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 2529450' -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 : 2481130 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 : ['2529450'] with mtr_portfolio_ids : ['20070508'] and first list_photo_ids : [] new path : /proc/2481130/ 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 : 8.237404823303223 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 Wed Feb 12 08:50:48 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 : 6198 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-12 08:50:51.604299: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-12 08:50:51.631289: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-12 08:50:51.632838: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fbeb8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-12 08:50:51.632897: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-12 08:50:51.636223: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-12 08:50:51.776052: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x288ee300 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-12 08:50:51.776115: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-12 08:50:51.777187: 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 2025-02-12 08:50:51.777617: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 08:50:51.780122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 08:50:51.782688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 08:50:51.783174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 08:50:51.785830: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 08:50:51.787190: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 08:50:51.792613: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 08:50:51.794186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 08:50:51.794322: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 08:50:51.798128: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 08:50:51.798151: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 08:50:51.798163: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 08:50:51.799578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5684 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. 2025-02-12 08:50:52.084711: 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 2025-02-12 08:50:52.084882: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 08:50:52.084909: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 08:50:52.084949: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 08:50:52.084971: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 08:50:52.084993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 08:50:52.085015: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 08:50:52.085036: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 08:50:52.086289: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 08:50:52.094414: 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 2025-02-12 08:50:52.094525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-12 08:50:52.094546: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 08:50:52.094566: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-12 08:50:52.094585: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-12 08:50:52.094603: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-12 08:50:52.094621: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-12 08:50:52.094641: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 08:50:52.098333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-12 08:50:52.098387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-12 08:50:52.098398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-12 08:50:52.098408: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-12 08:50:52.099753: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5684 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 : [] 2025-02-12 08:51:00.153397: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-12 08:51:00.375641: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-12 08:51:02.041548: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.288956: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.289815: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.290643: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.291523: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.292359: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.293196: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2282521344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.293255: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.297638: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.297676: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.305453: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.305489: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.306117: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.306137: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.312861: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.312890: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.313400: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.313416: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.344491: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.344527: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.345016: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.345032: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.350813: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.350840: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.351336: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.351353: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-12 08:51:02.385585: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.386126: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.387927: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.388413: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.432723: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.433244: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.435318: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.435845: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.462337: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.462846: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.464411: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.464909: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.470526: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.471031: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.472642: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.473121: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.478721: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.479207: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.480697: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.481234: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.508648: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.509198: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.509747: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.510270: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.514020: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.514553: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.530590: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.531179: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.531694: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.532202: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.545074: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.545611: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.546121: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.546653: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.551114: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.551643: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.556253: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.556779: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.568984: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.569481: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.573578: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.574057: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.574530: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.575028: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.575758: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.576272: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.587218: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.587798: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.588329: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.588825: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.589299: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.589772: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.590248: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.590723: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.600273: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.600783: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.606957: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.607476: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.637153: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.637256: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-02-12 08:51:02.638354: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.639329: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.646401: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.646882: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.647421: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.647932: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.655886: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.656409: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.672116: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.672667: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.673196: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.673733: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.678238: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.678793: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.679361: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.679887: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.680994: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.691477: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.692037: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.701414: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.701984: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.702527: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.703089: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.703663: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-12 08:51:02.704242: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 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: -118.44219 max: 149.04531 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 9.751319885253906e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.00040078163146972656 length of segment : 20 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 167 time to create 1 rle with old method : 0.00037360191345214844 length of segment : 33 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 56 time to create 1 rle with old method : 0.00014591217041015625 length of segment : 19 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.010724782943725586 length of segment : 10 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.00045680999755859375 length of segment : 27 time for calcul the mask position with numpy : 0.00014328956604003906 nb_pixel_total : 1999 time to create 1 rle with old method : 0.004116535186767578 length of segment : 79 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.45391 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 : 8.177757263183594e-05 nb_pixel_total : 487 time to create 1 rle with old method : 0.0010857582092285156 length of segment : 55 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 2484 time to create 1 rle with old method : 0.0031232833862304688 length of segment : 57 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 1029 time to create 1 rle with old method : 0.0013332366943359375 length of segment : 41 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.90313 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 : 7.081031799316406e-05 nb_pixel_total : 1970 time to create 1 rle with old method : 0.0029141902923583984 length of segment : 43 time for calcul the mask position with numpy : 0.0001068115234375 nb_pixel_total : 2377 time to create 1 rle with old method : 0.0029413700103759766 length of segment : 98 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.00051116943359375 length of segment : 15 time for calcul the mask position with numpy : 0.0005900859832763672 nb_pixel_total : 1046 time to create 1 rle with old method : 0.002071380615234375 length of segment : 100 time for calcul the mask position with numpy : 0.00011587142944335938 nb_pixel_total : 1473 time to create 1 rle with old method : 0.001954793930053711 length of segment : 58 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -114.66094 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 : 0.0001201629638671875 nb_pixel_total : 6075 time to create 1 rle with old method : 0.007758617401123047 length of segment : 99 time for calcul the mask position with numpy : 9.012222290039062e-05 nb_pixel_total : 1425 time to create 1 rle with old method : 0.0018928050994873047 length of segment : 78 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 1093 time to create 1 rle with old method : 0.0014104843139648438 length of segment : 64 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 210 time to create 1 rle with old method : 0.0003147125244140625 length of segment : 18 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 719 time to create 1 rle with old method : 0.0009491443634033203 length of segment : 56 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 516 time to create 1 rle with old method : 0.0017788410186767578 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: 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 : 8.96453857421875e-05 nb_pixel_total : 860 time to create 1 rle with old method : 0.0022325515747070312 length of segment : 51 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 519 time to create 1 rle with old method : 0.0007207393646240234 length of segment : 28 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 1166 time to create 1 rle with old method : 0.001516103744506836 length of segment : 54 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 922 time to create 1 rle with old method : 0.0012960433959960938 length of segment : 58 time for calcul the mask position with numpy : 0.00011086463928222656 nb_pixel_total : 3068 time to create 1 rle with old method : 0.004208564758300781 length of segment : 52 time for calcul the mask position with numpy : 0.0005173683166503906 nb_pixel_total : 44986 time to create 1 rle with old method : 0.052583932876586914 length of segment : 232 Processing 1 images image shape: (400, 400, 3) min: 16.00000 max: 207.00000 molded_images shape: (1, 640, 640, 3) min: -93.84063 max: 80.18203 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0014469623565673828 nb_pixel_total : 152697 time to create 1 rle with new method : 0.002279520034790039 length of segment : 400 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.12188 max: 150.81875 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 1342 time to create 1 rle with old method : 0.0017507076263427734 length of segment : 48 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.00027251243591308594 length of segment : 13 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 456 time to create 1 rle with old method : 0.0006518363952636719 length of segment : 37 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 188 time to create 1 rle with old method : 0.00029087066650390625 length of segment : 18 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0006809234619140625 length of segment : 27 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 382 time to create 1 rle with old method : 0.0007023811340332031 length of segment : 19 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 62 time to create 1 rle with old method : 0.00011396408081054688 length of segment : 9 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0004329681396484375 length of segment : 16 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 1318 time to create 1 rle with old method : 0.0017368793487548828 length of segment : 38 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 204 time to create 1 rle with old method : 0.0003037452697753906 length of segment : 21 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 434 time to create 1 rle with old method : 0.0006508827209472656 length of segment : 20 time for calcul the mask position with numpy : 0.0003464221954345703 nb_pixel_total : 10066 time to create 1 rle with old method : 0.011623859405517578 length of segment : 269 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.00020694732666015625 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: -120.80938 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 : 6.914138793945312e-05 nb_pixel_total : 467 time to create 1 rle with old method : 0.0006730556488037109 length of segment : 28 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 531 time to create 1 rle with old method : 0.0007612705230712891 length of segment : 45 time for calcul the mask position with numpy : 0.0004856586456298828 nb_pixel_total : 9240 time to create 1 rle with old method : 0.01109457015991211 length of segment : 128 time for calcul the mask position with numpy : 0.00010800361633300781 nb_pixel_total : 991 time to create 1 rle with old method : 0.0013225078582763672 length of segment : 50 time for calcul the mask position with numpy : 7.271766662597656e-05 nb_pixel_total : 510 time to create 1 rle with old method : 0.0010111331939697266 length of segment : 27 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.0002608299255371094 length of segment : 9 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 425 time to create 1 rle with old method : 0.0008101463317871094 length of segment : 34 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.0003292560577392578 length of segment : 20 time for calcul the mask position with numpy : 9.369850158691406e-05 nb_pixel_total : 1055 time to create 1 rle with old method : 0.0013489723205566406 length of segment : 43 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 404 time to create 1 rle with old method : 0.0008075237274169922 length of segment : 21 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 63 time to create 1 rle with old method : 0.00017070770263671875 length of segment : 7 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 167 time to create 1 rle with old method : 0.00025010108947753906 length of segment : 16 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 335 time to create 1 rle with old method : 0.0004572868347167969 length of segment : 22 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.94609 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 : 6.365776062011719e-05 nb_pixel_total : 370 time to create 1 rle with old method : 0.0005438327789306641 length of segment : 28 time for calcul the mask position with numpy : 0.0002541542053222656 nb_pixel_total : 12152 time to create 1 rle with old method : 0.014136075973510742 length of segment : 133 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 726 time to create 1 rle with old method : 0.00096893310546875 length of segment : 32 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 1407 time to create 1 rle with old method : 0.0017855167388916016 length of segment : 43 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 1363 time to create 1 rle with old method : 0.0017426013946533203 length of segment : 55 time for calcul the mask position with numpy : 0.00011134147644042969 nb_pixel_total : 1817 time to create 1 rle with old method : 0.002308368682861328 length of segment : 83 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.00035643577575683594 length of segment : 22 time for calcul the mask position with numpy : 8.535385131835938e-05 nb_pixel_total : 1114 time to create 1 rle with old method : 0.001608133316040039 length of segment : 37 time for calcul the mask position with numpy : 0.00014138221740722656 nb_pixel_total : 1467 time to create 1 rle with old method : 0.001909494400024414 length of segment : 132 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 478 time to create 1 rle with old method : 0.0007123947143554688 length of segment : 48 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.0002994537353515625 length of segment : 21 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: 139.14687 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 220 time to create 1 rle with old method : 0.0003349781036376953 length of segment : 23 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 447 time to create 1 rle with old method : 0.0006313323974609375 length of segment : 47 time for calcul the mask position with numpy : 9.202957153320312e-05 nb_pixel_total : 824 time to create 1 rle with old method : 0.0010716915130615234 length of segment : 69 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 102 time to create 1 rle with old method : 0.0001952648162841797 length of segment : 12 time for calcul the mask position with numpy : 8.797645568847656e-05 nb_pixel_total : 851 time to create 1 rle with old method : 0.0017385482788085938 length of segment : 45 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 90 time to create 1 rle with old method : 0.00014853477478027344 length of segment : 15 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 1531 time to create 1 rle with old method : 0.0019321441650390625 length of segment : 75 Processing 1 images image shape: (280, 400, 3) min: 10.00000 max: 177.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 60.85391 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009555816650390625 nb_pixel_total : 106541 time to create 1 rle with old method : 0.11930084228515625 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: 143.14297 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 17 time for calcul the mask position with numpy : 0.00014495849609375 nb_pixel_total : 4733 time to create 1 rle with old method : 0.0059206485748291016 length of segment : 120 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 200 time to create 1 rle with old method : 0.00031065940856933594 length of segment : 21 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 1004 time to create 1 rle with old method : 0.0012674331665039062 length of segment : 41 time for calcul the mask position with numpy : 8.58306884765625e-05 nb_pixel_total : 1877 time to create 1 rle with old method : 0.0024459362030029297 length of segment : 74 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00026488304138183594 length of segment : 15 time for calcul the mask position with numpy : 0.000148773193359375 nb_pixel_total : 3269 time to create 1 rle with old method : 0.0059931278228759766 length of segment : 105 time for calcul the mask position with numpy : 0.0001010894775390625 nb_pixel_total : 485 time to create 1 rle with old method : 0.0011327266693115234 length of segment : 21 time for calcul the mask position with numpy : 0.00011157989501953125 nb_pixel_total : 1657 time to create 1 rle with old method : 0.0033457279205322266 length of segment : 76 time for calcul the mask position with numpy : 0.00011467933654785156 nb_pixel_total : 350 time to create 1 rle with old method : 0.0010204315185546875 length of segment : 23 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 371 time to create 1 rle with old method : 0.0009968280792236328 length of segment : 37 time for calcul the mask position with numpy : 0.0001239776611328125 nb_pixel_total : 737 time to create 1 rle with old method : 0.0015869140625 length of segment : 29 time for calcul the mask position with numpy : 9.107589721679688e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.00035762786865234375 length of segment : 17 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0003495216369628906 length of segment : 14 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 968 time to create 1 rle with old method : 0.0012328624725341797 length of segment : 40 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 935 time to create 1 rle with old method : 0.0012097358703613281 length of segment : 40 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.00025343894958496094 length of segment : 15 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 369 time to create 1 rle with old method : 0.0006000995635986328 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 : 42 time for calcul the mask position with numpy : 8.749961853027344e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.0003910064697265625 length of segment : 26 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 446 time to create 1 rle with old method : 0.0005788803100585938 length of segment : 35 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 290 time to create 1 rle with old method : 0.000457763671875 length of segment : 16 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 740 time to create 1 rle with old method : 0.0009772777557373047 length of segment : 49 time for calcul the mask position with numpy : 9.322166442871094e-05 nb_pixel_total : 899 time to create 1 rle with old method : 0.0017154216766357422 length of segment : 72 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 294 time to create 1 rle with old method : 0.0004382133483886719 length of segment : 22 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 477 time to create 1 rle with old method : 0.0006902217864990234 length of segment : 22 time for calcul the mask position with numpy : 0.0001506805419921875 nb_pixel_total : 488 time to create 1 rle with old method : 0.0008904933929443359 length of segment : 26 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 476 time to create 1 rle with old method : 0.0007328987121582031 length of segment : 22 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 657 time to create 1 rle with old method : 0.0009107589721679688 length of segment : 68 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 1454 time to create 1 rle with old method : 0.0018243789672851562 length of segment : 56 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 224 time to create 1 rle with old method : 0.0003662109375 length of segment : 17 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 349 time to create 1 rle with old method : 0.00047707557678222656 length of segment : 30 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 901 time to create 1 rle with old method : 0.0012145042419433594 length of segment : 46 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 1116 time to create 1 rle with old method : 0.0014739036560058594 length of segment : 49 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0005173683166503906 length of segment : 42 time for calcul the mask position with numpy : 7.343292236328125e-05 nb_pixel_total : 1008 time to create 1 rle with old method : 0.0013706684112548828 length of segment : 26 time for calcul the mask position with numpy : 0.0001926422119140625 nb_pixel_total : 5938 time to create 1 rle with old method : 0.007791757583618164 length of segment : 136 time for calcul the mask position with numpy : 9.322166442871094e-05 nb_pixel_total : 1523 time to create 1 rle with old method : 0.001931905746459961 length of segment : 59 time for calcul the mask position with numpy : 8.797645568847656e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.0005385875701904297 length of segment : 24 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.0003924369812011719 length of segment : 47 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 239 time to create 1 rle with old method : 0.0003509521484375 length of segment : 32 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002608299255371094 length of segment : 16 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 782 time to create 1 rle with old method : 0.001016378402709961 length of segment : 52 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 446 time to create 1 rle with old method : 0.0006501674652099609 length of segment : 34 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002739429473876953 length of segment : 38 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 584 time to create 1 rle with old method : 0.0007669925689697266 length of segment : 34 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 963 time to create 1 rle with old method : 0.001294851303100586 length of segment : 45 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 704 time to create 1 rle with old method : 0.0010061264038085938 length of segment : 90 time for calcul the mask position with numpy : 0.00010728836059570312 nb_pixel_total : 711 time to create 1 rle with old method : 0.0017347335815429688 length of segment : 28 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.0001697540283203125 length of segment : 16 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00020122528076171875 length of segment : 6 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 377 time to create 1 rle with old method : 0.0005383491516113281 length of segment : 22 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 804 time to create 1 rle with old method : 0.0010459423065185547 length of segment : 33 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 1099 time to create 1 rle with old method : 0.001394510269165039 length of segment : 45 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 475 time to create 1 rle with old method : 0.0006427764892578125 length of segment : 32 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 515 time to create 1 rle with old method : 0.0007138252258300781 length of segment : 28 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 743 time to create 1 rle with old method : 0.0009768009185791016 length of segment : 28 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006823539733886719 length of segment : 36 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 775 time to create 1 rle with old method : 0.0009799003601074219 length of segment : 39 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 545 time to create 1 rle with old method : 0.0006966590881347656 length of segment : 35 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 1243 time to create 1 rle with old method : 0.0015528202056884766 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: 145.64297 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 453 time to create 1 rle with old method : 0.0006198883056640625 length of segment : 27 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 898 time to create 1 rle with old method : 0.0012288093566894531 length of segment : 67 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 555 time to create 1 rle with old method : 0.0007367134094238281 length of segment : 36 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 1994 time to create 1 rle with old method : 0.0025634765625 length of segment : 56 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.00046634674072265625 length of segment : 27 time for calcul the mask position with numpy : 3.218650817871094e-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 : 3.695487976074219e-05 nb_pixel_total : 367 time to create 1 rle with old method : 0.0004913806915283203 length of segment : 33 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 589 time to create 1 rle with old method : 0.0008387565612792969 length of segment : 54 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.00020074844360351562 length of segment : 15 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 320 time to create 1 rle with old method : 0.0004658699035644531 length of segment : 20 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.0001595020294189453 length of segment : 8 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.00015473365783691406 length of segment : 20 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 114 time to create 1 rle with old method : 0.0001857280731201172 length of segment : 11 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 495 time to create 1 rle with old method : 0.0006771087646484375 length of segment : 22 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: 136.85000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.00034546852111816406 length of segment : 14 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 562 time to create 1 rle with old method : 0.0006899833679199219 length of segment : 52 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 69 time to create 1 rle with old method : 0.0001289844512939453 length of segment : 8 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.00022554397583007812 length of segment : 30 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 122 time to create 1 rle with old method : 0.00021314620971679688 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: -118.33672 max: 148.74062 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.00026702880859375 length of segment : 26 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002961158752441406 length of segment : 32 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 57 time to create 1 rle with old method : 0.00013375282287597656 length of segment : 18 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 218 time to create 1 rle with old method : 0.0003046989440917969 length of segment : 29 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 2032 time to create 1 rle with old method : 0.002629518508911133 length of segment : 77 time for calcul the mask position with numpy : 0.00010895729064941406 nb_pixel_total : 2092 time to create 1 rle with old method : 0.0026395320892333984 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: -119.68438 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 : 6.937980651855469e-05 nb_pixel_total : 2564 time to create 1 rle with old method : 0.0033292770385742188 length of segment : 52 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.00026988983154296875 length of segment : 45 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 78 time to create 1 rle with old method : 0.0002484321594238281 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: -121.36797 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 : 6.4849853515625e-05 nb_pixel_total : 2237 time to create 1 rle with old method : 0.0029070377349853516 length of segment : 44 time for calcul the mask position with numpy : 0.00010037422180175781 nb_pixel_total : 1909 time to create 1 rle with old method : 0.002438068389892578 length of segment : 57 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.00028586387634277344 length of segment : 11 time for calcul the mask position with numpy : 8.511543273925781e-05 nb_pixel_total : 4169 time to create 1 rle with old method : 0.0048847198486328125 length of segment : 53 time for calcul the mask position with numpy : 8.440017700195312e-05 nb_pixel_total : 2266 time to create 1 rle with old method : 0.00287628173828125 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: -113.58672 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 : 0.00012350082397460938 nb_pixel_total : 6310 time to create 1 rle with old method : 0.0077037811279296875 length of segment : 112 time for calcul the mask position with numpy : 0.0001163482666015625 nb_pixel_total : 1420 time to create 1 rle with old method : 0.0030405521392822266 length of segment : 89 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 879 time to create 1 rle with old method : 0.001119852066040039 length of segment : 68 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 789 time to create 1 rle with old method : 0.0010569095611572266 length of segment : 76 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 : 5 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0007476806640625 length of segment : 28 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 727 time to create 1 rle with old method : 0.0013020038604736328 length of segment : 52 time for calcul the mask position with numpy : 0.00010013580322265625 nb_pixel_total : 942 time to create 1 rle with old method : 0.0016307830810546875 length of segment : 53 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 953 time to create 1 rle with old method : 0.0012922286987304688 length of segment : 46 time for calcul the mask position with numpy : 0.00010013580322265625 nb_pixel_total : 2916 time to create 1 rle with old method : 0.003877878189086914 length of segment : 64 Processing 1 images image shape: (400, 400, 3) min: 17.00000 max: 208.00000 molded_images shape: (1, 640, 640, 3) min: -97.85234 max: 81.41250 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: -119.53594 max: 150.51406 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 1322 time to create 1 rle with old method : 0.0016200542449951172 length of segment : 47 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002994537353515625 length of segment : 13 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002753734588623047 length of segment : 17 time for calcul the mask position with numpy : 8.344650268554688e-05 nb_pixel_total : 1623 time to create 1 rle with old method : 0.002144336700439453 length of segment : 44 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 409 time to create 1 rle with old method : 0.0005769729614257812 length of segment : 32 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 411 time to create 1 rle with old method : 0.0006728172302246094 length of segment : 18 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 533 time to create 1 rle with old method : 0.0007493495941162109 length of segment : 28 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 111 time to create 1 rle with old method : 0.00019884109497070312 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: -120.45000 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 : 4.982948303222656e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0006513595581054688 length of segment : 28 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0007078647613525391 length of segment : 37 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 792 time to create 1 rle with old method : 0.0010607242584228516 length of segment : 40 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.00030159950256347656 length of segment : 18 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 515 time to create 1 rle with old method : 0.0007243156433105469 length of segment : 26 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 850 time to create 1 rle with old method : 0.0011012554168701172 length of segment : 42 time for calcul the mask position with numpy : 0.00039196014404296875 nb_pixel_total : 16330 time to create 1 rle with old method : 0.019904136657714844 length of segment : 233 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 1095 time to create 1 rle with old method : 0.001394510269165039 length of segment : 45 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 580 time to create 1 rle with old method : 0.0008671283721923828 length of segment : 24 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 1026 time to create 1 rle with old method : 0.0013349056243896484 length of segment : 46 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 141 time to create 1 rle with old method : 0.00020003318786621094 length of segment : 31 time for calcul the mask position with numpy : 0.00012302398681640625 nb_pixel_total : 5501 time to create 1 rle with old method : 0.007031679153442383 length of segment : 42 time for calcul the mask position with numpy : 0.00020599365234375 nb_pixel_total : 3058 time to create 1 rle with old method : 0.004029035568237305 length of segment : 128 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.23516 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 : 5.173683166503906e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004930496215820312 length of segment : 24 time for calcul the mask position with numpy : 0.00021839141845703125 nb_pixel_total : 12567 time to create 1 rle with old method : 0.014270782470703125 length of segment : 133 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 659 time to create 1 rle with old method : 0.0008897781372070312 length of segment : 31 time for calcul the mask position with numpy : 0.00013899803161621094 nb_pixel_total : 1867 time to create 1 rle with old method : 0.0023326873779296875 length of segment : 153 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 1377 time to create 1 rle with old method : 0.0016641616821289062 length of segment : 42 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 716 time to create 1 rle with old method : 0.0009512901306152344 length of segment : 66 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.00032448768615722656 length of segment : 23 time for calcul the mask position with numpy : 0.0001468658447265625 nb_pixel_total : 3974 time to create 1 rle with old method : 0.004655599594116211 length of segment : 147 time for calcul the mask position with numpy : 9.465217590332031e-05 nb_pixel_total : 1361 time to create 1 rle with old method : 0.0018553733825683594 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: 139.60000 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 : 232 time to create 1 rle with old method : 0.0003387928009033203 length of segment : 21 time for calcul the mask position with numpy : 0.00010132789611816406 nb_pixel_total : 734 time to create 1 rle with old method : 0.0009837150573730469 length of segment : 61 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.0003762245178222656 length of segment : 45 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 789 time to create 1 rle with old method : 0.0010404586791992188 length of segment : 47 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 358 time to create 1 rle with old method : 0.0005376338958740234 length of segment : 41 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 86 time to create 1 rle with old method : 0.0001430511474609375 length of segment : 15 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 527 time to create 1 rle with old method : 0.0007326602935791016 length of segment : 53 time for calcul the mask position with numpy : 8.511543273925781e-05 nb_pixel_total : 1512 time to create 1 rle with old method : 0.0018506050109863281 length of segment : 75 time for calcul the mask position with numpy : 0.00030994415283203125 nb_pixel_total : 2652 time to create 1 rle with old method : 0.003131866455078125 length of segment : 180 Processing 1 images image shape: (280, 400, 3) min: 11.00000 max: 181.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 64.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0012025833129882812 nb_pixel_total : 106448 time to create 1 rle with old method : 0.12039470672607422 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: 144.93203 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 17 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 917 time to create 1 rle with old method : 0.0011665821075439453 length of segment : 41 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 499 time to create 1 rle with old method : 0.0007152557373046875 length of segment : 23 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001895427703857422 length of segment : 19 time for calcul the mask position with numpy : 0.0001583099365234375 nb_pixel_total : 5087 time to create 1 rle with old method : 0.006326913833618164 length of segment : 97 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 576 time to create 1 rle with old method : 0.0008080005645751953 length of segment : 26 time for calcul the mask position with numpy : 0.00016379356384277344 nb_pixel_total : 5175 time to create 1 rle with old method : 0.006081104278564453 length of segment : 75 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 614 time to create 1 rle with old method : 0.0008251667022705078 length of segment : 37 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 1732 time to create 1 rle with old method : 0.002128124237060547 length of segment : 57 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 370 time to create 1 rle with old method : 0.0005490779876708984 length of segment : 22 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.00024771690368652344 length of segment : 15 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 639 time to create 1 rle with old method : 0.0008406639099121094 length of segment : 52 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0002460479736328125 length of segment : 14 time for calcul the mask position with numpy : 8.296966552734375e-05 nb_pixel_total : 1529 time to create 1 rle with old method : 0.002001523971557617 length of segment : 81 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.00027179718017578125 length of segment : 19 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.00020432472229003906 length of segment : 15 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 377 time to create 1 rle with old method : 0.0005407333374023438 length of segment : 23 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 1141 time to create 1 rle with old method : 0.0013699531555175781 length of segment : 46 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 : 9.083747863769531e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002830028533935547 length of segment : 26 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 451 time to create 1 rle with old method : 0.0006039142608642578 length of segment : 36 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 284 time to create 1 rle with old method : 0.0004382133483886719 length of segment : 20 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 910 time to create 1 rle with old method : 0.0011839866638183594 length of segment : 45 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.0004138946533203125 length of segment : 14 time for calcul the mask position with numpy : 0.00016045570373535156 nb_pixel_total : 3167 time to create 1 rle with old method : 0.003922700881958008 length of segment : 145 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 1038 time to create 1 rle with old method : 0.0013356208801269531 length of segment : 67 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 598 time to create 1 rle with old method : 0.0008420944213867188 length of segment : 25 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 519 time to create 1 rle with old method : 0.0007455348968505859 length of segment : 25 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 1236 time to create 1 rle with old method : 0.00156402587890625 length of segment : 60 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 1515 time to create 1 rle with old method : 0.0018901824951171875 length of segment : 56 time for calcul the mask position with numpy : 6.794929504394531e-05 nb_pixel_total : 348 time to create 1 rle with old method : 0.0005125999450683594 length of segment : 42 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 164 time to create 1 rle with old method : 0.0002751350402832031 length of segment : 14 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 870 time to create 1 rle with old method : 0.0012149810791015625 length of segment : 31 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 765 time to create 1 rle with old method : 0.0010426044464111328 length of segment : 30 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 941 time to create 1 rle with old method : 0.0012938976287841797 length of segment : 42 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004558563232421875 length of segment : 28 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 959 time to create 1 rle with old method : 0.0012769699096679688 length of segment : 40 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 605 time to create 1 rle with old method : 0.0008075237274169922 length of segment : 64 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 290 time to create 1 rle with old method : 0.0004105567932128906 length of segment : 22 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 468 time to create 1 rle with old method : 0.0006990432739257812 length of segment : 44 time for calcul the mask position with numpy : 7.82012939453125e-05 nb_pixel_total : 1182 time to create 1 rle with old method : 0.0014805793762207031 length of segment : 60 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 349 time to create 1 rle with old method : 0.00048470497131347656 length of segment : 29 time for calcul the mask position with numpy : 9.918212890625e-05 nb_pixel_total : 610 time to create 1 rle with old method : 0.0008268356323242188 length of segment : 50 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 510 time to create 1 rle with old method : 0.0006732940673828125 length of segment : 25 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 454 time to create 1 rle with old method : 0.0006380081176757812 length of segment : 21 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 1272 time to create 1 rle with old method : 0.0015349388122558594 length of segment : 47 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 895 time to create 1 rle with old method : 0.0011625289916992188 length of segment : 37 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.0002186298370361328 length of segment : 32 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005924701690673828 length of segment : 36 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.0003597736358642578 length of segment : 21 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 481 time to create 1 rle with old method : 0.0006592273712158203 length of segment : 31 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 174 time to create 1 rle with old method : 0.00029730796813964844 length of segment : 17 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 430 time to create 1 rle with old method : 0.0006160736083984375 length of segment : 36 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 170 time to create 1 rle with old method : 0.0003323554992675781 length of segment : 16 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.00036644935607910156 length of segment : 33 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 163 time to create 1 rle with old method : 0.0002856254577636719 length of segment : 12 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 679 time to create 1 rle with old method : 0.0009434223175048828 length of segment : 29 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 1254 time to create 1 rle with old method : 0.0016183853149414062 length of segment : 45 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 699 time to create 1 rle with old method : 0.0009684562683105469 length of segment : 28 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 853 time to create 1 rle with old method : 0.0011913776397705078 length of segment : 31 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 960 time to create 1 rle with old method : 0.0012881755828857422 length of segment : 38 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 485 time to create 1 rle with old method : 0.0006701946258544922 length of segment : 30 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 1340 time to create 1 rle with old method : 0.0016491413116455078 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: 145.52187 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 18 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 55 time to create 1 rle with old method : 0.00014066696166992188 length of segment : 12 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 446 time to create 1 rle with old method : 0.0008785724639892578 length of segment : 27 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 469 time to create 1 rle with old method : 0.001081705093383789 length of segment : 38 time for calcul the mask position with numpy : 8.344650268554688e-05 nb_pixel_total : 1988 time to create 1 rle with old method : 0.0024938583374023438 length of segment : 57 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 596 time to create 1 rle with old method : 0.0011370182037353516 length of segment : 58 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.00022292137145996094 length of segment : 17 time for calcul the mask position with numpy : 9.775161743164062e-05 nb_pixel_total : 539 time to create 1 rle with old method : 0.0008432865142822266 length of segment : 43 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.00044035911560058594 length of segment : 28 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 452 time to create 1 rle with old method : 0.0006461143493652344 length of segment : 34 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00020813941955566406 length of segment : 12 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004885196685791016 length of segment : 19 time for calcul the mask position with numpy : 9.512901306152344e-05 nb_pixel_total : 824 time to create 1 rle with old method : 0.0011012554168701172 length of segment : 70 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 138 time to create 1 rle with old method : 0.0002613067626953125 length of segment : 12 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 1167 time to create 1 rle with old method : 0.0015933513641357422 length of segment : 45 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 85 time to create 1 rle with old method : 0.0001583099365234375 length of segment : 12 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 666 time to create 1 rle with old method : 0.0008594989776611328 length of segment : 63 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0004305839538574219 length of segment : 30 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 294 time to create 1 rle with old method : 0.00043201446533203125 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: 136.85000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 106 time to create 1 rle with old method : 0.00019931793212890625 length of segment : 11 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.0001926422119140625 length of segment : 17 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 79 time to create 1 rle with old method : 0.00015616416931152344 length of segment : 9 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.00029158592224121094 length of segment : 12 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 54 time to create 1 rle with old method : 0.0001304149627685547 length of segment : 10 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 454 time to create 1 rle with old method : 0.0006580352783203125 length of segment : 55 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: -118.65313 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 : 5.8650970458984375e-05 nb_pixel_total : 208 time to create 1 rle with old method : 0.0003407001495361328 length of segment : 22 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 230 time to create 1 rle with old method : 0.00035953521728515625 length of segment : 38 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 48 time to create 1 rle with old method : 0.00010013580322265625 length of segment : 12 time for calcul the mask position with numpy : 8.940696716308594e-05 nb_pixel_total : 2730 time to create 1 rle with old method : 0.0034427642822265625 length of segment : 76 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 273 time to create 1 rle with old method : 0.00039577484130859375 length of segment : 34 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 403 time to create 1 rle with old method : 0.0008442401885986328 length of segment : 51 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 1331 time to create 1 rle with old method : 0.0024919509887695312 length of segment : 77 time for calcul the mask position with numpy : 0.00014543533325195312 nb_pixel_total : 2542 time to create 1 rle with old method : 0.003162860870361328 length of segment : 81 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 52 time to create 1 rle with old method : 0.00010275840759277344 length of segment : 15 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.16875 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 : 6.651878356933594e-05 nb_pixel_total : 2495 time to create 1 rle with old method : 0.002996206283569336 length of segment : 55 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 858 time to create 1 rle with old method : 0.0010821819305419922 length of segment : 48 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 707 time to create 1 rle with old method : 0.0009362697601318359 length of segment : 41 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 30 time to create 1 rle with old method : 8.869171142578125e-05 length of segment : 5 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 2227 time to create 1 rle with old method : 0.0028336048126220703 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: -120.65313 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 : 6.127357482910156e-05 nb_pixel_total : 2287 time to create 1 rle with old method : 0.003010988235473633 length of segment : 46 time for calcul the mask position with numpy : 0.00012183189392089844 nb_pixel_total : 1992 time to create 1 rle with old method : 0.002536773681640625 length of segment : 58 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 100 time to create 1 rle with old method : 0.00033211708068847656 length of segment : 15 time for calcul the mask position with numpy : 0.00011014938354492188 nb_pixel_total : 2372 time to create 1 rle with old method : 0.002983570098876953 length of segment : 58 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.01641 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 : 8.368492126464844e-05 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0014858245849609375 length of segment : 79 time for calcul the mask position with numpy : 0.0002224445343017578 nb_pixel_total : 6489 time to create 1 rle with old method : 0.0078046321868896484 length of segment : 124 time for calcul the mask position with numpy : 0.0001354217529296875 nb_pixel_total : 927 time to create 1 rle with old method : 0.0012288093566894531 length of segment : 44 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 1048 time to create 1 rle with old method : 0.0013840198516845703 length of segment : 54 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 201 time to create 1 rle with old method : 0.0003147125244140625 length of segment : 17 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 364 time to create 1 rle with old method : 0.0005035400390625 length of segment : 37 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1045 time to create 1 rle with old method : 0.0013055801391601562 length of segment : 60 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.00023293495178222656 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: 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 : 5.602836608886719e-05 nb_pixel_total : 550 time to create 1 rle with old method : 0.0007541179656982422 length of segment : 30 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 797 time to create 1 rle with old method : 0.0010690689086914062 length of segment : 48 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 1271 time to create 1 rle with old method : 0.0016050338745117188 length of segment : 55 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 449 time to create 1 rle with old method : 0.0006229877471923828 length of segment : 41 time for calcul the mask position with numpy : 0.00010633468627929688 nb_pixel_total : 2745 time to create 1 rle with old method : 0.0033731460571289062 length of segment : 53 time for calcul the mask position with numpy : 0.0001068115234375 nb_pixel_total : 1701 time to create 1 rle with old method : 0.003092527389526367 length of segment : 167 time for calcul the mask position with numpy : 0.0006146430969238281 nb_pixel_total : 54009 time to create 1 rle with old method : 0.06360983848571777 length of segment : 291 Processing 1 images image shape: (400, 400, 3) min: 14.00000 max: 205.00000 molded_images shape: (1, 640, 640, 3) min: -92.41875 max: 76.95156 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: -118.85234 max: 149.42422 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 8.654594421386719e-05 nb_pixel_total : 1228 time to create 1 rle with old method : 0.0020716190338134766 length of segment : 48 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.0004024505615234375 length of segment : 20 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 449 time to create 1 rle with old method : 0.0005943775177001953 length of segment : 39 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 82 time to create 1 rle with old method : 0.00014734268188476562 length of segment : 11 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 574 time to create 1 rle with old method : 0.0008218288421630859 length of segment : 33 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 157 time to create 1 rle with old method : 0.00029540061950683594 length of segment : 13 time for calcul the mask position with numpy : 0.00014853477478027344 nb_pixel_total : 3612 time to create 1 rle with old method : 0.004561424255371094 length of segment : 144 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 327 time to create 1 rle with old method : 0.0005872249603271484 length of segment : 23 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 459 time to create 1 rle with old method : 0.0007905960083007812 length of segment : 37 time for calcul the mask position with numpy : 8.487701416015625e-05 nb_pixel_total : 1447 time to create 1 rle with old method : 0.0019838809967041016 length of segment : 48 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 271 time to create 1 rle with old method : 0.00041294097900390625 length of segment : 23 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00019598007202148438 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: -122.57109 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 : 4.792213439941406e-05 nb_pixel_total : 456 time to create 1 rle with old method : 0.0006282329559326172 length of segment : 30 time for calcul the mask position with numpy : 0.0002772808074951172 nb_pixel_total : 4229 time to create 1 rle with old method : 0.005445241928100586 length of segment : 144 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 402 time to create 1 rle with old method : 0.0005695819854736328 length of segment : 19 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 1053 time to create 1 rle with old method : 0.0013751983642578125 length of segment : 43 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 461 time to create 1 rle with old method : 0.0007181167602539062 length of segment : 35 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 434 time to create 1 rle with old method : 0.0006124973297119141 length of segment : 20 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 521 time to create 1 rle with old method : 0.0008165836334228516 length of segment : 27 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 58 time to create 1 rle with old method : 0.00023508071899414062 length of segment : 8 time for calcul the mask position with numpy : 0.00045108795166015625 nb_pixel_total : 13730 time to create 1 rle with old method : 0.015837430953979492 length of segment : 210 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002834796905517578 length of segment : 19 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 1019 time to create 1 rle with old method : 0.001310586929321289 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: -122.57109 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 : 5.53131103515625e-05 nb_pixel_total : 406 time to create 1 rle with old method : 0.0005826950073242188 length of segment : 28 time for calcul the mask position with numpy : 0.0001285076141357422 nb_pixel_total : 2107 time to create 1 rle with old method : 0.0026624202728271484 length of segment : 143 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 683 time to create 1 rle with old method : 0.0009472370147705078 length of segment : 31 time for calcul the mask position with numpy : 0.0002300739288330078 nb_pixel_total : 12298 time to create 1 rle with old method : 0.014525175094604492 length of segment : 136 time for calcul the mask position with numpy : 0.00011348724365234375 nb_pixel_total : 1358 time to create 1 rle with old method : 0.0020170211791992188 length of segment : 41 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 1335 time to create 1 rle with old method : 0.0018525123596191406 length of segment : 41 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.0003418922424316406 length of segment : 25 time for calcul the mask position with numpy : 0.00012636184692382812 nb_pixel_total : 3533 time to create 1 rle with old method : 0.004370689392089844 length of segment : 142 time for calcul the mask position with numpy : 0.00018262863159179688 nb_pixel_total : 8924 time to create 1 rle with old method : 0.010921478271484375 length of segment : 114 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: 141.23672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 0.00012159347534179688 nb_pixel_total : 743 time to create 1 rle with old method : 0.001027822494506836 length of segment : 69 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0006287097930908203 length of segment : 50 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 67 time to create 1 rle with old method : 0.0001804828643798828 length of segment : 23 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 233 time to create 1 rle with old method : 0.0003719329833984375 length of segment : 18 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 220 time to create 1 rle with old method : 0.0003132820129394531 length of segment : 21 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 808 time to create 1 rle with old method : 0.0010383129119873047 length of segment : 43 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 530 time to create 1 rle with old method : 0.0007588863372802734 length of segment : 30 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 1547 time to create 1 rle with old method : 0.002023935317993164 length of segment : 76 time for calcul the mask position with numpy : 0.0002548694610595703 nb_pixel_total : 2432 time to create 1 rle with old method : 0.0037276744842529297 length of segment : 182 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 533 time to create 1 rle with old method : 0.0007593631744384766 length of segment : 31 Processing 1 images image shape: (280, 400, 3) min: 12.00000 max: 176.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 58.95156 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0014922618865966797 nb_pixel_total : 106285 time to create 1 rle with old method : 0.11538887023925781 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: 143.18984 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 0.00014495849609375 nb_pixel_total : 5021 time to create 1 rle with old method : 0.00623011589050293 length of segment : 89 time for calcul the mask position with numpy : 0.00012731552124023438 nb_pixel_total : 6627 time to create 1 rle with old method : 0.008230447769165039 length of segment : 91 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 890 time to create 1 rle with old method : 0.001165628433227539 length of segment : 41 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.00018262863159179688 length of segment : 16 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 352 time to create 1 rle with old method : 0.0005195140838623047 length of segment : 21 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0007097721099853516 length of segment : 23 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 627 time to create 1 rle with old method : 0.0008566379547119141 length of segment : 26 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.00017690658569335938 length of segment : 21 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 1795 time to create 1 rle with old method : 0.002274751663208008 length of segment : 72 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 203 time to create 1 rle with old method : 0.0002837181091308594 length of segment : 21 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 300 time to create 1 rle with old method : 0.0004410743713378906 length of segment : 26 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.00022721290588378906 length of segment : 15 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 1224 time to create 1 rle with old method : 0.0014979839324951172 length of segment : 48 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 607 time to create 1 rle with old method : 0.0008127689361572266 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 : 42 time for calcul the mask position with numpy : 9.822845458984375e-05 nb_pixel_total : 271 time to create 1 rle with old method : 0.0005505084991455078 length of segment : 16 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 464 time to create 1 rle with old method : 0.0005998611450195312 length of segment : 36 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.00026416778564453125 length of segment : 26 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 796 time to create 1 rle with old method : 0.0010530948638916016 length of segment : 40 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0004146099090576172 length of segment : 19 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 209 time to create 1 rle with old method : 0.0003521442413330078 length of segment : 17 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 486 time to create 1 rle with old method : 0.0006961822509765625 length of segment : 26 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 1468 time to create 1 rle with old method : 0.0017867088317871094 length of segment : 60 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 862 time to create 1 rle with old method : 0.0011341571807861328 length of segment : 52 time for calcul the mask position with numpy : 0.00017142295837402344 nb_pixel_total : 5231 time to create 1 rle with old method : 0.005891084671020508 length of segment : 122 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 306 time to create 1 rle with old method : 0.0004241466522216797 length of segment : 23 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 341 time to create 1 rle with old method : 0.00048351287841796875 length of segment : 35 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 976 time to create 1 rle with old method : 0.0012793540954589844 length of segment : 40 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 873 time to create 1 rle with old method : 0.0010673999786376953 length of segment : 67 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 467 time to create 1 rle with old method : 0.0006389617919921875 length of segment : 20 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 630 time to create 1 rle with old method : 0.0008702278137207031 length of segment : 38 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 365 time to create 1 rle with old method : 0.0005166530609130859 length of segment : 29 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.00048828125 length of segment : 29 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 544 time to create 1 rle with old method : 0.0007832050323486328 length of segment : 23 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 241 time to create 1 rle with old method : 0.0003459453582763672 length of segment : 19 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 586 time to create 1 rle with old method : 0.0007905960083007812 length of segment : 55 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0005495548248291016 length of segment : 40 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 453 time to create 1 rle with old method : 0.0006287097930908203 length of segment : 41 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 238 time to create 1 rle with old method : 0.000335693359375 length of segment : 34 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 849 time to create 1 rle with old method : 0.0011789798736572266 length of segment : 26 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 527 time to create 1 rle with old method : 0.0006771087646484375 length of segment : 35 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0006463527679443359 length of segment : 36 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 930 time to create 1 rle with old method : 0.0012214183807373047 length of segment : 46 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 873 time to create 1 rle with old method : 0.0011043548583984375 length of segment : 36 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 1079 time to create 1 rle with old method : 0.001283884048461914 length of segment : 46 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0008530616760253906 length of segment : 19 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.0011866092681884766 length of segment : 36 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 192 time to create 1 rle with old method : 0.0004394054412841797 length of segment : 30 time for calcul the mask position with numpy : 8.654594421386719e-05 nb_pixel_total : 1056 time to create 1 rle with old method : 0.001382589340209961 length of segment : 47 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 676 time to create 1 rle with old method : 0.0009338855743408203 length of segment : 26 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 798 time to create 1 rle with old method : 0.0010371208190917969 length of segment : 35 time for calcul the mask position with numpy : 8.845329284667969e-05 nb_pixel_total : 1203 time to create 1 rle with old method : 0.001683950424194336 length of segment : 76 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 1175 time to create 1 rle with old method : 0.0014984607696533203 length of segment : 48 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 1030 time to create 1 rle with old method : 0.0014376640319824219 length of segment : 39 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 635 time to create 1 rle with old method : 0.0008320808410644531 length of segment : 36 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 468 time to create 1 rle with old method : 0.0006616115570068359 length of segment : 33 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 867 time to create 1 rle with old method : 0.0012128353118896484 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: 145.52578 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 20 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 444 time to create 1 rle with old method : 0.0006251335144042969 length of segment : 27 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 51 time to create 1 rle with old method : 9.131431579589844e-05 length of segment : 11 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 2575 time to create 1 rle with old method : 0.0032205581665039062 length of segment : 69 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.0002276897430419922 length of segment : 13 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 289 time to create 1 rle with old method : 0.0004279613494873047 length of segment : 19 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 280 time to create 1 rle with old method : 0.00039267539978027344 length of segment : 25 time for calcul the mask position with numpy : 0.00010323524475097656 nb_pixel_total : 1261 time to create 1 rle with old method : 0.0016505718231201172 length of segment : 74 time for calcul the mask position with numpy : 9.775161743164062e-05 nb_pixel_total : 1451 time to create 1 rle with old method : 0.0017898082733154297 length of segment : 101 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 144 time to create 1 rle with old method : 0.0002231597900390625 length of segment : 16 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 142 time to create 1 rle with old method : 0.00019407272338867188 length of segment : 26 time for calcul the mask position with numpy : 8.559226989746094e-05 nb_pixel_total : 1540 time to create 1 rle with old method : 0.001964092254638672 length of segment : 91 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 522 time to create 1 rle with old method : 0.0007541179656982422 length of segment : 62 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 664 time to create 1 rle with old method : 0.0008723735809326172 length of segment : 39 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 113 time to create 1 rle with old method : 0.0001952648162841797 length of segment : 10 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0004763603210449219 length of segment : 28 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 65 time to create 1 rle with old method : 0.00011324882507324219 length of segment : 14 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.0002429485321044922 length of segment : 13 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 587 time to create 1 rle with old method : 0.0009758472442626953 length of segment : 46 time for calcul the mask position with numpy : 9.417533874511719e-05 nb_pixel_total : 2234 time to create 1 rle with old method : 0.0029456615447998047 length of segment : 151 time for calcul the mask position with numpy : 9.441375732421875e-05 nb_pixel_total : 1771 time to create 1 rle with old method : 0.002314329147338867 length of segment : 88 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: 135.78750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 144 time to create 1 rle with old method : 0.00024175643920898438 length of segment : 12 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 268 time to create 1 rle with old method : 0.00042557716369628906 length of segment : 18 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 69 time to create 1 rle with old method : 0.00015687942504882812 length of segment : 9 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0007472038269042969 length of segment : 58 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.000339508056640625 length of segment : 14 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.0002028942108154297 length of segment : 29 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: -118.07891 max: 149.03359 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 195 time to create 1 rle with old method : 0.00030922889709472656 length of segment : 34 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.00029921531677246094 length of segment : 8 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 52 time to create 1 rle with old method : 9.560585021972656e-05 length of segment : 19 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.0002818107604980469 length of segment : 26 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00012683868408203125 length of segment : 17 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 261 time to create 1 rle with old method : 0.00041174888610839844 length of segment : 34 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 311 time to create 1 rle with old method : 0.00046896934509277344 length of segment : 32 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 284 time to create 1 rle with old method : 0.00042819976806640625 length of segment : 34 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.00030517578125 length of segment : 41 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 192 time to create 1 rle with old method : 0.0003066062927246094 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: -116.37188 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 : 6.580352783203125e-05 nb_pixel_total : 2512 time to create 1 rle with old method : 0.0030679702758789062 length of segment : 53 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 770 time to create 1 rle with old method : 0.0017404556274414062 length of segment : 48 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 36 time to create 1 rle with old method : 8.463859558105469e-05 length of segment : 6 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.0002665519714355469 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: -120.75469 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 : 5.936622619628906e-05 nb_pixel_total : 1488 time to create 1 rle with old method : 0.0019609928131103516 length of segment : 40 time for calcul the mask position with numpy : 9.799003601074219e-05 nb_pixel_total : 4289 time to create 1 rle with old method : 0.00548100471496582 length of segment : 56 time for calcul the mask position with numpy : 0.0001068115234375 nb_pixel_total : 1856 time to create 1 rle with old method : 0.0024695396423339844 length of segment : 65 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 320 time to create 1 rle with old method : 0.0008070468902587891 length of segment : 17 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 2134 time to create 1 rle with old method : 0.0028514862060546875 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: -116.98516 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 : 0.00013780593872070312 nb_pixel_total : 6069 time to create 1 rle with old method : 0.007682323455810547 length of segment : 102 time for calcul the mask position with numpy : 9.107589721679688e-05 nb_pixel_total : 1766 time to create 1 rle with old method : 0.0022890567779541016 length of segment : 106 time for calcul the mask position with numpy : 8.296966552734375e-05 nb_pixel_total : 1140 time to create 1 rle with old method : 0.0014410018920898438 length of segment : 56 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 200 time to create 1 rle with old method : 0.0003066062927246094 length of segment : 18 time for calcul the mask position with numpy : 8.344650268554688e-05 nb_pixel_total : 1774 time to create 1 rle with old method : 0.0021867752075195312 length of segment : 103 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0009365081787109375 length of segment : 11 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 1065 time to create 1 rle with old method : 0.0012977123260498047 length of segment : 59 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0006062984466552734 length of segment : 22 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 740 time to create 1 rle with old method : 0.0009768009185791016 length of segment : 43 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.00021529197692871094 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: 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 : 4.363059997558594e-05 nb_pixel_total : 442 time to create 1 rle with old method : 0.000579833984375 length of segment : 29 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 863 time to create 1 rle with old method : 0.001155853271484375 length of segment : 51 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 644 time to create 1 rle with old method : 0.0010242462158203125 length of segment : 50 time for calcul the mask position with numpy : 9.083747863769531e-05 nb_pixel_total : 2819 time to create 1 rle with old method : 0.0035364627838134766 length of segment : 53 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1090 time to create 1 rle with old method : 0.0013494491577148438 length of segment : 51 time for calcul the mask position with numpy : 0.0006203651428222656 nb_pixel_total : 53251 time to create 1 rle with old method : 0.06461811065673828 length of segment : 281 time for calcul the mask position with numpy : 8.630752563476562e-05 nb_pixel_total : 1236 time to create 1 rle with old method : 0.0019106864929199219 length of segment : 52 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 2889 time to create 1 rle with old method : 0.0036628246307373047 length of segment : 54 Processing 1 images image shape: (400, 400, 3) min: 15.00000 max: 202.00000 molded_images shape: (1, 640, 640, 3) min: -94.14141 max: 72.68203 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: -118.91484 max: 150.33828 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1353 time to create 1 rle with old method : 0.0020990371704101562 length of segment : 46 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.00029468536376953125 length of segment : 18 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.0002765655517578125 length of segment : 13 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 111 time to create 1 rle with old method : 0.00019097328186035156 length of segment : 12 time for calcul the mask position with numpy : 0.00010991096496582031 nb_pixel_total : 1967 time to create 1 rle with old method : 0.002656698226928711 length of segment : 58 time for calcul the mask position with numpy : 0.00012183189392089844 nb_pixel_total : 485 time to create 1 rle with old method : 0.0006206035614013672 length of segment : 43 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 337 time to create 1 rle with old method : 0.00046563148498535156 length of segment : 27 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 112 time to create 1 rle with old method : 0.0001919269561767578 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: -121.23516 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 : 5.340576171875e-05 nb_pixel_total : 636 time to create 1 rle with old method : 0.000982046127319336 length of segment : 54 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006768703460693359 length of segment : 29 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 676 time to create 1 rle with old method : 0.0009028911590576172 length of segment : 32 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 263 time to create 1 rle with old method : 0.00040531158447265625 length of segment : 24 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 511 time to create 1 rle with old method : 0.0007364749908447266 length of segment : 25 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 1039 time to create 1 rle with old method : 0.0013740062713623047 length of segment : 42 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.00019216537475585938 length of segment : 9 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 111 time to create 1 rle with old method : 0.0001919269561767578 length of segment : 15 time for calcul the mask position with numpy : 0.00036525726318359375 nb_pixel_total : 9306 time to create 1 rle with old method : 0.011236906051635742 length of segment : 156 time for calcul the mask position with numpy : 0.00011777877807617188 nb_pixel_total : 1030 time to create 1 rle with old method : 0.001373291015625 length of segment : 46 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 1173 time to create 1 rle with old method : 0.0015361309051513672 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: -122.55938 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 : 5.459785461425781e-05 nb_pixel_total : 356 time to create 1 rle with old method : 0.0006191730499267578 length of segment : 27 time for calcul the mask position with numpy : 0.00018644332885742188 nb_pixel_total : 12642 time to create 1 rle with old method : 0.014583587646484375 length of segment : 136 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 1330 time to create 1 rle with old method : 0.0016732215881347656 length of segment : 40 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 718 time to create 1 rle with old method : 0.0009572505950927734 length of segment : 31 time for calcul the mask position with numpy : 0.0001347064971923828 nb_pixel_total : 1968 time to create 1 rle with old method : 0.0025284290313720703 length of segment : 158 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 648 time to create 1 rle with old method : 0.0008628368377685547 length of segment : 39 time for calcul the mask position with numpy : 9.417533874511719e-05 nb_pixel_total : 1011 time to create 1 rle with old method : 0.0014684200286865234 length of segment : 35 time for calcul the mask position with numpy : 0.00010204315185546875 nb_pixel_total : 3118 time to create 1 rle with old method : 0.00391077995300293 length of segment : 136 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 1075 time to create 1 rle with old method : 0.001535654067993164 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: 141.35000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 270 time to create 1 rle with old method : 0.0003974437713623047 length of segment : 24 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 215 time to create 1 rle with old method : 0.00033354759216308594 length of segment : 41 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 474 time to create 1 rle with old method : 0.0006666183471679688 length of segment : 49 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 1264 time to create 1 rle with old method : 0.0015556812286376953 length of segment : 50 time for calcul the mask position with numpy : 9.441375732421875e-05 nb_pixel_total : 1536 time to create 1 rle with old method : 0.002027750015258789 length of segment : 77 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 809 time to create 1 rle with old method : 0.0010721683502197266 length of segment : 41 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.00018906593322753906 length of segment : 11 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 806 time to create 1 rle with old method : 0.0010600090026855469 length of segment : 68 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.00032973289489746094 length of segment : 21 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 552 time to create 1 rle with old method : 0.0007588863372802734 length of segment : 58 time for calcul the mask position with numpy : 0.00020742416381835938 nb_pixel_total : 2393 time to create 1 rle with old method : 0.002908945083618164 length of segment : 169 Processing 1 images image shape: (280, 400, 3) min: 4.00000 max: 177.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 61.45937 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0008924007415771484 nb_pixel_total : 106685 time to create 1 rle with old method : 0.12960314750671387 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: 145.53359 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 942 time to create 1 rle with old method : 0.0012156963348388672 length of segment : 45 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0005955696105957031 length of segment : 25 time for calcul the mask position with numpy : 0.00015163421630859375 nb_pixel_total : 5689 time to create 1 rle with old method : 0.00793313980102539 length of segment : 105 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 157 time to create 1 rle with old method : 0.0003330707550048828 length of segment : 14 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 217 time to create 1 rle with old method : 0.0003783702850341797 length of segment : 21 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 1740 time to create 1 rle with old method : 0.002241373062133789 length of segment : 71 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00020837783813476562 length of segment : 16 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 313 time to create 1 rle with old method : 0.00046563148498535156 length of segment : 26 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 123 time to create 1 rle with old method : 0.00018596649169921875 length of segment : 19 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 115 time to create 1 rle with old method : 0.00017642974853515625 length of segment : 18 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 1856 time to create 1 rle with old method : 0.002859830856323242 length of segment : 50 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.00024008750915527344 length of segment : 14 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 863 time to create 1 rle with old method : 0.0011417865753173828 length of segment : 55 time for calcul the mask position with numpy : 0.0001327991485595703 nb_pixel_total : 3686 time to create 1 rle with old method : 0.004507780075073242 length of segment : 125 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 : 7.367134094238281e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.0002913475036621094 length of segment : 26 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0006799697875976562 length of segment : 37 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 284 time to create 1 rle with old method : 0.00043964385986328125 length of segment : 15 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 603 time to create 1 rle with old method : 0.0007982254028320312 length of segment : 35 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 284 time to create 1 rle with old method : 0.0004391670227050781 length of segment : 21 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003643035888671875 length of segment : 33 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 219 time to create 1 rle with old method : 0.00033593177795410156 length of segment : 17 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0007038116455078125 length of segment : 23 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 1113 time to create 1 rle with old method : 0.0019631385803222656 length of segment : 47 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 432 time to create 1 rle with old method : 0.0006175041198730469 length of segment : 33 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 786 time to create 1 rle with old method : 0.001032114028930664 length of segment : 56 time for calcul the mask position with numpy : 9.179115295410156e-05 nb_pixel_total : 496 time to create 1 rle with old method : 0.0009572505950927734 length of segment : 31 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 365 time to create 1 rle with old method : 0.0004868507385253906 length of segment : 29 time for calcul the mask position with numpy : 0.00010037422180175781 nb_pixel_total : 455 time to create 1 rle with old method : 0.0006275177001953125 length of segment : 22 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 303 time to create 1 rle with old method : 0.0006639957427978516 length of segment : 23 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 1433 time to create 1 rle with old method : 0.0017676353454589844 length of segment : 54 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 364 time to create 1 rle with old method : 0.0005273818969726562 length of segment : 38 time for calcul the mask position with numpy : 0.0006961822509765625 nb_pixel_total : 7369 time to create 1 rle with old method : 0.014598846435546875 length of segment : 153 time for calcul the mask position with numpy : 0.000148773193359375 nb_pixel_total : 1486 time to create 1 rle with old method : 0.003022909164428711 length of segment : 55 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 934 time to create 1 rle with old method : 0.0012485980987548828 length of segment : 42 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 466 time to create 1 rle with old method : 0.0006833076477050781 length of segment : 19 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 758 time to create 1 rle with old method : 0.0009348392486572266 length of segment : 64 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 737 time to create 1 rle with old method : 0.0009968280792236328 length of segment : 30 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 968 time to create 1 rle with old method : 0.001340627670288086 length of segment : 43 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 898 time to create 1 rle with old method : 0.0012562274932861328 length of segment : 30 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 841 time to create 1 rle with old method : 0.0010840892791748047 length of segment : 34 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 1273 time to create 1 rle with old method : 0.001644134521484375 length of segment : 48 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 192 time to create 1 rle with old method : 0.0003180503845214844 length of segment : 15 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.0001926422119140625 length of segment : 13 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 685 time to create 1 rle with old method : 0.0008974075317382812 length of segment : 54 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 435 time to create 1 rle with old method : 0.0005600452423095703 length of segment : 36 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 857 time to create 1 rle with old method : 0.0011284351348876953 length of segment : 36 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 1217 time to create 1 rle with old method : 0.0015113353729248047 length of segment : 49 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 566 time to create 1 rle with old method : 0.0007414817810058594 length of segment : 37 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 556 time to create 1 rle with old method : 0.0007390975952148438 length of segment : 38 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: 145.49844 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 18 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 418 time to create 1 rle with old method : 0.0006351470947265625 length of segment : 18 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 421 time to create 1 rle with old method : 0.0005884170532226562 length of segment : 26 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 317 time to create 1 rle with old method : 0.00042724609375 length of segment : 28 time for calcul the mask position with numpy : 9.512901306152344e-05 nb_pixel_total : 1780 time to create 1 rle with old method : 0.0022966861724853516 length of segment : 63 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.0002167224884033203 length of segment : 14 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 859 time to create 1 rle with old method : 0.0012772083282470703 length of segment : 46 time for calcul the mask position with numpy : 0.00017571449279785156 nb_pixel_total : 1338 time to create 1 rle with old method : 0.001749277114868164 length of segment : 93 time for calcul the mask position with numpy : 8.845329284667969e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.00021600723266601562 length of segment : 15 time for calcul the mask position with numpy : 0.0001316070556640625 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0015883445739746094 length of segment : 61 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 253 time to create 1 rle with old method : 0.0004038810729980469 length of segment : 16 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 43 time to create 1 rle with old method : 9.894371032714844e-05 length of segment : 11 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 513 time to create 1 rle with old method : 0.0007033348083496094 length of segment : 40 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 136 time to create 1 rle with old method : 0.0002655982971191406 length of segment : 11 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 69 time to create 1 rle with old method : 0.00013566017150878906 length of segment : 16 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 306 time to create 1 rle with old method : 0.0004305839538574219 length of segment : 28 time for calcul the mask position with numpy : 0.0001838207244873047 nb_pixel_total : 1557 time to create 1 rle with old method : 0.0019986629486083984 length of segment : 107 time for calcul the mask position with numpy : 0.00018095970153808594 nb_pixel_total : 1891 time to create 1 rle with old method : 0.0025377273559570312 length of segment : 74 time for calcul the mask position with numpy : 0.00012755393981933594 nb_pixel_total : 1708 time to create 1 rle with old method : 0.0022819042205810547 length of segment : 80 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: 135.28750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.0002391338348388672 length of segment : 13 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 84 time to create 1 rle with old method : 0.00016951560974121094 length of segment : 10 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 263 time to create 1 rle with old method : 0.0004241466522216797 length of segment : 19 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 230 time to create 1 rle with old method : 0.0005180835723876953 length of segment : 39 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.0003178119659423828 length of segment : 13 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 179 time to create 1 rle with old method : 0.0002651214599609375 length of segment : 30 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: -118.33672 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 : 0.00010204315185546875 nb_pixel_total : 48 time to create 1 rle with old method : 0.000156402587890625 length of segment : 13 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.00030732154846191406 length of segment : 33 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 261 time to create 1 rle with old method : 0.0004341602325439453 length of segment : 33 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 223 time to create 1 rle with old method : 0.0003554821014404297 length of segment : 27 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 217 time to create 1 rle with old method : 0.0003910064697265625 length of segment : 10 time for calcul the mask position with numpy : 8.368492126464844e-05 nb_pixel_total : 280 time to create 1 rle with old method : 0.0006389617919921875 length of segment : 35 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.0003654956817626953 length of segment : 22 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 27 time to create 1 rle with old method : 0.00013327598571777344 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: -116.44219 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 : 0.00012946128845214844 nb_pixel_total : 2771 time to create 1 rle with old method : 0.003470182418823242 length of segment : 57 time for calcul the mask position with numpy : 8.654594421386719e-05 nb_pixel_total : 712 time to create 1 rle with old method : 0.0009624958038330078 length of segment : 51 time for calcul the mask position with numpy : 9.226799011230469e-05 nb_pixel_total : 1043 time to create 1 rle with old method : 0.0015223026275634766 length of segment : 32 time for calcul the mask position with numpy : 9.560585021972656e-05 nb_pixel_total : 62 time to create 1 rle with old method : 0.00012946128845214844 length of segment : 17 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.04766 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 : 6.031990051269531e-05 nb_pixel_total : 1723 time to create 1 rle with old method : 0.002358675003051758 length of segment : 30 time for calcul the mask position with numpy : 0.00012946128845214844 nb_pixel_total : 2182 time to create 1 rle with old method : 0.0035228729248046875 length of segment : 82 time for calcul the mask position with numpy : 8.368492126464844e-05 nb_pixel_total : 2343 time to create 1 rle with old method : 0.003009319305419922 length of segment : 117 time for calcul the mask position with numpy : 0.00010752677917480469 nb_pixel_total : 1613 time to create 1 rle with old method : 0.0021600723266601562 length of segment : 128 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.0004925727844238281 length of segment : 15 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.37969 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 : 0.0001323223114013672 nb_pixel_total : 6412 time to create 1 rle with old method : 0.00844717025756836 length of segment : 124 time for calcul the mask position with numpy : 0.000141143798828125 nb_pixel_total : 1294 time to create 1 rle with old method : 0.0018107891082763672 length of segment : 96 time for calcul the mask position with numpy : 0.00012350082397460938 nb_pixel_total : 917 time to create 1 rle with old method : 0.0012314319610595703 length of segment : 49 time for calcul the mask position with numpy : 9.131431579589844e-05 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0013692378997802734 length of segment : 70 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.00028133392333984375 length of segment : 17 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 873 time to create 1 rle with old method : 0.0015788078308105469 length of segment : 58 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 : 8 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 540 time to create 1 rle with old method : 0.0008146762847900391 length of segment : 29 time for calcul the mask position with numpy : 8.344650268554688e-05 nb_pixel_total : 948 time to create 1 rle with old method : 0.0015530586242675781 length of segment : 52 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 950 time to create 1 rle with old method : 0.0011723041534423828 length of segment : 52 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 1114 time to create 1 rle with old method : 0.0013935565948486328 length of segment : 75 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 2966 time to create 1 rle with old method : 0.003628253936767578 length of segment : 51 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 147 time to create 1 rle with old method : 0.0002467632293701172 length of segment : 17 time for calcul the mask position with numpy : 9.72747802734375e-05 nb_pixel_total : 903 time to create 1 rle with old method : 0.0011756420135498047 length of segment : 96 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1051 time to create 1 rle with old method : 0.0013341903686523438 length of segment : 51 Processing 1 images image shape: (400, 400, 3) min: 12.00000 max: 209.00000 molded_images shape: (1, 640, 640, 3) min: -97.98125 max: 80.95156 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0014493465423583984 nb_pixel_total : 151054 time to create 1 rle with new method : 0.002146482467651367 length of segment : 398 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.02031 max: 149.42422 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1299 time to create 1 rle with old method : 0.0016329288482666016 length of segment : 48 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00020837783813476562 length of segment : 12 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 151 time to create 1 rle with old method : 0.0002410411834716797 length of segment : 13 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.0003209114074707031 length of segment : 19 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 388 time to create 1 rle with old method : 0.0005724430084228516 length of segment : 23 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 1426 time to create 1 rle with old method : 0.0018846988677978516 length of segment : 39 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.0001926422119140625 length of segment : 12 time for calcul the mask position with numpy : 0.0001876354217529297 nb_pixel_total : 7171 time to create 1 rle with old method : 0.008629798889160156 length of segment : 89 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0005290508270263672 length of segment : 33 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 65 time to create 1 rle with old method : 0.00013399124145507812 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: -121.05938 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 : 7.677078247070312e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0009150505065917969 length of segment : 29 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 530 time to create 1 rle with old method : 0.0010395050048828125 length of segment : 28 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 658 time to create 1 rle with old method : 0.0010051727294921875 length of segment : 20 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 1047 time to create 1 rle with old method : 0.0013570785522460938 length of segment : 44 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 73 time to create 1 rle with old method : 0.00014662742614746094 length of segment : 8 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 364 time to create 1 rle with old method : 0.0006420612335205078 length of segment : 37 time for calcul the mask position with numpy : 0.0005278587341308594 nb_pixel_total : 12982 time to create 1 rle with old method : 0.015813112258911133 length of segment : 174 time for calcul the mask position with numpy : 0.00014781951904296875 nb_pixel_total : 5818 time to create 1 rle with old method : 0.007447481155395508 length of segment : 42 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.00019121170043945312 length of segment : 8 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.83281 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 : 4.100799560546875e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004999637603759766 length of segment : 24 time for calcul the mask position with numpy : 7.271766662597656e-05 nb_pixel_total : 737 time to create 1 rle with old method : 0.0009989738464355469 length of segment : 31 time for calcul the mask position with numpy : 0.0002193450927734375 nb_pixel_total : 13288 time to create 1 rle with old method : 0.01567220687866211 length of segment : 142 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 1354 time to create 1 rle with old method : 0.0016410350799560547 length of segment : 42 time for calcul the mask position with numpy : 0.00014543533325195312 nb_pixel_total : 1910 time to create 1 rle with old method : 0.002450704574584961 length of segment : 138 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 1332 time to create 1 rle with old method : 0.0017638206481933594 length of segment : 41 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 291 time to create 1 rle with old method : 0.0004024505615234375 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: 139.93203 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006909370422363281 length of segment : 48 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 33 time to create 1 rle with old method : 6.699562072753906e-05 length of segment : 10 time for calcul the mask position with numpy : 0.00010228157043457031 nb_pixel_total : 862 time to create 1 rle with old method : 0.001252889633178711 length of segment : 73 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 209 time to create 1 rle with old method : 0.0003478527069091797 length of segment : 24 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 804 time to create 1 rle with old method : 0.0010523796081542969 length of segment : 46 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.0004096031188964844 length of segment : 18 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 128 time to create 1 rle with old method : 0.00019598007202148438 length of segment : 19 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 944 time to create 1 rle with old method : 0.0012423992156982422 length of segment : 66 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 821 time to create 1 rle with old method : 0.0010881423950195312 length of segment : 46 time for calcul the mask position with numpy : 0.00034117698669433594 nb_pixel_total : 2401 time to create 1 rle with old method : 0.0029332637786865234 length of segment : 176 Processing 1 images image shape: (280, 400, 3) min: 10.00000 max: 171.