_03-02-2025_08_55_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080881 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080881 AND mptpi.`type`=4230 To do Qualite : 0.24129448446306662 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080882_03-02-2025_08_57_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080882 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080882 AND mptpi.`type`=4230 To do Qualite : 0.22958120197847603 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080883_03-02-2025_09_03_14.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080883 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080883 AND mptpi.`type`=4230 To do Qualite : 0.2118123777119743 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080884_03-02-2025_09_23_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080884 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080884 AND mptpi.`type`=4230 To do Qualite : 0.2177765159748498 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080885_06-02-2025_10_47_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080885 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080885 AND mptpi.`type`=4230 To do Qualite : 0.25988353253598656 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080886_06-02-2025_11_19_27.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080886 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080886 AND mptpi.`type`=4230 To do Qualite : 0.24419084116603645 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080887_03-02-2025_09_22_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080887 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080887 AND mptpi.`type`=4230 To do Qualite : 0.2590502793769312 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080888_03-02-2025_09_33_43.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080888 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080888 AND mptpi.`type`=4230 To do Qualite : 0.24733017620698095 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080889_03-02-2025_09_37_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080889 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080889 AND mptpi.`type`=4230 To do Qualite : 0.23934256539917748 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080890_03-02-2025_09_42_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080890 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080890 AND mptpi.`type`=4230 To do Qualite : 0.25727449361918203 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080891_03-02-2025_09_47_39.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080891 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080891 AND mptpi.`type`=4230 To do Qualite : 0.19419321171174883 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080892_30-01-2025_19_47_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080892 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080892 AND mptpi.`type`=4230 To do Qualite : 0.21426636804686136 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080893_30-01-2025_18_33_20.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080893 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080893 AND mptpi.`type`=4230 To do Qualite : 0.1897019073157801 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20080894_30-01-2025_18_48_24.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20080894 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20080894 AND mptpi.`type`=4230 To do Qualite : 0.22247523923137189 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20082394_30-01-2025_19_20_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20082394 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20082394 AND mptpi.`type`=4230 To do Qualite : 0.23701978675033195 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20084871_03-02-2025_10_52_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20084871 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20084871 AND mptpi.`type`=4230 To do Qualite : 0.20064659500037274 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20084872_03-02-2025_10_59_06.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20084872 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20084872 AND mptpi.`type`=4230 To do Qualite : 0.1699529342858993 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20084873_30-01-2025_20_47_30.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20084873 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20084873 AND mptpi.`type`=4230 To do Qualite : 0.166152764323985 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085531_06-02-2025_23_08_35.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085531 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085531 AND mptpi.`type`=4230 To do Qualite : 0.1767779738809873 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085532_03-02-2025_11_38_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085532 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085532 AND mptpi.`type`=4230 To do Qualite : 0.17468735006178784 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085533_03-02-2025_11_42_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085533 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085533 AND mptpi.`type`=4230 To do Qualite : 0.16244421947131724 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085534_03-02-2025_11_47_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085534 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085534 AND mptpi.`type`=4230 To do Qualite : 0.18584121080168442 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085535_03-02-2025_11_52_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085535 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085535 AND mptpi.`type`=4230 To do Qualite : 0.210986415770356 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085537_03-02-2025_11_57_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085537 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085537 AND mptpi.`type`=4230 To do Qualite : 0.19674334175642583 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085539_10-02-2025_12_08_11.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085539 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085539 AND mptpi.`type`=4230 To do Qualite : 0.18604783977232067 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085541_03-02-2025_12_08_02.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085541 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085541 AND mptpi.`type`=4230 To do Qualite : 0.17437462703722073 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085543_03-02-2025_12_13_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085543 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085543 AND mptpi.`type`=4230 To do Qualite : 0.20372862977481582 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20085545_30-01-2025_21_47_10.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20085545 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20085545 AND mptpi.`type`=4230 To do Qualite : 0.19749032146012782 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20086674_03-02-2025_12_25_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20086674 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20086674 AND mptpi.`type`=4230 To do Qualite : 0.2056645522742653 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20086675_03-02-2025_12_35_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20086675 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20086675 AND mptpi.`type`=4230 To do Qualite : 0.20232831200947957 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20086676_03-02-2025_12_49_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20086676 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20086676 AND mptpi.`type`=4230 To do Qualite : 0.18481668903006088 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20086677_30-01-2025_21_36_10.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20086677 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20086677 AND mptpi.`type`=4230 To do Qualite : 0.1888519201882366 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20086678_30-01-2025_21_48_56.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20086678 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20086678 AND mptpi.`type`=4230 To do Qualite : 0.17868921238530078 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20086679_03-02-2025_12_46_58.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20086679 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11415 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11419 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11419 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11416 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11417 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11417 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11422 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 2 of step 11418 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11419 doesn't seem to be define in the database( WARNING : output 1 of step 11415 have datatype=7 whereas input 2 of step 11419 have datatype=None WARNING : type of output 3 of step 11419 doesn't seem to be define in the database( WARNING : type of input 1 of step 11416 doesn't seem to be define in the database( WARNING : type of output 1 of step 11416 doesn't seem to be define in the database( WARNING : type of input 3 of step 11417 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11416 have datatype=10 whereas input 0 of step 11420 have datatype=18 WARNING : type of input 5 of step 11418 doesn't seem to be define in the database( WARNING : output 0 of step 11420 have datatype=11 whereas input 5 of step 11418 have datatype=None WARNING : type of input 2 of step 11416 doesn't seem to be define in the database( WARNING : output 0 of step 11421 have datatype=5 whereas input 2 of step 11416 have datatype=None WARNING : output 0 of step 11418 have datatype=10 whereas input 0 of step 11422 have datatype=18 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20086679 AND mptpi.`type`=4230 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'30012025': {'nb_upload': 64, 'nb_taggue_class': 64, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1332934252, 1332934245, 1332934219, 1332934207, 1332934203, 1332934198, 1332934177, 1332934128, 1332934123, 1332934118, 1332934112, 1332934055, 1332934051, 1332933775, 1332933772, 1332933762, 1332933747, 1332933425, 1332933124, 1332933033, 1332933026] Looping around the photos to save general results len do output : 1 /20068969Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934252', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934245', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934219', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934207', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934203', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934198', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934177', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934128', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934123', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934118', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934112', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934055', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332934051', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933775', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933772', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933762', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933747', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933425', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933124', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933033', None, None, None, None, None, '2529344') ('4234', None, None, None, None, None, None, None, '2529344') ('4234', None, '1332933026', None, None, None, None, None, '2529344') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 22 time used for this insertion : 0.2433779239654541 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.31286334991455 time spend to save output : 0.2437288761138916 total time spend for step 11 : 12.556592226028442 caffe_path_current : About to save ! 2 After save, about to update current ! update_current_state 298.57user 145.30system 30:14.46elapsed 24%CPU (0avgtext+0avgdata 3795604maxresident)k 641616inputs+167056outputs (4206major+17218818minor)pagefaults 0swaps