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 53.55703 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.001293182373046875 nb_pixel_total : 106316 time to create 1 rle with old method : 0.12051916122436523 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: 143.45547 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 532 time to create 1 rle with old method : 0.0007677078247070312 length of segment : 24 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 950 time to create 1 rle with old method : 0.00118255615234375 length of segment : 42 time for calcul the mask position with numpy : 0.00011610984802246094 nb_pixel_total : 5645 time to create 1 rle with old method : 0.0069065093994140625 length of segment : 88 time for calcul the mask position with numpy : 9.489059448242188e-05 nb_pixel_total : 128 time to create 1 rle with old method : 0.0002205371856689453 length of segment : 21 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 676 time to create 1 rle with old method : 0.0009622573852539062 length of segment : 28 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 800 time to create 1 rle with old method : 0.0010609626770019531 length of segment : 39 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 314 time to create 1 rle with old method : 0.0004582405090332031 length of segment : 25 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 443 time to create 1 rle with old method : 0.0005891323089599609 length of segment : 33 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00021767616271972656 length of segment : 15 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 2009 time to create 1 rle with old method : 0.002427339553833008 length of segment : 50 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 368 time to create 1 rle with old method : 0.0005362033843994141 length of segment : 23 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 164 time to create 1 rle with old method : 0.00024700164794921875 length of segment : 15 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.00025534629821777344 length of segment : 15 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 381 time to create 1 rle with old method : 0.0005574226379394531 length of segment : 23 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.00030803680419921875 length of segment : 20 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 1050 time to create 1 rle with old method : 0.0013079643249511719 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 : 41 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.00026726722717285156 length of segment : 26 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 435 time to create 1 rle with old method : 0.0005669593811035156 length of segment : 36 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 727 time to create 1 rle with old method : 0.0009462833404541016 length of segment : 41 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 695 time to create 1 rle with old method : 0.0009059906005859375 length of segment : 45 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 298 time to create 1 rle with old method : 0.0007848739624023438 length of segment : 16 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.0007479190826416016 length of segment : 22 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.0006666183471679688 length of segment : 24 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 307 time to create 1 rle with old method : 0.00043010711669921875 length of segment : 24 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 966 time to create 1 rle with old method : 0.0013053417205810547 length of segment : 42 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1005 time to create 1 rle with old method : 0.0013568401336669922 length of segment : 53 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 521 time to create 1 rle with old method : 0.0007429122924804688 length of segment : 27 time for calcul the mask position with numpy : 9.72747802734375e-05 nb_pixel_total : 1266 time to create 1 rle with old method : 0.0021848678588867188 length of segment : 31 time for calcul the mask position with numpy : 9.417533874511719e-05 nb_pixel_total : 1231 time to create 1 rle with old method : 0.0015723705291748047 length of segment : 45 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 1610 time to create 1 rle with old method : 0.002044677734375 length of segment : 58 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 813 time to create 1 rle with old method : 0.0010843276977539062 length of segment : 35 time for calcul the mask position with numpy : 0.0001697540283203125 nb_pixel_total : 7385 time to create 1 rle with old method : 0.008633136749267578 length of segment : 138 time for calcul the mask position with numpy : 7.104873657226562e-05 nb_pixel_total : 421 time to create 1 rle with old method : 0.0005900859832763672 length of segment : 45 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 651 time to create 1 rle with old method : 0.0009386539459228516 length of segment : 36 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 338 time to create 1 rle with old method : 0.0004725456237792969 length of segment : 38 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 1530 time to create 1 rle with old method : 0.001958131790161133 length of segment : 53 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 582 time to create 1 rle with old method : 0.0008463859558105469 length of segment : 42 time for calcul the mask position with numpy : 0.00010824203491210938 nb_pixel_total : 1000 time to create 1 rle with old method : 0.0019488334655761719 length of segment : 41 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 524 time to create 1 rle with old method : 0.0007541179656982422 length of segment : 22 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 425 time to create 1 rle with old method : 0.0006136894226074219 length of segment : 34 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 85 time to create 1 rle with old method : 0.00024366378784179688 length of segment : 10 time for calcul the mask position with numpy : 9.226799011230469e-05 nb_pixel_total : 558 time to create 1 rle with old method : 0.0011279582977294922 length of segment : 38 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 368 time to create 1 rle with old method : 0.0005011558532714844 length of segment : 29 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 365 time to create 1 rle with old method : 0.0005693435668945312 length of segment : 19 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 633 time to create 1 rle with old method : 0.0008220672607421875 length of segment : 40 time for calcul the mask position with numpy : 8.940696716308594e-05 nb_pixel_total : 971 time to create 1 rle with old method : 0.0011968612670898438 length of segment : 68 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 768 time to create 1 rle with old method : 0.0012602806091308594 length of segment : 28 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 1189 time to create 1 rle with old method : 0.001474142074584961 length of segment : 47 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 1408 time to create 1 rle with old method : 0.0017962455749511719 length of segment : 61 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0006325244903564453 length of segment : 35 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003647804260253906 length of segment : 18 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 892 time to create 1 rle with old method : 0.0011706352233886719 length of segment : 39 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0005815029144287109 length of segment : 31 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 749 time to create 1 rle with old method : 0.0010037422180175781 length of segment : 28 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 882 time to create 1 rle with old method : 0.0011811256408691406 length of segment : 34 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 345 time to create 1 rle with old method : 0.0007126331329345703 length of segment : 29 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 502 time to create 1 rle with old method : 0.0006830692291259766 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: 145.52187 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 17 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 344 time to create 1 rle with old method : 0.0005338191986083984 length of segment : 17 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 623 time to create 1 rle with old method : 0.0009086132049560547 length of segment : 45 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 441 time to create 1 rle with old method : 0.0006680488586425781 length of segment : 26 time for calcul the mask position with numpy : 8.440017700195312e-05 nb_pixel_total : 2336 time to create 1 rle with old method : 0.003552675247192383 length of segment : 65 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 665 time to create 1 rle with old method : 0.0009167194366455078 length of segment : 55 time for calcul the mask position with numpy : 8.7738037109375e-05 nb_pixel_total : 451 time to create 1 rle with old method : 0.0009467601776123047 length of segment : 27 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 598 time to create 1 rle with old method : 0.0008208751678466797 length of segment : 38 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 928 time to create 1 rle with old method : 0.0013058185577392578 length of segment : 51 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0005097389221191406 length of segment : 34 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.00023555755615234375 length of segment : 16 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 324 time to create 1 rle with old method : 0.0004923343658447266 length of segment : 20 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006966590881347656 length of segment : 24 time for calcul the mask position with numpy : 7.82012939453125e-05 nb_pixel_total : 325 time to create 1 rle with old method : 0.0007472038269042969 length of segment : 25 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 177 time to create 1 rle with old method : 0.00036525726318359375 length of segment : 27 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 139 time to create 1 rle with old method : 0.0002067089080810547 length of segment : 27 time for calcul the mask position with numpy : 8.58306884765625e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0008206367492675781 length of segment : 38 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00020623207092285156 length of segment : 13 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: 132.10000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 508 time to create 1 rle with old method : 0.0011322498321533203 length of segment : 42 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 297 time to create 1 rle with old method : 0.0008065700531005859 length of segment : 22 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.00029730796813964844 length of segment : 30 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.0003554821014404297 length of segment : 13 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 84 time to create 1 rle with old method : 0.0001933574676513672 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: -118.26250 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 : 6.651878356933594e-05 nb_pixel_total : 208 time to create 1 rle with old method : 0.00046443939208984375 length of segment : 26 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 42 time to create 1 rle with old method : 8.034706115722656e-05 length of segment : 20 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.00035572052001953125 length of segment : 10 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 560 time to create 1 rle with old method : 0.0008256435394287109 length of segment : 63 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 93 time to create 1 rle with old method : 0.00014829635620117188 length of segment : 17 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 54 time to create 1 rle with old method : 9.441375732421875e-05 length of segment : 17 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 545 time to create 1 rle with old method : 0.0007994174957275391 length of segment : 41 time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 2494 time to create 1 rle with old method : 0.0032248497009277344 length of segment : 85 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 203 time to create 1 rle with old method : 0.000308990478515625 length of segment : 34 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.48516 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 : 0.000156402587890625 nb_pixel_total : 2802 time to create 1 rle with old method : 0.0036003589630126953 length of segment : 57 time for calcul the mask position with numpy : 0.00011157989501953125 nb_pixel_total : 863 time to create 1 rle with old method : 0.0013039112091064453 length of segment : 39 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.00036525726318359375 length of segment : 34 time for calcul the mask position with numpy : 0.00010776519775390625 nb_pixel_total : 2042 time to create 1 rle with old method : 0.002742767333984375 length of segment : 73 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00020551681518554688 length of segment : 24 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 488 time to create 1 rle with old method : 0.0006697177886962891 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: -121.43047 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 : 0.00012183189392089844 nb_pixel_total : 2039 time to create 1 rle with old method : 0.003204345703125 length of segment : 59 time for calcul the mask position with numpy : 0.0001251697540283203 nb_pixel_total : 1718 time to create 1 rle with old method : 0.0027735233306884766 length of segment : 45 time for calcul the mask position with numpy : 0.00012612342834472656 nb_pixel_total : 947 time to create 1 rle with old method : 0.001447916030883789 length of segment : 49 time for calcul the mask position with numpy : 0.00011968612670898438 nb_pixel_total : 889 time to create 1 rle with old method : 0.0014984607696533203 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: -112.39141 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 : 0.0001819133758544922 nb_pixel_total : 6151 time to create 1 rle with old method : 0.008536577224731445 length of segment : 132 time for calcul the mask position with numpy : 0.00013303756713867188 nb_pixel_total : 887 time to create 1 rle with old method : 0.0013384819030761719 length of segment : 61 time for calcul the mask position with numpy : 0.00011539459228515625 nb_pixel_total : 1242 time to create 1 rle with old method : 0.0017161369323730469 length of segment : 85 time for calcul the mask position with numpy : 0.00010967254638671875 nb_pixel_total : 1057 time to create 1 rle with old method : 0.0014557838439941406 length of segment : 56 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.00032973289489746094 length of segment : 17 time for calcul the mask position with numpy : 9.298324584960938e-05 nb_pixel_total : 1265 time to create 1 rle with old method : 0.0018084049224853516 length of segment : 36 time for calcul the mask position with numpy : 0.0001697540283203125 nb_pixel_total : 1247 time to create 1 rle with old method : 0.0017666816711425781 length of segment : 76 time for calcul the mask position with numpy : 9.775161743164062e-05 nb_pixel_total : 1502 time to create 1 rle with old method : 0.0020284652709960938 length of segment : 58 time for calcul the mask position with numpy : 9.036064147949219e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.00026297569274902344 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: 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 : 0.00011706352233886719 nb_pixel_total : 672 time to create 1 rle with old method : 0.0009176731109619141 length of segment : 39 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 932 time to create 1 rle with old method : 0.0012652873992919922 length of segment : 51 time for calcul the mask position with numpy : 0.00011372566223144531 nb_pixel_total : 595 time to create 1 rle with old method : 0.00080108642578125 length of segment : 53 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 985 time to create 1 rle with old method : 0.0013151168823242188 length of segment : 49 time for calcul the mask position with numpy : 0.0007035732269287109 nb_pixel_total : 54761 time to create 1 rle with old method : 0.06061220169067383 length of segment : 287 Processing 1 images image shape: (400, 400, 3) min: 14.00000 max: 198.00000 molded_images shape: (1, 640, 640, 3) min: -93.99297 max: 71.47500 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: -117.01250 max: 149.96719 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 0.0001049041748046875 nb_pixel_total : 1256 time to create 1 rle with old method : 0.0019719600677490234 length of segment : 44 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.0003883838653564453 length of segment : 13 time for calcul the mask position with numpy : 9.083747863769531e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.0003769397735595703 length of segment : 19 time for calcul the mask position with numpy : 0.00011682510375976562 nb_pixel_total : 1635 time to create 1 rle with old method : 0.002177715301513672 length of segment : 45 time for calcul the mask position with numpy : 0.00011706352233886719 nb_pixel_total : 120 time to create 1 rle with old method : 0.00025010108947753906 length of segment : 12 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0005128383636474609 length of segment : 18 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 74 time to create 1 rle with old method : 0.0001704692840576172 length of segment : 10 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 762 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: -120.61797 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 : 8.749961853027344e-05 nb_pixel_total : 487 time to create 1 rle with old method : 0.0007119178771972656 length of segment : 29 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 1074 time to create 1 rle with old method : 0.001432180404663086 length of segment : 45 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.0002396106719970703 length of segment : 9 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002868175506591797 length of segment : 22 time for calcul the mask position with numpy : 0.0004000663757324219 nb_pixel_total : 4381 time to create 1 rle with old method : 0.005372047424316406 length of segment : 147 time for calcul the mask position with numpy : 0.00012063980102539062 nb_pixel_total : 282 time to create 1 rle with old method : 0.0005435943603515625 length of segment : 32 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 1147 time to create 1 rle with old method : 0.0015456676483154297 length of segment : 48 time for calcul the mask position with numpy : 0.00014138221740722656 nb_pixel_total : 972 time to create 1 rle with old method : 0.001422882080078125 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: -122.64922 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 : 0.00028586387634277344 nb_pixel_total : 14687 time to create 1 rle with old method : 0.024136781692504883 length of segment : 148 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 692 time to create 1 rle with old method : 0.0013048648834228516 length of segment : 31 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 1390 time to create 1 rle with old method : 0.001772165298461914 length of segment : 43 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 618 time to create 1 rle with old method : 0.0008497238159179688 length of segment : 63 time for calcul the mask position with numpy : 0.00019025802612304688 nb_pixel_total : 1326 time to create 1 rle with old method : 0.0017614364624023438 length of segment : 127 time for calcul the mask position with numpy : 0.0002288818359375 nb_pixel_total : 1484 time to create 1 rle with old method : 0.0020585060119628906 length of segment : 48 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.00031948089599609375 length of segment : 21 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 248 time to create 1 rle with old method : 0.0003628730773925781 length of segment : 23 time for calcul the mask position with numpy : 8.678436279296875e-05 nb_pixel_total : 1399 time to create 1 rle with old method : 0.0018296241760253906 length of segment : 46 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: 142.09219 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 550 time to create 1 rle with old method : 0.0007648468017578125 length of segment : 56 time for calcul the mask position with numpy : 8.344650268554688e-05 nb_pixel_total : 807 time to create 1 rle with old method : 0.001069784164428711 length of segment : 43 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 323 time to create 1 rle with old method : 0.00045180320739746094 length of segment : 24 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 14 time to create 1 rle with old method : 5.030632019042969e-05 length of segment : 8 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 366 time to create 1 rle with old method : 0.0006420612335205078 length of segment : 52 time for calcul the mask position with numpy : 9.1552734375e-05 nb_pixel_total : 1454 time to create 1 rle with old method : 0.0018467903137207031 length of segment : 68 Processing 1 images image shape: (280, 400, 3) min: 6.00000 max: 182.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 53.63125 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0013074874877929688 nb_pixel_total : 106533 time to create 1 rle with old method : 0.12611126899719238 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: 143.14297 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 20 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 986 time to create 1 rle with old method : 0.0012011528015136719 length of segment : 46 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 530 time to create 1 rle with old method : 0.0007584095001220703 length of segment : 23 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.00021219253540039062 length of segment : 15 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 335 time to create 1 rle with old method : 0.0004994869232177734 length of segment : 27 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.00022101402282714844 length of segment : 13 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 1916 time to create 1 rle with old method : 0.0023169517517089844 length of segment : 53 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00018095970153808594 length of segment : 18 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 2809 time to create 1 rle with old method : 0.003505229949951172 length of segment : 126 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 149 time to create 1 rle with old method : 0.00023412704467773438 length of segment : 14 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002684593200683594 length of segment : 20 time for calcul the mask position with numpy : 0.00011467933654785156 nb_pixel_total : 5033 time to create 1 rle with old method : 0.005859375 length of segment : 102 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 1174 time to create 1 rle with old method : 0.0014815330505371094 length of segment : 46 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 569 time to create 1 rle with old method : 0.000759124755859375 length of segment : 26 time for calcul the mask position with numpy : 0.00010347366333007812 nb_pixel_total : 4796 time to create 1 rle with old method : 0.0055158138275146484 length of segment : 97 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 2019 time to create 1 rle with old method : 0.002479076385498047 length of segment : 59 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.0005049705505371094 length of segment : 20 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 1038 time to create 1 rle with old method : 0.0012814998626708984 length of segment : 44 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002319812774658203 length of segment : 14 time for calcul the mask position with numpy : 8.678436279296875e-05 nb_pixel_total : 3446 time to create 1 rle with old method : 0.004098415374755859 length of segment : 123 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 375 time to create 1 rle with old method : 0.0005474090576171875 length of segment : 24 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 : 42 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 177 time to create 1 rle with old method : 0.0002732276916503906 length of segment : 25 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.0005235671997070312 length of segment : 35 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 270 time to create 1 rle with old method : 0.0004494190216064453 length of segment : 14 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 310 time to create 1 rle with old method : 0.00044918060302734375 length of segment : 23 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002987384796142578 length of segment : 17 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 629 time to create 1 rle with old method : 0.0008339881896972656 length of segment : 37 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 862 time to create 1 rle with old method : 0.0011746883392333984 length of segment : 25 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 917 time to create 1 rle with old method : 0.0012483596801757812 length of segment : 38 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 290 time to create 1 rle with old method : 0.0006396770477294922 length of segment : 21 time for calcul the mask position with numpy : 0.00018548965454101562 nb_pixel_total : 6111 time to create 1 rle with old method : 0.009779691696166992 length of segment : 139 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 534 time to create 1 rle with old method : 0.0009357929229736328 length of segment : 37 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 343 time to create 1 rle with old method : 0.0006322860717773438 length of segment : 34 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 482 time to create 1 rle with old method : 0.0009224414825439453 length of segment : 26 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 757 time to create 1 rle with old method : 0.001100778579711914 length of segment : 86 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 1113 time to create 1 rle with old method : 0.0014336109161376953 length of segment : 45 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 915 time to create 1 rle with old method : 0.0011417865753173828 length of segment : 66 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 1158 time to create 1 rle with old method : 0.00145721435546875 length of segment : 46 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 329 time to create 1 rle with old method : 0.0004432201385498047 length of segment : 28 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006568431854248047 length of segment : 32 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 289 time to create 1 rle with old method : 0.0003948211669921875 length of segment : 23 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0023069381713867188 length of segment : 66 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 395 time to create 1 rle with old method : 0.0005631446838378906 length of segment : 35 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 734 time to create 1 rle with old method : 0.0009698867797851562 length of segment : 29 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 243 time to create 1 rle with old method : 0.00037741661071777344 length of segment : 47 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 898 time to create 1 rle with old method : 0.0011837482452392578 length of segment : 39 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 629 time to create 1 rle with old method : 0.0007946491241455078 length of segment : 32 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 688 time to create 1 rle with old method : 0.0008802413940429688 length of segment : 37 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0006921291351318359 length of segment : 28 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 696 time to create 1 rle with old method : 0.0011408329010009766 length of segment : 26 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 323 time to create 1 rle with old method : 0.0004477500915527344 length of segment : 28 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 280 time to create 1 rle with old method : 0.0004382133483886719 length of segment : 51 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 1528 time to create 1 rle with old method : 0.0018513202667236328 length of segment : 59 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0006492137908935547 length of segment : 22 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 480 time to create 1 rle with old method : 0.0006780624389648438 length of segment : 21 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.0001881122589111328 length of segment : 12 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 1610 time to create 1 rle with old method : 0.002109050750732422 length of segment : 59 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 411 time to create 1 rle with old method : 0.0005767345428466797 length of segment : 22 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 774 time to create 1 rle with old method : 0.0015480518341064453 length of segment : 35 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 412 time to create 1 rle with old method : 0.0008261203765869141 length of segment : 35 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 889 time to create 1 rle with old method : 0.0017054080963134766 length of segment : 35 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006961822509765625 length of segment : 27 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 1250 time to create 1 rle with old method : 0.0015780925750732422 length of segment : 47 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: 145.59219 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 426 time to create 1 rle with old method : 0.0011138916015625 length of segment : 27 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0005185604095458984 length of segment : 15 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 541 time to create 1 rle with old method : 0.0007009506225585938 length of segment : 33 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 75 time to create 1 rle with old method : 0.00013637542724609375 length of segment : 10 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 392 time to create 1 rle with old method : 0.000522613525390625 length of segment : 30 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 2155 time to create 1 rle with old method : 0.0026865005493164062 length of segment : 60 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 511 time to create 1 rle with old method : 0.0007116794586181641 length of segment : 51 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 667 time to create 1 rle with old method : 0.0009419918060302734 length of segment : 54 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.00023674964904785156 length of segment : 23 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.00017499923706054688 length of segment : 21 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 1273 time to create 1 rle with old method : 0.0015976428985595703 length of segment : 92 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 559 time to create 1 rle with old method : 0.0007517337799072266 length of segment : 24 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.00019121170043945312 length of segment : 15 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: 141.28750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.00031447410583496094 length of segment : 12 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 556 time to create 1 rle with old method : 0.0010151863098144531 length of segment : 47 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.0004057884216308594 length of segment : 13 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.00040221214294433594 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: -118.54375 max: 148.74062 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.0004909038543701172 length of segment : 27 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.0004363059997558594 length of segment : 32 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 66 time to create 1 rle with old method : 0.0001556873321533203 length of segment : 19 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 227 time to create 1 rle with old method : 0.0010833740234375 length of segment : 31 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 57 time to create 1 rle with old method : 0.00014138221740722656 length of segment : 18 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.0004620552062988281 length of segment : 9 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 257 time to create 1 rle with old method : 0.0005419254302978516 length of segment : 26 time for calcul the mask position with numpy : 0.00011134147644042969 nb_pixel_total : 2226 time to create 1 rle with old method : 0.004001617431640625 length of segment : 72 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 213 time to create 1 rle with old method : 0.0004265308380126953 length of segment : 26 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 324 time to create 1 rle with old method : 0.0007307529449462891 length of segment : 48 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 174 time to create 1 rle with old method : 0.0003523826599121094 length of segment : 23 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 1073 time to create 1 rle with old method : 0.002110719680786133 length of segment : 76 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.66094 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 : 0.0001277923583984375 nb_pixel_total : 2498 time to create 1 rle with old method : 0.004876136779785156 length of segment : 52 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.0003910064697265625 length of segment : 44 time for calcul the mask position with numpy : 0.00011205673217773438 nb_pixel_total : 805 time to create 1 rle with old method : 0.0017194747924804688 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: -120.65703 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 : 9.131431579589844e-05 nb_pixel_total : 1441 time to create 1 rle with old method : 0.003026723861694336 length of segment : 28 time for calcul the mask position with numpy : 0.0001399517059326172 nb_pixel_total : 2008 time to create 1 rle with old method : 0.0039141178131103516 length of segment : 61 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -115.34063 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 : 0.00014901161193847656 nb_pixel_total : 7387 time to create 1 rle with old method : 0.009521245956420898 length of segment : 136 time for calcul the mask position with numpy : 9.608268737792969e-05 nb_pixel_total : 1473 time to create 1 rle with old method : 0.0019605159759521484 length of segment : 79 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1347 time to create 1 rle with old method : 0.0017631053924560547 length of segment : 64 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 791 time to create 1 rle with old method : 0.001134634017944336 length of segment : 75 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002770423889160156 length of segment : 17 time for calcul the mask position with numpy : 6.866455078125e-05 nb_pixel_total : 1069 time to create 1 rle with old method : 0.0016062259674072266 length of segment : 27 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 436 time to create 1 rle with old method : 0.00125885009765625 length of segment : 11 time for calcul the mask position with numpy : 0.0001308917999267578 nb_pixel_total : 2564 time to create 1 rle with old method : 0.004886150360107422 length of segment : 134 time for calcul the mask position with numpy : 0.0001246929168701172 nb_pixel_total : 2298 time to create 1 rle with old method : 0.004464864730834961 length of segment : 134 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 : 8 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 525 time to create 1 rle with old method : 0.0006916522979736328 length of segment : 28 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 987 time to create 1 rle with old method : 0.0012454986572265625 length of segment : 54 time for calcul the mask position with numpy : 0.00011086463928222656 nb_pixel_total : 1101 time to create 1 rle with old method : 0.0021505355834960938 length of segment : 52 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 816 time to create 1 rle with old method : 0.0016121864318847656 length of segment : 74 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.0002999305725097656 length of segment : 11 time for calcul the mask position with numpy : 0.00010180473327636719 nb_pixel_total : 2130 time to create 1 rle with old method : 0.004306793212890625 length of segment : 50 time for calcul the mask position with numpy : 8.249282836914062e-05 nb_pixel_total : 1055 time to create 1 rle with old method : 0.0020494461059570312 length of segment : 51 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 167 time to create 1 rle with old method : 0.0003960132598876953 length of segment : 18 Processing 1 images image shape: (400, 400, 3) min: 17.00000 max: 209.00000 molded_images shape: (1, 640, 640, 3) min: -96.12578 max: 81.76406 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: -119.84063 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 : 5.435943603515625e-05 nb_pixel_total : 1415 time to create 1 rle with old method : 0.0017173290252685547 length of segment : 49 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.0002224445343017578 length of segment : 12 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1484 time to create 1 rle with old method : 0.0019872188568115234 length of segment : 39 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 486 time to create 1 rle with old method : 0.0006630420684814453 length of segment : 33 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 204 time to create 1 rle with old method : 0.0002887248992919922 length of segment : 19 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 164 time to create 1 rle with old method : 0.000255584716796875 length of segment : 13 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 539 time to create 1 rle with old method : 0.0007071495056152344 length of segment : 39 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 82 time to create 1 rle with old method : 0.00014281272888183594 length of segment : 10 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 973 time to create 1 rle with old method : 0.001271963119506836 length of segment : 44 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 1403 time to create 1 rle with old method : 0.0019121170043945312 length of segment : 37 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 250 time to create 1 rle with old method : 0.0005505084991455078 length of segment : 21 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.0003139972686767578 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: -120.90703 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 : 7.963180541992188e-05 nb_pixel_total : 458 time to create 1 rle with old method : 0.0006475448608398438 length of segment : 28 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 1146 time to create 1 rle with old method : 0.001499176025390625 length of segment : 42 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 620 time to create 1 rle with old method : 0.0011866092681884766 length of segment : 29 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001957416534423828 length of segment : 8 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 348 time to create 1 rle with old method : 0.0004944801330566406 length of segment : 38 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0005128383636474609 length of segment : 44 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 230 time to create 1 rle with old method : 0.0004448890686035156 length of segment : 30 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 473 time to create 1 rle with old method : 0.0006456375122070312 length of segment : 41 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 1243 time to create 1 rle with old method : 0.0015959739685058594 length of segment : 48 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 179 time to create 1 rle with old method : 0.0003046989440917969 length of segment : 14 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 528 time to create 1 rle with old method : 0.0007545948028564453 length of segment : 41 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 10 time to create 1 rle with old method : 7.081031799316406e-05 length of segment : 5 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1113 time to create 1 rle with old method : 0.0014684200286865234 length of segment : 47 time for calcul the mask position with numpy : 0.0005087852478027344 nb_pixel_total : 13953 time to create 1 rle with old method : 0.01648998260498047 length of segment : 216 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.51250 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 : 0.0002727508544921875 nb_pixel_total : 14691 time to create 1 rle with old method : 0.024370431900024414 length of segment : 147 time for calcul the mask position with numpy : 0.0001380443572998047 nb_pixel_total : 689 time to create 1 rle with old method : 0.001291036605834961 length of segment : 31 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 1372 time to create 1 rle with old method : 0.0024025440216064453 length of segment : 41 time for calcul the mask position with numpy : 0.00013184547424316406 nb_pixel_total : 1756 time to create 1 rle with old method : 0.0030257701873779297 length of segment : 148 time for calcul the mask position with numpy : 0.00010037422180175781 nb_pixel_total : 509 time to create 1 rle with old method : 0.0006525516510009766 length of segment : 53 time for calcul the mask position with numpy : 0.00010418891906738281 nb_pixel_total : 1194 time to create 1 rle with old method : 0.0016674995422363281 length of segment : 39 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002510547637939453 length of segment : 24 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 1361 time to create 1 rle with old method : 0.0017833709716796875 length of segment : 47 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: 142.45547 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.00032973289489746094 length of segment : 25 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 590 time to create 1 rle with old method : 0.0011508464813232422 length of segment : 64 time for calcul the mask position with numpy : 0.0002658367156982422 nb_pixel_total : 3557 time to create 1 rle with old method : 0.006136655807495117 length of segment : 198 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 726 time to create 1 rle with old method : 0.0013058185577392578 length of segment : 40 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 850 time to create 1 rle with old method : 0.0015141963958740234 length of segment : 46 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 246 time to create 1 rle with old method : 0.0005583763122558594 length of segment : 18 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 253 time to create 1 rle with old method : 0.0005588531494140625 length of segment : 19 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.00036406517028808594 length of segment : 20 length of segment : 0 Processing 1 images image shape: (280, 400, 3) min: 2.00000 max: 172.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 51.16250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0011513233184814453 nb_pixel_total : 106190 time to create 1 rle with old method : 0.13260865211486816 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: 144.93203 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 18 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 502 time to create 1 rle with old method : 0.0007240772247314453 length of segment : 23 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 970 time to create 1 rle with old method : 0.0015494823455810547 length of segment : 44 time for calcul the mask position with numpy : 0.00011730194091796875 nb_pixel_total : 4702 time to create 1 rle with old method : 0.005900859832763672 length of segment : 104 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00018596649169921875 length of segment : 19 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00019931793212890625 length of segment : 15 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.0002384185791015625 length of segment : 13 time for calcul the mask position with numpy : 0.00011849403381347656 nb_pixel_total : 3035 time to create 1 rle with old method : 0.00485682487487793 length of segment : 86 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 513 time to create 1 rle with old method : 0.0006859302520751953 length of segment : 46 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 1219 time to create 1 rle with old method : 0.0015621185302734375 length of segment : 36 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.0003268718719482422 length of segment : 16 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 643 time to create 1 rle with old method : 0.0008573532104492188 length of segment : 84 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 376 time to create 1 rle with old method : 0.0006835460662841797 length of segment : 22 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 200 time to create 1 rle with old method : 0.0002849102020263672 length of segment : 19 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 1179 time to create 1 rle with old method : 0.001531362533569336 length of segment : 35 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 1156 time to create 1 rle with old method : 0.0014872550964355469 length of segment : 47 time for calcul the mask position with numpy : 0.00011014938354492188 nb_pixel_total : 3261 time to create 1 rle with old method : 0.004612445831298828 length of segment : 120 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 378 time to create 1 rle with old method : 0.0005435943603515625 length of segment : 22 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 381 time to create 1 rle with old method : 0.0005559921264648438 length of segment : 23 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 : 32 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002472400665283203 length of segment : 26 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 287 time to create 1 rle with old method : 0.00043702125549316406 length of segment : 16 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 454 time to create 1 rle with old method : 0.0006515979766845703 length of segment : 36 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 442 time to create 1 rle with old method : 0.0005850791931152344 length of segment : 36 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 304 time to create 1 rle with old method : 0.00045180320739746094 length of segment : 21 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 730 time to create 1 rle with old method : 0.0009875297546386719 length of segment : 44 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 203 time to create 1 rle with old method : 0.00032401084899902344 length of segment : 20 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 525 time to create 1 rle with old method : 0.0007350444793701172 length of segment : 23 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 1357 time to create 1 rle with old method : 0.0018711090087890625 length of segment : 34 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 310 time to create 1 rle with old method : 0.00043511390686035156 length of segment : 23 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 452 time to create 1 rle with old method : 0.0006494522094726562 length of segment : 24 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 1565 time to create 1 rle with old method : 0.001992464065551758 length of segment : 59 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 1542 time to create 1 rle with old method : 0.0019388198852539062 length of segment : 58 time for calcul the mask position with numpy : 0.00016498565673828125 nb_pixel_total : 8233 time to create 1 rle with old method : 0.009172439575195312 length of segment : 169 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 233 time to create 1 rle with old method : 0.0004756450653076172 length of segment : 17 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 568 time to create 1 rle with old method : 0.0012891292572021484 length of segment : 26 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.0006639957427978516 length of segment : 32 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0019152164459228516 length of segment : 42 time for calcul the mask position with numpy : 6.0558319091796875e-05 nb_pixel_total : 1052 time to create 1 rle with old method : 0.0018978118896484375 length of segment : 41 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 502 time to create 1 rle with old method : 0.00092315673828125 length of segment : 63 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 355 time to create 1 rle with old method : 0.0006237030029296875 length of segment : 28 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 827 time to create 1 rle with old method : 0.0014715194702148438 length of segment : 29 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 976 time to create 1 rle with old method : 0.001744985580444336 length of segment : 45 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 900 time to create 1 rle with old method : 0.001489877700805664 length of segment : 66 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 440 time to create 1 rle with old method : 0.0007958412170410156 length of segment : 30 time for calcul the mask position with numpy : 6.604194641113281e-05 nb_pixel_total : 684 time to create 1 rle with old method : 0.0012352466583251953 length of segment : 44 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 273 time to create 1 rle with old method : 0.0003936290740966797 length of segment : 34 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 851 time to create 1 rle with old method : 0.0011065006256103516 length of segment : 36 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 358 time to create 1 rle with old method : 0.0004911422729492188 length of segment : 30 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 177 time to create 1 rle with old method : 0.000293731689453125 length of segment : 17 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0013649463653564453 length of segment : 46 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 604 time to create 1 rle with old method : 0.0008883476257324219 length of segment : 22 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: 144.91250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 17 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 496 time to create 1 rle with old method : 0.0007071495056152344 length of segment : 35 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 2244 time to create 1 rle with old method : 0.002832174301147461 length of segment : 67 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 382 time to create 1 rle with old method : 0.0007081031799316406 length of segment : 41 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 720 time to create 1 rle with old method : 0.0009882450103759766 length of segment : 47 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.0003859996795654297 length of segment : 18 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 433 time to create 1 rle with old method : 0.00061798095703125 length of segment : 26 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 778 time to create 1 rle with old method : 0.0010044574737548828 length of segment : 40 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 1156 time to create 1 rle with old method : 0.0014786720275878906 length of segment : 84 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 525 time to create 1 rle with old method : 0.0007243156433105469 length of segment : 22 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 90 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 : 9 time to create 1 rle with old method : 5.626678466796875e-05 length of segment : 5 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.00024700164794921875 length of segment : 16 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 1964 time to create 1 rle with old method : 0.002540111541748047 length of segment : 77 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00028395652770996094 length of segment : 14 time for calcul the mask position with numpy : 0.00011777877807617188 nb_pixel_total : 3671 time to create 1 rle with old method : 0.004538297653198242 length of segment : 151 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 1950 time to create 1 rle with old method : 0.0024118423461914062 length of segment : 102 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 123 time to create 1 rle with old method : 0.00018358230590820312 length of segment : 15 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: 135.28750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00021195411682128906 length of segment : 12 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006625652313232422 length of segment : 44 time for calcul the mask position with numpy : 2.9087066650390625e-05 nb_pixel_total : 76 time to create 1 rle with old method : 0.00014662742614746094 length of segment : 9 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 174 time to create 1 rle with old method : 0.00031304359436035156 length of segment : 13 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00020956993103027344 length of segment : 24 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.0002982616424560547 length of segment : 13 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.00029754638671875 length of segment : 13 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: -118.18438 max: 148.37344 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 235 time to create 1 rle with old method : 0.00035762786865234375 length of segment : 33 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002663135528564453 length of segment : 22 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 47 time to create 1 rle with old method : 9.083747863769531e-05 length of segment : 14 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 222 time to create 1 rle with old method : 0.0003325939178466797 length of segment : 40 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003554821014404297 length of segment : 26 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 55 time to create 1 rle with old method : 0.0001010894775390625 length of segment : 17 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 238 time to create 1 rle with old method : 0.0003542900085449219 length of segment : 42 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 269 time to create 1 rle with old method : 0.0003895759582519531 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: -117.99297 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 : 0.00010919570922851562 nb_pixel_total : 2359 time to create 1 rle with old method : 0.004845619201660156 length of segment : 73 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 2608 time to create 1 rle with old method : 0.0045795440673828125 length of segment : 53 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.0002872943878173828 length of segment : 21 time for calcul the mask position with numpy : 0.0004334449768066406 nb_pixel_total : 15277 time to create 1 rle with old method : 0.02467942237854004 length of segment : 453 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.99297 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 : 0.00017380714416503906 nb_pixel_total : 2147 time to create 1 rle with old method : 0.005215883255004883 length of segment : 83 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 1631 time to create 1 rle with old method : 0.003055572509765625 length of segment : 32 time for calcul the mask position with numpy : 0.00010180473327636719 nb_pixel_total : 1452 time to create 1 rle with old method : 0.0028085708618164062 length of segment : 55 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -116.68047 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 : 0.0001652240753173828 nb_pixel_total : 6162 time to create 1 rle with old method : 0.010530948638916016 length of segment : 125 time for calcul the mask position with numpy : 0.0001068115234375 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002152681350708008 length of segment : 75 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 1013 time to create 1 rle with old method : 0.0018436908721923828 length of segment : 51 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 890 time to create 1 rle with old method : 0.0016133785247802734 length of segment : 49 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 180 time to create 1 rle with old method : 0.0003705024719238281 length of segment : 16 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 837 time to create 1 rle with old method : 0.0012574195861816406 length of segment : 63 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1576 time to create 1 rle with old method : 0.002018451690673828 length of segment : 34 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 : 7 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 493 time to create 1 rle with old method : 0.0006761550903320312 length of segment : 27 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 2734 time to create 1 rle with old method : 0.0034236907958984375 length of segment : 58 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 1099 time to create 1 rle with old method : 0.0013937950134277344 length of segment : 53 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 989 time to create 1 rle with old method : 0.0012640953063964844 length of segment : 54 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 1113 time to create 1 rle with old method : 0.0013718605041503906 length of segment : 73 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.00023102760314941406 length of segment : 17 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 1089 time to create 1 rle with old method : 0.0013689994812011719 length of segment : 52 Processing 1 images image shape: (400, 400, 3) min: 16.00000 max: 194.00000 molded_images shape: (1, 640, 640, 3) min: -96.12578 max: 69.83437 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: -118.02031 max: 149.34609 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.0001087188720703125 nb_pixel_total : 1324 time to create 1 rle with old method : 0.0017507076263427734 length of segment : 47 time for calcul the mask position with numpy : 0.00011277198791503906 nb_pixel_total : 165 time to create 1 rle with old method : 0.0002963542938232422 length of segment : 13 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.00031828880310058594 length of segment : 19 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 385 time to create 1 rle with old method : 0.0006895065307617188 length of segment : 32 time for calcul the mask position with numpy : 8.916854858398438e-05 nb_pixel_total : 322 time to create 1 rle with old method : 0.0007638931274414062 length of segment : 19 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 331 time to create 1 rle with old method : 0.000522613525390625 length of segment : 18 time for calcul the mask position with numpy : 7.271766662597656e-05 nb_pixel_total : 1478 time to create 1 rle with old method : 0.0020291805267333984 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: -120.80547 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 : 5.030632019042969e-05 nb_pixel_total : 459 time to create 1 rle with old method : 0.0006473064422607422 length of segment : 28 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.0002090930938720703 length of segment : 9 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 292 time to create 1 rle with old method : 0.0004112720489501953 length of segment : 33 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 987 time to create 1 rle with old method : 0.0013020038604736328 length of segment : 41 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 983 time to create 1 rle with old method : 0.0012652873992919922 length of segment : 45 time for calcul the mask position with numpy : 0.0004749298095703125 nb_pixel_total : 17765 time to create 1 rle with old method : 0.021413326263427734 length of segment : 285 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 537 time to create 1 rle with old method : 0.0007839202880859375 length of segment : 27 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 513 time to create 1 rle with old method : 0.0007638931274414062 length of segment : 33 time for calcul the mask position with numpy : 7.557868957519531e-05 nb_pixel_total : 73 time to create 1 rle with old method : 0.0003273487091064453 length of segment : 20 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 278 time to create 1 rle with old method : 0.0003981590270996094 length of segment : 25 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 133 time to create 1 rle with old method : 0.00025081634521484375 length of segment : 11 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 1150 time to create 1 rle with old method : 0.0016472339630126953 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: -123.19219 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 : 0.00010418891906738281 nb_pixel_total : 1802 time to create 1 rle with old method : 0.0021860599517822266 length of segment : 144 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 719 time to create 1 rle with old method : 0.0009815692901611328 length of segment : 31 time for calcul the mask position with numpy : 0.00023794174194335938 nb_pixel_total : 14990 time to create 1 rle with old method : 0.017540931701660156 length of segment : 145 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 1386 time to create 1 rle with old method : 0.0017628669738769531 length of segment : 42 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 885 time to create 1 rle with old method : 0.0011620521545410156 length of segment : 73 time for calcul the mask position with numpy : 0.00018715858459472656 nb_pixel_total : 3922 time to create 1 rle with old method : 0.00512242317199707 length of segment : 143 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0005245208740234375 length of segment : 25 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 1010 time to create 1 rle with old method : 0.0012636184692382812 length of segment : 39 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 1112 time to create 1 rle with old method : 0.0015947818756103516 length of segment : 38 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 280 time to create 1 rle with old method : 0.0003781318664550781 length of segment : 33 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: 140.77969 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 541 time to create 1 rle with old method : 0.0007493495941162109 length of segment : 55 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 834 time to create 1 rle with old method : 0.0010764598846435547 length of segment : 47 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 349 time to create 1 rle with old method : 0.0005316734313964844 length of segment : 33 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.00019431114196777344 length of segment : 20 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 251 time to create 1 rle with old method : 0.00040030479431152344 length of segment : 17 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.00026798248291015625 length of segment : 20 time for calcul the mask position with numpy : 8.702278137207031e-05 nb_pixel_total : 756 time to create 1 rle with old method : 0.0010213851928710938 length of segment : 63 length of segment : 0 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 854 time to create 1 rle with old method : 0.0011026859283447266 length of segment : 49 Processing 1 images image shape: (280, 400, 3) min: 10.00000 max: 169.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 51.49453 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0012912750244140625 nb_pixel_total : 106930 time to create 1 rle with old method : 0.11787867546081543 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: 144.93203 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 23 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 505 time to create 1 rle with old method : 0.0007479190826416016 length of segment : 23 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 974 time to create 1 rle with old method : 0.0012619495391845703 length of segment : 46 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 222 time to create 1 rle with old method : 0.0003285408020019531 length of segment : 21 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 136 time to create 1 rle with old method : 0.0002257823944091797 length of segment : 17 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 587 time to create 1 rle with old method : 0.0008065700531005859 length of segment : 26 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001685619354248047 length of segment : 19 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 317 time to create 1 rle with old method : 0.00047779083251953125 length of segment : 26 time for calcul the mask position with numpy : 0.0001163482666015625 nb_pixel_total : 3979 time to create 1 rle with old method : 0.0050890445709228516 length of segment : 121 time for calcul the mask position with numpy : 9.012222290039062e-05 nb_pixel_total : 240 time to create 1 rle with old method : 0.0004951953887939453 length of segment : 46 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 929 time to create 1 rle with old method : 0.001781463623046875 length of segment : 46 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 1881 time to create 1 rle with old method : 0.0023241043090820312 length of segment : 57 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 391 time to create 1 rle with old method : 0.0005824565887451172 length of segment : 24 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.0004754066467285156 length of segment : 18 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 1081 time to create 1 rle with old method : 0.0013723373413085938 length of segment : 47 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.00022983551025390625 length of segment : 15 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 1141 time to create 1 rle with old method : 0.0013911724090576172 length of segment : 46 time for calcul the mask position with numpy : 9.822845458984375e-05 nb_pixel_total : 3440 time to create 1 rle with old method : 0.00407862663269043 length of segment : 118 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006694793701171875 length of segment : 34 time for calcul the mask position with numpy : 0.00015473365783691406 nb_pixel_total : 4673 time to create 1 rle with old method : 0.005616426467895508 length of segment : 115 time for calcul the mask position with numpy : 0.00016570091247558594 nb_pixel_total : 5512 time to create 1 rle with old method : 0.006575345993041992 length of segment : 101 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.0001938343048095703 length of segment : 19 time for calcul the mask position with numpy : 0.000125885009765625 nb_pixel_total : 3247 time to create 1 rle with old method : 0.004385948181152344 length of segment : 95 time for calcul the mask position with numpy : 9.703636169433594e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005567073822021484 length of segment : 22 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 : 37 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002465248107910156 length of segment : 25 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 860 time to create 1 rle with old method : 0.0010561943054199219 length of segment : 58 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 306 time to create 1 rle with old method : 0.0004477500915527344 length of segment : 16 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 417 time to create 1 rle with old method : 0.0006086826324462891 length of segment : 36 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 311 time to create 1 rle with old method : 0.0003936290740966797 length of segment : 24 time for calcul the mask position with numpy : 7.104873657226562e-05 nb_pixel_total : 1406 time to create 1 rle with old method : 0.0017054080963134766 length of segment : 44 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 398 time to create 1 rle with old method : 0.0005004405975341797 length of segment : 34 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 315 time to create 1 rle with old method : 0.0004203319549560547 length of segment : 23 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 231 time to create 1 rle with old method : 0.0003154277801513672 length of segment : 18 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 973 time to create 1 rle with old method : 0.0011560916900634766 length of segment : 41 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 940 time to create 1 rle with old method : 0.0010623931884765625 length of segment : 66 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.00027823448181152344 length of segment : 45 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 420 time to create 1 rle with old method : 0.00055694580078125 length of segment : 22 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 1593 time to create 1 rle with old method : 0.0018508434295654297 length of segment : 57 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 856 time to create 1 rle with old method : 0.0010154247283935547 length of segment : 35 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.0004506111145019531 length of segment : 28 time for calcul the mask position with numpy : 0.00013899803161621094 nb_pixel_total : 5891 time to create 1 rle with old method : 0.006394386291503906 length of segment : 145 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 700 time to create 1 rle with old method : 0.0009305477142333984 length of segment : 53 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006368160247802734 length of segment : 58 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 508 time to create 1 rle with old method : 0.0006039142608642578 length of segment : 35 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 1071 time to create 1 rle with old method : 0.0012364387512207031 length of segment : 42 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0012514591217041016 length of segment : 50 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 535 time to create 1 rle with old method : 0.0006616115570068359 length of segment : 43 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 338 time to create 1 rle with old method : 0.00041794776916503906 length of segment : 29 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004131793975830078 length of segment : 17 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 86 time to create 1 rle with old method : 0.00016069412231445312 length of segment : 33 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002677440643310547 length of segment : 14 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1570 time to create 1 rle with old method : 0.0018322467803955078 length of segment : 58 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 352 time to create 1 rle with old method : 0.0004565715789794922 length of segment : 22 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 872 time to create 1 rle with old method : 0.001089334487915039 length of segment : 33 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 231 time to create 1 rle with old method : 0.000293731689453125 length of segment : 33 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 1142 time to create 1 rle with old method : 0.0013439655303955078 length of segment : 45 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1612 time to create 1 rle with old method : 0.0018253326416015625 length of segment : 58 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 751 time to create 1 rle with old method : 0.0009255409240722656 length of segment : 27 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 842 time to create 1 rle with old method : 0.0009982585906982422 length of segment : 36 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 557 time to create 1 rle with old method : 0.0007205009460449219 length of segment : 26 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 262 time to create 1 rle with old method : 0.0003383159637451172 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: 146.39297 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 18 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 364 time to create 1 rle with old method : 0.0004944801330566406 length of segment : 16 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005919933319091797 length of segment : 26 time for calcul the mask position with numpy : 2.9087066650390625e-05 nb_pixel_total : 139 time to create 1 rle with old method : 0.00020647048950195312 length of segment : 14 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 987 time to create 1 rle with old method : 0.0012540817260742188 length of segment : 63 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 545 time to create 1 rle with old method : 0.0006501674652099609 length of segment : 37 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 340 time to create 1 rle with old method : 0.0004246234893798828 length of segment : 30 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 2016 time to create 1 rle with old method : 0.0023355484008789062 length of segment : 64 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 722 time to create 1 rle with old method : 0.0009033679962158203 length of segment : 61 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 355 time to create 1 rle with old method : 0.0005857944488525391 length of segment : 37 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 410 time to create 1 rle with old method : 0.00048613548278808594 length of segment : 30 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 188 time to create 1 rle with old method : 0.00025272369384765625 length of segment : 24 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1007 time to create 1 rle with old method : 0.0013365745544433594 length of segment : 82 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.00017142295837402344 length of segment : 13 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.0001666545867919922 length of segment : 28 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 3047 time to create 1 rle with old method : 0.003427267074584961 length of segment : 124 time for calcul the mask position with numpy : 2.9087066650390625e-05 nb_pixel_total : 123 time to create 1 rle with old method : 0.00017905235290527344 length of segment : 15 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0003871917724609375 length of segment : 18 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 538 time to create 1 rle with old method : 0.0006506443023681641 length of segment : 40 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: 134.03750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 130 time to create 1 rle with old method : 0.00021839141845703125 length of segment : 12 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004210472106933594 length of segment : 55 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 202 time to create 1 rle with old method : 0.00030922889709472656 length of segment : 14 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 113 time to create 1 rle with old method : 0.0001971721649169922 length of segment : 22 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 198 time to create 1 rle with old method : 0.00028133392333984375 length of segment : 15 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 73 time to create 1 rle with old method : 0.00013113021850585938 length of segment : 9 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.0001850128173828125 length of segment : 28 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: -118.25469 max: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 805 time to create 1 rle with old method : 0.0010275840759277344 length of segment : 42 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 56 time to create 1 rle with old method : 9.989738464355469e-05 length of segment : 19 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 224 time to create 1 rle with old method : 0.00039196014404296875 length of segment : 12 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 248 time to create 1 rle with old method : 0.00034689903259277344 length of segment : 31 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 917 time to create 1 rle with old method : 0.001207113265991211 length of segment : 32 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 102 time to create 1 rle with old method : 0.00015997886657714844 length of segment : 17 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 58 time to create 1 rle with old method : 0.00010371208190917969 length of segment : 17 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 371 time to create 1 rle with old method : 0.0005116462707519531 length of segment : 37 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 262 time to create 1 rle with old method : 0.0003809928894042969 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: -116.07891 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 : 7.915496826171875e-05 nb_pixel_total : 2295 time to create 1 rle with old method : 0.002629518508911133 length of segment : 92 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 2772 time to create 1 rle with old method : 0.0032329559326171875 length of segment : 55 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 867 time to create 1 rle with old method : 0.0010581016540527344 length of segment : 51 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.00035858154296875 length of segment : 38 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 644 time to create 1 rle with old method : 0.0008037090301513672 length of segment : 48 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.77031 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 : 8.487701416015625e-05 nb_pixel_total : 2501 time to create 1 rle with old method : 0.00307464599609375 length of segment : 94 time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 2620 time to create 1 rle with old method : 0.0032052993774414062 length of segment : 36 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 337 time to create 1 rle with old method : 0.0006091594696044922 length of segment : 31 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 533 time to create 1 rle with old method : 0.0009205341339111328 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: -118.33281 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 : 0.000125885009765625 nb_pixel_total : 6405 time to create 1 rle with old method : 0.00771784782409668 length of segment : 130 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 1297 time to create 1 rle with old method : 0.0017085075378417969 length of segment : 76 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 950 time to create 1 rle with old method : 0.0011873245239257812 length of segment : 73 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002758502960205078 length of segment : 17 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 1099 time to create 1 rle with old method : 0.0014030933380126953 length of segment : 73 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 : 4.57763671875e-05 nb_pixel_total : 514 time to create 1 rle with old method : 0.000690460205078125 length of segment : 27 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 1137 time to create 1 rle with old method : 0.0013689994812011719 length of segment : 52 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 835 time to create 1 rle with old method : 0.0010669231414794922 length of segment : 49 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 589 time to create 1 rle with old method : 0.0007598400115966797 length of segment : 49 time for calcul the mask position with numpy : 0.0005195140838623047 nb_pixel_total : 47623 time to create 1 rle with old method : 0.07126402854919434 length of segment : 259 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 2928 time to create 1 rle with old method : 0.003692626953125 length of segment : 50 Processing 1 images image shape: (400, 400, 3) min: 13.00000 max: 209.00000 molded_images shape: (1, 640, 640, 3) min: -94.70000 max: 81.76406 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: -117.28594 max: 151.06094 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 297 time to create 1 rle with old method : 0.000484466552734375 length of segment : 14 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 1280 time to create 1 rle with old method : 0.0015592575073242188 length of segment : 50 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 1775 time to create 1 rle with old method : 0.0022830963134765625 length of segment : 49 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 500 time to create 1 rle with old method : 0.0006539821624755859 length of segment : 43 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00019669532775878906 length of segment : 12 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 814 time to create 1 rle with old method : 0.0011148452758789062 length of segment : 37 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 440 time to create 1 rle with old method : 0.0006136894226074219 length of segment : 33 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.0001895427703857422 length of segment : 12 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 167 time to create 1 rle with old method : 0.00026297569274902344 length of segment : 13 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 213 time to create 1 rle with old method : 0.00030493736267089844 length of segment : 22 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0006222724914550781 length of segment : 58 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.58281 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 : 6.103515625e-05 nb_pixel_total : 469 time to create 1 rle with old method : 0.0006673336029052734 length of segment : 29 time for calcul the mask position with numpy : 8.940696716308594e-05 nb_pixel_total : 3446 time to create 1 rle with old method : 0.004433393478393555 length of segment : 58 time for calcul the mask position with numpy : 0.0003483295440673828 nb_pixel_total : 19910 time to create 1 rle with old method : 0.021574735641479492 length of segment : 212 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.0004878044128417969 length of segment : 42 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.00026106834411621094 length of segment : 11 time for calcul the mask position with numpy : 9.083747863769531e-05 nb_pixel_total : 2341 time to create 1 rle with old method : 0.0027382373809814453 length of segment : 81 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 554 time to create 1 rle with old method : 0.0007257461547851562 length of segment : 34 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1001 time to create 1 rle with old method : 0.0014009475708007812 length of segment : 37 time for calcul the mask position with numpy : 8.940696716308594e-05 nb_pixel_total : 2980 time to create 1 rle with old method : 0.0035784244537353516 length of segment : 58 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 623 time to create 1 rle with old method : 0.0008456707000732422 length of segment : 47 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 359 time to create 1 rle with old method : 0.00043845176696777344 length of segment : 37 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 254 time to create 1 rle with old method : 0.00036454200744628906 length of segment : 24 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 284 time to create 1 rle with old method : 0.00044345855712890625 length of segment : 13 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 2351 time to create 1 rle with old method : 0.0028543472290039062 length of segment : 89 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 518 time to create 1 rle with old method : 0.0006756782531738281 length of segment : 21 time for calcul the mask position with numpy : 0.00044918060302734375 nb_pixel_total : 29613 time to create 1 rle with old method : 0.03301572799682617 length of segment : 229 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 3007 time to create 1 rle with old method : 0.0035648345947265625 length of segment : 111 time for calcul the mask position with numpy : 8.368492126464844e-05 nb_pixel_total : 3016 time to create 1 rle with old method : 0.0035409927368164062 length of segment : 59 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.37578 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 : 9.202957153320312e-05 nb_pixel_total : 1730 time to create 1 rle with old method : 0.002025604248046875 length of segment : 149 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 767 time to create 1 rle with old method : 0.0010318756103515625 length of segment : 69 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 1424 time to create 1 rle with old method : 0.0017507076263427734 length of segment : 45 time for calcul the mask position with numpy : 0.00021386146545410156 nb_pixel_total : 14816 time to create 1 rle with old method : 0.01640796661376953 length of segment : 162 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 336 time to create 1 rle with old method : 0.0004558563232421875 length of segment : 28 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 310 time to create 1 rle with old method : 0.00038361549377441406 length of segment : 21 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 1123 time to create 1 rle with old method : 0.0015444755554199219 length of segment : 36 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 188 time to create 1 rle with old method : 0.0002887248992919922 length of segment : 22 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 3114 time to create 1 rle with old method : 0.00345611572265625 length of segment : 113 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.00048804283142089844 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: 140.10781 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 459 time to create 1 rle with old method : 0.00069427490234375 length of segment : 52 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 602 time to create 1 rle with old method : 0.0007829666137695312 length of segment : 62 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.00024247169494628906 length of segment : 20 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 1139 time to create 1 rle with old method : 0.001432180404663086 length of segment : 52 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 94 time to create 1 rle with old method : 0.00017499923706054688 length of segment : 11 time for calcul the mask position with numpy : 0.00010800361633300781 nb_pixel_total : 641 time to create 1 rle with old method : 0.0008835792541503906 length of segment : 56 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 711 time to create 1 rle with old method : 0.0009355545043945312 length of segment : 39 time for calcul the mask position with numpy : 0.0001819133758544922 nb_pixel_total : 2330 time to create 1 rle with old method : 0.0029404163360595703 length of segment : 173 Processing 1 images image shape: (280, 400, 3) min: 9.00000 max: 173.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 53.32266 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0010390281677246094 nb_pixel_total : 106445 time to create 1 rle with old method : 0.11719393730163574 length of segment : 285 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: 144.01406 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 1053 time to create 1 rle with old method : 0.0012574195861816406 length of segment : 47 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 115 time to create 1 rle with old method : 0.00019931793212890625 length of segment : 15 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.00019049644470214844 length of segment : 20 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003345012664794922 length of segment : 22 time for calcul the mask position with numpy : 0.0001277923583984375 nb_pixel_total : 4662 time to create 1 rle with old method : 0.005927324295043945 length of segment : 114 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006432533264160156 length of segment : 33 time for calcul the mask position with numpy : 0.0001366138458251953 nb_pixel_total : 7499 time to create 1 rle with old method : 0.008706092834472656 length of segment : 70 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005617141723632812 length of segment : 28 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 720 time to create 1 rle with old method : 0.0008969306945800781 length of segment : 30 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 299 time to create 1 rle with old method : 0.0004062652587890625 length of segment : 25 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.00020575523376464844 length of segment : 48 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 1829 time to create 1 rle with old method : 0.0023620128631591797 length of segment : 73 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 1232 time to create 1 rle with old method : 0.0014786720275878906 length of segment : 45 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 427 time to create 1 rle with old method : 0.0006124973297119141 length of segment : 26 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 412 time to create 1 rle with old method : 0.0005743503570556641 length of segment : 23 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 : 8.535385131835938e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.00024509429931640625 length of segment : 25 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 292 time to create 1 rle with old method : 0.0004792213439941406 length of segment : 15 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 416 time to create 1 rle with old method : 0.0005602836608886719 length of segment : 36 time for calcul the mask position with numpy : 8.416175842285156e-05 nb_pixel_total : 1063 time to create 1 rle with old method : 0.001795053482055664 length of segment : 41 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1443 time to create 1 rle with old method : 0.00231170654296875 length of segment : 30 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 798 time to create 1 rle with old method : 0.0014290809631347656 length of segment : 43 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 428 time to create 1 rle with old method : 0.0009047985076904297 length of segment : 34 time for calcul the mask position with numpy : 8.559226989746094e-05 nb_pixel_total : 534 time to create 1 rle with old method : 0.0010454654693603516 length of segment : 44 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 845 time to create 1 rle with old method : 0.0012810230255126953 length of segment : 63 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 1414 time to create 1 rle with old method : 0.0020117759704589844 length of segment : 62 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 296 time to create 1 rle with old method : 0.0003993511199951172 length of segment : 23 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.00022125244140625 length of segment : 17 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 949 time to create 1 rle with old method : 0.0013163089752197266 length of segment : 40 time for calcul the mask position with numpy : 0.00024580955505371094 nb_pixel_total : 5363 time to create 1 rle with old method : 0.006726980209350586 length of segment : 138 time for calcul the mask position with numpy : 6.389617919921875e-05 nb_pixel_total : 560 time to create 1 rle with old method : 0.0007541179656982422 length of segment : 38 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 292 time to create 1 rle with old method : 0.0003809928894042969 length of segment : 20 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 227 time to create 1 rle with old method : 0.0003192424774169922 length of segment : 19 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0005986690521240234 length of segment : 31 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 446 time to create 1 rle with old method : 0.0006005764007568359 length of segment : 20 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.0002155303955078125 length of segment : 15 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.00023365020751953125 length of segment : 29 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 220 time to create 1 rle with old method : 0.00030112266540527344 length of segment : 31 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 614 time to create 1 rle with old method : 0.0008661746978759766 length of segment : 43 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 546 time to create 1 rle with old method : 0.0007359981536865234 length of segment : 27 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002682209014892578 length of segment : 34 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 387 time to create 1 rle with old method : 0.0005557537078857422 length of segment : 24 time for calcul the mask position with numpy : 9.274482727050781e-05 nb_pixel_total : 1577 time to create 1 rle with old method : 0.0022001266479492188 length of segment : 57 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 867 time to create 1 rle with old method : 0.0010864734649658203 length of segment : 35 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 1186 time to create 1 rle with old method : 0.001378774642944336 length of segment : 50 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 815 time to create 1 rle with old method : 0.0010085105895996094 length of segment : 33 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0004906654357910156 length of segment : 22 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 309 time to create 1 rle with old method : 0.0004673004150390625 length of segment : 41 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 405 time to create 1 rle with old method : 0.0004966259002685547 length of segment : 30 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 811 time to create 1 rle with old method : 0.0009777545928955078 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: 146.45937 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 953 time to create 1 rle with old method : 0.0012454986572265625 length of segment : 63 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 331 time to create 1 rle with old method : 0.000446319580078125 length of segment : 17 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 427 time to create 1 rle with old method : 0.0005540847778320312 length of segment : 27 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 839 time to create 1 rle with old method : 0.001009225845336914 length of segment : 60 time for calcul the mask position with numpy : 8.082389831542969e-05 nb_pixel_total : 1818 time to create 1 rle with old method : 0.0021636486053466797 length of segment : 63 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 100 time to create 1 rle with old method : 0.00015616416931152344 length of segment : 14 time for calcul the mask position with numpy : 0.0001049041748046875 nb_pixel_total : 4596 time to create 1 rle with old method : 0.0052225589752197266 length of segment : 144 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 147 time to create 1 rle with old method : 0.00021195411682128906 length of segment : 15 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 307 time to create 1 rle with old method : 0.000423431396484375 length of segment : 39 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 136 time to create 1 rle with old method : 0.00019073486328125 length of segment : 27 time for calcul the mask position with numpy : 0.00010657310485839844 nb_pixel_total : 4817 time to create 1 rle with old method : 0.005518198013305664 length of segment : 140 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 453 time to create 1 rle with old method : 0.0005724430084228516 length of segment : 33 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 681 time to create 1 rle with old method : 0.0008425712585449219 length of segment : 40 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.00022864341735839844 length of segment : 12 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.00023651123046875 length of segment : 19 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 1829 time to create 1 rle with old method : 0.0023326873779296875 length of segment : 65 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: 134.28750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.0002410411834716797 length of segment : 12 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.00033164024353027344 length of segment : 12 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 592 time to create 1 rle with old method : 0.0007734298706054688 length of segment : 48 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 142 time to create 1 rle with old method : 0.00019669532775878906 length of segment : 28 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 189 time to create 1 rle with old method : 0.0003008842468261719 length of segment : 13 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: -118.44219 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 : 5.7697296142578125e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.0002930164337158203 length of segment : 25 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 51 time to create 1 rle with old method : 9.393692016601562e-05 length of segment : 17 time for calcul the mask position with numpy : 9.250640869140625e-05 nb_pixel_total : 1701 time to create 1 rle with old method : 0.0021898746490478516 length of segment : 68 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 285 time to create 1 rle with old method : 0.0004589557647705078 length of segment : 10 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.0003974437713623047 length of segment : 32 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002925395965576172 length of segment : 34 time for calcul the mask position with numpy : 0.00016355514526367188 nb_pixel_total : 886 time to create 1 rle with old method : 0.0011875629425048828 length of segment : 70 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 60 time to create 1 rle with old method : 0.00012254714965820312 length of segment : 19 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 376 time to create 1 rle with old method : 0.000598907470703125 length of segment : 51 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 237 time to create 1 rle with old method : 0.0003464221954345703 length of segment : 37 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 1260 time to create 1 rle with old method : 0.0015916824340820312 length of segment : 68 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 278 time to create 1 rle with old method : 0.0003604888916015625 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: -114.71172 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 : 7.152557373046875e-05 nb_pixel_total : 2546 time to create 1 rle with old method : 0.0031595230102539062 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: -121.66875 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 : 0.00011372566223144531 nb_pixel_total : 3561 time to create 1 rle with old method : 0.0040509700775146484 length of segment : 88 time for calcul the mask position with numpy : 0.00012373924255371094 nb_pixel_total : 3097 time to create 1 rle with old method : 0.004923105239868164 length of segment : 131 time for calcul the mask position with numpy : 0.00011682510375976562 nb_pixel_total : 4463 time to create 1 rle with old method : 0.0050563812255859375 length of segment : 108 time for calcul the mask position with numpy : 0.00010585784912109375 nb_pixel_total : 3489 time to create 1 rle with old method : 0.00392603874206543 length of segment : 135 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.01250 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 : 0.000133514404296875 nb_pixel_total : 5908 time to create 1 rle with old method : 0.006768703460693359 length of segment : 99 time for calcul the mask position with numpy : 0.00010228157043457031 nb_pixel_total : 1469 time to create 1 rle with old method : 0.0018215179443359375 length of segment : 86 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 966 time to create 1 rle with old method : 0.001171112060546875 length of segment : 76 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 1768 time to create 1 rle with old method : 0.0021517276763916016 length of segment : 27 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 1006 time to create 1 rle with old method : 0.0011806488037109375 length of segment : 59 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.00025963783264160156 length of segment : 17 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 632 time to create 1 rle with old method : 0.0008099079132080078 length of segment : 63 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.0002503395080566406 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: 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 : 5.8650970458984375e-05 nb_pixel_total : 853 time to create 1 rle with old method : 0.0010776519775390625 length of segment : 49 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 514 time to create 1 rle with old method : 0.0006341934204101562 length of segment : 29 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 87 time to create 1 rle with old method : 0.00015282630920410156 length of segment : 9 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 1107 time to create 1 rle with old method : 0.0013000965118408203 length of segment : 52 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.00024199485778808594 length of segment : 18 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 985 time to create 1 rle with old method : 0.0012431144714355469 length of segment : 51 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 576 time to create 1 rle with old method : 0.0007100105285644531 length of segment : 54 time for calcul the mask position with numpy : 0.0007188320159912109 nb_pixel_total : 60989 time to create 1 rle with old method : 0.0649566650390625 length of segment : 299 Processing 1 images image shape: (400, 400, 3) min: 14.00000 max: 194.00000 molded_images shape: (1, 640, 640, 3) min: -96.12578 max: 67.47500 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: -119.31328 max: 150.27969 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 1327 time to create 1 rle with old method : 0.0016880035400390625 length of segment : 51 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.0006618499755859375 length of segment : 33 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 113 time to create 1 rle with old method : 0.00017881393432617188 length of segment : 11 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 1434 time to create 1 rle with old method : 0.0019042491912841797 length of segment : 37 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 150 time to create 1 rle with old method : 0.00021886825561523438 length of segment : 12 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 2045 time to create 1 rle with old method : 0.002513408660888672 length of segment : 63 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 367 time to create 1 rle with old method : 0.0005145072937011719 length of segment : 25 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 1523 time to create 1 rle with old method : 0.0020172595977783203 length of segment : 36 time for calcul the mask position with numpy : 0.00010085105895996094 nb_pixel_total : 3307 time to create 1 rle with old method : 0.0038213729858398438 length of segment : 94 time for calcul the mask position with numpy : 9.918212890625e-05 nb_pixel_total : 2806 time to create 1 rle with old method : 0.003259420394897461 length of segment : 89 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 284 time to create 1 rle with old method : 0.0003654956817626953 length of segment : 27 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 324 time to create 1 rle with old method : 0.0005242824554443359 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: -121.74297 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 : 4.935264587402344e-05 nb_pixel_total : 477 time to create 1 rle with old method : 0.0006184577941894531 length of segment : 29 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.000209808349609375 length of segment : 13 time for calcul the mask position with numpy : 0.00012183189392089844 nb_pixel_total : 4110 time to create 1 rle with old method : 0.005065202713012695 length of segment : 123 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016927719116210938 length of segment : 17 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 1857 time to create 1 rle with old method : 0.0024399757385253906 length of segment : 36 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 263 time to create 1 rle with old method : 0.00037932395935058594 length of segment : 13 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 300 time to create 1 rle with old method : 0.00039649009704589844 length of segment : 20 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.0004124641418457031 length of segment : 19 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 3767 time to create 1 rle with old method : 0.004518270492553711 length of segment : 55 time for calcul the mask position with numpy : 0.0002276897430419922 nb_pixel_total : 1379 time to create 1 rle with old method : 0.0018124580383300781 length of segment : 101 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.000682830810546875 length of segment : 19 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 1360 time to create 1 rle with old method : 0.0018818378448486328 length of segment : 49 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 319 time to create 1 rle with old method : 0.0005939006805419922 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: -121.94219 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 : 0.000125885009765625 nb_pixel_total : 3470 time to create 1 rle with old method : 0.00411534309387207 length of segment : 144 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 959 time to create 1 rle with old method : 0.0013070106506347656 length of segment : 36 time for calcul the mask position with numpy : 0.0002925395965576172 nb_pixel_total : 13661 time to create 1 rle with old method : 0.015675067901611328 length of segment : 127 time for calcul the mask position with numpy : 0.00012159347534179688 nb_pixel_total : 1564 time to create 1 rle with old method : 0.002028226852416992 length of segment : 144 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.0003101825714111328 length of segment : 10 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 93 time to create 1 rle with old method : 0.0001933574676513672 length of segment : 14 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 1088 time to create 1 rle with old method : 0.0015442371368408203 length of segment : 34 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: 136.49453 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 94 time to create 1 rle with old method : 0.00022983551025390625 length of segment : 23 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 659 time to create 1 rle with old method : 0.00089263916015625 length of segment : 67 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 1177 time to create 1 rle with old method : 0.0013959407806396484 length of segment : 63 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.000164031982421875 length of segment : 16 time for calcul the mask position with numpy : 2.8133392333984375e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00017380714416503906 length of segment : 11 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 803 time to create 1 rle with old method : 0.0009522438049316406 length of segment : 46 time for calcul the mask position with numpy : 0.00015854835510253906 nb_pixel_total : 713 time to create 1 rle with old method : 0.0011973381042480469 length of segment : 65 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 908 time to create 1 rle with old method : 0.0013113021850585938 length of segment : 53 time for calcul the mask position with numpy : 0.00023055076599121094 nb_pixel_total : 3020 time to create 1 rle with old method : 0.0036652088165283203 length of segment : 201 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 822 time to create 1 rle with old method : 0.0010790824890136719 length of segment : 45 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 1346 time to create 1 rle with old method : 0.0017595291137695312 length of segment : 76 Processing 1 images image shape: (280, 400, 3) min: 0.00000 max: 177.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 57.05703 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009129047393798828 nb_pixel_total : 106704 time to create 1 rle with old method : 0.11406588554382324 length of segment : 285 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: 148.22500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 965 time to create 1 rle with old method : 0.0012240409851074219 length of segment : 46 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 204 time to create 1 rle with old method : 0.0003006458282470703 length of segment : 21 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.0001926422119140625 length of segment : 15 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.0002143383026123047 length of segment : 21 time for calcul the mask position with numpy : 0.0001316070556640625 nb_pixel_total : 5095 time to create 1 rle with old method : 0.006270408630371094 length of segment : 97 time for calcul the mask position with numpy : 6.914138793945312e-05 nb_pixel_total : 2017 time to create 1 rle with old method : 0.002511262893676758 length of segment : 55 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 326 time to create 1 rle with old method : 0.00048065185546875 length of segment : 27 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 1750 time to create 1 rle with old method : 0.002283334732055664 length of segment : 70 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 650 time to create 1 rle with old method : 0.0008594989776611328 length of segment : 35 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005922317504882812 length of segment : 23 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 164 time to create 1 rle with old method : 0.00026607513427734375 length of segment : 14 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 666 time to create 1 rle with old method : 0.0008745193481445312 length of segment : 35 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.0002129077911376953 length of segment : 15 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002582073211669922 length of segment : 16 time for calcul the mask position with numpy : 0.00011086463928222656 nb_pixel_total : 4079 time to create 1 rle with old method : 0.004897594451904297 length of segment : 125 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 1228 time to create 1 rle with old method : 0.0020613670349121094 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 : 46 time for calcul the mask position with numpy : 6.771087646484375e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.00033020973205566406 length of segment : 25 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 309 time to create 1 rle with old method : 0.003331422805786133 length of segment : 22 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 418 time to create 1 rle with old method : 0.0007641315460205078 length of segment : 37 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 764 time to create 1 rle with old method : 0.0014297962188720703 length of segment : 39 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.0004093647003173828 length of segment : 18 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 432 time to create 1 rle with old method : 0.0008454322814941406 length of segment : 25 time for calcul the mask position with numpy : 9.274482727050781e-05 nb_pixel_total : 885 time to create 1 rle with old method : 0.0016829967498779297 length of segment : 26 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 291 time to create 1 rle with old method : 0.0006163120269775391 length of segment : 15 time for calcul the mask position with numpy : 8.249282836914062e-05 nb_pixel_total : 1124 time to create 1 rle with old method : 0.0019617080688476562 length of segment : 44 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.00041866302490234375 length of segment : 33 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0008118152618408203 length of segment : 30 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 815 time to create 1 rle with old method : 0.0009865760803222656 length of segment : 67 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 233 time to create 1 rle with old method : 0.0003650188446044922 length of segment : 31 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 481 time to create 1 rle with old method : 0.0006382465362548828 length of segment : 21 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 455 time to create 1 rle with old method : 0.0006575584411621094 length of segment : 36 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 239 time to create 1 rle with old method : 0.0003352165222167969 length of segment : 18 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 1037 time to create 1 rle with old method : 0.001444101333618164 length of segment : 52 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 297 time to create 1 rle with old method : 0.0004055500030517578 length of segment : 24 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 461 time to create 1 rle with old method : 0.0006024837493896484 length of segment : 34 time for calcul the mask position with numpy : 0.00017333030700683594 nb_pixel_total : 5412 time to create 1 rle with old method : 0.006309032440185547 length of segment : 139 time for calcul the mask position with numpy : 8.893013000488281e-05 nb_pixel_total : 1209 time to create 1 rle with old method : 0.0016181468963623047 length of segment : 54 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 450 time to create 1 rle with old method : 0.0006785392761230469 length of segment : 45 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 421 time to create 1 rle with old method : 0.0005383491516113281 length of segment : 32 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 1022 time to create 1 rle with old method : 0.0013401508331298828 length of segment : 41 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 1107 time to create 1 rle with old method : 0.0015044212341308594 length of segment : 50 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 1560 time to create 1 rle with old method : 0.0018324851989746094 length of segment : 58 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 845 time to create 1 rle with old method : 0.0011107921600341797 length of segment : 34 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.000255584716796875 length of segment : 14 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 331 time to create 1 rle with old method : 0.0004699230194091797 length of segment : 28 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 994 time to create 1 rle with old method : 0.0013086795806884766 length of segment : 39 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 412 time to create 1 rle with old method : 0.0005433559417724609 length of segment : 39 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.000308990478515625 length of segment : 33 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 417 time to create 1 rle with old method : 0.0005781650543212891 length of segment : 22 time for calcul the mask position with numpy : 7.343292236328125e-05 nb_pixel_total : 1583 time to create 1 rle with old method : 0.0019943714141845703 length of segment : 61 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 717 time to create 1 rle with old method : 0.0009570121765136719 length of segment : 27 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.0006897449493408203 length of segment : 25 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 1132 time to create 1 rle with old method : 0.001453399658203125 length of segment : 44 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 209 time to create 1 rle with old method : 0.0002849102020263672 length of segment : 44 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 1036 time to create 1 rle with old method : 0.0013494491577148438 length of segment : 44 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 498 time to create 1 rle with old method : 0.000675201416015625 length of segment : 26 time for calcul the mask position with numpy : 8.559226989746094e-05 nb_pixel_total : 404 time to create 1 rle with old method : 0.0010919570922851562 length of segment : 23 time for calcul the mask position with numpy : 8.678436279296875e-05 nb_pixel_total : 1213 time to create 1 rle with old method : 0.001871347427368164 length of segment : 46 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 717 time to create 1 rle with old method : 0.0014815330505371094 length of segment : 27 time for calcul the mask position with numpy : 0.00018167495727539062 nb_pixel_total : 1693 time to create 1 rle with old method : 0.0028734207153320312 length of segment : 60 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 424 time to create 1 rle with old method : 0.0009369850158691406 length of segment : 32 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0009212493896484375 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: 145.92422 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0010159015655517578 length of segment : 26 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 492 time to create 1 rle with old method : 0.0009245872497558594 length of segment : 37 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 427 time to create 1 rle with old method : 0.0008337497711181641 length of segment : 20 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 1630 time to create 1 rle with old method : 0.0029125213623046875 length of segment : 55 time for calcul the mask position with numpy : 0.0001251697540283203 nb_pixel_total : 1739 time to create 1 rle with old method : 0.0031621456146240234 length of segment : 87 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 273 time to create 1 rle with old method : 0.0005419254302978516 length of segment : 25 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.00029850006103515625 length of segment : 12 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0005676746368408203 length of segment : 24 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 151 time to create 1 rle with old method : 0.0003249645233154297 length of segment : 16 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 338 time to create 1 rle with old method : 0.0006406307220458984 length of segment : 30 time for calcul the mask position with numpy : 0.0001239776611328125 nb_pixel_total : 2759 time to create 1 rle with old method : 0.004819154739379883 length of segment : 123 time for calcul the mask position with numpy : 9.465217590332031e-05 nb_pixel_total : 1831 time to create 1 rle with old method : 0.0023589134216308594 length of segment : 108 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.00023031234741210938 length of segment : 11 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 1783 time to create 1 rle with old method : 0.002316713333129883 length of segment : 70 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: 137.78750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 113 time to create 1 rle with old method : 0.00020456314086914062 length of segment : 12 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 210 time to create 1 rle with old method : 0.0003514289855957031 length of segment : 13 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.000629425048828125 length of segment : 38 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.00022983551025390625 length of segment : 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: -118.46563 max: 149.04531 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 53 time to create 1 rle with old method : 9.512901306152344e-05 length of segment : 18 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.0002720355987548828 length of segment : 26 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.0002567768096923828 length of segment : 28 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 140 time to create 1 rle with old method : 0.0002186298370361328 length of segment : 23 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 64 time to create 1 rle with old method : 0.00010967254638671875 length of segment : 18 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.0003237724304199219 length of segment : 34 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 40 time to create 1 rle with old method : 8.249282836914062e-05 length of segment : 30 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 231 time to create 1 rle with old method : 0.0003294944763183594 length of segment : 31 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.00022864341735839844 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: -114.39531 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 : 6.103515625e-05 nb_pixel_total : 1261 time to create 1 rle with old method : 0.0018875598907470703 length of segment : 42 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 2346 time to create 1 rle with old method : 0.0028100013732910156 length of segment : 50 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 779 time to create 1 rle with old method : 0.000978231430053711 length of segment : 51 time for calcul the mask position with numpy : 7.176399230957031e-05 nb_pixel_total : 2399 time to create 1 rle with old method : 0.0031163692474365234 length of segment : 48 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.59844 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 : 9.298324584960938e-05 nb_pixel_total : 2938 time to create 1 rle with old method : 0.0036449432373046875 length of segment : 118 time for calcul the mask position with numpy : 0.000102996826171875 nb_pixel_total : 4285 time to create 1 rle with old method : 0.005318880081176758 length of segment : 89 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 241 time to create 1 rle with old method : 0.0004153251647949219 length of segment : 11 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 2481 time to create 1 rle with old method : 0.0034034252166748047 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: -122.71172 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 : 0.00016760826110839844 nb_pixel_total : 7180 time to create 1 rle with old method : 0.009107112884521484 length of segment : 140 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 898 time to create 1 rle with old method : 0.0016241073608398438 length of segment : 59 time for calcul the mask position with numpy : 0.00013303756713867188 nb_pixel_total : 1407 time to create 1 rle with old method : 0.0025548934936523438 length of segment : 95 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 614 time to create 1 rle with old method : 0.0011146068572998047 length of segment : 54 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 1179 time to create 1 rle with old method : 0.0026235580444335938 length of segment : 65 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.0004856586456298828 length of segment : 21 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.00033211708068847656 length of segment : 21 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.0003521442413330078 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: 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 : 5.030632019042969e-05 nb_pixel_total : 520 time to create 1 rle with old method : 0.0007195472717285156 length of segment : 29 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 915 time to create 1 rle with old method : 0.0011973381042480469 length of segment : 51 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 1085 time to create 1 rle with old method : 0.0013399124145507812 length of segment : 53 time for calcul the mask position with numpy : 7.343292236328125e-05 nb_pixel_total : 2315 time to create 1 rle with old method : 0.0028922557830810547 length of segment : 53 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 1253 time to create 1 rle with old method : 0.002173900604248047 length of segment : 82 time for calcul the mask position with numpy : 0.0007288455963134766 nb_pixel_total : 48501 time to create 1 rle with old method : 0.06081748008728027 length of segment : 280 Processing 1 images image shape: (400, 400, 3) min: 13.00000 max: 195.00000 molded_images shape: (1, 640, 640, 3) min: -98.79766 max: 71.31094 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: -120.72734 max: 151.06094 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 9.965896606445312e-05 nb_pixel_total : 4428 time to create 1 rle with old method : 0.005383729934692383 length of segment : 118 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 153 time to create 1 rle with old method : 0.00024819374084472656 length of segment : 12 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 1453 time to create 1 rle with old method : 0.0018532276153564453 length of segment : 45 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 115 time to create 1 rle with old method : 0.00026106834411621094 length of segment : 11 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 1029 time to create 1 rle with old method : 0.0013971328735351562 length of segment : 32 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 777 time to create 1 rle with old method : 0.0010190010070800781 length of segment : 39 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 320 time to create 1 rle with old method : 0.00047326087951660156 length of segment : 37 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0005397796630859375 length of segment : 19 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 239 time to create 1 rle with old method : 0.00034928321838378906 length of segment : 21 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 288 time to create 1 rle with old method : 0.0004391670227050781 length of segment : 33 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 101 time to create 1 rle with old method : 0.00016927719116210938 length of segment : 11 time for calcul the mask position with numpy : 0.00012373924255371094 nb_pixel_total : 2745 time to create 1 rle with old method : 0.003587484359741211 length of segment : 126 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.00024127960205078125 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: -122.89922 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 : 4.673004150390625e-05 nb_pixel_total : 704 time to create 1 rle with old method : 0.0008807182312011719 length of segment : 53 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006575584411621094 length of segment : 29 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 583 time to create 1 rle with old method : 0.0008802413940429688 length of segment : 35 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 1428 time to create 1 rle with old method : 0.0018205642700195312 length of segment : 94 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 597 time to create 1 rle with old method : 0.0009291172027587891 length of segment : 15 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 700 time to create 1 rle with old method : 0.0009038448333740234 length of segment : 31 time for calcul the mask position with numpy : 0.00014901161193847656 nb_pixel_total : 7112 time to create 1 rle with old method : 0.008394718170166016 length of segment : 92 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 466 time to create 1 rle with old method : 0.0006577968597412109 length of segment : 35 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0004513263702392578 length of segment : 21 time for calcul the mask position with numpy : 0.00010728836059570312 nb_pixel_total : 1871 time to create 1 rle with old method : 0.0026688575744628906 length of segment : 86 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.00034308433532714844 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: -121.55938 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 : 5.7697296142578125e-05 nb_pixel_total : 1064 time to create 1 rle with old method : 0.0015249252319335938 length of segment : 36 time for calcul the mask position with numpy : 8.7738037109375e-05 nb_pixel_total : 2042 time to create 1 rle with old method : 0.0025463104248046875 length of segment : 140 time for calcul the mask position with numpy : 0.00023555755615234375 nb_pixel_total : 15981 time to create 1 rle with old method : 0.018740177154541016 length of segment : 151 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 96 time to create 1 rle with old method : 0.00019884109497070312 length of segment : 14 time for calcul the mask position with numpy : 0.00011539459228515625 nb_pixel_total : 4520 time to create 1 rle with old method : 0.0053865909576416016 length of segment : 156 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 1388 time to create 1 rle with old method : 0.001741647720336914 length of segment : 41 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 404 time to create 1 rle with old method : 0.0005712509155273438 length of segment : 47 time for calcul the mask position with numpy : 9.72747802734375e-05 nb_pixel_total : 2842 time to create 1 rle with old method : 0.0034513473510742188 length of segment : 98 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.00025343894958496094 length of segment : 21 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: 136.15078 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 8 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 492 time to create 1 rle with old method : 0.0006754398345947266 length of segment : 55 time for calcul the mask position with numpy : 0.00010013580322265625 nb_pixel_total : 550 time to create 1 rle with old method : 0.0008001327514648438 length of segment : 68 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 987 time to create 1 rle with old method : 0.0012164115905761719 length of segment : 49 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.00019288063049316406 length of segment : 11 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 826 time to create 1 rle with old method : 0.00104522705078125 length of segment : 46 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 816 time to create 1 rle with old method : 0.0010571479797363281 length of segment : 44 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 1396 time to create 1 rle with old method : 0.0017387866973876953 length of segment : 69 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.0001881122589111328 length of segment : 19 Processing 1 images image shape: (280, 400, 3) min: 7.00000 max: 179.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 63.09219 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0008754730224609375 nb_pixel_total : 106492 time to create 1 rle with old method : 0.11730551719665527 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: 150.09219 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 17 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 500 time to create 1 rle with old method : 0.0007507801055908203 length of segment : 23 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 881 time to create 1 rle with old method : 0.0011353492736816406 length of segment : 47 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 424 time to create 1 rle with old method : 0.0006620883941650391 length of segment : 36 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 680 time to create 1 rle with old method : 0.0009374618530273438 length of segment : 26 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.00018143653869628906 length of segment : 15 time for calcul the mask position with numpy : 7.915496826171875e-05 nb_pixel_total : 1073 time to create 1 rle with old method : 0.0012683868408203125 length of segment : 50 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 532 time to create 1 rle with old method : 0.0006930828094482422 length of segment : 34 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 1470 time to create 1 rle with old method : 0.0018935203552246094 length of segment : 50 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 1636 time to create 1 rle with old method : 0.0021347999572753906 length of segment : 68 time for calcul the mask position with numpy : 0.0001227855682373047 nb_pixel_total : 7904 time to create 1 rle with old method : 0.009494543075561523 length of segment : 69 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 298 time to create 1 rle with old method : 0.0004382133483886719 length of segment : 25 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 109 time to create 1 rle with old method : 0.00016427040100097656 length of segment : 19 time for calcul the mask position with numpy : 2.7894973754882812e-05 nb_pixel_total : 209 time to create 1 rle with old method : 0.0002846717834472656 length of segment : 20 time for calcul the mask position with numpy : 0.00010585784912109375 nb_pixel_total : 3480 time to create 1 rle with old method : 0.004192829132080078 length of segment : 85 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 381 time to create 1 rle with old method : 0.0005047321319580078 length of segment : 21 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 3700 time to create 1 rle with old method : 0.004244089126586914 length of segment : 115 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 369 time to create 1 rle with old method : 0.0004987716674804688 length of segment : 20 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 : 47 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002601146697998047 length of segment : 25 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 317 time to create 1 rle with old method : 0.0004544258117675781 length of segment : 22 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 270 time to create 1 rle with old method : 0.00042176246643066406 length of segment : 16 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 445 time to create 1 rle with old method : 0.0005745887756347656 length of segment : 39 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 241 time to create 1 rle with old method : 0.00036907196044921875 length of segment : 17 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 899 time to create 1 rle with old method : 0.0011548995971679688 length of segment : 46 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 1005 time to create 1 rle with old method : 0.0013434886932373047 length of segment : 40 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 1463 time to create 1 rle with old method : 0.0018579959869384766 length of segment : 80 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 164 time to create 1 rle with old method : 0.00025844573974609375 length of segment : 33 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 579 time to create 1 rle with old method : 0.0007951259613037109 length of segment : 89 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 628 time to create 1 rle with old method : 0.0008080005645751953 length of segment : 24 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 307 time to create 1 rle with old method : 0.00042939186096191406 length of segment : 22 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 580 time to create 1 rle with old method : 0.0008440017700195312 length of segment : 28 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 1528 time to create 1 rle with old method : 0.0019183158874511719 length of segment : 63 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 1263 time to create 1 rle with old method : 0.0015654563903808594 length of segment : 51 time for calcul the mask position with numpy : 0.00014710426330566406 nb_pixel_total : 6667 time to create 1 rle with old method : 0.007694721221923828 length of segment : 147 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 956 time to create 1 rle with old method : 0.0012578964233398438 length of segment : 36 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 974 time to create 1 rle with old method : 0.0012412071228027344 length of segment : 41 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 493 time to create 1 rle with old method : 0.0006129741668701172 length of segment : 23 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 820 time to create 1 rle with old method : 0.0010461807250976562 length of segment : 36 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.0002124309539794922 length of segment : 17 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.00026869773864746094 length of segment : 15 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 122 time to create 1 rle with old method : 0.00020575523376464844 length of segment : 15 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.0002543926239013672 length of segment : 37 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 1131 time to create 1 rle with old method : 0.0013451576232910156 length of segment : 77 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 407 time to create 1 rle with old method : 0.0006043910980224609 length of segment : 21 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 430 time to create 1 rle with old method : 0.0006072521209716797 length of segment : 19 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 699 time to create 1 rle with old method : 0.0009107589721679688 length of segment : 36 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 210 time to create 1 rle with old method : 0.0002892017364501953 length of segment : 28 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 996 time to create 1 rle with old method : 0.0012803077697753906 length of segment : 44 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005581378936767578 length of segment : 30 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 345 time to create 1 rle with old method : 0.0004851818084716797 length of segment : 26 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 351 time to create 1 rle with old method : 0.00045037269592285156 length of segment : 31 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 1544 time to create 1 rle with old method : 0.0018928050994873047 length of segment : 56 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 1477 time to create 1 rle with old method : 0.0017783641815185547 length of segment : 57 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 835 time to create 1 rle with old method : 0.0010821819305419922 length of segment : 36 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 1591 time to create 1 rle with old method : 0.0019431114196777344 length of segment : 56 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 402 time to create 1 rle with old method : 0.0005650520324707031 length of segment : 32 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.0004878044128417969 length of segment : 29 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 599 time to create 1 rle with old method : 0.0008080005645751953 length of segment : 24 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 645 time to create 1 rle with old method : 0.0008881092071533203 length of segment : 34 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 894 time to create 1 rle with old method : 0.0011603832244873047 length of segment : 35 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 1299 time to create 1 rle with old method : 0.0015130043029785156 length of segment : 52 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 777 time to create 1 rle with old method : 0.0010874271392822266 length of segment : 35 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 949 time to create 1 rle with old method : 0.0012793540954589844 length of segment : 35 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 549 time to create 1 rle with old method : 0.00074005126953125 length of segment : 31 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 617 time to create 1 rle with old method : 0.0008592605590820312 length of segment : 26 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: 145.92422 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 208 time to create 1 rle with old method : 0.00034046173095703125 length of segment : 16 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 2056 time to create 1 rle with old method : 0.0026154518127441406 length of segment : 89 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 1661 time to create 1 rle with old method : 0.002100706100463867 length of segment : 57 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 582 time to create 1 rle with old method : 0.0007719993591308594 length of segment : 44 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.00020647048950195312 length of segment : 12 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 433 time to create 1 rle with old method : 0.0005750656127929688 length of segment : 33 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 319 time to create 1 rle with old method : 0.0004291534423828125 length of segment : 26 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 703 time to create 1 rle with old method : 0.0009491443634033203 length of segment : 42 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.00019812583923339844 length of segment : 15 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 396 time to create 1 rle with old method : 0.000637054443359375 length of segment : 20 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 372 time to create 1 rle with old method : 0.0005283355712890625 length of segment : 25 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 727 time to create 1 rle with old method : 0.0010974407196044922 length of segment : 110 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 2233 time to create 1 rle with old method : 0.0027589797973632812 length of segment : 85 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 2109 time to create 1 rle with old method : 0.002618551254272461 length of segment : 89 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: 137.41250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 139 time to create 1 rle with old method : 0.00023746490478515625 length of segment : 13 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 178 time to create 1 rle with old method : 0.00030684471130371094 length of segment : 12 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0006687641143798828 length of segment : 45 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.000324249267578125 length of segment : 30 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.00016498565673828125 length of segment : 25 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 225 time to create 1 rle with old method : 0.00034308433532714844 length of segment : 29 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: -118.78203 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 : 3.838539123535156e-05 nb_pixel_total : 296 time to create 1 rle with old method : 0.0005247592926025391 length of segment : 11 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 41 time to create 1 rle with old method : 7.724761962890625e-05 length of segment : 15 time for calcul the mask position with numpy : 6.985664367675781e-05 nb_pixel_total : 633 time to create 1 rle with old method : 0.0012903213500976562 length of segment : 59 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 316 time to create 1 rle with old method : 0.0005908012390136719 length of segment : 23 time for calcul the mask position with numpy : 0.00029850006103515625 nb_pixel_total : 9277 time to create 1 rle with old method : 0.01589035987854004 length of segment : 104 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 249 time to create 1 rle with old method : 0.00035262107849121094 length of segment : 35 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 409 time to create 1 rle with old method : 0.0005841255187988281 length of segment : 49 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 213 time to create 1 rle with old method : 0.00030541419982910156 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: -116.37188 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 : 4.935264587402344e-05 nb_pixel_total : 144 time to create 1 rle with old method : 0.0003325939178466797 length of segment : 27 time for calcul the mask position with numpy : 9.846687316894531e-05 nb_pixel_total : 2582 time to create 1 rle with old method : 0.00452113151550293 length of segment : 56 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 274 time to create 1 rle with old method : 0.0005452632904052734 length of segment : 32 time for calcul the mask position with numpy : 9.918212890625e-05 nb_pixel_total : 1973 time to create 1 rle with old method : 0.0034415721893310547 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: -119.46953 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 : 8.845329284667969e-05 nb_pixel_total : 3637 time to create 1 rle with old method : 0.004548311233520508 length of segment : 144 time for calcul the mask position with numpy : 9.989738464355469e-05 nb_pixel_total : 2609 time to create 1 rle with old method : 0.003239870071411133 length of segment : 98 time for calcul the mask position with numpy : 9.322166442871094e-05 nb_pixel_total : 3279 time to create 1 rle with old method : 0.004034280776977539 length of segment : 121 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -113.03203 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 : 0.0001399517059326172 nb_pixel_total : 7508 time to create 1 rle with old method : 0.009713172912597656 length of segment : 134 time for calcul the mask position with numpy : 8.440017700195312e-05 nb_pixel_total : 1492 time to create 1 rle with old method : 0.001987934112548828 length of segment : 82 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 691 time to create 1 rle with old method : 0.0009565353393554688 length of segment : 77 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002956390380859375 length of segment : 16 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 928 time to create 1 rle with old method : 0.0011470317840576172 length of segment : 53 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.000274658203125 length of segment : 23 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 565 time to create 1 rle with old method : 0.0007655620574951172 length of segment : 67 time for calcul the mask position with numpy : 7.653236389160156e-05 nb_pixel_total : 1768 time to create 1 rle with old method : 0.0022630691528320312 length of segment : 25 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 : 4.076957702636719e-05 nb_pixel_total : 531 time to create 1 rle with old method : 0.000667572021484375 length of segment : 28 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 881 time to create 1 rle with old method : 0.0011637210845947266 length of segment : 53 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 1337 time to create 1 rle with old method : 0.0016753673553466797 length of segment : 55 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 2771 time to create 1 rle with old method : 0.0035457611083984375 length of segment : 50 time for calcul the mask position with numpy : 0.00045609474182128906 nb_pixel_total : 42092 time to create 1 rle with old method : 0.04815840721130371 length of segment : 276 time for calcul the mask position with numpy : 8.7738037109375e-05 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0022687911987304688 length of segment : 55 Processing 1 images image shape: (400, 400, 3) min: 17.00000 max: 203.00000 molded_images shape: (1, 640, 640, 3) min: -93.84063 max: 73.91250 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: -119.31719 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 : 5.030632019042969e-05 nb_pixel_total : 1238 time to create 1 rle with old method : 0.0016019344329833984 length of segment : 44 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 118 time to create 1 rle with old method : 0.0002014636993408203 length of segment : 12 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 2104 time to create 1 rle with old method : 0.0027916431427001953 length of segment : 50 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 1178 time to create 1 rle with old method : 0.001520395278930664 length of segment : 61 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 371 time to create 1 rle with old method : 0.0005791187286376953 length of segment : 15 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.00020170211791992188 length of segment : 12 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 884 time to create 1 rle with old method : 0.0012106895446777344 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: -119.23125 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 : 8.177757263183594e-05 nb_pixel_total : 468 time to create 1 rle with old method : 0.0009262561798095703 length of segment : 29 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 87 time to create 1 rle with old method : 0.0002071857452392578 length of segment : 16 time for calcul the mask position with numpy : 0.00030922889709472656 nb_pixel_total : 12928 time to create 1 rle with old method : 0.02034163475036621 length of segment : 117 time for calcul the mask position with numpy : 0.00013566017150878906 nb_pixel_total : 2062 time to create 1 rle with old method : 0.0036783218383789062 length of segment : 62 time for calcul the mask position with numpy : 7.390975952148438e-05 nb_pixel_total : 717 time to create 1 rle with old method : 0.0010387897491455078 length of segment : 32 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 771 time to create 1 rle with old method : 0.001003265380859375 length of segment : 39 time for calcul the mask position with numpy : 0.00010848045349121094 nb_pixel_total : 994 time to create 1 rle with old method : 0.0013489723205566406 length of segment : 119 time for calcul the mask position with numpy : 8.082389831542969e-05 nb_pixel_total : 1349 time to create 1 rle with old method : 0.0019011497497558594 length of segment : 79 time for calcul the mask position with numpy : 0.00016617774963378906 nb_pixel_total : 2612 time to create 1 rle with old method : 0.003793478012084961 length of segment : 103 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.00036597251892089844 length of segment : 19 time for calcul the mask position with numpy : 0.0002582073211669922 nb_pixel_total : 3699 time to create 1 rle with old method : 0.00490117073059082 length of segment : 112 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 66 time to create 1 rle with old method : 0.00013136863708496094 length of segment : 7 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.52422 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 : 9.036064147949219e-05 nb_pixel_total : 2039 time to create 1 rle with old method : 0.0024085044860839844 length of segment : 153 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 713 time to create 1 rle with old method : 0.0009686946868896484 length of segment : 70 time for calcul the mask position with numpy : 0.00021910667419433594 nb_pixel_total : 11780 time to create 1 rle with old method : 0.014739513397216797 length of segment : 154 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 1208 time to create 1 rle with old method : 0.0014977455139160156 length of segment : 38 time for calcul the mask position with numpy : 0.00021886825561523438 nb_pixel_total : 13876 time to create 1 rle with old method : 0.016382455825805664 length of segment : 119 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 1107 time to create 1 rle with old method : 0.0013384819030761719 length of segment : 97 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 157 time to create 1 rle with old method : 0.00023555755615234375 length of segment : 20 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 244 time to create 1 rle with old method : 0.0003452301025390625 length of segment : 22 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 970 time to create 1 rle with old method : 0.0014095306396484375 length of segment : 37 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004737377166748047 length of segment : 42 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: 139.43203 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 9.036064147949219e-05 nb_pixel_total : 848 time to create 1 rle with old method : 0.0010972023010253906 length of segment : 72 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.00039005279541015625 length of segment : 20 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 1040 time to create 1 rle with old method : 0.0015034675598144531 length of segment : 51 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 1774 time to create 1 rle with old method : 0.002145051956176758 length of segment : 88 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 791 time to create 1 rle with old method : 0.0010721683502197266 length of segment : 45 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 219 time to create 1 rle with old method : 0.0003514289855957031 length of segment : 23 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.00023245811462402344 length of segment : 23 time for calcul the mask position with numpy : 0.00020074844360351562 nb_pixel_total : 3861 time to create 1 rle with old method : 0.004701852798461914 length of segment : 212 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 783 time to create 1 rle with old method : 0.0010342597961425781 length of segment : 43 Processing 1 images image shape: (280, 400, 3) min: 5.00000 max: 182.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 57.29141 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009458065032958984 nb_pixel_total : 106307 time to create 1 rle with old method : 0.12097978591918945 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 : 4.4345855712890625e-05 nb_pixel_total : 500 time to create 1 rle with old method : 0.0007355213165283203 length of segment : 23 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 958 time to create 1 rle with old method : 0.0012226104736328125 length of segment : 41 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 151 time to create 1 rle with old method : 0.0002446174621582031 length of segment : 15 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 611 time to create 1 rle with old method : 0.0008032321929931641 length of segment : 55 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 1727 time to create 1 rle with old method : 0.0021715164184570312 length of segment : 51 time for calcul the mask position with numpy : 0.00011920928955078125 nb_pixel_total : 4056 time to create 1 rle with old method : 0.004915475845336914 length of segment : 93 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 367 time to create 1 rle with old method : 0.0005393028259277344 length of segment : 20 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 800 time to create 1 rle with old method : 0.0011012554168701172 length of segment : 57 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 699 time to create 1 rle with old method : 0.0009288787841796875 length of segment : 28 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 694 time to create 1 rle with old method : 0.0009279251098632812 length of segment : 28 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.00025010108947753906 length of segment : 15 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.000263214111328125 length of segment : 17 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 376 time to create 1 rle with old method : 0.0005424022674560547 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 : 36 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 455 time to create 1 rle with old method : 0.0006079673767089844 length of segment : 39 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.00023698806762695312 length of segment : 25 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 295 time to create 1 rle with old method : 0.00043392181396484375 length of segment : 21 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 834 time to create 1 rle with old method : 0.0014786720275878906 length of segment : 41 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 1765 time to create 1 rle with old method : 0.0031347274780273438 length of segment : 77 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.0005552768707275391 length of segment : 15 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 877 time to create 1 rle with old method : 0.0015621185302734375 length of segment : 41 time for calcul the mask position with numpy : 0.0001850128173828125 nb_pixel_total : 6762 time to create 1 rle with old method : 0.010412454605102539 length of segment : 140 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 544 time to create 1 rle with old method : 0.0007395744323730469 length of segment : 45 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.00061798095703125 length of segment : 33 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.00030112266540527344 length of segment : 32 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00022411346435546875 length of segment : 18 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 992 time to create 1 rle with old method : 0.001336812973022461 length of segment : 39 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 299 time to create 1 rle with old method : 0.0003955364227294922 length of segment : 23 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 429 time to create 1 rle with old method : 0.0006151199340820312 length of segment : 33 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 548 time to create 1 rle with old method : 0.0007843971252441406 length of segment : 25 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.0002048015594482422 length of segment : 18 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 379 time to create 1 rle with old method : 0.0005526542663574219 length of segment : 26 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 859 time to create 1 rle with old method : 0.0011208057403564453 length of segment : 34 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 765 time to create 1 rle with old method : 0.001010894775390625 length of segment : 29 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 1197 time to create 1 rle with old method : 0.001512765884399414 length of segment : 50 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 1416 time to create 1 rle with old method : 0.0017714500427246094 length of segment : 56 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 638 time to create 1 rle with old method : 0.0008969306945800781 length of segment : 30 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 1009 time to create 1 rle with old method : 0.001352548599243164 length of segment : 37 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 204 time to create 1 rle with old method : 0.00028204917907714844 length of segment : 24 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 234 time to create 1 rle with old method : 0.0003380775451660156 length of segment : 19 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 382 time to create 1 rle with old method : 0.0005443096160888672 length of segment : 19 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 1495 time to create 1 rle with old method : 0.0017771720886230469 length of segment : 61 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.00027108192443847656 length of segment : 15 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 486 time to create 1 rle with old method : 0.0007197856903076172 length of segment : 24 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0005774497985839844 length of segment : 33 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 847 time to create 1 rle with old method : 0.0011434555053710938 length of segment : 34 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 1223 time to create 1 rle with old method : 0.0015094280242919922 length of segment : 50 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 690 time to create 1 rle with old method : 0.0012555122375488281 length of segment : 26 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0007078647613525391 length of segment : 21 time for calcul the mask position with numpy : 8.034706115722656e-05 nb_pixel_total : 1419 time to create 1 rle with old method : 0.0021514892578125 length of segment : 57 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: 147.43984 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 18 time for calcul the mask position with numpy : 0.00010657310485839844 nb_pixel_total : 475 time to create 1 rle with old method : 0.0011281967163085938 length of segment : 33 time for calcul the mask position with numpy : 0.00010895729064941406 nb_pixel_total : 1591 time to create 1 rle with old method : 0.0028533935546875 length of segment : 50 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 324 time to create 1 rle with old method : 0.0006403923034667969 length of segment : 21 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 331 time to create 1 rle with old method : 0.0006427764892578125 length of segment : 26 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 344 time to create 1 rle with old method : 0.0006620883941650391 length of segment : 27 time for calcul the mask position with numpy : 9.989738464355469e-05 nb_pixel_total : 754 time to create 1 rle with old method : 0.0015344619750976562 length of segment : 54 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 51 time to create 1 rle with old method : 0.0001342296600341797 length of segment : 10 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 410 time to create 1 rle with old method : 0.0008022785186767578 length of segment : 26 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0008924007415771484 length of segment : 26 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 672 time to create 1 rle with old method : 0.0008895397186279297 length of segment : 59 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00017833709716796875 length of segment : 12 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 251 time to create 1 rle with old method : 0.0003750324249267578 length of segment : 21 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 114 time to create 1 rle with old method : 0.00018095970153808594 length of segment : 14 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005750656127929688 length of segment : 23 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 111 time to create 1 rle with old method : 0.0001647472381591797 length of segment : 27 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 560 time to create 1 rle with old method : 0.0007505416870117188 length of segment : 26 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 1371 time to create 1 rle with old method : 0.001806497573852539 length of segment : 64 time for calcul the mask position with numpy : 6.866455078125e-05 nb_pixel_total : 1164 time to create 1 rle with old method : 0.0015325546264648438 length of segment : 75 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: 139.78750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 555 time to create 1 rle with old method : 0.0006923675537109375 length of segment : 47 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 210 time to create 1 rle with old method : 0.00035834312438964844 length of segment : 12 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.00019621849060058594 length of segment : 23 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.00017571449279785156 length of segment : 30 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 79 time to create 1 rle with old method : 0.0001366138458251953 length of segment : 9 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 82 time to create 1 rle with old method : 0.00016546249389648438 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: -118.27031 max: 148.80312 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 8.559226989746094e-05 nb_pixel_total : 95 time to create 1 rle with old method : 0.00025725364685058594 length of segment : 16 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.0001800060272216797 length of segment : 19 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.001027822494506836 length of segment : 57 time for calcul the mask position with numpy : 0.00010752677917480469 nb_pixel_total : 1895 time to create 1 rle with old method : 0.0037546157836914062 length of segment : 52 time for calcul the mask position with numpy : 0.00039839744567871094 nb_pixel_total : 9560 time to create 1 rle with old method : 0.01837158203125 length of segment : 175 time for calcul the mask position with numpy : 9.870529174804688e-05 nb_pixel_total : 227 time to create 1 rle with old method : 0.0006008148193359375 length of segment : 29 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.0006778240203857422 length of segment : 32 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 164 time to create 1 rle with old method : 0.0004513263702392578 length of segment : 8 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.0004630088806152344 length of segment : 25 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 333 time to create 1 rle with old method : 0.0006954669952392578 length of segment : 24 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 282 time to create 1 rle with old method : 0.000629425048828125 length of segment : 34 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 375 time to create 1 rle with old method : 0.0007488727569580078 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: -118.14531 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 : 5.245208740234375e-05 nb_pixel_total : 641 time to create 1 rle with old method : 0.0008633136749267578 length of segment : 33 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 2461 time to create 1 rle with old method : 0.003118753433227539 length of segment : 51 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 747 time to create 1 rle with old method : 0.0009667873382568359 length of segment : 45 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 141 time to create 1 rle with old method : 0.00029349327087402344 length of segment : 30 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 117 time to create 1 rle with old method : 0.0002052783966064453 length of segment : 14 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 1737 time to create 1 rle with old method : 0.0021224021911621094 length of segment : 84 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.53203 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 : 0.00015854835510253906 nb_pixel_total : 1800 time to create 1 rle with old method : 0.0070497989654541016 length of segment : 106 time for calcul the mask position with numpy : 0.0002014636993408203 nb_pixel_total : 3239 time to create 1 rle with old method : 0.007398843765258789 length of segment : 43 time for calcul the mask position with numpy : 0.0002608299255371094 nb_pixel_total : 5951 time to create 1 rle with old method : 0.011511802673339844 length of segment : 106 time for calcul the mask position with numpy : 0.00015592575073242188 nb_pixel_total : 2331 time to create 1 rle with old method : 0.0030128955841064453 length of segment : 98 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0007982254028320312 length of segment : 26 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 480 time to create 1 rle with old method : 0.0007138252258300781 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: -118.20781 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 : 8.821487426757812e-05 nb_pixel_total : 1406 time to create 1 rle with old method : 0.002614259719848633 length of segment : 80 time for calcul the mask position with numpy : 0.00018095970153808594 nb_pixel_total : 6729 time to create 1 rle with old method : 0.010097265243530273 length of segment : 108 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 716 time to create 1 rle with old method : 0.0009462833404541016 length of segment : 58 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 206 time to create 1 rle with old method : 0.0003173351287841797 length of segment : 17 time for calcul the mask position with numpy : 8.726119995117188e-05 nb_pixel_total : 2039 time to create 1 rle with old method : 0.0038597583770751953 length of segment : 31 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 896 time to create 1 rle with old method : 0.0012011528015136719 length of segment : 71 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 252 time to create 1 rle with old method : 0.0005514621734619141 length of segment : 21 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 635 time to create 1 rle with old method : 0.0011382102966308594 length of segment : 54 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 : 5 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 512 time to create 1 rle with old method : 0.0006902217864990234 length of segment : 28 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 873 time to create 1 rle with old method : 0.0010991096496582031 length of segment : 51 time for calcul the mask position with numpy : 9.226799011230469e-05 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0015537738800048828 length of segment : 54 time for calcul the mask position with numpy : 0.0006237030029296875 nb_pixel_total : 59637 time to create 1 rle with old method : 0.07117700576782227 length of segment : 307 time for calcul the mask position with numpy : 0.00010895729064941406 nb_pixel_total : 1288 time to create 1 rle with old method : 0.0016155242919921875 length of segment : 55 Processing 1 images image shape: (400, 400, 3) min: 18.00000 max: 210.00000 molded_images shape: (1, 640, 640, 3) min: -93.52813 max: 82.07656 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.001287221908569336 nb_pixel_total : 151957 time to create 1 rle with new method : 0.002164125442504883 length of segment : 395 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.50078 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 : 0.00010704994201660156 nb_pixel_total : 1349 time to create 1 rle with old method : 0.002310037612915039 length of segment : 47 time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 1599 time to create 1 rle with old method : 0.0029096603393554688 length of segment : 33 time for calcul the mask position with numpy : 8.130073547363281e-05 nb_pixel_total : 1225 time to create 1 rle with old method : 0.0024118423461914062 length of segment : 32 time for calcul the mask position with numpy : 0.00012230873107910156 nb_pixel_total : 3659 time to create 1 rle with old method : 0.0043332576751708984 length of segment : 113 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 489 time to create 1 rle with old method : 0.0007448196411132812 length of segment : 24 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 513 time to create 1 rle with old method : 0.0007326602935791016 length of segment : 33 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0009531974792480469 length of segment : 33 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.0005261898040771484 length of segment : 19 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 728 time to create 1 rle with old method : 0.0009214878082275391 length of segment : 39 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 302 time to create 1 rle with old method : 0.0004336833953857422 length of segment : 21 time for calcul the mask position with numpy : 0.00011658668518066406 nb_pixel_total : 2237 time to create 1 rle with old method : 0.00350189208984375 length of segment : 53 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 105 time to create 1 rle with old method : 0.0002639293670654297 length of segment : 13 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.00023412704467773438 length of segment : 18 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00020194053649902344 length of segment : 15 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.0002474784851074219 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: -122.12969 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 : 7.510185241699219e-05 nb_pixel_total : 472 time to create 1 rle with old method : 0.0009317398071289062 length of segment : 28 time for calcul the mask position with numpy : 0.00011515617370605469 nb_pixel_total : 1382 time to create 1 rle with old method : 0.002555370330810547 length of segment : 86 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00018477439880371094 length of segment : 19 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 534 time to create 1 rle with old method : 0.0008578300476074219 length of segment : 39 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 707 time to create 1 rle with old method : 0.0009021759033203125 length of segment : 32 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 326 time to create 1 rle with old method : 0.0004661083221435547 length of segment : 21 time for calcul the mask position with numpy : 0.00024366378784179688 nb_pixel_total : 11232 time to create 1 rle with old method : 0.01314234733581543 length of segment : 183 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 349 time to create 1 rle with old method : 0.0005159378051757812 length of segment : 16 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 1164 time to create 1 rle with old method : 0.0014879703521728516 length of segment : 49 time for calcul the mask position with numpy : 8.273124694824219e-05 nb_pixel_total : 2965 time to create 1 rle with old method : 0.003786325454711914 length of segment : 78 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 60 time to create 1 rle with old method : 0.00012135505676269531 length of segment : 8 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 495 time to create 1 rle with old method : 0.0006923675537109375 length of segment : 26 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 834 time to create 1 rle with old method : 0.001070261001586914 length of segment : 30 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 241 time to create 1 rle with old method : 0.0003762245178222656 length of segment : 13 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 262 time to create 1 rle with old method : 0.00040841102600097656 length of segment : 32 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 788 time to create 1 rle with old method : 0.0010526180267333984 length of segment : 42 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 743 time to create 1 rle with old method : 0.0009963512420654297 length of segment : 24 time for calcul the mask position with numpy : 0.00013208389282226562 nb_pixel_total : 2070 time to create 1 rle with old method : 0.0025382041931152344 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: -123.70000 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 : 0.00012183189392089844 nb_pixel_total : 5898 time to create 1 rle with old method : 0.007185935974121094 length of segment : 124 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 1241 time to create 1 rle with old method : 0.0015664100646972656 length of segment : 40 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 1710 time to create 1 rle with old method : 0.0021262168884277344 length of segment : 130 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 779 time to create 1 rle with old method : 0.0010128021240234375 length of segment : 38 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 1077 time to create 1 rle with old method : 0.0013608932495117188 length of segment : 90 time for calcul the mask position with numpy : 0.0001010894775390625 nb_pixel_total : 3712 time to create 1 rle with old method : 0.004413127899169922 length of segment : 156 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 823 time to create 1 rle with old method : 0.0012335777282714844 length of segment : 38 time for calcul the mask position with numpy : 0.00012922286987304688 nb_pixel_total : 6925 time to create 1 rle with old method : 0.008195877075195312 length of segment : 95 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 998 time to create 1 rle with old method : 0.0012192726135253906 length of segment : 101 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: 143.52969 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0007359981536865234 length of segment : 72 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 1174 time to create 1 rle with old method : 0.0014653205871582031 length of segment : 57 time for calcul the mask position with numpy : 6.198883056640625e-05 nb_pixel_total : 1650 time to create 1 rle with old method : 0.002086162567138672 length of segment : 82 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 801 time to create 1 rle with old method : 0.0009915828704833984 length of segment : 42 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 92 time to create 1 rle with old method : 0.0001761913299560547 length of segment : 22 time for calcul the mask position with numpy : 0.00017404556274414062 nb_pixel_total : 3326 time to create 1 rle with old method : 0.003938913345336914 length of segment : 204 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 809 time to create 1 rle with old method : 0.0010273456573486328 length of segment : 42 Processing 1 images image shape: (280, 400, 3) min: 13.00000 max: 178.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 58.96719 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0008795261383056641 nb_pixel_total : 106245 time to create 1 rle with old method : 0.11530780792236328 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 : 6.413459777832031e-05 nb_pixel_total : 512 time to create 1 rle with old method : 0.0007941722869873047 length of segment : 25 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 617 time to create 1 rle with old method : 0.0011494159698486328 length of segment : 26 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 933 time to create 1 rle with old method : 0.0016701221466064453 length of segment : 39 time for calcul the mask position with numpy : 7.367134094238281e-05 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0021338462829589844 length of segment : 56 time for calcul the mask position with numpy : 0.0001666545867919922 nb_pixel_total : 4356 time to create 1 rle with old method : 0.007257938385009766 length of segment : 84 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 724 time to create 1 rle with old method : 0.0013458728790283203 length of segment : 38 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 989 time to create 1 rle with old method : 0.0017058849334716797 length of segment : 48 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00018930435180664062 length of segment : 14 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 140 time to create 1 rle with old method : 0.0002129077911376953 length of segment : 15 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 2154 time to create 1 rle with old method : 0.002620220184326172 length of segment : 79 time for calcul the mask position with numpy : 5.841255187988281e-05 nb_pixel_total : 1752 time to create 1 rle with old method : 0.002222299575805664 length of segment : 52 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.000301361083984375 length of segment : 21 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.0005664825439453125 length of segment : 22 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.0002753734588623047 length of segment : 20 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 365 time to create 1 rle with old method : 0.0004894733428955078 length of segment : 20 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 386 time to create 1 rle with old method : 0.0005509853363037109 length of segment : 22 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 : 5.936622619628906e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002613067626953125 length of segment : 26 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 418 time to create 1 rle with old method : 0.0005419254302978516 length of segment : 38 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 301 time to create 1 rle with old method : 0.00044083595275878906 length of segment : 21 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 250 time to create 1 rle with old method : 0.00037217140197753906 length of segment : 26 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 824 time to create 1 rle with old method : 0.0010595321655273438 length of segment : 48 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 253 time to create 1 rle with old method : 0.00040030479431152344 length of segment : 13 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 782 time to create 1 rle with old method : 0.0010237693786621094 length of segment : 58 time for calcul the mask position with numpy : 0.00014019012451171875 nb_pixel_total : 6403 time to create 1 rle with old method : 0.00754857063293457 length of segment : 132 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1368 time to create 1 rle with old method : 0.001733541488647461 length of segment : 50 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 562 time to create 1 rle with old method : 0.0007736682891845703 length of segment : 25 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.000217437744140625 length of segment : 32 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.00031757354736328125 length of segment : 38 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.00023484230041503906 length of segment : 17 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 1019 time to create 1 rle with old method : 0.0013461112976074219 length of segment : 40 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 844 time to create 1 rle with old method : 0.0010440349578857422 length of segment : 64 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002524852752685547 length of segment : 18 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 487 time to create 1 rle with old method : 0.0006895065307617188 length of segment : 41 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 843 time to create 1 rle with old method : 0.001093149185180664 length of segment : 41 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 986 time to create 1 rle with old method : 0.0013027191162109375 length of segment : 96 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 761 time to create 1 rle with old method : 0.0010287761688232422 length of segment : 27 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 891 time to create 1 rle with old method : 0.0011410713195800781 length of segment : 37 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 951 time to create 1 rle with old method : 0.0012996196746826172 length of segment : 27 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 402 time to create 1 rle with old method : 0.0007047653198242188 length of segment : 13 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 1307 time to create 1 rle with old method : 0.0016260147094726562 length of segment : 53 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.0002639293670654297 length of segment : 14 time for calcul the mask position with numpy : 7.867813110351562e-05 nb_pixel_total : 1508 time to create 1 rle with old method : 0.0019109249114990234 length of segment : 54 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 288 time to create 1 rle with old method : 0.0003924369812011719 length of segment : 31 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 385 time to create 1 rle with old method : 0.0005331039428710938 length of segment : 30 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 905 time to create 1 rle with old method : 0.0012123584747314453 length of segment : 36 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 879 time to create 1 rle with old method : 0.001195669174194336 length of segment : 37 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 700 time to create 1 rle with old method : 0.0009765625 length of segment : 30 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 97 time to create 1 rle with old method : 0.00018310546875 length of segment : 11 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 280 time to create 1 rle with old method : 0.0004253387451171875 length of segment : 40 time for calcul the mask position with numpy : 7.319450378417969e-05 nb_pixel_total : 1392 time to create 1 rle with old method : 0.00173187255859375 length of segment : 58 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 433 time to create 1 rle with old method : 0.0005695819854736328 length of segment : 31 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 308 time to create 1 rle with old method : 0.0004177093505859375 length of segment : 25 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 822 time to create 1 rle with old method : 0.0010802745819091797 length of segment : 33 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 410 time to create 1 rle with old method : 0.0006043910980224609 length of segment : 21 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 262 time to create 1 rle with old method : 0.0003819465637207031 length of segment : 23 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 435 time to create 1 rle with old method : 0.0006167888641357422 length of segment : 22 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: 146.26016 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 518 time to create 1 rle with old method : 0.0006847381591796875 length of segment : 38 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 432 time to create 1 rle with old method : 0.0006029605865478516 length of segment : 26 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1575 time to create 1 rle with old method : 0.002007007598876953 length of segment : 51 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 812 time to create 1 rle with old method : 0.0011186599731445312 length of segment : 67 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.00045609474182128906 length of segment : 29 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 931 time to create 1 rle with old method : 0.0012941360473632812 length of segment : 52 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 478 time to create 1 rle with old method : 0.0005991458892822266 length of segment : 32 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 1779 time to create 1 rle with old method : 0.002263307571411133 length of segment : 66 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 201 time to create 1 rle with old method : 0.00029158592224121094 length of segment : 17 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 569 time to create 1 rle with old method : 0.0008249282836914062 length of segment : 32 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.00019788742065429688 length of segment : 16 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 626 time to create 1 rle with old method : 0.0008141994476318359 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: 132.03750 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 521 time to create 1 rle with old method : 0.0006964206695556641 length of segment : 43 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001575946807861328 length of segment : 21 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.00031065940856933594 length of segment : 12 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 114 time to create 1 rle with old method : 0.00020694732666015625 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: -118.43047 max: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 0.00024628639221191406 nb_pixel_total : 13369 time to create 1 rle with old method : 0.01519775390625 length of segment : 178 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 58 time to create 1 rle with old method : 0.00011467933654785156 length of segment : 16 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 98 time to create 1 rle with old method : 0.000148773193359375 length of segment : 17 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 197 time to create 1 rle with old method : 0.0002913475036621094 length of segment : 29 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 351 time to create 1 rle with old method : 0.0004928112030029297 length of segment : 27 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 253 time to create 1 rle with old method : 0.00037097930908203125 length of segment : 27 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 273 time to create 1 rle with old method : 0.0005412101745605469 length of segment : 12 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002989768981933594 length of segment : 22 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 158 time to create 1 rle with old method : 0.00031280517578125 length of segment : 36 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.0005519390106201172 length of segment : 56 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 : 4 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 2955 time to create 1 rle with old method : 0.0051691532135009766 length of segment : 58 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 759 time to create 1 rle with old method : 0.001451253890991211 length of segment : 46 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 1865 time to create 1 rle with old method : 0.003551483154296875 length of segment : 55 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 537 time to create 1 rle with old method : 0.0010333061218261719 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: -120.56328 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 : 3.695487976074219e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.0003514289855957031 length of segment : 11 time for calcul the mask position with numpy : 0.00010895729064941406 nb_pixel_total : 2633 time to create 1 rle with old method : 0.003787517547607422 length of segment : 31 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 605 time to create 1 rle with old method : 0.0010352134704589844 length of segment : 20 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 2389 time to create 1 rle with old method : 0.002965211868286133 length of segment : 97 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.0003800392150878906 length of segment : 8 time for calcul the mask position with numpy : 9.250640869140625e-05 nb_pixel_total : 2915 time to create 1 rle with old method : 0.003552675247192383 length of segment : 113 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 : 7 time for calcul the mask position with numpy : 0.00015497207641601562 nb_pixel_total : 6001 time to create 1 rle with old method : 0.007888078689575195 length of segment : 96 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 1558 time to create 1 rle with old method : 0.0020232200622558594 length of segment : 77 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 894 time to create 1 rle with old method : 0.0011835098266601562 length of segment : 69 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.00029754638671875 length of segment : 17 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 157 time to create 1 rle with old method : 0.00022530555725097656 length of segment : 21 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 876 time to create 1 rle with old method : 0.0013649463653564453 length of segment : 62 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 542 time to create 1 rle with old method : 0.0007143020629882812 length of segment : 47 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 : 5 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 550 time to create 1 rle with old method : 0.0007107257843017578 length of segment : 30 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0013523101806640625 length of segment : 50 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 855 time to create 1 rle with old method : 0.0012497901916503906 length of segment : 51 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 726 time to create 1 rle with old method : 0.000934600830078125 length of segment : 49 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 106 time to create 1 rle with old method : 0.0001876354217529297 length of segment : 10 Processing 1 images image shape: (400, 400, 3) min: 16.00000 max: 205.00000 molded_images shape: (1, 640, 640, 3) min: -93.84063 max: 75.93203 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: -117.77422 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 : 7.867813110351562e-05 nb_pixel_total : 1424 time to create 1 rle with old method : 0.002346515655517578 length of segment : 48 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1615 time to create 1 rle with old method : 0.0027403831481933594 length of segment : 37 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.0002808570861816406 length of segment : 18 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 220 time to create 1 rle with old method : 0.00043511390686035156 length of segment : 20 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 449 time to create 1 rle with old method : 0.0008504390716552734 length of segment : 34 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 205 time to create 1 rle with old method : 0.00038433074951171875 length of segment : 19 time for calcul the mask position with numpy : 7.128715515136719e-05 nb_pixel_total : 2019 time to create 1 rle with old method : 0.0034477710723876953 length of segment : 41 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 681 time to create 1 rle with old method : 0.001186370849609375 length of segment : 37 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 623 time to create 1 rle with old method : 0.001142740249633789 length of segment : 32 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 67 time to create 1 rle with old method : 0.00017404556274414062 length of segment : 9 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00021791458129882812 length of segment : 5 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 2527 time to create 1 rle with old method : 0.0032312870025634766 length of segment : 63 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 935 time to create 1 rle with old method : 0.0013570785522460938 length of segment : 52 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.0002434253692626953 length of segment : 19 time for calcul the mask position with numpy : 8.106231689453125e-05 nb_pixel_total : 2675 time to create 1 rle with old method : 0.003234386444091797 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: -123.20000 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 : 5.507469177246094e-05 nb_pixel_total : 463 time to create 1 rle with old method : 0.0006952285766601562 length of segment : 28 time for calcul the mask position with numpy : 7.200241088867188e-05 nb_pixel_total : 2695 time to create 1 rle with old method : 0.0034341812133789062 length of segment : 106 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 326 time to create 1 rle with old method : 0.0004553794860839844 length of segment : 21 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 106 time to create 1 rle with old method : 0.00016450881958007812 length of segment : 16 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 727 time to create 1 rle with old method : 0.0011920928955078125 length of segment : 31 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 101 time to create 1 rle with old method : 0.0001609325408935547 length of segment : 16 time for calcul the mask position with numpy : 7.295608520507812e-05 nb_pixel_total : 2717 time to create 1 rle with old method : 0.0032262802124023438 length of segment : 82 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 822 time to create 1 rle with old method : 0.0010669231414794922 length of segment : 34 time for calcul the mask position with numpy : 0.00024390220642089844 nb_pixel_total : 8656 time to create 1 rle with old method : 0.010393857955932617 length of segment : 141 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 759 time to create 1 rle with old method : 0.0009675025939941406 length of segment : 37 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 239 time to create 1 rle with old method : 0.0003514289855957031 length of segment : 52 time for calcul the mask position with numpy : 9.72747802734375e-05 nb_pixel_total : 1834 time to create 1 rle with old method : 0.0023202896118164062 length of segment : 67 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 1085 time to create 1 rle with old method : 0.001317739486694336 length of segment : 47 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 67 time to create 1 rle with old method : 0.00013017654418945312 length of segment : 7 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 335 time to create 1 rle with old method : 0.00043964385986328125 length of segment : 19 time for calcul the mask position with numpy : 0.00012111663818359375 nb_pixel_total : 3894 time to create 1 rle with old method : 0.005002021789550781 length of segment : 175 Processing 1 images image shape: (400, 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 : 9 time for calcul the mask position with numpy : 0.00014352798461914062 nb_pixel_total : 5712 time to create 1 rle with old method : 0.0065305233001708984 length of segment : 104 time for calcul the mask position with numpy : 0.00010371208190917969 nb_pixel_total : 2136 time to create 1 rle with old method : 0.0026438236236572266 length of segment : 161 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 1286 time to create 1 rle with old method : 0.0015587806701660156 length of segment : 42 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 92 time to create 1 rle with old method : 0.00018644332885742188 length of segment : 13 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 1029 time to create 1 rle with old method : 0.0014615058898925781 length of segment : 36 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 260 time to create 1 rle with old method : 0.0003571510314941406 length of segment : 39 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 244 time to create 1 rle with old method : 0.0004668235778808594 length of segment : 19 time for calcul the mask position with numpy : 8.559226989746094e-05 nb_pixel_total : 1100 time to create 1 rle with old method : 0.0013766288757324219 length of segment : 93 time for calcul the mask position with numpy : 0.00012350082397460938 nb_pixel_total : 5525 time to create 1 rle with old method : 0.006473541259765625 length of segment : 90 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: 139.43203 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 1014 time to create 1 rle with old method : 0.0012700557708740234 length of segment : 47 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 139 time to create 1 rle with old method : 0.0002789497375488281 length of segment : 22 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 800 time to create 1 rle with old method : 0.0010294914245605469 length of segment : 43 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 589 time to create 1 rle with old method : 0.0007944107055664062 length of segment : 64 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 1633 time to create 1 rle with old method : 0.002020597457885742 length of segment : 81 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.000339508056640625 length of segment : 10 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 116 time to create 1 rle with old method : 0.00028061866760253906 length of segment : 18 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 808 time to create 1 rle with old method : 0.0010523796081542969 length of segment : 44 time for calcul the mask position with numpy : 9.393692016601562e-05 nb_pixel_total : 914 time to create 1 rle with old method : 0.0012073516845703125 length of segment : 103 Processing 1 images image shape: (280, 400, 3) min: 8.00000 max: 181.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 64.68984 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.002362966537475586 nb_pixel_total : 106246 time to create 1 rle with old method : 0.11564803123474121 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 : 19 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 537 time to create 1 rle with old method : 0.0010428428649902344 length of segment : 25 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 302 time to create 1 rle with old method : 0.0006101131439208984 length of segment : 27 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 155 time to create 1 rle with old method : 0.00033855438232421875 length of segment : 14 time for calcul the mask position with numpy : 8.893013000488281e-05 nb_pixel_total : 2214 time to create 1 rle with old method : 0.0038535594940185547 length of segment : 58 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 932 time to create 1 rle with old method : 0.0011594295501708984 length of segment : 46 time for calcul the mask position with numpy : 0.00011777877807617188 nb_pixel_total : 3899 time to create 1 rle with old method : 0.004866600036621094 length of segment : 100 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1152 time to create 1 rle with old method : 0.0014576911926269531 length of segment : 45 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 91 time to create 1 rle with old method : 0.0001819133758544922 length of segment : 14 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 609 time to create 1 rle with old method : 0.0008523464202880859 length of segment : 44 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.0002582073211669922 length of segment : 20 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 361 time to create 1 rle with old method : 0.0005140304565429688 length of segment : 20 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 671 time to create 1 rle with old method : 0.0008594989776611328 length of segment : 63 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 505 time to create 1 rle with old method : 0.0006968975067138672 length of segment : 62 time for calcul the mask position with numpy : 8.678436279296875e-05 nb_pixel_total : 1377 time to create 1 rle with old method : 0.0017638206481933594 length of segment : 100 time for calcul the mask position with numpy : 0.00010180473327636719 nb_pixel_total : 3807 time to create 1 rle with old method : 0.004460811614990234 length of segment : 124 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.0002849102020263672 length of segment : 20 time for calcul the mask position with numpy : 8.487701416015625e-05 nb_pixel_total : 3028 time to create 1 rle with old method : 0.0036640167236328125 length of segment : 115 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 382 time to create 1 rle with old method : 0.0005950927734375 length of segment : 21 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005354881286621094 length of segment : 24 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 : 0.00011992454528808594 nb_pixel_total : 175 time to create 1 rle with old method : 0.0003669261932373047 length of segment : 25 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 317 time to create 1 rle with old method : 0.00045299530029296875 length of segment : 22 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 770 time to create 1 rle with old method : 0.00098419189453125 length of segment : 38 time for calcul the mask position with numpy : 6.651878356933594e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0007164478302001953 length of segment : 39 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 280 time to create 1 rle with old method : 0.0006320476531982422 length of segment : 16 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 236 time to create 1 rle with old method : 0.00036025047302246094 length of segment : 20 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 539 time to create 1 rle with old method : 0.000988006591796875 length of segment : 23 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 222 time to create 1 rle with old method : 0.0004782676696777344 length of segment : 56 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 426 time to create 1 rle with old method : 0.0005626678466796875 length of segment : 30 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1304 time to create 1 rle with old method : 0.0015740394592285156 length of segment : 52 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 945 time to create 1 rle with old method : 0.0012693405151367188 length of segment : 27 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 953 time to create 1 rle with old method : 0.0012736320495605469 length of segment : 38 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 1006 time to create 1 rle with old method : 0.0013365745544433594 length of segment : 47 time for calcul the mask position with numpy : 0.00012803077697753906 nb_pixel_total : 2116 time to create 1 rle with old method : 0.0025708675384521484 length of segment : 88 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 904 time to create 1 rle with old method : 0.0010960102081298828 length of segment : 35 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 486 time to create 1 rle with old method : 0.0006225109100341797 length of segment : 35 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 1394 time to create 1 rle with old method : 0.001712799072265625 length of segment : 51 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 551 time to create 1 rle with old method : 0.0007011890411376953 length of segment : 37 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 304 time to create 1 rle with old method : 0.0004417896270751953 length of segment : 23 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.001024484634399414 length of segment : 40 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 416 time to create 1 rle with old method : 0.0007188320159912109 length of segment : 15 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.0005517005920410156 length of segment : 30 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 243 time to create 1 rle with old method : 0.0003783702850341797 length of segment : 42 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 209 time to create 1 rle with old method : 0.00027370452880859375 length of segment : 24 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 756 time to create 1 rle with old method : 0.0009634494781494141 length of segment : 75 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 431 time to create 1 rle with old method : 0.0006203651428222656 length of segment : 36 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 704 time to create 1 rle with old method : 0.0009543895721435547 length of segment : 28 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 238 time to create 1 rle with old method : 0.00031304359436035156 length of segment : 33 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 770 time to create 1 rle with old method : 0.0009057521820068359 length of segment : 49 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 395 time to create 1 rle with old method : 0.0006098747253417969 length of segment : 52 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0007078647613525391 length of segment : 23 time for calcul the mask position with numpy : 6.723403930664062e-05 nb_pixel_total : 347 time to create 1 rle with old method : 0.0005664825439453125 length of segment : 21 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 875 time to create 1 rle with old method : 0.0011935234069824219 length of segment : 36 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0007910728454589844 length of segment : 21 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.000993490219116211 length of segment : 35 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 975 time to create 1 rle with old method : 0.0018818378448486328 length of segment : 42 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 417 time to create 1 rle with old method : 0.000732421875 length of segment : 34 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0007662773132324219 length of segment : 27 time for calcul the mask position with numpy : 6.937980651855469e-05 nb_pixel_total : 470 time to create 1 rle with old method : 0.0006461143493652344 length of segment : 38 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: 149.24062 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 504 time to create 1 rle with old method : 0.0006773471832275391 length of segment : 37 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 421 time to create 1 rle with old method : 0.0005719661712646484 length of segment : 21 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 529 time to create 1 rle with old method : 0.0008008480072021484 length of segment : 49 time for calcul the mask position with numpy : 7.510185241699219e-05 nb_pixel_total : 1680 time to create 1 rle with old method : 0.0021104812622070312 length of segment : 69 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 236 time to create 1 rle with old method : 0.0003440380096435547 length of segment : 21 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 392 time to create 1 rle with old method : 0.0008258819580078125 length of segment : 37 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.0001900196075439453 length of segment : 13 length of segment : 0 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 442 time to create 1 rle with old method : 0.0006268024444580078 length of segment : 30 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.0002105236053466797 length of segment : 15 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 2264 time to create 1 rle with old method : 0.0027823448181152344 length of segment : 89 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 492 time to create 1 rle with old method : 0.0006954669952392578 length of segment : 26 time for calcul the mask position with numpy : 9.059906005859375e-05 nb_pixel_total : 632 time to create 1 rle with old method : 0.0010156631469726562 length of segment : 51 time for calcul the mask position with numpy : 9.465217590332031e-05 nb_pixel_total : 2063 time to create 1 rle with old method : 0.0026009082794189453 length of segment : 93 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: 139.97500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 535 time to create 1 rle with old method : 0.0007076263427734375 length of segment : 42 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 140 time to create 1 rle with old method : 0.00024509429931640625 length of segment : 13 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.0003008842468261719 length of segment : 12 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.0001773834228515625 length of segment : 25 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.00016045570373535156 length of segment : 25 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: -118.53203 max: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 0.00031256675720214844 nb_pixel_total : 18922 time to create 1 rle with old method : 0.02218914031982422 length of segment : 165 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 302 time to create 1 rle with old method : 0.0004944801330566406 length of segment : 12 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.0004086494445800781 length of segment : 43 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 67 time to create 1 rle with old method : 0.00013136863708496094 length of segment : 21 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003809928894042969 length of segment : 30 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 54 time to create 1 rle with old method : 0.00010228157043457031 length of segment : 17 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 95 time to create 1 rle with old method : 0.000148773193359375 length of segment : 16 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 367 time to create 1 rle with old method : 0.0008492469787597656 length of segment : 26 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.00045943260192871094 length of segment : 37 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.00025153160095214844 length of segment : 29 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 356 time to create 1 rle with old method : 0.0004811286926269531 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: -117.97734 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 : 8.344650268554688e-05 nb_pixel_total : 2799 time to create 1 rle with old method : 0.0034210681915283203 length of segment : 55 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 425 time to create 1 rle with old method : 0.0006012916564941406 length of segment : 28 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 297 time to create 1 rle with old method : 0.0004553794860839844 length of segment : 36 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 1643 time to create 1 rle with old method : 0.002089977264404297 length of segment : 34 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 331 time to create 1 rle with old method : 0.0004928112030029297 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: -118.02422 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 : 3.9577484130859375e-05 nb_pixel_total : 329 time to create 1 rle with old method : 0.00048732757568359375 length of segment : 15 time for calcul the mask position with numpy : 9.512901306152344e-05 nb_pixel_total : 2275 time to create 1 rle with old method : 0.002917766571044922 length of segment : 92 time for calcul the mask position with numpy : 0.00010585784912109375 nb_pixel_total : 2752 time to create 1 rle with old method : 0.0033636093139648438 length of segment : 118 time for calcul the mask position with numpy : 0.00010848045349121094 nb_pixel_total : 2053 time to create 1 rle with old method : 0.0027434825897216797 length of segment : 30 time for calcul the mask position with numpy : 9.918212890625e-05 nb_pixel_total : 3100 time to create 1 rle with old method : 0.004005908966064453 length of segment : 55 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.87188 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 : 6.413459777832031e-05 nb_pixel_total : 1836 time to create 1 rle with old method : 0.0023665428161621094 length of segment : 78 time for calcul the mask position with numpy : 0.0001571178436279297 nb_pixel_total : 7676 time to create 1 rle with old method : 0.010135412216186523 length of segment : 138 time for calcul the mask position with numpy : 8.440017700195312e-05 nb_pixel_total : 462 time to create 1 rle with old method : 0.0007278919219970703 length of segment : 60 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 590 time to create 1 rle with old method : 0.0008273124694824219 length of segment : 63 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 200 time to create 1 rle with old method : 0.00031304359436035156 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: 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 : 5.173683166503906e-05 nb_pixel_total : 526 time to create 1 rle with old method : 0.0007367134094238281 length of segment : 30 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1404 time to create 1 rle with old method : 0.0017590522766113281 length of segment : 54 time for calcul the mask position with numpy : 7.987022399902344e-05 nb_pixel_total : 2922 time to create 1 rle with old method : 0.005327463150024414 length of segment : 56 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 917 time to create 1 rle with old method : 0.0012443065643310547 length of segment : 52 time for calcul the mask position with numpy : 0.0005478858947753906 nb_pixel_total : 46125 time to create 1 rle with old method : 0.050959110260009766 length of segment : 238 time for calcul the mask position with numpy : 0.0006053447723388672 nb_pixel_total : 49233 time to create 1 rle with old method : 0.05598092079162598 length of segment : 265 Processing 1 images image shape: (400, 400, 3) min: 19.00000 max: 214.00000 molded_images shape: (1, 640, 640, 3) min: -95.46563 max: 86.22109 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0013720989227294922 nb_pixel_total : 151408 time to create 1 rle with new method : 0.0021567344665527344 length of segment : 395 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.98125 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 : 7.700920104980469e-05 nb_pixel_total : 1319 time to create 1 rle with old method : 0.0016334056854248047 length of segment : 47 time for calcul the mask position with numpy : 0.0002410411834716797 nb_pixel_total : 7702 time to create 1 rle with old method : 0.009233713150024414 length of segment : 156 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.0002810955047607422 length of segment : 17 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 1700 time to create 1 rle with old method : 0.0021996498107910156 length of segment : 35 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00013375282287597656 length of segment : 10 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 411 time to create 1 rle with old method : 0.0005943775177001953 length of segment : 44 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 307 time to create 1 rle with old method : 0.0005054473876953125 length of segment : 18 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 279 time to create 1 rle with old method : 0.0004601478576660156 length of segment : 16 time for calcul the mask position with numpy : 8.916854858398438e-05 nb_pixel_total : 3209 time to create 1 rle with old method : 0.0040416717529296875 length of segment : 59 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 145 time to create 1 rle with old method : 0.00024175643920898438 length of segment : 18 time for calcul the mask position with numpy : 9.036064147949219e-05 nb_pixel_total : 3070 time to create 1 rle with old method : 0.0036211013793945312 length of segment : 68 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.71953 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 : 5.364418029785156e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006806850433349609 length of segment : 28 time for calcul the mask position with numpy : 0.0003490447998046875 nb_pixel_total : 13342 time to create 1 rle with old method : 0.016778945922851562 length of segment : 119 time for calcul the mask position with numpy : 0.0001327991485595703 nb_pixel_total : 3189 time to create 1 rle with old method : 0.0037415027618408203 length of segment : 73 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 662 time to create 1 rle with old method : 0.0008623600006103516 length of segment : 30 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.0003230571746826172 length of segment : 13 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 548 time to create 1 rle with old method : 0.0008668899536132812 length of segment : 41 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 121 time to create 1 rle with old method : 0.0002295970916748047 length of segment : 18 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 75 time to create 1 rle with old method : 0.00014472007751464844 length of segment : 8 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 448 time to create 1 rle with old method : 0.0006866455078125 length of segment : 20 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 708 time to create 1 rle with old method : 0.0009391307830810547 length of segment : 32 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 831 time to create 1 rle with old method : 0.001077413558959961 length of segment : 38 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 1652 time to create 1 rle with old method : 0.002016782760620117 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: -123.70000 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 : 5.364418029785156e-05 nb_pixel_total : 1234 time to create 1 rle with old method : 0.0014414787292480469 length of segment : 41 time for calcul the mask position with numpy : 0.0001227855682373047 nb_pixel_total : 1203 time to create 1 rle with old method : 0.0015869140625 length of segment : 100 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 424 time to create 1 rle with old method : 0.0006468296051025391 length of segment : 29 time for calcul the mask position with numpy : 0.00013756752014160156 nb_pixel_total : 4254 time to create 1 rle with old method : 0.005269527435302734 length of segment : 153 time for calcul the mask position with numpy : 7.271766662597656e-05 nb_pixel_total : 821 time to create 1 rle with old method : 0.0014750957489013672 length of segment : 37 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 170 time to create 1 rle with old method : 0.0003883838653564453 length of segment : 11 time for calcul the mask position with numpy : 8.153915405273438e-05 nb_pixel_total : 1069 time to create 1 rle with old method : 0.001893758773803711 length of segment : 93 time for calcul the mask position with numpy : 7.43865966796875e-05 nb_pixel_total : 623 time to create 1 rle with old method : 0.0011966228485107422 length of segment : 59 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: 140.14297 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 445 time to create 1 rle with old method : 0.0006506443023681641 length of segment : 62 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 1005 time to create 1 rle with old method : 0.0012714862823486328 length of segment : 51 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.00040721893310546875 length of segment : 49 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 846 time to create 1 rle with old method : 0.0010859966278076172 length of segment : 44 time for calcul the mask position with numpy : 0.0002110004425048828 nb_pixel_total : 3816 time to create 1 rle with old method : 0.004714012145996094 length of segment : 209 time for calcul the mask position with numpy : 0.0001277923583984375 nb_pixel_total : 1603 time to create 1 rle with old method : 0.002078533172607422 length of segment : 74 Processing 1 images image shape: (280, 400, 3) min: 14.00000 max: 185.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 63.98281 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0011837482452392578 nb_pixel_total : 106282 time to create 1 rle with old method : 0.13292980194091797 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 : 21 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 1286 time to create 1 rle with old method : 0.00162506103515625 length of segment : 51 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 982 time to create 1 rle with old method : 0.0012211799621582031 length of segment : 46 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 1115 time to create 1 rle with old method : 0.001443624496459961 length of segment : 54 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 345 time to create 1 rle with old method : 0.0004990100860595703 length of segment : 19 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 84 time to create 1 rle with old method : 0.00015306472778320312 length of segment : 14 time for calcul the mask position with numpy : 0.00010704994201660156 nb_pixel_total : 4512 time to create 1 rle with old method : 0.006860971450805664 length of segment : 100 time for calcul the mask position with numpy : 7.62939453125e-05 nb_pixel_total : 507 time to create 1 rle with old method : 0.0007319450378417969 length of segment : 23 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 179 time to create 1 rle with old method : 0.0002582073211669922 length of segment : 19 time for calcul the mask position with numpy : 0.00010538101196289062 nb_pixel_total : 1472 time to create 1 rle with old method : 0.001790761947631836 length of segment : 50 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 44 time to create 1 rle with old method : 0.00010323524475097656 length of segment : 9 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.0002372264862060547 length of segment : 17 time for calcul the mask position with numpy : 0.00010275840759277344 nb_pixel_total : 1119 time to create 1 rle with old method : 0.0013730525970458984 length of segment : 47 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 696 time to create 1 rle with old method : 0.0009248256683349609 length of segment : 38 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 131 time to create 1 rle with old method : 0.00020194053649902344 length of segment : 13 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 380 time to create 1 rle with old method : 0.0005080699920654297 length of segment : 41 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 628 time to create 1 rle with old method : 0.0008645057678222656 length of segment : 34 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.00017714500427246094 length of segment : 13 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 379 time to create 1 rle with old method : 0.0005466938018798828 length of segment : 22 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.0004892349243164062 length of segment : 28 time for calcul the mask position with numpy : 0.00011539459228515625 nb_pixel_total : 2496 time to create 1 rle with old method : 0.002897977828979492 length of segment : 96 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 365 time to create 1 rle with old method : 0.0005400180816650391 length of segment : 20 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 : 5.7697296142578125e-05 nb_pixel_total : 296 time to create 1 rle with old method : 0.00041937828063964844 length of segment : 20 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002493858337402344 length of segment : 25 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 755 time to create 1 rle with old method : 0.0010535717010498047 length of segment : 39 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 450 time to create 1 rle with old method : 0.00063323974609375 length of segment : 39 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 404 time to create 1 rle with old method : 0.0005815029144287109 length of segment : 20 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002963542938232422 length of segment : 17 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 277 time to create 1 rle with old method : 0.00042724609375 length of segment : 15 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.0002543926239013672 length of segment : 18 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 408 time to create 1 rle with old method : 0.0005393028259277344 length of segment : 29 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 966 time to create 1 rle with old method : 0.0012831687927246094 length of segment : 29 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 919 time to create 1 rle with old method : 0.0012502670288085938 length of segment : 35 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 510 time to create 1 rle with old method : 0.0007002353668212891 length of segment : 23 time for calcul the mask position with numpy : 0.0001800060272216797 nb_pixel_total : 6888 time to create 1 rle with old method : 0.008208990097045898 length of segment : 121 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 1054 time to create 1 rle with old method : 0.0013666152954101562 length of segment : 60 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 371 time to create 1 rle with old method : 0.0006651878356933594 length of segment : 13 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 1504 time to create 1 rle with old method : 0.0018749237060546875 length of segment : 55 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 290 time to create 1 rle with old method : 0.00039458274841308594 length of segment : 22 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 1044 time to create 1 rle with old method : 0.0012748241424560547 length of segment : 46 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 367 time to create 1 rle with old method : 0.000507354736328125 length of segment : 21 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 834 time to create 1 rle with old method : 0.001069784164428711 length of segment : 36 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 136 time to create 1 rle with old method : 0.00023937225341796875 length of segment : 32 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 745 time to create 1 rle with old method : 0.0010302066802978516 length of segment : 27 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.00021457672119140625 length of segment : 17 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 179 time to create 1 rle with old method : 0.0002484321594238281 length of segment : 24 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 457 time to create 1 rle with old method : 0.0006635189056396484 length of segment : 18 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 1049 time to create 1 rle with old method : 0.001369476318359375 length of segment : 37 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 451 time to create 1 rle with old method : 0.0005702972412109375 length of segment : 47 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 885 time to create 1 rle with old method : 0.0010859966278076172 length of segment : 66 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 156 time to create 1 rle with old method : 0.00024628639221191406 length of segment : 14 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 376 time to create 1 rle with old method : 0.000522613525390625 length of segment : 21 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 616 time to create 1 rle with old method : 0.0008358955383300781 length of segment : 104 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 1335 time to create 1 rle with old method : 0.0016748905181884766 length of segment : 46 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 388 time to create 1 rle with old method : 0.0005557537078857422 length of segment : 34 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 809 time to create 1 rle with old method : 0.001032114028930664 length of segment : 65 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 1021 time to create 1 rle with old method : 0.0013089179992675781 length of segment : 39 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006520748138427734 length of segment : 28 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006840229034423828 length of segment : 23 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 669 time to create 1 rle with old method : 0.0008835792541503906 length of segment : 27 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 995 time to create 1 rle with old method : 0.001251220703125 length of segment : 38 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 757 time to create 1 rle with old method : 0.0010142326354980469 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: 147.36953 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 223 time to create 1 rle with old method : 0.00031757354736328125 length of segment : 20 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 441 time to create 1 rle with old method : 0.0006263256072998047 length of segment : 26 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.00020575523376464844 length of segment : 10 time for calcul the mask position with numpy : 7.772445678710938e-05 nb_pixel_total : 1838 time to create 1 rle with old method : 0.002344369888305664 length of segment : 90 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 523 time to create 1 rle with old method : 0.0007116794586181641 length of segment : 37 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 564 time to create 1 rle with old method : 0.0007636547088623047 length of segment : 38 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 713 time to create 1 rle with old method : 0.0009431838989257812 length of segment : 68 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 2460 time to create 1 rle with old method : 0.003027677536010742 length of segment : 72 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 405 time to create 1 rle with old method : 0.0005326271057128906 length of segment : 33 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.00024056434631347656 length of segment : 26 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 205 time to create 1 rle with old method : 0.0002715587615966797 length of segment : 28 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 282 time to create 1 rle with old method : 0.0004062652587890625 length of segment : 21 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 112 time to create 1 rle with old method : 0.0001780986785888672 length of segment : 14 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: 138.85000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.0003285408020019531 length of segment : 13 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.0002162456512451172 length of segment : 12 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.0003120899200439453 length of segment : 37 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 180 time to create 1 rle with old method : 0.0002894401550292969 length of segment : 13 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: -118.00469 max: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 665 time to create 1 rle with old method : 0.0009245872497558594 length of segment : 61 time for calcul the mask position with numpy : 0.00039887428283691406 nb_pixel_total : 17664 time to create 1 rle with old method : 0.020683765411376953 length of segment : 174 time for calcul the mask position with numpy : 7.891654968261719e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.00022411346435546875 length of segment : 19 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 51 time to create 1 rle with old method : 0.00011801719665527344 length of segment : 18 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.00036072731018066406 length of segment : 26 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 92 time to create 1 rle with old method : 0.00014328956604003906 length of segment : 16 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 660 time to create 1 rle with old method : 0.0009305477142333984 length of segment : 47 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 242 time to create 1 rle with old method : 0.0003399848937988281 length of segment : 28 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 222 time to create 1 rle with old method : 0.00029850006103515625 length of segment : 25 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 1268 time to create 1 rle with old method : 0.0017523765563964844 length of segment : 79 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -119.01250 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 : 8.869171142578125e-05 nb_pixel_total : 2790 time to create 1 rle with old method : 0.0034394264221191406 length of segment : 62 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 807 time to create 1 rle with old method : 0.0010380744934082031 length of segment : 44 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 1982 time to create 1 rle with old method : 0.002506732940673828 length of segment : 38 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 317 time to create 1 rle with old method : 0.0004584789276123047 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: -121.51641 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 : 4.9114227294921875e-05 nb_pixel_total : 1195 time to create 1 rle with old method : 0.0015692710876464844 length of segment : 32 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 2206 time to create 1 rle with old method : 0.002972126007080078 length of segment : 28 time for calcul the mask position with numpy : 7.677078247070312e-05 nb_pixel_total : 762 time to create 1 rle with old method : 0.001165151596069336 length of segment : 96 time for calcul the mask position with numpy : 8.225440979003906e-05 nb_pixel_total : 2353 time to create 1 rle with old method : 0.0029594898223876953 length of segment : 64 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.0005393028259277344 length of segment : 20 time for calcul the mask position with numpy : 8.320808410644531e-05 nb_pixel_total : 1916 time to create 1 rle with old method : 0.002419710159301758 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: -117.40313 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 : 0.000133514404296875 nb_pixel_total : 8561 time to create 1 rle with old method : 0.009854555130004883 length of segment : 128 time for calcul the mask position with numpy : 7.700920104980469e-05 nb_pixel_total : 1819 time to create 1 rle with old method : 0.0023288726806640625 length of segment : 80 time for calcul the mask position with numpy : 7.009506225585938e-05 nb_pixel_total : 483 time to create 1 rle with old method : 0.0010328292846679688 length of segment : 68 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00019598007202148438 length of segment : 16 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.0003135204315185547 length of segment : 17 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 938 time to create 1 rle with old method : 0.0011260509490966797 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: 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 : 4.8160552978515625e-05 nb_pixel_total : 935 time to create 1 rle with old method : 0.001255035400390625 length of segment : 52 time for calcul the mask position with numpy : 6.794929504394531e-05 nb_pixel_total : 553 time to create 1 rle with old method : 0.0011091232299804688 length of segment : 31 time for calcul the mask position with numpy : 7.343292236328125e-05 nb_pixel_total : 598 time to create 1 rle with old method : 0.0011987686157226562 length of segment : 53 time for calcul the mask position with numpy : 0.0006046295166015625 nb_pixel_total : 46720 time to create 1 rle with old method : 0.05570244789123535 length of segment : 389 Processing 1 images image shape: (400, 400, 3) min: 22.00000 max: 216.00000 molded_images shape: (1, 640, 640, 3) min: -93.68828 max: 88.26406 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0013575553894042969 nb_pixel_total : 153441 time to create 1 rle with new method : 0.0020303726196289062 length of segment : 399 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 : 9 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 1402 time to create 1 rle with old method : 0.0017368793487548828 length of segment : 47 time for calcul the mask position with numpy : 0.000225067138671875 nb_pixel_total : 3741 time to create 1 rle with old method : 0.005552768707275391 length of segment : 90 time for calcul the mask position with numpy : 8.940696716308594e-05 nb_pixel_total : 391 time to create 1 rle with old method : 0.0006883144378662109 length of segment : 20 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 77 time to create 1 rle with old method : 0.00014019012451171875 length of segment : 11 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005769729614257812 length of segment : 32 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 149 time to create 1 rle with old method : 0.0002474784851074219 length of segment : 18 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 651 time to create 1 rle with old method : 0.0008676052093505859 length of segment : 35 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 197 time to create 1 rle with old method : 0.00029587745666503906 length of segment : 22 time for calcul the mask position with numpy : 7.152557373046875e-05 nb_pixel_total : 1907 time to create 1 rle with old method : 0.0036468505859375 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: -123.01641 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 : 6.461143493652344e-05 nb_pixel_total : 476 time to create 1 rle with old method : 0.0006563663482666016 length of segment : 28 time for calcul the mask position with numpy : 0.0002892017364501953 nb_pixel_total : 14278 time to create 1 rle with old method : 0.017109394073486328 length of segment : 153 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00016736984252929688 length of segment : 17 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005497932434082031 length of segment : 25 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 717 time to create 1 rle with old method : 0.0009512901306152344 length of segment : 34 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 595 time to create 1 rle with old method : 0.0008101463317871094 length of segment : 28 time for calcul the mask position with numpy : 7.748603820800781e-05 nb_pixel_total : 1669 time to create 1 rle with old method : 0.0022966861724853516 length of segment : 53 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 842 time to create 1 rle with old method : 0.0011374950408935547 length of segment : 27 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 500 time to create 1 rle with old method : 0.0006756782531738281 length of segment : 25 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 261 time to create 1 rle with old method : 0.00043272972106933594 length of segment : 18 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.0002682209014892578 length of segment : 17 time for calcul the mask position with numpy : 9.012222290039062e-05 nb_pixel_total : 930 time to create 1 rle with old method : 0.0013229846954345703 length of segment : 37 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 728 time to create 1 rle with old method : 0.0009584426879882812 length of segment : 34 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 66 time to create 1 rle with old method : 0.0001308917999267578 length of segment : 7 Processing 1 images image shape: (400, 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 : 5 time for calcul the mask position with numpy : 9.131431579589844e-05 nb_pixel_total : 1661 time to create 1 rle with old method : 0.002089977264404297 length of segment : 124 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 1328 time to create 1 rle with old method : 0.0016283988952636719 length of segment : 40 time for calcul the mask position with numpy : 0.00011467933654785156 nb_pixel_total : 3327 time to create 1 rle with old method : 0.003996133804321289 length of segment : 146 time for calcul the mask position with numpy : 0.00011038780212402344 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0017137527465820312 length of segment : 43 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 941 time to create 1 rle with old method : 0.0012350082397460938 length of segment : 81 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: 138.52969 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 1288 time to create 1 rle with old method : 0.0016138553619384766 length of segment : 56 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003871917724609375 length of segment : 38 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 863 time to create 1 rle with old method : 0.0011053085327148438 length of segment : 47 time for calcul the mask position with numpy : 0.00019598007202148438 nb_pixel_total : 4116 time to create 1 rle with old method : 0.004939079284667969 length of segment : 214 time for calcul the mask position with numpy : 0.00015044212341308594 nb_pixel_total : 314 time to create 1 rle with old method : 0.0007131099700927734 length of segment : 42 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 236 time to create 1 rle with old method : 0.0005502700805664062 length of segment : 24 Processing 1 images image shape: (280, 400, 3) min: 11.00000 max: 187.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 69.45937 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009224414825439453 nb_pixel_total : 106478 time to create 1 rle with old method : 0.11394047737121582 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: 150.63125 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 16 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 530 time to create 1 rle with old method : 0.0007560253143310547 length of segment : 25 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 641 time to create 1 rle with old method : 0.0008280277252197266 length of segment : 24 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 742 time to create 1 rle with old method : 0.0009965896606445312 length of segment : 38 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 170 time to create 1 rle with old method : 0.0002493858337402344 length of segment : 20 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.0002903938293457031 length of segment : 17 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002104520797729492 length of segment : 45 time for calcul the mask position with numpy : 0.0002124309539794922 nb_pixel_total : 4752 time to create 1 rle with old method : 0.008254766464233398 length of segment : 113 time for calcul the mask position with numpy : 5.507469177246094e-05 nb_pixel_total : 383 time to create 1 rle with old method : 0.0005776882171630859 length of segment : 23 time for calcul the mask position with numpy : 0.00012254714965820312 nb_pixel_total : 3916 time to create 1 rle with old method : 0.004945516586303711 length of segment : 97 time for calcul the mask position with numpy : 8.106231689453125e-05 nb_pixel_total : 980 time to create 1 rle with old method : 0.0012629032135009766 length of segment : 43 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.0003275871276855469 length of segment : 18 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 643 time to create 1 rle with old method : 0.0008668899536132812 length of segment : 38 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 1356 time to create 1 rle with old method : 0.0017027854919433594 length of segment : 45 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.00025653839111328125 length of segment : 16 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 367 time to create 1 rle with old method : 0.0005099773406982422 length of segment : 21 time for calcul the mask position with numpy : 0.00011706352233886719 nb_pixel_total : 3356 time to create 1 rle with old method : 0.004389286041259766 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 : 41 time for calcul the mask position with numpy : 8.869171142578125e-05 nb_pixel_total : 169 time to create 1 rle with old method : 0.00041961669921875 length of segment : 25 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 414 time to create 1 rle with old method : 0.0005631446838378906 length of segment : 39 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 303 time to create 1 rle with old method : 0.00043845176696777344 length of segment : 21 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 806 time to create 1 rle with old method : 0.0010495185852050781 length of segment : 38 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 192 time to create 1 rle with old method : 0.0003147125244140625 length of segment : 16 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 302 time to create 1 rle with old method : 0.00045871734619140625 length of segment : 16 time for calcul the mask position with numpy : 9.393692016601562e-05 nb_pixel_total : 1221 time to create 1 rle with old method : 0.0019714832305908203 length of segment : 52 time for calcul the mask position with numpy : 5.602836608886719e-05 nb_pixel_total : 441 time to create 1 rle with old method : 0.0005819797515869141 length of segment : 29 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 713 time to create 1 rle with old method : 0.0013704299926757812 length of segment : 28 time for calcul the mask position with numpy : 7.486343383789062e-05 nb_pixel_total : 1090 time to create 1 rle with old method : 0.0015301704406738281 length of segment : 28 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 1008 time to create 1 rle with old method : 0.0019354820251464844 length of segment : 39 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 836 time to create 1 rle with old method : 0.001077413558959961 length of segment : 36 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 282 time to create 1 rle with old method : 0.0003974437713623047 length of segment : 23 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1263 time to create 1 rle with old method : 0.00223541259765625 length of segment : 50 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 182 time to create 1 rle with old method : 0.00032520294189453125 length of segment : 32 time for calcul the mask position with numpy : 0.00018858909606933594 nb_pixel_total : 7695 time to create 1 rle with old method : 0.00864863395690918 length of segment : 157 time for calcul the mask position with numpy : 7.05718994140625e-05 nb_pixel_total : 974 time to create 1 rle with old method : 0.0017066001892089844 length of segment : 37 time for calcul the mask position with numpy : 8.082389831542969e-05 nb_pixel_total : 1338 time to create 1 rle with old method : 0.0016999244689941406 length of segment : 52 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 507 time to create 1 rle with old method : 0.0006716251373291016 length of segment : 34 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 403 time to create 1 rle with old method : 0.0005543231964111328 length of segment : 29 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 209 time to create 1 rle with old method : 0.0002923011779785156 length of segment : 25 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 477 time to create 1 rle with old method : 0.0006663799285888672 length of segment : 36 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 903 time to create 1 rle with old method : 0.001232147216796875 length of segment : 31 time for calcul the mask position with numpy : 7.963180541992188e-05 nb_pixel_total : 534 time to create 1 rle with old method : 0.001010894775390625 length of segment : 23 time for calcul the mask position with numpy : 7.2479248046875e-05 nb_pixel_total : 1433 time to create 1 rle with old method : 0.0018184185028076172 length of segment : 57 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002465248107910156 length of segment : 16 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.0002930164337158203 length of segment : 15 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 375 time to create 1 rle with old method : 0.0005364418029785156 length of segment : 32 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 473 time to create 1 rle with old method : 0.0006818771362304688 length of segment : 27 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 618 time to create 1 rle with old method : 0.0009002685546875 length of segment : 23 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 570 time to create 1 rle with old method : 0.0007777214050292969 length of segment : 26 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 142 time to create 1 rle with old method : 0.00024318695068359375 length of segment : 40 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 1531 time to create 1 rle with old method : 0.0018050670623779297 length of segment : 63 time for calcul the mask position with numpy : 5.1975250244140625e-05 nb_pixel_total : 674 time to create 1 rle with old method : 0.0010075569152832031 length of segment : 32 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005521774291992188 length of segment : 28 time for calcul the mask position with numpy : 8.249282836914062e-05 nb_pixel_total : 149 time to create 1 rle with old method : 0.00036144256591796875 length of segment : 17 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 1281 time to create 1 rle with old method : 0.0014951229095458984 length of segment : 52 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 413 time to create 1 rle with old method : 0.0007758140563964844 length of segment : 21 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 427 time to create 1 rle with old method : 0.0006978511810302734 length of segment : 20 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 914 time to create 1 rle with old method : 0.001219034194946289 length of segment : 35 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 427 time to create 1 rle with old method : 0.0006000995635986328 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: 148.16250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 433 time to create 1 rle with old method : 0.0006322860717773438 length of segment : 26 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 229 time to create 1 rle with old method : 0.00032830238342285156 length of segment : 18 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 416 time to create 1 rle with old method : 0.0006721019744873047 length of segment : 23 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 90 time to create 1 rle with old method : 0.00015282630920410156 length of segment : 13 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 235 time to create 1 rle with old method : 0.00048351287841796875 length of segment : 34 time for calcul the mask position with numpy : 6.866455078125e-05 nb_pixel_total : 611 time to create 1 rle with old method : 0.0008699893951416016 length of segment : 51 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 562 time to create 1 rle with old method : 0.0007555484771728516 length of segment : 34 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.0005407333374023438 length of segment : 28 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 247 time to create 1 rle with old method : 0.00033473968505859375 length of segment : 31 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 1852 time to create 1 rle with old method : 0.002318143844604492 length of segment : 51 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 404 time to create 1 rle with old method : 0.0006163120269775391 length of segment : 20 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 480 time to create 1 rle with old method : 0.0006344318389892578 length of segment : 36 time for calcul the mask position with numpy : 7.462501525878906e-05 nb_pixel_total : 1680 time to create 1 rle with old method : 0.0021266937255859375 length of segment : 68 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.0001857280731201172 length of segment : 15 time for calcul the mask position with numpy : 2.9087066650390625e-05 nb_pixel_total : 127 time to create 1 rle with old method : 0.0001900196075439453 length of segment : 15 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: 136.91250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 223 time to create 1 rle with old method : 0.0003845691680908203 length of segment : 12 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001456737518310547 length of segment : 25 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 119 time to create 1 rle with old method : 0.0002110004425048828 length of segment : 11 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 238 time to create 1 rle with old method : 0.00036025047302246094 length of segment : 37 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: -118.46953 max: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 0.0002472400665283203 nb_pixel_total : 14529 time to create 1 rle with old method : 0.01715564727783203 length of segment : 131 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 511 time to create 1 rle with old method : 0.0007557868957519531 length of segment : 57 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 60 time to create 1 rle with old method : 0.00010919570922851562 length of segment : 18 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 316 time to create 1 rle with old method : 0.0004382133483886719 length of segment : 34 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 97 time to create 1 rle with old method : 0.00015592575073242188 length of segment : 16 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002608299255371094 length of segment : 25 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 245 time to create 1 rle with old method : 0.0005733966827392578 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: -117.66094 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 : 0.00011444091796875 nb_pixel_total : 2636 time to create 1 rle with old method : 0.0036737918853759766 length of segment : 60 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 853 time to create 1 rle with old method : 0.0010945796966552734 length of segment : 57 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 1038 time to create 1 rle with old method : 0.0013968944549560547 length of segment : 40 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0005702972412109375 length of segment : 26 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 692 time to create 1 rle with old method : 0.0009508132934570312 length of segment : 39 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 794 time to create 1 rle with old method : 0.0009999275207519531 length of segment : 48 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.61797 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 : 4.76837158203125e-05 nb_pixel_total : 1108 time to create 1 rle with old method : 0.0017359256744384766 length of segment : 29 time for calcul the mask position with numpy : 8.392333984375e-05 nb_pixel_total : 2079 time to create 1 rle with old method : 0.0026121139526367188 length of segment : 56 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 475 time to create 1 rle with old method : 0.0010848045349121094 length of segment : 30 time for calcul the mask position with numpy : 8.20159912109375e-05 nb_pixel_total : 2610 time to create 1 rle with old method : 0.0035505294799804688 length of segment : 82 time for calcul the mask position with numpy : 8.058547973632812e-05 nb_pixel_total : 110 time to create 1 rle with old method : 0.0003886222839355469 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: -119.36406 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 : 0.00014090538024902344 nb_pixel_total : 9029 time to create 1 rle with old method : 0.010799884796142578 length of segment : 128 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 1112 time to create 1 rle with old method : 0.0013713836669921875 length of segment : 52 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 1760 time to create 1 rle with old method : 0.0022420883178710938 length of segment : 74 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 205 time to create 1 rle with old method : 0.00029540061950683594 length of segment : 17 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 950 time to create 1 rle with old method : 0.0011682510375976562 length of segment : 64 time for calcul the mask position with numpy : 6.961822509765625e-05 nb_pixel_total : 1180 time to create 1 rle with old method : 0.0015323162078857422 length of segment : 76 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 : 7 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 572 time to create 1 rle with old method : 0.0007781982421875 length of segment : 30 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 892 time to create 1 rle with old method : 0.0011470317840576172 length of segment : 52 time for calcul the mask position with numpy : 8.082389831542969e-05 nb_pixel_total : 2516 time to create 1 rle with old method : 0.003134489059448242 length of segment : 49 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 577 time to create 1 rle with old method : 0.0007512569427490234 length of segment : 48 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 1149 time to create 1 rle with old method : 0.0014603137969970703 length of segment : 53 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 1045 time to create 1 rle with old method : 0.0013241767883300781 length of segment : 52 time for calcul the mask position with numpy : 0.0006229877471923828 nb_pixel_total : 62410 time to create 1 rle with old method : 0.06993341445922852 length of segment : 278 Processing 1 images image shape: (400, 400, 3) min: 17.00000 max: 214.00000 molded_images shape: (1, 640, 640, 3) min: -95.46563 max: 86.22109 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0018923282623291016 nb_pixel_total : 151965 time to create 1 rle with new method : 0.003150463104248047 length of segment : 401 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: 151.07266 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1380 time to create 1 rle with old method : 0.0024154186248779297 length of segment : 46 time for calcul the mask position with numpy : 5.078315734863281e-05 nb_pixel_total : 683 time to create 1 rle with old method : 0.0012428760528564453 length of segment : 38 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.0003533363342285156 length of segment : 19 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 77 time to create 1 rle with old method : 0.00018286705017089844 length of segment : 11 time for calcul the mask position with numpy : 0.0001709461212158203 nb_pixel_total : 4918 time to create 1 rle with old method : 0.008337020874023438 length of segment : 136 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 466 time to create 1 rle with old method : 0.0010068416595458984 length of segment : 19 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 1592 time to create 1 rle with old method : 0.002875804901123047 length of segment : 34 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -122.70000 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 : 4.38690185546875e-05 nb_pixel_total : 461 time to create 1 rle with old method : 0.0006887912750244141 length of segment : 28 time for calcul the mask position with numpy : 0.0002391338348388672 nb_pixel_total : 14112 time to create 1 rle with old method : 0.01567816734313965 length of segment : 169 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 440 time to create 1 rle with old method : 0.0005784034729003906 length of segment : 40 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.00017452239990234375 length of segment : 17 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 2670 time to create 1 rle with old method : 0.0032989978790283203 length of segment : 59 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 764 time to create 1 rle with old method : 0.0009877681732177734 length of segment : 40 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 882 time to create 1 rle with old method : 0.0011172294616699219 length of segment : 34 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 325 time to create 1 rle with old method : 0.0004487037658691406 length of segment : 22 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 817 time to create 1 rle with old method : 0.0011034011840820312 length of segment : 24 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 709 time to create 1 rle with old method : 0.0009000301361083984 length of segment : 32 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 601 time to create 1 rle with old method : 0.0008018016815185547 length of segment : 27 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 779 time to create 1 rle with old method : 0.0010159015655517578 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: -123.37578 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 : 8.416175842285156e-05 nb_pixel_total : 1575 time to create 1 rle with old method : 0.0019383430480957031 length of segment : 127 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 1376 time to create 1 rle with old method : 0.0018503665924072266 length of segment : 40 time for calcul the mask position with numpy : 9.012222290039062e-05 nb_pixel_total : 644 time to create 1 rle with old method : 0.0008490085601806641 length of segment : 65 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 212 time to create 1 rle with old method : 0.00030875205993652344 length of segment : 22 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 1328 time to create 1 rle with old method : 0.0016608238220214844 length of segment : 40 time for calcul the mask position with numpy : 0.00010752677917480469 nb_pixel_total : 3185 time to create 1 rle with old method : 0.0038292407989501953 length of segment : 142 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 528 time to create 1 rle with old method : 0.0006937980651855469 length of segment : 31 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 243 time to create 1 rle with old method : 0.0003409385681152344 length of segment : 22 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 818 time to create 1 rle with old method : 0.0010633468627929688 length of segment : 78 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: 138.40859 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 5.1021575927734375e-05 nb_pixel_total : 1388 time to create 1 rle with old method : 0.0016887187957763672 length of segment : 56 time for calcul the mask position with numpy : 6.818771362304688e-05 nb_pixel_total : 1013 time to create 1 rle with old method : 0.0013229846954345703 length of segment : 79 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 870 time to create 1 rle with old method : 0.0011072158813476562 length of segment : 46 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 35 time to create 1 rle with old method : 8.630752563476562e-05 length of segment : 10 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.0001785755157470703 length of segment : 19 Processing 1 images image shape: (280, 400, 3) min: 14.00000 max: 179.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 62.02969 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0008842945098876953 nb_pixel_total : 106491 time to create 1 rle with old method : 0.11448383331298828 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: 148.60000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 19 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 526 time to create 1 rle with old method : 0.00074005126953125 length of segment : 25 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 186 time to create 1 rle with old method : 0.0002713203430175781 length of segment : 22 time for calcul the mask position with numpy : 0.00010991096496582031 nb_pixel_total : 5059 time to create 1 rle with old method : 0.006119728088378906 length of segment : 105 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 1309 time to create 1 rle with old method : 0.0016658306121826172 length of segment : 59 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.00023865699768066406 length of segment : 20 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 864 time to create 1 rle with old method : 0.0011222362518310547 length of segment : 39 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 1797 time to create 1 rle with old method : 0.0022711753845214844 length of segment : 60 time for calcul the mask position with numpy : 0.00010824203491210938 nb_pixel_total : 5736 time to create 1 rle with old method : 0.006921291351318359 length of segment : 97 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 1242 time to create 1 rle with old method : 0.0015685558319091797 length of segment : 56 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 1241 time to create 1 rle with old method : 0.0015442371368408203 length of segment : 60 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 112 time to create 1 rle with old method : 0.0001919269561767578 length of segment : 15 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 1115 time to create 1 rle with old method : 0.0014102458953857422 length of segment : 41 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 1044 time to create 1 rle with old method : 0.0013058185577392578 length of segment : 40 time for calcul the mask position with numpy : 6.842613220214844e-05 nb_pixel_total : 1754 time to create 1 rle with old method : 0.0021882057189941406 length of segment : 70 time for calcul the mask position with numpy : 0.0001327991485595703 nb_pixel_total : 3025 time to create 1 rle with old method : 0.003668546676635742 length of segment : 107 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002503395080566406 length of segment : 17 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1819 time to create 1 rle with old method : 0.0022056102752685547 length of segment : 52 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 388 time to create 1 rle with old method : 0.0005688667297363281 length of segment : 22 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 398 time to create 1 rle with old method : 0.0005240440368652344 length of segment : 23 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 : 9.059906005859375e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0005998611450195312 length of segment : 38 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 158 time to create 1 rle with old method : 0.00023889541625976562 length of segment : 24 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 319 time to create 1 rle with old method : 0.0004646778106689453 length of segment : 22 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0002551078796386719 length of segment : 12 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 1474 time to create 1 rle with old method : 0.0018768310546875 length of segment : 54 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 264 time to create 1 rle with old method : 0.0003905296325683594 length of segment : 38 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 813 time to create 1 rle with old method : 0.0010478496551513672 length of segment : 45 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 1104 time to create 1 rle with old method : 0.0014896392822265625 length of segment : 32 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 271 time to create 1 rle with old method : 0.0004239082336425781 length of segment : 16 time for calcul the mask position with numpy : 0.00015020370483398438 nb_pixel_total : 6794 time to create 1 rle with old method : 0.007756948471069336 length of segment : 121 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 460 time to create 1 rle with old method : 0.0006594657897949219 length of segment : 24 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 937 time to create 1 rle with old method : 0.0011751651763916016 length of segment : 97 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 289 time to create 1 rle with old method : 0.00039649009704589844 length of segment : 22 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 356 time to create 1 rle with old method : 0.0005242824554443359 length of segment : 30 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 731 time to create 1 rle with old method : 0.0010030269622802734 length of segment : 29 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 1219 time to create 1 rle with old method : 0.0015282630920410156 length of segment : 46 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 553 time to create 1 rle with old method : 0.0007417201995849609 length of segment : 71 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 134 time to create 1 rle with old method : 0.00022482872009277344 length of segment : 17 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 550 time to create 1 rle with old method : 0.0007383823394775391 length of segment : 24 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 1046 time to create 1 rle with old method : 0.0014116764068603516 length of segment : 37 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 1051 time to create 1 rle with old method : 0.0013844966888427734 length of segment : 60 time for calcul the mask position with numpy : 5.316734313964844e-05 nb_pixel_total : 786 time to create 1 rle with old method : 0.0010402202606201172 length of segment : 32 time for calcul the mask position with numpy : 4.553794860839844e-05 nb_pixel_total : 485 time to create 1 rle with old method : 0.0006842613220214844 length of segment : 19 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 870 time to create 1 rle with old method : 0.0010821819305419922 length of segment : 64 time for calcul the mask position with numpy : 7.081031799316406e-05 nb_pixel_total : 436 time to create 1 rle with old method : 0.0008809566497802734 length of segment : 35 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 435 time to create 1 rle with old method : 0.0006220340728759766 length of segment : 36 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 1180 time to create 1 rle with old method : 0.0015063285827636719 length of segment : 55 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 390 time to create 1 rle with old method : 0.0005781650543212891 length of segment : 19 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 429 time to create 1 rle with old method : 0.0005779266357421875 length of segment : 31 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004782676696777344 length of segment : 32 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 556 time to create 1 rle with old method : 0.0007355213165283203 length of segment : 34 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 1331 time to create 1 rle with old method : 0.001697540283203125 length of segment : 56 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 1008 time to create 1 rle with old method : 0.0013425350189208984 length of segment : 40 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 311 time to create 1 rle with old method : 0.0004534721374511719 length of segment : 21 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 395 time to create 1 rle with old method : 0.0005204677581787109 length of segment : 29 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: 146.82266 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 12 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 406 time to create 1 rle with old method : 0.0006048679351806641 length of segment : 26 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 872 time to create 1 rle with old method : 0.0011930465698242188 length of segment : 74 time for calcul the mask position with numpy : 0.00013685226440429688 nb_pixel_total : 5511 time to create 1 rle with old method : 0.006233692169189453 length of segment : 112 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 418 time to create 1 rle with old method : 0.0005772113800048828 length of segment : 21 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.00017380714416503906 length of segment : 14 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0007276535034179688 length of segment : 24 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 452 time to create 1 rle with old method : 0.0006194114685058594 length of segment : 27 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 552 time to create 1 rle with old method : 0.0007331371307373047 length of segment : 37 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 392 time to create 1 rle with old method : 0.0005197525024414062 length of segment : 27 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 757 time to create 1 rle with old method : 0.001016855239868164 length of segment : 61 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 1526 time to create 1 rle with old method : 0.0019483566284179688 length of segment : 90 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 141 time to create 1 rle with old method : 0.0002090930938720703 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: 137.22500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 4 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 523 time to create 1 rle with old method : 0.0006589889526367188 length of segment : 41 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 141 time to create 1 rle with old method : 0.0001926422119140625 length of segment : 26 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 129 time to create 1 rle with old method : 0.0002231597900390625 length of segment : 12 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 208 time to create 1 rle with old method : 0.0003371238708496094 length of segment : 13 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: -117.96172 max: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 243 time to create 1 rle with old method : 0.0004353523254394531 length of segment : 31 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 46 time to create 1 rle with old method : 0.000110626220703125 length of segment : 14 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.0008788108825683594 length of segment : 57 time for calcul the mask position with numpy : 0.0002224445343017578 nb_pixel_total : 14066 time to create 1 rle with old method : 0.01642894744873047 length of segment : 170 time for calcul the mask position with numpy : 0.00011754035949707031 nb_pixel_total : 1891 time to create 1 rle with old method : 0.0024306774139404297 length of segment : 64 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.0003063678741455078 length of segment : 31 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00015282630920410156 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: -115.82891 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 : 6.890296936035156e-05 nb_pixel_total : 2417 time to create 1 rle with old method : 0.002967357635498047 length of segment : 52 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 683 time to create 1 rle with old method : 0.0008807182312011719 length of segment : 37 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 624 time to create 1 rle with old method : 0.0008759498596191406 length of segment : 50 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005397796630859375 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: -121.56719 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 : 4.887580871582031e-05 nb_pixel_total : 1140 time to create 1 rle with old method : 0.00153350830078125 length of segment : 30 time for calcul the mask position with numpy : 8.0108642578125e-05 nb_pixel_total : 1926 time to create 1 rle with old method : 0.002412080764770508 length of segment : 57 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1789 time to create 1 rle with old method : 0.0025327205657958984 length of segment : 24 time for calcul the mask position with numpy : 6.532669067382812e-05 nb_pixel_total : 1045 time to create 1 rle with old method : 0.0016448497772216797 length of segment : 29 time for calcul the mask position with numpy : 5.221366882324219e-05 nb_pixel_total : 702 time to create 1 rle with old method : 0.0011446475982666016 length of segment : 43 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 2226 time to create 1 rle with old method : 0.002833843231201172 length of segment : 56 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.01250 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 : 6.127357482910156e-05 nb_pixel_total : 1144 time to create 1 rle with old method : 0.0015187263488769531 length of segment : 81 time for calcul the mask position with numpy : 0.00012159347534179688 nb_pixel_total : 5531 time to create 1 rle with old method : 0.006755828857421875 length of segment : 103 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 558 time to create 1 rle with old method : 0.0007677078247070312 length of segment : 63 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.0002994537353515625 length of segment : 17 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 152 time to create 1 rle with old method : 0.00024771690368652344 length of segment : 20 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 : 7 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0006110668182373047 length of segment : 28 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 1065 time to create 1 rle with old method : 0.0013267993927001953 length of segment : 52 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 817 time to create 1 rle with old method : 0.0010366439819335938 length of segment : 50 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 651 time to create 1 rle with old method : 0.0008473396301269531 length of segment : 54 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001862049102783203 length of segment : 10 time for calcul the mask position with numpy : 0.0005910396575927734 nb_pixel_total : 55090 time to create 1 rle with old method : 0.06258296966552734 length of segment : 326 time for calcul the mask position with numpy : 0.0004298686981201172 nb_pixel_total : 37807 time to create 1 rle with old method : 0.04100990295410156 length of segment : 271 Processing 1 images image shape: (400, 400, 3) min: 21.00000 max: 204.00000 molded_images shape: (1, 640, 640, 3) min: -93.68828 max: 77.36953 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: -118.72344 max: 150.90469 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 7.843971252441406e-05 nb_pixel_total : 1341 time to create 1 rle with old method : 0.0023336410522460938 length of segment : 46 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 1932 time to create 1 rle with old method : 0.003390073776245117 length of segment : 35 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 1524 time to create 1 rle with old method : 0.002736806869506836 length of segment : 35 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.00033020973205566406 length of segment : 17 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 68 time to create 1 rle with old method : 0.00016570091247558594 length of segment : 11 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 126 time to create 1 rle with old method : 0.00027823448181152344 length of segment : 16 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 558 time to create 1 rle with old method : 0.0009989738464355469 length of segment : 48 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 564 time to create 1 rle with old method : 0.0010571479797363281 length of segment : 33 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 144 time to create 1 rle with old method : 0.0003361701965332031 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: -122.08281 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 : 4.38690185546875e-05 nb_pixel_total : 452 time to create 1 rle with old method : 0.0006206035614013672 length of segment : 27 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 499 time to create 1 rle with old method : 0.0006589889526367188 length of segment : 46 time for calcul the mask position with numpy : 0.00023126602172851562 nb_pixel_total : 13960 time to create 1 rle with old method : 0.02022552490234375 length of segment : 129 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001862049102783203 length of segment : 18 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 390 time to create 1 rle with old method : 0.000530242919921875 length of segment : 26 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 817 time to create 1 rle with old method : 0.0010807514190673828 length of segment : 35 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 761 time to create 1 rle with old method : 0.0010111331939697266 length of segment : 32 time for calcul the mask position with numpy : 5.054473876953125e-05 nb_pixel_total : 125 time to create 1 rle with old method : 0.00032639503479003906 length of segment : 16 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 65 time to create 1 rle with old method : 0.0002200603485107422 length of segment : 7 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 728 time to create 1 rle with old method : 0.0009479522705078125 length of segment : 33 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 677 time to create 1 rle with old method : 0.0008981227874755859 length of segment : 33 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 672 time to create 1 rle with old method : 0.000942230224609375 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: -123.66484 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 : 8.749961853027344e-05 nb_pixel_total : 1797 time to create 1 rle with old method : 0.0038673877716064453 length of segment : 138 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 1203 time to create 1 rle with old method : 0.0014307498931884766 length of segment : 40 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 625 time to create 1 rle with old method : 0.0009245872497558594 length of segment : 25 time for calcul the mask position with numpy : 0.00010514259338378906 nb_pixel_total : 4307 time to create 1 rle with old method : 0.005326509475708008 length of segment : 168 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 845 time to create 1 rle with old method : 0.001096487045288086 length of segment : 90 time for calcul the mask position with numpy : 8.654594421386719e-05 nb_pixel_total : 2401 time to create 1 rle with old method : 0.0028870105743408203 length of segment : 160 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 774 time to create 1 rle with old method : 0.0009980201721191406 length of segment : 41 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 87 time to create 1 rle with old method : 0.00019478797912597656 length of segment : 14 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 1067 time to create 1 rle with old method : 0.001531362533569336 length of segment : 46 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 2308 time to create 1 rle with old method : 0.002866983413696289 length of segment : 61 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 615 time to create 1 rle with old method : 0.0008211135864257812 length of segment : 80 time for calcul the mask position with numpy : 6.103515625e-05 nb_pixel_total : 2494 time to create 1 rle with old method : 0.0030667781829833984 length of segment : 67 time for calcul the mask position with numpy : 0.0001385211944580078 nb_pixel_total : 6156 time to create 1 rle with old method : 0.007566213607788086 length of segment : 116 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: 138.75625 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 : 178 time to create 1 rle with old method : 0.0002892017364501953 length of segment : 34 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 790 time to create 1 rle with old method : 0.0010378360748291016 length of segment : 43 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 556 time to create 1 rle with old method : 0.0007529258728027344 length of segment : 57 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 1170 time to create 1 rle with old method : 0.0014700889587402344 length of segment : 52 time for calcul the mask position with numpy : 0.00013494491577148438 nb_pixel_total : 2179 time to create 1 rle with old method : 0.002749919891357422 length of segment : 158 Processing 1 images image shape: (280, 400, 3) min: 9.00000 max: 182.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 62.99453 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009045600891113281 nb_pixel_total : 105605 time to create 1 rle with old method : 0.11688470840454102 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 : 17 time for calcul the mask position with numpy : 7.343292236328125e-05 nb_pixel_total : 896 time to create 1 rle with old method : 0.0016324520111083984 length of segment : 44 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 509 time to create 1 rle with old method : 0.0009899139404296875 length of segment : 24 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 199 time to create 1 rle with old method : 0.00040984153747558594 length of segment : 20 time for calcul the mask position with numpy : 5.5789947509765625e-05 nb_pixel_total : 1136 time to create 1 rle with old method : 0.0019867420196533203 length of segment : 43 time for calcul the mask position with numpy : 0.00015425682067871094 nb_pixel_total : 4697 time to create 1 rle with old method : 0.007974386215209961 length of segment : 113 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 758 time to create 1 rle with old method : 0.0013914108276367188 length of segment : 55 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1207 time to create 1 rle with old method : 0.0020766258239746094 length of segment : 52 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 209 time to create 1 rle with old method : 0.00029158592224121094 length of segment : 21 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 216 time to create 1 rle with old method : 0.00030517578125 length of segment : 21 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.0007641315460205078 length of segment : 24 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 1532 time to create 1 rle with old method : 0.0019044876098632812 length of segment : 57 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 78 time to create 1 rle with old method : 0.00014829635620117188 length of segment : 14 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 350 time to create 1 rle with old method : 0.00051116943359375 length of segment : 20 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 565 time to create 1 rle with old method : 0.0007271766662597656 length of segment : 34 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 363 time to create 1 rle with old method : 0.0005123615264892578 length of segment : 21 time for calcul the mask position with numpy : 5.412101745605469e-05 nb_pixel_total : 1406 time to create 1 rle with old method : 0.0018260478973388672 length of segment : 63 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.0005180835723876953 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 : 41 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 164 time to create 1 rle with old method : 0.00024771690368652344 length of segment : 25 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004565715789794922 length of segment : 22 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 2047 time to create 1 rle with old method : 0.0027124881744384766 length of segment : 59 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0005345344543457031 length of segment : 38 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 734 time to create 1 rle with old method : 0.0009756088256835938 length of segment : 44 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 318 time to create 1 rle with old method : 0.0005249977111816406 length of segment : 51 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 288 time to create 1 rle with old method : 0.00043702125549316406 length of segment : 18 time for calcul the mask position with numpy : 4.506111145019531e-05 nb_pixel_total : 872 time to create 1 rle with old method : 0.0010814666748046875 length of segment : 64 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 216 time to create 1 rle with old method : 0.00029850006103515625 length of segment : 25 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 444 time to create 1 rle with old method : 0.0008304119110107422 length of segment : 18 time for calcul the mask position with numpy : 6.175041198730469e-05 nb_pixel_total : 949 time to create 1 rle with old method : 0.0015499591827392578 length of segment : 40 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 151 time to create 1 rle with old method : 0.00034737586975097656 length of segment : 16 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 591 time to create 1 rle with old method : 0.0008108615875244141 length of segment : 70 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 365 time to create 1 rle with old method : 0.0004734992980957031 length of segment : 29 time for calcul the mask position with numpy : 8.988380432128906e-05 nb_pixel_total : 760 time to create 1 rle with old method : 0.0017604827880859375 length of segment : 42 time for calcul the mask position with numpy : 0.00011777877807617188 nb_pixel_total : 3266 time to create 1 rle with old method : 0.003996133804321289 length of segment : 147 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.0002689361572265625 length of segment : 35 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 180 time to create 1 rle with old method : 0.00024580955505371094 length of segment : 18 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002598762512207031 length of segment : 14 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 143 time to create 1 rle with old method : 0.0002143383026123047 length of segment : 14 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 852 time to create 1 rle with old method : 0.00113677978515625 length of segment : 31 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 231 time to create 1 rle with old method : 0.00034236907958984375 length of segment : 36 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 780 time to create 1 rle with old method : 0.0009961128234863281 length of segment : 29 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 562 time to create 1 rle with old method : 0.0007429122924804688 length of segment : 24 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.0005621910095214844 length of segment : 32 time for calcul the mask position with numpy : 3.886222839355469e-05 nb_pixel_total : 497 time to create 1 rle with old method : 0.0007083415985107422 length of segment : 21 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0003960132598876953 length of segment : 22 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0005707740783691406 length of segment : 26 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 1229 time to create 1 rle with old method : 0.0015053749084472656 length of segment : 45 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 227 time to create 1 rle with old method : 0.0003349781036376953 length of segment : 18 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 869 time to create 1 rle with old method : 0.0011289119720458984 length of segment : 36 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 1325 time to create 1 rle with old method : 0.0016667842864990234 length of segment : 47 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 375 time to create 1 rle with old method : 0.0005340576171875 length of segment : 20 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.0003170967102050781 length of segment : 12 time for calcul the mask position with numpy : 5.435943603515625e-05 nb_pixel_total : 1445 time to create 1 rle with old method : 0.0017092227935791016 length of segment : 55 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 415 time to create 1 rle with old method : 0.0006000995635986328 length of segment : 22 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 1535 time to create 1 rle with old method : 0.0019350051879882812 length of segment : 57 time for calcul the mask position with numpy : 5.245208740234375e-05 nb_pixel_total : 978 time to create 1 rle with old method : 0.0012946128845214844 length of segment : 39 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 379 time to create 1 rle with old method : 0.0005297660827636719 length of segment : 22 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 951 time to create 1 rle with old method : 0.0012333393096923828 length of segment : 39 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 716 time to create 1 rle with old method : 0.0009553432464599609 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: 149.35781 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 515 time to create 1 rle with old method : 0.0009701251983642578 length of segment : 36 time for calcul the mask position with numpy : 8.106231689453125e-05 nb_pixel_total : 1342 time to create 1 rle with old method : 0.0024404525756835938 length of segment : 68 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 437 time to create 1 rle with old method : 0.0008459091186523438 length of segment : 26 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 471 time to create 1 rle with old method : 0.0009143352508544922 length of segment : 22 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.0005364418029785156 length of segment : 34 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.0001919269561767578 length of segment : 13 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.00030732154846191406 length of segment : 18 time for calcul the mask position with numpy : 2.86102294921875e-05 nb_pixel_total : 77 time to create 1 rle with old method : 0.00012874603271484375 length of segment : 11 time for calcul the mask position with numpy : 2.9087066650390625e-05 nb_pixel_total : 120 time to create 1 rle with old method : 0.00018310546875 length of segment : 15 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.0003743171691894531 length of segment : 16 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 304 time to create 1 rle with old method : 0.0004215240478515625 length of segment : 28 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 334 time to create 1 rle with old method : 0.0004677772521972656 length of segment : 30 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 493 time to create 1 rle with old method : 0.0006642341613769531 length of segment : 27 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 113 time to create 1 rle with old method : 0.00017142295837402344 length of segment : 14 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: 141.35000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 525 time to create 1 rle with old method : 0.0008959770202636719 length of segment : 40 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 133 time to create 1 rle with old method : 0.0002760887145996094 length of segment : 12 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002799034118652344 length of segment : 31 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 179 time to create 1 rle with old method : 0.00031185150146484375 length of segment : 12 time for calcul the mask position with numpy : 2.86102294921875e-05 nb_pixel_total : 75 time to create 1 rle with old method : 0.0001304149627685547 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: -118.26641 max: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 13 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 170 time to create 1 rle with old method : 0.0003185272216796875 length of segment : 10 time for calcul the mask position with numpy : 0.000270843505859375 nb_pixel_total : 16728 time to create 1 rle with old method : 0.02103710174560547 length of segment : 174 time for calcul the mask position with numpy : 4.4345855712890625e-05 nb_pixel_total : 249 time to create 1 rle with old method : 0.0003497600555419922 length of segment : 36 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 194 time to create 1 rle with old method : 0.00029587745666503906 length of segment : 32 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 45 time to create 1 rle with old method : 0.00010418891906738281 length of segment : 12 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 253 time to create 1 rle with old method : 0.0003540515899658203 length of segment : 27 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 256 time to create 1 rle with old method : 0.0003635883331298828 length of segment : 33 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 231 time to create 1 rle with old method : 0.00032019615173339844 length of segment : 25 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 216 time to create 1 rle with old method : 0.00032067298889160156 length of segment : 34 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003635883331298828 length of segment : 33 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 302 time to create 1 rle with old method : 0.0004055500030517578 length of segment : 25 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 42 time to create 1 rle with old method : 8.559226989746094e-05 length of segment : 19 time for calcul the mask position with numpy : 4.935264587402344e-05 nb_pixel_total : 1323 time to create 1 rle with old method : 0.0017733573913574219 length of segment : 69 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -117.84453 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 : 6.413459777832031e-05 nb_pixel_total : 2373 time to create 1 rle with old method : 0.002960681915283203 length of segment : 52 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 2527 time to create 1 rle with old method : 0.0031096935272216797 length of segment : 53 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 650 time to create 1 rle with old method : 0.0008871555328369141 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: -121.35234 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 : 1573 time to create 1 rle with old method : 0.002054929733276367 length of segment : 23 time for calcul the mask position with numpy : 9.179115295410156e-05 nb_pixel_total : 2978 time to create 1 rle with old method : 0.003610849380493164 length of segment : 149 time for calcul the mask position with numpy : 5.91278076171875e-05 nb_pixel_total : 1110 time to create 1 rle with old method : 0.0014841556549072266 length of segment : 33 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 1053 time to create 1 rle with old method : 0.0015497207641601562 length of segment : 36 time for calcul the mask position with numpy : 0.0001068115234375 nb_pixel_total : 1357 time to create 1 rle with old method : 0.002605915069580078 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: -118.26641 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 : 6.0558319091796875e-05 nb_pixel_total : 1495 time to create 1 rle with old method : 0.0018897056579589844 length of segment : 76 time for calcul the mask position with numpy : 0.00012826919555664062 nb_pixel_total : 6758 time to create 1 rle with old method : 0.008027791976928711 length of segment : 97 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 698 time to create 1 rle with old method : 0.0009343624114990234 length of segment : 61 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 536 time to create 1 rle with old method : 0.000720977783203125 length of segment : 58 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 1226 time to create 1 rle with old method : 0.0015497207641601562 length of segment : 98 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 216 time to create 1 rle with old method : 0.0003218650817871094 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: 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 : 5.078315734863281e-05 nb_pixel_total : 512 time to create 1 rle with old method : 0.0009539127349853516 length of segment : 28 time for calcul the mask position with numpy : 7.796287536621094e-05 nb_pixel_total : 910 time to create 1 rle with old method : 0.0019948482513427734 length of segment : 53 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 485 time to create 1 rle with old method : 0.001093149185180664 length of segment : 45 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 1519 time to create 1 rle with old method : 0.0018610954284667969 length of segment : 55 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 1531 time to create 1 rle with old method : 0.001922607421875 length of segment : 57 time for calcul the mask position with numpy : 0.0005910396575927734 nb_pixel_total : 57779 time to create 1 rle with old method : 0.06337213516235352 length of segment : 280 Processing 1 images image shape: (400, 400, 3) min: 20.00000 max: 203.00000 molded_images shape: (1, 640, 640, 3) min: -89.77813 max: 76.11953 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: -120.67656 max: 150.90469 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 1436 time to create 1 rle with old method : 0.0025343894958496094 length of segment : 47 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 63 time to create 1 rle with old method : 0.00016188621520996094 length of segment : 10 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 150 time to create 1 rle with old method : 0.0003027915954589844 length of segment : 17 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.0006632804870605469 length of segment : 18 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 1568 time to create 1 rle with old method : 0.0028221607208251953 length of segment : 34 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -120.73906 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 : 6.580352783203125e-05 nb_pixel_total : 904 time to create 1 rle with old method : 0.0011336803436279297 length of segment : 79 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 622 time to create 1 rle with old method : 0.0009140968322753906 length of segment : 43 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 455 time to create 1 rle with old method : 0.0006244182586669922 length of segment : 26 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 500 time to create 1 rle with old method : 0.0006835460662841797 length of segment : 29 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.00016355514526367188 length of segment : 15 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 844 time to create 1 rle with old method : 0.0010442733764648438 length of segment : 47 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 1869 time to create 1 rle with old method : 0.0022640228271484375 length of segment : 49 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 858 time to create 1 rle with old method : 0.0011272430419921875 length of segment : 31 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 394 time to create 1 rle with old method : 0.0005896091461181641 length of segment : 21 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 358 time to create 1 rle with old method : 0.0004794597625732422 length of segment : 36 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 710 time to create 1 rle with old method : 0.0009286403656005859 length of segment : 34 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 738 time to create 1 rle with old method : 0.0009083747863769531 length of segment : 58 time for calcul the mask position with numpy : 6.437301635742188e-05 nb_pixel_total : 1701 time to create 1 rle with old method : 0.0021619796752929688 length of segment : 77 time for calcul the mask position with numpy : 5.7220458984375e-05 nb_pixel_total : 1134 time to create 1 rle with old method : 0.0015633106231689453 length of segment : 51 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 113 time to create 1 rle with old method : 0.0001747608184814453 length of segment : 17 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 1006 time to create 1 rle with old method : 0.0012745857238769531 length of segment : 52 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 699 time to create 1 rle with old method : 0.0009732246398925781 length of segment : 32 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 1177 time to create 1 rle with old method : 0.0015552043914794922 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: -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 : 4.458427429199219e-05 nb_pixel_total : 558 time to create 1 rle with old method : 0.0007669925689697266 length of segment : 27 time for calcul the mask position with numpy : 8.463859558105469e-05 nb_pixel_total : 2099 time to create 1 rle with old method : 0.002568960189819336 length of segment : 141 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 168 time to create 1 rle with old method : 0.00028133392333984375 length of segment : 11 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 798 time to create 1 rle with old method : 0.0010008811950683594 length of segment : 37 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 1023 time to create 1 rle with old method : 0.0014543533325195312 length of segment : 39 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 765 time to create 1 rle with old method : 0.0009906291961669922 length of segment : 80 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 433 time to create 1 rle with old method : 0.0005822181701660156 length of segment : 27 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001957416534423828 length of segment : 13 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.0003180503845214844 length of segment : 12 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 356 time to create 1 rle with old method : 0.00051116943359375 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: 144.93203 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004630088806152344 length of segment : 56 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 788 time to create 1 rle with old method : 0.0010156631469726562 length of segment : 45 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 564 time to create 1 rle with old method : 0.0007879734039306641 length of segment : 57 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 526 time to create 1 rle with old method : 0.0007562637329101562 length of segment : 31 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 1258 time to create 1 rle with old method : 0.0015819072723388672 length of segment : 54 time for calcul the mask position with numpy : 0.0001735687255859375 nb_pixel_total : 4574 time to create 1 rle with old method : 0.005916118621826172 length of segment : 209 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1448 time to create 1 rle with old method : 0.0017864704132080078 length of segment : 72 time for calcul the mask position with numpy : 6.580352783203125e-05 nb_pixel_total : 968 time to create 1 rle with old method : 0.0013570785522460938 length of segment : 89 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.00017714500427246094 length of segment : 16 Processing 1 images image shape: (280, 400, 3) min: 9.00000 max: 186.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 64.42422 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0008718967437744141 nb_pixel_total : 106713 time to create 1 rle with old method : 0.11754465103149414 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 : 4.9114227294921875e-05 nb_pixel_total : 993 time to create 1 rle with old method : 0.001260519027709961 length of segment : 41 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 502 time to create 1 rle with old method : 0.0007169246673583984 length of segment : 23 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 1271 time to create 1 rle with old method : 0.0015871524810791016 length of segment : 62 time for calcul the mask position with numpy : 4.458427429199219e-05 nb_pixel_total : 723 time to create 1 rle with old method : 0.0009758472442626953 length of segment : 49 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.000244140625 length of segment : 18 time for calcul the mask position with numpy : 0.00012993812561035156 nb_pixel_total : 5232 time to create 1 rle with old method : 0.0060808658599853516 length of segment : 102 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 112 time to create 1 rle with old method : 0.000186920166015625 length of segment : 15 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 1145 time to create 1 rle with old method : 0.001432180404663086 length of segment : 42 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 211 time to create 1 rle with old method : 0.0002956390380859375 length of segment : 22 time for calcul the mask position with numpy : 5.888938903808594e-05 nb_pixel_total : 1566 time to create 1 rle with old method : 0.001973867416381836 length of segment : 57 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.00028967857360839844 length of segment : 20 time for calcul the mask position with numpy : 6.365776062011719e-05 nb_pixel_total : 1895 time to create 1 rle with old method : 0.0023734569549560547 length of segment : 70 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 146 time to create 1 rle with old method : 0.00021028518676757812 length of segment : 15 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 343 time to create 1 rle with old method : 0.00048422813415527344 length of segment : 18 time for calcul the mask position with numpy : 0.00010895729064941406 nb_pixel_total : 4032 time to create 1 rle with old method : 0.004750251770019531 length of segment : 120 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 : 6.198883056640625e-05 nb_pixel_total : 173 time to create 1 rle with old method : 0.00026869773864746094 length of segment : 25 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 753 time to create 1 rle with old method : 0.0009741783142089844 length of segment : 36 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 452 time to create 1 rle with old method : 0.0005939006805419922 length of segment : 38 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 158 time to create 1 rle with old method : 0.0002620220184326172 length of segment : 23 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 309 time to create 1 rle with old method : 0.00044918060302734375 length of segment : 23 time for calcul the mask position with numpy : 5.698204040527344e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.00075531005859375 length of segment : 69 time for calcul the mask position with numpy : 4.220008850097656e-05 nb_pixel_total : 817 time to create 1 rle with old method : 0.0010347366333007812 length of segment : 34 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.00043392181396484375 length of segment : 16 time for calcul the mask position with numpy : 7.224082946777344e-05 nb_pixel_total : 2055 time to create 1 rle with old method : 0.002628803253173828 length of segment : 66 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 786 time to create 1 rle with old method : 0.000985860824584961 length of segment : 61 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 185 time to create 1 rle with old method : 0.00028061866760253906 length of segment : 54 time for calcul the mask position with numpy : 9.417533874511719e-05 nb_pixel_total : 3058 time to create 1 rle with old method : 0.0035440921783447266 length of segment : 97 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 794 time to create 1 rle with old method : 0.0010180473327636719 length of segment : 73 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 444 time to create 1 rle with old method : 0.0006525516510009766 length of segment : 19 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 840 time to create 1 rle with old method : 0.0011360645294189453 length of segment : 36 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 1613 time to create 1 rle with old method : 0.0020294189453125 length of segment : 55 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 277 time to create 1 rle with old method : 0.0003910064697265625 length of segment : 23 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 164 time to create 1 rle with old method : 0.0002627372741699219 length of segment : 15 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 794 time to create 1 rle with old method : 0.0010385513305664062 length of segment : 35 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 163 time to create 1 rle with old method : 0.00023889541625976562 length of segment : 25 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005702972412109375 length of segment : 20 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 397 time to create 1 rle with old method : 0.0005304813385009766 length of segment : 29 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 419 time to create 1 rle with old method : 0.0006310939788818359 length of segment : 33 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 391 time to create 1 rle with old method : 0.0006003379821777344 length of segment : 21 time for calcul the mask position with numpy : 4.38690185546875e-05 nb_pixel_total : 827 time to create 1 rle with old method : 0.0011327266693115234 length of segment : 30 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 422 time to create 1 rle with old method : 0.0006022453308105469 length of segment : 21 time for calcul the mask position with numpy : 5.53131103515625e-05 nb_pixel_total : 282 time to create 1 rle with old method : 0.0004425048828125 length of segment : 23 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 158 time to create 1 rle with old method : 0.00023889541625976562 length of segment : 18 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 432 time to create 1 rle with old method : 0.0006093978881835938 length of segment : 25 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.00027942657470703125 length of segment : 17 time for calcul the mask position with numpy : 5.0067901611328125e-05 nb_pixel_total : 961 time to create 1 rle with old method : 0.0012302398681640625 length of segment : 38 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 181 time to create 1 rle with old method : 0.0002446174621582031 length of segment : 23 time for calcul the mask position with numpy : 3.6716461181640625e-05 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005507469177246094 length of segment : 29 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 721 time to create 1 rle with old method : 0.0009872913360595703 length of segment : 29 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 411 time to create 1 rle with old method : 0.0005869865417480469 length of segment : 33 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 555 time to create 1 rle with old method : 0.0007565021514892578 length of segment : 24 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 1355 time to create 1 rle with old method : 0.0017173290252685547 length of segment : 47 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 377 time to create 1 rle with old method : 0.0005340576171875 length of segment : 23 time for calcul the mask position with numpy : 7.104873657226562e-05 nb_pixel_total : 1574 time to create 1 rle with old method : 0.001998424530029297 length of segment : 57 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: 149.64297 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 15 time for calcul the mask position with numpy : 5.626678466796875e-05 nb_pixel_total : 575 time to create 1 rle with old method : 0.0009326934814453125 length of segment : 37 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 136 time to create 1 rle with old method : 0.0002086162567138672 length of segment : 16 time for calcul the mask position with numpy : 6.318092346191406e-05 nb_pixel_total : 1762 time to create 1 rle with old method : 0.002226114273071289 length of segment : 54 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 213 time to create 1 rle with old method : 0.0003771781921386719 length of segment : 18 time for calcul the mask position with numpy : 7.82012939453125e-05 nb_pixel_total : 727 time to create 1 rle with old method : 0.0013418197631835938 length of segment : 48 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 381 time to create 1 rle with old method : 0.0005075931549072266 length of segment : 31 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 660 time to create 1 rle with old method : 0.0010020732879638672 length of segment : 39 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 355 time to create 1 rle with old method : 0.0004825592041015625 length of segment : 31 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 96 time to create 1 rle with old method : 0.000156402587890625 length of segment : 13 time for calcul the mask position with numpy : 8.654594421386719e-05 nb_pixel_total : 3308 time to create 1 rle with old method : 0.00408482551574707 length of segment : 130 time for calcul the mask position with numpy : 7.724761962890625e-05 nb_pixel_total : 2197 time to create 1 rle with old method : 0.0026106834411621094 length of segment : 97 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 278 time to create 1 rle with old method : 0.00038886070251464844 length of segment : 21 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0006134510040283203 length of segment : 28 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 302 time to create 1 rle with old method : 0.0003933906555175781 length of segment : 30 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 2021 time to create 1 rle with old method : 0.0025780200958251953 length of segment : 94 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: 136.22500 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 time for calcul the mask position with numpy : 6.079673767089844e-05 nb_pixel_total : 116 time to create 1 rle with old method : 0.0002837181091308594 length of segment : 10 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 413 time to create 1 rle with old method : 0.0007073879241943359 length of segment : 46 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 171 time to create 1 rle with old method : 0.0002338886260986328 length of segment : 28 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 177 time to create 1 rle with old method : 0.00028204917907714844 length of segment : 12 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 133 time to create 1 rle with old method : 0.000225067138671875 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: -118.38750 max: 145.73672 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 : 229 time to create 1 rle with old method : 0.0003204345703125 length of segment : 25 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.0002224445343017578 length of segment : 7 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 57 time to create 1 rle with old method : 0.00010204315185546875 length of segment : 17 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 343 time to create 1 rle with old method : 0.0005424022674560547 length of segment : 49 time for calcul the mask position with numpy : 0.00023937225341796875 nb_pixel_total : 6095 time to create 1 rle with old method : 0.007615804672241211 length of segment : 233 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 283 time to create 1 rle with old method : 0.0003998279571533203 length of segment : 34 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 309 time to create 1 rle with old method : 0.0004642009735107422 length of segment : 31 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 52 time to create 1 rle with old method : 9.107589721679688e-05 length of segment : 15 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.00028395652770996094 length of segment : 37 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 107 time to create 1 rle with old method : 0.0001728534698486328 length of segment : 22 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -118.78594 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 : 8.130073547363281e-05 nb_pixel_total : 2455 time to create 1 rle with old method : 0.0036246776580810547 length of segment : 53 time for calcul the mask position with numpy : 6.151199340820312e-05 nb_pixel_total : 2590 time to create 1 rle with old method : 0.0033006668090820312 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: -121.22344 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 : 9.703636169433594e-05 nb_pixel_total : 4193 time to create 1 rle with old method : 0.005194425582885742 length of segment : 150 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1490 time to create 1 rle with old method : 0.0022783279418945312 length of segment : 13 time for calcul the mask position with numpy : 6.341934204101562e-05 nb_pixel_total : 1149 time to create 1 rle with old method : 0.0017893314361572266 length of segment : 28 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 1483 time to create 1 rle with old method : 0.0022706985473632812 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: -123.09063 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 : 9.107589721679688e-05 nb_pixel_total : 1636 time to create 1 rle with old method : 0.0024971961975097656 length of segment : 80 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 747 time to create 1 rle with old method : 0.0009567737579345703 length of segment : 69 time for calcul the mask position with numpy : 0.00018668174743652344 nb_pixel_total : 11353 time to create 1 rle with old method : 0.014473438262939453 length of segment : 107 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 149 time to create 1 rle with old method : 0.00021147727966308594 length of segment : 22 time for calcul the mask position with numpy : 4.982948303222656e-05 nb_pixel_total : 726 time to create 1 rle with old method : 0.0009884834289550781 length of segment : 66 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 851 time to create 1 rle with old method : 0.0010514259338378906 length of segment : 58 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 257 time to create 1 rle with old method : 0.00034928321838378906 length of segment : 22 time for calcul the mask position with numpy : 3.123283386230469e-05 nb_pixel_total : 213 time to create 1 rle with old method : 0.00031685829162597656 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: 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 : 7.2479248046875e-05 nb_pixel_total : 1127 time to create 1 rle with old method : 0.0014209747314453125 length of segment : 52 time for calcul the mask position with numpy : 5.2928924560546875e-05 nb_pixel_total : 504 time to create 1 rle with old method : 0.0007340908050537109 length of segment : 28 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 479 time to create 1 rle with old method : 0.0006363391876220703 length of segment : 46 time for calcul the mask position with numpy : 5.984306335449219e-05 nb_pixel_total : 941 time to create 1 rle with old method : 0.0012354850769042969 length of segment : 52 time for calcul the mask position with numpy : 0.0007214546203613281 nb_pixel_total : 66392 time to create 1 rle with old method : 0.07142424583435059 length of segment : 312 Processing 1 images image shape: (400, 400, 3) min: 19.00000 max: 203.00000 molded_images shape: (1, 640, 640, 3) min: -91.86016 max: 76.11953 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: -118.96172 max: 149.39687 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 10 time for calcul the mask position with numpy : 7.033348083496094e-05 nb_pixel_total : 1475 time to create 1 rle with old method : 0.002596139907836914 length of segment : 47 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 73 time to create 1 rle with old method : 0.00017333030700683594 length of segment : 11 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0003275871276855469 length of segment : 16 time for calcul the mask position with numpy : 6.556510925292969e-05 nb_pixel_total : 1627 time to create 1 rle with old method : 0.002942323684692383 length of segment : 34 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1659 time to create 1 rle with old method : 0.0029053688049316406 length of segment : 36 time for calcul the mask position with numpy : 5.3882598876953125e-05 nb_pixel_total : 456 time to create 1 rle with old method : 0.0008485317230224609 length of segment : 41 time for calcul the mask position with numpy : 0.00012683868408203125 nb_pixel_total : 3242 time to create 1 rle with old method : 0.006032705307006836 length of segment : 76 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 367 time to create 1 rle with old method : 0.0007474422454833984 length of segment : 20 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 419 time to create 1 rle with old method : 0.0005891323089599609 length of segment : 36 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 390 time to create 1 rle with old method : 0.0006248950958251953 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: -120.75469 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 : 5.173683166503906e-05 nb_pixel_total : 491 time to create 1 rle with old method : 0.0006492137908935547 length of segment : 29 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 529 time to create 1 rle with old method : 0.0007266998291015625 length of segment : 26 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 97 time to create 1 rle with old method : 0.00015664100646972656 length of segment : 16 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 818 time to create 1 rle with old method : 0.001051187515258789 length of segment : 37 time for calcul the mask position with numpy : 0.00016760826110839844 nb_pixel_total : 8815 time to create 1 rle with old method : 0.01071786880493164 length of segment : 164 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 1021 time to create 1 rle with old method : 0.0013546943664550781 length of segment : 46 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 142 time to create 1 rle with old method : 0.0002579689025878906 length of segment : 15 time for calcul the mask position with numpy : 0.0002186298370361328 nb_pixel_total : 11203 time to create 1 rle with old method : 0.01320648193359375 length of segment : 133 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 938 time to create 1 rle with old method : 0.0011742115020751953 length of segment : 32 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 1058 time to create 1 rle with old method : 0.0014519691467285156 length of segment : 42 time for calcul the mask position with numpy : 4.00543212890625e-05 nb_pixel_total : 574 time to create 1 rle with old method : 0.0007197856903076172 length of segment : 66 time for calcul the mask position with numpy : 5.340576171875e-05 nb_pixel_total : 2179 time to create 1 rle with old method : 0.002786874771118164 length of segment : 56 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 72 time to create 1 rle with old method : 0.0001246929168701172 length of segment : 9 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 684 time to create 1 rle with old method : 0.0008420944213867188 length of segment : 44 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 948 time to create 1 rle with old method : 0.0012099742889404297 length of segment : 33 time for calcul the mask position with numpy : 0.00030303001403808594 nb_pixel_total : 5974 time to create 1 rle with old method : 0.007595062255859375 length of segment : 198 Processing 1 images image shape: (400, 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 : 8 time for calcul the mask position with numpy : 8.487701416015625e-05 nb_pixel_total : 1831 time to create 1 rle with old method : 0.002173185348510742 length of segment : 145 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 1366 time to create 1 rle with old method : 0.00170135498046875 length of segment : 41 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 1783 time to create 1 rle with old method : 0.0022516250610351562 length of segment : 67 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 1079 time to create 1 rle with old method : 0.0015256404876708984 length of segment : 40 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 101 time to create 1 rle with old method : 0.0002009868621826172 length of segment : 14 time for calcul the mask position with numpy : 8.96453857421875e-05 nb_pixel_total : 2232 time to create 1 rle with old method : 0.0027539730072021484 length of segment : 72 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 815 time to create 1 rle with old method : 0.0010728836059570312 length of segment : 82 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 753 time to create 1 rle with old method : 0.0010502338409423828 length of segment : 75 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: 143.67812 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 : 491 time to create 1 rle with old method : 0.0006847381591796875 length of segment : 49 time for calcul the mask position with numpy : 4.744529724121094e-05 nb_pixel_total : 1491 time to create 1 rle with old method : 0.001833200454711914 length of segment : 60 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 1595 time to create 1 rle with old method : 0.0020275115966796875 length of segment : 83 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 357 time to create 1 rle with old method : 0.0005719661712646484 length of segment : 46 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 804 time to create 1 rle with old method : 0.0010340213775634766 length of segment : 44 time for calcul the mask position with numpy : 0.0001709461212158203 nb_pixel_total : 3856 time to create 1 rle with old method : 0.005031585693359375 length of segment : 215 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 96 time to create 1 rle with old method : 0.00018167495727539062 length of segment : 12 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 799 time to create 1 rle with old method : 0.001058816909790039 length of segment : 65 Processing 1 images image shape: (280, 400, 3) min: 12.00000 max: 191.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 71.10781 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009188652038574219 nb_pixel_total : 106532 time to create 1 rle with old method : 0.11510634422302246 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: 148.25234 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 17 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 491 time to create 1 rle with old method : 0.0007081031799316406 length of segment : 23 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 916 time to create 1 rle with old method : 0.0011425018310546875 length of segment : 44 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 214 time to create 1 rle with old method : 0.00029349327087402344 length of segment : 21 time for calcul the mask position with numpy : 9.5367431640625e-05 nb_pixel_total : 5407 time to create 1 rle with old method : 0.006403446197509766 length of segment : 93 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 160 time to create 1 rle with old method : 0.00025391578674316406 length of segment : 14 time for calcul the mask position with numpy : 0.00010991096496582031 nb_pixel_total : 3436 time to create 1 rle with old method : 0.0044252872467041016 length of segment : 91 time for calcul the mask position with numpy : 3.2901763916015625e-05 nb_pixel_total : 153 time to create 1 rle with old method : 0.0002429485321044922 length of segment : 15 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 1170 time to create 1 rle with old method : 0.001428365707397461 length of segment : 46 time for calcul the mask position with numpy : 5.7697296142578125e-05 nb_pixel_total : 1447 time to create 1 rle with old method : 0.0018553733825683594 length of segment : 90 time for calcul the mask position with numpy : 6.4849853515625e-05 nb_pixel_total : 1947 time to create 1 rle with old method : 0.0023920536041259766 length of segment : 58 time for calcul the mask position with numpy : 2.8848648071289062e-05 nb_pixel_total : 108 time to create 1 rle with old method : 0.0001518726348876953 length of segment : 18 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001938343048095703 length of segment : 19 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006566047668457031 length of segment : 36 time for calcul the mask position with numpy : 5.14984130859375e-05 nb_pixel_total : 2021 time to create 1 rle with old method : 0.0023713111877441406 length of segment : 58 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 348 time to create 1 rle with old method : 0.0005092620849609375 length of segment : 19 time for calcul the mask position with numpy : 3.337860107421875e-05 nb_pixel_total : 385 time to create 1 rle with old method : 0.0005559921264648438 length of segment : 23 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 362 time to create 1 rle with old method : 0.0005273818969726562 length of segment : 22 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 : 6.413459777832031e-05 nb_pixel_total : 162 time to create 1 rle with old method : 0.00024509429931640625 length of segment : 25 time for calcul the mask position with numpy : 3.814697265625e-05 nb_pixel_total : 399 time to create 1 rle with old method : 0.000537872314453125 length of segment : 38 time for calcul the mask position with numpy : 4.363059997558594e-05 nb_pixel_total : 905 time to create 1 rle with old method : 0.001155853271484375 length of segment : 47 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 996 time to create 1 rle with old method : 0.0013267993927001953 length of segment : 38 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 952 time to create 1 rle with old method : 0.0012161731719970703 length of segment : 42 time for calcul the mask position with numpy : 3.504753112792969e-05 nb_pixel_total : 295 time to create 1 rle with old method : 0.0004227161407470703 length of segment : 22 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 807 time to create 1 rle with old method : 0.0010917186737060547 length of segment : 95 time for calcul the mask position with numpy : 8.177757263183594e-05 nb_pixel_total : 2488 time to create 1 rle with old method : 0.002976655960083008 length of segment : 77 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 953 time to create 1 rle with old method : 0.0012664794921875 length of segment : 29 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 286 time to create 1 rle with old method : 0.0004343986511230469 length of segment : 16 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 488 time to create 1 rle with old method : 0.0006837844848632812 length of segment : 24 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 509 time to create 1 rle with old method : 0.0006811618804931641 length of segment : 34 time for calcul the mask position with numpy : 4.2438507080078125e-05 nb_pixel_total : 755 time to create 1 rle with old method : 0.0010192394256591797 length of segment : 32 time for calcul the mask position with numpy : 3.218650817871094e-05 nb_pixel_total : 157 time to create 1 rle with old method : 0.00026488304138183594 length of segment : 15 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 597 time to create 1 rle with old method : 0.0007791519165039062 length of segment : 56 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 441 time to create 1 rle with old method : 0.0006270408630371094 length of segment : 34 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 538 time to create 1 rle with old method : 0.0007381439208984375 length of segment : 46 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 506 time to create 1 rle with old method : 0.0007274150848388672 length of segment : 20 time for calcul the mask position with numpy : 3.0279159545898438e-05 nb_pixel_total : 161 time to create 1 rle with old method : 0.0002541542053222656 length of segment : 15 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 1691 time to create 1 rle with old method : 0.002173185348510742 length of segment : 57 time for calcul the mask position with numpy : 5.364418029785156e-05 nb_pixel_total : 1251 time to create 1 rle with old method : 0.0015575885772705078 length of segment : 49 time for calcul the mask position with numpy : 4.9114227294921875e-05 nb_pixel_total : 887 time to create 1 rle with old method : 0.00122833251953125 length of segment : 36 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 174 time to create 1 rle with old method : 0.00026726722717285156 length of segment : 44 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 551 time to create 1 rle with old method : 0.0007348060607910156 length of segment : 23 time for calcul the mask position with numpy : 3.170967102050781e-05 nb_pixel_total : 176 time to create 1 rle with old method : 0.0002613067626953125 length of segment : 19 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 410 time to create 1 rle with old method : 0.0005881786346435547 length of segment : 23 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 461 time to create 1 rle with old method : 0.000682830810546875 length of segment : 31 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 403 time to create 1 rle with old method : 0.0005440711975097656 length of segment : 29 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 79 time to create 1 rle with old method : 0.00013875961303710938 length of segment : 26 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005242824554443359 length of segment : 29 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 298 time to create 1 rle with old method : 0.0004143714904785156 length of segment : 24 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 914 time to create 1 rle with old method : 0.0012192726135253906 length of segment : 38 time for calcul the mask position with numpy : 3.314018249511719e-05 nb_pixel_total : 159 time to create 1 rle with old method : 0.00023317337036132812 length of segment : 26 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 913 time to create 1 rle with old method : 0.0012104511260986328 length of segment : 34 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 144 time to create 1 rle with old method : 0.00022935867309570312 length of segment : 18 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 886 time to create 1 rle with old method : 0.0012187957763671875 length of segment : 35 time for calcul the mask position with numpy : 3.457069396972656e-05 nb_pixel_total : 322 time to create 1 rle with old method : 0.0005235671997070312 length of segment : 20 time for calcul the mask position with numpy : 6.67572021484375e-05 nb_pixel_total : 624 time to create 1 rle with old method : 0.001361846923828125 length of segment : 36 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 822 time to create 1 rle with old method : 0.0011229515075683594 length of segment : 29 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 432 time to create 1 rle with old method : 0.000606536865234375 length of segment : 22 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 700 time to create 1 rle with old method : 0.0009043216705322266 length of segment : 50 time for calcul the mask position with numpy : 4.9591064453125e-05 nb_pixel_total : 919 time to create 1 rle with old method : 0.0012097358703613281 length of segment : 32 time for calcul the mask position with numpy : 6.413459777832031e-05 nb_pixel_total : 1601 time to create 1 rle with old method : 0.002049684524536133 length of segment : 55 time for calcul the mask position with numpy : 5.936622619628906e-05 nb_pixel_total : 30 time to create 1 rle with old method : 7.486343383789062e-05 length of segment : 10 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: 148.42812 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 95 time to create 1 rle with old method : 0.00016880035400390625 length of segment : 12 time for calcul the mask position with numpy : 4.1484832763671875e-05 nb_pixel_total : 252 time to create 1 rle with old method : 0.0005209445953369141 length of segment : 34 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 707 time to create 1 rle with old method : 0.0009024143218994141 length of segment : 52 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 348 time to create 1 rle with old method : 0.00046896934509277344 length of segment : 26 time for calcul the mask position with numpy : 6.270408630371094e-05 nb_pixel_total : 840 time to create 1 rle with old method : 0.0011649131774902344 length of segment : 61 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 2 time to create 1 rle with old method : 1.9073486328125e-05 length of segment : 2 time for calcul the mask position with numpy : 2.86102294921875e-05 nb_pixel_total : 93 time to create 1 rle with old method : 0.0001575946807861328 length of segment : 12 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.0002086162567138672 length of segment : 15 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.00032901763916015625 length of segment : 20 time for calcul the mask position with numpy : 4.863739013671875e-05 nb_pixel_total : 1168 time to create 1 rle with old method : 0.0014874935150146484 length of segment : 48 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 244 time to create 1 rle with old method : 0.00034999847412109375 length of segment : 19 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 668 time to create 1 rle with old method : 0.0008859634399414062 length of segment : 30 time for calcul the mask position with numpy : 6.699562072753906e-05 nb_pixel_total : 2222 time to create 1 rle with old method : 0.002820253372192383 length of segment : 97 time for calcul the mask position with numpy : 0.00010061264038085938 nb_pixel_total : 4829 time to create 1 rle with old method : 0.005710124969482422 length of segment : 166 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: 130.41250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 573 time to create 1 rle with old method : 0.0007374286651611328 length of segment : 47 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 153 time to create 1 rle with old method : 0.00026416778564453125 length of segment : 27 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 166 time to create 1 rle with old method : 0.0002865791320800781 length of segment : 13 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 93 time to create 1 rle with old method : 0.000179290771484375 length of segment : 10 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 136 time to create 1 rle with old method : 0.0001926422119140625 length of segment : 28 time for calcul the mask position with numpy : 2.86102294921875e-05 nb_pixel_total : 70 time to create 1 rle with old method : 0.00013375282287597656 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: 145.73672 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 11 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 42 time to create 1 rle with old method : 8.535385131835938e-05 length of segment : 15 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0006568431854248047 length of segment : 65 time for calcul the mask position with numpy : 4.0531158447265625e-05 nb_pixel_total : 191 time to create 1 rle with old method : 0.00026607513427734375 length of segment : 27 time for calcul the mask position with numpy : 6.222724914550781e-05 nb_pixel_total : 1755 time to create 1 rle with old method : 0.0022537708282470703 length of segment : 64 time for calcul the mask position with numpy : 3.1948089599609375e-05 nb_pixel_total : 243 time to create 1 rle with old method : 0.00034046173095703125 length of segment : 25 time for calcul the mask position with numpy : 3.3855438232421875e-05 nb_pixel_total : 242 time to create 1 rle with old method : 0.0003581047058105469 length of segment : 31 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 274 time to create 1 rle with old method : 0.0003993511199951172 length of segment : 31 time for calcul the mask position with numpy : 3.24249267578125e-05 nb_pixel_total : 227 time to create 1 rle with old method : 0.0003254413604736328 length of segment : 27 time for calcul the mask position with numpy : 5.793571472167969e-05 nb_pixel_total : 1449 time to create 1 rle with old method : 0.001863241195678711 length of segment : 60 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 244 time to create 1 rle with old method : 0.00035262107849121094 length of segment : 31 time for calcul the mask position with numpy : 3.552436828613281e-05 nb_pixel_total : 228 time to create 1 rle with old method : 0.0003120899200439453 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: -117.89141 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 : 4.9591064453125e-05 nb_pixel_total : 154 time to create 1 rle with old method : 0.0002951622009277344 length of segment : 25 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 2428 time to create 1 rle with old method : 0.0030221939086914062 length of segment : 51 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 664 time to create 1 rle with old method : 0.0008831024169921875 length of segment : 36 time for calcul the mask position with numpy : 3.4809112548828125e-05 nb_pixel_total : 313 time to create 1 rle with old method : 0.0004267692565917969 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: -121.93047 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 : 9.036064147949219e-05 nb_pixel_total : 3890 time to create 1 rle with old method : 0.004620790481567383 length of segment : 141 time for calcul the mask position with numpy : 6.628036499023438e-05 nb_pixel_total : 2048 time to create 1 rle with old method : 0.002824544906616211 length of segment : 40 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 340 time to create 1 rle with old method : 0.0004711151123046875 length of segment : 24 time for calcul the mask position with numpy : 5.817413330078125e-05 nb_pixel_total : 1604 time to create 1 rle with old method : 0.002434253692626953 length of segment : 14 time for calcul the mask position with numpy : 6.890296936035156e-05 nb_pixel_total : 1484 time to create 1 rle with old method : 0.002305746078491211 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: -120.80547 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 : 6.866455078125e-05 nb_pixel_total : 1357 time to create 1 rle with old method : 0.00182342529296875 length of segment : 77 time for calcul the mask position with numpy : 0.00016570091247558594 nb_pixel_total : 10546 time to create 1 rle with old method : 0.012442588806152344 length of segment : 92 time for calcul the mask position with numpy : 6.127357482910156e-05 nb_pixel_total : 900 time to create 1 rle with old method : 0.0011837482452392578 length of segment : 82 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 662 time to create 1 rle with old method : 0.000904083251953125 length of segment : 58 time for calcul the mask position with numpy : 3.910064697265625e-05 nb_pixel_total : 196 time to create 1 rle with old method : 0.0002732276916503906 length of segment : 28 time for calcul the mask position with numpy : 5.173683166503906e-05 nb_pixel_total : 203 time to create 1 rle with old method : 0.000377655029296875 length of segment : 18 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 1074 time to create 1 rle with old method : 0.0013010501861572266 length of segment : 82 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 : 5 time for calcul the mask position with numpy : 5.555152893066406e-05 nb_pixel_total : 85 time to create 1 rle with old method : 0.0002646446228027344 length of segment : 9 time for calcul the mask position with numpy : 5.459785461425781e-05 nb_pixel_total : 496 time to create 1 rle with old method : 0.0010662078857421875 length of segment : 30 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 1425 time to create 1 rle with old method : 0.0017864704132080078 length of segment : 55 time for calcul the mask position with numpy : 4.57763671875e-05 nb_pixel_total : 507 time to create 1 rle with old method : 0.0006728172302246094 length of segment : 44 time for calcul the mask position with numpy : 8.106231689453125e-05 nb_pixel_total : 886 time to create 1 rle with old method : 0.00122833251953125 length of segment : 51 Processing 1 images image shape: (400, 400, 3) min: 17.00000 max: 201.00000 molded_images shape: (1, 640, 640, 3) min: -90.28594 max: 75.05703 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: -114.91875 max: 147.35000 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 9 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 1686 time to create 1 rle with old method : 0.002132415771484375 length of segment : 35 time for calcul the mask position with numpy : 7.581710815429688e-05 nb_pixel_total : 1292 time to create 1 rle with old method : 0.0019888877868652344 length of segment : 45 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 270 time to create 1 rle with old method : 0.00042057037353515625 length of segment : 14 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 66 time to create 1 rle with old method : 0.00011968612670898438 length of segment : 10 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 551 time to create 1 rle with old method : 0.0006926059722900391 length of segment : 46 time for calcul the mask position with numpy : 3.075599670410156e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.00025534629821777344 length of segment : 18 time for calcul the mask position with numpy : 3.266334533691406e-05 nb_pixel_total : 207 time to create 1 rle with old method : 0.00029468536376953125 length of segment : 17 time for calcul the mask position with numpy : 6.461143493652344e-05 nb_pixel_total : 1931 time to create 1 rle with old method : 0.002978801727294922 length of segment : 51 time for calcul the mask position with numpy : 4.410743713378906e-05 nb_pixel_total : 351 time to create 1 rle with old method : 0.0005898475646972656 length of segment : 17 Processing 1 images image shape: (400, 400, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -121.55938 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 : 4.839897155761719e-05 nb_pixel_total : 490 time to create 1 rle with old method : 0.0006885528564453125 length of segment : 26 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 501 time to create 1 rle with old method : 0.0007030963897705078 length of segment : 29 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 739 time to create 1 rle with old method : 0.0011153221130371094 length of segment : 36 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 577 time to create 1 rle with old method : 0.0007615089416503906 length of segment : 27 time for calcul the mask position with numpy : 0.00015497207641601562 nb_pixel_total : 5984 time to create 1 rle with old method : 0.007496356964111328 length of segment : 121 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 730 time to create 1 rle with old method : 0.0009081363677978516 length of segment : 34 time for calcul the mask position with numpy : 4.124641418457031e-05 nb_pixel_total : 577 time to create 1 rle with old method : 0.0007610321044921875 length of segment : 47 time for calcul the mask position with numpy : 2.956390380859375e-05 nb_pixel_total : 104 time to create 1 rle with old method : 0.00016546249389648438 length of segment : 15 time for calcul the mask position with numpy : 3.743171691894531e-05 nb_pixel_total : 472 time to create 1 rle with old method : 0.0007371902465820312 length of segment : 33 time for calcul the mask position with numpy : 3.933906555175781e-05 nb_pixel_total : 629 time to create 1 rle with old method : 0.00079345703125 length of segment : 65 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 97 time to create 1 rle with old method : 0.00015783309936523438 length of segment : 15 time for calcul the mask position with numpy : 4.029273986816406e-05 nb_pixel_total : 623 time to create 1 rle with old method : 0.0009238719940185547 length of segment : 41 time for calcul the mask position with numpy : 0.0002849102020263672 nb_pixel_total : 9004 time to create 1 rle with old method : 0.011349678039550781 length of segment : 174 Processing 1 images image shape: (400, 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 : 8 time for calcul the mask position with numpy : 8.821487426757812e-05 nb_pixel_total : 1732 time to create 1 rle with old method : 0.002055644989013672 length of segment : 138 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 1109 time to create 1 rle with old method : 0.0014493465423583984 length of segment : 75 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 1326 time to create 1 rle with old method : 0.0016422271728515625 length of segment : 42 time for calcul the mask position with numpy : 5.650520324707031e-05 nb_pixel_total : 1265 time to create 1 rle with old method : 0.001691579818725586 length of segment : 40 time for calcul the mask position with numpy : 0.00014853477478027344 nb_pixel_total : 1835 time to create 1 rle with old method : 0.004236936569213867 length of segment : 67 time for calcul the mask position with numpy : 5.8650970458984375e-05 nb_pixel_total : 86 time to create 1 rle with old method : 0.00024080276489257812 length of segment : 13 time for calcul the mask position with numpy : 9.131431579589844e-05 nb_pixel_total : 1416 time to create 1 rle with old method : 0.0020284652709960938 length of segment : 99 time for calcul the mask position with numpy : 7.534027099609375e-05 nb_pixel_total : 1235 time to create 1 rle with old method : 0.0017354488372802734 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: 143.81484 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 7 time for calcul the mask position with numpy : 5.030632019042969e-05 nb_pixel_total : 55 time to create 1 rle with old method : 0.00019621849060058594 length of segment : 16 time for calcul the mask position with numpy : 7.414817810058594e-05 nb_pixel_total : 955 time to create 1 rle with old method : 0.0012619495391845703 length of segment : 71 time for calcul the mask position with numpy : 4.649162292480469e-05 nb_pixel_total : 806 time to create 1 rle with old method : 0.0010273456573486328 length of segment : 45 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 846 time to create 1 rle with old method : 0.0010933876037597656 length of segment : 49 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 300 time to create 1 rle with old method : 0.0004477500915527344 length of segment : 20 time for calcul the mask position with numpy : 9.34600830078125e-05 nb_pixel_total : 1602 time to create 1 rle with old method : 0.0021665096282958984 length of segment : 108 time for calcul the mask position with numpy : 0.00025773048400878906 nb_pixel_total : 3235 time to create 1 rle with old method : 0.004148721694946289 length of segment : 208 Processing 1 images image shape: (280, 400, 3) min: 11.00000 max: 187.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 70.25234 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 1 time for calcul the mask position with numpy : 0.0009424686431884766 nb_pixel_total : 106172 time to create 1 rle with old method : 0.1270580291748047 length of segment : 285 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: 148.52578 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 6.031990051269531e-05 nb_pixel_total : 992 time to create 1 rle with old method : 0.001661539077758789 length of segment : 45 time for calcul the mask position with numpy : 4.839897155761719e-05 nb_pixel_total : 503 time to create 1 rle with old method : 0.0007166862487792969 length of segment : 24 time for calcul the mask position with numpy : 4.5299530029296875e-05 nb_pixel_total : 716 time to create 1 rle with old method : 0.0009555816650390625 length of segment : 46 time for calcul the mask position with numpy : 5.9604644775390625e-05 nb_pixel_total : 1734 time to create 1 rle with old method : 0.002229452133178711 length of segment : 59 time for calcul the mask position with numpy : 0.00021505355834960938 nb_pixel_total : 4654 time to create 1 rle with old method : 0.005919218063354492 length of segment : 117 time for calcul the mask position with numpy : 5.125999450683594e-05 nb_pixel_total : 187 time to create 1 rle with old method : 0.00027060508728027344 length of segment : 20 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 612 time to create 1 rle with old method : 0.0008521080017089844 length of segment : 27 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 135 time to create 1 rle with old method : 0.00021767616271972656 length of segment : 13 time for calcul the mask position with numpy : 3.0040740966796875e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.00025177001953125 length of segment : 19 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 1287 time to create 1 rle with old method : 0.0021948814392089844 length of segment : 51 time for calcul the mask position with numpy : 3.8623809814453125e-05 nb_pixel_total : 28 time to create 1 rle with old method : 8.726119995117188e-05 length of segment : 11 time for calcul the mask position with numpy : 3.838539123535156e-05 nb_pixel_total : 494 time to create 1 rle with old method : 0.0007014274597167969 length of segment : 29 time for calcul the mask position with numpy : 3.719329833984375e-05 nb_pixel_total : 388 time to create 1 rle with old method : 0.0005681514739990234 length of segment : 23 time for calcul the mask position with numpy : 3.790855407714844e-05 nb_pixel_total : 400 time to create 1 rle with old method : 0.000560760498046875 length of segment : 23 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 : 6.794929504394531e-05 nb_pixel_total : 967 time to create 1 rle with old method : 0.001211404800415039 length of segment : 47 time for calcul the mask position with numpy : 6.29425048828125e-05 nb_pixel_total : 175 time to create 1 rle with old method : 0.0004019737243652344 length of segment : 25 time for calcul the mask position with numpy : 4.482269287109375e-05 nb_pixel_total : 405 time to create 1 rle with old method : 0.000537872314453125 length of segment : 37 time for calcul the mask position with numpy : 3.647804260253906e-05 nb_pixel_total : 305 time to create 1 rle with old method : 0.0004363059997558594 length of segment : 21 time for calcul the mask position with numpy : 4.8160552978515625e-05 nb_pixel_total : 811 time to create 1 rle with old method : 0.0009922981262207031 length of segment : 62 time for calcul the mask position with numpy : 4.076957702636719e-05 nb_pixel_total : 801 time to create 1 rle with old method : 0.0009818077087402344 length of segment : 34 time for calcul the mask position with numpy : 3.695487976074219e-05 nb_pixel_total : 409 time to create 1 rle with old method : 0.0005800724029541016 length of segment : 24 time for calcul the mask position with numpy : 3.62396240234375e-05 nb_pixel_total : 457 time to create 1 rle with old method : 0.0006296634674072266 length of segment : 20 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 165 time to create 1 rle with old method : 0.0003228187561035156 length of segment : 18 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 693 time to create 1 rle with old method : 0.0009412765502929688 length of segment : 29 time for calcul the mask position with numpy : 0.00010275840759277344 nb_pixel_total : 3199 time to create 1 rle with old method : 0.0038001537322998047 length of segment : 146 time for calcul the mask position with numpy : 6.246566772460938e-05 nb_pixel_total : 912 time to create 1 rle with old method : 0.0012378692626953125 length of segment : 29 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 1445 time to create 1 rle with old method : 0.001814126968383789 length of segment : 57 time for calcul the mask position with numpy : 4.172325134277344e-05 nb_pixel_total : 522 time to create 1 rle with old method : 0.0007181167602539062 length of segment : 35 time for calcul the mask position with numpy : 5.745887756347656e-05 nb_pixel_total : 1627 time to create 1 rle with old method : 0.0020296573638916016 length of segment : 55 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 629 time to create 1 rle with old method : 0.0013439655303955078 length of segment : 29 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.00025844573974609375 length of segment : 23 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 285 time to create 1 rle with old method : 0.0004413127899169922 length of segment : 18 time for calcul the mask position with numpy : 3.147125244140625e-05 nb_pixel_total : 298 time to create 1 rle with old method : 0.00037288665771484375 length of segment : 23 time for calcul the mask position with numpy : 3.4332275390625e-05 nb_pixel_total : 193 time to create 1 rle with old method : 0.00028324127197265625 length of segment : 42 time for calcul the mask position with numpy : 4.6253204345703125e-05 nb_pixel_total : 957 time to create 1 rle with old method : 0.0011916160583496094 length of segment : 39 time for calcul the mask position with numpy : 4.601478576660156e-05 nb_pixel_total : 950 time to create 1 rle with old method : 0.001224517822265625 length of segment : 40 time for calcul the mask position with numpy : 5.269050598144531e-05 nb_pixel_total : 1224 time to create 1 rle with old method : 0.0015358924865722656 length of segment : 51 time for calcul the mask position with numpy : 2.9325485229492188e-05 nb_pixel_total : 137 time to create 1 rle with old method : 0.0002079010009765625 length of segment : 13 time for calcul the mask position with numpy : 4.792213439941406e-05 nb_pixel_total : 735 time to create 1 rle with old method : 0.0009491443634033203 length of segment : 61 time for calcul the mask position with numpy : 3.528594970703125e-05 nb_pixel_total : 446 time to create 1 rle with old method : 0.0006182193756103516 length of segment : 22 time for calcul the mask position with numpy : 3.0994415283203125e-05 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005590915679931641 length of segment : 23 time for calcul the mask position with numpy : 3.5762786865234375e-05 nb_pixel_total : 387 time to create 1 rle with old method : 0.0004923343658447266 length of segment : 31 time for calcul the mask position with numpy : 4.7206878662109375e-05 nb_pixel_total : 475 time to create 1 rle with old method : 0.0005960464477539062 length of segment : 44 time for calcul the mask position with numpy : 4.267692565917969e-05 nb_pixel_total : 226 time to create 1 rle with old method : 0.0003402233123779297 length of segment : 34 time for calcul the mask position with numpy : 4.673004150390625e-05 nb_pixel_total : 1250 time to create 1 rle with old method : 0.0015218257904052734 length of segment : 51 time for calcul the mask position with numpy : 0.0001373291015625 nb_pixel_total : 2070 time to create 1 rle with old method : 0.0026993751525878906 length of segment : 109 time for calcul the mask position with numpy : 6.747245788574219e-05 nb_pixel_total : 893 time to create 1 rle with old method : 0.0011906623840332031 length of segment : 37 time for calcul the mask position with numpy : 5.6743621826171875e-05 nb_pixel_total : 1114 time to create 1 rle with old method : 0.0014691352844238281 length of segment : 47 time for calcul the mask position with numpy : 6.508827209472656e-05 nb_pixel_total : 1621 time to create 1 rle with old method : 0.0020172595977783203 length of segment : 53 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 158 time to create 1 rle with old method : 0.00028586387634277344 length of segment : 18 time for calcul the mask position with numpy : 3.600120544433594e-05 nb_pixel_total : 438 time to create 1 rle with old method : 0.0005943775177001953 length of segment : 30 time for calcul the mask position with numpy : 4.696846008300781e-05 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005910396575927734 length of segment : 28 time for calcul the mask position with numpy : 3.9577484130859375e-05 nb_pixel_total : 326 time to create 1 rle with old method : 0.0004317760467529297 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: 149.19766 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 14 time for calcul the mask position with numpy : 4.76837158203125e-05 nb_pixel_total : 619 time to create 1 rle with old method : 0.0008249282836914062 length of segment : 41 time for calcul the mask position with numpy : 6.008148193359375e-05 nb_pixel_total : 1201 time to create 1 rle with old method : 0.0015306472778320312 length of segment : 63 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 538 time to create 1 rle with old method : 0.0008051395416259766 length of segment : 21 time for calcul the mask position with numpy : 5.4836273193359375e-05 nb_pixel_total : 1533 time to create 1 rle with old method : 0.0019500255584716797 length of segment : 44 time for calcul the mask position with numpy : 3.981590270996094e-05 nb_pixel_total : 342 time to create 1 rle with old method : 0.0004897117614746094 length of segment : 29 time for calcul the mask position with numpy : 3.0517578125e-05 nb_pixel_total : 94 time to create 1 rle with old method : 0.0001556873321533203 length of segment : 13 length of segment : 0 time for calcul the mask position with numpy : 2.9802322387695312e-05 nb_pixel_total : 148 time to create 1 rle with old method : 0.0002162456512451172 length of segment : 16 time for calcul the mask position with numpy : 4.3392181396484375e-05 nb_pixel_total : 330 time to create 1 rle with old method : 0.0005359649658203125 length of segment : 32 time for calcul the mask position with numpy : 3.7670135498046875e-05 nb_pixel_total : 257 time to create 1 rle with old method : 0.00036406517028808594 length of segment : 21 time for calcul the mask position with numpy : 9.775161743164062e-05 nb_pixel_total : 1741 time to create 1 rle with old method : 0.002190113067626953 length of segment : 92 time for calcul the mask position with numpy : 4.887580871582031e-05 nb_pixel_total : 340 time to create 1 rle with old method : 0.0004773139953613281 length of segment : 26 time for calcul the mask position with numpy : 4.100799560546875e-05 nb_pixel_total : 429 time to create 1 rle with old method : 0.0006184577941894531 length of segment : 26 time for calcul the mask position with numpy : 9.799003601074219e-05 nb_pixel_total : 3965 time to create 1 rle with old method : 0.005032062530517578 length of segment : 118 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: 134.16250 image_metas shape: (1, 17) min: 0.00000 max: 640.00000 nb d'objets trouves : 6 time for calcul the mask position with numpy : 4.291534423828125e-05 nb_pixel_total : 553 time to create 1 rle with old method : 0.0007219314575195312 length of segment : 43 time for calcul the mask position with numpy : 4.315376281738281e-05 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001900196075439453 length of segment : 12 time for calcul the mask position with numpy : 3.361701965332031e-05 nb_pixel_total : 170 time to create 1 rle with old method : 0.0002911090850830078 length of segment : 11 time for calcul the mask position with numpy : 3.409385681152344e-05 nb_pixel_total : 124 time to create 1 rle with old method : 0.00017762184143066406 length of segment : 25 time for calcul the mask position with numpy : 0.00012636184692382812 nb_pixel_total : 2271 time to create 1 rle with old method : 0.0033795833587646484 length of segment : 193 time for calcul the mask position with numpy : 4.1961669921875e-05 nb_pixel_total : 184 time to create 1 rle with old method : 0.0003161430358886719 length of segment : 13 Detection mask done ! Trying to reset tf kernel 2482351 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 9581 tf kernel not reseted sub process len(results) : 3143 len(list_Values) 3143 None max_time_sub_proc : 3600 parent process len(results) : 0 len(list_Values) 3143 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 : 10774 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 3143 chid ids of type : 4228 Number RLEs to save : 148399 save missing photos in datou_result : time spend for datou_step_exec : 90.72126579284668 time spend to save output : 9.555238962173462 total time spend for step 1 : 100.27650475502014 step2:brightness Wed Feb 12 08:52:29 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 treat image : temp/1739346645_2481130_1332937882_e944c0de912fc856fdb408c569545bd7.jpg treat image : temp/1739346645_2481130_1332937879_c8037c3ba633c17b7808b44bcf50f78d.jpg treat image : temp/1739346645_2481130_1332937852_09c7a4e40b802f7752d88bc39b006598.jpg treat image : temp/1739346645_2481130_1332937850_e985596b0c43080d57eb9b49eca8d2ff.jpg treat image : temp/1739346645_2481130_1332937845_97a33840da1030bab2fe6ba7df69a0ec.jpg treat image : temp/1739346645_2481130_1332937839_4b2927d31b67d7cf2f7f4f5de31a3a86.jpg treat image : temp/1739346645_2481130_1332937831_1c0d68913d5ed15f8a6970841b1156c4.jpg treat image : temp/1739346645_2481130_1332937826_0b34695840b1b39a69e80b4b15646a44.jpg treat image : temp/1739346645_2481130_1332937795_c5d3616cc6ba60daf9855db191f57e55.jpg treat image : temp/1739346645_2481130_1332937790_f9a4236afb4b016ee1d5891dd2b09f30.jpg treat image : temp/1739346645_2481130_1332937783_d310c4116e6103f2fe976ffaea03ea87.jpg treat image : temp/1739346645_2481130_1332937779_0b68e322ea90cb314371202b7dfd001c.jpg treat image : temp/1739346645_2481130_1332937775_812747ff4004414a3617d3808a244bc2.jpg treat image : temp/1739346645_2481130_1332937771_a632b60e9a4e8fb48ef0d8ed832a24b4.jpg treat image : temp/1739346645_2481130_1332937745_72d90ae0f3c28fa01b632f1d96e0f732.jpg treat image : temp/1739346645_2481130_1332937740_9e12d0ffb7ebbd813701d45da213595a.jpg treat image : temp/1739346645_2481130_1332937735_e6c379996fd124a98e58beb472ea0a20.jpg treat image : temp/1739346645_2481130_1332937728_93a96dbafd0ff2c267b47adea8414e1d.jpg treat image : temp/1739346645_2481130_1332937720_382575f404d47269f22011d7799e8b81.jpg treat image : temp/1739346645_2481130_1332937713_6ab3b5f83a6fe4731bba5a91adccae19.jpg treat image : temp/1739346645_2481130_1332937694_5134b9e16e22d2f54fee0ea441a513d2.jpg 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 : 0.01198434829711914 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 : 0.014319896697998047 save missing photos in datou_result : time spend for datou_step_exec : 5.702317953109741 time spend to save output : 0.0314788818359375 total time spend for step 2 : 5.733796834945679 step3:blur_detection Wed Feb 12 08:52:34 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 methode: ratio et variance treat image : temp/1739346645_2481130_1332937882_e944c0de912fc856fdb408c569545bd7.jpg resize: (1080, 1920) 1332937882 -7.779968145687247 treat image : temp/1739346645_2481130_1332937879_c8037c3ba633c17b7808b44bcf50f78d.jpg resize: (1080, 1920) 1332937879 -7.775083382821967 treat image : temp/1739346645_2481130_1332937852_09c7a4e40b802f7752d88bc39b006598.jpg resize: (1080, 1920) 1332937852 -7.76211971613327 treat image : temp/1739346645_2481130_1332937850_e985596b0c43080d57eb9b49eca8d2ff.jpg resize: (1080, 1920) 1332937850 -7.753704476939944 treat image : temp/1739346645_2481130_1332937845_97a33840da1030bab2fe6ba7df69a0ec.jpg resize: (1080, 1920) 1332937845 -7.757662503865591 treat image : temp/1739346645_2481130_1332937839_4b2927d31b67d7cf2f7f4f5de31a3a86.jpg resize: (1080, 1920) 1332937839 -7.75355035884945 treat image : temp/1739346645_2481130_1332937831_1c0d68913d5ed15f8a6970841b1156c4.jpg resize: (1080, 1920) 1332937831 -7.749716586537498 treat image : temp/1739346645_2481130_1332937826_0b34695840b1b39a69e80b4b15646a44.jpg resize: (1080, 1920) 1332937826 -7.735172358592695 treat image : temp/1739346645_2481130_1332937795_c5d3616cc6ba60daf9855db191f57e55.jpg resize: (1080, 1920) 1332937795 -7.723696918767472 treat image : temp/1739346645_2481130_1332937790_f9a4236afb4b016ee1d5891dd2b09f30.jpg resize: (1080, 1920) 1332937790 -7.741549851799454 treat image : temp/1739346645_2481130_1332937783_d310c4116e6103f2fe976ffaea03ea87.jpg resize: (1080, 1920) 1332937783 -7.736430657661612 treat image : temp/1739346645_2481130_1332937779_0b68e322ea90cb314371202b7dfd001c.jpg resize: (1080, 1920) 1332937779 -7.770792002591815 treat image : temp/1739346645_2481130_1332937775_812747ff4004414a3617d3808a244bc2.jpg resize: (1080, 1920) 1332937775 -7.843782170196549 treat image : temp/1739346645_2481130_1332937771_a632b60e9a4e8fb48ef0d8ed832a24b4.jpg resize: (1080, 1920) 1332937771 -7.8468820751613455 treat image : temp/1739346645_2481130_1332937745_72d90ae0f3c28fa01b632f1d96e0f732.jpg resize: (1080, 1920) 1332937745 -7.827786240339442 treat image : temp/1739346645_2481130_1332937740_9e12d0ffb7ebbd813701d45da213595a.jpg resize: (1080, 1920) 1332937740 -7.830637711339109 treat image : temp/1739346645_2481130_1332937735_e6c379996fd124a98e58beb472ea0a20.jpg resize: (1080, 1920) 1332937735 -7.8365095951384705 treat image : temp/1739346645_2481130_1332937728_93a96dbafd0ff2c267b47adea8414e1d.jpg resize: (1080, 1920) 1332937728 -7.873750553107825 treat image : temp/1739346645_2481130_1332937720_382575f404d47269f22011d7799e8b81.jpg resize: (1080, 1920) 1332937720 -7.918271148765961 treat image : temp/1739346645_2481130_1332937713_6ab3b5f83a6fe4731bba5a91adccae19.jpg resize: (1080, 1920) 1332937713 -7.922149427565916 treat image : temp/1739346645_2481130_1332937694_5134b9e16e22d2f54fee0ea441a513d2.jpg resize: (1080, 1920) 1332937694 -7.92305057161556 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 : 0.012191057205200195 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 : 0.07167911529541016 save missing photos in datou_result : time spend for datou_step_exec : 19.844736576080322 time spend to save output : 0.08819985389709473 total time spend for step 3 : 19.932936429977417 step4:rle_unique_nms_with_priority Wed Feb 12 08:52:54 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 3143 chid ids of type : 4228 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 147 nb_hashtags : 7 time to prepare the origin masks : 1.184464454650879 time for calcul the mask position with numpy : 0.034879207611083984 nb_pixel_total : 1639252 time to create 1 rle with new method : 0.27406930923461914 time for calcul the mask position with numpy : 0.008559465408325195 nb_pixel_total : 56 time to create 1 rle with old method : 8.940696716308594e-05 time for calcul the mask position with numpy : 0.008539915084838867 nb_pixel_total : 1029 time to create 1 rle with old method : 0.0012595653533935547 time for calcul the mask position with numpy : 0.008488893508911133 nb_pixel_total : 226 time to create 1 rle with old method : 0.0002810955047607422 time for calcul the mask position with numpy : 0.008508443832397461 nb_pixel_total : 148 time to create 1 rle with old method : 0.0001971721649169922 time for calcul the mask position with numpy : 0.008457422256469727 nb_pixel_total : 167 time to create 1 rle with old method : 0.0002257823944091797 time for calcul the mask position with numpy : 0.00859689712524414 nb_pixel_total : 44986 time to create 1 rle with old method : 0.048941612243652344 time for calcul the mask position with numpy : 0.008615970611572266 nb_pixel_total : 2484 time to create 1 rle with old method : 0.0028781890869140625 time for calcul the mask position with numpy : 0.008566141128540039 nb_pixel_total : 487 time to create 1 rle with old method : 0.0006029605865478516 time for calcul the mask position with numpy : 0.008604764938354492 nb_pixel_total : 225 time to create 1 rle with old method : 0.0003020763397216797 time for calcul the mask position with numpy : 0.008609533309936523 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006709098815917969 time for calcul the mask position with numpy : 0.008518457412719727 nb_pixel_total : 1046 time to create 1 rle with old method : 0.0015559196472167969 time for calcul the mask position with numpy : 0.008715629577636719 nb_pixel_total : 1999 time to create 1 rle with old method : 0.0023651123046875 time for calcul the mask position with numpy : 0.008594751358032227 nb_pixel_total : 1093 time to create 1 rle with old method : 0.0012791156768798828 time for calcul the mask position with numpy : 0.008571624755859375 nb_pixel_total : 17 time to create 1 rle with old method : 6.461143493652344e-05 time for calcul the mask position with numpy : 0.008606433868408203 nb_pixel_total : 210 time to create 1 rle with old method : 0.00026416778564453125 time for calcul the mask position with numpy : 0.008491039276123047 nb_pixel_total : 1425 time to create 1 rle with old method : 0.0016777515411376953 time for calcul the mask position with numpy : 0.008369207382202148 nb_pixel_total : 3068 time to create 1 rle with old method : 0.0034017562866210938 time for calcul the mask position with numpy : 0.008520364761352539 nb_pixel_total : 1166 time to create 1 rle with old method : 0.0013616085052490234 time for calcul the mask position with numpy : 0.008501052856445312 nb_pixel_total : 2377 time to create 1 rle with old method : 0.002681732177734375 time for calcul the mask position with numpy : 0.01139068603515625 nb_pixel_total : 1473 time to create 1 rle with old method : 0.0030422210693359375 time for calcul the mask position with numpy : 0.010221242904663086 nb_pixel_total : 6075 time to create 1 rle with old method : 0.0067234039306640625 time for calcul the mask position with numpy : 0.008982658386230469 nb_pixel_total : 922 time to create 1 rle with old method : 0.0013394355773925781 time for calcul the mask position with numpy : 0.009264469146728516 nb_pixel_total : 519 time to create 1 rle with old method : 0.0005664825439453125 time for calcul the mask position with numpy : 0.00839090347290039 nb_pixel_total : 860 time to create 1 rle with old method : 0.0016183853149414062 time for calcul the mask position with numpy : 0.008342981338500977 nb_pixel_total : 1970 time to create 1 rle with old method : 0.0022525787353515625 time for calcul the mask position with numpy : 0.008376121520996094 nb_pixel_total : 192 time to create 1 rle with old method : 0.00025653839111328125 time for calcul the mask position with numpy : 0.008797645568847656 nb_pixel_total : 152697 time to create 1 rle with new method : 0.058867692947387695 time for calcul the mask position with numpy : 0.008428573608398438 nb_pixel_total : 1363 time to create 1 rle with old method : 0.0015721321105957031 time for calcul the mask position with numpy : 0.008358001708984375 nb_pixel_total : 12152 time to create 1 rle with old method : 0.013209819793701172 time for calcul the mask position with numpy : 0.008385181427001953 nb_pixel_total : 167 time to create 1 rle with old method : 0.0001964569091796875 time for calcul the mask position with numpy : 0.008312463760375977 nb_pixel_total : 9240 time to create 1 rle with old method : 0.010477542877197266 time for calcul the mask position with numpy : 0.008398294448852539 nb_pixel_total : 62 time to create 1 rle with old method : 7.891654968261719e-05 time for calcul the mask position with numpy : 0.00833749771118164 nb_pixel_total : 1817 time to create 1 rle with old method : 0.0020627975463867188 time for calcul the mask position with numpy : 0.008403539657592773 nb_pixel_total : 18 time to create 1 rle with old method : 3.504753112792969e-05 time for calcul the mask position with numpy : 0.00837254524230957 nb_pixel_total : 188 time to create 1 rle with old method : 0.00021791458129882812 time for calcul the mask position with numpy : 0.008555412292480469 nb_pixel_total : 370 time to create 1 rle with old method : 0.00044465065002441406 time for calcul the mask position with numpy : 0.00865626335144043 nb_pixel_total : 1055 time to create 1 rle with old method : 0.0012197494506835938 time for calcul the mask position with numpy : 0.008612871170043945 nb_pixel_total : 726 time to create 1 rle with old method : 0.0009601116180419922 time for calcul the mask position with numpy : 0.008551359176635742 nb_pixel_total : 1407 time to create 1 rle with old method : 0.0016055107116699219 time for calcul the mask position with numpy : 0.008555889129638672 nb_pixel_total : 851 time to create 1 rle with old method : 0.0010118484497070312 time for calcul the mask position with numpy : 0.008596181869506836 nb_pixel_total : 194 time to create 1 rle with old method : 0.000247955322265625 time for calcul the mask position with numpy : 0.008530855178833008 nb_pixel_total : 1342 time to create 1 rle with old method : 0.0015523433685302734 time for calcul the mask position with numpy : 0.008505582809448242 nb_pixel_total : 225 time to create 1 rle with old method : 0.00029730796813964844 time for calcul the mask position with numpy : 0.008426666259765625 nb_pixel_total : 478 time to create 1 rle with old method : 0.0005333423614501953 time for calcul the mask position with numpy : 0.0084381103515625 nb_pixel_total : 168 time to create 1 rle with old method : 0.00020360946655273438 time for calcul the mask position with numpy : 0.0083770751953125 nb_pixel_total : 9619 time to create 1 rle with old method : 0.010782718658447266 time for calcul the mask position with numpy : 0.008386611938476562 nb_pixel_total : 90 time to create 1 rle with old method : 0.00010967254638671875 time for calcul the mask position with numpy : 0.008380889892578125 nb_pixel_total : 991 time to create 1 rle with old method : 0.0011265277862548828 time for calcul the mask position with numpy : 0.008419275283813477 nb_pixel_total : 110 time to create 1 rle with old method : 0.00014781951904296875 time for calcul the mask position with numpy : 0.00830841064453125 nb_pixel_total : 1467 time to create 1 rle with old method : 0.0016872882843017578 time for calcul the mask position with numpy : 0.00834798812866211 nb_pixel_total : 447 time to create 1 rle with old method : 0.0005011558532714844 time for calcul the mask position with numpy : 0.008361101150512695 nb_pixel_total : 497 time to create 1 rle with old method : 0.0005848407745361328 time for calcul the mask position with numpy : 0.008366584777832031 nb_pixel_total : 217 time to create 1 rle with old method : 0.0002734661102294922 time for calcul the mask position with numpy : 0.00838470458984375 nb_pixel_total : 118 time to create 1 rle with old method : 0.00014281272888183594 time for calcul the mask position with numpy : 0.008352994918823242 nb_pixel_total : 510 time to create 1 rle with old method : 0.0006043910980224609 time for calcul the mask position with numpy : 0.00838160514831543 nb_pixel_total : 382 time to create 1 rle with old method : 0.00044226646423339844 time for calcul the mask position with numpy : 0.008353710174560547 nb_pixel_total : 824 time to create 1 rle with old method : 0.0009648799896240234 time for calcul the mask position with numpy : 0.008394241333007812 nb_pixel_total : 1318 time to create 1 rle with old method : 0.001523733139038086 time for calcul the mask position with numpy : 0.00835561752319336 nb_pixel_total : 102 time to create 1 rle with old method : 0.0001270771026611328 time for calcul the mask position with numpy : 0.00838923454284668 nb_pixel_total : 1114 time to create 1 rle with old method : 0.001295328140258789 time for calcul the mask position with numpy : 0.008391618728637695 nb_pixel_total : 456 time to create 1 rle with old method : 0.0005357265472412109 time for calcul the mask position with numpy : 0.008380889892578125 nb_pixel_total : 1531 time to create 1 rle with old method : 0.0017406940460205078 time for calcul the mask position with numpy : 0.008369922637939453 nb_pixel_total : 220 time to create 1 rle with old method : 0.0002593994140625 time for calcul the mask position with numpy : 0.008354663848876953 nb_pixel_total : 63 time to create 1 rle with old method : 8.320808410644531e-05 time for calcul the mask position with numpy : 0.008372306823730469 nb_pixel_total : 434 time to create 1 rle with old method : 0.0005121231079101562 time for calcul the mask position with numpy : 0.0083770751953125 nb_pixel_total : 283 time to create 1 rle with old method : 0.0003376007080078125 time for calcul the mask position with numpy : 0.008356571197509766 nb_pixel_total : 404 time to create 1 rle with old method : 0.0004749298095703125 time for calcul the mask position with numpy : 0.008352518081665039 nb_pixel_total : 467 time to create 1 rle with old method : 0.0005404949188232422 time for calcul the mask position with numpy : 0.00841212272644043 nb_pixel_total : 525 time to create 1 rle with old method : 0.0006124973297119141 time for calcul the mask position with numpy : 0.008634805679321289 nb_pixel_total : 106541 time to create 1 rle with old method : 0.11618542671203613 time for calcul the mask position with numpy : 0.008394002914428711 nb_pixel_total : 446 time to create 1 rle with old method : 0.0005147457122802734 time for calcul the mask position with numpy : 0.008376121520996094 nb_pixel_total : 122 time to create 1 rle with old method : 0.0002067089080810547 time for calcul the mask position with numpy : 0.00838160514831543 nb_pixel_total : 154 time to create 1 rle with old method : 0.00018715858459472656 time for calcul the mask position with numpy : 0.008361339569091797 nb_pixel_total : 176 time to create 1 rle with old method : 0.00020194053649902344 time for calcul the mask position with numpy : 0.008399724960327148 nb_pixel_total : 305 time to create 1 rle with old method : 0.00035953521728515625 time for calcul the mask position with numpy : 0.008398771286010742 nb_pixel_total : 349 time to create 1 rle with old method : 0.00040411949157714844 time for calcul the mask position with numpy : 0.008396625518798828 nb_pixel_total : 1994 time to create 1 rle with old method : 0.002271413803100586 time for calcul the mask position with numpy : 0.008373022079467773 nb_pixel_total : 495 time to create 1 rle with old method : 0.00054168701171875 time for calcul the mask position with numpy : 0.008374452590942383 nb_pixel_total : 350 time to create 1 rle with old method : 0.0004127025604248047 time for calcul the mask position with numpy : 0.008369207382202148 nb_pixel_total : 29 time to create 1 rle with old method : 5.53131103515625e-05 time for calcul the mask position with numpy : 0.008370161056518555 nb_pixel_total : 377 time to create 1 rle with old method : 0.00042557716369628906 time for calcul the mask position with numpy : 0.008357524871826172 nb_pixel_total : 199 time to create 1 rle with old method : 0.0002892017364501953 time for calcul the mask position with numpy : 0.008346796035766602 nb_pixel_total : 371 time to create 1 rle with old method : 0.0004322528839111328 time for calcul the mask position with numpy : 0.008360624313354492 nb_pixel_total : 107 time to create 1 rle with old method : 0.00013065338134765625 time for calcul the mask position with numpy : 0.00837850570678711 nb_pixel_total : 224 time to create 1 rle with old method : 0.0002675056457519531 time for calcul the mask position with numpy : 0.008374691009521484 nb_pixel_total : 200 time to create 1 rle with old method : 0.00022292137145996094 time for calcul the mask position with numpy : 0.008382320404052734 nb_pixel_total : 1008 time to create 1 rle with old method : 0.0011699199676513672 time for calcul the mask position with numpy : 0.008394718170166016 nb_pixel_total : 488 time to create 1 rle with old method : 0.0005800724029541016 time for calcul the mask position with numpy : 0.008382558822631836 nb_pixel_total : 160 time to create 1 rle with old method : 0.0001938343048095703 time for calcul the mask position with numpy : 0.008361577987670898 nb_pixel_total : 477 time to create 1 rle with old method : 0.0005309581756591797 time for calcul the mask position with numpy : 0.008487939834594727 nb_pixel_total : 12 time to create 1 rle with old method : 6.842613220214844e-05 time for calcul the mask position with numpy : 0.009784698486328125 nb_pixel_total : 584 time to create 1 rle with old method : 0.0006840229034423828 time for calcul the mask position with numpy : 0.00838923454284668 nb_pixel_total : 516 time to create 1 rle with old method : 0.0006237030029296875 time for calcul the mask position with numpy : 0.009703397750854492 nb_pixel_total : 711 time to create 1 rle with old method : 0.0008325576782226562 time for calcul the mask position with numpy : 0.008574247360229492 nb_pixel_total : 515 time to create 1 rle with old method : 0.0006015300750732422 time for calcul the mask position with numpy : 0.008380889892578125 nb_pixel_total : 33 time to create 1 rle with old method : 7.319450378417969e-05 time for calcul the mask position with numpy : 0.008354663848876953 nb_pixel_total : 75 time to create 1 rle with old method : 0.00012540817260742188 time for calcul the mask position with numpy : 0.008369207382202148 nb_pixel_total : 206 time to create 1 rle with old method : 0.0002582073211669922 time for calcul the mask position with numpy : 0.008356571197509766 nb_pixel_total : 901 time to create 1 rle with old method : 0.0010542869567871094 time for calcul the mask position with numpy : 0.008375883102416992 nb_pixel_total : 127 time to create 1 rle with old method : 0.0001556873321533203 time for calcul the mask position with numpy : 0.00837850570678711 nb_pixel_total : 453 time to create 1 rle with old method : 0.0005252361297607422 time for calcul the mask position with numpy : 0.008343935012817383 nb_pixel_total : 1657 time to create 1 rle with old method : 0.001905679702758789 time for calcul the mask position with numpy : 0.008357048034667969 nb_pixel_total : 320 time to create 1 rle with old method : 0.0003750324249267578 time for calcul the mask position with numpy : 0.008374452590942383 nb_pixel_total : 476 time to create 1 rle with old method : 0.0007314682006835938 time for calcul the mask position with numpy : 0.008350133895874023 nb_pixel_total : 5904 time to create 1 rle with old method : 0.006346940994262695 time for calcul the mask position with numpy : 0.008366823196411133 nb_pixel_total : 899 time to create 1 rle with old method : 0.0010442733764648438 time for calcul the mask position with numpy : 0.00833582878112793 nb_pixel_total : 4733 time to create 1 rle with old method : 0.0052738189697265625 time for calcul the mask position with numpy : 0.008349895477294922 nb_pixel_total : 94 time to create 1 rle with old method : 0.0001480579376220703 time for calcul the mask position with numpy : 0.008356094360351562 nb_pixel_total : 1454 time to create 1 rle with old method : 0.0016205310821533203 time for calcul the mask position with numpy : 0.008385419845581055 nb_pixel_total : 117 time to create 1 rle with old method : 0.00014328956604003906 time for calcul the mask position with numpy : 0.008403539657592773 nb_pixel_total : 152 time to create 1 rle with old method : 0.00019097328186035156 time for calcul the mask position with numpy : 0.008386850357055664 nb_pixel_total : 740 time to create 1 rle with old method : 0.0008618831634521484 time for calcul the mask position with numpy : 0.008353948593139648 nb_pixel_total : 704 time to create 1 rle with old method : 0.0008301734924316406 time for calcul the mask position with numpy : 0.008321523666381836 nb_pixel_total : 562 time to create 1 rle with old method : 0.0006706714630126953 time for calcul the mask position with numpy : 0.008385419845581055 nb_pixel_total : 114 time to create 1 rle with old method : 0.00013947486877441406 time for calcul the mask position with numpy : 0.008344411849975586 nb_pixel_total : 290 time to create 1 rle with old method : 0.0003304481506347656 time for calcul the mask position with numpy : 0.008368253707885742 nb_pixel_total : 239 time to create 1 rle with old method : 0.0002760887145996094 time for calcul the mask position with numpy : 0.008358478546142578 nb_pixel_total : 737 time to create 1 rle with old method : 0.0008487701416015625 time for calcul the mask position with numpy : 0.008351802825927734 nb_pixel_total : 44 time to create 1 rle with old method : 6.961822509765625e-05 time for calcul the mask position with numpy : 0.008340597152709961 nb_pixel_total : 347 time to create 1 rle with old method : 0.00040459632873535156 time for calcul the mask position with numpy : 0.008376359939575195 nb_pixel_total : 69 time to create 1 rle with old method : 7.987022399902344e-05 time for calcul the mask position with numpy : 0.008361577987670898 nb_pixel_total : 168 time to create 1 rle with old method : 0.00019407272338867188 time for calcul the mask position with numpy : 0.008384227752685547 nb_pixel_total : 5 time to create 1 rle with old method : 2.0503997802734375e-05 time for calcul the mask position with numpy : 0.00834798812866211 nb_pixel_total : 1004 time to create 1 rle with old method : 0.0011143684387207031 time for calcul the mask position with numpy : 0.008382558822631836 nb_pixel_total : 152 time to create 1 rle with old method : 0.0001850128173828125 time for calcul the mask position with numpy : 0.008348226547241211 nb_pixel_total : 28 time to create 1 rle with old method : 5.936622619628906e-05 time for calcul the mask position with numpy : 0.008215188980102539 nb_pixel_total : 446 time to create 1 rle with old method : 0.0005240440368652344 time for calcul the mask position with numpy : 0.008146524429321289 nb_pixel_total : 72 time to create 1 rle with old method : 9.942054748535156e-05 time for calcul the mask position with numpy : 0.00821542739868164 nb_pixel_total : 100 time to create 1 rle with old method : 0.000133514404296875 time for calcul the mask position with numpy : 0.008337974548339844 nb_pixel_total : 350 time to create 1 rle with old method : 0.00040435791015625 time for calcul the mask position with numpy : 0.008111953735351562 nb_pixel_total : 127 time to create 1 rle with old method : 0.00013899803161621094 time for calcul the mask position with numpy : 0.008047819137573242 nb_pixel_total : 1116 time to create 1 rle with old method : 0.001293182373046875 time for calcul the mask position with numpy : 0.007947444915771484 nb_pixel_total : 555 time to create 1 rle with old method : 0.0006163120269775391 time for calcul the mask position with numpy : 0.0079803466796875 nb_pixel_total : 898 time to create 1 rle with old method : 0.0010440349578857422 time for calcul the mask position with numpy : 0.007978439331054688 nb_pixel_total : 294 time to create 1 rle with old method : 0.0003299713134765625 time for calcul the mask position with numpy : 0.007972240447998047 nb_pixel_total : 1877 time to create 1 rle with old method : 0.002027750015258789 time for calcul the mask position with numpy : 0.007942914962768555 nb_pixel_total : 28 time to create 1 rle with old method : 4.982948303222656e-05 time for calcul the mask position with numpy : 0.008175134658813477 nb_pixel_total : 3269 time to create 1 rle with old method : 0.0036487579345703125 time for calcul the mask position with numpy : 0.008213043212890625 nb_pixel_total : 103 time to create 1 rle with old method : 0.00011539459228515625 time for calcul the mask position with numpy : 0.008141040802001953 nb_pixel_total : 657 time to create 1 rle with old method : 0.0007786750793457031 time for calcul the mask position with numpy : 0.00798177719116211 nb_pixel_total : 171 time to create 1 rle with old method : 0.00022125244140625 time for calcul the mask position with numpy : 0.008322000503540039 nb_pixel_total : 146 time to create 1 rle with old method : 0.00017833709716796875 time for calcul the mask position with numpy : 0.00792837142944336 nb_pixel_total : 1099 time to create 1 rle with old method : 0.0012171268463134766 time for calcul the mask position with numpy : 0.008260488510131836 nb_pixel_total : 367 time to create 1 rle with old method : 0.00042700767517089844 time for calcul the mask position with numpy : 0.008208990097045898 nb_pixel_total : 935 time to create 1 rle with old method : 0.0010199546813964844 time for calcul the mask position with numpy : 0.008086681365966797 nb_pixel_total : 475 time to create 1 rle with old method : 0.0005230903625488281 time for calcul the mask position with numpy : 0.007967948913574219 nb_pixel_total : 104 time to create 1 rle with old method : 0.0001304149627685547 create new chi : 1.941145896911621 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 : 0.006684303283691406 batch 1 Loaded 148 chid ids of type : 4230 Number RLEs to save : 13928 TO DO : save crop sub photo not yet done ! save time : 1.4057116508483887 nb_obj : 148 nb_hashtags : 7 time to prepare the origin masks : 2.3272287845611572 time for calcul the mask position with numpy : 0.5022850036621094 nb_pixel_total : 1827191 time to create 1 rle with new method : 0.08254551887512207 time for calcul the mask position with numpy : 0.00979471206665039 nb_pixel_total : 57 time to create 1 rle with old method : 9.1552734375e-05 time for calcul the mask position with numpy : 0.00609135627746582 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002646446228027344 time for calcul the mask position with numpy : 0.006016969680786133 nb_pixel_total : 218 time to create 1 rle with old method : 0.00027179718017578125 time for calcul the mask position with numpy : 0.00811004638671875 nb_pixel_total : 166 time to create 1 rle with old method : 0.0002143383026123047 time for calcul the mask position with numpy : 0.008371591567993164 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002472400665283203 time for calcul the mask position with numpy : 0.008733272552490234 nb_pixel_total : 2564 time to create 1 rle with old method : 0.002971649169921875 time for calcul the mask position with numpy : 0.0098114013671875 nb_pixel_total : 78 time to create 1 rle with old method : 0.0001316070556640625 time for calcul the mask position with numpy : 0.009855270385742188 nb_pixel_total : 2032 time to create 1 rle with old method : 0.002366781234741211 time for calcul the mask position with numpy : 0.009932994842529297 nb_pixel_total : 137 time to create 1 rle with old method : 0.00021386146545410156 time for calcul the mask position with numpy : 0.009256601333618164 nb_pixel_total : 879 time to create 1 rle with old method : 0.0010492801666259766 time for calcul the mask position with numpy : 0.009592294692993164 nb_pixel_total : 36 time to create 1 rle with old method : 8.702278137207031e-05 time for calcul the mask position with numpy : 0.006026029586791992 nb_pixel_total : 1420 time to create 1 rle with old method : 0.0016989707946777344 time for calcul the mask position with numpy : 0.00601959228515625 nb_pixel_total : 4169 time to create 1 rle with old method : 0.0045070648193359375 time for calcul the mask position with numpy : 0.006104707717895508 nb_pixel_total : 2916 time to create 1 rle with old method : 0.003357410430908203 time for calcul the mask position with numpy : 0.00600743293762207 nb_pixel_total : 953 time to create 1 rle with old method : 0.0011017322540283203 time for calcul the mask position with numpy : 0.006035804748535156 nb_pixel_total : 1909 time to create 1 rle with old method : 0.002164602279663086 time for calcul the mask position with numpy : 0.006178379058837891 nb_pixel_total : 510 time to create 1 rle with old method : 0.0006628036499023438 time for calcul the mask position with numpy : 0.006006479263305664 nb_pixel_total : 6310 time to create 1 rle with old method : 0.007283687591552734 time for calcul the mask position with numpy : 0.009881973266601562 nb_pixel_total : 727 time to create 1 rle with old method : 0.0008597373962402344 time for calcul the mask position with numpy : 0.00974273681640625 nb_pixel_total : 506 time to create 1 rle with old method : 0.0005986690521240234 time for calcul the mask position with numpy : 0.009060382843017578 nb_pixel_total : 942 time to create 1 rle with old method : 0.0011096000671386719 time for calcul the mask position with numpy : 0.008397340774536133 nb_pixel_total : 2237 time to create 1 rle with old method : 0.002471923828125 time for calcul the mask position with numpy : 0.009804964065551758 nb_pixel_total : 61 time to create 1 rle with old method : 0.0001246929168701172 time for calcul the mask position with numpy : 0.009867668151855469 nb_pixel_total : 3974 time to create 1 rle with old method : 0.004588603973388672 time for calcul the mask position with numpy : 0.009096622467041016 nb_pixel_total : 1907 time to create 1 rle with old method : 0.0030732154846191406 time for calcul the mask position with numpy : 0.01074671745300293 nb_pixel_total : 12567 time to create 1 rle with old method : 0.013991355895996094 time for calcul the mask position with numpy : 0.012984275817871094 nb_pixel_total : 15891 time to create 1 rle with old method : 0.018194198608398438 time for calcul the mask position with numpy : 0.009890317916870117 nb_pixel_total : 2501 time to create 1 rle with old method : 0.0035352706909179688 time for calcul the mask position with numpy : 0.010584354400634766 nb_pixel_total : 850 time to create 1 rle with old method : 0.000989675521850586 time for calcul the mask position with numpy : 0.010019540786743164 nb_pixel_total : 187 time to create 1 rle with old method : 0.00022363662719726562 time for calcul the mask position with numpy : 0.009402990341186523 nb_pixel_total : 339 time to create 1 rle with old method : 0.0003962516784667969 time for calcul the mask position with numpy : 0.009725332260131836 nb_pixel_total : 659 time to create 1 rle with old method : 0.0007801055908203125 time for calcul the mask position with numpy : 0.009822368621826172 nb_pixel_total : 792 time to create 1 rle with old method : 0.0009214878082275391 time for calcul the mask position with numpy : 0.010189294815063477 nb_pixel_total : 1377 time to create 1 rle with old method : 0.0015540122985839844 time for calcul the mask position with numpy : 0.009968042373657227 nb_pixel_total : 789 time to create 1 rle with old method : 0.0009279251098632812 time for calcul the mask position with numpy : 0.010236501693725586 nb_pixel_total : 1322 time to create 1 rle with old method : 0.0015039443969726562 time for calcul the mask position with numpy : 0.010007143020629883 nb_pixel_total : 141 time to create 1 rle with old method : 0.0001723766326904297 time for calcul the mask position with numpy : 0.009861469268798828 nb_pixel_total : 716 time to create 1 rle with old method : 0.0008463859558105469 time for calcul the mask position with numpy : 0.0099639892578125 nb_pixel_total : 206 time to create 1 rle with old method : 0.0002651214599609375 time for calcul the mask position with numpy : 0.009899377822875977 nb_pixel_total : 183 time to create 1 rle with old method : 0.0002682209014892578 time for calcul the mask position with numpy : 0.009676456451416016 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002028942108154297 time for calcul the mask position with numpy : 0.009592771530151367 nb_pixel_total : 2652 time to create 1 rle with old method : 0.002889394760131836 time for calcul the mask position with numpy : 0.009714365005493164 nb_pixel_total : 86 time to create 1 rle with old method : 0.00012230873107910156 time for calcul the mask position with numpy : 0.01002812385559082 nb_pixel_total : 1867 time to create 1 rle with old method : 0.002194643020629883 time for calcul the mask position with numpy : 0.009950637817382812 nb_pixel_total : 21 time to create 1 rle with old method : 7.152557373046875e-05 time for calcul the mask position with numpy : 0.009998798370361328 nb_pixel_total : 1091 time to create 1 rle with old method : 0.0012881755828857422 time for calcul the mask position with numpy : 0.009976625442504883 nb_pixel_total : 527 time to create 1 rle with old method : 0.0006868839263916016 time for calcul the mask position with numpy : 0.009690999984741211 nb_pixel_total : 465 time to create 1 rle with old method : 0.0005843639373779297 time for calcul the mask position with numpy : 0.006129026412963867 nb_pixel_total : 533 time to create 1 rle with old method : 0.0006444454193115234 time for calcul the mask position with numpy : 0.006318807601928711 nb_pixel_total : 196 time to create 1 rle with old method : 0.00023937225341796875 time for calcul the mask position with numpy : 0.006170749664306641 nb_pixel_total : 411 time to create 1 rle with old method : 0.0005278587341308594 time for calcul the mask position with numpy : 0.006182432174682617 nb_pixel_total : 515 time to create 1 rle with old method : 0.0006041526794433594 time for calcul the mask position with numpy : 0.006136417388916016 nb_pixel_total : 734 time to create 1 rle with old method : 0.000873565673828125 time for calcul the mask position with numpy : 0.006329774856567383 nb_pixel_total : 1623 time to create 1 rle with old method : 0.0019116401672363281 time for calcul the mask position with numpy : 0.007195949554443359 nb_pixel_total : 111 time to create 1 rle with old method : 0.00015473365783691406 time for calcul the mask position with numpy : 0.006498575210571289 nb_pixel_total : 358 time to create 1 rle with old method : 0.0004467964172363281 time for calcul the mask position with numpy : 0.006256580352783203 nb_pixel_total : 1361 time to create 1 rle with old method : 0.0015459060668945312 time for calcul the mask position with numpy : 0.0061838626861572266 nb_pixel_total : 409 time to create 1 rle with old method : 0.0004906654357910156 time for calcul the mask position with numpy : 0.00594329833984375 nb_pixel_total : 1512 time to create 1 rle with old method : 0.0017511844635009766 time for calcul the mask position with numpy : 0.006119728088378906 nb_pixel_total : 580 time to create 1 rle with old method : 0.0006988048553466797 time for calcul the mask position with numpy : 0.0060613155364990234 nb_pixel_total : 232 time to create 1 rle with old method : 0.00027942657470703125 time for calcul the mask position with numpy : 0.006136417388916016 nb_pixel_total : 470 time to create 1 rle with old method : 0.0005636215209960938 time for calcul the mask position with numpy : 0.0071947574615478516 nb_pixel_total : 106448 time to create 1 rle with old method : 0.1149146556854248 time for calcul the mask position with numpy : 0.006998300552368164 nb_pixel_total : 451 time to create 1 rle with old method : 0.0005426406860351562 time for calcul the mask position with numpy : 0.006260871887207031 nb_pixel_total : 106 time to create 1 rle with old method : 0.0001480579376220703 time for calcul the mask position with numpy : 0.0065310001373291016 nb_pixel_total : 85 time to create 1 rle with old method : 0.00012683868408203125 time for calcul the mask position with numpy : 0.006301164627075195 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002257823944091797 time for calcul the mask position with numpy : 0.006310939788818359 nb_pixel_total : 172 time to create 1 rle with old method : 0.000232696533203125 time for calcul the mask position with numpy : 0.006613016128540039 nb_pixel_total : 290 time to create 1 rle with old method : 0.0003516674041748047 time for calcul the mask position with numpy : 0.006351470947265625 nb_pixel_total : 154 time to create 1 rle with old method : 0.0002040863037109375 time for calcul the mask position with numpy : 0.00635981559753418 nb_pixel_total : 19 time to create 1 rle with old method : 5.53131103515625e-05 time for calcul the mask position with numpy : 0.0066874027252197266 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004019737243652344 time for calcul the mask position with numpy : 0.006429433822631836 nb_pixel_total : 54 time to create 1 rle with old method : 8.749961853027344e-05 time for calcul the mask position with numpy : 0.006333351135253906 nb_pixel_total : 1988 time to create 1 rle with old method : 0.002332448959350586 time for calcul the mask position with numpy : 0.006350278854370117 nb_pixel_total : 16 time to create 1 rle with old method : 6.127357482910156e-05 time for calcul the mask position with numpy : 0.006300687789916992 nb_pixel_total : 370 time to create 1 rle with old method : 0.00046062469482421875 time for calcul the mask position with numpy : 0.006189584732055664 nb_pixel_total : 1732 time to create 1 rle with old method : 0.0020270347595214844 time for calcul the mask position with numpy : 0.006144523620605469 nb_pixel_total : 481 time to create 1 rle with old method : 0.0005731582641601562 time for calcul the mask position with numpy : 0.006194353103637695 nb_pixel_total : 20 time to create 1 rle with old method : 5.1021575927734375e-05 time for calcul the mask position with numpy : 0.006300926208496094 nb_pixel_total : 286 time to create 1 rle with old method : 0.00033855438232421875 time for calcul the mask position with numpy : 0.006875276565551758 nb_pixel_total : 119 time to create 1 rle with old method : 0.0001583099365234375 time for calcul the mask position with numpy : 0.006067037582397461 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001556873321533203 time for calcul the mask position with numpy : 0.006340980529785156 nb_pixel_total : 191 time to create 1 rle with old method : 0.00022673606872558594 time for calcul the mask position with numpy : 0.008681058883666992 nb_pixel_total : 631 time to create 1 rle with old method : 0.00140380859375 time for calcul the mask position with numpy : 0.00634455680847168 nb_pixel_total : 870 time to create 1 rle with old method : 0.0010495185852050781 time for calcul the mask position with numpy : 0.005998134613037109 nb_pixel_total : 164 time to create 1 rle with old method : 0.00021123886108398438 time for calcul the mask position with numpy : 0.006192922592163086 nb_pixel_total : 519 time to create 1 rle with old method : 0.0006210803985595703 time for calcul the mask position with numpy : 0.00605010986328125 nb_pixel_total : 117 time to create 1 rle with old method : 0.0001857280731201172 time for calcul the mask position with numpy : 0.00613093376159668 nb_pixel_total : 7 time to create 1 rle with old method : 4.124641418457031e-05 time for calcul the mask position with numpy : 0.006049156188964844 nb_pixel_total : 853 time to create 1 rle with old method : 0.0009799003601074219 time for calcul the mask position with numpy : 0.0061283111572265625 nb_pixel_total : 598 time to create 1 rle with old method : 0.0007159709930419922 time for calcul the mask position with numpy : 0.006323814392089844 nb_pixel_total : 485 time to create 1 rle with old method : 0.0005698204040527344 time for calcul the mask position with numpy : 0.006341695785522461 nb_pixel_total : 596 time to create 1 rle with old method : 0.0007224082946777344 time for calcul the mask position with numpy : 0.006471395492553711 nb_pixel_total : 256 time to create 1 rle with old method : 0.00030875205993652344 time for calcul the mask position with numpy : 0.006326436996459961 nb_pixel_total : 43 time to create 1 rle with old method : 9.989738464355469e-05 time for calcul the mask position with numpy : 0.007044076919555664 nb_pixel_total : 679 time to create 1 rle with old method : 0.0009982585906982422 time for calcul the mask position with numpy : 0.006534099578857422 nb_pixel_total : 765 time to create 1 rle with old method : 0.0008990764617919922 time for calcul the mask position with numpy : 0.006113529205322266 nb_pixel_total : 614 time to create 1 rle with old method : 0.0007340908050537109 time for calcul the mask position with numpy : 0.006289482116699219 nb_pixel_total : 468 time to create 1 rle with old method : 0.0005693435668945312 time for calcul the mask position with numpy : 0.007247447967529297 nb_pixel_total : 171 time to create 1 rle with old method : 0.00026917457580566406 time for calcul the mask position with numpy : 0.006374359130859375 nb_pixel_total : 941 time to create 1 rle with old method : 0.0011107921600341797 time for calcul the mask position with numpy : 0.0063018798828125 nb_pixel_total : 35 time to create 1 rle with old method : 9.1552734375e-05 time for calcul the mask position with numpy : 0.006237983703613281 nb_pixel_total : 446 time to create 1 rle with old method : 0.000537872314453125 time for calcul the mask position with numpy : 0.006170749664306641 nb_pixel_total : 1529 time to create 1 rle with old method : 0.0018568038940429688 time for calcul the mask position with numpy : 0.00635218620300293 nb_pixel_total : 24 time to create 1 rle with old method : 6.842613220214844e-05 time for calcul the mask position with numpy : 0.006283283233642578 nb_pixel_total : 610 time to create 1 rle with old method : 0.0006940364837646484 time for calcul the mask position with numpy : 0.006402492523193359 nb_pixel_total : 321 time to create 1 rle with old method : 0.0003960132598876953 time for calcul the mask position with numpy : 0.006234645843505859 nb_pixel_total : 3167 time to create 1 rle with old method : 0.0034935474395751953 time for calcul the mask position with numpy : 0.006264686584472656 nb_pixel_total : 5087 time to create 1 rle with old method : 0.005756855010986328 time for calcul the mask position with numpy : 0.0062541961669921875 nb_pixel_total : 1026 time to create 1 rle with old method : 0.0011868476867675781 time for calcul the mask position with numpy : 0.006157636642456055 nb_pixel_total : 313 time to create 1 rle with old method : 0.00041365623474121094 time for calcul the mask position with numpy : 0.006041765213012695 nb_pixel_total : 1236 time to create 1 rle with old method : 0.0013570785522460938 time for calcul the mask position with numpy : 0.006038665771484375 nb_pixel_total : 127 time to create 1 rle with old method : 0.00016927719116210938 time for calcul the mask position with numpy : 0.006009817123413086 nb_pixel_total : 910 time to create 1 rle with old method : 0.0010750293731689453 time for calcul the mask position with numpy : 0.0059587955474853516 nb_pixel_total : 454 time to create 1 rle with old method : 0.0006008148193359375 time for calcul the mask position with numpy : 0.006207466125488281 nb_pixel_total : 118 time to create 1 rle with old method : 0.00016045570373535156 time for calcul the mask position with numpy : 0.006529808044433594 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016546249389648438 time for calcul the mask position with numpy : 0.006211519241333008 nb_pixel_total : 264 time to create 1 rle with old method : 0.0003204345703125 time for calcul the mask position with numpy : 0.006189584732055664 nb_pixel_total : 824 time to create 1 rle with old method : 0.0009546279907226562 time for calcul the mask position with numpy : 0.006041765213012695 nb_pixel_total : 264 time to create 1 rle with old method : 0.00033736228942871094 time for calcul the mask position with numpy : 0.0061414241790771484 nb_pixel_total : 576 time to create 1 rle with old method : 0.0006902217864990234 time for calcul the mask position with numpy : 0.0062525272369384766 nb_pixel_total : 889 time to create 1 rle with old method : 0.0010488033294677734 time for calcul the mask position with numpy : 0.006041765213012695 nb_pixel_total : 1167 time to create 1 rle with old method : 0.0013821125030517578 time for calcul the mask position with numpy : 0.0060923099517822266 nb_pixel_total : 79 time to create 1 rle with old method : 0.00011181831359863281 time for calcul the mask position with numpy : 0.005983591079711914 nb_pixel_total : 13 time to create 1 rle with old method : 3.218650817871094e-05 time for calcul the mask position with numpy : 0.006123781204223633 nb_pixel_total : 917 time to create 1 rle with old method : 0.0010807514190673828 time for calcul the mask position with numpy : 0.006049633026123047 nb_pixel_total : 170 time to create 1 rle with old method : 0.00024247169494628906 time for calcul the mask position with numpy : 0.006001710891723633 nb_pixel_total : 430 time to create 1 rle with old method : 0.0005235671997070312 time for calcul the mask position with numpy : 0.006277561187744141 nb_pixel_total : 72 time to create 1 rle with old method : 0.00011444091796875 time for calcul the mask position with numpy : 0.006060361862182617 nb_pixel_total : 452 time to create 1 rle with old method : 0.0005383491516113281 time for calcul the mask position with numpy : 0.006313800811767578 nb_pixel_total : 119 time to create 1 rle with old method : 0.00017333030700683594 time for calcul the mask position with numpy : 0.00622868537902832 nb_pixel_total : 7 time to create 1 rle with old method : 4.6253204345703125e-05 time for calcul the mask position with numpy : 0.00598597526550293 nb_pixel_total : 143 time to create 1 rle with old method : 0.00018024444580078125 time for calcul the mask position with numpy : 0.00608515739440918 nb_pixel_total : 1182 time to create 1 rle with old method : 0.0013985633850097656 time for calcul the mask position with numpy : 0.00614476203918457 nb_pixel_total : 539 time to create 1 rle with old method : 0.0007035732269287109 time for calcul the mask position with numpy : 0.006090879440307617 nb_pixel_total : 284 time to create 1 rle with old method : 0.000335693359375 time for calcul the mask position with numpy : 0.0060024261474609375 nb_pixel_total : 499 time to create 1 rle with old method : 0.0005936622619628906 time for calcul the mask position with numpy : 0.0061473846435546875 nb_pixel_total : 5175 time to create 1 rle with old method : 0.005917072296142578 time for calcul the mask position with numpy : 0.006220340728759766 nb_pixel_total : 666 time to create 1 rle with old method : 0.0007703304290771484 time for calcul the mask position with numpy : 0.006784200668334961 nb_pixel_total : 605 time to create 1 rle with old method : 0.0007224082946777344 time for calcul the mask position with numpy : 0.005994319915771484 nb_pixel_total : 55 time to create 1 rle with old method : 8.082389831542969e-05 time for calcul the mask position with numpy : 0.006176948547363281 nb_pixel_total : 138 time to create 1 rle with old method : 0.0001876354217529297 time for calcul the mask position with numpy : 0.0059850215911865234 nb_pixel_total : 1141 time to create 1 rle with old method : 0.0013072490692138672 time for calcul the mask position with numpy : 0.006088972091674805 nb_pixel_total : 1272 time to create 1 rle with old method : 0.0014522075653076172 time for calcul the mask position with numpy : 0.0060367584228515625 nb_pixel_total : 469 time to create 1 rle with old method : 0.0006017684936523438 time for calcul the mask position with numpy : 0.006426572799682617 nb_pixel_total : 25 time to create 1 rle with old method : 7.390975952148438e-05 time for calcul the mask position with numpy : 0.005980730056762695 nb_pixel_total : 302 time to create 1 rle with old method : 0.000347137451171875 time for calcul the mask position with numpy : 0.008373022079467773 nb_pixel_total : 265 time to create 1 rle with old method : 0.0003218650817871094 create new chi : 1.9449868202209473 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 : 0.0024209022521972656 batch 1 Loaded 149 chid ids of type : 4230 Number RLEs to save : 13337 TO DO : save crop sub photo not yet done ! save time : 2.1376988887786865 nb_obj : 157 nb_hashtags : 7 time to prepare the origin masks : 1.6129767894744873 time for calcul the mask position with numpy : 0.05865621566772461 nb_pixel_total : 1763651 time to create 1 rle with new method : 0.4704122543334961 time for calcul the mask position with numpy : 0.00667572021484375 nb_pixel_total : 2227 time to create 1 rle with old method : 0.002611398696899414 time for calcul the mask position with numpy : 0.00651860237121582 nb_pixel_total : 1701 time to create 1 rle with old method : 0.002009153366088867 time for calcul the mask position with numpy : 0.00690150260925293 nb_pixel_total : 53880 time to create 1 rle with old method : 0.058568716049194336 time for calcul the mask position with numpy : 0.006726503372192383 nb_pixel_total : 273 time to create 1 rle with old method : 0.0003333091735839844 time for calcul the mask position with numpy : 0.00661158561706543 nb_pixel_total : 13 time to create 1 rle with old method : 4.506111145019531e-05 time for calcul the mask position with numpy : 0.006719827651977539 nb_pixel_total : 48 time to create 1 rle with old method : 0.00010657310485839844 time for calcul the mask position with numpy : 0.007129192352294922 nb_pixel_total : 208 time to create 1 rle with old method : 0.0002689361572265625 time for calcul the mask position with numpy : 0.0065577030181884766 nb_pixel_total : 134 time to create 1 rle with old method : 0.0002694129943847656 time for calcul the mask position with numpy : 0.006390810012817383 nb_pixel_total : 1331 time to create 1 rle with old method : 0.0015954971313476562 time for calcul the mask position with numpy : 0.006591320037841797 nb_pixel_total : 230 time to create 1 rle with old method : 0.0002989768981933594 time for calcul the mask position with numpy : 0.0067293643951416016 nb_pixel_total : 30 time to create 1 rle with old method : 6.031990051269531e-05 time for calcul the mask position with numpy : 0.006613492965698242 nb_pixel_total : 2495 time to create 1 rle with old method : 0.0028929710388183594 time for calcul the mask position with numpy : 0.006585359573364258 nb_pixel_total : 885 time to create 1 rle with old method : 0.0010418891906738281 time for calcul the mask position with numpy : 0.006645679473876953 nb_pixel_total : 2730 time to create 1 rle with old method : 0.0030782222747802734 time for calcul the mask position with numpy : 0.006773233413696289 nb_pixel_total : 96 time to create 1 rle with old method : 0.0001571178436279297 time for calcul the mask position with numpy : 0.0070629119873046875 nb_pixel_total : 858 time to create 1 rle with old method : 0.00104522705078125 time for calcul the mask position with numpy : 0.0066165924072265625 nb_pixel_total : 162 time to create 1 rle with old method : 0.00023627281188964844 time for calcul the mask position with numpy : 0.007635593414306641 nb_pixel_total : 927 time to create 1 rle with old method : 0.0010759830474853516 time for calcul the mask position with numpy : 0.007135868072509766 nb_pixel_total : 707 time to create 1 rle with old method : 0.0008368492126464844 time for calcul the mask position with numpy : 0.0067789554595947266 nb_pixel_total : 201 time to create 1 rle with old method : 0.0002560615539550781 time for calcul the mask position with numpy : 0.006791830062866211 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0012798309326171875 time for calcul the mask position with numpy : 0.006324291229248047 nb_pixel_total : 2745 time to create 1 rle with old method : 0.003223419189453125 time for calcul the mask position with numpy : 0.006570339202880859 nb_pixel_total : 1271 time to create 1 rle with old method : 0.0014946460723876953 time for calcul the mask position with numpy : 0.006125211715698242 nb_pixel_total : 1992 time to create 1 rle with old method : 0.002225160598754883 time for calcul the mask position with numpy : 0.006273746490478516 nb_pixel_total : 364 time to create 1 rle with old method : 0.00046515464782714844 time for calcul the mask position with numpy : 0.0063533782958984375 nb_pixel_total : 534 time to create 1 rle with old method : 0.0006678104400634766 time for calcul the mask position with numpy : 0.006388425827026367 nb_pixel_total : 6489 time to create 1 rle with old method : 0.0074443817138671875 time for calcul the mask position with numpy : 0.00629115104675293 nb_pixel_total : 161 time to create 1 rle with old method : 0.0002033710479736328 time for calcul the mask position with numpy : 0.007386684417724609 nb_pixel_total : 550 time to create 1 rle with old method : 0.000804901123046875 time for calcul the mask position with numpy : 0.007591962814331055 nb_pixel_total : 449 time to create 1 rle with old method : 0.0006945133209228516 time for calcul the mask position with numpy : 0.006600141525268555 nb_pixel_total : 797 time to create 1 rle with old method : 0.0009071826934814453 time for calcul the mask position with numpy : 0.0065305233001708984 nb_pixel_total : 2287 time to create 1 rle with old method : 0.002644062042236328 time for calcul the mask position with numpy : 0.00631403923034668 nb_pixel_total : 80 time to create 1 rle with old method : 0.0001475811004638672 time for calcul the mask position with numpy : 0.006498813629150391 nb_pixel_total : 4229 time to create 1 rle with old method : 0.005035877227783203 time for calcul the mask position with numpy : 0.006618022918701172 nb_pixel_total : 12298 time to create 1 rle with old method : 0.013890743255615234 time for calcul the mask position with numpy : 0.006282806396484375 nb_pixel_total : 3533 time to create 1 rle with old method : 0.004087209701538086 time for calcul the mask position with numpy : 0.006819725036621094 nb_pixel_total : 13568 time to create 1 rle with old method : 0.01564335823059082 time for calcul the mask position with numpy : 0.00678253173828125 nb_pixel_total : 82 time to create 1 rle with old method : 0.0001220703125 time for calcul the mask position with numpy : 0.006646156311035156 nb_pixel_total : 449 time to create 1 rle with old method : 0.0005373954772949219 time for calcul the mask position with numpy : 0.00648808479309082 nb_pixel_total : 94 time to create 1 rle with old method : 0.00016927719116210938 time for calcul the mask position with numpy : 0.006603240966796875 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002536773681640625 time for calcul the mask position with numpy : 0.006333112716674805 nb_pixel_total : 406 time to create 1 rle with old method : 0.00047326087951660156 time for calcul the mask position with numpy : 0.006224632263183594 nb_pixel_total : 683 time to create 1 rle with old method : 0.0008678436279296875 time for calcul the mask position with numpy : 0.006348133087158203 nb_pixel_total : 1358 time to create 1 rle with old method : 0.0015439987182617188 time for calcul the mask position with numpy : 0.01029348373413086 nb_pixel_total : 808 time to create 1 rle with old method : 0.0011703968048095703 time for calcul the mask position with numpy : 0.006758689880371094 nb_pixel_total : 1228 time to create 1 rle with old method : 0.0014078617095947266 time for calcul the mask position with numpy : 0.00631403923034668 nb_pixel_total : 225 time to create 1 rle with old method : 0.00028586387634277344 time for calcul the mask position with numpy : 0.006258249282836914 nb_pixel_total : 17 time to create 1 rle with old method : 6.580352783203125e-05 time for calcul the mask position with numpy : 0.006421566009521484 nb_pixel_total : 501 time to create 1 rle with old method : 0.0006155967712402344 time for calcul the mask position with numpy : 0.006358146667480469 nb_pixel_total : 67 time to create 1 rle with old method : 0.00011539459228515625 time for calcul the mask position with numpy : 0.00635075569152832 nb_pixel_total : 157 time to create 1 rle with old method : 0.00020432472229003906 time for calcul the mask position with numpy : 0.006351470947265625 nb_pixel_total : 2432 time to create 1 rle with old method : 0.0028541088104248047 time for calcul the mask position with numpy : 0.006629228591918945 nb_pixel_total : 3612 time to create 1 rle with old method : 0.004265785217285156 time for calcul the mask position with numpy : 0.006371736526489258 nb_pixel_total : 2107 time to create 1 rle with old method : 0.0024971961975097656 time for calcul the mask position with numpy : 0.00642085075378418 nb_pixel_total : 28 time to create 1 rle with old method : 6.747245788574219e-05 time for calcul the mask position with numpy : 0.006760597229003906 nb_pixel_total : 1053 time to create 1 rle with old method : 0.0012249946594238281 time for calcul the mask position with numpy : 0.006991863250732422 nb_pixel_total : 460 time to create 1 rle with old method : 0.0005540847778320312 time for calcul the mask position with numpy : 0.006618976593017578 nb_pixel_total : 574 time to create 1 rle with old method : 0.0006990432739257812 time for calcul the mask position with numpy : 0.006875276565551758 nb_pixel_total : 461 time to create 1 rle with old method : 0.0005419254302978516 time for calcul the mask position with numpy : 0.006585597991943359 nb_pixel_total : 327 time to create 1 rle with old method : 0.0003819465637207031 time for calcul the mask position with numpy : 0.006475210189819336 nb_pixel_total : 521 time to create 1 rle with old method : 0.0006246566772460938 time for calcul the mask position with numpy : 0.00711512565612793 nb_pixel_total : 743 time to create 1 rle with old method : 0.0008788108825683594 time for calcul the mask position with numpy : 0.006976604461669922 nb_pixel_total : 271 time to create 1 rle with old method : 0.0003275871276855469 time for calcul the mask position with numpy : 0.007023334503173828 nb_pixel_total : 1447 time to create 1 rle with old method : 0.0017094612121582031 time for calcul the mask position with numpy : 0.0065686702728271484 nb_pixel_total : 109 time to create 1 rle with old method : 0.00015020370483398438 time for calcul the mask position with numpy : 0.006747245788574219 nb_pixel_total : 233 time to create 1 rle with old method : 0.00042891502380371094 time for calcul the mask position with numpy : 0.010213851928710938 nb_pixel_total : 1335 time to create 1 rle with old method : 0.0022253990173339844 time for calcul the mask position with numpy : 0.009068727493286133 nb_pixel_total : 459 time to create 1 rle with old method : 0.0008120536804199219 time for calcul the mask position with numpy : 0.009948968887329102 nb_pixel_total : 1547 time to create 1 rle with old method : 0.002561807632446289 time for calcul the mask position with numpy : 0.007646322250366211 nb_pixel_total : 58 time to create 1 rle with old method : 0.00012612342834472656 time for calcul the mask position with numpy : 0.008880853652954102 nb_pixel_total : 220 time to create 1 rle with old method : 0.0003848075866699219 time for calcul the mask position with numpy : 0.011581182479858398 nb_pixel_total : 434 time to create 1 rle with old method : 0.0008146762847900391 time for calcul the mask position with numpy : 0.011891365051269531 nb_pixel_total : 456 time to create 1 rle with old method : 0.0005519390106201172 time for calcul the mask position with numpy : 0.010386943817138672 nb_pixel_total : 402 time to create 1 rle with old method : 0.0004780292510986328 time for calcul the mask position with numpy : 0.01048898696899414 nb_pixel_total : 106285 time to create 1 rle with old method : 0.11471986770629883 time for calcul the mask position with numpy : 0.009964227676391602 nb_pixel_total : 464 time to create 1 rle with old method : 0.0005507469177246094 time for calcul the mask position with numpy : 0.009943246841430664 nb_pixel_total : 144 time to create 1 rle with old method : 0.00020074844360351562 time for calcul the mask position with numpy : 0.012881994247436523 nb_pixel_total : 131 time to create 1 rle with old method : 0.0002498626708984375 time for calcul the mask position with numpy : 0.009443998336791992 nb_pixel_total : 148 time to create 1 rle with old method : 0.00022125244140625 time for calcul the mask position with numpy : 0.007520198822021484 nb_pixel_total : 664 time to create 1 rle with old method : 0.0007779598236083984 time for calcul the mask position with numpy : 0.009991168975830078 nb_pixel_total : 181 time to create 1 rle with old method : 0.00023031234741210938 time for calcul the mask position with numpy : 0.009667158126831055 nb_pixel_total : 306 time to create 1 rle with old method : 0.0003669261932373047 time for calcul the mask position with numpy : 0.010358810424804688 nb_pixel_total : 65 time to create 1 rle with old method : 9.655952453613281e-05 time for calcul the mask position with numpy : 0.010054826736450195 nb_pixel_total : 2575 time to create 1 rle with old method : 0.002962350845336914 time for calcul the mask position with numpy : 0.009922981262207031 nb_pixel_total : 365 time to create 1 rle with old method : 0.0004363059997558594 time for calcul the mask position with numpy : 0.009964704513549805 nb_pixel_total : 12 time to create 1 rle with old method : 3.361701965332031e-05 time for calcul the mask position with numpy : 0.009695768356323242 nb_pixel_total : 352 time to create 1 rle with old method : 0.0004200935363769531 time for calcul the mask position with numpy : 0.009982824325561523 nb_pixel_total : 372 time to create 1 rle with old method : 0.00044798851013183594 time for calcul the mask position with numpy : 0.010040283203125 nb_pixel_total : 120 time to create 1 rle with old method : 0.00015616416931152344 time for calcul the mask position with numpy : 0.010025262832641602 nb_pixel_total : 209 time to create 1 rle with old method : 0.0002639293670654297 time for calcul the mask position with numpy : 0.009955644607543945 nb_pixel_total : 203 time to create 1 rle with old method : 0.00025272369384765625 time for calcul the mask position with numpy : 0.010384082794189453 nb_pixel_total : 486 time to create 1 rle with old method : 0.0005972385406494141 time for calcul the mask position with numpy : 0.010142087936401367 nb_pixel_total : 849 time to create 1 rle with old method : 0.0009987354278564453 time for calcul the mask position with numpy : 0.010385513305664062 nb_pixel_total : 867 time to create 1 rle with old method : 0.0010197162628173828 time for calcul the mask position with numpy : 0.009897232055664062 nb_pixel_total : 544 time to create 1 rle with old method : 0.0007598400115966797 time for calcul the mask position with numpy : 0.010053157806396484 nb_pixel_total : 630 time to create 1 rle with old method : 0.0010383129119873047 time for calcul the mask position with numpy : 0.008587360382080078 nb_pixel_total : 31 time to create 1 rle with old method : 7.748603820800781e-05 time for calcul the mask position with numpy : 0.006772041320800781 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006589889526367188 time for calcul the mask position with numpy : 0.006369590759277344 nb_pixel_total : 241 time to create 1 rle with old method : 0.00030612945556640625 time for calcul the mask position with numpy : 0.006299495697021484 nb_pixel_total : 676 time to create 1 rle with old method : 0.0007491111755371094 time for calcul the mask position with numpy : 0.006288051605224609 nb_pixel_total : 930 time to create 1 rle with old method : 0.0015821456909179688 time for calcul the mask position with numpy : 0.007606983184814453 nb_pixel_total : 268 time to create 1 rle with old method : 0.0005002021789550781 time for calcul the mask position with numpy : 0.007561683654785156 nb_pixel_total : 76 time to create 1 rle with old method : 0.00023508071899414062 time for calcul the mask position with numpy : 0.007416486740112305 nb_pixel_total : 976 time to create 1 rle with old method : 0.0017576217651367188 time for calcul the mask position with numpy : 0.007681608200073242 nb_pixel_total : 20 time to create 1 rle with old method : 8.0108642578125e-05 time for calcul the mask position with numpy : 0.007456541061401367 nb_pixel_total : 156 time to create 1 rle with old method : 0.00020432472229003906 time for calcul the mask position with numpy : 0.0062983036041259766 nb_pixel_total : 553 time to create 1 rle with old method : 0.0006544589996337891 time for calcul the mask position with numpy : 0.006584882736206055 nb_pixel_total : 444 time to create 1 rle with old method : 0.0005125999450683594 time for calcul the mask position with numpy : 0.006184816360473633 nb_pixel_total : 467 time to create 1 rle with old method : 0.0006239414215087891 time for calcul the mask position with numpy : 0.00652313232421875 nb_pixel_total : 289 time to create 1 rle with old method : 0.00038051605224609375 time for calcul the mask position with numpy : 0.006929636001586914 nb_pixel_total : 300 time to create 1 rle with old method : 0.0003795623779296875 time for calcul the mask position with numpy : 0.006995201110839844 nb_pixel_total : 5206 time to create 1 rle with old method : 0.006094455718994141 time for calcul the mask position with numpy : 0.006255149841308594 nb_pixel_total : 5021 time to create 1 rle with old method : 0.0055217742919921875 time for calcul the mask position with numpy : 0.006455421447753906 nb_pixel_total : 871 time to create 1 rle with old method : 0.0010211467742919922 time for calcul the mask position with numpy : 0.0060689449310302734 nb_pixel_total : 1767 time to create 1 rle with old method : 0.0020461082458496094 time for calcul the mask position with numpy : 0.006287574768066406 nb_pixel_total : 1468 time to create 1 rle with old method : 0.0016918182373046875 time for calcul the mask position with numpy : 0.0060880184173583984 nb_pixel_total : 1451 time to create 1 rle with old method : 0.0015587806701660156 time for calcul the mask position with numpy : 0.006348848342895508 nb_pixel_total : 108 time to create 1 rle with old method : 0.00014925003051757812 time for calcul the mask position with numpy : 0.006098508834838867 nb_pixel_total : 796 time to create 1 rle with old method : 0.000904083251953125 time for calcul the mask position with numpy : 0.006226062774658203 nb_pixel_total : 334 time to create 1 rle with old method : 0.00047135353088378906 time for calcul the mask position with numpy : 0.006935834884643555 nb_pixel_total : 465 time to create 1 rle with old method : 0.0006210803985595703 time for calcul the mask position with numpy : 0.006475210189819336 nb_pixel_total : 113 time to create 1 rle with old method : 0.00015020370483398438 time for calcul the mask position with numpy : 0.006074428558349609 nb_pixel_total : 271 time to create 1 rle with old method : 0.0003287792205810547 time for calcul the mask position with numpy : 0.006487607955932617 nb_pixel_total : 238 time to create 1 rle with old method : 0.0002884864807128906 time for calcul the mask position with numpy : 0.006260395050048828 nb_pixel_total : 627 time to create 1 rle with old method : 0.0007388591766357422 time for calcul the mask position with numpy : 0.005991458892822266 nb_pixel_total : 5 time to create 1 rle with old method : 4.3392181396484375e-05 time for calcul the mask position with numpy : 0.005807161331176758 nb_pixel_total : 9 time to create 1 rle with old method : 5.030632019042969e-05 time for calcul the mask position with numpy : 0.00600743293762207 nb_pixel_total : 873 time to create 1 rle with old method : 0.0010466575622558594 time for calcul the mask position with numpy : 0.006757497787475586 nb_pixel_total : 69 time to create 1 rle with old method : 0.00010609626770019531 time for calcul the mask position with numpy : 0.006390810012817383 nb_pixel_total : 94 time to create 1 rle with old method : 0.00014448165893554688 time for calcul the mask position with numpy : 0.006347179412841797 nb_pixel_total : 890 time to create 1 rle with old method : 0.0009982585906982422 time for calcul the mask position with numpy : 0.006188154220581055 nb_pixel_total : 148 time to create 1 rle with old method : 0.00018310546875 time for calcul the mask position with numpy : 0.006135463714599609 nb_pixel_total : 470 time to create 1 rle with old method : 0.0005657672882080078 time for calcul the mask position with numpy : 0.006104707717895508 nb_pixel_total : 20 time to create 1 rle with old method : 6.914138793945312e-05 time for calcul the mask position with numpy : 0.0058155059814453125 nb_pixel_total : 181 time to create 1 rle with old method : 0.00022673606872558594 time for calcul the mask position with numpy : 0.006774425506591797 nb_pixel_total : 2232 time to create 1 rle with old method : 0.0026497840881347656 time for calcul the mask position with numpy : 0.0062885284423828125 nb_pixel_total : 180 time to create 1 rle with old method : 0.00023508071899414062 time for calcul the mask position with numpy : 0.006850004196166992 nb_pixel_total : 142 time to create 1 rle with old method : 0.0001690387725830078 time for calcul the mask position with numpy : 0.005839824676513672 nb_pixel_total : 1261 time to create 1 rle with old method : 0.0014286041259765625 time for calcul the mask position with numpy : 0.0058100223541259766 nb_pixel_total : 144 time to create 1 rle with old method : 0.00019073486328125 time for calcul the mask position with numpy : 0.006127357482910156 nb_pixel_total : 192 time to create 1 rle with old method : 0.0002396106719970703 time for calcul the mask position with numpy : 0.005865573883056641 nb_pixel_total : 862 time to create 1 rle with old method : 0.000972747802734375 time for calcul the mask position with numpy : 0.005926370620727539 nb_pixel_total : 283 time to create 1 rle with old method : 0.00031828880310058594 time for calcul the mask position with numpy : 0.0059964656829833984 nb_pixel_total : 498 time to create 1 rle with old method : 0.0005979537963867188 time for calcul the mask position with numpy : 0.006659746170043945 nb_pixel_total : 6627 time to create 1 rle with old method : 0.007489204406738281 time for calcul the mask position with numpy : 0.006065845489501953 nb_pixel_total : 1370 time to create 1 rle with old method : 0.0015819072723388672 time for calcul the mask position with numpy : 0.005871772766113281 nb_pixel_total : 341 time to create 1 rle with old method : 0.00038933753967285156 time for calcul the mask position with numpy : 0.005810737609863281 nb_pixel_total : 51 time to create 1 rle with old method : 7.295608520507812e-05 time for calcul the mask position with numpy : 0.00814366340637207 nb_pixel_total : 99 time to create 1 rle with old method : 0.00012969970703125 time for calcul the mask position with numpy : 0.007930755615234375 nb_pixel_total : 1079 time to create 1 rle with old method : 0.0012116432189941406 time for calcul the mask position with numpy : 0.008280515670776367 nb_pixel_total : 1224 time to create 1 rle with old method : 0.0013358592987060547 time for calcul the mask position with numpy : 0.00811767578125 nb_pixel_total : 280 time to create 1 rle with old method : 0.0003077983856201172 time for calcul the mask position with numpy : 0.007957220077514648 nb_pixel_total : 438 time to create 1 rle with old method : 0.0005068778991699219 time for calcul the mask position with numpy : 0.008144378662109375 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0012028217315673828 time for calcul the mask position with numpy : 0.008380651473999023 nb_pixel_total : 527 time to create 1 rle with old method : 0.0005686283111572266 time for calcul the mask position with numpy : 0.007910966873168945 nb_pixel_total : 67 time to create 1 rle with old method : 8.749961853027344e-05 time for calcul the mask position with numpy : 0.008029699325561523 nb_pixel_total : 453 time to create 1 rle with old method : 0.00047278404235839844 create new chi : 2.0476794242858887 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 : 0.002695798873901367 batch 1 Loaded 158 chid ids of type : 4230 Number RLEs to save : 14959 TO DO : save crop sub photo not yet done ! save time : 1.2733876705169678 nb_obj : 150 nb_hashtags : 8 time to prepare the origin masks : 1.8341925144195557 time for calcul the mask position with numpy : 0.02505207061767578 nb_pixel_total : 1789693 time to create 1 rle with new method : 0.04972505569458008 time for calcul the mask position with numpy : 0.006041049957275391 nb_pixel_total : 311 time to create 1 rle with old method : 0.0004138946533203125 time for calcul the mask position with numpy : 0.005813121795654297 nb_pixel_total : 23 time to create 1 rle with old method : 4.506111145019531e-05 time for calcul the mask position with numpy : 0.005951404571533203 nb_pixel_total : 84 time to create 1 rle with old method : 0.00012421607971191406 time for calcul the mask position with numpy : 0.006303548812866211 nb_pixel_total : 52 time to create 1 rle with old method : 8.654594421386719e-05 time for calcul the mask position with numpy : 0.005990028381347656 nb_pixel_total : 113 time to create 1 rle with old method : 0.00016379356384277344 time for calcul the mask position with numpy : 0.006532907485961914 nb_pixel_total : 51847 time to create 1 rle with old method : 0.06554508209228516 time for calcul the mask position with numpy : 0.006897687911987305 nb_pixel_total : 23 time to create 1 rle with old method : 8.130073547363281e-05 time for calcul the mask position with numpy : 0.006787776947021484 nb_pixel_total : 161 time to create 1 rle with old method : 0.0002930164337158203 time for calcul the mask position with numpy : 0.006813764572143555 nb_pixel_total : 165 time to create 1 rle with old method : 0.0002834796905517578 time for calcul the mask position with numpy : 0.006564617156982422 nb_pixel_total : 36 time to create 1 rle with old method : 8.606910705566406e-05 time for calcul the mask position with numpy : 0.006597280502319336 nb_pixel_total : 195 time to create 1 rle with old method : 0.0003592967987060547 time for calcul the mask position with numpy : 0.0066106319427490234 nb_pixel_total : 2512 time to create 1 rle with old method : 0.003997802734375 time for calcul the mask position with numpy : 0.006592988967895508 nb_pixel_total : 4 time to create 1 rle with old method : 2.86102294921875e-05 time for calcul the mask position with numpy : 0.005995750427246094 nb_pixel_total : 159 time to create 1 rle with old method : 0.00020742416381835938 time for calcul the mask position with numpy : 0.006196260452270508 nb_pixel_total : 501 time to create 1 rle with old method : 0.0006139278411865234 time for calcul the mask position with numpy : 0.005971670150756836 nb_pixel_total : 889 time to create 1 rle with old method : 0.0010361671447753906 time for calcul the mask position with numpy : 0.005926847457885742 nb_pixel_total : 770 time to create 1 rle with old method : 0.0009036064147949219 time for calcul the mask position with numpy : 0.005830526351928711 nb_pixel_total : 1005 time to create 1 rle with old method : 0.0011444091796875 time for calcul the mask position with numpy : 0.005771636962890625 nb_pixel_total : 10 time to create 1 rle with old method : 4.172325134277344e-05 time for calcul the mask position with numpy : 0.0057277679443359375 nb_pixel_total : 200 time to create 1 rle with old method : 0.00022983551025390625 time for calcul the mask position with numpy : 0.006109952926635742 nb_pixel_total : 1766 time to create 1 rle with old method : 0.0019335746765136719 time for calcul the mask position with numpy : 0.00603032112121582 nb_pixel_total : 372 time to create 1 rle with old method : 0.0004680156707763672 time for calcul the mask position with numpy : 0.0060405731201171875 nb_pixel_total : 4289 time to create 1 rle with old method : 0.0046694278717041016 time for calcul the mask position with numpy : 0.00614166259765625 nb_pixel_total : 94 time to create 1 rle with old method : 0.0002300739288330078 time for calcul the mask position with numpy : 0.006170511245727539 nb_pixel_total : 2819 time to create 1 rle with old method : 0.0031440258026123047 time for calcul the mask position with numpy : 0.006043672561645508 nb_pixel_total : 1774 time to create 1 rle with old method : 0.0029311180114746094 time for calcul the mask position with numpy : 0.006577730178833008 nb_pixel_total : 1090 time to create 1 rle with old method : 0.00180816650390625 time for calcul the mask position with numpy : 0.006571531295776367 nb_pixel_total : 159 time to create 1 rle with old method : 0.0003120899200439453 time for calcul the mask position with numpy : 0.006460428237915039 nb_pixel_total : 1856 time to create 1 rle with old method : 0.003116607666015625 time for calcul the mask position with numpy : 0.006545543670654297 nb_pixel_total : 430 time to create 1 rle with old method : 0.0007951259613037109 time for calcul the mask position with numpy : 0.006566047668457031 nb_pixel_total : 6069 time to create 1 rle with old method : 0.009840250015258789 time for calcul the mask position with numpy : 0.010620832443237305 nb_pixel_total : 148 time to create 1 rle with old method : 0.00026106834411621094 time for calcul the mask position with numpy : 0.006827592849731445 nb_pixel_total : 644 time to create 1 rle with old method : 0.001100301742553711 time for calcul the mask position with numpy : 0.006589412689208984 nb_pixel_total : 442 time to create 1 rle with old method : 0.0005238056182861328 time for calcul the mask position with numpy : 0.006139278411865234 nb_pixel_total : 863 time to create 1 rle with old method : 0.0010707378387451172 time for calcul the mask position with numpy : 0.006005525588989258 nb_pixel_total : 1488 time to create 1 rle with old method : 0.0015904903411865234 time for calcul the mask position with numpy : 0.006222248077392578 nb_pixel_total : 132 time to create 1 rle with old method : 0.0002014636993408203 time for calcul the mask position with numpy : 0.006083011627197266 nb_pixel_total : 12642 time to create 1 rle with old method : 0.01925063133239746 time for calcul the mask position with numpy : 0.0059909820556640625 nb_pixel_total : 3118 time to create 1 rle with old method : 0.003293752670288086 time for calcul the mask position with numpy : 0.0059812068939208984 nb_pixel_total : 8743 time to create 1 rle with old method : 0.009392499923706055 time for calcul the mask position with numpy : 0.0059888362884521484 nb_pixel_total : 485 time to create 1 rle with old method : 0.0005776882171630859 time for calcul the mask position with numpy : 0.006153106689453125 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002484321594238281 time for calcul the mask position with numpy : 0.0063211917877197266 nb_pixel_total : 356 time to create 1 rle with old method : 0.0004303455352783203 time for calcul the mask position with numpy : 0.0063555240631103516 nb_pixel_total : 718 time to create 1 rle with old method : 0.0008411407470703125 time for calcul the mask position with numpy : 0.006085872650146484 nb_pixel_total : 1330 time to create 1 rle with old method : 0.001619577407836914 time for calcul the mask position with numpy : 0.0061342716217041016 nb_pixel_total : 648 time to create 1 rle with old method : 0.0007710456848144531 time for calcul the mask position with numpy : 0.0058536529541015625 nb_pixel_total : 809 time to create 1 rle with old method : 0.000873565673828125 time for calcul the mask position with numpy : 0.005909919738769531 nb_pixel_total : 1353 time to create 1 rle with old method : 0.0014710426330566406 time for calcul the mask position with numpy : 0.005986928939819336 nb_pixel_total : 215 time to create 1 rle with old method : 0.0002582073211669922 time for calcul the mask position with numpy : 0.005915403366088867 nb_pixel_total : 270 time to create 1 rle with old method : 0.0003082752227783203 time for calcul the mask position with numpy : 0.006024599075317383 nb_pixel_total : 2380 time to create 1 rle with old method : 0.002641439437866211 time for calcul the mask position with numpy : 0.00590062141418457 nb_pixel_total : 161 time to create 1 rle with old method : 0.00020837783813476562 time for calcul the mask position with numpy : 0.00588536262512207 nb_pixel_total : 91 time to create 1 rle with old method : 0.00015664100646972656 time for calcul the mask position with numpy : 0.0058901309967041016 nb_pixel_total : 50 time to create 1 rle with old method : 0.000102996826171875 time for calcul the mask position with numpy : 0.005971193313598633 nb_pixel_total : 1968 time to create 1 rle with old method : 0.0022039413452148438 time for calcul the mask position with numpy : 0.006033658981323242 nb_pixel_total : 111 time to create 1 rle with old method : 0.0001552104949951172 time for calcul the mask position with numpy : 0.005805253982543945 nb_pixel_total : 1039 time to create 1 rle with old method : 0.0011680126190185547 time for calcul the mask position with numpy : 0.005857706069946289 nb_pixel_total : 123 time to create 1 rle with old method : 0.00017952919006347656 time for calcul the mask position with numpy : 0.005974292755126953 nb_pixel_total : 474 time to create 1 rle with old method : 0.000583648681640625 time for calcul the mask position with numpy : 0.006193637847900391 nb_pixel_total : 636 time to create 1 rle with old method : 0.0007815361022949219 time for calcul the mask position with numpy : 0.005919694900512695 nb_pixel_total : 134 time to create 1 rle with old method : 0.0001761913299560547 time for calcul the mask position with numpy : 0.0058596134185791016 nb_pixel_total : 337 time to create 1 rle with old method : 0.000408172607421875 time for calcul the mask position with numpy : 0.006470203399658203 nb_pixel_total : 107 time to create 1 rle with old method : 0.00013756752014160156 time for calcul the mask position with numpy : 0.00597834587097168 nb_pixel_total : 511 time to create 1 rle with old method : 0.0006043910980224609 time for calcul the mask position with numpy : 0.005967140197753906 nb_pixel_total : 806 time to create 1 rle with old method : 0.0009667873382568359 time for calcul the mask position with numpy : 0.006232023239135742 nb_pixel_total : 111 time to create 1 rle with old method : 0.00014853477478027344 time for calcul the mask position with numpy : 0.005962848663330078 nb_pixel_total : 1967 time to create 1 rle with old method : 0.0022928714752197266 time for calcul the mask position with numpy : 0.0058252811431884766 nb_pixel_total : 112 time to create 1 rle with old method : 0.00015735626220703125 time for calcul the mask position with numpy : 0.0058917999267578125 nb_pixel_total : 9 time to create 1 rle with old method : 3.24249267578125e-05 time for calcul the mask position with numpy : 0.006001472473144531 nb_pixel_total : 1264 time to create 1 rle with old method : 0.0014011859893798828 time for calcul the mask position with numpy : 0.005887269973754883 nb_pixel_total : 98 time to create 1 rle with old method : 0.00014328956604003906 time for calcul the mask position with numpy : 0.005870819091796875 nb_pixel_total : 1011 time to create 1 rle with old method : 0.0012235641479492188 time for calcul the mask position with numpy : 0.006084442138671875 nb_pixel_total : 86 time to create 1 rle with old method : 0.00016808509826660156 time for calcul the mask position with numpy : 0.005875110626220703 nb_pixel_total : 1536 time to create 1 rle with old method : 0.0017423629760742188 time for calcul the mask position with numpy : 0.005742073059082031 nb_pixel_total : 483 time to create 1 rle with old method : 0.0005910396575927734 time for calcul the mask position with numpy : 0.00583195686340332 nb_pixel_total : 676 time to create 1 rle with old method : 0.0008025169372558594 time for calcul the mask position with numpy : 0.0063974857330322266 nb_pixel_total : 106685 time to create 1 rle with old method : 0.11998295783996582 time for calcul the mask position with numpy : 0.006179332733154297 nb_pixel_total : 439 time to create 1 rle with old method : 0.000484466552734375 time for calcul the mask position with numpy : 0.005575418472290039 nb_pixel_total : 418 time to create 1 rle with old method : 0.0004513263702392578 time for calcul the mask position with numpy : 0.005644083023071289 nb_pixel_total : 131 time to create 1 rle with old method : 0.00016498565673828125 time for calcul the mask position with numpy : 0.005541324615478516 nb_pixel_total : 146 time to create 1 rle with old method : 0.00017189979553222656 time for calcul the mask position with numpy : 0.005552053451538086 nb_pixel_total : 173 time to create 1 rle with old method : 0.0002086162567138672 time for calcul the mask position with numpy : 0.005734443664550781 nb_pixel_total : 69 time to create 1 rle with old method : 9.870529174804688e-05 time for calcul the mask position with numpy : 0.0057179927825927734 nb_pixel_total : 303 time to create 1 rle with old method : 0.00033974647521972656 time for calcul the mask position with numpy : 0.005551576614379883 nb_pixel_total : 157 time to create 1 rle with old method : 0.0001850128173828125 time for calcul the mask position with numpy : 0.005700826644897461 nb_pixel_total : 365 time to create 1 rle with old method : 0.00040078163146972656 time for calcul the mask position with numpy : 0.005652904510498047 nb_pixel_total : 1780 time to create 1 rle with old method : 0.0018925666809082031 time for calcul the mask position with numpy : 0.006512880325317383 nb_pixel_total : 363 time to create 1 rle with old method : 0.0006272792816162109 time for calcul the mask position with numpy : 0.00652003288269043 nb_pixel_total : 455 time to create 1 rle with old method : 0.0005450248718261719 time for calcul the mask position with numpy : 0.005961418151855469 nb_pixel_total : 1856 time to create 1 rle with old method : 0.002149343490600586 time for calcul the mask position with numpy : 0.006811618804931641 nb_pixel_total : 123 time to create 1 rle with old method : 0.0002453327178955078 time for calcul the mask position with numpy : 0.006628990173339844 nb_pixel_total : 219 time to create 1 rle with old method : 0.00027251243591308594 time for calcul the mask position with numpy : 0.00606083869934082 nb_pixel_total : 217 time to create 1 rle with old method : 0.00025153160095214844 time for calcul the mask position with numpy : 0.006105661392211914 nb_pixel_total : 1 time to create 1 rle with old method : 1.9550323486328125e-05 time for calcul the mask position with numpy : 0.005892753601074219 nb_pixel_total : 898 time to create 1 rle with old method : 0.0010662078857421875 time for calcul the mask position with numpy : 0.005931377410888672 nb_pixel_total : 496 time to create 1 rle with old method : 0.0006067752838134766 time for calcul the mask position with numpy : 0.005936622619628906 nb_pixel_total : 1 time to create 1 rle with old method : 8.344650268554688e-05 time for calcul the mask position with numpy : 0.0059854984283447266 nb_pixel_total : 501 time to create 1 rle with old method : 0.0006003379821777344 time for calcul the mask position with numpy : 0.0057926177978515625 nb_pixel_total : 16 time to create 1 rle with old method : 5.054473876953125e-05 time for calcul the mask position with numpy : 0.005782604217529297 nb_pixel_total : 566 time to create 1 rle with old method : 0.0006773471832275391 time for calcul the mask position with numpy : 0.00565028190612793 nb_pixel_total : 737 time to create 1 rle with old method : 0.0008077621459960938 time for calcul the mask position with numpy : 0.005534172058105469 nb_pixel_total : 118 time to create 1 rle with old method : 0.000148773193359375 time for calcul the mask position with numpy : 0.0055468082427978516 nb_pixel_total : 263 time to create 1 rle with old method : 0.00029087066650390625 time for calcul the mask position with numpy : 0.005667448043823242 nb_pixel_total : 934 time to create 1 rle with old method : 0.0009620189666748047 time for calcul the mask position with numpy : 0.005714893341064453 nb_pixel_total : 56 time to create 1 rle with old method : 0.00011539459228515625 time for calcul the mask position with numpy : 0.005650043487548828 nb_pixel_total : 127 time to create 1 rle with old method : 0.00015044212341308594 time for calcul the mask position with numpy : 0.0056841373443603516 nb_pixel_total : 127 time to create 1 rle with old method : 0.0001704692840576172 time for calcul the mask position with numpy : 0.005665302276611328 nb_pixel_total : 421 time to create 1 rle with old method : 0.0004668235778808594 time for calcul the mask position with numpy : 0.0057239532470703125 nb_pixel_total : 466 time to create 1 rle with old method : 0.0005338191986083984 time for calcul the mask position with numpy : 0.005667686462402344 nb_pixel_total : 313 time to create 1 rle with old method : 0.0003979206085205078 time for calcul the mask position with numpy : 0.0059201717376708984 nb_pixel_total : 253 time to create 1 rle with old method : 0.0003066062927246094 time for calcul the mask position with numpy : 0.005666971206665039 nb_pixel_total : 450 time to create 1 rle with old method : 0.00048232078552246094 time for calcul the mask position with numpy : 0.00581669807434082 nb_pixel_total : 7315 time to create 1 rle with old method : 0.00821685791015625 time for calcul the mask position with numpy : 0.005682468414306641 nb_pixel_total : 863 time to create 1 rle with old method : 0.0009112358093261719 time for calcul the mask position with numpy : 0.005573272705078125 nb_pixel_total : 473 time to create 1 rle with old method : 0.0005242824554443359 time for calcul the mask position with numpy : 0.005626201629638672 nb_pixel_total : 5689 time to create 1 rle with old method : 0.006134748458862305 time for calcul the mask position with numpy : 0.0056383609771728516 nb_pixel_total : 1884 time to create 1 rle with old method : 0.0021381378173828125 time for calcul the mask position with numpy : 0.0055789947509765625 nb_pixel_total : 1433 time to create 1 rle with old method : 0.001628875732421875 time for calcul the mask position with numpy : 0.006103992462158203 nb_pixel_total : 603 time to create 1 rle with old method : 0.0006806850433349609 time for calcul the mask position with numpy : 0.005853891372680664 nb_pixel_total : 118 time to create 1 rle with old method : 0.00015997886657714844 time for calcul the mask position with numpy : 0.006033658981323242 nb_pixel_total : 324 time to create 1 rle with old method : 0.00041675567626953125 time for calcul the mask position with numpy : 0.0063707828521728516 nb_pixel_total : 213 time to create 1 rle with old method : 0.0003223419189453125 time for calcul the mask position with numpy : 0.006183624267578125 nb_pixel_total : 1338 time to create 1 rle with old method : 0.001653432846069336 time for calcul the mask position with numpy : 0.00641942024230957 nb_pixel_total : 230 time to create 1 rle with old method : 0.0002923011779785156 time for calcul the mask position with numpy : 0.006296873092651367 nb_pixel_total : 259 time to create 1 rle with old method : 0.0002923011779785156 time for calcul the mask position with numpy : 0.005968809127807617 nb_pixel_total : 284 time to create 1 rle with old method : 0.00030732154846191406 time for calcul the mask position with numpy : 0.005934476852416992 nb_pixel_total : 125 time to create 1 rle with old method : 0.00019311904907226562 time for calcul the mask position with numpy : 0.005840301513671875 nb_pixel_total : 43 time to create 1 rle with old method : 7.963180541992188e-05 time for calcul the mask position with numpy : 0.006021738052368164 nb_pixel_total : 841 time to create 1 rle with old method : 0.0009610652923583984 time for calcul the mask position with numpy : 0.006079196929931641 nb_pixel_total : 84 time to create 1 rle with old method : 0.00011897087097167969 time for calcul the mask position with numpy : 0.005991458892822266 nb_pixel_total : 942 time to create 1 rle with old method : 0.0009763240814208984 time for calcul the mask position with numpy : 0.005827903747558594 nb_pixel_total : 179 time to create 1 rle with old method : 0.00022411346435546875 time for calcul the mask position with numpy : 0.00585174560546875 nb_pixel_total : 85 time to create 1 rle with old method : 0.00014638900756835938 time for calcul the mask position with numpy : 0.0058133602142333984 nb_pixel_total : 432 time to create 1 rle with old method : 0.0005371570587158203 time for calcul the mask position with numpy : 0.005843400955200195 nb_pixel_total : 317 time to create 1 rle with old method : 0.0003857612609863281 time for calcul the mask position with numpy : 0.005799770355224609 nb_pixel_total : 127 time to create 1 rle with old method : 0.00017786026000976562 time for calcul the mask position with numpy : 0.0057680606842041016 nb_pixel_total : 126 time to create 1 rle with old method : 0.000156402587890625 time for calcul the mask position with numpy : 0.0058231353759765625 nb_pixel_total : 1113 time to create 1 rle with old method : 0.0012540817260742188 time for calcul the mask position with numpy : 0.00579380989074707 nb_pixel_total : 513 time to create 1 rle with old method : 0.0006160736083984375 time for calcul the mask position with numpy : 0.005987882614135742 nb_pixel_total : 762 time to create 1 rle with old method : 0.0008769035339355469 time for calcul the mask position with numpy : 0.006041049957275391 nb_pixel_total : 284 time to create 1 rle with old method : 0.00031375885009765625 time for calcul the mask position with numpy : 0.006003141403198242 nb_pixel_total : 1740 time to create 1 rle with old method : 0.0020575523376464844 time for calcul the mask position with numpy : 0.006150722503662109 nb_pixel_total : 786 time to create 1 rle with old method : 0.0008690357208251953 time for calcul the mask position with numpy : 0.006282806396484375 nb_pixel_total : 136 time to create 1 rle with old method : 0.00017380714416503906 time for calcul the mask position with numpy : 0.006373882293701172 nb_pixel_total : 43 time to create 1 rle with old method : 7.033348083496094e-05 time for calcul the mask position with numpy : 0.006119251251220703 nb_pixel_total : 22 time to create 1 rle with old method : 6.651878356933594e-05 time for calcul the mask position with numpy : 0.006163120269775391 nb_pixel_total : 1273 time to create 1 rle with old method : 0.0014367103576660156 time for calcul the mask position with numpy : 0.006123542785644531 nb_pixel_total : 859 time to create 1 rle with old method : 0.0010628700256347656 time for calcul the mask position with numpy : 0.0061435699462890625 nb_pixel_total : 306 time to create 1 rle with old method : 0.0003821849822998047 time for calcul the mask position with numpy : 0.0061187744140625 nb_pixel_total : 435 time to create 1 rle with old method : 0.0005240440368652344 create new chi : 1.3299386501312256 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 : 0.0026671886444091797 batch 1 Loaded 151 chid ids of type : 4230 Number RLEs to save : 13110 TO DO : save crop sub photo not yet done ! save time : 1.608708381652832 nb_obj : 148 nb_hashtags : 7 time to prepare the origin masks : 1.457862138748169 time for calcul the mask position with numpy : 0.016817569732666016 nb_pixel_total : 1678389 time to create 1 rle with new method : 0.034694671630859375 time for calcul the mask position with numpy : 0.006210803985595703 nb_pixel_total : 903 time to create 1 rle with old method : 0.0010602474212646484 time for calcul the mask position with numpy : 0.009687662124633789 nb_pixel_total : 63 time to create 1 rle with old method : 0.00022649765014648438 time for calcul the mask position with numpy : 0.009472131729125977 nb_pixel_total : 48 time to create 1 rle with old method : 7.43865966796875e-05 time for calcul the mask position with numpy : 0.00952768325805664 nb_pixel_total : 27 time to create 1 rle with old method : 6.508827209472656e-05 time for calcul the mask position with numpy : 0.005785703659057617 nb_pixel_total : 1043 time to create 1 rle with old method : 0.0011744499206542969 time for calcul the mask position with numpy : 0.0060269832611083984 nb_pixel_total : 261 time to create 1 rle with old method : 0.0003287792205810547 time for calcul the mask position with numpy : 0.005941629409790039 nb_pixel_total : 79 time to create 1 rle with old method : 0.00011324882507324219 time for calcul the mask position with numpy : 0.005909442901611328 nb_pixel_total : 223 time to create 1 rle with old method : 0.0002579689025878906 time for calcul the mask position with numpy : 0.005957603454589844 nb_pixel_total : 182 time to create 1 rle with old method : 0.0002338886260986328 time for calcul the mask position with numpy : 0.005749940872192383 nb_pixel_total : 2771 time to create 1 rle with old method : 0.0038917064666748047 time for calcul the mask position with numpy : 0.008920669555664062 nb_pixel_total : 217 time to create 1 rle with old method : 0.0002701282501220703 time for calcul the mask position with numpy : 0.009831428527832031 nb_pixel_total : 62 time to create 1 rle with old method : 9.179115295410156e-05 time for calcul the mask position with numpy : 0.010857343673706055 nb_pixel_total : 709 time to create 1 rle with old method : 0.0008528232574462891 time for calcul the mask position with numpy : 0.00977325439453125 nb_pixel_total : 712 time to create 1 rle with old method : 0.0007812976837158203 time for calcul the mask position with numpy : 0.009659051895141602 nb_pixel_total : 365 time to create 1 rle with old method : 0.0004286766052246094 time for calcul the mask position with numpy : 0.009388208389282227 nb_pixel_total : 874 time to create 1 rle with old method : 0.0009760856628417969 time for calcul the mask position with numpy : 0.00571131706237793 nb_pixel_total : 186 time to create 1 rle with old method : 0.00022649765014648438 time for calcul the mask position with numpy : 0.0056002140045166016 nb_pixel_total : 1294 time to create 1 rle with old method : 0.0014786720275878906 time for calcul the mask position with numpy : 0.005709409713745117 nb_pixel_total : 2966 time to create 1 rle with old method : 0.0032265186309814453 time for calcul the mask position with numpy : 0.00563359260559082 nb_pixel_total : 950 time to create 1 rle with old method : 0.001043081283569336 time for calcul the mask position with numpy : 0.006052970886230469 nb_pixel_total : 109 time to create 1 rle with old method : 0.00016760826110839844 time for calcul the mask position with numpy : 0.006209135055541992 nb_pixel_total : 2182 time to create 1 rle with old method : 0.002532958984375 time for calcul the mask position with numpy : 0.006250858306884766 nb_pixel_total : 2343 time to create 1 rle with old method : 0.002856731414794922 time for calcul the mask position with numpy : 0.005914211273193359 nb_pixel_total : 377 time to create 1 rle with old method : 0.0005013942718505859 time for calcul the mask position with numpy : 0.005914449691772461 nb_pixel_total : 6412 time to create 1 rle with old method : 0.006875038146972656 time for calcul the mask position with numpy : 0.00574493408203125 nb_pixel_total : 147 time to create 1 rle with old method : 0.00018095970153808594 time for calcul the mask position with numpy : 0.005780220031738281 nb_pixel_total : 1114 time to create 1 rle with old method : 0.0012421607971191406 time for calcul the mask position with numpy : 0.005725383758544922 nb_pixel_total : 540 time to create 1 rle with old method : 0.0006139278411865234 time for calcul the mask position with numpy : 0.0058324337005615234 nb_pixel_total : 948 time to create 1 rle with old method : 0.0010421276092529297 time for calcul the mask position with numpy : 0.0058324337005615234 nb_pixel_total : 1723 time to create 1 rle with old method : 0.0019235610961914062 time for calcul the mask position with numpy : 0.005824089050292969 nb_pixel_total : 104 time to create 1 rle with old method : 0.0001735687255859375 time for calcul the mask position with numpy : 0.006860017776489258 nb_pixel_total : 151054 time to create 1 rle with new method : 0.02773308753967285 time for calcul the mask position with numpy : 0.006850004196166992 nb_pixel_total : 5818 time to create 1 rle with old method : 0.006797075271606445 time for calcul the mask position with numpy : 0.006285905838012695 nb_pixel_total : 13288 time to create 1 rle with old method : 0.016315221786499023 time for calcul the mask position with numpy : 0.006926298141479492 nb_pixel_total : 65 time to create 1 rle with old method : 9.799003601074219e-05 time for calcul the mask position with numpy : 0.006993770599365234 nb_pixel_total : 225 time to create 1 rle with old method : 0.0004222393035888672 time for calcul the mask position with numpy : 0.006827592849731445 nb_pixel_total : 339 time to create 1 rle with old method : 0.00042247772216796875 time for calcul the mask position with numpy : 0.00613713264465332 nb_pixel_total : 737 time to create 1 rle with old method : 0.000965118408203125 time for calcul the mask position with numpy : 0.006724119186401367 nb_pixel_total : 12807 time to create 1 rle with old method : 0.014347314834594727 time for calcul the mask position with numpy : 0.005942344665527344 nb_pixel_total : 1354 time to create 1 rle with old method : 0.0015370845794677734 time for calcul the mask position with numpy : 0.0058727264404296875 nb_pixel_total : 804 time to create 1 rle with old method : 0.0009336471557617188 time for calcul the mask position with numpy : 0.005857229232788086 nb_pixel_total : 35 time to create 1 rle with old method : 8.416175842285156e-05 time for calcul the mask position with numpy : 0.006511211395263672 nb_pixel_total : 291 time to create 1 rle with old method : 0.0003628730773925781 time for calcul the mask position with numpy : 0.0061779022216796875 nb_pixel_total : 1299 time to create 1 rle with old method : 0.001497030258178711 time for calcul the mask position with numpy : 0.00647425651550293 nb_pixel_total : 7171 time to create 1 rle with old method : 0.008432149887084961 time for calcul the mask position with numpy : 0.0065577030181884766 nb_pixel_total : 33 time to create 1 rle with old method : 6.532669067382812e-05 time for calcul the mask position with numpy : 0.006376028060913086 nb_pixel_total : 151 time to create 1 rle with old method : 0.00020170211791992188 time for calcul the mask position with numpy : 0.006219625473022461 nb_pixel_total : 2401 time to create 1 rle with old method : 0.002795696258544922 time for calcul the mask position with numpy : 0.006163835525512695 nb_pixel_total : 128 time to create 1 rle with old method : 0.00019049644470214844 time for calcul the mask position with numpy : 0.006083965301513672 nb_pixel_total : 1910 time to create 1 rle with old method : 0.00226593017578125 time for calcul the mask position with numpy : 0.005890846252441406 nb_pixel_total : 119 time to create 1 rle with old method : 0.000255584716796875 time for calcul the mask position with numpy : 0.005846977233886719 nb_pixel_total : 1047 time to create 1 rle with old method : 0.0012290477752685547 time for calcul the mask position with numpy : 0.006085872650146484 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006053447723388672 time for calcul the mask position with numpy : 0.005898237228393555 nb_pixel_total : 364 time to create 1 rle with old method : 0.00048804283142089844 time for calcul the mask position with numpy : 0.0060122013092041016 nb_pixel_total : 98 time to create 1 rle with old method : 0.00014638900756835938 time for calcul the mask position with numpy : 0.0060236454010009766 nb_pixel_total : 530 time to create 1 rle with old method : 0.0006597042083740234 time for calcul the mask position with numpy : 0.005728960037231445 nb_pixel_total : 862 time to create 1 rle with old method : 0.0009577274322509766 time for calcul the mask position with numpy : 0.005618095397949219 nb_pixel_total : 388 time to create 1 rle with old method : 0.00045228004455566406 time for calcul the mask position with numpy : 0.005842685699462891 nb_pixel_total : 1426 time to create 1 rle with old method : 0.0016436576843261719 time for calcul the mask position with numpy : 0.006409883499145508 nb_pixel_total : 110 time to create 1 rle with old method : 0.00015425682067871094 time for calcul the mask position with numpy : 0.006052255630493164 nb_pixel_total : 264 time to create 1 rle with old method : 0.00034236907958984375 time for calcul the mask position with numpy : 0.006082296371459961 nb_pixel_total : 1332 time to create 1 rle with old method : 0.0015304088592529297 time for calcul the mask position with numpy : 0.005974292755126953 nb_pixel_total : 776 time to create 1 rle with old method : 0.0009455680847167969 time for calcul the mask position with numpy : 0.006211280822753906 nb_pixel_total : 372 time to create 1 rle with old method : 0.0004646778106689453 time for calcul the mask position with numpy : 0.006330966949462891 nb_pixel_total : 658 time to create 1 rle with old method : 0.0008254051208496094 time for calcul the mask position with numpy : 0.0068359375 nb_pixel_total : 209 time to create 1 rle with old method : 0.0002703666687011719 time for calcul the mask position with numpy : 0.0067217350006103516 nb_pixel_total : 73 time to create 1 rle with old method : 0.0001308917999267578 time for calcul the mask position with numpy : 0.006560087203979492 nb_pixel_total : 501 time to create 1 rle with old method : 0.0006775856018066406 time for calcul the mask position with numpy : 0.006803274154663086 nb_pixel_total : 106316 time to create 1 rle with old method : 0.11965632438659668 time for calcul the mask position with numpy : 0.006007671356201172 nb_pixel_total : 435 time to create 1 rle with old method : 0.0005311965942382812 time for calcul the mask position with numpy : 0.006296396255493164 nb_pixel_total : 344 time to create 1 rle with old method : 0.0004107952117919922 time for calcul the mask position with numpy : 0.00635075569152832 nb_pixel_total : 124 time to create 1 rle with old method : 0.00018095970153808594 time for calcul the mask position with numpy : 0.0063250064849853516 nb_pixel_total : 164 time to create 1 rle with old method : 0.0003521442413330078 time for calcul the mask position with numpy : 0.006571054458618164 nb_pixel_total : 178 time to create 1 rle with old method : 0.00025177001953125 time for calcul the mask position with numpy : 0.0062067508697509766 nb_pixel_total : 307 time to create 1 rle with old method : 0.0003752708435058594 time for calcul the mask position with numpy : 0.006426334381103516 nb_pixel_total : 2336 time to create 1 rle with old method : 0.0026366710662841797 time for calcul the mask position with numpy : 0.006148099899291992 nb_pixel_total : 6 time to create 1 rle with old method : 2.7418136596679688e-05 time for calcul the mask position with numpy : 0.006328582763671875 nb_pixel_total : 368 time to create 1 rle with old method : 0.0004210472106933594 time for calcul the mask position with numpy : 0.005971670150756836 nb_pixel_total : 516 time to create 1 rle with old method : 0.0005710124969482422 time for calcul the mask position with numpy : 0.0057697296142578125 nb_pixel_total : 368 time to create 1 rle with old method : 0.0004343986511230469 time for calcul the mask position with numpy : 0.005934000015258789 nb_pixel_total : 16 time to create 1 rle with old method : 6.103515625e-05 time for calcul the mask position with numpy : 0.006005525588989258 nb_pixel_total : 2009 time to create 1 rle with old method : 0.0024051666259765625 time for calcul the mask position with numpy : 0.006462574005126953 nb_pixel_total : 365 time to create 1 rle with old method : 0.0005042552947998047 time for calcul the mask position with numpy : 0.006105899810791016 nb_pixel_total : 869 time to create 1 rle with old method : 0.0010466575622558594 time for calcul the mask position with numpy : 0.006082057952880859 nb_pixel_total : 437 time to create 1 rle with old method : 0.0007081031799316406 time for calcul the mask position with numpy : 0.0064830780029296875 nb_pixel_total : 85 time to create 1 rle with old method : 0.0001289844512939453 time for calcul the mask position with numpy : 0.005977630615234375 nb_pixel_total : 260 time to create 1 rle with old method : 0.00032591819763183594 time for calcul the mask position with numpy : 0.006067514419555664 nb_pixel_total : 128 time to create 1 rle with old method : 0.0001647472381591797 time for calcul the mask position with numpy : 0.006098508834838867 nb_pixel_total : 1266 time to create 1 rle with old method : 0.0021381378173828125 time for calcul the mask position with numpy : 0.0065724849700927734 nb_pixel_total : 225 time to create 1 rle with old method : 0.0002703666687011719 time for calcul the mask position with numpy : 0.006189823150634766 nb_pixel_total : 892 time to create 1 rle with old method : 0.001013040542602539 time for calcul the mask position with numpy : 0.006119728088378906 nb_pixel_total : 521 time to create 1 rle with old method : 0.0006289482116699219 time for calcul the mask position with numpy : 0.006052494049072266 nb_pixel_total : 68 time to create 1 rle with old method : 0.0001277923583984375 time for calcul the mask position with numpy : 0.00583195686340332 nb_pixel_total : 154 time to create 1 rle with old method : 0.00019431114196777344 time for calcul the mask position with numpy : 0.00599217414855957 nb_pixel_total : 524 time to create 1 rle with old method : 0.0006015300750732422 time for calcul the mask position with numpy : 0.006752490997314453 nb_pixel_total : 633 time to create 1 rle with old method : 0.0007319450378417969 time for calcul the mask position with numpy : 0.005978584289550781 nb_pixel_total : 665 time to create 1 rle with old method : 0.0007860660552978516 time for calcul the mask position with numpy : 0.005831480026245117 nb_pixel_total : 240 time to create 1 rle with old method : 0.00030112266540527344 time for calcul the mask position with numpy : 0.005902290344238281 nb_pixel_total : 768 time to create 1 rle with old method : 0.0009031295776367188 time for calcul the mask position with numpy : 0.0058727264404296875 nb_pixel_total : 8 time to create 1 rle with old method : 5.7697296142578125e-05 time for calcul the mask position with numpy : 0.006009101867675781 nb_pixel_total : 21 time to create 1 rle with old method : 5.221366882324219e-05 time for calcul the mask position with numpy : 0.006452798843383789 nb_pixel_total : 651 time to create 1 rle with old method : 0.000812530517578125 time for calcul the mask position with numpy : 0.006102561950683594 nb_pixel_total : 297 time to create 1 rle with old method : 0.0006260871887207031 time for calcul the mask position with numpy : 0.005866050720214844 nb_pixel_total : 966 time to create 1 rle with old method : 0.001035928726196289 time for calcul the mask position with numpy : 0.005807399749755859 nb_pixel_total : 84 time to create 1 rle with old method : 0.0001995563507080078 time for calcul the mask position with numpy : 0.005879402160644531 nb_pixel_total : 695 time to create 1 rle with old method : 0.0007987022399902344 time for calcul the mask position with numpy : 0.005821704864501953 nb_pixel_total : 441 time to create 1 rle with old method : 0.0004971027374267578 time for calcul the mask position with numpy : 0.005759716033935547 nb_pixel_total : 324 time to create 1 rle with old method : 0.0004093647003173828 time for calcul the mask position with numpy : 0.00594782829284668 nb_pixel_total : 298 time to create 1 rle with old method : 0.000354766845703125 time for calcul the mask position with numpy : 0.005915641784667969 nb_pixel_total : 554 time to create 1 rle with old method : 0.0006666183471679688 time for calcul the mask position with numpy : 0.006333351135253906 nb_pixel_total : 145 time to create 1 rle with old method : 0.00019669532775878906 time for calcul the mask position with numpy : 0.0061757564544677734 nb_pixel_total : 7357 time to create 1 rle with old method : 0.008290290832519531 time for calcul the mask position with numpy : 0.006114482879638672 nb_pixel_total : 5645 time to create 1 rle with old method : 0.006358146667480469 time for calcul the mask position with numpy : 0.005911111831665039 nb_pixel_total : 1610 time to create 1 rle with old method : 0.0024993419647216797 time for calcul the mask position with numpy : 0.00584721565246582 nb_pixel_total : 727 time to create 1 rle with old method : 0.0008542537689208984 time for calcul the mask position with numpy : 0.005762815475463867 nb_pixel_total : 372 time to create 1 rle with old method : 0.0004363059997558594 time for calcul the mask position with numpy : 0.005925655364990234 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016760826110839844 time for calcul the mask position with numpy : 0.005877971649169922 nb_pixel_total : 338 time to create 1 rle with old method : 0.0004055500030517578 time for calcul the mask position with numpy : 0.005871295928955078 nb_pixel_total : 508 time to create 1 rle with old method : 0.0006468296051025391 time for calcul the mask position with numpy : 0.005936384201049805 nb_pixel_total : 124 time to create 1 rle with old method : 0.00017189979553222656 time for calcul the mask position with numpy : 0.005799770355224609 nb_pixel_total : 42 time to create 1 rle with old method : 0.00011110305786132812 time for calcul the mask position with numpy : 0.005825519561767578 nb_pixel_total : 298 time to create 1 rle with old method : 0.0003535747528076172 time for calcul the mask position with numpy : 0.0062596797943115234 nb_pixel_total : 676 time to create 1 rle with old method : 0.0008172988891601562 time for calcul the mask position with numpy : 0.005994081497192383 nb_pixel_total : 813 time to create 1 rle with old method : 0.000926971435546875 time for calcul the mask position with numpy : 0.005773067474365234 nb_pixel_total : 84 time to create 1 rle with old method : 0.00011968612670898438 time for calcul the mask position with numpy : 0.005936145782470703 nb_pixel_total : 152 time to create 1 rle with old method : 0.00018835067749023438 time for calcul the mask position with numpy : 0.0058841705322265625 nb_pixel_total : 950 time to create 1 rle with old method : 0.0010561943054199219 time for calcul the mask position with numpy : 0.007231473922729492 nb_pixel_total : 22 time to create 1 rle with old method : 6.365776062011719e-05 time for calcul the mask position with numpy : 0.0056912899017333984 nb_pixel_total : 425 time to create 1 rle with old method : 0.0005548000335693359 time for calcul the mask position with numpy : 0.005765438079833984 nb_pixel_total : 443 time to create 1 rle with old method : 0.0004818439483642578 time for calcul the mask position with numpy : 0.005702495574951172 nb_pixel_total : 139 time to create 1 rle with old method : 0.00017976760864257812 time for calcul the mask position with numpy : 0.0056073665618896484 nb_pixel_total : 451 time to create 1 rle with old method : 0.0005509853363037109 time for calcul the mask position with numpy : 0.0056362152099609375 nb_pixel_total : 134 time to create 1 rle with old method : 0.0001780986785888672 time for calcul the mask position with numpy : 0.005791902542114258 nb_pixel_total : 94 time to create 1 rle with old method : 0.00014328956604003906 time for calcul the mask position with numpy : 0.0059206485748291016 nb_pixel_total : 1005 time to create 1 rle with old method : 0.001123666763305664 time for calcul the mask position with numpy : 0.005747556686401367 nb_pixel_total : 301 time to create 1 rle with old method : 0.00035190582275390625 time for calcul the mask position with numpy : 0.0058612823486328125 nb_pixel_total : 598 time to create 1 rle with old method : 0.0006363391876220703 time for calcul the mask position with numpy : 0.005750894546508789 nb_pixel_total : 623 time to create 1 rle with old method : 0.0007157325744628906 time for calcul the mask position with numpy : 0.005776166915893555 nb_pixel_total : 532 time to create 1 rle with old method : 0.0005953311920166016 time for calcul the mask position with numpy : 0.005973339080810547 nb_pixel_total : 393 time to create 1 rle with old method : 0.0005791187286376953 time for calcul the mask position with numpy : 0.005816221237182617 nb_pixel_total : 502 time to create 1 rle with old method : 0.0006105899810791016 time for calcul the mask position with numpy : 0.00610041618347168 nb_pixel_total : 800 time to create 1 rle with old method : 0.0008375644683837891 time for calcul the mask position with numpy : 0.0059163570404052734 nb_pixel_total : 40 time to create 1 rle with old method : 7.605552673339844e-05 time for calcul the mask position with numpy : 0.005724668502807617 nb_pixel_total : 928 time to create 1 rle with old method : 0.0010218620300292969 time for calcul the mask position with numpy : 0.005710124969482422 nb_pixel_total : 1231 time to create 1 rle with old method : 0.0013737678527832031 time for calcul the mask position with numpy : 0.0057604312896728516 nb_pixel_total : 313 time to create 1 rle with old method : 0.00039958953857421875 time for calcul the mask position with numpy : 0.005781412124633789 nb_pixel_total : 325 time to create 1 rle with old method : 0.0003752708435058594 time for calcul the mask position with numpy : 0.005777597427368164 nb_pixel_total : 582 time to create 1 rle with old method : 0.0006654262542724609 create new chi : 1.297583818435669 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 : 0.004596233367919922 batch 1 Loaded 149 chid ids of type : 4230 Number RLEs to save : 13129 TO DO : save crop sub photo not yet done ! save time : 1.051910400390625 nb_obj : 143 nb_hashtags : 7 time to prepare the origin masks : 1.367487907409668 time for calcul the mask position with numpy : 0.020761489868164062 nb_pixel_total : 1795167 time to create 1 rle with new method : 0.037596940994262695 time for calcul the mask position with numpy : 0.006884098052978516 nb_pixel_total : 2042 time to create 1 rle with old method : 0.002239227294921875 time for calcul the mask position with numpy : 0.007817983627319336 nb_pixel_total : 247 time to create 1 rle with old method : 0.00030875205993652344 time for calcul the mask position with numpy : 0.006627321243286133 nb_pixel_total : 54761 time to create 1 rle with old method : 0.05949592590332031 time for calcul the mask position with numpy : 0.006460905075073242 nb_pixel_total : 42 time to create 1 rle with old method : 7.128715515136719e-05 time for calcul the mask position with numpy : 0.006236076354980469 nb_pixel_total : 21 time to create 1 rle with old method : 4.887580871582031e-05 time for calcul the mask position with numpy : 0.005978107452392578 nb_pixel_total : 560 time to create 1 rle with old method : 0.0006761550903320312 time for calcul the mask position with numpy : 0.006380319595336914 nb_pixel_total : 9 time to create 1 rle with old method : 3.409385681152344e-05 time for calcul the mask position with numpy : 0.006400585174560547 nb_pixel_total : 208 time to create 1 rle with old method : 0.0002684593200683594 time for calcul the mask position with numpy : 0.006032705307006836 nb_pixel_total : 117 time to create 1 rle with old method : 0.00021529197692871094 time for calcul the mask position with numpy : 0.006058692932128906 nb_pixel_total : 112 time to create 1 rle with old method : 0.00018167495727539062 time for calcul the mask position with numpy : 0.006551027297973633 nb_pixel_total : 78 time to create 1 rle with old method : 0.0001239776611328125 time for calcul the mask position with numpy : 0.007149934768676758 nb_pixel_total : 2802 time to create 1 rle with old method : 0.0032231807708740234 time for calcul the mask position with numpy : 0.0061337947845458984 nb_pixel_total : 206 time to create 1 rle with old method : 0.0002536773681640625 time for calcul the mask position with numpy : 0.006148338317871094 nb_pixel_total : 1074 time to create 1 rle with old method : 0.0011456012725830078 time for calcul the mask position with numpy : 0.006009340286254883 nb_pixel_total : 2494 time to create 1 rle with old method : 0.0028884410858154297 time for calcul the mask position with numpy : 0.006585359573364258 nb_pixel_total : 887 time to create 1 rle with old method : 0.0010235309600830078 time for calcul the mask position with numpy : 0.006177425384521484 nb_pixel_total : 488 time to create 1 rle with old method : 0.0005981922149658203 time for calcul the mask position with numpy : 0.0067138671875 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006928443908691406 time for calcul the mask position with numpy : 0.006539344787597656 nb_pixel_total : 198 time to create 1 rle with old method : 0.0002422332763671875 time for calcul the mask position with numpy : 0.0068798065185546875 nb_pixel_total : 1242 time to create 1 rle with old method : 0.0014030933380126953 time for calcul the mask position with numpy : 0.005951404571533203 nb_pixel_total : 889 time to create 1 rle with old method : 0.0010268688201904297 time for calcul the mask position with numpy : 0.005894660949707031 nb_pixel_total : 863 time to create 1 rle with old method : 0.0009763240814208984 time for calcul the mask position with numpy : 0.006009101867675781 nb_pixel_total : 985 time to create 1 rle with old method : 0.0011701583862304688 time for calcul the mask position with numpy : 0.006688594818115234 nb_pixel_total : 2039 time to create 1 rle with old method : 0.0025026798248291016 time for calcul the mask position with numpy : 0.006374835968017578 nb_pixel_total : 947 time to create 1 rle with old method : 0.0011146068572998047 time for calcul the mask position with numpy : 0.006040811538696289 nb_pixel_total : 6151 time to create 1 rle with old method : 0.007462024688720703 time for calcul the mask position with numpy : 0.006363391876220703 nb_pixel_total : 165 time to create 1 rle with old method : 0.00020575523376464844 time for calcul the mask position with numpy : 0.006282329559326172 nb_pixel_total : 672 time to create 1 rle with old method : 0.0007913112640380859 time for calcul the mask position with numpy : 0.005845785140991211 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007181167602539062 time for calcul the mask position with numpy : 0.006241559982299805 nb_pixel_total : 1502 time to create 1 rle with old method : 0.0015780925750732422 time for calcul the mask position with numpy : 0.0064105987548828125 nb_pixel_total : 932 time to create 1 rle with old method : 0.0011415481567382812 time for calcul the mask position with numpy : 0.00613093376159668 nb_pixel_total : 1718 time to create 1 rle with old method : 0.0020508766174316406 time for calcul the mask position with numpy : 0.00632023811340332 nb_pixel_total : 1259 time to create 1 rle with old method : 0.0015065670013427734 time for calcul the mask position with numpy : 0.0064525604248046875 nb_pixel_total : 14687 time to create 1 rle with old method : 0.015984535217285156 time for calcul the mask position with numpy : 0.006770133972167969 nb_pixel_total : 3443 time to create 1 rle with old method : 0.003913402557373047 time for calcul the mask position with numpy : 0.006746530532836914 nb_pixel_total : 74 time to create 1 rle with old method : 0.0001068115234375 time for calcul the mask position with numpy : 0.006004810333251953 nb_pixel_total : 187 time to create 1 rle with old method : 0.00023555755615234375 time for calcul the mask position with numpy : 0.006045103073120117 nb_pixel_total : 202 time to create 1 rle with old method : 0.00023794174194335938 time for calcul the mask position with numpy : 0.006986141204833984 nb_pixel_total : 1074 time to create 1 rle with old method : 0.002052783966064453 time for calcul the mask position with numpy : 0.00612640380859375 nb_pixel_total : 692 time to create 1 rle with old method : 0.0008134841918945312 time for calcul the mask position with numpy : 0.005919933319091797 nb_pixel_total : 1390 time to create 1 rle with old method : 0.0015223026275634766 time for calcul the mask position with numpy : 0.006006717681884766 nb_pixel_total : 807 time to create 1 rle with old method : 0.0009016990661621094 time for calcul the mask position with numpy : 0.005968570709228516 nb_pixel_total : 194 time to create 1 rle with old method : 0.00024437904357910156 time for calcul the mask position with numpy : 0.005961418151855469 nb_pixel_total : 1399 time to create 1 rle with old method : 0.0015065670013427734 time for calcul the mask position with numpy : 0.006003618240356445 nb_pixel_total : 1256 time to create 1 rle with old method : 0.0014355182647705078 time for calcul the mask position with numpy : 0.0059278011322021484 nb_pixel_total : 248 time to create 1 rle with old method : 0.0002999305725097656 time for calcul the mask position with numpy : 0.005835056304931641 nb_pixel_total : 618 time to create 1 rle with old method : 0.0007495880126953125 time for calcul the mask position with numpy : 0.005968809127807617 nb_pixel_total : 323 time to create 1 rle with old method : 0.00036597251892089844 time for calcul the mask position with numpy : 0.005951642990112305 nb_pixel_total : 14 time to create 1 rle with old method : 3.8623809814453125e-05 time for calcul the mask position with numpy : 0.005980968475341797 nb_pixel_total : 155 time to create 1 rle with old method : 0.00019884109497070312 time for calcul the mask position with numpy : 0.009977102279663086 nb_pixel_total : 35 time to create 1 rle with old method : 8.296966552734375e-05 time for calcul the mask position with numpy : 0.009922266006469727 nb_pixel_total : 120 time to create 1 rle with old method : 0.0001647472381591797 time for calcul the mask position with numpy : 0.009844303131103516 nb_pixel_total : 1147 time to create 1 rle with old method : 0.0013759136199951172 time for calcul the mask position with numpy : 0.00990438461303711 nb_pixel_total : 1326 time to create 1 rle with old method : 0.0015506744384765625 time for calcul the mask position with numpy : 0.009719371795654297 nb_pixel_total : 550 time to create 1 rle with old method : 0.0006628036499023438 time for calcul the mask position with numpy : 0.009624004364013672 nb_pixel_total : 282 time to create 1 rle with old method : 0.0003600120544433594 time for calcul the mask position with numpy : 0.009648799896240234 nb_pixel_total : 124 time to create 1 rle with old method : 0.00017213821411132812 time for calcul the mask position with numpy : 0.009554386138916016 nb_pixel_total : 762 time to create 1 rle with old method : 0.00089263916015625 time for calcul the mask position with numpy : 0.010073184967041016 nb_pixel_total : 339 time to create 1 rle with old method : 0.00040912628173828125 time for calcul the mask position with numpy : 0.010068893432617188 nb_pixel_total : 1635 time to create 1 rle with old method : 0.001840353012084961 time for calcul the mask position with numpy : 0.009764671325683594 nb_pixel_total : 366 time to create 1 rle with old method : 0.0004563331604003906 time for calcul the mask position with numpy : 0.009783029556274414 nb_pixel_total : 1484 time to create 1 rle with old method : 0.001621246337890625 time for calcul the mask position with numpy : 0.0065860748291015625 nb_pixel_total : 1454 time to create 1 rle with old method : 0.0016913414001464844 time for calcul the mask position with numpy : 0.006119728088378906 nb_pixel_total : 487 time to create 1 rle with old method : 0.0006163120269775391 time for calcul the mask position with numpy : 0.0070743560791015625 nb_pixel_total : 106533 time to create 1 rle with old method : 0.11267352104187012 time for calcul the mask position with numpy : 0.00608515739440918 nb_pixel_total : 399 time to create 1 rle with old method : 0.0004925727844238281 time for calcul the mask position with numpy : 0.006400108337402344 nb_pixel_total : 362 time to create 1 rle with old method : 0.00043487548828125 time for calcul the mask position with numpy : 0.00612187385559082 nb_pixel_total : 126 time to create 1 rle with old method : 0.0002739429473876953 time for calcul the mask position with numpy : 0.007510185241699219 nb_pixel_total : 21 time to create 1 rle with old method : 6.699562072753906e-05 time for calcul the mask position with numpy : 0.0072476863861083984 nb_pixel_total : 149 time to create 1 rle with old method : 0.000194549560546875 time for calcul the mask position with numpy : 0.007338762283325195 nb_pixel_total : 177 time to create 1 rle with old method : 0.00039958953857421875 time for calcul the mask position with numpy : 0.00719451904296875 nb_pixel_total : 289 time to create 1 rle with old method : 0.00034046173095703125 time for calcul the mask position with numpy : 0.006504535675048828 nb_pixel_total : 137 time to create 1 rle with old method : 0.0001823902130126953 time for calcul the mask position with numpy : 0.006173372268676758 nb_pixel_total : 2155 time to create 1 rle with old method : 0.0028493404388427734 time for calcul the mask position with numpy : 0.0062329769134521484 nb_pixel_total : 329 time to create 1 rle with old method : 0.0004265308380126953 time for calcul the mask position with numpy : 0.006496906280517578 nb_pixel_total : 559 time to create 1 rle with old method : 0.0006506443023681641 time for calcul the mask position with numpy : 0.005970478057861328 nb_pixel_total : 118 time to create 1 rle with old method : 0.0001728534698486328 time for calcul the mask position with numpy : 0.005950927734375 nb_pixel_total : 29 time to create 1 rle with old method : 7.2479248046875e-05 time for calcul the mask position with numpy : 0.005753993988037109 nb_pixel_total : 350 time to create 1 rle with old method : 0.0004267692565917969 time for calcul the mask position with numpy : 0.006470441818237305 nb_pixel_total : 1916 time to create 1 rle with old method : 0.0022153854370117188 time for calcul the mask position with numpy : 0.006364107131958008 nb_pixel_total : 411 time to create 1 rle with old method : 0.0005061626434326172 time for calcul the mask position with numpy : 0.0061914920806884766 nb_pixel_total : 183 time to create 1 rle with old method : 0.00039196014404296875 time for calcul the mask position with numpy : 0.0064771175384521484 nb_pixel_total : 109 time to create 1 rle with old method : 0.0001513957977294922 time for calcul the mask position with numpy : 0.006228923797607422 nb_pixel_total : 182 time to create 1 rle with old method : 0.00023794174194335938 time for calcul the mask position with numpy : 0.00644373893737793 nb_pixel_total : 190 time to create 1 rle with old method : 0.0002391338348388672 time for calcul the mask position with numpy : 0.006002664566040039 nb_pixel_total : 862 time to create 1 rle with old method : 0.0010266304016113281 time for calcul the mask position with numpy : 0.005956172943115234 nb_pixel_total : 482 time to create 1 rle with old method : 0.0005862712860107422 time for calcul the mask position with numpy : 0.006375789642333984 nb_pixel_total : 143 time to create 1 rle with old method : 0.0002205371856689453 time for calcul the mask position with numpy : 0.006361484527587891 nb_pixel_total : 774 time to create 1 rle with old method : 0.0008914470672607422 time for calcul the mask position with numpy : 0.005890369415283203 nb_pixel_total : 152 time to create 1 rle with old method : 0.000186920166015625 time for calcul the mask position with numpy : 0.005807161331176758 nb_pixel_total : 460 time to create 1 rle with old method : 0.0005540847778320312 time for calcul the mask position with numpy : 0.006034135818481445 nb_pixel_total : 629 time to create 1 rle with old method : 0.0011749267578125 time for calcul the mask position with numpy : 0.006127595901489258 nb_pixel_total : 30 time to create 1 rle with old method : 8.368492126464844e-05 time for calcul the mask position with numpy : 0.005975961685180664 nb_pixel_total : 69 time to create 1 rle with old method : 0.0001804828643798828 time for calcul the mask position with numpy : 0.0063817501068115234 nb_pixel_total : 511 time to create 1 rle with old method : 0.0006389617919921875 time for calcul the mask position with numpy : 0.006108283996582031 nb_pixel_total : 290 time to create 1 rle with old method : 0.0003418922424316406 time for calcul the mask position with numpy : 0.005840301513671875 nb_pixel_total : 734 time to create 1 rle with old method : 0.0008728504180908203 time for calcul the mask position with numpy : 0.005967140197753906 nb_pixel_total : 56 time to create 1 rle with old method : 8.20159912109375e-05 time for calcul the mask position with numpy : 0.006058692932128906 nb_pixel_total : 17 time to create 1 rle with old method : 6.389617919921875e-05 time for calcul the mask position with numpy : 0.006191730499267578 nb_pixel_total : 117 time to create 1 rle with old method : 0.00015687942504882812 time for calcul the mask position with numpy : 0.006099700927734375 nb_pixel_total : 171 time to create 1 rle with old method : 0.00021338462829589844 time for calcul the mask position with numpy : 0.006000041961669922 nb_pixel_total : 917 time to create 1 rle with old method : 0.0010652542114257812 time for calcul the mask position with numpy : 0.006432294845581055 nb_pixel_total : 426 time to create 1 rle with old method : 0.0005266666412353516 time for calcul the mask position with numpy : 0.006417751312255859 nb_pixel_total : 335 time to create 1 rle with old method : 0.0003993511199951172 time for calcul the mask position with numpy : 0.006463527679443359 nb_pixel_total : 2275 time to create 1 rle with old method : 0.0028219223022460938 time for calcul the mask position with numpy : 0.007094144821166992 nb_pixel_total : 2798 time to create 1 rle with old method : 0.0031142234802246094 time for calcul the mask position with numpy : 0.006265401840209961 nb_pixel_total : 828 time to create 1 rle with old method : 0.0009372234344482422 time for calcul the mask position with numpy : 0.0067403316497802734 nb_pixel_total : 6106 time to create 1 rle with old method : 0.007055759429931641 time for calcul the mask position with numpy : 0.0062601566314697266 nb_pixel_total : 243 time to create 1 rle with old method : 0.00030612945556640625 time for calcul the mask position with numpy : 0.006680011749267578 nb_pixel_total : 144 time to create 1 rle with old method : 0.00021576881408691406 time for calcul the mask position with numpy : 0.006654262542724609 nb_pixel_total : 1320 time to create 1 rle with old method : 0.0016314983367919922 time for calcul the mask position with numpy : 0.00728607177734375 nb_pixel_total : 35 time to create 1 rle with old method : 9.059906005859375e-05 time for calcul the mask position with numpy : 0.006618022918701172 nb_pixel_total : 303 time to create 1 rle with old method : 0.0004572868347167969 time for calcul the mask position with numpy : 0.00667262077331543 nb_pixel_total : 127 time to create 1 rle with old method : 0.00017833709716796875 time for calcul the mask position with numpy : 0.006844043731689453 nb_pixel_total : 629 time to create 1 rle with old method : 0.0009813308715820312 time for calcul the mask position with numpy : 0.007025241851806641 nb_pixel_total : 757 time to create 1 rle with old method : 0.0009267330169677734 time for calcul the mask position with numpy : 0.0067424774169921875 nb_pixel_total : 556 time to create 1 rle with old method : 0.0007989406585693359 time for calcul the mask position with numpy : 0.006379127502441406 nb_pixel_total : 296 time to create 1 rle with old method : 0.0004215240478515625 time for calcul the mask position with numpy : 0.006333351135253906 nb_pixel_total : 270 time to create 1 rle with old method : 0.00045299530029296875 time for calcul the mask position with numpy : 0.006632566452026367 nb_pixel_total : 1273 time to create 1 rle with old method : 0.001584768295288086 time for calcul the mask position with numpy : 0.0071048736572265625 nb_pixel_total : 569 time to create 1 rle with old method : 0.0007989406585693359 time for calcul the mask position with numpy : 0.006578922271728516 nb_pixel_total : 307 time to create 1 rle with old method : 0.00039196014404296875 time for calcul the mask position with numpy : 0.0068628787994384766 nb_pixel_total : 986 time to create 1 rle with old method : 0.0012218952178955078 time for calcul the mask position with numpy : 0.0063495635986328125 nb_pixel_total : 18 time to create 1 rle with old method : 9.846687316894531e-05 time for calcul the mask position with numpy : 0.006452798843383789 nb_pixel_total : 395 time to create 1 rle with old method : 0.00048470497131347656 time for calcul the mask position with numpy : 0.0066263675689697266 nb_pixel_total : 392 time to create 1 rle with old method : 0.00045490264892578125 time for calcul the mask position with numpy : 0.006283998489379883 nb_pixel_total : 66 time to create 1 rle with old method : 0.00017547607421875 time for calcul the mask position with numpy : 0.006704807281494141 nb_pixel_total : 120 time to create 1 rle with old method : 0.00023412704467773438 time for calcul the mask position with numpy : 0.006195068359375 nb_pixel_total : 626 time to create 1 rle with old method : 0.0007989406585693359 time for calcul the mask position with numpy : 0.006718158721923828 nb_pixel_total : 1113 time to create 1 rle with old method : 0.0013725757598876953 time for calcul the mask position with numpy : 0.006170749664306641 nb_pixel_total : 310 time to create 1 rle with old method : 0.0003566741943359375 time for calcul the mask position with numpy : 0.006185293197631836 nb_pixel_total : 541 time to create 1 rle with old method : 0.0006375312805175781 time for calcul the mask position with numpy : 0.0062067508697509766 nb_pixel_total : 530 time to create 1 rle with old method : 0.0006396770477294922 time for calcul the mask position with numpy : 0.0062313079833984375 nb_pixel_total : 4790 time to create 1 rle with old method : 0.005299806594848633 time for calcul the mask position with numpy : 0.006139039993286133 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006849765777587891 time for calcul the mask position with numpy : 0.0063800811767578125 nb_pixel_total : 120 time to create 1 rle with old method : 0.00015664100646972656 time for calcul the mask position with numpy : 0.006470441818237305 nb_pixel_total : 1158 time to create 1 rle with old method : 0.001293182373046875 time for calcul the mask position with numpy : 0.006513118743896484 nb_pixel_total : 1174 time to create 1 rle with old method : 0.001539468765258789 time for calcul the mask position with numpy : 0.006185770034790039 nb_pixel_total : 104 time to create 1 rle with old method : 0.00016069412231445312 time for calcul the mask position with numpy : 0.006437540054321289 nb_pixel_total : 2 time to create 1 rle with old method : 2.288818359375e-05 time for calcul the mask position with numpy : 0.006519794464111328 nb_pixel_total : 534 time to create 1 rle with old method : 0.0006103515625 time for calcul the mask position with numpy : 0.006110429763793945 nb_pixel_total : 34 time to create 1 rle with old method : 8.797645568847656e-05 time for calcul the mask position with numpy : 0.006127357482910156 nb_pixel_total : 501 time to create 1 rle with old method : 0.0005946159362792969 create new chi : 1.3279986381530762 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 : 0.0025081634521484375 batch 1 Loaded 144 chid ids of type : 4230 Number RLEs to save : 12715 TO DO : save crop sub photo not yet done ! save time : 1.3175647258758545 nb_obj : 150 nb_hashtags : 7 time to prepare the origin masks : 1.7570979595184326 time for calcul the mask position with numpy : 0.05703091621398926 nb_pixel_total : 1830393 time to create 1 rle with new method : 0.35520052909851074 time for calcul the mask position with numpy : 0.00757598876953125 nb_pixel_total : 66 time to create 1 rle with old method : 0.00010132789611816406 time for calcul the mask position with numpy : 0.007503032684326172 nb_pixel_total : 84 time to create 1 rle with old method : 0.00014019012451171875 time for calcul the mask position with numpy : 0.007155418395996094 nb_pixel_total : 27 time to create 1 rle with old method : 6.103515625e-05 time for calcul the mask position with numpy : 0.00769805908203125 nb_pixel_total : 14 time to create 1 rle with old method : 4.38690185546875e-05 time for calcul the mask position with numpy : 0.00735783576965332 nb_pixel_total : 36 time to create 1 rle with old method : 6.961822509765625e-05 time for calcul the mask position with numpy : 0.007942438125610352 nb_pixel_total : 1073 time to create 1 rle with old method : 0.0012652873992919922 time for calcul the mask position with numpy : 0.007038116455078125 nb_pixel_total : 156 time to create 1 rle with old method : 0.0002288818359375 time for calcul the mask position with numpy : 0.006749391555786133 nb_pixel_total : 65 time to create 1 rle with old method : 0.0001766681671142578 time for calcul the mask position with numpy : 0.0064427852630615234 nb_pixel_total : 227 time to create 1 rle with old method : 0.00028395652770996094 time for calcul the mask position with numpy : 0.006429433822631836 nb_pixel_total : 191 time to create 1 rle with old method : 0.0002551078796386719 time for calcul the mask position with numpy : 0.007081270217895508 nb_pixel_total : 207 time to create 1 rle with old method : 0.0002689361572265625 time for calcul the mask position with numpy : 0.006902933120727539 nb_pixel_total : 2498 time to create 1 rle with old method : 0.0027618408203125 time for calcul the mask position with numpy : 0.007773637771606445 nb_pixel_total : 186 time to create 1 rle with old method : 0.00030350685119628906 time for calcul the mask position with numpy : 0.006677389144897461 nb_pixel_total : 436 time to create 1 rle with old method : 0.0005609989166259766 time for calcul the mask position with numpy : 0.006420612335205078 nb_pixel_total : 2226 time to create 1 rle with old method : 0.003008604049682617 time for calcul the mask position with numpy : 0.007418394088745117 nb_pixel_total : 1347 time to create 1 rle with old method : 0.0016665458679199219 time for calcul the mask position with numpy : 0.006838083267211914 nb_pixel_total : 805 time to create 1 rle with old method : 0.0009524822235107422 time for calcul the mask position with numpy : 0.006492137908935547 nb_pixel_total : 66 time to create 1 rle with old method : 0.00013875961303710938 time for calcul the mask position with numpy : 0.006788015365600586 nb_pixel_total : 182 time to create 1 rle with old method : 0.00023174285888671875 time for calcul the mask position with numpy : 0.009665489196777344 nb_pixel_total : 1473 time to create 1 rle with old method : 0.0017485618591308594 time for calcul the mask position with numpy : 0.011194229125976562 nb_pixel_total : 2130 time to create 1 rle with old method : 0.0024619102478027344 time for calcul the mask position with numpy : 0.009519577026367188 nb_pixel_total : 2564 time to create 1 rle with old method : 0.002988576889038086 time for calcul the mask position with numpy : 0.010271072387695312 nb_pixel_total : 7 time to create 1 rle with old method : 5.173683166503906e-05 time for calcul the mask position with numpy : 0.010135173797607422 nb_pixel_total : 1101 time to create 1 rle with old method : 0.0013475418090820312 time for calcul the mask position with numpy : 0.008550167083740234 nb_pixel_total : 114 time to create 1 rle with old method : 0.00020384788513183594 time for calcul the mask position with numpy : 0.006894350051879883 nb_pixel_total : 2008 time to create 1 rle with old method : 0.0024292469024658203 time for calcul the mask position with numpy : 0.007467985153198242 nb_pixel_total : 7387 time to create 1 rle with old method : 0.008611440658569336 time for calcul the mask position with numpy : 0.006551980972290039 nb_pixel_total : 167 time to create 1 rle with old method : 0.00021386146545410156 time for calcul the mask position with numpy : 0.006748199462890625 nb_pixel_total : 105 time to create 1 rle with old method : 0.00014781951904296875 time for calcul the mask position with numpy : 0.006554841995239258 nb_pixel_total : 816 time to create 1 rle with old method : 0.0009739398956298828 time for calcul the mask position with numpy : 0.006621360778808594 nb_pixel_total : 525 time to create 1 rle with old method : 0.0006542205810546875 time for calcul the mask position with numpy : 0.006562232971191406 nb_pixel_total : 987 time to create 1 rle with old method : 0.0011832714080810547 time for calcul the mask position with numpy : 0.0066072940826416016 nb_pixel_total : 1441 time to create 1 rle with old method : 0.0016956329345703125 time for calcul the mask position with numpy : 0.006642580032348633 nb_pixel_total : 1069 time to create 1 rle with old method : 0.0012471675872802734 time for calcul the mask position with numpy : 0.0064907073974609375 nb_pixel_total : 14691 time to create 1 rle with old method : 0.016312599182128906 time for calcul the mask position with numpy : 0.006334066390991211 nb_pixel_total : 82 time to create 1 rle with old method : 0.00011801719665527344 time for calcul the mask position with numpy : 0.0062923431396484375 nb_pixel_total : 13742 time to create 1 rle with old method : 0.014610528945922852 time for calcul the mask position with numpy : 0.006366729736328125 nb_pixel_total : 539 time to create 1 rle with old method : 0.0006434917449951172 time for calcul the mask position with numpy : 0.006304740905761719 nb_pixel_total : 204 time to create 1 rle with old method : 0.0002696514129638672 time for calcul the mask position with numpy : 0.006444215774536133 nb_pixel_total : 10 time to create 1 rle with old method : 3.838539123535156e-05 time for calcul the mask position with numpy : 0.006256818771362305 nb_pixel_total : 1146 time to create 1 rle with old method : 0.0013375282287597656 time for calcul the mask position with numpy : 0.006063938140869141 nb_pixel_total : 689 time to create 1 rle with old method : 0.0008361339569091797 time for calcul the mask position with numpy : 0.006119251251220703 nb_pixel_total : 1372 time to create 1 rle with old method : 0.001512765884399414 time for calcul the mask position with numpy : 0.006175994873046875 nb_pixel_total : 726 time to create 1 rle with old method : 0.0007736682891845703 time for calcul the mask position with numpy : 0.006075382232666016 nb_pixel_total : 1361 time to create 1 rle with old method : 0.001434326171875 time for calcul the mask position with numpy : 0.006190776824951172 nb_pixel_total : 1415 time to create 1 rle with old method : 0.0016627311706542969 time for calcul the mask position with numpy : 0.006268739700317383 nb_pixel_total : 134 time to create 1 rle with old method : 0.0001888275146484375 time for calcul the mask position with numpy : 0.006047487258911133 nb_pixel_total : 162 time to create 1 rle with old method : 0.00020623207092285156 time for calcul the mask position with numpy : 0.006079673767089844 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001575946807861328 time for calcul the mask position with numpy : 0.0062713623046875 nb_pixel_total : 509 time to create 1 rle with old method : 0.0006041526794433594 time for calcul the mask position with numpy : 0.007848501205444336 nb_pixel_total : 164 time to create 1 rle with old method : 0.00020933151245117188 time for calcul the mask position with numpy : 0.005955934524536133 nb_pixel_total : 3557 time to create 1 rle with old method : 0.0040721893310546875 time for calcul the mask position with numpy : 0.006172895431518555 nb_pixel_total : 278 time to create 1 rle with old method : 0.0003228187561035156 time for calcul the mask position with numpy : 0.006186723709106445 nb_pixel_total : 1756 time to create 1 rle with old method : 0.0020198822021484375 time for calcul the mask position with numpy : 0.005961179733276367 nb_pixel_total : 973 time to create 1 rle with old method : 0.001123666763305664 time for calcul the mask position with numpy : 0.005825519561767578 nb_pixel_total : 4 time to create 1 rle with old method : 2.7179718017578125e-05 time for calcul the mask position with numpy : 0.006090879440307617 nb_pixel_total : 1243 time to create 1 rle with old method : 0.0013697147369384766 time for calcul the mask position with numpy : 0.00584721565246582 nb_pixel_total : 127 time to create 1 rle with old method : 0.00017452239990234375 time for calcul the mask position with numpy : 0.005927085876464844 nb_pixel_total : 590 time to create 1 rle with old method : 0.0006995201110839844 time for calcul the mask position with numpy : 0.0061740875244140625 nb_pixel_total : 230 time to create 1 rle with old method : 0.0003116130828857422 time for calcul the mask position with numpy : 0.00617218017578125 nb_pixel_total : 179 time to create 1 rle with old method : 0.00024199485778808594 time for calcul the mask position with numpy : 0.0061299800872802734 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001995563507080078 time for calcul the mask position with numpy : 0.006543636322021484 nb_pixel_total : 1484 time to create 1 rle with old method : 0.0015492439270019531 time for calcul the mask position with numpy : 0.006349086761474609 nb_pixel_total : 107 time to create 1 rle with old method : 0.00015020370483398438 time for calcul the mask position with numpy : 0.0064775943756103516 nb_pixel_total : 246 time to create 1 rle with old method : 0.0003197193145751953 time for calcul the mask position with numpy : 0.006231784820556641 nb_pixel_total : 1194 time to create 1 rle with old method : 0.0013675689697265625 time for calcul the mask position with numpy : 0.006201028823852539 nb_pixel_total : 16 time to create 1 rle with old method : 6.031990051269531e-05 time for calcul the mask position with numpy : 0.006124019622802734 nb_pixel_total : 486 time to create 1 rle with old method : 0.0005629062652587891 time for calcul the mask position with numpy : 0.006353855133056641 nb_pixel_total : 1403 time to create 1 rle with old method : 0.0016465187072753906 time for calcul the mask position with numpy : 0.006229400634765625 nb_pixel_total : 620 time to create 1 rle with old method : 0.0007724761962890625 time for calcul the mask position with numpy : 0.006577014923095703 nb_pixel_total : 473 time to create 1 rle with old method : 0.0005424022674560547 time for calcul the mask position with numpy : 0.006223440170288086 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002262592315673828 time for calcul the mask position with numpy : 0.006013631820678711 nb_pixel_total : 250 time to create 1 rle with old method : 0.0002887248992919922 time for calcul the mask position with numpy : 0.0060253143310546875 nb_pixel_total : 528 time to create 1 rle with old method : 0.0006382465362548828 time for calcul the mask position with numpy : 0.006664752960205078 nb_pixel_total : 458 time to create 1 rle with old method : 0.0005509853363037109 time for calcul the mask position with numpy : 0.006274223327636719 nb_pixel_total : 279 time to create 1 rle with old method : 0.0003414154052734375 time for calcul the mask position with numpy : 0.0071790218353271484 nb_pixel_total : 106190 time to create 1 rle with old method : 0.11796307563781738 time for calcul the mask position with numpy : 0.0064427852630615234 nb_pixel_total : 442 time to create 1 rle with old method : 0.0005888938903808594 time for calcul the mask position with numpy : 0.006381034851074219 nb_pixel_total : 525 time to create 1 rle with old method : 0.0006113052368164062 time for calcul the mask position with numpy : 0.006456851959228516 nb_pixel_total : 118 time to create 1 rle with old method : 0.00016498565673828125 time for calcul the mask position with numpy : 0.006590366363525391 nb_pixel_total : 778 time to create 1 rle with old method : 0.0009214878082275391 time for calcul the mask position with numpy : 0.006422281265258789 nb_pixel_total : 162 time to create 1 rle with old method : 0.00020885467529296875 time for calcul the mask position with numpy : 0.00644373893737793 nb_pixel_total : 310 time to create 1 rle with old method : 0.00037217140197753906 time for calcul the mask position with numpy : 0.006226778030395508 nb_pixel_total : 120 time to create 1 rle with old method : 0.00015878677368164062 time for calcul the mask position with numpy : 0.006373405456542969 nb_pixel_total : 20 time to create 1 rle with old method : 4.744529724121094e-05 time for calcul the mask position with numpy : 0.006119728088378906 nb_pixel_total : 355 time to create 1 rle with old method : 0.00041937828063964844 time for calcul the mask position with numpy : 0.006549358367919922 nb_pixel_total : 2244 time to create 1 rle with old method : 0.0025849342346191406 time for calcul the mask position with numpy : 0.006236553192138672 nb_pixel_total : 376 time to create 1 rle with old method : 0.00045561790466308594 time for calcul the mask position with numpy : 0.006243705749511719 nb_pixel_total : 11 time to create 1 rle with old method : 3.814697265625e-05 time for calcul the mask position with numpy : 0.006222963333129883 nb_pixel_total : 5 time to create 1 rle with old method : 2.4080276489257812e-05 time for calcul the mask position with numpy : 0.006247997283935547 nb_pixel_total : 117 time to create 1 rle with old method : 0.00016760826110839844 time for calcul the mask position with numpy : 0.0062291622161865234 nb_pixel_total : 1219 time to create 1 rle with old method : 0.00142669677734375 time for calcul the mask position with numpy : 0.006201267242431641 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016188621520996094 time for calcul the mask position with numpy : 0.006012678146362305 nb_pixel_total : 203 time to create 1 rle with old method : 0.0002658367156982422 time for calcul the mask position with numpy : 0.006020069122314453 nb_pixel_total : 5 time to create 1 rle with old method : 5.626678466796875e-05 time for calcul the mask position with numpy : 0.006010770797729492 nb_pixel_total : 200 time to create 1 rle with old method : 0.00024890899658203125 time for calcul the mask position with numpy : 0.006201267242431641 nb_pixel_total : 1357 time to create 1 rle with old method : 0.0015807151794433594 time for calcul the mask position with numpy : 0.006091117858886719 nb_pixel_total : 452 time to create 1 rle with old method : 0.0005640983581542969 time for calcul the mask position with numpy : 0.006135463714599609 nb_pixel_total : 166 time to create 1 rle with old method : 0.0002105236053466797 time for calcul the mask position with numpy : 0.006078481674194336 nb_pixel_total : 102 time to create 1 rle with old method : 0.00017404556274414062 time for calcul the mask position with numpy : 0.006143808364868164 nb_pixel_total : 519 time to create 1 rle with old method : 0.0006430149078369141 time for calcul the mask position with numpy : 0.0060367584228515625 nb_pixel_total : 720 time to create 1 rle with old method : 0.0009243488311767578 time for calcul the mask position with numpy : 0.006123542785644531 nb_pixel_total : 233 time to create 1 rle with old method : 0.00028896331787109375 time for calcul the mask position with numpy : 0.006012678146362305 nb_pixel_total : 827 time to create 1 rle with old method : 0.0009772777557373047 time for calcul the mask position with numpy : 0.0060825347900390625 nb_pixel_total : 684 time to create 1 rle with old method : 0.0008361339569091797 time for calcul the mask position with numpy : 0.006277561187744141 nb_pixel_total : 174 time to create 1 rle with old method : 0.00023245811462402344 time for calcul the mask position with numpy : 0.006119728088378906 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0012593269348144531 time for calcul the mask position with numpy : 0.006330966949462891 nb_pixel_total : 27 time to create 1 rle with old method : 8.20159912109375e-05 time for calcul the mask position with numpy : 0.0062160491943359375 nb_pixel_total : 8 time to create 1 rle with old method : 4.649162292480469e-05 time for calcul the mask position with numpy : 0.006374359130859375 nb_pixel_total : 186 time to create 1 rle with old method : 0.00025200843811035156 time for calcul the mask position with numpy : 0.007232189178466797 nb_pixel_total : 433 time to create 1 rle with old method : 0.0005252361297607422 time for calcul the mask position with numpy : 0.0063779354095458984 nb_pixel_total : 643 time to create 1 rle with old method : 0.0011582374572753906 time for calcul the mask position with numpy : 0.006789684295654297 nb_pixel_total : 525 time to create 1 rle with old method : 0.0008704662322998047 time for calcul the mask position with numpy : 0.0068242549896240234 nb_pixel_total : 181 time to create 1 rle with old method : 0.0004093647003173828 time for calcul the mask position with numpy : 0.006908416748046875 nb_pixel_total : 8112 time to create 1 rle with old method : 0.01283121109008789 time for calcul the mask position with numpy : 0.006764650344848633 nb_pixel_total : 4702 time to create 1 rle with old method : 0.0078046321868896484 time for calcul the mask position with numpy : 0.007119894027709961 nb_pixel_total : 240 time to create 1 rle with old method : 0.0004229545593261719 time for calcul the mask position with numpy : 0.006929159164428711 nb_pixel_total : 34 time to create 1 rle with old method : 0.0001342296600341797 time for calcul the mask position with numpy : 0.006684541702270508 nb_pixel_total : 1924 time to create 1 rle with old method : 0.0031206607818603516 time for calcul the mask position with numpy : 0.0073583126068115234 nb_pixel_total : 45 time to create 1 rle with old method : 0.00014519691467285156 time for calcul the mask position with numpy : 0.006496906280517578 nb_pixel_total : 1565 time to create 1 rle with old method : 0.0018050670623779297 time for calcul the mask position with numpy : 0.006451845169067383 nb_pixel_total : 730 time to create 1 rle with old method : 0.0008726119995117188 time for calcul the mask position with numpy : 0.006363868713378906 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016546249389648438 time for calcul the mask position with numpy : 0.006246089935302734 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006227493286132812 time for calcul the mask position with numpy : 0.006093502044677734 nb_pixel_total : 9 time to create 1 rle with old method : 4.00543212890625e-05 time for calcul the mask position with numpy : 0.006287336349487305 nb_pixel_total : 165 time to create 1 rle with old method : 0.00019884109497070312 time for calcul the mask position with numpy : 0.00639033317565918 nb_pixel_total : 273 time to create 1 rle with old method : 0.00032711029052734375 time for calcul the mask position with numpy : 0.010005474090576172 nb_pixel_total : 1156 time to create 1 rle with old method : 0.0013637542724609375 time for calcul the mask position with numpy : 0.0062351226806640625 nb_pixel_total : 287 time to create 1 rle with old method : 0.0003638267517089844 time for calcul the mask position with numpy : 0.006072044372558594 nb_pixel_total : 851 time to create 1 rle with old method : 0.0009579658508300781 time for calcul the mask position with numpy : 0.006054401397705078 nb_pixel_total : 76 time to create 1 rle with old method : 0.00011301040649414062 time for calcul the mask position with numpy : 0.006070375442504883 nb_pixel_total : 970 time to create 1 rle with old method : 0.0012290477752685547 time for calcul the mask position with numpy : 0.006174802780151367 nb_pixel_total : 513 time to create 1 rle with old method : 0.07120633125305176 time for calcul the mask position with numpy : 0.006609201431274414 nb_pixel_total : 177 time to create 1 rle with old method : 0.00023221969604492188 time for calcul the mask position with numpy : 0.006659030914306641 nb_pixel_total : 454 time to create 1 rle with old method : 0.0007474422454833984 time for calcul the mask position with numpy : 0.006682634353637695 nb_pixel_total : 382 time to create 1 rle with old method : 0.0005548000335693359 time for calcul the mask position with numpy : 0.008282661437988281 nb_pixel_total : 3671 time to create 1 rle with old method : 0.005567073822021484 time for calcul the mask position with numpy : 0.008855581283569336 nb_pixel_total : 9 time to create 1 rle with old method : 4.363059997558594e-05 time for calcul the mask position with numpy : 0.00863027572631836 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001773834228515625 time for calcul the mask position with numpy : 0.008682727813720703 nb_pixel_total : 57 time to create 1 rle with old method : 0.00019216537475585938 time for calcul the mask position with numpy : 0.008260488510131836 nb_pixel_total : 976 time to create 1 rle with old method : 0.0011942386627197266 time for calcul the mask position with numpy : 0.007517814636230469 nb_pixel_total : 304 time to create 1 rle with old method : 0.00040602684020996094 time for calcul the mask position with numpy : 0.00771021842956543 nb_pixel_total : 496 time to create 1 rle with old method : 0.0006048679351806641 time for calcul the mask position with numpy : 0.007375478744506836 nb_pixel_total : 502 time to create 1 rle with old method : 0.0008599758148193359 time for calcul the mask position with numpy : 0.007354021072387695 nb_pixel_total : 3035 time to create 1 rle with old method : 0.004716157913208008 time for calcul the mask position with numpy : 0.008111715316772461 nb_pixel_total : 502 time to create 1 rle with old method : 0.0006647109985351562 time for calcul the mask position with numpy : 0.007654666900634766 nb_pixel_total : 1156 time to create 1 rle with old method : 0.001451253890991211 time for calcul the mask position with numpy : 0.007657527923583984 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0012285709381103516 time for calcul the mask position with numpy : 0.010290384292602539 nb_pixel_total : 135 time to create 1 rle with old method : 0.000316619873046875 time for calcul the mask position with numpy : 0.011378049850463867 nb_pixel_total : 440 time to create 1 rle with old method : 0.0006148815155029297 create new chi : 1.8107268810272217 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 : 0.003218412399291992 batch 1 Loaded 151 chid ids of type : 4230 Number RLEs to save : 12847 TO DO : save crop sub photo not yet done ! save time : 1.5276546478271484 nb_obj : 151 nb_hashtags : 8 time to prepare the origin masks : 1.4316062927246094 time for calcul the mask position with numpy : 0.12703609466552734 nb_pixel_total : 1813565 time to create 1 rle with new method : 0.08304738998413086 time for calcul the mask position with numpy : 0.006159305572509766 nb_pixel_total : 2359 time to create 1 rle with old method : 0.002751588821411133 time for calcul the mask position with numpy : 0.0058422088623046875 nb_pixel_total : 12 time to create 1 rle with old method : 4.029273986816406e-05 time for calcul the mask position with numpy : 0.005936145782470703 nb_pixel_total : 100 time to create 1 rle with old method : 0.000152587890625 time for calcul the mask position with numpy : 0.005938529968261719 nb_pixel_total : 47 time to create 1 rle with old method : 7.796287536621094e-05 time for calcul the mask position with numpy : 0.005856990814208984 nb_pixel_total : 53 time to create 1 rle with old method : 9.202957153320312e-05 time for calcul the mask position with numpy : 0.005776405334472656 nb_pixel_total : 129 time to create 1 rle with old method : 0.0001723766326904297 time for calcul the mask position with numpy : 0.0057108402252197266 nb_pixel_total : 159 time to create 1 rle with old method : 0.00018858909606933594 time for calcul the mask position with numpy : 0.005937814712524414 nb_pixel_total : 62 time to create 1 rle with old method : 0.00010538101196289062 time for calcul the mask position with numpy : 0.00571441650390625 nb_pixel_total : 222 time to create 1 rle with old method : 0.00027489662170410156 time for calcul the mask position with numpy : 0.005768537521362305 nb_pixel_total : 235 time to create 1 rle with old method : 0.0003063678741455078 time for calcul the mask position with numpy : 0.005715131759643555 nb_pixel_total : 2608 time to create 1 rle with old method : 0.0028748512268066406 time for calcul the mask position with numpy : 0.0057048797607421875 nb_pixel_total : 15277 time to create 1 rle with old method : 0.016209840774536133 time for calcul the mask position with numpy : 0.005800485610961914 nb_pixel_total : 699 time to create 1 rle with old method : 0.0007541179656982422 time for calcul the mask position with numpy : 0.005597352981567383 nb_pixel_total : 1013 time to create 1 rle with old method : 0.0011126995086669922 time for calcul the mask position with numpy : 0.005578279495239258 nb_pixel_total : 104 time to create 1 rle with old method : 0.0001647472381591797 time for calcul the mask position with numpy : 0.00557708740234375 nb_pixel_total : 180 time to create 1 rle with old method : 0.0001983642578125 time for calcul the mask position with numpy : 0.005819559097290039 nb_pixel_total : 1452 time to create 1 rle with old method : 0.0015566349029541016 time for calcul the mask position with numpy : 0.005688190460205078 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0012395381927490234 time for calcul the mask position with numpy : 0.005532026290893555 nb_pixel_total : 2734 time to create 1 rle with old method : 0.0031430721282958984 time for calcul the mask position with numpy : 0.005709409713745117 nb_pixel_total : 1099 time to create 1 rle with old method : 0.0011470317840576172 time for calcul the mask position with numpy : 0.005654335021972656 nb_pixel_total : 11 time to create 1 rle with old method : 4.7206878662109375e-05 time for calcul the mask position with numpy : 0.00561976432800293 nb_pixel_total : 2147 time to create 1 rle with old method : 0.0023136138916015625 time for calcul the mask position with numpy : 0.005593299865722656 nb_pixel_total : 6162 time to create 1 rle with old method : 0.006852388381958008 time for calcul the mask position with numpy : 0.005835533142089844 nb_pixel_total : 154 time to create 1 rle with old method : 0.0001838207244873047 time for calcul the mask position with numpy : 0.006007194519042969 nb_pixel_total : 1112 time to create 1 rle with old method : 0.0012664794921875 time for calcul the mask position with numpy : 0.005873680114746094 nb_pixel_total : 493 time to create 1 rle with old method : 0.0006003379821777344 time for calcul the mask position with numpy : 0.005894660949707031 nb_pixel_total : 989 time to create 1 rle with old method : 0.0010671615600585938 time for calcul the mask position with numpy : 0.005978584289550781 nb_pixel_total : 1631 time to create 1 rle with old method : 0.0017902851104736328 time for calcul the mask position with numpy : 0.005799293518066406 nb_pixel_total : 1575 time to create 1 rle with old method : 0.0017445087432861328 time for calcul the mask position with numpy : 0.0059664249420166016 nb_pixel_total : 14990 time to create 1 rle with old method : 0.01594233512878418 time for calcul the mask position with numpy : 0.005950212478637695 nb_pixel_total : 3922 time to create 1 rle with old method : 0.00426030158996582 time for calcul the mask position with numpy : 0.0060079097747802734 nb_pixel_total : 17765 time to create 1 rle with old method : 0.018699169158935547 time for calcul the mask position with numpy : 0.005846261978149414 nb_pixel_total : 14 time to create 1 rle with old method : 3.886222839355469e-05 time for calcul the mask position with numpy : 0.005832195281982422 nb_pixel_total : 207 time to create 1 rle with old method : 0.0002300739288330078 time for calcul the mask position with numpy : 0.009337663650512695 nb_pixel_total : 513 time to create 1 rle with old method : 0.0006158351898193359 time for calcul the mask position with numpy : 0.005734920501708984 nb_pixel_total : 362 time to create 1 rle with old method : 0.0003902912139892578 time for calcul the mask position with numpy : 0.005804300308227539 nb_pixel_total : 983 time to create 1 rle with old method : 0.0010540485382080078 time for calcul the mask position with numpy : 0.005716085433959961 nb_pixel_total : 719 time to create 1 rle with old method : 0.0007865428924560547 time for calcul the mask position with numpy : 0.005789041519165039 nb_pixel_total : 1010 time to create 1 rle with old method : 0.0011093616485595703 time for calcul the mask position with numpy : 0.0058176517486572266 nb_pixel_total : 1386 time to create 1 rle with old method : 0.0013861656188964844 time for calcul the mask position with numpy : 0.006120443344116211 nb_pixel_total : 34 time to create 1 rle with old method : 8.273124694824219e-05 time for calcul the mask position with numpy : 0.006064891815185547 nb_pixel_total : 834 time to create 1 rle with old method : 0.0009670257568359375 time for calcul the mask position with numpy : 0.005957841873168945 nb_pixel_total : 280 time to create 1 rle with old method : 0.00033926963806152344 time for calcul the mask position with numpy : 0.00586247444152832 nb_pixel_total : 885 time to create 1 rle with old method : 0.0010180473327636719 time for calcul the mask position with numpy : 0.00582122802734375 nb_pixel_total : 1324 time to create 1 rle with old method : 0.0015380382537841797 time for calcul the mask position with numpy : 0.006006956100463867 nb_pixel_total : 349 time to create 1 rle with old method : 0.0004265308380126953 time for calcul the mask position with numpy : 0.006018400192260742 nb_pixel_total : 165 time to create 1 rle with old method : 0.00020051002502441406 time for calcul the mask position with numpy : 0.0058443546295166016 nb_pixel_total : 129 time to create 1 rle with old method : 0.0001723766326904297 time for calcul the mask position with numpy : 0.0057332515716552734 nb_pixel_total : 188 time to create 1 rle with old method : 0.00021529197692871094 time for calcul the mask position with numpy : 0.005948543548583984 nb_pixel_total : 1802 time to create 1 rle with old method : 0.0020787715911865234 time for calcul the mask position with numpy : 0.0058667659759521484 nb_pixel_total : 164 time to create 1 rle with old method : 0.0002751350402832031 time for calcul the mask position with numpy : 0.0069310665130615234 nb_pixel_total : 541 time to create 1 rle with old method : 0.0006206035614013672 time for calcul the mask position with numpy : 0.009790897369384766 nb_pixel_total : 987 time to create 1 rle with old method : 0.0010554790496826172 time for calcul the mask position with numpy : 0.010045766830444336 nb_pixel_total : 62 time to create 1 rle with old method : 0.00016641616821289062 time for calcul the mask position with numpy : 0.006533622741699219 nb_pixel_total : 120 time to create 1 rle with old method : 0.00022840499877929688 time for calcul the mask position with numpy : 0.006466865539550781 nb_pixel_total : 537 time to create 1 rle with old method : 0.0009381771087646484 time for calcul the mask position with numpy : 0.006579160690307617 nb_pixel_total : 756 time to create 1 rle with old method : 0.0015246868133544922 time for calcul the mask position with numpy : 0.006563425064086914 nb_pixel_total : 251 time to create 1 rle with old method : 0.0004639625549316406 time for calcul the mask position with numpy : 0.0066335201263427734 nb_pixel_total : 1112 time to create 1 rle with old method : 0.0019304752349853516 time for calcul the mask position with numpy : 0.00644683837890625 nb_pixel_total : 385 time to create 1 rle with old method : 0.0006849765777587891 time for calcul the mask position with numpy : 0.006646156311035156 nb_pixel_total : 1478 time to create 1 rle with old method : 0.0025000572204589844 time for calcul the mask position with numpy : 0.006585597991943359 nb_pixel_total : 189 time to create 1 rle with old method : 0.0003402233123779297 time for calcul the mask position with numpy : 0.00655674934387207 nb_pixel_total : 322 time to create 1 rle with old method : 0.0005621910095214844 time for calcul the mask position with numpy : 0.006413698196411133 nb_pixel_total : 331 time to create 1 rle with old method : 0.0004515647888183594 time for calcul the mask position with numpy : 0.006150960922241211 nb_pixel_total : 459 time to create 1 rle with old method : 0.0005524158477783203 time for calcul the mask position with numpy : 0.006470441818237305 nb_pixel_total : 350 time to create 1 rle with old method : 0.0004315376281738281 time for calcul the mask position with numpy : 0.006815910339355469 nb_pixel_total : 106930 time to create 1 rle with old method : 0.11551785469055176 time for calcul the mask position with numpy : 0.0062105655670166016 nb_pixel_total : 417 time to create 1 rle with old method : 0.0005037784576416016 time for calcul the mask position with numpy : 0.006219148635864258 nb_pixel_total : 364 time to create 1 rle with old method : 0.0004405975341796875 time for calcul the mask position with numpy : 0.006104946136474609 nb_pixel_total : 130 time to create 1 rle with old method : 0.00017571449279785156 time for calcul the mask position with numpy : 0.006002664566040039 nb_pixel_total : 12 time to create 1 rle with old method : 3.600120544433594e-05 time for calcul the mask position with numpy : 0.006173372268676758 nb_pixel_total : 155 time to create 1 rle with old method : 0.00020766258239746094 time for calcul the mask position with numpy : 0.0064373016357421875 nb_pixel_total : 172 time to create 1 rle with old method : 0.000331878662109375 time for calcul the mask position with numpy : 0.007326602935791016 nb_pixel_total : 311 time to create 1 rle with old method : 0.0004611015319824219 time for calcul the mask position with numpy : 0.006249189376831055 nb_pixel_total : 2016 time to create 1 rle with old method : 0.0023250579833984375 time for calcul the mask position with numpy : 0.006089687347412109 nb_pixel_total : 338 time to create 1 rle with old method : 0.00040411949157714844 time for calcul the mask position with numpy : 0.005988359451293945 nb_pixel_total : 14 time to create 1 rle with old method : 4.887580871582031e-05 time for calcul the mask position with numpy : 0.00601959228515625 nb_pixel_total : 391 time to create 1 rle with old method : 0.00047326087951660156 time for calcul the mask position with numpy : 0.006098031997680664 nb_pixel_total : 352 time to create 1 rle with old method : 0.0004253387451171875 time for calcul the mask position with numpy : 0.0061795711517333984 nb_pixel_total : 1881 time to create 1 rle with old method : 0.0021975040435791016 time for calcul the mask position with numpy : 0.006095409393310547 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002906322479248047 time for calcul the mask position with numpy : 0.006039857864379883 nb_pixel_total : 52 time to create 1 rle with old method : 9.5367431640625e-05 time for calcul the mask position with numpy : 0.006020069122314453 nb_pixel_total : 1406 time to create 1 rle with old method : 0.0016481876373291016 time for calcul the mask position with numpy : 0.006064653396606445 nb_pixel_total : 113 time to create 1 rle with old method : 0.0001704692840576172 time for calcul the mask position with numpy : 0.006122589111328125 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001423358917236328 time for calcul the mask position with numpy : 0.0061283111572265625 nb_pixel_total : 20 time to create 1 rle with old method : 5.125999450683594e-05 time for calcul the mask position with numpy : 0.006105661392211914 nb_pixel_total : 231 time to create 1 rle with old method : 0.00035309791564941406 time for calcul the mask position with numpy : 0.006028652191162109 nb_pixel_total : 222 time to create 1 rle with old method : 0.0002598762512207031 time for calcul the mask position with numpy : 0.0060884952545166016 nb_pixel_total : 420 time to create 1 rle with old method : 0.0005333423614501953 time for calcul the mask position with numpy : 0.006155490875244141 nb_pixel_total : 872 time to create 1 rle with old method : 0.0009837150573730469 time for calcul the mask position with numpy : 0.0060291290283203125 nb_pixel_total : 321 time to create 1 rle with old method : 0.0003840923309326172 time for calcul the mask position with numpy : 0.0060825347900390625 nb_pixel_total : 529 time to create 1 rle with old method : 0.0006589889526367188 time for calcul the mask position with numpy : 0.006715059280395508 nb_pixel_total : 722 time to create 1 rle with old method : 0.0009009838104248047 time for calcul the mask position with numpy : 0.00606536865234375 nb_pixel_total : 240 time to create 1 rle with old method : 0.00029587745666503906 time for calcul the mask position with numpy : 0.00606083869934082 nb_pixel_total : 751 time to create 1 rle with old method : 0.0008494853973388672 time for calcul the mask position with numpy : 0.006010770797729492 nb_pixel_total : 700 time to create 1 rle with old method : 0.0008509159088134766 time for calcul the mask position with numpy : 0.006011247634887695 nb_pixel_total : 15 time to create 1 rle with old method : 3.7670135498046875e-05 time for calcul the mask position with numpy : 0.006137847900390625 nb_pixel_total : 506 time to create 1 rle with old method : 0.0005724430084228516 time for calcul the mask position with numpy : 0.006071805953979492 nb_pixel_total : 202 time to create 1 rle with old method : 0.0002639293670654297 time for calcul the mask position with numpy : 0.006081819534301758 nb_pixel_total : 973 time to create 1 rle with old method : 0.0011436939239501953 time for calcul the mask position with numpy : 0.006328582763671875 nb_pixel_total : 534 time to create 1 rle with old method : 0.0006368160247802734 time for calcul the mask position with numpy : 0.006070375442504883 nb_pixel_total : 191 time to create 1 rle with old method : 0.00026106834411621094 time for calcul the mask position with numpy : 0.006073951721191406 nb_pixel_total : 422 time to create 1 rle with old method : 0.0004897117614746094 time for calcul the mask position with numpy : 0.006099700927734375 nb_pixel_total : 286 time to create 1 rle with old method : 0.00035381317138671875 time for calcul the mask position with numpy : 0.0062944889068603516 nb_pixel_total : 317 time to create 1 rle with old method : 0.00037598609924316406 time for calcul the mask position with numpy : 0.006206989288330078 nb_pixel_total : 19 time to create 1 rle with old method : 5.626678466796875e-05 time for calcul the mask position with numpy : 0.006152153015136719 nb_pixel_total : 3421 time to create 1 rle with old method : 0.0039713382720947266 time for calcul the mask position with numpy : 0.006087779998779297 nb_pixel_total : 1379 time to create 1 rle with old method : 0.0017173290252685547 time for calcul the mask position with numpy : 0.006357908248901367 nb_pixel_total : 940 time to create 1 rle with old method : 0.0010762214660644531 time for calcul the mask position with numpy : 0.00622105598449707 nb_pixel_total : 929 time to create 1 rle with old method : 0.0012090206146240234 time for calcul the mask position with numpy : 0.006254673004150391 nb_pixel_total : 5868 time to create 1 rle with old method : 0.006728649139404297 time for calcul the mask position with numpy : 0.006170511245727539 nb_pixel_total : 46 time to create 1 rle with old method : 0.0001049041748046875 time for calcul the mask position with numpy : 0.006287097930908203 nb_pixel_total : 1007 time to create 1 rle with old method : 0.0012087821960449219 time for calcul the mask position with numpy : 0.006332874298095703 nb_pixel_total : 1593 time to create 1 rle with old method : 0.0018558502197265625 time for calcul the mask position with numpy : 0.006715297698974609 nb_pixel_total : 860 time to create 1 rle with old method : 0.0010035037994384766 time for calcul the mask position with numpy : 0.0061686038970947266 nb_pixel_total : 136 time to create 1 rle with old method : 0.0001857280731201172 time for calcul the mask position with numpy : 0.006221771240234375 nb_pixel_total : 9 time to create 1 rle with old method : 6.461143493652344e-05 time for calcul the mask position with numpy : 0.006275177001953125 nb_pixel_total : 321 time to create 1 rle with old method : 0.00039768218994140625 time for calcul the mask position with numpy : 0.006208896636962891 nb_pixel_total : 139 time to create 1 rle with old method : 0.0001888275146484375 time for calcul the mask position with numpy : 0.00629115104675293 nb_pixel_total : 35 time to create 1 rle with old method : 7.295608520507812e-05 time for calcul the mask position with numpy : 0.006163835525512695 nb_pixel_total : 231 time to create 1 rle with old method : 0.0002887248992919922 time for calcul the mask position with numpy : 0.006376504898071289 nb_pixel_total : 306 time to create 1 rle with old method : 0.0003783702850341797 time for calcul the mask position with numpy : 0.006153583526611328 nb_pixel_total : 929 time to create 1 rle with old method : 0.0010952949523925781 time for calcul the mask position with numpy : 0.00624847412109375 nb_pixel_total : 587 time to create 1 rle with old method : 0.0007064342498779297 time for calcul the mask position with numpy : 0.007102251052856445 nb_pixel_total : 13 time to create 1 rle with old method : 5.364418029785156e-05 time for calcul the mask position with numpy : 0.00610661506652832 nb_pixel_total : 856 time to create 1 rle with old method : 0.0009582042694091797 time for calcul the mask position with numpy : 0.0061588287353515625 nb_pixel_total : 73 time to create 1 rle with old method : 0.00011372566223144531 time for calcul the mask position with numpy : 0.006690025329589844 nb_pixel_total : 974 time to create 1 rle with old method : 0.0011394023895263672 time for calcul the mask position with numpy : 0.006089687347412109 nb_pixel_total : 146 time to create 1 rle with old method : 0.00018310546875 time for calcul the mask position with numpy : 0.006075859069824219 nb_pixel_total : 398 time to create 1 rle with old method : 0.0004868507385253906 time for calcul the mask position with numpy : 0.0060689449310302734 nb_pixel_total : 409 time to create 1 rle with old method : 0.0005247592926025391 time for calcul the mask position with numpy : 0.00616455078125 nb_pixel_total : 126 time to create 1 rle with old method : 0.00016880035400390625 time for calcul the mask position with numpy : 0.006071567535400391 nb_pixel_total : 3047 time to create 1 rle with old method : 0.003381490707397461 time for calcul the mask position with numpy : 0.006186723709106445 nb_pixel_total : 70 time to create 1 rle with old method : 0.00011754035949707031 time for calcul the mask position with numpy : 0.006266117095947266 nb_pixel_total : 123 time to create 1 rle with old method : 0.0001690387725830078 time for calcul the mask position with numpy : 0.006127595901489258 nb_pixel_total : 1056 time to create 1 rle with old method : 0.0012667179107666016 time for calcul the mask position with numpy : 0.006183147430419922 nb_pixel_total : 987 time to create 1 rle with old method : 0.001216888427734375 time for calcul the mask position with numpy : 0.00619196891784668 nb_pixel_total : 315 time to create 1 rle with old method : 0.0003933906555175781 time for calcul the mask position with numpy : 0.0062487125396728516 nb_pixel_total : 33 time to create 1 rle with old method : 7.82012939453125e-05 time for calcul the mask position with numpy : 0.006043910980224609 nb_pixel_total : 545 time to create 1 rle with old method : 0.0006458759307861328 time for calcul the mask position with numpy : 0.006041765213012695 nb_pixel_total : 505 time to create 1 rle with old method : 0.0006062984466552734 time for calcul the mask position with numpy : 0.006111621856689453 nb_pixel_total : 3979 time to create 1 rle with old method : 0.0046465396881103516 time for calcul the mask position with numpy : 0.006039142608642578 nb_pixel_total : 43 time to create 1 rle with old method : 8.749961853027344e-05 time for calcul the mask position with numpy : 0.006011962890625 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006113052368164062 time for calcul the mask position with numpy : 0.0061283111572265625 nb_pixel_total : 1081 time to create 1 rle with old method : 0.0012004375457763672 time for calcul the mask position with numpy : 0.006010770797729492 nb_pixel_total : 63 time to create 1 rle with old method : 0.00010848045349121094 time for calcul the mask position with numpy : 0.005989551544189453 nb_pixel_total : 340 time to create 1 rle with old method : 0.00036025047302246094 time for calcul the mask position with numpy : 0.0057888031005859375 nb_pixel_total : 210 time to create 1 rle with old method : 0.0002732276916503906 time for calcul the mask position with numpy : 0.005646228790283203 nb_pixel_total : 1071 time to create 1 rle with old method : 0.001132965087890625 time for calcul the mask position with numpy : 0.007916450500488281 nb_pixel_total : 508 time to create 1 rle with old method : 0.0005166530609130859 time for calcul the mask position with numpy : 0.007779836654663086 nb_pixel_total : 90 time to create 1 rle with old method : 0.00010895729064941406 create new chi : 1.4510605335235596 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 : 0.0025415420532226562 batch 1 Loaded 152 chid ids of type : 4230 Number RLEs to save : 14081 TO DO : save crop sub photo not yet done ! save time : 1.11661958694458 nb_obj : 145 nb_hashtags : 7 time to prepare the origin masks : 1.4539709091186523 time for calcul the mask position with numpy : 0.01995372772216797 nb_pixel_total : 1756434 time to create 1 rle with new method : 0.03826189041137695 time for calcul the mask position with numpy : 0.006375312805175781 nb_pixel_total : 2295 time to create 1 rle with old method : 0.002680540084838867 time for calcul the mask position with numpy : 0.00635981559753418 nb_pixel_total : 50 time to create 1 rle with old method : 8.916854858398438e-05 time for calcul the mask position with numpy : 0.006195783615112305 nb_pixel_total : 25 time to create 1 rle with old method : 5.555152893066406e-05 time for calcul the mask position with numpy : 0.0065271854400634766 nb_pixel_total : 35 time to create 1 rle with old method : 9.608268737792969e-05 time for calcul the mask position with numpy : 0.010104656219482422 nb_pixel_total : 867 time to create 1 rle with old method : 0.0010559558868408203 time for calcul the mask position with numpy : 0.006316184997558594 nb_pixel_total : 56 time to create 1 rle with old method : 8.654594421386719e-05 time for calcul the mask position with numpy : 0.008263111114501953 nb_pixel_total : 16 time to create 1 rle with old method : 7.081031799316406e-05 time for calcul the mask position with numpy : 0.007193565368652344 nb_pixel_total : 56 time to create 1 rle with old method : 0.0001308917999267578 time for calcul the mask position with numpy : 0.008201360702514648 nb_pixel_total : 248 time to create 1 rle with old method : 0.0003542900085449219 time for calcul the mask position with numpy : 0.007764339447021484 nb_pixel_total : 805 time to create 1 rle with old method : 0.0009865760803222656 time for calcul the mask position with numpy : 0.00844430923461914 nb_pixel_total : 129 time to create 1 rle with old method : 0.0003368854522705078 time for calcul the mask position with numpy : 0.009866476058959961 nb_pixel_total : 2772 time to create 1 rle with old method : 0.003927707672119141 time for calcul the mask position with numpy : 0.00632476806640625 nb_pixel_total : 47623 time to create 1 rle with old method : 0.05232882499694824 time for calcul the mask position with numpy : 0.005991220474243164 nb_pixel_total : 224 time to create 1 rle with old method : 0.0003139972686767578 time for calcul the mask position with numpy : 0.006040334701538086 nb_pixel_total : 192 time to create 1 rle with old method : 0.0003192424774169922 time for calcul the mask position with numpy : 0.00727534294128418 nb_pixel_total : 950 time to create 1 rle with old method : 0.00113677978515625 time for calcul the mask position with numpy : 0.006322383880615234 nb_pixel_total : 185 time to create 1 rle with old method : 0.00024175643920898438 time for calcul the mask position with numpy : 0.006604909896850586 nb_pixel_total : 1297 time to create 1 rle with old method : 0.0016398429870605469 time for calcul the mask position with numpy : 0.006155252456665039 nb_pixel_total : 533 time to create 1 rle with old method : 0.0007011890411376953 time for calcul the mask position with numpy : 0.0061817169189453125 nb_pixel_total : 2620 time to create 1 rle with old method : 0.0030546188354492188 time for calcul the mask position with numpy : 0.00646662712097168 nb_pixel_total : 2928 time to create 1 rle with old method : 0.003357410430908203 time for calcul the mask position with numpy : 0.0061190128326416016 nb_pixel_total : 1137 time to create 1 rle with old method : 0.001253366470336914 time for calcul the mask position with numpy : 0.006379604339599609 nb_pixel_total : 2501 time to create 1 rle with old method : 0.002718687057495117 time for calcul the mask position with numpy : 0.0059893131256103516 nb_pixel_total : 6405 time to create 1 rle with old method : 0.0074808597564697266 time for calcul the mask position with numpy : 0.006043434143066406 nb_pixel_total : 589 time to create 1 rle with old method : 0.0007076263427734375 time for calcul the mask position with numpy : 0.0060825347900390625 nb_pixel_total : 514 time to create 1 rle with old method : 0.0006084442138671875 time for calcul the mask position with numpy : 0.006052732467651367 nb_pixel_total : 835 time to create 1 rle with old method : 0.0009598731994628906 time for calcul the mask position with numpy : 0.005910396575927734 nb_pixel_total : 337 time to create 1 rle with old method : 0.0004467964172363281 time for calcul the mask position with numpy : 0.0059740543365478516 nb_pixel_total : 518 time to create 1 rle with old method : 0.000614166259765625 time for calcul the mask position with numpy : 0.006017446517944336 nb_pixel_total : 363 time to create 1 rle with old method : 0.0004334449768066406 time for calcul the mask position with numpy : 0.005722999572753906 nb_pixel_total : 3446 time to create 1 rle with old method : 0.0038673877716064453 time for calcul the mask position with numpy : 0.006041288375854492 nb_pixel_total : 3114 time to create 1 rle with old method : 0.003559589385986328 time for calcul the mask position with numpy : 0.0060062408447265625 nb_pixel_total : 284 time to create 1 rle with old method : 0.0003502368927001953 time for calcul the mask position with numpy : 0.005974292755126953 nb_pixel_total : 14816 time to create 1 rle with old method : 0.015260696411132812 time for calcul the mask position with numpy : 0.0061147212982177734 nb_pixel_total : 9460 time to create 1 rle with old method : 0.010665178298950195 time for calcul the mask position with numpy : 0.0059201717376708984 nb_pixel_total : 470 time to create 1 rle with old method : 0.0005543231964111328 time for calcul the mask position with numpy : 0.005852937698364258 nb_pixel_total : 500 time to create 1 rle with old method : 0.0005600452423095703 time for calcul the mask position with numpy : 0.009859085083007812 nb_pixel_total : 91 time to create 1 rle with old method : 0.00013184547424316406 time for calcul the mask position with numpy : 0.009897708892822266 nb_pixel_total : 621 time to create 1 rle with old method : 0.0007452964782714844 time for calcul the mask position with numpy : 0.009983062744140625 nb_pixel_total : 19910 time to create 1 rle with old method : 0.02121448516845703 time for calcul the mask position with numpy : 0.009673357009887695 nb_pixel_total : 2980 time to create 1 rle with old method : 0.003353118896484375 time for calcul the mask position with numpy : 0.009631156921386719 nb_pixel_total : 213 time to create 1 rle with old method : 0.0002636909484863281 time for calcul the mask position with numpy : 0.009714126586914062 nb_pixel_total : 92 time to create 1 rle with old method : 0.00017023086547851562 time for calcul the mask position with numpy : 0.009809017181396484 nb_pixel_total : 336 time to create 1 rle with old method : 0.0003693103790283203 time for calcul the mask position with numpy : 0.009783029556274414 nb_pixel_total : 304 time to create 1 rle with old method : 0.00037026405334472656 time for calcul the mask position with numpy : 0.009763479232788086 nb_pixel_total : 1424 time to create 1 rle with old method : 0.0016407966613769531 time for calcul the mask position with numpy : 0.009905576705932617 nb_pixel_total : 711 time to create 1 rle with old method : 0.0008199214935302734 time for calcul the mask position with numpy : 0.00975942611694336 nb_pixel_total : 1280 time to create 1 rle with old method : 0.0014882087707519531 time for calcul the mask position with numpy : 0.009607076644897461 nb_pixel_total : 767 time to create 1 rle with old method : 0.0009012222290039062 time for calcul the mask position with numpy : 0.00970602035522461 nb_pixel_total : 188 time to create 1 rle with old method : 0.00022482872009277344 time for calcul the mask position with numpy : 0.009637117385864258 nb_pixel_total : 459 time to create 1 rle with old method : 0.0005319118499755859 time for calcul the mask position with numpy : 0.006327390670776367 nb_pixel_total : 359 time to create 1 rle with old method : 0.00040459632873535156 time for calcul the mask position with numpy : 0.005707263946533203 nb_pixel_total : 167 time to create 1 rle with old method : 0.0002071857452392578 time for calcul the mask position with numpy : 0.0058748722076416016 nb_pixel_total : 2330 time to create 1 rle with old method : 0.002519369125366211 time for calcul the mask position with numpy : 0.005949974060058594 nb_pixel_total : 578 time to create 1 rle with old method : 0.0007393360137939453 time for calcul the mask position with numpy : 0.00595855712890625 nb_pixel_total : 2341 time to create 1 rle with old method : 0.0026366710662841797 time for calcul the mask position with numpy : 0.005781412124633789 nb_pixel_total : 111 time to create 1 rle with old method : 0.00020051002502441406 time for calcul the mask position with numpy : 0.005627870559692383 nb_pixel_total : 1730 time to create 1 rle with old method : 0.00194549560546875 time for calcul the mask position with numpy : 0.005807161331176758 nb_pixel_total : 117 time to create 1 rle with old method : 0.00015974044799804688 time for calcul the mask position with numpy : 0.005770683288574219 nb_pixel_total : 602 time to create 1 rle with old method : 0.0006992816925048828 time for calcul the mask position with numpy : 0.005793333053588867 nb_pixel_total : 943 time to create 1 rle with old method : 0.0011179447174072266 time for calcul the mask position with numpy : 0.005918741226196289 nb_pixel_total : 297 time to create 1 rle with old method : 0.0003426074981689453 time for calcul the mask position with numpy : 0.006059408187866211 nb_pixel_total : 641 time to create 1 rle with old method : 0.0007586479187011719 time for calcul the mask position with numpy : 0.005772829055786133 nb_pixel_total : 1775 time to create 1 rle with old method : 0.0020837783813476562 time for calcul the mask position with numpy : 0.005957126617431641 nb_pixel_total : 107 time to create 1 rle with old method : 0.0002040863037109375 time for calcul the mask position with numpy : 0.005939960479736328 nb_pixel_total : 1139 time to create 1 rle with old method : 0.001196146011352539 time for calcul the mask position with numpy : 0.005953311920166016 nb_pixel_total : 94 time to create 1 rle with old method : 0.00012636184692382812 time for calcul the mask position with numpy : 0.005802154541015625 nb_pixel_total : 1123 time to create 1 rle with old method : 0.0013744831085205078 time for calcul the mask position with numpy : 0.0058536529541015625 nb_pixel_total : 440 time to create 1 rle with old method : 0.0005316734313964844 time for calcul the mask position with numpy : 0.005900144577026367 nb_pixel_total : 814 time to create 1 rle with old method : 0.0009007453918457031 time for calcul the mask position with numpy : 0.005925893783569336 nb_pixel_total : 181 time to create 1 rle with old method : 0.00022602081298828125 time for calcul the mask position with numpy : 0.005837202072143555 nb_pixel_total : 254 time to create 1 rle with old method : 0.00031876564025878906 time for calcul the mask position with numpy : 0.005715608596801758 nb_pixel_total : 469 time to create 1 rle with old method : 0.0005314350128173828 time for calcul the mask position with numpy : 0.005698680877685547 nb_pixel_total : 340 time to create 1 rle with old method : 0.0003943443298339844 time for calcul the mask position with numpy : 0.006463766098022461 nb_pixel_total : 106445 time to create 1 rle with old method : 0.11079788208007812 time for calcul the mask position with numpy : 0.0058536529541015625 nb_pixel_total : 331 time to create 1 rle with old method : 0.0003476142883300781 time for calcul the mask position with numpy : 0.00580596923828125 nb_pixel_total : 416 time to create 1 rle with old method : 0.0004978179931640625 time for calcul the mask position with numpy : 0.006307840347290039 nb_pixel_total : 134 time to create 1 rle with old method : 0.0001583099365234375 time for calcul the mask position with numpy : 0.005673408508300781 nb_pixel_total : 453 time to create 1 rle with old method : 0.00054168701171875 time for calcul the mask position with numpy : 0.0058248043060302734 nb_pixel_total : 161 time to create 1 rle with old method : 0.00018906593322753906 time for calcul the mask position with numpy : 0.005755186080932617 nb_pixel_total : 296 time to create 1 rle with old method : 0.0003528594970703125 time for calcul the mask position with numpy : 0.0057485103607177734 nb_pixel_total : 1818 time to create 1 rle with old method : 0.0019991397857666016 time for calcul the mask position with numpy : 0.005868196487426758 nb_pixel_total : 405 time to create 1 rle with old method : 0.0004374980926513672 time for calcul the mask position with numpy : 0.0058405399322509766 nb_pixel_total : 11 time to create 1 rle with old method : 4.863739013671875e-05 time for calcul the mask position with numpy : 0.005827188491821289 nb_pixel_total : 427 time to create 1 rle with old method : 0.0004863739013671875 time for calcul the mask position with numpy : 0.0058710575103759766 nb_pixel_total : 387 time to create 1 rle with old method : 0.0004642009735107422 time for calcul the mask position with numpy : 0.0058290958404541016 nb_pixel_total : 1232 time to create 1 rle with old method : 0.0013518333435058594 time for calcul the mask position with numpy : 0.005820035934448242 nb_pixel_total : 220 time to create 1 rle with old method : 0.0002529621124267578 time for calcul the mask position with numpy : 0.005847454071044922 nb_pixel_total : 127 time to create 1 rle with old method : 0.00016570091247558594 time for calcul the mask position with numpy : 0.005777120590209961 nb_pixel_total : 229 time to create 1 rle with old method : 0.0002682209014892578 time for calcul the mask position with numpy : 0.006072044372558594 nb_pixel_total : 143 time to create 1 rle with old method : 0.00018835067749023438 time for calcul the mask position with numpy : 0.005877494812011719 nb_pixel_total : 1443 time to create 1 rle with old method : 0.0016410350799560547 time for calcul the mask position with numpy : 0.005937099456787109 nb_pixel_total : 362 time to create 1 rle with old method : 0.0004496574401855469 time for calcul the mask position with numpy : 0.005900859832763672 nb_pixel_total : 100 time to create 1 rle with old method : 0.00013756752014160156 time for calcul the mask position with numpy : 0.0058689117431640625 nb_pixel_total : 867 time to create 1 rle with old method : 0.0010628700256347656 time for calcul the mask position with numpy : 0.0058441162109375 nb_pixel_total : 116 time to create 1 rle with old method : 0.00015878677368164062 time for calcul the mask position with numpy : 0.005883932113647461 nb_pixel_total : 140 time to create 1 rle with old method : 0.00018024444580078125 time for calcul the mask position with numpy : 0.0058901309967041016 nb_pixel_total : 681 time to create 1 rle with old method : 0.00074005126953125 time for calcul the mask position with numpy : 0.005660533905029297 nb_pixel_total : 227 time to create 1 rle with old method : 0.0002777576446533203 time for calcul the mask position with numpy : 0.005636930465698242 nb_pixel_total : 614 time to create 1 rle with old method : 0.0007197856903076172 time for calcul the mask position with numpy : 0.005764007568359375 nb_pixel_total : 522 time to create 1 rle with old method : 0.0005955696105957031 time for calcul the mask position with numpy : 0.005708217620849609 nb_pixel_total : 196 time to create 1 rle with old method : 0.00023221969604492188 time for calcul the mask position with numpy : 0.0056915283203125 nb_pixel_total : 1063 time to create 1 rle with old method : 0.0012009143829345703 time for calcul the mask position with numpy : 0.005661487579345703 nb_pixel_total : 100 time to create 1 rle with old method : 0.00016236305236816406 time for calcul the mask position with numpy : 0.005833148956298828 nb_pixel_total : 189 time to create 1 rle with old method : 0.0002288818359375 time for calcul the mask position with numpy : 0.0057599544525146484 nb_pixel_total : 427 time to create 1 rle with old method : 0.0004553794860839844 time for calcul the mask position with numpy : 0.005862712860107422 nb_pixel_total : 393 time to create 1 rle with old method : 0.00043487548828125 time for calcul the mask position with numpy : 0.005910634994506836 nb_pixel_total : 446 time to create 1 rle with old method : 0.0005431175231933594 time for calcul the mask position with numpy : 0.005982875823974609 nb_pixel_total : 299 time to create 1 rle with old method : 0.0003542900085449219 time for calcul the mask position with numpy : 0.006021261215209961 nb_pixel_total : 5176 time to create 1 rle with old method : 0.006003856658935547 time for calcul the mask position with numpy : 0.006835460662841797 nb_pixel_total : 845 time to create 1 rle with old method : 0.0010044574737548828 time for calcul the mask position with numpy : 0.005962371826171875 nb_pixel_total : 1828 time to create 1 rle with old method : 0.0021109580993652344 time for calcul the mask position with numpy : 0.006084442138671875 nb_pixel_total : 4662 time to create 1 rle with old method : 0.005477190017700195 time for calcul the mask position with numpy : 0.006092548370361328 nb_pixel_total : 218 time to create 1 rle with old method : 0.00037026405334472656 time for calcul the mask position with numpy : 0.0062634944915771484 nb_pixel_total : 1414 time to create 1 rle with old method : 0.001615285873413086 time for calcul the mask position with numpy : 0.0060901641845703125 nb_pixel_total : 798 time to create 1 rle with old method : 0.0009248256683349609 time for calcul the mask position with numpy : 0.006003141403198242 nb_pixel_total : 115 time to create 1 rle with old method : 0.00023794174194335938 time for calcul the mask position with numpy : 0.006021738052368164 nb_pixel_total : 592 time to create 1 rle with old method : 0.0006906986236572266 time for calcul the mask position with numpy : 0.006018400192260742 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002052783966064453 time for calcul the mask position with numpy : 0.0059511661529541016 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002071857452392578 time for calcul the mask position with numpy : 0.0058901309967041016 nb_pixel_total : 292 time to create 1 rle with old method : 0.00035309791564941406 time for calcul the mask position with numpy : 0.005861997604370117 nb_pixel_total : 186 time to create 1 rle with old method : 0.00021982192993164062 time for calcul the mask position with numpy : 0.005927324295043945 nb_pixel_total : 720 time to create 1 rle with old method : 0.0008480548858642578 time for calcul the mask position with numpy : 0.0057866573333740234 nb_pixel_total : 35 time to create 1 rle with old method : 7.939338684082031e-05 time for calcul the mask position with numpy : 0.00569915771484375 nb_pixel_total : 815 time to create 1 rle with old method : 0.0008642673492431641 time for calcul the mask position with numpy : 0.005829572677612305 nb_pixel_total : 1053 time to create 1 rle with old method : 0.0011279582977294922 time for calcul the mask position with numpy : 0.005637645721435547 nb_pixel_total : 142 time to create 1 rle with old method : 0.0001838207244873047 time for calcul the mask position with numpy : 0.005980491638183594 nb_pixel_total : 143 time to create 1 rle with old method : 0.0001823902130126953 time for calcul the mask position with numpy : 0.0060079097747802734 nb_pixel_total : 428 time to create 1 rle with old method : 0.0005550384521484375 time for calcul the mask position with numpy : 0.005976438522338867 nb_pixel_total : 4398 time to create 1 rle with old method : 0.004770755767822266 time for calcul the mask position with numpy : 0.005758047103881836 nb_pixel_total : 114 time to create 1 rle with old method : 0.0002524852752685547 time for calcul the mask position with numpy : 0.005881547927856445 nb_pixel_total : 136 time to create 1 rle with old method : 0.00016808509826660156 time for calcul the mask position with numpy : 0.005892515182495117 nb_pixel_total : 301 time to create 1 rle with old method : 0.0003616809844970703 time for calcul the mask position with numpy : 0.005876064300537109 nb_pixel_total : 534 time to create 1 rle with old method : 0.0006747245788574219 time for calcul the mask position with numpy : 0.006019115447998047 nb_pixel_total : 147 time to create 1 rle with old method : 0.00019121170043945312 time for calcul the mask position with numpy : 0.005941629409790039 nb_pixel_total : 953 time to create 1 rle with old method : 0.0010762214660644531 time for calcul the mask position with numpy : 0.005867719650268555 nb_pixel_total : 949 time to create 1 rle with old method : 0.0010569095611572266 time for calcul the mask position with numpy : 0.005986928939819336 nb_pixel_total : 839 time to create 1 rle with old method : 0.0008745193481445312 time for calcul the mask position with numpy : 0.0059871673583984375 nb_pixel_total : 1829 time to create 1 rle with old method : 0.002101898193359375 time for calcul the mask position with numpy : 0.005970954895019531 nb_pixel_total : 7499 time to create 1 rle with old method : 0.008339166641235352 time for calcul the mask position with numpy : 0.0057828426361083984 nb_pixel_total : 8 time to create 1 rle with old method : 3.528594970703125e-05 time for calcul the mask position with numpy : 0.006101369857788086 nb_pixel_total : 438 time to create 1 rle with old method : 0.0004973411560058594 time for calcul the mask position with numpy : 0.005976438522338867 nb_pixel_total : 1186 time to create 1 rle with old method : 0.001373291015625 time for calcul the mask position with numpy : 0.005997896194458008 nb_pixel_total : 560 time to create 1 rle with old method : 0.0006706714630126953 time for calcul the mask position with numpy : 0.006039619445800781 nb_pixel_total : 546 time to create 1 rle with old method : 0.0006442070007324219 create new chi : 1.3491156101226807 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 : 0.0027909278869628906 batch 1 Loaded 146 chid ids of type : 4230 Number RLEs to save : 14179 TO DO : save crop sub photo not yet done ! save time : 0.9185349941253662 nb_obj : 157 nb_hashtags : 7 time to prepare the origin masks : 1.4275896549224854 time for calcul the mask position with numpy : 0.1312558650970459 nb_pixel_total : 1770300 time to create 1 rle with new method : 0.08977055549621582 time for calcul the mask position with numpy : 0.0064775943756103516 nb_pixel_total : 60989 time to create 1 rle with old method : 0.06688833236694336 time for calcul the mask position with numpy : 0.006494283676147461 nb_pixel_total : 22 time to create 1 rle with old method : 6.461143493652344e-05 time for calcul the mask position with numpy : 0.011183023452758789 nb_pixel_total : 27 time to create 1 rle with old method : 6.0558319091796875e-05 time for calcul the mask position with numpy : 0.010761499404907227 nb_pixel_total : 1260 time to create 1 rle with old method : 0.0023164749145507812 time for calcul the mask position with numpy : 0.00795602798461914 nb_pixel_total : 51 time to create 1 rle with old method : 8.106231689453125e-05 time for calcul the mask position with numpy : 0.006090879440307617 nb_pixel_total : 260 time to create 1 rle with old method : 0.0003294944763183594 time for calcul the mask position with numpy : 0.005924224853515625 nb_pixel_total : 50 time to create 1 rle with old method : 8.797645568847656e-05 time for calcul the mask position with numpy : 0.005776166915893555 nb_pixel_total : 186 time to create 1 rle with old method : 0.0002872943878173828 time for calcul the mask position with numpy : 0.005926370620727539 nb_pixel_total : 89 time to create 1 rle with old method : 0.00015997886657714844 time for calcul the mask position with numpy : 0.005979061126708984 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002300739288330078 time for calcul the mask position with numpy : 0.006333351135253906 nb_pixel_total : 2546 time to create 1 rle with old method : 0.002706289291381836 time for calcul the mask position with numpy : 0.006196737289428711 nb_pixel_total : 285 time to create 1 rle with old method : 0.00034737586975097656 time for calcul the mask position with numpy : 0.00658106803894043 nb_pixel_total : 1006 time to create 1 rle with old method : 0.0015840530395507812 time for calcul the mask position with numpy : 0.0060656070709228516 nb_pixel_total : 543 time to create 1 rle with old method : 0.0006613731384277344 time for calcul the mask position with numpy : 0.006058692932128906 nb_pixel_total : 1701 time to create 1 rle with old method : 0.002129793167114258 time for calcul the mask position with numpy : 0.006301403045654297 nb_pixel_total : 846 time to create 1 rle with old method : 0.0010242462158203125 time for calcul the mask position with numpy : 0.006043434143066406 nb_pixel_total : 40 time to create 1 rle with old method : 9.369850158691406e-05 time for calcul the mask position with numpy : 0.006055116653442383 nb_pixel_total : 35 time to create 1 rle with old method : 5.984306335449219e-05 time for calcul the mask position with numpy : 0.006184577941894531 nb_pixel_total : 1469 time to create 1 rle with old method : 0.0016732215881347656 time for calcul the mask position with numpy : 0.006098270416259766 nb_pixel_total : 1107 time to create 1 rle with old method : 0.001247406005859375 time for calcul the mask position with numpy : 0.00677490234375 nb_pixel_total : 633 time to create 1 rle with old method : 0.0009257793426513672 time for calcul the mask position with numpy : 0.006111860275268555 nb_pixel_total : 3097 time to create 1 rle with old method : 0.0033693313598632812 time for calcul the mask position with numpy : 0.007359981536865234 nb_pixel_total : 5908 time to create 1 rle with old method : 0.007816314697265625 time for calcul the mask position with numpy : 0.007162809371948242 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002472400665283203 time for calcul the mask position with numpy : 0.00713038444519043 nb_pixel_total : 176 time to create 1 rle with old method : 0.00025534629821777344 time for calcul the mask position with numpy : 0.0075075626373291016 nb_pixel_total : 2 time to create 1 rle with old method : 2.4080276489257812e-05 time for calcul the mask position with numpy : 0.007095813751220703 nb_pixel_total : 87 time to create 1 rle with old method : 0.00012755393981933594 time for calcul the mask position with numpy : 0.006342411041259766 nb_pixel_total : 934 time to create 1 rle with old method : 0.0012133121490478516 time for calcul the mask position with numpy : 0.006992816925048828 nb_pixel_total : 3561 time to create 1 rle with old method : 0.004596233367919922 time for calcul the mask position with numpy : 0.005990028381347656 nb_pixel_total : 514 time to create 1 rle with old method : 0.0006425380706787109 time for calcul the mask position with numpy : 0.006131649017333984 nb_pixel_total : 576 time to create 1 rle with old method : 0.0006146430969238281 time for calcul the mask position with numpy : 0.006075143814086914 nb_pixel_total : 853 time to create 1 rle with old method : 0.0010564327239990234 time for calcul the mask position with numpy : 0.006190299987792969 nb_pixel_total : 1768 time to create 1 rle with old method : 0.0020225048065185547 time for calcul the mask position with numpy : 0.006369829177856445 nb_pixel_total : 131 time to create 1 rle with old method : 0.0001804828643798828 time for calcul the mask position with numpy : 0.0061419010162353516 nb_pixel_total : 156 time to create 1 rle with old method : 0.00027751922607421875 time for calcul the mask position with numpy : 0.0061490535736083984 nb_pixel_total : 422 time to create 1 rle with old method : 0.00048232078552246094 time for calcul the mask position with numpy : 0.006062984466552734 nb_pixel_total : 263 time to create 1 rle with old method : 0.0003247261047363281 time for calcul the mask position with numpy : 0.0061838626861572266 nb_pixel_total : 971 time to create 1 rle with old method : 0.0011718273162841797 time for calcul the mask position with numpy : 0.006449699401855469 nb_pixel_total : 1857 time to create 1 rle with old method : 0.0021283626556396484 time for calcul the mask position with numpy : 0.006090879440307617 nb_pixel_total : 4 time to create 1 rle with old method : 2.5033950805664062e-05 time for calcul the mask position with numpy : 0.00615239143371582 nb_pixel_total : 3470 time to create 1 rle with old method : 0.004847288131713867 time for calcul the mask position with numpy : 0.006797075271606445 nb_pixel_total : 13661 time to create 1 rle with old method : 0.015427827835083008 time for calcul the mask position with numpy : 0.006418466567993164 nb_pixel_total : 3307 time to create 1 rle with old method : 0.003703594207763672 time for calcul the mask position with numpy : 0.006646394729614258 nb_pixel_total : 4110 time to create 1 rle with old method : 0.004845380783081055 time for calcul the mask position with numpy : 0.006640434265136719 nb_pixel_total : 284 time to create 1 rle with old method : 0.00035691261291503906 time for calcul the mask position with numpy : 0.005841493606567383 nb_pixel_total : 3 time to create 1 rle with old method : 2.8848648071289062e-05 time for calcul the mask position with numpy : 0.009391546249389648 nb_pixel_total : 1379 time to create 1 rle with old method : 0.0024194717407226562 time for calcul the mask position with numpy : 0.010510921478271484 nb_pixel_total : 959 time to create 1 rle with old method : 0.0010881423950195312 time for calcul the mask position with numpy : 0.009553194046020508 nb_pixel_total : 803 time to create 1 rle with old method : 0.0009248256683349609 time for calcul the mask position with numpy : 0.0098419189453125 nb_pixel_total : 48 time to create 1 rle with old method : 9.918212890625e-05 time for calcul the mask position with numpy : 0.00996708869934082 nb_pixel_total : 1327 time to create 1 rle with old method : 0.0015537738800048828 time for calcul the mask position with numpy : 0.00603938102722168 nb_pixel_total : 94 time to create 1 rle with old method : 0.00014925003051757812 time for calcul the mask position with numpy : 0.005959272384643555 nb_pixel_total : 2984 time to create 1 rle with old method : 0.003438711166381836 time for calcul the mask position with numpy : 0.006102561950683594 nb_pixel_total : 150 time to create 1 rle with old method : 0.00021958351135253906 time for calcul the mask position with numpy : 0.006089687347412109 nb_pixel_total : 110 time to create 1 rle with old method : 0.0001506805419921875 time for calcul the mask position with numpy : 0.00597691535949707 nb_pixel_total : 113 time to create 1 rle with old method : 0.00015401840209960938 time for calcul the mask position with numpy : 0.006038665771484375 nb_pixel_total : 659 time to create 1 rle with old method : 0.0007104873657226562 time for calcul the mask position with numpy : 0.005993366241455078 nb_pixel_total : 1564 time to create 1 rle with old method : 0.0019850730895996094 time for calcul the mask position with numpy : 0.006103992462158203 nb_pixel_total : 367 time to create 1 rle with old method : 0.00045228004455566406 time for calcul the mask position with numpy : 0.0060541629791259766 nb_pixel_total : 324 time to create 1 rle with old method : 0.00040793418884277344 time for calcul the mask position with numpy : 0.00585174560546875 nb_pixel_total : 119 time to create 1 rle with old method : 0.00015926361083984375 time for calcul the mask position with numpy : 0.005808115005493164 nb_pixel_total : 2045 time to create 1 rle with old method : 0.0023996829986572266 time for calcul the mask position with numpy : 0.00607752799987793 nb_pixel_total : 711 time to create 1 rle with old method : 0.0008473396301269531 time for calcul the mask position with numpy : 0.005883455276489258 nb_pixel_total : 908 time to create 1 rle with old method : 0.0010666847229003906 time for calcul the mask position with numpy : 0.006027936935424805 nb_pixel_total : 1434 time to create 1 rle with old method : 0.0016720294952392578 time for calcul the mask position with numpy : 0.005870819091796875 nb_pixel_total : 1177 time to create 1 rle with old method : 0.0013735294342041016 time for calcul the mask position with numpy : 0.007616758346557617 nb_pixel_total : 99 time to create 1 rle with old method : 0.00014162063598632812 time for calcul the mask position with numpy : 0.0060727596282958984 nb_pixel_total : 1088 time to create 1 rle with old method : 0.0012884140014648438 time for calcul the mask position with numpy : 0.006135225296020508 nb_pixel_total : 1523 time to create 1 rle with old method : 0.001705169677734375 time for calcul the mask position with numpy : 0.006108760833740234 nb_pixel_total : 479 time to create 1 rle with old method : 0.0005767345428466797 time for calcul the mask position with numpy : 0.0061032772064208984 nb_pixel_total : 1346 time to create 1 rle with old method : 0.001607656478881836 time for calcul the mask position with numpy : 0.006465911865234375 nb_pixel_total : 3767 time to create 1 rle with old method : 0.004407167434692383 time for calcul the mask position with numpy : 0.0061953067779541016 nb_pixel_total : 300 time to create 1 rle with old method : 0.0003638267517089844 time for calcul the mask position with numpy : 0.0062541961669921875 nb_pixel_total : 477 time to create 1 rle with old method : 0.0005590915679931641 time for calcul the mask position with numpy : 0.0063457489013671875 nb_pixel_total : 301 time to create 1 rle with old method : 0.00038695335388183594 time for calcul the mask position with numpy : 0.0062105655670166016 nb_pixel_total : 93 time to create 1 rle with old method : 0.0001304149627685547 time for calcul the mask position with numpy : 0.00665736198425293 nb_pixel_total : 106704 time to create 1 rle with old method : 0.11364340782165527 time for calcul the mask position with numpy : 0.006274223327636719 nb_pixel_total : 418 time to create 1 rle with old method : 0.0004801750183105469 time for calcul the mask position with numpy : 0.006129264831542969 nb_pixel_total : 412 time to create 1 rle with old method : 0.0005259513854980469 time for calcul the mask position with numpy : 0.006136894226074219 nb_pixel_total : 427 time to create 1 rle with old method : 0.0004780292510986328 time for calcul the mask position with numpy : 0.006299734115600586 nb_pixel_total : 113 time to create 1 rle with old method : 0.00015807151794433594 time for calcul the mask position with numpy : 0.006148576736450195 nb_pixel_total : 164 time to create 1 rle with old method : 0.00020647048950195312 time for calcul the mask position with numpy : 0.0061817169189453125 nb_pixel_total : 175 time to create 1 rle with old method : 0.00022339820861816406 time for calcul the mask position with numpy : 0.006495952606201172 nb_pixel_total : 297 time to create 1 rle with old method : 0.0003445148468017578 time for calcul the mask position with numpy : 0.006105661392211914 nb_pixel_total : 135 time to create 1 rle with old method : 0.00018024444580078125 time for calcul the mask position with numpy : 0.0063512325286865234 nb_pixel_total : 438 time to create 1 rle with old method : 0.0008757114410400391 time for calcul the mask position with numpy : 0.0067446231842041016 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005083084106445312 time for calcul the mask position with numpy : 0.0060460567474365234 nb_pixel_total : 1630 time to create 1 rle with old method : 0.0018987655639648438 time for calcul the mask position with numpy : 0.006038665771484375 nb_pixel_total : 417 time to create 1 rle with old method : 0.0005030632019042969 time for calcul the mask position with numpy : 0.006045818328857422 nb_pixel_total : 2017 time to create 1 rle with old method : 0.0023479461669921875 time for calcul the mask position with numpy : 0.005909919738769531 nb_pixel_total : 756 time to create 1 rle with old method : 0.0008683204650878906 time for calcul the mask position with numpy : 0.006109714508056641 nb_pixel_total : 214 time to create 1 rle with old method : 0.0002777576446533203 time for calcul the mask position with numpy : 0.006178855895996094 nb_pixel_total : 131 time to create 1 rle with old method : 0.00018453598022460938 time for calcul the mask position with numpy : 0.00615239143371582 nb_pixel_total : 9 time to create 1 rle with old method : 4.601478576660156e-05 time for calcul the mask position with numpy : 0.006180763244628906 nb_pixel_total : 126 time to create 1 rle with old method : 0.00016760826110839844 time for calcul the mask position with numpy : 0.006325721740722656 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002391338348388672 time for calcul the mask position with numpy : 0.006161928176879883 nb_pixel_total : 1036 time to create 1 rle with old method : 0.0012090206146240234 time for calcul the mask position with numpy : 0.0073850154876708984 nb_pixel_total : 885 time to create 1 rle with old method : 0.0009515285491943359 time for calcul the mask position with numpy : 0.006433963775634766 nb_pixel_total : 204 time to create 1 rle with old method : 0.00025844573974609375 time for calcul the mask position with numpy : 0.00599980354309082 nb_pixel_total : 432 time to create 1 rle with old method : 0.0005292892456054688 time for calcul the mask position with numpy : 0.00581812858581543 nb_pixel_total : 169 time to create 1 rle with old method : 0.00021576881408691406 time for calcul the mask position with numpy : 0.006100177764892578 nb_pixel_total : 313 time to create 1 rle with old method : 0.00037980079650878906 time for calcul the mask position with numpy : 0.0059626102447509766 nb_pixel_total : 137 time to create 1 rle with old method : 0.0001819133758544922 time for calcul the mask position with numpy : 0.006240367889404297 nb_pixel_total : 239 time to create 1 rle with old method : 0.0003559589385986328 time for calcul the mask position with numpy : 0.007367849349975586 nb_pixel_total : 17 time to create 1 rle with old method : 7.772445678710938e-05 time for calcul the mask position with numpy : 0.007246732711791992 nb_pixel_total : 717 time to create 1 rle with old method : 0.0008456707000732422 time for calcul the mask position with numpy : 0.005917787551879883 nb_pixel_total : 273 time to create 1 rle with old method : 0.0003535747528076172 time for calcul the mask position with numpy : 0.005860328674316406 nb_pixel_total : 1037 time to create 1 rle with old method : 0.0011208057403564453 time for calcul the mask position with numpy : 0.006083488464355469 nb_pixel_total : 180 time to create 1 rle with old method : 0.00039076805114746094 time for calcul the mask position with numpy : 0.006020784378051758 nb_pixel_total : 650 time to create 1 rle with old method : 0.0007431507110595703 time for calcul the mask position with numpy : 0.0059490203857421875 nb_pixel_total : 42 time to create 1 rle with old method : 9.131431579589844e-05 time for calcul the mask position with numpy : 0.005957126617431641 nb_pixel_total : 210 time to create 1 rle with old method : 0.0002677440643310547 time for calcul the mask position with numpy : 0.005950212478637695 nb_pixel_total : 852 time to create 1 rle with old method : 0.0009915828704833984 time for calcul the mask position with numpy : 0.005811929702758789 nb_pixel_total : 400 time to create 1 rle with old method : 0.000469207763671875 time for calcul the mask position with numpy : 0.0060155391693115234 nb_pixel_total : 152 time to create 1 rle with old method : 0.0002257823944091797 time for calcul the mask position with numpy : 0.006005048751831055 nb_pixel_total : 481 time to create 1 rle with old method : 0.0005793571472167969 time for calcul the mask position with numpy : 0.0060040950775146484 nb_pixel_total : 296 time to create 1 rle with old method : 0.00037550926208496094 time for calcul the mask position with numpy : 0.00608062744140625 nb_pixel_total : 5095 time to create 1 rle with old method : 0.005882978439331055 time for calcul the mask position with numpy : 0.005999565124511719 nb_pixel_total : 815 time to create 1 rle with old method : 0.0008981227874755859 time for calcul the mask position with numpy : 0.006160736083984375 nb_pixel_total : 5180 time to create 1 rle with old method : 0.005860567092895508 time for calcul the mask position with numpy : 0.0061266422271728516 nb_pixel_total : 124 time to create 1 rle with old method : 0.000179290771484375 time for calcul the mask position with numpy : 0.0058438777923583984 nb_pixel_total : 1605 time to create 1 rle with old method : 0.0017499923706054688 time for calcul the mask position with numpy : 0.006039619445800781 nb_pixel_total : 4 time to create 1 rle with old method : 4.5299530029296875e-05 time for calcul the mask position with numpy : 0.006078958511352539 nb_pixel_total : 29 time to create 1 rle with old method : 7.200241088867188e-05 time for calcul the mask position with numpy : 0.006017208099365234 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005047321319580078 time for calcul the mask position with numpy : 0.0061228275299072266 nb_pixel_total : 198 time to create 1 rle with old method : 0.0002968311309814453 time for calcul the mask position with numpy : 0.006034135818481445 nb_pixel_total : 101 time to create 1 rle with old method : 0.00016355514526367188 time for calcul the mask position with numpy : 0.005840301513671875 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006356239318847656 time for calcul the mask position with numpy : 0.006021976470947266 nb_pixel_total : 1560 time to create 1 rle with old method : 0.0017991065979003906 time for calcul the mask position with numpy : 0.005942821502685547 nb_pixel_total : 109 time to create 1 rle with old method : 0.00014734268188476562 time for calcul the mask position with numpy : 0.005984783172607422 nb_pixel_total : 52 time to create 1 rle with old method : 0.00011801719665527344 time for calcul the mask position with numpy : 0.006001949310302734 nb_pixel_total : 764 time to create 1 rle with old method : 0.0008690357208251953 time for calcul the mask position with numpy : 0.005904197692871094 nb_pixel_total : 479 time to create 1 rle with old method : 0.0005702972412109375 time for calcul the mask position with numpy : 0.005995750427246094 nb_pixel_total : 185 time to create 1 rle with old method : 0.00023984909057617188 time for calcul the mask position with numpy : 0.0060198307037353516 nb_pixel_total : 291 time to create 1 rle with old method : 0.00033926963806152344 time for calcul the mask position with numpy : 0.0061435699462890625 nb_pixel_total : 233 time to create 1 rle with old method : 0.00028777122497558594 time for calcul the mask position with numpy : 0.006204366683959961 nb_pixel_total : 845 time to create 1 rle with old method : 0.000985860824584961 time for calcul the mask position with numpy : 0.008432149887084961 nb_pixel_total : 965 time to create 1 rle with old method : 0.0010993480682373047 time for calcul the mask position with numpy : 0.00815272331237793 nb_pixel_total : 455 time to create 1 rle with old method : 0.0005409717559814453 time for calcul the mask position with numpy : 0.008068084716796875 nb_pixel_total : 1739 time to create 1 rle with old method : 0.002011537551879883 time for calcul the mask position with numpy : 0.008324384689331055 nb_pixel_total : 2634 time to create 1 rle with old method : 0.0028641223907470703 time for calcul the mask position with numpy : 0.008314132690429688 nb_pixel_total : 54 time to create 1 rle with old method : 7.987022399902344e-05 time for calcul the mask position with numpy : 0.008264303207397461 nb_pixel_total : 150 time to create 1 rle with old method : 0.00018095970153808594 time for calcul the mask position with numpy : 0.008079767227172852 nb_pixel_total : 1124 time to create 1 rle with old method : 0.0012772083282470703 time for calcul the mask position with numpy : 0.008304595947265625 nb_pixel_total : 492 time to create 1 rle with old method : 0.00055694580078125 time for calcul the mask position with numpy : 0.00829768180847168 nb_pixel_total : 309 time to create 1 rle with old method : 0.0003643035888671875 time for calcul the mask position with numpy : 0.008375406265258789 nb_pixel_total : 1750 time to create 1 rle with old method : 0.0020313262939453125 time for calcul the mask position with numpy : 0.008159637451171875 nb_pixel_total : 461 time to create 1 rle with old method : 0.0004982948303222656 time for calcul the mask position with numpy : 0.008087396621704102 nb_pixel_total : 129 time to create 1 rle with old method : 0.0001437664031982422 time for calcul the mask position with numpy : 0.007999181747436523 nb_pixel_total : 1228 time to create 1 rle with old method : 0.0013759136199951172 time for calcul the mask position with numpy : 0.008211374282836914 nb_pixel_total : 98 time to create 1 rle with old method : 0.00013327598571777344 time for calcul the mask position with numpy : 0.008352279663085938 nb_pixel_total : 1132 time to create 1 rle with old method : 0.0012829303741455078 time for calcul the mask position with numpy : 0.00833272933959961 nb_pixel_total : 209 time to create 1 rle with old method : 0.00025010108947753906 time for calcul the mask position with numpy : 0.008292913436889648 nb_pixel_total : 338 time to create 1 rle with old method : 0.000354766845703125 time for calcul the mask position with numpy : 0.0083465576171875 nb_pixel_total : 421 time to create 1 rle with old method : 0.0004825592041015625 time for calcul the mask position with numpy : 0.008130550384521484 nb_pixel_total : 498 time to create 1 rle with old method : 0.0005788803100585938 time for calcul the mask position with numpy : 0.008331537246704102 nb_pixel_total : 286 time to create 1 rle with old method : 0.0003287792205810547 create new chi : 1.6255836486816406 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 : 0.002703428268432617 batch 1 Loaded 158 chid ids of type : 4230 Number RLEs to save : 14221 TO DO : save crop sub photo not yet done ! save time : 1.8845930099487305 nb_obj : 157 nb_hashtags : 7 time to prepare the origin masks : 1.541576623916626 time for calcul the mask position with numpy : 0.2018437385559082 nb_pixel_total : 1770177 time to create 1 rle with new method : 0.08133268356323242 time for calcul the mask position with numpy : 0.006524562835693359 nb_pixel_total : 48501 time to create 1 rle with old method : 0.052696943283081055 time for calcul the mask position with numpy : 0.00628662109375 nb_pixel_total : 95 time to create 1 rle with old method : 0.0002086162567138672 time for calcul the mask position with numpy : 0.00603175163269043 nb_pixel_total : 22 time to create 1 rle with old method : 4.887580871582031e-05 time for calcul the mask position with numpy : 0.006039619445800781 nb_pixel_total : 73 time to create 1 rle with old method : 0.00012087821960449219 time for calcul the mask position with numpy : 0.006040334701538086 nb_pixel_total : 40 time to create 1 rle with old method : 7.534027099609375e-05 time for calcul the mask position with numpy : 0.0060520172119140625 nb_pixel_total : 1261 time to create 1 rle with old method : 0.0014853477478027344 time for calcul the mask position with numpy : 0.006028652191162109 nb_pixel_total : 53 time to create 1 rle with old method : 8.20159912109375e-05 time for calcul the mask position with numpy : 0.006024837493896484 nb_pixel_total : 66 time to create 1 rle with old method : 0.00010585784912109375 time for calcul the mask position with numpy : 0.0059010982513427734 nb_pixel_total : 166 time to create 1 rle with old method : 0.00022840499877929688 time for calcul the mask position with numpy : 0.006195783615112305 nb_pixel_total : 140 time to create 1 rle with old method : 0.00018930435180664062 time for calcul the mask position with numpy : 0.006012916564941406 nb_pixel_total : 178 time to create 1 rle with old method : 0.0002372264862060547 time for calcul the mask position with numpy : 0.006251335144042969 nb_pixel_total : 252 time to create 1 rle with old method : 0.0003902912139892578 time for calcul the mask position with numpy : 0.006520748138427734 nb_pixel_total : 2346 time to create 1 rle with old method : 0.00255584716796875 time for calcul the mask position with numpy : 0.006075382232666016 nb_pixel_total : 898 time to create 1 rle with old method : 0.001050710678100586 time for calcul the mask position with numpy : 0.006028890609741211 nb_pixel_total : 779 time to create 1 rle with old method : 0.0009183883666992188 time for calcul the mask position with numpy : 0.006026506423950195 nb_pixel_total : 558 time to create 1 rle with old method : 0.0006973743438720703 time for calcul the mask position with numpy : 0.006014108657836914 nb_pixel_total : 259 time to create 1 rle with old method : 0.0003097057342529297 time for calcul the mask position with numpy : 0.006071805953979492 nb_pixel_total : 507 time to create 1 rle with old method : 0.0006229877471923828 time for calcul the mask position with numpy : 0.005910634994506836 nb_pixel_total : 1407 time to create 1 rle with old method : 0.001650094985961914 time for calcul the mask position with numpy : 0.005994558334350586 nb_pixel_total : 100 time to create 1 rle with old method : 0.00013136863708496094 time for calcul the mask position with numpy : 0.005990743637084961 nb_pixel_total : 241 time to create 1 rle with old method : 0.0003070831298828125 time for calcul the mask position with numpy : 0.005985736846923828 nb_pixel_total : 2481 time to create 1 rle with old method : 0.002901792526245117 time for calcul the mask position with numpy : 0.006067991256713867 nb_pixel_total : 2315 time to create 1 rle with old method : 0.0026531219482421875 time for calcul the mask position with numpy : 0.006040334701538086 nb_pixel_total : 1085 time to create 1 rle with old method : 0.001283884048461914 time for calcul the mask position with numpy : 0.005993366241455078 nb_pixel_total : 2938 time to create 1 rle with old method : 0.003408670425415039 time for calcul the mask position with numpy : 0.006061553955078125 nb_pixel_total : 4285 time to create 1 rle with old method : 0.004889011383056641 time for calcul the mask position with numpy : 0.0060503482818603516 nb_pixel_total : 7180 time to create 1 rle with old method : 0.008278846740722656 time for calcul the mask position with numpy : 0.006247997283935547 nb_pixel_total : 1253 time to create 1 rle with old method : 0.001415252685546875 time for calcul the mask position with numpy : 0.006156444549560547 nb_pixel_total : 168 time to create 1 rle with old method : 0.00020575523376464844 time for calcul the mask position with numpy : 0.0062503814697265625 nb_pixel_total : 520 time to create 1 rle with old method : 0.0006253719329833984 time for calcul the mask position with numpy : 0.006188631057739258 nb_pixel_total : 915 time to create 1 rle with old method : 0.0010840892791748047 time for calcul the mask position with numpy : 0.006165027618408203 nb_pixel_total : 597 time to create 1 rle with old method : 0.0007750988006591797 time for calcul the mask position with numpy : 0.006025552749633789 nb_pixel_total : 15981 time to create 1 rle with old method : 0.017154216766357422 time for calcul the mask position with numpy : 0.006070613861083984 nb_pixel_total : 121 time to create 1 rle with old method : 0.00017547607421875 time for calcul the mask position with numpy : 0.005795717239379883 nb_pixel_total : 1428 time to create 1 rle with old method : 0.0016169548034667969 time for calcul the mask position with numpy : 0.005944967269897461 nb_pixel_total : 4428 time to create 1 rle with old method : 0.005034685134887695 time for calcul the mask position with numpy : 0.005928754806518555 nb_pixel_total : 4520 time to create 1 rle with old method : 0.005137205123901367 time for calcul the mask position with numpy : 0.006086111068725586 nb_pixel_total : 2836 time to create 1 rle with old method : 0.003330707550048828 time for calcul the mask position with numpy : 0.006239652633666992 nb_pixel_total : 225 time to create 1 rle with old method : 0.00028324127197265625 time for calcul the mask position with numpy : 0.006238222122192383 nb_pixel_total : 1493 time to create 1 rle with old method : 0.0017709732055664062 time for calcul the mask position with numpy : 0.00620269775390625 nb_pixel_total : 700 time to create 1 rle with old method : 0.0007917881011962891 time for calcul the mask position with numpy : 0.006231784820556641 nb_pixel_total : 583 time to create 1 rle with old method : 0.0008103847503662109 time for calcul the mask position with numpy : 0.006226301193237305 nb_pixel_total : 239 time to create 1 rle with old method : 0.00033736228942871094 time for calcul the mask position with numpy : 0.0064275264739990234 nb_pixel_total : 1388 time to create 1 rle with old method : 0.0016131401062011719 time for calcul the mask position with numpy : 0.006260395050048828 nb_pixel_total : 826 time to create 1 rle with old method : 0.0009639263153076172 time for calcul the mask position with numpy : 0.005995988845825195 nb_pixel_total : 23 time to create 1 rle with old method : 7.390975952148438e-05 time for calcul the mask position with numpy : 0.005927085876464844 nb_pixel_total : 286 time to create 1 rle with old method : 0.000331878662109375 time for calcul the mask position with numpy : 0.006211280822753906 nb_pixel_total : 1453 time to create 1 rle with old method : 0.0016484260559082031 time for calcul the mask position with numpy : 0.006022453308105469 nb_pixel_total : 168 time to create 1 rle with old method : 0.00021767616271972656 time for calcul the mask position with numpy : 0.006268739700317383 nb_pixel_total : 404 time to create 1 rle with old method : 0.0004925727844238281 time for calcul the mask position with numpy : 0.00640869140625 nb_pixel_total : 7112 time to create 1 rle with old method : 0.007688283920288086 time for calcul the mask position with numpy : 0.006617307662963867 nb_pixel_total : 153 time to create 1 rle with old method : 0.0002040863037109375 time for calcul the mask position with numpy : 0.006331443786621094 nb_pixel_total : 125 time to create 1 rle with old method : 0.00016927719116210938 time for calcul the mask position with numpy : 0.006041049957275391 nb_pixel_total : 2412 time to create 1 rle with old method : 0.002901315689086914 time for calcul the mask position with numpy : 0.00641632080078125 nb_pixel_total : 2042 time to create 1 rle with old method : 0.002402067184448242 time for calcul the mask position with numpy : 0.006194591522216797 nb_pixel_total : 115 time to create 1 rle with old method : 0.0001575946807861328 time for calcul the mask position with numpy : 0.0061571598052978516 nb_pixel_total : 704 time to create 1 rle with old method : 0.0008425712585449219 time for calcul the mask position with numpy : 0.006531953811645508 nb_pixel_total : 492 time to create 1 rle with old method : 0.0005769729614257812 time for calcul the mask position with numpy : 0.006326436996459961 nb_pixel_total : 288 time to create 1 rle with old method : 0.0003676414489746094 time for calcul the mask position with numpy : 0.006156444549560547 nb_pixel_total : 550 time to create 1 rle with old method : 0.0006794929504394531 time for calcul the mask position with numpy : 0.005974292755126953 nb_pixel_total : 363 time to create 1 rle with old method : 0.00044918060302734375 time for calcul the mask position with numpy : 0.0061109066009521484 nb_pixel_total : 1029 time to create 1 rle with old method : 0.0011630058288574219 time for calcul the mask position with numpy : 0.009561538696289062 nb_pixel_total : 97 time to create 1 rle with old method : 0.00013184547424316406 time for calcul the mask position with numpy : 0.0062830448150634766 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001373291015625 time for calcul the mask position with numpy : 0.006149768829345703 nb_pixel_total : 987 time to create 1 rle with old method : 0.0011601448059082031 time for calcul the mask position with numpy : 0.0058972835540771484 nb_pixel_total : 1064 time to create 1 rle with old method : 0.0012743473052978516 time for calcul the mask position with numpy : 0.005937099456787109 nb_pixel_total : 320 time to create 1 rle with old method : 0.00036025047302246094 time for calcul the mask position with numpy : 0.0058019161224365234 nb_pixel_total : 777 time to create 1 rle with old method : 0.0008945465087890625 time for calcul the mask position with numpy : 0.005932807922363281 nb_pixel_total : 1396 time to create 1 rle with old method : 0.0015442371368408203 time for calcul the mask position with numpy : 0.0058209896087646484 nb_pixel_total : 466 time to create 1 rle with old method : 0.0005402565002441406 time for calcul the mask position with numpy : 0.006001710891723633 nb_pixel_total : 483 time to create 1 rle with old method : 0.0005741119384765625 time for calcul the mask position with numpy : 0.0058252811431884766 nb_pixel_total : 96 time to create 1 rle with old method : 0.0001430511474609375 time for calcul the mask position with numpy : 0.005912065505981445 nb_pixel_total : 445 time to create 1 rle with old method : 0.0005335807800292969 time for calcul the mask position with numpy : 0.006476402282714844 nb_pixel_total : 106492 time to create 1 rle with old method : 0.11095452308654785 time for calcul the mask position with numpy : 0.006749391555786133 nb_pixel_total : 396 time to create 1 rle with old method : 0.00044345855712890625 time for calcul the mask position with numpy : 0.005858898162841797 nb_pixel_total : 139 time to create 1 rle with old method : 0.00016951560974121094 time for calcul the mask position with numpy : 0.005849123001098633 nb_pixel_total : 172 time to create 1 rle with old method : 0.00021791458129882812 time for calcul the mask position with numpy : 0.005866289138793945 nb_pixel_total : 372 time to create 1 rle with old method : 0.00040340423583984375 time for calcul the mask position with numpy : 0.00594329833984375 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005025863647460938 time for calcul the mask position with numpy : 0.005946159362792969 nb_pixel_total : 381 time to create 1 rle with old method : 0.0004630088806152344 time for calcul the mask position with numpy : 0.005883216857910156 nb_pixel_total : 1661 time to create 1 rle with old method : 0.0018887519836425781 time for calcul the mask position with numpy : 0.005774497985839844 nb_pixel_total : 407 time to create 1 rle with old method : 0.00048351287841796875 time for calcul the mask position with numpy : 0.00596928596496582 nb_pixel_total : 485 time to create 1 rle with old method : 0.0005364418029785156 time for calcul the mask position with numpy : 0.0059316158294677734 nb_pixel_total : 1470 time to create 1 rle with old method : 0.0017077922821044922 time for calcul the mask position with numpy : 0.006018877029418945 nb_pixel_total : 214 time to create 1 rle with old method : 0.00027632713317871094 time for calcul the mask position with numpy : 0.0059680938720703125 nb_pixel_total : 22 time to create 1 rle with old method : 5.698204040527344e-05 time for calcul the mask position with numpy : 0.006014823913574219 nb_pixel_total : 5 time to create 1 rle with old method : 3.743171691894531e-05 time for calcul the mask position with numpy : 0.0059223175048828125 nb_pixel_total : 1005 time to create 1 rle with old method : 0.0011053085327148438 time for calcul the mask position with numpy : 0.006027698516845703 nb_pixel_total : 109 time to create 1 rle with old method : 0.00014543533325195312 time for calcul the mask position with numpy : 0.006018400192260742 nb_pixel_total : 208 time to create 1 rle with old method : 0.0002598762512207031 time for calcul the mask position with numpy : 0.0059397220611572266 nb_pixel_total : 241 time to create 1 rle with old method : 0.0003001689910888672 time for calcul the mask position with numpy : 0.0060274600982666016 nb_pixel_total : 996 time to create 1 rle with old method : 0.0011785030364990234 time for calcul the mask position with numpy : 0.00597834587097168 nb_pixel_total : 209 time to create 1 rle with old method : 0.0002617835998535156 time for calcul the mask position with numpy : 0.006088733673095703 nb_pixel_total : 52 time to create 1 rle with old method : 0.00011348724365234375 time for calcul the mask position with numpy : 0.005989789962768555 nb_pixel_total : 580 time to create 1 rle with old method : 0.0006623268127441406 time for calcul the mask position with numpy : 0.005897045135498047 nb_pixel_total : 37 time to create 1 rle with old method : 6.365776062011719e-05 time for calcul the mask position with numpy : 0.0057697296142578125 nb_pixel_total : 59 time to create 1 rle with old method : 9.846687316894531e-05 time for calcul the mask position with numpy : 0.0059735774993896484 nb_pixel_total : 48 time to create 1 rle with old method : 9.083747863769531e-05 time for calcul the mask position with numpy : 0.00572657585144043 nb_pixel_total : 703 time to create 1 rle with old method : 0.0008065700531005859 time for calcul the mask position with numpy : 0.005965232849121094 nb_pixel_total : 307 time to create 1 rle with old method : 0.0003769397735595703 time for calcul the mask position with numpy : 0.005893707275390625 nb_pixel_total : 20 time to create 1 rle with old method : 5.817413330078125e-05 time for calcul the mask position with numpy : 0.005957365036010742 nb_pixel_total : 628 time to create 1 rle with old method : 0.0007386207580566406 time for calcul the mask position with numpy : 0.0057086944580078125 nb_pixel_total : 89 time to create 1 rle with old method : 0.000152587890625 time for calcul the mask position with numpy : 0.005784749984741211 nb_pixel_total : 699 time to create 1 rle with old method : 0.0007810592651367188 time for calcul the mask position with numpy : 0.0058176517486572266 nb_pixel_total : 532 time to create 1 rle with old method : 0.0006632804870605469 time for calcul the mask position with numpy : 0.005893707275390625 nb_pixel_total : 53 time to create 1 rle with old method : 0.0001010894775390625 time for calcul the mask position with numpy : 0.0058710575103759766 nb_pixel_total : 178 time to create 1 rle with old method : 0.00023698806762695312 time for calcul the mask position with numpy : 0.005827426910400391 nb_pixel_total : 956 time to create 1 rle with old method : 0.0011241436004638672 time for calcul the mask position with numpy : 0.005850553512573242 nb_pixel_total : 178 time to create 1 rle with old method : 0.0002288818359375 time for calcul the mask position with numpy : 0.0057828426361083984 nb_pixel_total : 2233 time to create 1 rle with old method : 0.002662181854248047 time for calcul the mask position with numpy : 0.005980730056762695 nb_pixel_total : 424 time to create 1 rle with old method : 0.0005717277526855469 time for calcul the mask position with numpy : 0.005845308303833008 nb_pixel_total : 35 time to create 1 rle with old method : 9.965896606445312e-05 time for calcul the mask position with numpy : 0.00599360466003418 nb_pixel_total : 44 time to create 1 rle with old method : 9.322166442871094e-05 time for calcul the mask position with numpy : 0.0058612823486328125 nb_pixel_total : 298 time to create 1 rle with old method : 0.0003428459167480469 time for calcul the mask position with numpy : 0.007927656173706055 nb_pixel_total : 1463 time to create 1 rle with old method : 0.0016200542449951172 time for calcul the mask position with numpy : 0.009556055068969727 nb_pixel_total : 345 time to create 1 rle with old method : 0.00039124488830566406 time for calcul the mask position with numpy : 0.00958704948425293 nb_pixel_total : 1016 time to create 1 rle with old method : 0.0012600421905517578 time for calcul the mask position with numpy : 0.009570598602294922 nb_pixel_total : 6613 time to create 1 rle with old method : 0.0073773860931396484 time for calcul the mask position with numpy : 0.009773731231689453 nb_pixel_total : 3480 time to create 1 rle with old method : 0.0039196014404296875 time for calcul the mask position with numpy : 0.00982975959777832 nb_pixel_total : 331 time to create 1 rle with old method : 0.0004165172576904297 time for calcul the mask position with numpy : 0.009623527526855469 nb_pixel_total : 1544 time to create 1 rle with old method : 0.0018000602722167969 time for calcul the mask position with numpy : 0.009700775146484375 nb_pixel_total : 26 time to create 1 rle with old method : 6.580352783203125e-05 time for calcul the mask position with numpy : 0.009779214859008789 nb_pixel_total : 899 time to create 1 rle with old method : 0.0010676383972167969 time for calcul the mask position with numpy : 0.009951353073120117 nb_pixel_total : 103 time to create 1 rle with old method : 0.00014925003051757812 time for calcul the mask position with numpy : 0.009735822677612305 nb_pixel_total : 36 time to create 1 rle with old method : 9.489059448242188e-05 time for calcul the mask position with numpy : 0.009829998016357422 nb_pixel_total : 497 time to create 1 rle with old method : 0.0005986690521240234 time for calcul the mask position with numpy : 0.009716987609863281 nb_pixel_total : 164 time to create 1 rle with old method : 0.0002028942108154297 time for calcul the mask position with numpy : 0.009693622589111328 nb_pixel_total : 270 time to create 1 rle with old method : 0.0003190040588378906 time for calcul the mask position with numpy : 0.009684562683105469 nb_pixel_total : 680 time to create 1 rle with old method : 0.0008137226104736328 time for calcul the mask position with numpy : 0.009801626205444336 nb_pixel_total : 805 time to create 1 rle with old method : 0.0009453296661376953 time for calcul the mask position with numpy : 0.008296728134155273 nb_pixel_total : 1564 time to create 1 rle with old method : 0.0017485618591308594 time for calcul the mask position with numpy : 0.005917549133300781 nb_pixel_total : 42 time to create 1 rle with old method : 7.43865966796875e-05 time for calcul the mask position with numpy : 0.005766868591308594 nb_pixel_total : 881 time to create 1 rle with old method : 0.0010728836059570312 time for calcul the mask position with numpy : 0.005851030349731445 nb_pixel_total : 118 time to create 1 rle with old method : 0.00014400482177734375 time for calcul the mask position with numpy : 0.0057756900787353516 nb_pixel_total : 122 time to create 1 rle with old method : 0.0001609325408935547 time for calcul the mask position with numpy : 0.005879878997802734 nb_pixel_total : 53 time to create 1 rle with old method : 0.00010418891906738281 time for calcul the mask position with numpy : 0.005701780319213867 nb_pixel_total : 351 time to create 1 rle with old method : 0.0004107952117919922 time for calcul the mask position with numpy : 0.005939006805419922 nb_pixel_total : 83 time to create 1 rle with old method : 0.0001227855682373047 time for calcul the mask position with numpy : 0.005915641784667969 nb_pixel_total : 2056 time to create 1 rle with old method : 0.0023026466369628906 time for calcul the mask position with numpy : 0.005979776382446289 nb_pixel_total : 126 time to create 1 rle with old method : 0.00016808509826660156 time for calcul the mask position with numpy : 0.005887508392333984 nb_pixel_total : 654 time to create 1 rle with old method : 0.0007965564727783203 time for calcul the mask position with numpy : 0.005785703659057617 nb_pixel_total : 38 time to create 1 rle with old method : 7.62939453125e-05 time for calcul the mask position with numpy : 0.005812644958496094 nb_pixel_total : 1528 time to create 1 rle with old method : 0.0017955303192138672 time for calcul the mask position with numpy : 0.0058820247650146484 nb_pixel_total : 582 time to create 1 rle with old method : 0.0006594657897949219 time for calcul the mask position with numpy : 0.0058057308197021484 nb_pixel_total : 210 time to create 1 rle with old method : 0.00024271011352539062 time for calcul the mask position with numpy : 0.0060122013092041016 nb_pixel_total : 317 time to create 1 rle with old method : 0.00038886070251464844 time for calcul the mask position with numpy : 0.005902767181396484 nb_pixel_total : 500 time to create 1 rle with old method : 0.0005753040313720703 time for calcul the mask position with numpy : 0.006226062774658203 nb_pixel_total : 7904 time to create 1 rle with old method : 0.008489847183227539 time for calcul the mask position with numpy : 0.005876302719116211 nb_pixel_total : 579 time to create 1 rle with old method : 0.0007197856903076172 time for calcul the mask position with numpy : 0.006433248519897461 nb_pixel_total : 117 time to create 1 rle with old method : 0.0002295970916748047 time for calcul the mask position with numpy : 0.0064318180084228516 nb_pixel_total : 1263 time to create 1 rle with old method : 0.0019996166229248047 time for calcul the mask position with numpy : 0.006412506103515625 nb_pixel_total : 46 time to create 1 rle with old method : 0.00012135505676269531 time for calcul the mask position with numpy : 0.006380558013916016 nb_pixel_total : 1073 time to create 1 rle with old method : 0.0017192363739013672 time for calcul the mask position with numpy : 0.006408214569091797 nb_pixel_total : 168 time to create 1 rle with old method : 0.0005602836608886719 time for calcul the mask position with numpy : 0.006422281265258789 nb_pixel_total : 319 time to create 1 rle with old method : 0.0005276203155517578 time for calcul the mask position with numpy : 0.006476402282714844 nb_pixel_total : 361 time to create 1 rle with old method : 0.0006337165832519531 time for calcul the mask position with numpy : 0.006426334381103516 nb_pixel_total : 135 time to create 1 rle with old method : 0.00024056434631347656 create new chi : 1.646972417831421 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 : 0.0036025047302246094 batch 1 Loaded 158 chid ids of type : 4230 Number RLEs to save : 14278 TO DO : save crop sub photo not yet done ! save time : 1.2487797737121582 nb_obj : 141 nb_hashtags : 7 time to prepare the origin masks : 1.3628544807434082 time for calcul the mask position with numpy : 0.018483638763427734 nb_pixel_total : 1783911 time to create 1 rle with new method : 0.052681684494018555 time for calcul the mask position with numpy : 0.006180763244628906 nb_pixel_total : 41 time to create 1 rle with old method : 6.890296936035156e-05 time for calcul the mask position with numpy : 0.005933284759521484 nb_pixel_total : 633 time to create 1 rle with old method : 0.0007967948913574219 time for calcul the mask position with numpy : 0.006032466888427734 nb_pixel_total : 42 time to create 1 rle with old method : 7.2479248046875e-05 time for calcul the mask position with numpy : 0.005925178527832031 nb_pixel_total : 144 time to create 1 rle with old method : 0.0002682209014892578 time for calcul the mask position with numpy : 0.006165266036987305 nb_pixel_total : 316 time to create 1 rle with old method : 0.0004076957702636719 time for calcul the mask position with numpy : 0.0059490203857421875 nb_pixel_total : 95 time to create 1 rle with old method : 0.00015401840209960938 time for calcul the mask position with numpy : 0.006416797637939453 nb_pixel_total : 17 time to create 1 rle with old method : 0.00011110305786132812 time for calcul the mask position with numpy : 0.006385326385498047 nb_pixel_total : 2582 time to create 1 rle with old method : 0.0030624866485595703 time for calcul the mask position with numpy : 0.007173299789428711 nb_pixel_total : 42092 time to create 1 rle with old method : 0.05202007293701172 time for calcul the mask position with numpy : 0.0071561336517333984 nb_pixel_total : 274 time to create 1 rle with old method : 0.0003495216369628906 time for calcul the mask position with numpy : 0.007001161575317383 nb_pixel_total : 296 time to create 1 rle with old method : 0.00041365623474121094 time for calcul the mask position with numpy : 0.006587028503417969 nb_pixel_total : 850 time to create 1 rle with old method : 0.0009906291961669922 time for calcul the mask position with numpy : 0.0064013004302978516 nb_pixel_total : 9277 time to create 1 rle with old method : 0.011587858200073242 time for calcul the mask position with numpy : 0.008354425430297852 nb_pixel_total : 1973 time to create 1 rle with old method : 0.002818584442138672 time for calcul the mask position with numpy : 0.0071277618408203125 nb_pixel_total : 691 time to create 1 rle with old method : 0.0009319782257080078 time for calcul the mask position with numpy : 0.007306098937988281 nb_pixel_total : 121 time to create 1 rle with old method : 0.0002498626708984375 time for calcul the mask position with numpy : 0.007254123687744141 nb_pixel_total : 183 time to create 1 rle with old method : 0.00024771690368652344 time for calcul the mask position with numpy : 0.008034944534301758 nb_pixel_total : 1492 time to create 1 rle with old method : 0.002713441848754883 time for calcul the mask position with numpy : 0.008061885833740234 nb_pixel_total : 2771 time to create 1 rle with old method : 0.003596067428588867 time for calcul the mask position with numpy : 0.00809168815612793 nb_pixel_total : 1337 time to create 1 rle with old method : 0.0017669200897216797 time for calcul the mask position with numpy : 0.007436037063598633 nb_pixel_total : 88 time to create 1 rle with old method : 0.00015020370483398438 time for calcul the mask position with numpy : 0.007486820220947266 nb_pixel_total : 817 time to create 1 rle with old method : 0.001165628433227539 time for calcul the mask position with numpy : 0.007811546325683594 nb_pixel_total : 2609 time to create 1 rle with old method : 0.003752470016479492 time for calcul the mask position with numpy : 0.008400201797485352 nb_pixel_total : 3637 time to create 1 rle with old method : 0.004810810089111328 time for calcul the mask position with numpy : 0.0076541900634765625 nb_pixel_total : 7508 time to create 1 rle with old method : 0.008938789367675781 time for calcul the mask position with numpy : 0.006761312484741211 nb_pixel_total : 185 time to create 1 rle with old method : 0.00024628639221191406 time for calcul the mask position with numpy : 0.0072252750396728516 nb_pixel_total : 531 time to create 1 rle with old method : 0.0006697177886962891 time for calcul the mask position with numpy : 0.00755620002746582 nb_pixel_total : 881 time to create 1 rle with old method : 0.0011870861053466797 time for calcul the mask position with numpy : 0.007569789886474609 nb_pixel_total : 1019 time to create 1 rle with old method : 0.0014352798461914062 time for calcul the mask position with numpy : 0.007172346115112305 nb_pixel_total : 2030 time to create 1 rle with old method : 0.0024864673614501953 time for calcul the mask position with numpy : 0.00645136833190918 nb_pixel_total : 11780 time to create 1 rle with old method : 0.01320505142211914 time for calcul the mask position with numpy : 0.008962869644165039 nb_pixel_total : 2170 time to create 1 rle with old method : 0.0049915313720703125 time for calcul the mask position with numpy : 0.0065555572509765625 nb_pixel_total : 2575 time to create 1 rle with old method : 0.0031049251556396484 time for calcul the mask position with numpy : 0.00616145133972168 nb_pixel_total : 994 time to create 1 rle with old method : 0.001135110855102539 time for calcul the mask position with numpy : 0.006042003631591797 nb_pixel_total : 1178 time to create 1 rle with old method : 0.0013284683227539062 time for calcul the mask position with numpy : 0.006055355072021484 nb_pixel_total : 11 time to create 1 rle with old method : 6.413459777832031e-05 time for calcul the mask position with numpy : 0.005914926528930664 nb_pixel_total : 717 time to create 1 rle with old method : 0.0008246898651123047 time for calcul the mask position with numpy : 0.006184577941894531 nb_pixel_total : 1349 time to create 1 rle with old method : 0.001622915267944336 time for calcul the mask position with numpy : 0.0061380863189697266 nb_pixel_total : 1107 time to create 1 rle with old method : 0.0013141632080078125 time for calcul the mask position with numpy : 0.006162881851196289 nb_pixel_total : 2062 time to create 1 rle with old method : 0.0024285316467285156 time for calcul the mask position with numpy : 0.007122039794921875 nb_pixel_total : 1208 time to create 1 rle with old method : 0.0023241043090820312 time for calcul the mask position with numpy : 0.007217884063720703 nb_pixel_total : 791 time to create 1 rle with old method : 0.0010676383972167969 time for calcul the mask position with numpy : 0.006573915481567383 nb_pixel_total : 12928 time to create 1 rle with old method : 0.016192913055419922 time for calcul the mask position with numpy : 0.007631540298461914 nb_pixel_total : 157 time to create 1 rle with old method : 0.00024580955505371094 time for calcul the mask position with numpy : 0.00825047492980957 nb_pixel_total : 13 time to create 1 rle with old method : 7.486343383789062e-05 time for calcul the mask position with numpy : 0.007561445236206055 nb_pixel_total : 1238 time to create 1 rle with old method : 0.00142669677734375 time for calcul the mask position with numpy : 0.0069773197174072266 nb_pixel_total : 713 time to create 1 rle with old method : 0.0008542537689208984 time for calcul the mask position with numpy : 0.006930828094482422 nb_pixel_total : 42 time to create 1 rle with old method : 8.535385131835938e-05 time for calcul the mask position with numpy : 0.00719451904296875 nb_pixel_total : 243 time to create 1 rle with old method : 0.0003399848937988281 time for calcul the mask position with numpy : 0.0077381134033203125 nb_pixel_total : 207 time to create 1 rle with old method : 0.0003361701965332031 time for calcul the mask position with numpy : 0.007333040237426758 nb_pixel_total : 3856 time to create 1 rle with old method : 0.005300998687744141 time for calcul the mask position with numpy : 0.007782697677612305 nb_pixel_total : 155 time to create 1 rle with old method : 0.0002357959747314453 time for calcul the mask position with numpy : 0.008069515228271484 nb_pixel_total : 2039 time to create 1 rle with old method : 0.0023636817932128906 time for calcul the mask position with numpy : 0.007382869720458984 nb_pixel_total : 121 time to create 1 rle with old method : 0.00017595291137695312 time for calcul the mask position with numpy : 0.007113933563232422 nb_pixel_total : 848 time to create 1 rle with old method : 0.0011212825775146484 time for calcul the mask position with numpy : 0.007268428802490234 nb_pixel_total : 371 time to create 1 rle with old method : 0.0005135536193847656 time for calcul the mask position with numpy : 0.00701904296875 nb_pixel_total : 87 time to create 1 rle with old method : 0.0001316070556640625 time for calcul the mask position with numpy : 0.006531715393066406 nb_pixel_total : 771 time to create 1 rle with old method : 0.0009758472442626953 time for calcul the mask position with numpy : 0.0066378116607666016 nb_pixel_total : 2104 time to create 1 rle with old method : 0.002619028091430664 time for calcul the mask position with numpy : 0.006669759750366211 nb_pixel_total : 118 time to create 1 rle with old method : 0.0001575946807861328 time for calcul the mask position with numpy : 0.007638692855834961 nb_pixel_total : 116 time to create 1 rle with old method : 0.00024700164794921875 time for calcul the mask position with numpy : 0.008818864822387695 nb_pixel_total : 1040 time to create 1 rle with old method : 0.0017404556274414062 time for calcul the mask position with numpy : 0.007951974868774414 nb_pixel_total : 970 time to create 1 rle with old method : 0.001333475112915039 time for calcul the mask position with numpy : 0.007524728775024414 nb_pixel_total : 1774 time to create 1 rle with old method : 0.002251863479614258 time for calcul the mask position with numpy : 0.007787227630615234 nb_pixel_total : 66 time to create 1 rle with old method : 0.0001552104949951172 time for calcul the mask position with numpy : 0.0076487064361572266 nb_pixel_total : 245 time to create 1 rle with old method : 0.000354766845703125 time for calcul the mask position with numpy : 0.007042407989501953 nb_pixel_total : 468 time to create 1 rle with old method : 0.0005774497985839844 time for calcul the mask position with numpy : 0.007092952728271484 nb_pixel_total : 455 time to create 1 rle with old method : 0.0006122589111328125 time for calcul the mask position with numpy : 0.007275581359863281 nb_pixel_total : 106307 time to create 1 rle with old method : 0.1204385757446289 time for calcul the mask position with numpy : 0.006441593170166016 nb_pixel_total : 422 time to create 1 rle with old method : 0.0005395412445068359 time for calcul the mask position with numpy : 0.0066297054290771484 nb_pixel_total : 406 time to create 1 rle with old method : 0.0004830360412597656 time for calcul the mask position with numpy : 0.0067594051361083984 nb_pixel_total : 82 time to create 1 rle with old method : 0.00013375282287597656 time for calcul the mask position with numpy : 0.00713658332824707 nb_pixel_total : 51 time to create 1 rle with old method : 8.225440979003906e-05 time for calcul the mask position with numpy : 0.007500648498535156 nb_pixel_total : 160 time to create 1 rle with old method : 0.0003063678741455078 time for calcul the mask position with numpy : 0.006394624710083008 nb_pixel_total : 161 time to create 1 rle with old method : 0.0002129077911376953 time for calcul the mask position with numpy : 0.006289958953857422 nb_pixel_total : 299 time to create 1 rle with old method : 0.0003604888916015625 time for calcul the mask position with numpy : 0.0060272216796875 nb_pixel_total : 151 time to create 1 rle with old method : 0.00018858909606933594 time for calcul the mask position with numpy : 0.006112575531005859 nb_pixel_total : 471 time to create 1 rle with old method : 0.000537872314453125 time for calcul the mask position with numpy : 0.006112098693847656 nb_pixel_total : 560 time to create 1 rle with old method : 0.0006723403930664062 time for calcul the mask position with numpy : 0.006172657012939453 nb_pixel_total : 9 time to create 1 rle with old method : 7.176399230957031e-05 time for calcul the mask position with numpy : 0.007420778274536133 nb_pixel_total : 367 time to create 1 rle with old method : 0.00044417381286621094 time for calcul the mask position with numpy : 0.0063555240631103516 nb_pixel_total : 39 time to create 1 rle with old method : 8.916854858398438e-05 time for calcul the mask position with numpy : 0.0065860748291015625 nb_pixel_total : 1591 time to create 1 rle with old method : 0.0017948150634765625 time for calcul the mask position with numpy : 0.006360054016113281 nb_pixel_total : 382 time to create 1 rle with old method : 0.00047397613525390625 time for calcul the mask position with numpy : 0.006356716156005859 nb_pixel_total : 187 time to create 1 rle with old method : 0.0002536773681640625 time for calcul the mask position with numpy : 0.0064318180084228516 nb_pixel_total : 1765 time to create 1 rle with old method : 0.0021042823791503906 time for calcul the mask position with numpy : 0.00659632682800293 nb_pixel_total : 1727 time to create 1 rle with old method : 0.0019996166229248047 time for calcul the mask position with numpy : 0.006358623504638672 nb_pixel_total : 110 time to create 1 rle with old method : 0.0001499652862548828 time for calcul the mask position with numpy : 0.006093502044677734 nb_pixel_total : 251 time to create 1 rle with old method : 0.0002949237823486328 time for calcul the mask position with numpy : 0.006046295166015625 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001671314239501953 time for calcul the mask position with numpy : 0.006066083908081055 nb_pixel_total : 548 time to create 1 rle with old method : 0.0006551742553710938 time for calcul the mask position with numpy : 0.006103992462158203 nb_pixel_total : 148 time to create 1 rle with old method : 0.00023627281188964844 time for calcul the mask position with numpy : 0.0063610076904296875 nb_pixel_total : 847 time to create 1 rle with old method : 0.0009570121765136719 time for calcul the mask position with numpy : 0.00658726692199707 nb_pixel_total : 171 time to create 1 rle with old method : 0.0002067089080810547 time for calcul the mask position with numpy : 0.006522655487060547 nb_pixel_total : 672 time to create 1 rle with old method : 0.0008182525634765625 time for calcul the mask position with numpy : 0.006593465805053711 nb_pixel_total : 234 time to create 1 rle with old method : 0.00036644935607910156 time for calcul the mask position with numpy : 0.006327629089355469 nb_pixel_total : 765 time to create 1 rle with old method : 0.0008502006530761719 time for calcul the mask position with numpy : 0.006542682647705078 nb_pixel_total : 638 time to create 1 rle with old method : 0.0007343292236328125 time for calcul the mask position with numpy : 0.006446361541748047 nb_pixel_total : 210 time to create 1 rle with old method : 0.00024580955505371094 time for calcul the mask position with numpy : 0.0066792964935302734 nb_pixel_total : 992 time to create 1 rle with old method : 0.0014431476593017578 time for calcul the mask position with numpy : 0.006725311279296875 nb_pixel_total : 49 time to create 1 rle with old method : 9.655952453613281e-05 time for calcul the mask position with numpy : 0.0065839290618896484 nb_pixel_total : 169 time to create 1 rle with old method : 0.00021600723266601562 time for calcul the mask position with numpy : 0.0062868595123291016 nb_pixel_total : 410 time to create 1 rle with old method : 0.0005419254302978516 time for calcul the mask position with numpy : 0.0065457820892333984 nb_pixel_total : 5 time to create 1 rle with old method : 2.765655517578125e-05 time for calcul the mask position with numpy : 0.006628990173339844 nb_pixel_total : 800 time to create 1 rle with old method : 0.0009377002716064453 time for calcul the mask position with numpy : 0.0063800811767578125 nb_pixel_total : 379 time to create 1 rle with old method : 0.0004894733428955078 time for calcul the mask position with numpy : 0.0062100887298583984 nb_pixel_total : 6735 time to create 1 rle with old method : 0.007880687713623047 time for calcul the mask position with numpy : 0.006123065948486328 nb_pixel_total : 4056 time to create 1 rle with old method : 0.004774570465087891 time for calcul the mask position with numpy : 0.0065765380859375 nb_pixel_total : 14 time to create 1 rle with old method : 6.365776062011719e-05 time for calcul the mask position with numpy : 0.0059356689453125 nb_pixel_total : 1243 time to create 1 rle with old method : 0.0013866424560546875 time for calcul the mask position with numpy : 0.006017446517944336 nb_pixel_total : 119 time to create 1 rle with old method : 0.0001914501190185547 time for calcul the mask position with numpy : 0.0062274932861328125 nb_pixel_total : 1416 time to create 1 rle with old method : 0.0016052722930908203 time for calcul the mask position with numpy : 0.006095409393310547 nb_pixel_total : 834 time to create 1 rle with old method : 0.0010068416595458984 time for calcul the mask position with numpy : 0.006159067153930664 nb_pixel_total : 555 time to create 1 rle with old method : 0.0006344318389892578 time for calcul the mask position with numpy : 0.006093025207519531 nb_pixel_total : 176 time to create 1 rle with old method : 0.00023055076599121094 time for calcul the mask position with numpy : 0.006909370422363281 nb_pixel_total : 256 time to create 1 rle with old method : 0.00033926963806152344 time for calcul the mask position with numpy : 0.007292509078979492 nb_pixel_total : 31 time to create 1 rle with old method : 0.0001327991485595703 time for calcul the mask position with numpy : 0.006799221038818359 nb_pixel_total : 699 time to create 1 rle with old method : 0.0011796951293945312 time for calcul the mask position with numpy : 0.006639003753662109 nb_pixel_total : 12 time to create 1 rle with old method : 8.392333984375e-05 time for calcul the mask position with numpy : 0.0066530704498291016 nb_pixel_total : 859 time to create 1 rle with old method : 0.0014238357543945312 time for calcul the mask position with numpy : 0.006495475769042969 nb_pixel_total : 79 time to create 1 rle with old method : 0.00015497207641601562 time for calcul the mask position with numpy : 0.0065419673919677734 nb_pixel_total : 611 time to create 1 rle with old method : 0.0011370182037353516 time for calcul the mask position with numpy : 0.00650787353515625 nb_pixel_total : 121 time to create 1 rle with old method : 0.0002224445343017578 time for calcul the mask position with numpy : 0.00654911994934082 nb_pixel_total : 958 time to create 1 rle with old method : 0.0016188621520996094 time for calcul the mask position with numpy : 0.006575822830200195 nb_pixel_total : 12 time to create 1 rle with old method : 6.151199340820312e-05 time for calcul the mask position with numpy : 0.006515026092529297 nb_pixel_total : 429 time to create 1 rle with old method : 0.0007350444793701172 time for calcul the mask position with numpy : 0.0064885616302490234 nb_pixel_total : 111 time to create 1 rle with old method : 0.00015091896057128906 time for calcul the mask position with numpy : 0.006156206130981445 nb_pixel_total : 344 time to create 1 rle with old method : 0.0004107952117919922 time for calcul the mask position with numpy : 0.006037712097167969 nb_pixel_total : 114 time to create 1 rle with old method : 0.00014495849609375 time for calcul the mask position with numpy : 0.005852937698364258 nb_pixel_total : 731 time to create 1 rle with old method : 0.0008928775787353516 time for calcul the mask position with numpy : 0.005966663360595703 nb_pixel_total : 877 time to create 1 rle with old method : 0.0009560585021972656 time for calcul the mask position with numpy : 0.00740814208984375 nb_pixel_total : 295 time to create 1 rle with old method : 0.00034356117248535156 time for calcul the mask position with numpy : 0.009590387344360352 nb_pixel_total : 475 time to create 1 rle with old method : 0.000518798828125 time for calcul the mask position with numpy : 0.00958395004272461 nb_pixel_total : 500 time to create 1 rle with old method : 0.0005600452423095703 time for calcul the mask position with numpy : 0.009600162506103516 nb_pixel_total : 204 time to create 1 rle with old method : 0.000255584716796875 time for calcul the mask position with numpy : 0.010149717330932617 nb_pixel_total : 544 time to create 1 rle with old method : 0.0006546974182128906 time for calcul the mask position with numpy : 0.010068178176879883 nb_pixel_total : 1197 time to create 1 rle with old method : 0.0012950897216796875 time for calcul the mask position with numpy : 0.010012388229370117 nb_pixel_total : 29 time to create 1 rle with old method : 8.153915405273438e-05 time for calcul the mask position with numpy : 0.009649276733398438 nb_pixel_total : 331 time to create 1 rle with old method : 0.0003674030303955078 time for calcul the mask position with numpy : 0.009686708450317383 nb_pixel_total : 324 time to create 1 rle with old method : 0.00036454200744628906 time for calcul the mask position with numpy : 0.009671449661254883 nb_pixel_total : 120 time to create 1 rle with old method : 0.000152587890625 create new chi : 1.4155950546264648 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 : 0.002579927444458008 batch 1 Loaded 142 chid ids of type : 4230 Number RLEs to save : 13451 TO DO : save crop sub photo not yet done ! save time : 0.7590322494506836 nb_obj : 158 nb_hashtags : 8 time to prepare the origin masks : 1.5515286922454834 time for calcul the mask position with numpy : 0.06356596946716309 nb_pixel_total : 1603393 time to create 1 rle with new method : 0.1381840705871582 time for calcul the mask position with numpy : 0.0067119598388671875 nb_pixel_total : 59637 time to create 1 rle with old method : 0.06791496276855469 time for calcul the mask position with numpy : 0.006411075592041016 nb_pixel_total : 357 time to create 1 rle with old method : 0.0004341602325439453 time for calcul the mask position with numpy : 0.0063173770904541016 nb_pixel_total : 27 time to create 1 rle with old method : 5.745887756347656e-05 time for calcul the mask position with numpy : 0.006305694580078125 nb_pixel_total : 68 time to create 1 rle with old method : 0.00010061264038085938 time for calcul the mask position with numpy : 0.006425380706787109 nb_pixel_total : 95 time to create 1 rle with old method : 0.0001342296600341797 time for calcul the mask position with numpy : 0.006210803985595703 nb_pixel_total : 35 time to create 1 rle with old method : 7.677078247070312e-05 time for calcul the mask position with numpy : 0.006694793701171875 nb_pixel_total : 379 time to create 1 rle with old method : 0.0004954338073730469 time for calcul the mask position with numpy : 0.006546497344970703 nb_pixel_total : 16 time to create 1 rle with old method : 5.459785461425781e-05 time for calcul the mask position with numpy : 0.006344795227050781 nb_pixel_total : 48 time to create 1 rle with old method : 9.489059448242188e-05 time for calcul the mask position with numpy : 0.006376743316650391 nb_pixel_total : 95 time to create 1 rle with old method : 0.00011944770812988281 time for calcul the mask position with numpy : 0.006560802459716797 nb_pixel_total : 2461 time to create 1 rle with old method : 0.0029289722442626953 time for calcul the mask position with numpy : 0.006407260894775391 nb_pixel_total : 141 time to create 1 rle with old method : 0.00021767616271972656 time for calcul the mask position with numpy : 0.0069468021392822266 nb_pixel_total : 164 time to create 1 rle with old method : 0.0003330707550048828 time for calcul the mask position with numpy : 0.006630897521972656 nb_pixel_total : 9448 time to create 1 rle with old method : 0.010626792907714844 time for calcul the mask position with numpy : 0.006370067596435547 nb_pixel_total : 1895 time to create 1 rle with old method : 0.0021355152130126953 time for calcul the mask position with numpy : 0.006379365921020508 nb_pixel_total : 252 time to create 1 rle with old method : 0.00030040740966796875 time for calcul the mask position with numpy : 0.006185293197631836 nb_pixel_total : 57 time to create 1 rle with old method : 9.72747802734375e-05 time for calcul the mask position with numpy : 0.006003379821777344 nb_pixel_total : 249 time to create 1 rle with old method : 0.0003113746643066406 time for calcul the mask position with numpy : 0.006171226501464844 nb_pixel_total : 747 time to create 1 rle with old method : 0.0008940696716308594 time for calcul the mask position with numpy : 0.006230831146240234 nb_pixel_total : 716 time to create 1 rle with old method : 0.0008547306060791016 time for calcul the mask position with numpy : 0.0066416263580322266 nb_pixel_total : 1175 time to create 1 rle with old method : 0.0013425350189208984 time for calcul the mask position with numpy : 0.006289243698120117 nb_pixel_total : 641 time to create 1 rle with old method : 0.0008339881896972656 time for calcul the mask position with numpy : 0.0063397884368896484 nb_pixel_total : 206 time to create 1 rle with old method : 0.0002741813659667969 time for calcul the mask position with numpy : 0.006222724914550781 nb_pixel_total : 1406 time to create 1 rle with old method : 0.0016779899597167969 time for calcul the mask position with numpy : 0.006593465805053711 nb_pixel_total : 3239 time to create 1 rle with old method : 0.003710508346557617 time for calcul the mask position with numpy : 0.006602764129638672 nb_pixel_total : 1227 time to create 1 rle with old method : 0.0016171932220458984 time for calcul the mask position with numpy : 0.006605863571166992 nb_pixel_total : 87 time to create 1 rle with old method : 0.0001666545867919922 time for calcul the mask position with numpy : 0.006854534149169922 nb_pixel_total : 2331 time to create 1 rle with old method : 0.0026657581329345703 time for calcul the mask position with numpy : 0.0068514347076416016 nb_pixel_total : 6729 time to create 1 rle with old method : 0.007880449295043945 time for calcul the mask position with numpy : 0.006410360336303711 nb_pixel_total : 532 time to create 1 rle with old method : 0.0006465911865234375 time for calcul the mask position with numpy : 0.006916999816894531 nb_pixel_total : 7 time to create 1 rle with old method : 6.413459777832031e-05 time for calcul the mask position with numpy : 0.006514072418212891 nb_pixel_total : 1800 time to create 1 rle with old method : 0.0021529197692871094 time for calcul the mask position with numpy : 0.006403446197509766 nb_pixel_total : 4354 time to create 1 rle with old method : 0.005994558334350586 time for calcul the mask position with numpy : 0.006662130355834961 nb_pixel_total : 512 time to create 1 rle with old method : 0.0006265640258789062 time for calcul the mask position with numpy : 0.006582498550415039 nb_pixel_total : 873 time to create 1 rle with old method : 0.001127481460571289 time for calcul the mask position with numpy : 0.006779670715332031 nb_pixel_total : 1513 time to create 1 rle with old method : 0.002872467041015625 time for calcul the mask position with numpy : 0.006509304046630859 nb_pixel_total : 117 time to create 1 rle with old method : 0.00017142295837402344 time for calcul the mask position with numpy : 0.006604433059692383 nb_pixel_total : 349 time to create 1 rle with old method : 0.0004432201385498047 time for calcul the mask position with numpy : 0.007497310638427734 nb_pixel_total : 151957 time to create 1 rle with new method : 0.11304807662963867 time for calcul the mask position with numpy : 0.006474971771240234 nb_pixel_total : 788 time to create 1 rle with old method : 0.0009908676147460938 time for calcul the mask position with numpy : 0.006598949432373047 nb_pixel_total : 5898 time to create 1 rle with old method : 0.0066678524017333984 time for calcul the mask position with numpy : 0.0062177181243896484 nb_pixel_total : 105 time to create 1 rle with old method : 0.0001571178436279297 time for calcul the mask position with numpy : 0.006257057189941406 nb_pixel_total : 1382 time to create 1 rle with old method : 0.001680612564086914 time for calcul the mask position with numpy : 0.006346702575683594 nb_pixel_total : 23 time to create 1 rle with old method : 7.104873657226562e-05 time for calcul the mask position with numpy : 0.006165742874145508 nb_pixel_total : 1015 time to create 1 rle with old method : 0.0012149810791015625 time for calcul the mask position with numpy : 0.006073474884033203 nb_pixel_total : 1179 time to create 1 rle with old method : 0.0014345645904541016 time for calcul the mask position with numpy : 0.005872488021850586 nb_pixel_total : 3712 time to create 1 rle with old method : 0.004241943359375 time for calcul the mask position with numpy : 0.006082773208618164 nb_pixel_total : 3659 time to create 1 rle with old method : 0.004044532775878906 time for calcul the mask position with numpy : 0.006110191345214844 nb_pixel_total : 241 time to create 1 rle with old method : 0.0002982616424560547 time for calcul the mask position with numpy : 0.006141185760498047 nb_pixel_total : 302 time to create 1 rle with old method : 0.00039887428283691406 time for calcul the mask position with numpy : 0.006768941879272461 nb_pixel_total : 707 time to create 1 rle with old method : 0.0008792877197265625 time for calcul the mask position with numpy : 0.007029056549072266 nb_pixel_total : 743 time to create 1 rle with old method : 0.000978231430053711 time for calcul the mask position with numpy : 0.0064716339111328125 nb_pixel_total : 534 time to create 1 rle with old method : 0.0007193088531494141 time for calcul the mask position with numpy : 0.006269931793212891 nb_pixel_total : 79 time to create 1 rle with old method : 0.00014853477478027344 time for calcul the mask position with numpy : 0.0076067447662353516 nb_pixel_total : 779 time to create 1 rle with old method : 0.001336812973022461 time for calcul the mask position with numpy : 0.007082939147949219 nb_pixel_total : 1077 time to create 1 rle with old method : 0.0012958049774169922 time for calcul the mask position with numpy : 0.006375789642333984 nb_pixel_total : 2902 time to create 1 rle with old method : 0.0033681392669677734 time for calcul the mask position with numpy : 0.006441831588745117 nb_pixel_total : 172 time to create 1 rle with old method : 0.00022077560424804688 time for calcul the mask position with numpy : 0.006214618682861328 nb_pixel_total : 1241 time to create 1 rle with old method : 0.001378774642944336 time for calcul the mask position with numpy : 0.006201505661010742 nb_pixel_total : 11232 time to create 1 rle with old method : 0.012828350067138672 time for calcul the mask position with numpy : 0.0061419010162353516 nb_pixel_total : 801 time to create 1 rle with old method : 0.0009381771087646484 time for calcul the mask position with numpy : 0.006159305572509766 nb_pixel_total : 23 time to create 1 rle with old method : 5.7220458984375e-05 time for calcul the mask position with numpy : 0.006251096725463867 nb_pixel_total : 1349 time to create 1 rle with old method : 0.0015876293182373047 time for calcul the mask position with numpy : 0.006202220916748047 nb_pixel_total : 92 time to create 1 rle with old method : 0.00013947486877441406 time for calcul the mask position with numpy : 0.006078243255615234 nb_pixel_total : 3326 time to create 1 rle with old method : 0.0036890506744384766 time for calcul the mask position with numpy : 0.006120443344116211 nb_pixel_total : 1710 time to create 1 rle with old method : 0.0018935203552246094 time for calcul the mask position with numpy : 0.00619053840637207 nb_pixel_total : 536 time to create 1 rle with old method : 0.0006208419799804688 time for calcul the mask position with numpy : 0.00606536865234375 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0013077259063720703 time for calcul the mask position with numpy : 0.005968570709228516 nb_pixel_total : 357 time to create 1 rle with old method : 0.0004341602325439453 time for calcul the mask position with numpy : 0.0060617923736572266 nb_pixel_total : 489 time to create 1 rle with old method : 0.0005617141723632812 time for calcul the mask position with numpy : 0.005994081497192383 nb_pixel_total : 124 time to create 1 rle with old method : 0.00016069412231445312 time for calcul the mask position with numpy : 0.006003618240356445 nb_pixel_total : 1225 time to create 1 rle with old method : 0.0012993812561035156 time for calcul the mask position with numpy : 0.006083488464355469 nb_pixel_total : 798 time to create 1 rle with old method : 0.0009586811065673828 time for calcul the mask position with numpy : 0.006049394607543945 nb_pixel_total : 506 time to create 1 rle with old method : 0.0006122589111328125 time for calcul the mask position with numpy : 0.0061261653900146484 nb_pixel_total : 728 time to create 1 rle with old method : 0.0008041858673095703 time for calcul the mask position with numpy : 0.006709575653076172 nb_pixel_total : 1174 time to create 1 rle with old method : 0.0016703605651855469 time for calcul the mask position with numpy : 0.006339311599731445 nb_pixel_total : 823 time to create 1 rle with old method : 0.0010066032409667969 time for calcul the mask position with numpy : 0.0061511993408203125 nb_pixel_total : 1599 time to create 1 rle with old method : 0.0019795894622802734 time for calcul the mask position with numpy : 0.006335735321044922 nb_pixel_total : 470 time to create 1 rle with old method : 0.0005650520324707031 time for calcul the mask position with numpy : 0.0059757232666015625 nb_pixel_total : 156 time to create 1 rle with old method : 0.0001850128173828125 time for calcul the mask position with numpy : 0.006459951400756836 nb_pixel_total : 18 time to create 1 rle with old method : 4.1484832763671875e-05 time for calcul the mask position with numpy : 0.00610804557800293 nb_pixel_total : 1650 time to create 1 rle with old method : 0.0018110275268554688 time for calcul the mask position with numpy : 0.00607752799987793 nb_pixel_total : 60 time to create 1 rle with old method : 9.083747863769531e-05 time for calcul the mask position with numpy : 0.006155490875244141 nb_pixel_total : 326 time to create 1 rle with old method : 0.00038743019104003906 time for calcul the mask position with numpy : 0.0064411163330078125 nb_pixel_total : 834 time to create 1 rle with old method : 0.000986337661743164 time for calcul the mask position with numpy : 0.007141590118408203 nb_pixel_total : 472 time to create 1 rle with old method : 0.0005309581756591797 time for calcul the mask position with numpy : 0.006431102752685547 nb_pixel_total : 193 time to create 1 rle with old method : 0.0002529621124267578 time for calcul the mask position with numpy : 0.006112098693847656 nb_pixel_total : 569 time to create 1 rle with old method : 0.0006697177886962891 time for calcul the mask position with numpy : 0.006341457366943359 nb_pixel_total : 106245 time to create 1 rle with old method : 0.1151113510131836 time for calcul the mask position with numpy : 0.006649494171142578 nb_pixel_total : 418 time to create 1 rle with old method : 0.0007920265197753906 time for calcul the mask position with numpy : 0.007493019104003906 nb_pixel_total : 114 time to create 1 rle with old method : 0.0002446174621582031 time for calcul the mask position with numpy : 0.009325265884399414 nb_pixel_total : 97 time to create 1 rle with old method : 0.00018405914306640625 time for calcul the mask position with numpy : 0.0068149566650390625 nb_pixel_total : 626 time to create 1 rle with old method : 0.0008389949798583984 time for calcul the mask position with numpy : 0.006818056106567383 nb_pixel_total : 169 time to create 1 rle with old method : 0.00021910667419433594 time for calcul the mask position with numpy : 0.00664210319519043 nb_pixel_total : 308 time to create 1 rle with old method : 0.00036978721618652344 time for calcul the mask position with numpy : 0.00691533088684082 nb_pixel_total : 140 time to create 1 rle with old method : 0.00019884109497070312 time for calcul the mask position with numpy : 0.007529735565185547 nb_pixel_total : 385 time to create 1 rle with old method : 0.0004985332489013672 time for calcul the mask position with numpy : 0.006834506988525391 nb_pixel_total : 3 time to create 1 rle with old method : 2.956390380859375e-05 time for calcul the mask position with numpy : 0.006328582763671875 nb_pixel_total : 4 time to create 1 rle with old method : 4.124641418457031e-05 time for calcul the mask position with numpy : 0.0065860748291015625 nb_pixel_total : 399 time to create 1 rle with old method : 0.0004680156707763672 time for calcul the mask position with numpy : 0.0069501399993896484 nb_pixel_total : 1575 time to create 1 rle with old method : 0.0017087459564208984 time for calcul the mask position with numpy : 0.006464719772338867 nb_pixel_total : 25 time to create 1 rle with old method : 7.581710815429688e-05 time for calcul the mask position with numpy : 0.006398916244506836 nb_pixel_total : 410 time to create 1 rle with old method : 0.0004925727844238281 time for calcul the mask position with numpy : 0.006669521331787109 nb_pixel_total : 226 time to create 1 rle with old method : 0.0002884864807128906 time for calcul the mask position with numpy : 0.00612187385559082 nb_pixel_total : 1752 time to create 1 rle with old method : 0.001941680908203125 time for calcul the mask position with numpy : 0.006346464157104492 nb_pixel_total : 1503 time to create 1 rle with old method : 0.0017499923706054688 time for calcul the mask position with numpy : 0.006284475326538086 nb_pixel_total : 250 time to create 1 rle with old method : 0.0002834796905517578 time for calcul the mask position with numpy : 0.0061931610107421875 nb_pixel_total : 201 time to create 1 rle with old method : 0.0002493858337402344 time for calcul the mask position with numpy : 0.00619196891784668 nb_pixel_total : 207 time to create 1 rle with old method : 0.00025391578674316406 time for calcul the mask position with numpy : 0.006595134735107422 nb_pixel_total : 4 time to create 1 rle with old method : 3.24249267578125e-05 time for calcul the mask position with numpy : 0.00642085075378418 nb_pixel_total : 951 time to create 1 rle with old method : 0.0010635852813720703 time for calcul the mask position with numpy : 0.006395816802978516 nb_pixel_total : 562 time to create 1 rle with old method : 0.0006237030029296875 time for calcul the mask position with numpy : 0.006220102310180664 nb_pixel_total : 22 time to create 1 rle with old method : 7.319450378417969e-05 time for calcul the mask position with numpy : 0.006301164627075195 nb_pixel_total : 905 time to create 1 rle with old method : 0.001516580581665039 time for calcul the mask position with numpy : 0.00957179069519043 nb_pixel_total : 171 time to create 1 rle with old method : 0.00029659271240234375 time for calcul the mask position with numpy : 0.006613492965698242 nb_pixel_total : 402 time to create 1 rle with old method : 0.0007314682006835938 time for calcul the mask position with numpy : 0.008210182189941406 nb_pixel_total : 761 time to create 1 rle with old method : 0.0012881755828857422 time for calcul the mask position with numpy : 0.009775400161743164 nb_pixel_total : 700 time to create 1 rle with old method : 0.0011568069458007812 time for calcul the mask position with numpy : 0.006701946258544922 nb_pixel_total : 724 time to create 1 rle with old method : 0.0011827945709228516 time for calcul the mask position with numpy : 0.007825136184692383 nb_pixel_total : 185 time to create 1 rle with old method : 0.0003657341003417969 time for calcul the mask position with numpy : 0.008270740509033203 nb_pixel_total : 1019 time to create 1 rle with old method : 0.0016753673553466797 time for calcul the mask position with numpy : 0.008915901184082031 nb_pixel_total : 280 time to create 1 rle with old method : 0.0005326271057128906 time for calcul the mask position with numpy : 0.007460117340087891 nb_pixel_total : 432 time to create 1 rle with old method : 0.000522613525390625 time for calcul the mask position with numpy : 0.006430149078369141 nb_pixel_total : 166 time to create 1 rle with old method : 0.00020074844360351562 time for calcul the mask position with numpy : 0.006506204605102539 nb_pixel_total : 939 time to create 1 rle with old method : 0.0011212825775146484 time for calcul the mask position with numpy : 0.006235599517822266 nb_pixel_total : 989 time to create 1 rle with old method : 0.0011458396911621094 time for calcul the mask position with numpy : 0.006636857986450195 nb_pixel_total : 801 time to create 1 rle with old method : 0.0008804798126220703 time for calcul the mask position with numpy : 0.0061757564544677734 nb_pixel_total : 6385 time to create 1 rle with old method : 0.007153511047363281 time for calcul the mask position with numpy : 0.006744861602783203 nb_pixel_total : 4356 time to create 1 rle with old method : 0.005030155181884766 time for calcul the mask position with numpy : 0.006285905838012695 nb_pixel_total : 1779 time to create 1 rle with old method : 0.0019452571868896484 time for calcul the mask position with numpy : 0.006134986877441406 nb_pixel_total : 140 time to create 1 rle with old method : 0.0002892017364501953 time for calcul the mask position with numpy : 0.006207704544067383 nb_pixel_total : 824 time to create 1 rle with old method : 0.0009694099426269531 time for calcul the mask position with numpy : 0.006273984909057617 nb_pixel_total : 104 time to create 1 rle with old method : 0.00014066696166992188 time for calcul the mask position with numpy : 0.006306886672973633 nb_pixel_total : 1307 time to create 1 rle with old method : 0.0013728141784667969 time for calcul the mask position with numpy : 0.006337881088256836 nb_pixel_total : 521 time to create 1 rle with old method : 0.0006246566772460938 time for calcul the mask position with numpy : 0.0063474178314208984 nb_pixel_total : 121 time to create 1 rle with old method : 0.0001647472381591797 time for calcul the mask position with numpy : 0.006155967712402344 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0013942718505859375 time for calcul the mask position with numpy : 0.00619816780090332 nb_pixel_total : 288 time to create 1 rle with old method : 0.0003230571746826172 time for calcul the mask position with numpy : 0.005965232849121094 nb_pixel_total : 253 time to create 1 rle with old method : 0.00032019615173339844 time for calcul the mask position with numpy : 0.006491661071777344 nb_pixel_total : 617 time to create 1 rle with old method : 0.0007240772247314453 time for calcul the mask position with numpy : 0.006256818771362305 nb_pixel_total : 818 time to create 1 rle with old method : 0.0009715557098388672 time for calcul the mask position with numpy : 0.006240367889404297 nb_pixel_total : 933 time to create 1 rle with old method : 0.0010831356048583984 time for calcul the mask position with numpy : 0.008502483367919922 nb_pixel_total : 487 time to create 1 rle with old method : 0.0005860328674316406 time for calcul the mask position with numpy : 0.008579015731811523 nb_pixel_total : 103 time to create 1 rle with old method : 0.0001354217529296875 time for calcul the mask position with numpy : 0.008490562438964844 nb_pixel_total : 362 time to create 1 rle with old method : 0.0004017353057861328 time for calcul the mask position with numpy : 0.009969472885131836 nb_pixel_total : 129 time to create 1 rle with old method : 0.00016045570373535156 time for calcul the mask position with numpy : 0.008427143096923828 nb_pixel_total : 812 time to create 1 rle with old method : 0.0009694099426269531 time for calcul the mask position with numpy : 0.008453369140625 nb_pixel_total : 2154 time to create 1 rle with old method : 0.00244140625 time for calcul the mask position with numpy : 0.008504152297973633 nb_pixel_total : 843 time to create 1 rle with old method : 0.0009524822235107422 time for calcul the mask position with numpy : 0.00824427604675293 nb_pixel_total : 518 time to create 1 rle with old method : 0.0005609989166259766 time for calcul the mask position with numpy : 0.008414506912231445 nb_pixel_total : 301 time to create 1 rle with old method : 0.0003635883331298828 time for calcul the mask position with numpy : 0.008488178253173828 nb_pixel_total : 512 time to create 1 rle with old method : 0.0006086826324462891 time for calcul the mask position with numpy : 0.008723020553588867 nb_pixel_total : 782 time to create 1 rle with old method : 0.000926971435546875 time for calcul the mask position with numpy : 0.008410453796386719 nb_pixel_total : 1368 time to create 1 rle with old method : 0.0015168190002441406 time for calcul the mask position with numpy : 0.00822138786315918 nb_pixel_total : 885 time to create 1 rle with old method : 0.0010371208190917969 time for calcul the mask position with numpy : 0.008322477340698242 nb_pixel_total : 334 time to create 1 rle with old method : 0.0003905296325683594 time for calcul the mask position with numpy : 0.008350610733032227 nb_pixel_total : 433 time to create 1 rle with old method : 0.0004954338073730469 time for calcul the mask position with numpy : 0.00850057601928711 nb_pixel_total : 152 time to create 1 rle with old method : 0.00021195411682128906 create new chi : 1.7655868530273438 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 : 0.0032339096069335938 batch 1 Loaded 159 chid ids of type : 4230 Number RLEs to save : 15469 TO DO : save crop sub photo not yet done ! save time : 0.8645966053009033 nb_obj : 157 nb_hashtags : 8 time to prepare the origin masks : 1.440380573272705 time for calcul the mask position with numpy : 0.020116806030273438 nb_pixel_total : 1834791 time to create 1 rle with new method : 0.07507777214050293 time for calcul the mask position with numpy : 0.006200551986694336 nb_pixel_total : 351 time to create 1 rle with old method : 0.0003962516784667969 time for calcul the mask position with numpy : 0.0059850215911865234 nb_pixel_total : 58 time to create 1 rle with old method : 8.416175842285156e-05 time for calcul the mask position with numpy : 0.005893707275390625 nb_pixel_total : 72 time to create 1 rle with old method : 0.0001246929168701172 time for calcul the mask position with numpy : 0.006529331207275391 nb_pixel_total : 759 time to create 1 rle with old method : 0.0009114742279052734 time for calcul the mask position with numpy : 0.006072521209716797 nb_pixel_total : 1367 time to create 1 rle with old method : 0.0016756057739257812 time for calcul the mask position with numpy : 0.007936954498291016 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002033710479736328 time for calcul the mask position with numpy : 0.0058062076568603516 nb_pixel_total : 197 time to create 1 rle with old method : 0.0002276897430419922 time for calcul the mask position with numpy : 0.005869388580322266 nb_pixel_total : 19 time to create 1 rle with old method : 6.699562072753906e-05 time for calcul the mask position with numpy : 0.006851673126220703 nb_pixel_total : 31 time to create 1 rle with old method : 7.009506225585938e-05 time for calcul the mask position with numpy : 0.005926847457885742 nb_pixel_total : 98 time to create 1 rle with old method : 0.0001227855682373047 time for calcul the mask position with numpy : 0.006106138229370117 nb_pixel_total : 1 time to create 1 rle with old method : 2.193450927734375e-05 time for calcul the mask position with numpy : 0.006003618240356445 nb_pixel_total : 2955 time to create 1 rle with old method : 0.0032453536987304688 time for calcul the mask position with numpy : 0.006043672561645508 nb_pixel_total : 273 time to create 1 rle with old method : 0.0003616809844970703 time for calcul the mask position with numpy : 0.0059320926666259766 nb_pixel_total : 775 time to create 1 rle with old method : 0.0008990764617919922 time for calcul the mask position with numpy : 0.006033182144165039 nb_pixel_total : 13369 time to create 1 rle with old method : 0.014533042907714844 time for calcul the mask position with numpy : 0.006157398223876953 nb_pixel_total : 894 time to create 1 rle with old method : 0.0009751319885253906 time for calcul the mask position with numpy : 0.005982875823974609 nb_pixel_total : 47 time to create 1 rle with old method : 9.894371032714844e-05 time for calcul the mask position with numpy : 0.0061969757080078125 nb_pixel_total : 199 time to create 1 rle with old method : 0.0002579689025878906 time for calcul the mask position with numpy : 0.0061206817626953125 nb_pixel_total : 1558 time to create 1 rle with old method : 0.0018870830535888672 time for calcul the mask position with numpy : 0.0060427188873291016 nb_pixel_total : 2633 time to create 1 rle with old method : 0.0031151771545410156 time for calcul the mask position with numpy : 0.005950450897216797 nb_pixel_total : 1078 time to create 1 rle with old method : 0.0012636184692382812 time for calcul the mask position with numpy : 0.006209850311279297 nb_pixel_total : 738 time to create 1 rle with old method : 0.0009353160858154297 time for calcul the mask position with numpy : 0.006230354309082031 nb_pixel_total : 2389 time to create 1 rle with old method : 0.0027856826782226562 time for calcul the mask position with numpy : 0.006407260894775391 nb_pixel_total : 6001 time to create 1 rle with old method : 0.006979227066040039 time for calcul the mask position with numpy : 0.006141185760498047 nb_pixel_total : 157 time to create 1 rle with old method : 0.00019741058349609375 time for calcul the mask position with numpy : 0.006748676300048828 nb_pixel_total : 106 time to create 1 rle with old method : 0.00014972686767578125 time for calcul the mask position with numpy : 0.00875091552734375 nb_pixel_total : 550 time to create 1 rle with old method : 0.0008068084716796875 time for calcul the mask position with numpy : 0.006724119186401367 nb_pixel_total : 726 time to create 1 rle with old method : 0.0008480548858642578 time for calcul the mask position with numpy : 0.006106376647949219 nb_pixel_total : 855 time to create 1 rle with old method : 0.0008955001831054688 time for calcul the mask position with numpy : 0.006535768508911133 nb_pixel_total : 605 time to create 1 rle with old method : 0.0007519721984863281 time for calcul the mask position with numpy : 0.00624847412109375 nb_pixel_total : 196 time to create 1 rle with old method : 0.00026035308837890625 time for calcul the mask position with numpy : 0.006181955337524414 nb_pixel_total : 165 time to create 1 rle with old method : 0.0002219676971435547 time for calcul the mask position with numpy : 0.006304740905761719 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001499652862548828 time for calcul the mask position with numpy : 0.006306886672973633 nb_pixel_total : 251 time to create 1 rle with old method : 0.00038242340087890625 time for calcul the mask position with numpy : 0.006276845932006836 nb_pixel_total : 3822 time to create 1 rle with old method : 0.004511594772338867 time for calcul the mask position with numpy : 0.0062716007232666016 nb_pixel_total : 5712 time to create 1 rle with old method : 0.0063018798828125 time for calcul the mask position with numpy : 0.006525516510009766 nb_pixel_total : 2695 time to create 1 rle with old method : 0.003033876419067383 time for calcul the mask position with numpy : 0.006447315216064453 nb_pixel_total : 1834 time to create 1 rle with old method : 0.0021343231201171875 time for calcul the mask position with numpy : 0.00629878044128418 nb_pixel_total : 908 time to create 1 rle with old method : 0.0010488033294677734 time for calcul the mask position with numpy : 0.006112813949584961 nb_pixel_total : 727 time to create 1 rle with old method : 0.0008454322814941406 time for calcul the mask position with numpy : 0.0061740875244140625 nb_pixel_total : 1100 time to create 1 rle with old method : 0.0012669563293457031 time for calcul the mask position with numpy : 0.0060117244720458984 nb_pixel_total : 205 time to create 1 rle with old method : 0.0003104209899902344 time for calcul the mask position with numpy : 0.006316423416137695 nb_pixel_total : 2717 time to create 1 rle with old method : 0.003141164779663086 time for calcul the mask position with numpy : 0.006448507308959961 nb_pixel_total : 335 time to create 1 rle with old method : 0.0005903244018554688 time for calcul the mask position with numpy : 0.0064733028411865234 nb_pixel_total : 239 time to create 1 rle with old method : 0.00046062469482421875 time for calcul the mask position with numpy : 0.006806612014770508 nb_pixel_total : 124 time to create 1 rle with old method : 0.00027942657470703125 time for calcul the mask position with numpy : 0.006691455841064453 nb_pixel_total : 1 time to create 1 rle with old method : 3.719329833984375e-05 time for calcul the mask position with numpy : 0.008360624313354492 nb_pixel_total : 1286 time to create 1 rle with old method : 0.001728057861328125 time for calcul the mask position with numpy : 0.005972862243652344 nb_pixel_total : 800 time to create 1 rle with old method : 0.0008807182312011719 time for calcul the mask position with numpy : 0.0060694217681884766 nb_pixel_total : 8573 time to create 1 rle with old method : 0.009546279907226562 time for calcul the mask position with numpy : 0.006053924560546875 nb_pixel_total : 23 time to create 1 rle with old method : 6.914138793945312e-05 time for calcul the mask position with numpy : 0.006223201751708984 nb_pixel_total : 260 time to create 1 rle with old method : 0.00032138824462890625 time for calcul the mask position with numpy : 0.006088972091674805 nb_pixel_total : 1424 time to create 1 rle with old method : 0.0016434192657470703 time for calcul the mask position with numpy : 0.006142854690551758 nb_pixel_total : 139 time to create 1 rle with old method : 0.0001881122589111328 time for calcul the mask position with numpy : 0.006241798400878906 nb_pixel_total : 116 time to create 1 rle with old method : 0.00015783309936523438 time for calcul the mask position with numpy : 0.005919933319091797 nb_pixel_total : 2136 time to create 1 rle with old method : 0.002408266067504883 time for calcul the mask position with numpy : 0.0059697628021240234 nb_pixel_total : 67 time to create 1 rle with old method : 0.000102996826171875 time for calcul the mask position with numpy : 0.006463527679443359 nb_pixel_total : 1085 time to create 1 rle with old method : 0.0012905597686767578 time for calcul the mask position with numpy : 0.006129741668701172 nb_pixel_total : 589 time to create 1 rle with old method : 0.0007174015045166016 time for calcul the mask position with numpy : 0.006283998489379883 nb_pixel_total : 2019 time to create 1 rle with old method : 0.002385377883911133 time for calcul the mask position with numpy : 0.006554603576660156 nb_pixel_total : 6 time to create 1 rle with old method : 3.123283386230469e-05 time for calcul the mask position with numpy : 0.006145000457763672 nb_pixel_total : 178 time to create 1 rle with old method : 0.0003001689910888672 time for calcul the mask position with numpy : 0.006114959716796875 nb_pixel_total : 106 time to create 1 rle with old method : 0.000148773193359375 time for calcul the mask position with numpy : 0.0061762332916259766 nb_pixel_total : 627 time to create 1 rle with old method : 0.0008230209350585938 time for calcul the mask position with numpy : 0.006181478500366211 nb_pixel_total : 623 time to create 1 rle with old method : 0.0007643699645996094 time for calcul the mask position with numpy : 0.006256103515625 nb_pixel_total : 759 time to create 1 rle with old method : 0.0008790493011474609 time for calcul the mask position with numpy : 0.006383180618286133 nb_pixel_total : 935 time to create 1 rle with old method : 0.0011684894561767578 time for calcul the mask position with numpy : 0.007026195526123047 nb_pixel_total : 220 time to create 1 rle with old method : 0.00043773651123046875 time for calcul the mask position with numpy : 0.007224082946777344 nb_pixel_total : 108 time to create 1 rle with old method : 0.00015163421630859375 time for calcul the mask position with numpy : 0.00608372688293457 nb_pixel_total : 1014 time to create 1 rle with old method : 0.0011754035949707031 time for calcul the mask position with numpy : 0.005994558334350586 nb_pixel_total : 1615 time to create 1 rle with old method : 0.0018236637115478516 time for calcul the mask position with numpy : 0.007107973098754883 nb_pixel_total : 1029 time to create 1 rle with old method : 0.0012025833129882812 time for calcul the mask position with numpy : 0.006165504455566406 nb_pixel_total : 135 time to create 1 rle with old method : 0.0002162456512451172 time for calcul the mask position with numpy : 0.006029605865478516 nb_pixel_total : 449 time to create 1 rle with old method : 0.0005223751068115234 time for calcul the mask position with numpy : 0.006073951721191406 nb_pixel_total : 681 time to create 1 rle with old method : 0.0008060932159423828 time for calcul the mask position with numpy : 0.006156206130981445 nb_pixel_total : 1633 time to create 1 rle with old method : 0.001825571060180664 time for calcul the mask position with numpy : 0.0058858394622802734 nb_pixel_total : 67 time to create 1 rle with old method : 0.00017023086547851562 time for calcul the mask position with numpy : 0.0061643123626708984 nb_pixel_total : 326 time to create 1 rle with old method : 0.00038886070251464844 time for calcul the mask position with numpy : 0.006123781204223633 nb_pixel_total : 822 time to create 1 rle with old method : 0.0009493827819824219 time for calcul the mask position with numpy : 0.006088972091674805 nb_pixel_total : 463 time to create 1 rle with old method : 0.0005168914794921875 time for calcul the mask position with numpy : 0.00619196891784668 nb_pixel_total : 92 time to create 1 rle with old method : 0.00014662742614746094 time for calcul the mask position with numpy : 0.0071659088134765625 nb_pixel_total : 106246 time to create 1 rle with old method : 0.11786794662475586 time for calcul the mask position with numpy : 0.0062825679779052734 nb_pixel_total : 437 time to create 1 rle with old method : 0.0005009174346923828 time for calcul the mask position with numpy : 0.0059888362884521484 nb_pixel_total : 421 time to create 1 rle with old method : 0.0005142688751220703 time for calcul the mask position with numpy : 0.0060656070709228516 nb_pixel_total : 140 time to create 1 rle with old method : 0.00017499923706054688 time for calcul the mask position with numpy : 0.006045818328857422 nb_pixel_total : 155 time to create 1 rle with old method : 0.00021600723266601562 time for calcul the mask position with numpy : 0.006200551986694336 nb_pixel_total : 171 time to create 1 rle with old method : 0.00034928321838378906 time for calcul the mask position with numpy : 0.006288051605224609 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002205371856689453 time for calcul the mask position with numpy : 0.00640559196472168 nb_pixel_total : 304 time to create 1 rle with old method : 0.0003685951232910156 time for calcul the mask position with numpy : 0.005956411361694336 nb_pixel_total : 426 time to create 1 rle with old method : 0.00046133995056152344 time for calcul the mask position with numpy : 0.006056785583496094 nb_pixel_total : 1680 time to create 1 rle with old method : 0.0019004344940185547 time for calcul the mask position with numpy : 0.005874156951904297 nb_pixel_total : 492 time to create 1 rle with old method : 0.0005960464477539062 time for calcul the mask position with numpy : 0.0058841705322265625 nb_pixel_total : 222 time to create 1 rle with old method : 0.0002810955047607422 time for calcul the mask position with numpy : 0.005921602249145508 nb_pixel_total : 2214 time to create 1 rle with old method : 0.0024747848510742188 time for calcul the mask position with numpy : 0.005901813507080078 nb_pixel_total : 25 time to create 1 rle with old method : 6.818771362304688e-05 time for calcul the mask position with numpy : 0.00716853141784668 nb_pixel_total : 361 time to create 1 rle with old method : 0.00048232078552246094 time for calcul the mask position with numpy : 0.006110429763793945 nb_pixel_total : 14 time to create 1 rle with old method : 6.246566772460938e-05 time for calcul the mask position with numpy : 0.005782604217529297 nb_pixel_total : 71 time to create 1 rle with old method : 0.00011801719665527344 time for calcul the mask position with numpy : 0.0059795379638671875 nb_pixel_total : 457 time to create 1 rle with old method : 0.0005762577056884766 time for calcul the mask position with numpy : 0.005883693695068359 nb_pixel_total : 347 time to create 1 rle with old method : 0.00038623809814453125 time for calcul the mask position with numpy : 0.0058977603912353516 nb_pixel_total : 609 time to create 1 rle with old method : 0.0007526874542236328 time for calcul the mask position with numpy : 0.005837202072143555 nb_pixel_total : 1006 time to create 1 rle with old method : 0.0011692047119140625 time for calcul the mask position with numpy : 0.005877494812011719 nb_pixel_total : 236 time to create 1 rle with old method : 0.0003001689910888672 time for calcul the mask position with numpy : 0.005738735198974609 nb_pixel_total : 236 time to create 1 rle with old method : 0.0002803802490234375 time for calcul the mask position with numpy : 0.006188631057739258 nb_pixel_total : 194 time to create 1 rle with old method : 0.0002493858337402344 time for calcul the mask position with numpy : 0.00616002082824707 nb_pixel_total : 945 time to create 1 rle with old method : 0.0011959075927734375 time for calcul the mask position with numpy : 0.006259441375732422 nb_pixel_total : 105 time to create 1 rle with old method : 0.0002472400665283203 time for calcul the mask position with numpy : 0.006093025207519531 nb_pixel_total : 539 time to create 1 rle with old method : 0.0005733966827392578 time for calcul the mask position with numpy : 0.0063304901123046875 nb_pixel_total : 875 time to create 1 rle with old method : 0.0011153221130371094 time for calcul the mask position with numpy : 0.006107330322265625 nb_pixel_total : 416 time to create 1 rle with old method : 0.0004799365997314453 time for calcul the mask position with numpy : 0.006797075271606445 nb_pixel_total : 52 time to create 1 rle with old method : 0.00011849403381347656 time for calcul the mask position with numpy : 0.006872415542602539 nb_pixel_total : 172 time to create 1 rle with old method : 0.0003330707550048828 time for calcul the mask position with numpy : 0.007062673568725586 nb_pixel_total : 704 time to create 1 rle with old method : 0.0010480880737304688 time for calcul the mask position with numpy : 0.007359027862548828 nb_pixel_total : 395 time to create 1 rle with old method : 0.0006518363952636719 time for calcul the mask position with numpy : 0.0067291259765625 nb_pixel_total : 953 time to create 1 rle with old method : 0.0012509822845458984 time for calcul the mask position with numpy : 0.0068318843841552734 nb_pixel_total : 173 time to create 1 rle with old method : 0.0003027915954589844 time for calcul the mask position with numpy : 0.007517337799072266 nb_pixel_total : 216 time to create 1 rle with old method : 0.00039768218994140625 time for calcul the mask position with numpy : 0.007334470748901367 nb_pixel_total : 108 time to create 1 rle with old method : 0.00019407272338867188 time for calcul the mask position with numpy : 0.006772279739379883 nb_pixel_total : 483 time to create 1 rle with old method : 0.0005955696105957031 time for calcul the mask position with numpy : 0.006664752960205078 nb_pixel_total : 2264 time to create 1 rle with old method : 0.002830028533935547 time for calcul the mask position with numpy : 0.006448984146118164 nb_pixel_total : 32 time to create 1 rle with old method : 0.00010132789611816406 time for calcul the mask position with numpy : 0.006643772125244141 nb_pixel_total : 505 time to create 1 rle with old method : 0.0006372928619384766 time for calcul the mask position with numpy : 0.006793498992919922 nb_pixel_total : 302 time to create 1 rle with old method : 0.0003979206085205078 time for calcul the mask position with numpy : 0.006104707717895508 nb_pixel_total : 732 time to create 1 rle with old method : 0.0010228157043457031 time for calcul the mask position with numpy : 0.0061109066009521484 nb_pixel_total : 2116 time to create 1 rle with old method : 0.0025441646575927734 time for calcul the mask position with numpy : 0.006537675857543945 nb_pixel_total : 56 time to create 1 rle with old method : 0.00019669532775878906 time for calcul the mask position with numpy : 0.006998300552368164 nb_pixel_total : 3899 time to create 1 rle with old method : 0.004971981048583984 time for calcul the mask position with numpy : 0.006359100341796875 nb_pixel_total : 243 time to create 1 rle with old method : 0.00030994415283203125 time for calcul the mask position with numpy : 0.006198883056640625 nb_pixel_total : 455 time to create 1 rle with old method : 0.0005307197570800781 time for calcul the mask position with numpy : 0.005959033966064453 nb_pixel_total : 91 time to create 1 rle with old method : 0.00013899803161621094 time for calcul the mask position with numpy : 0.006012678146362305 nb_pixel_total : 770 time to create 1 rle with old method : 0.0008981227874755859 time for calcul the mask position with numpy : 0.006171226501464844 nb_pixel_total : 535 time to create 1 rle with old method : 0.0006985664367675781 time for calcul the mask position with numpy : 0.005976438522338867 nb_pixel_total : 1394 time to create 1 rle with old method : 0.0015175342559814453 time for calcul the mask position with numpy : 0.006241559982299805 nb_pixel_total : 1310 time to create 1 rle with old method : 0.0015244483947753906 time for calcul the mask position with numpy : 0.006130695343017578 nb_pixel_total : 238 time to create 1 rle with old method : 0.0003094673156738281 time for calcul the mask position with numpy : 0.006255149841308594 nb_pixel_total : 280 time to create 1 rle with old method : 0.0003237724304199219 time for calcul the mask position with numpy : 0.006230592727661133 nb_pixel_total : 904 time to create 1 rle with old method : 0.0011363029479980469 time for calcul the mask position with numpy : 0.006317853927612305 nb_pixel_total : 671 time to create 1 rle with old method : 0.0008358955383300781 time for calcul the mask position with numpy : 0.006757974624633789 nb_pixel_total : 932 time to create 1 rle with old method : 0.0011799335479736328 time for calcul the mask position with numpy : 0.006834745407104492 nb_pixel_total : 126 time to create 1 rle with old method : 0.0001704692840576172 time for calcul the mask position with numpy : 0.006295919418334961 nb_pixel_total : 431 time to create 1 rle with old method : 0.0005426406860351562 time for calcul the mask position with numpy : 0.006593942642211914 nb_pixel_total : 11 time to create 1 rle with old method : 5.173683166503906e-05 time for calcul the mask position with numpy : 0.006447315216064453 nb_pixel_total : 440 time to create 1 rle with old method : 0.000560760498046875 time for calcul the mask position with numpy : 0.0060846805572509766 nb_pixel_total : 168 time to create 1 rle with old method : 0.00023674964904785156 time for calcul the mask position with numpy : 0.006532430648803711 nb_pixel_total : 126 time to create 1 rle with old method : 0.00017523765563964844 time for calcul the mask position with numpy : 0.006078958511352539 nb_pixel_total : 529 time to create 1 rle with old method : 0.0006804466247558594 time for calcul the mask position with numpy : 0.00620269775390625 nb_pixel_total : 504 time to create 1 rle with old method : 0.0006167888641357422 time for calcul the mask position with numpy : 0.006073951721191406 nb_pixel_total : 317 time to create 1 rle with old method : 0.0004143714904785156 time for calcul the mask position with numpy : 0.006092071533203125 nb_pixel_total : 537 time to create 1 rle with old method : 0.0006706714630126953 time for calcul the mask position with numpy : 0.0062236785888671875 nb_pixel_total : 209 time to create 1 rle with old method : 0.0002589225769042969 time for calcul the mask position with numpy : 0.006144046783447266 nb_pixel_total : 486 time to create 1 rle with old method : 0.0005996227264404297 time for calcul the mask position with numpy : 0.006170988082885742 nb_pixel_total : 1304 time to create 1 rle with old method : 0.0014553070068359375 time for calcul the mask position with numpy : 0.0061244964599609375 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005452632904052734 time for calcul the mask position with numpy : 0.006148576736450195 nb_pixel_total : 392 time to create 1 rle with old method : 0.000492095947265625 time for calcul the mask position with numpy : 0.0061533451080322266 nb_pixel_total : 1152 time to create 1 rle with old method : 0.0014090538024902344 time for calcul the mask position with numpy : 0.006209611892700195 nb_pixel_total : 551 time to create 1 rle with old method : 0.0006966590881347656 time for calcul the mask position with numpy : 0.006339311599731445 nb_pixel_total : 25 time to create 1 rle with old method : 6.914138793945312e-05 create new chi : 1.3763272762298584 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 : 0.006356239318847656 batch 1 Loaded 158 chid ids of type : 4230 Number RLEs to save : 13516 TO DO : save crop sub photo not yet done ! save time : 0.7938129901885986 nb_obj : 149 nb_hashtags : 7 time to prepare the origin masks : 1.4569001197814941 time for calcul the mask position with numpy : 0.01843571662902832 nb_pixel_total : 1610453 time to create 1 rle with new method : 0.04178571701049805 time for calcul the mask position with numpy : 0.00670313835144043 nb_pixel_total : 29 time to create 1 rle with old method : 6.842613220214844e-05 time for calcul the mask position with numpy : 0.006495952606201172 nb_pixel_total : 67 time to create 1 rle with old method : 0.00010514259338378906 time for calcul the mask position with numpy : 0.006917238235473633 nb_pixel_total : 114 time to create 1 rle with old method : 0.0001819133758544922 time for calcul the mask position with numpy : 0.006483316421508789 nb_pixel_total : 141 time to create 1 rle with old method : 0.00018835067749023438 time for calcul the mask position with numpy : 0.006089925765991211 nb_pixel_total : 425 time to create 1 rle with old method : 0.0005216598510742188 time for calcul the mask position with numpy : 0.006325721740722656 nb_pixel_total : 6788 time to create 1 rle with old method : 0.008068323135375977 time for calcul the mask position with numpy : 0.006600141525268555 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003654956817626953 time for calcul the mask position with numpy : 0.006215810775756836 nb_pixel_total : 57 time to create 1 rle with old method : 0.0001087188720703125 time for calcul the mask position with numpy : 0.0061342716217041016 nb_pixel_total : 297 time to create 1 rle with old method : 0.0003654956817626953 time for calcul the mask position with numpy : 0.006684303283691406 nb_pixel_total : 367 time to create 1 rle with old method : 0.0005369186401367188 time for calcul the mask position with numpy : 0.006555795669555664 nb_pixel_total : 18 time to create 1 rle with old method : 4.887580871582031e-05 time for calcul the mask position with numpy : 0.006650209426879883 nb_pixel_total : 46125 time to create 1 rle with old method : 0.05161905288696289 time for calcul the mask position with numpy : 0.0062580108642578125 nb_pixel_total : 57 time to create 1 rle with old method : 9.1552734375e-05 time for calcul the mask position with numpy : 0.006265163421630859 nb_pixel_total : 26 time to create 1 rle with old method : 5.650520324707031e-05 time for calcul the mask position with numpy : 0.006144285202026367 nb_pixel_total : 2799 time to create 1 rle with old method : 0.003245830535888672 time for calcul the mask position with numpy : 0.006735086441040039 nb_pixel_total : 302 time to create 1 rle with old method : 0.0003724098205566406 time for calcul the mask position with numpy : 0.006110429763793945 nb_pixel_total : 18922 time to create 1 rle with old method : 0.020472288131713867 time for calcul the mask position with numpy : 0.006276845932006836 nb_pixel_total : 150 time to create 1 rle with old method : 0.00029015541076660156 time for calcul the mask position with numpy : 0.0064198970794677734 nb_pixel_total : 462 time to create 1 rle with old method : 0.0005540847778320312 time for calcul the mask position with numpy : 0.0063402652740478516 nb_pixel_total : 1643 time to create 1 rle with old method : 0.0019254684448242188 time for calcul the mask position with numpy : 0.006403207778930664 nb_pixel_total : 200 time to create 1 rle with old method : 0.0002562999725341797 time for calcul the mask position with numpy : 0.006159543991088867 nb_pixel_total : 1836 time to create 1 rle with old method : 0.002097606658935547 time for calcul the mask position with numpy : 0.006335020065307617 nb_pixel_total : 2053 time to create 1 rle with old method : 0.0023794174194335938 time for calcul the mask position with numpy : 0.005923748016357422 nb_pixel_total : 1416 time to create 1 rle with old method : 0.001661062240600586 time for calcul the mask position with numpy : 0.006220340728759766 nb_pixel_total : 2922 time to create 1 rle with old method : 0.0033500194549560547 time for calcul the mask position with numpy : 0.006363391876220703 nb_pixel_total : 1404 time to create 1 rle with old method : 0.001636505126953125 time for calcul the mask position with numpy : 0.006322383880615234 nb_pixel_total : 725 time to create 1 rle with old method : 0.0009579658508300781 time for calcul the mask position with numpy : 0.006175518035888672 nb_pixel_total : 2275 time to create 1 rle with old method : 0.0025386810302734375 time for calcul the mask position with numpy : 0.0063436031341552734 nb_pixel_total : 7676 time to create 1 rle with old method : 0.008445978164672852 time for calcul the mask position with numpy : 0.006065845489501953 nb_pixel_total : 526 time to create 1 rle with old method : 0.0006303787231445312 time for calcul the mask position with numpy : 0.0061261653900146484 nb_pixel_total : 917 time to create 1 rle with old method : 0.0010542869567871094 time for calcul the mask position with numpy : 0.00623011589050293 nb_pixel_total : 329 time to create 1 rle with old method : 0.0004086494445800781 time for calcul the mask position with numpy : 0.006888389587402344 nb_pixel_total : 151408 time to create 1 rle with new method : 0.02919626235961914 time for calcul the mask position with numpy : 0.006089687347412109 nb_pixel_total : 548 time to create 1 rle with old method : 0.0006573200225830078 time for calcul the mask position with numpy : 0.0062901973724365234 nb_pixel_total : 3189 time to create 1 rle with old method : 0.0037751197814941406 time for calcul the mask position with numpy : 0.0066051483154296875 nb_pixel_total : 4254 time to create 1 rle with old method : 0.004842996597290039 time for calcul the mask position with numpy : 0.006104230880737305 nb_pixel_total : 70 time to create 1 rle with old method : 0.00010609626770019531 time for calcul the mask position with numpy : 0.006182193756103516 nb_pixel_total : 662 time to create 1 rle with old method : 0.0007898807525634766 time for calcul the mask position with numpy : 0.005704402923583984 nb_pixel_total : 424 time to create 1 rle with old method : 0.0004775524139404297 time for calcul the mask position with numpy : 0.005844593048095703 nb_pixel_total : 1652 time to create 1 rle with old method : 0.001882791519165039 time for calcul the mask position with numpy : 0.005961179733276367 nb_pixel_total : 1069 time to create 1 rle with old method : 0.0012104511260986328 time for calcul the mask position with numpy : 0.005823850631713867 nb_pixel_total : 821 time to create 1 rle with old method : 0.0009665489196777344 time for calcul the mask position with numpy : 0.0057184696197509766 nb_pixel_total : 194 time to create 1 rle with old method : 0.00024056434631347656 time for calcul the mask position with numpy : 0.005886554718017578 nb_pixel_total : 170 time to create 1 rle with old method : 0.00022029876708984375 time for calcul the mask position with numpy : 0.005889177322387695 nb_pixel_total : 185 time to create 1 rle with old method : 0.0002148151397705078 time for calcul the mask position with numpy : 0.0058574676513671875 nb_pixel_total : 1234 time to create 1 rle with old method : 0.0013473033905029297 time for calcul the mask position with numpy : 0.0058345794677734375 nb_pixel_total : 846 time to create 1 rle with old method : 0.0010058879852294922 time for calcul the mask position with numpy : 0.0058171749114990234 nb_pixel_total : 13342 time to create 1 rle with old method : 0.014224767684936523 time for calcul the mask position with numpy : 0.0058176517486572266 nb_pixel_total : 1319 time to create 1 rle with old method : 0.0015950202941894531 time for calcul the mask position with numpy : 0.007934331893920898 nb_pixel_total : 623 time to create 1 rle with old method : 0.0013172626495361328 time for calcul the mask position with numpy : 0.009042501449584961 nb_pixel_total : 245 time to create 1 rle with old method : 0.00031876564025878906 time for calcul the mask position with numpy : 0.005974769592285156 nb_pixel_total : 7702 time to create 1 rle with old method : 0.008397102355957031 time for calcul the mask position with numpy : 0.0061037540435791016 nb_pixel_total : 3816 time to create 1 rle with old method : 0.004271030426025391 time for calcul the mask position with numpy : 0.006664752960205078 nb_pixel_total : 445 time to create 1 rle with old method : 0.0005478858947753906 time for calcul the mask position with numpy : 0.005831241607666016 nb_pixel_total : 1203 time to create 1 rle with old method : 0.0012822151184082031 time for calcul the mask position with numpy : 0.005748748779296875 nb_pixel_total : 2 time to create 1 rle with old method : 2.288818359375e-05 time for calcul the mask position with numpy : 0.00583195686340332 nb_pixel_total : 99 time to create 1 rle with old method : 0.00024080276489257812 time for calcul the mask position with numpy : 0.005822658538818359 nb_pixel_total : 3209 time to create 1 rle with old method : 0.003465890884399414 time for calcul the mask position with numpy : 0.006011486053466797 nb_pixel_total : 121 time to create 1 rle with old method : 0.00015234947204589844 time for calcul the mask position with numpy : 0.005937099456787109 nb_pixel_total : 831 time to create 1 rle with old method : 0.0009438991546630859 time for calcul the mask position with numpy : 0.0057446956634521484 nb_pixel_total : 1005 time to create 1 rle with old method : 0.001123666763305664 time for calcul the mask position with numpy : 0.005830287933349609 nb_pixel_total : 1700 time to create 1 rle with old method : 0.0018985271453857422 time for calcul the mask position with numpy : 0.005948781967163086 nb_pixel_total : 411 time to create 1 rle with old method : 0.0004646778106689453 time for calcul the mask position with numpy : 0.0057582855224609375 nb_pixel_total : 145 time to create 1 rle with old method : 0.00019073486328125 time for calcul the mask position with numpy : 0.005874156951904297 nb_pixel_total : 448 time to create 1 rle with old method : 0.0005443096160888672 time for calcul the mask position with numpy : 0.005897045135498047 nb_pixel_total : 1603 time to create 1 rle with old method : 0.0017580986022949219 time for calcul the mask position with numpy : 0.005882740020751953 nb_pixel_total : 75 time to create 1 rle with old method : 0.00011348724365234375 time for calcul the mask position with numpy : 0.005713701248168945 nb_pixel_total : 307 time to create 1 rle with old method : 0.0003788471221923828 time for calcul the mask position with numpy : 0.005873441696166992 nb_pixel_total : 708 time to create 1 rle with old method : 0.0007579326629638672 time for calcul the mask position with numpy : 0.005697011947631836 nb_pixel_total : 471 time to create 1 rle with old method : 0.0005090236663818359 time for calcul the mask position with numpy : 0.005743980407714844 nb_pixel_total : 450 time to create 1 rle with old method : 0.0005438327789306641 time for calcul the mask position with numpy : 0.006352663040161133 nb_pixel_total : 106282 time to create 1 rle with old method : 0.1127476692199707 time for calcul the mask position with numpy : 0.006185293197631836 nb_pixel_total : 345 time to create 1 rle with old method : 0.0004086494445800781 time for calcul the mask position with numpy : 0.006082773208618164 nb_pixel_total : 120 time to create 1 rle with old method : 0.00016641616821289062 time for calcul the mask position with numpy : 0.006321430206298828 nb_pixel_total : 172 time to create 1 rle with old method : 0.0002186298370361328 time for calcul the mask position with numpy : 0.006204843521118164 nb_pixel_total : 161 time to create 1 rle with old method : 0.000213623046875 time for calcul the mask position with numpy : 0.0063169002532958984 nb_pixel_total : 290 time to create 1 rle with old method : 0.00035691261291503906 time for calcul the mask position with numpy : 0.006021022796630859 nb_pixel_total : 408 time to create 1 rle with old method : 0.0004913806915283203 time for calcul the mask position with numpy : 0.0063054561614990234 nb_pixel_total : 108 time to create 1 rle with old method : 0.0002110004425048828 time for calcul the mask position with numpy : 0.006093263626098633 nb_pixel_total : 2460 time to create 1 rle with old method : 0.0027153491973876953 time for calcul the mask position with numpy : 0.005993843078613281 nb_pixel_total : 379 time to create 1 rle with old method : 0.00046133995056152344 time for calcul the mask position with numpy : 0.005933523178100586 nb_pixel_total : 10 time to create 1 rle with old method : 4.100799560546875e-05 time for calcul the mask position with numpy : 0.005872011184692383 nb_pixel_total : 110 time to create 1 rle with old method : 0.00014710426330566406 time for calcul the mask position with numpy : 0.006102561950683594 nb_pixel_total : 404 time to create 1 rle with old method : 0.00046825408935546875 time for calcul the mask position with numpy : 0.006012678146362305 nb_pixel_total : 457 time to create 1 rle with old method : 0.0005464553833007812 time for calcul the mask position with numpy : 0.006005764007568359 nb_pixel_total : 44 time to create 1 rle with old method : 7.796287536621094e-05 time for calcul the mask position with numpy : 0.0060956478118896484 nb_pixel_total : 1472 time to create 1 rle with old method : 0.0017383098602294922 time for calcul the mask position with numpy : 0.006441354751586914 nb_pixel_total : 1042 time to create 1 rle with old method : 0.0013263225555419922 time for calcul the mask position with numpy : 0.0064051151275634766 nb_pixel_total : 223 time to create 1 rle with old method : 0.00026488304138183594 time for calcul the mask position with numpy : 0.006484270095825195 nb_pixel_total : 190 time to create 1 rle with old method : 0.00024962425231933594 time for calcul the mask position with numpy : 0.006420135498046875 nb_pixel_total : 966 time to create 1 rle with old method : 0.0011260509490966797 time for calcul the mask position with numpy : 0.0064165592193603516 nb_pixel_total : 179 time to create 1 rle with old method : 0.0002338886260986328 time for calcul the mask position with numpy : 0.0064814090728759766 nb_pixel_total : 510 time to create 1 rle with old method : 0.0007045269012451172 time for calcul the mask position with numpy : 0.006596088409423828 nb_pixel_total : 919 time to create 1 rle with old method : 0.0011153221130371094 time for calcul the mask position with numpy : 0.0066301822662353516 nb_pixel_total : 173 time to create 1 rle with old method : 0.0002560615539550781 time for calcul the mask position with numpy : 0.006507158279418945 nb_pixel_total : 371 time to create 1 rle with old method : 0.0005137920379638672 time for calcul the mask position with numpy : 0.006714582443237305 nb_pixel_total : 35 time to create 1 rle with old method : 0.00010037422180175781 time for calcul the mask position with numpy : 0.006903886795043945 nb_pixel_total : 564 time to create 1 rle with old method : 0.0007181167602539062 time for calcul the mask position with numpy : 0.006786823272705078 nb_pixel_total : 49 time to create 1 rle with old method : 0.00013709068298339844 time for calcul the mask position with numpy : 0.007215976715087891 nb_pixel_total : 12 time to create 1 rle with old method : 5.7697296142578125e-05 time for calcul the mask position with numpy : 0.006882190704345703 nb_pixel_total : 745 time to create 1 rle with old method : 0.0008845329284667969 time for calcul the mask position with numpy : 0.006972789764404297 nb_pixel_total : 17 time to create 1 rle with old method : 5.125999450683594e-05 time for calcul the mask position with numpy : 0.007226705551147461 nb_pixel_total : 696 time to create 1 rle with old method : 0.0008680820465087891 time for calcul the mask position with numpy : 0.0067446231842041016 nb_pixel_total : 367 time to create 1 rle with old method : 0.0005238056182861328 time for calcul the mask position with numpy : 0.00725865364074707 nb_pixel_total : 199 time to create 1 rle with old method : 0.0002734661102294922 time for calcul the mask position with numpy : 0.006973981857299805 nb_pixel_total : 1049 time to create 1 rle with old method : 0.001291513442993164 time for calcul the mask position with numpy : 0.006520509719848633 nb_pixel_total : 44 time to create 1 rle with old method : 8.559226989746094e-05 time for calcul the mask position with numpy : 0.006003618240356445 nb_pixel_total : 39 time to create 1 rle with old method : 8.869171142578125e-05 time for calcul the mask position with numpy : 0.006023883819580078 nb_pixel_total : 451 time to create 1 rle with old method : 0.0005438327789306641 time for calcul the mask position with numpy : 0.005893707275390625 nb_pixel_total : 180 time to create 1 rle with old method : 0.0002307891845703125 time for calcul the mask position with numpy : 0.005875110626220703 nb_pixel_total : 441 time to create 1 rle with old method : 0.0005087852478027344 time for calcul the mask position with numpy : 0.005971431732177734 nb_pixel_total : 156 time to create 1 rle with old method : 0.0002048015594482422 time for calcul the mask position with numpy : 0.005896329879760742 nb_pixel_total : 7 time to create 1 rle with old method : 5.173683166503906e-05 time for calcul the mask position with numpy : 0.005961418151855469 nb_pixel_total : 368 time to create 1 rle with old method : 0.0004563331604003906 time for calcul the mask position with numpy : 0.0059223175048828125 nb_pixel_total : 1286 time to create 1 rle with old method : 0.0015368461608886719 time for calcul the mask position with numpy : 0.00594639778137207 nb_pixel_total : 333 time to create 1 rle with old method : 0.0004017353057861328 time for calcul the mask position with numpy : 0.0059163570404052734 nb_pixel_total : 457 time to create 1 rle with old method : 0.0005211830139160156 time for calcul the mask position with numpy : 0.005932807922363281 nb_pixel_total : 4512 time to create 1 rle with old method : 0.005254030227661133 time for calcul the mask position with numpy : 0.0060198307037353516 nb_pixel_total : 223 time to create 1 rle with old method : 0.00030732154846191406 time for calcul the mask position with numpy : 0.005999326705932617 nb_pixel_total : 6862 time to create 1 rle with old method : 0.0073277950286865234 time for calcul the mask position with numpy : 0.005864858627319336 nb_pixel_total : 615 time to create 1 rle with old method : 0.0007393360137939453 time for calcul the mask position with numpy : 0.005889415740966797 nb_pixel_total : 1504 time to create 1 rle with old method : 0.0017399787902832031 time for calcul the mask position with numpy : 0.005829811096191406 nb_pixel_total : 84 time to create 1 rle with old method : 0.000110626220703125 time for calcul the mask position with numpy : 0.005789279937744141 nb_pixel_total : 755 time to create 1 rle with old method : 0.0009047985076904297 time for calcul the mask position with numpy : 0.0057680606842041016 nb_pixel_total : 199 time to create 1 rle with old method : 0.0002522468566894531 time for calcul the mask position with numpy : 0.005985260009765625 nb_pixel_total : 123 time to create 1 rle with old method : 0.00016045570373535156 time for calcul the mask position with numpy : 0.005800724029541016 nb_pixel_total : 136 time to create 1 rle with old method : 0.00016832351684570312 time for calcul the mask position with numpy : 0.005867719650268555 nb_pixel_total : 277 time to create 1 rle with old method : 0.0003421306610107422 time for calcul the mask position with numpy : 0.00587010383605957 nb_pixel_total : 1115 time to create 1 rle with old method : 0.0012073516845703125 time for calcul the mask position with numpy : 0.0059282779693603516 nb_pixel_total : 834 time to create 1 rle with old method : 0.0009276866912841797 time for calcul the mask position with numpy : 0.005751609802246094 nb_pixel_total : 982 time to create 1 rle with old method : 0.0013365745544433594 time for calcul the mask position with numpy : 0.005820274353027344 nb_pixel_total : 388 time to create 1 rle with old method : 0.0004715919494628906 time for calcul the mask position with numpy : 0.005967617034912109 nb_pixel_total : 115 time to create 1 rle with old method : 0.00016427040100097656 time for calcul the mask position with numpy : 0.005788087844848633 nb_pixel_total : 1838 time to create 1 rle with old method : 0.0020568370819091797 time for calcul the mask position with numpy : 0.005835294723510742 nb_pixel_total : 112 time to create 1 rle with old method : 0.0001513957977294922 time for calcul the mask position with numpy : 0.005873680114746094 nb_pixel_total : 1885 time to create 1 rle with old method : 0.002203226089477539 time for calcul the mask position with numpy : 0.005979299545288086 nb_pixel_total : 1054 time to create 1 rle with old method : 0.0012171268463134766 time for calcul the mask position with numpy : 0.006002664566040039 nb_pixel_total : 523 time to create 1 rle with old method : 0.0006363391876220703 time for calcul the mask position with numpy : 0.006041288375854492 nb_pixel_total : 233 time to create 1 rle with old method : 0.0002951622009277344 time for calcul the mask position with numpy : 0.005998849868774414 nb_pixel_total : 296 time to create 1 rle with old method : 0.0003349781036376953 time for calcul the mask position with numpy : 0.0058023929595947266 nb_pixel_total : 507 time to create 1 rle with old method : 0.000621795654296875 time for calcul the mask position with numpy : 0.005978107452392578 nb_pixel_total : 179 time to create 1 rle with old method : 0.0002181529998779297 time for calcul the mask position with numpy : 0.005903720855712891 nb_pixel_total : 809 time to create 1 rle with old method : 0.0008838176727294922 time for calcul the mask position with numpy : 0.00620722770690918 nb_pixel_total : 1335 time to create 1 rle with old method : 0.0015125274658203125 time for calcul the mask position with numpy : 0.0058481693267822266 nb_pixel_total : 1119 time to create 1 rle with old method : 0.0013039112091064453 time for calcul the mask position with numpy : 0.0058171749114990234 nb_pixel_total : 147 time to create 1 rle with old method : 0.00018906593322753906 time for calcul the mask position with numpy : 0.005871772766113281 nb_pixel_total : 376 time to create 1 rle with old method : 0.0004658699035644531 time for calcul the mask position with numpy : 0.0061187744140625 nb_pixel_total : 282 time to create 1 rle with old method : 0.0003554821014404297 time for calcul the mask position with numpy : 0.006047248840332031 nb_pixel_total : 135 time to create 1 rle with old method : 0.00017833709716796875 create new chi : 1.363504409790039 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 : 0.0027937889099121094 batch 1 Loaded 150 chid ids of type : 4230 Number RLEs to save : 14270 TO DO : save crop sub photo not yet done ! save time : 0.7982847690582275 nb_obj : 142 nb_hashtags : 7 time to prepare the origin masks : 1.549912452697754 time for calcul the mask position with numpy : 0.01850414276123047 nb_pixel_total : 1625372 time to create 1 rle with new method : 0.03733682632446289 time for calcul the mask position with numpy : 0.006783485412597656 nb_pixel_total : 46720 time to create 1 rle with old method : 0.05106544494628906 time for calcul the mask position with numpy : 0.007565021514892578 nb_pixel_total : 61 time to create 1 rle with old method : 0.00011515617370605469 time for calcul the mask position with numpy : 0.005949497222900391 nb_pixel_total : 72 time to create 1 rle with old method : 0.0001628398895263672 time for calcul the mask position with numpy : 0.006204128265380859 nb_pixel_total : 9 time to create 1 rle with old method : 3.9577484130859375e-05 time for calcul the mask position with numpy : 0.007333993911743164 nb_pixel_total : 1268 time to create 1 rle with old method : 0.0014998912811279297 time for calcul the mask position with numpy : 0.006495952606201172 nb_pixel_total : 665 time to create 1 rle with old method : 0.0008432865142822266 time for calcul the mask position with numpy : 0.006530284881591797 nb_pixel_total : 317 time to create 1 rle with old method : 0.00043272972106933594 time for calcul the mask position with numpy : 0.0069980621337890625 nb_pixel_total : 19 time to create 1 rle with old method : 4.57763671875e-05 time for calcul the mask position with numpy : 0.006569385528564453 nb_pixel_total : 184 time to create 1 rle with old method : 0.00023746490478515625 time for calcul the mask position with numpy : 0.006309986114501953 nb_pixel_total : 95 time to create 1 rle with old method : 0.00018906593322753906 time for calcul the mask position with numpy : 0.0061299800872802734 nb_pixel_total : 62 time to create 1 rle with old method : 9.059906005859375e-05 time for calcul the mask position with numpy : 0.006657123565673828 nb_pixel_total : 2790 time to create 1 rle with old method : 0.0036954879760742188 time for calcul the mask position with numpy : 0.006616115570068359 nb_pixel_total : 833 time to create 1 rle with old method : 0.0010166168212890625 time for calcul the mask position with numpy : 0.006659269332885742 nb_pixel_total : 17664 time to create 1 rle with old method : 0.019593000411987305 time for calcul the mask position with numpy : 0.006188154220581055 nb_pixel_total : 1982 time to create 1 rle with old method : 0.002318143844604492 time for calcul the mask position with numpy : 0.006554603576660156 nb_pixel_total : 483 time to create 1 rle with old method : 0.0006034374237060547 time for calcul the mask position with numpy : 0.006435394287109375 nb_pixel_total : 807 time to create 1 rle with old method : 0.0010099411010742188 time for calcul the mask position with numpy : 0.006166219711303711 nb_pixel_total : 211 time to create 1 rle with old method : 0.00027751922607421875 time for calcul the mask position with numpy : 0.006432533264160156 nb_pixel_total : 1819 time to create 1 rle with old method : 0.003290414810180664 time for calcul the mask position with numpy : 0.0066683292388916016 nb_pixel_total : 2206 time to create 1 rle with old method : 0.0031948089599609375 time for calcul the mask position with numpy : 0.006078004837036133 nb_pixel_total : 2353 time to create 1 rle with old method : 0.002773761749267578 time for calcul the mask position with numpy : 0.007026195526123047 nb_pixel_total : 96 time to create 1 rle with old method : 0.0001690387725830078 time for calcul the mask position with numpy : 0.006173372268676758 nb_pixel_total : 762 time to create 1 rle with old method : 0.0009095668792724609 time for calcul the mask position with numpy : 0.00643610954284668 nb_pixel_total : 8561 time to create 1 rle with old method : 0.009511232376098633 time for calcul the mask position with numpy : 0.006291389465332031 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001685619354248047 time for calcul the mask position with numpy : 0.006230354309082031 nb_pixel_total : 553 time to create 1 rle with old method : 0.0007202625274658203 time for calcul the mask position with numpy : 0.006653308868408203 nb_pixel_total : 598 time to create 1 rle with old method : 0.0007410049438476562 time for calcul the mask position with numpy : 0.006573677062988281 nb_pixel_total : 935 time to create 1 rle with old method : 0.0010864734649658203 time for calcul the mask position with numpy : 0.006383419036865234 nb_pixel_total : 1195 time to create 1 rle with old method : 0.0013570785522460938 time for calcul the mask position with numpy : 0.007338285446166992 nb_pixel_total : 39 time to create 1 rle with old method : 0.00010728836059570312 time for calcul the mask position with numpy : 0.0070896148681640625 nb_pixel_total : 153441 time to create 1 rle with new method : 0.028977632522583008 time for calcul the mask position with numpy : 0.006278276443481445 nb_pixel_total : 3327 time to create 1 rle with old method : 0.003751039505004883 time for calcul the mask position with numpy : 0.0066683292388916016 nb_pixel_total : 930 time to create 1 rle with old method : 0.0011143684387207031 time for calcul the mask position with numpy : 0.007541179656982422 nb_pixel_total : 77 time to create 1 rle with old method : 0.0001163482666015625 time for calcul the mask position with numpy : 0.006012678146362305 nb_pixel_total : 407 time to create 1 rle with old method : 0.0005066394805908203 time for calcul the mask position with numpy : 0.00615692138671875 nb_pixel_total : 842 time to create 1 rle with old method : 0.0009989738464355469 time for calcul the mask position with numpy : 0.006243467330932617 nb_pixel_total : 728 time to create 1 rle with old method : 0.0008575916290283203 time for calcul the mask position with numpy : 0.006514549255371094 nb_pixel_total : 595 time to create 1 rle with old method : 0.0009415149688720703 time for calcul the mask position with numpy : 0.0063016414642333984 nb_pixel_total : 500 time to create 1 rle with old method : 0.0005307197570800781 time for calcul the mask position with numpy : 0.0067098140716552734 nb_pixel_total : 197 time to create 1 rle with old method : 0.0003750324249267578 time for calcul the mask position with numpy : 0.0071239471435546875 nb_pixel_total : 941 time to create 1 rle with old method : 0.001069784164428711 time for calcul the mask position with numpy : 0.006208896636962891 nb_pixel_total : 737 time to create 1 rle with old method : 0.0009303092956542969 time for calcul the mask position with numpy : 0.006399393081665039 nb_pixel_total : 14278 time to create 1 rle with old method : 0.015541553497314453 time for calcul the mask position with numpy : 0.006326436996459961 nb_pixel_total : 863 time to create 1 rle with old method : 0.0012769699096679688 time for calcul the mask position with numpy : 0.006036996841430664 nb_pixel_total : 1328 time to create 1 rle with old method : 0.0014700889587402344 time for calcul the mask position with numpy : 0.006159782409667969 nb_pixel_total : 1402 time to create 1 rle with old method : 0.0015082359313964844 time for calcul the mask position with numpy : 0.005977630615234375 nb_pixel_total : 240 time to create 1 rle with old method : 0.0003025531768798828 time for calcul the mask position with numpy : 0.0060808658599853516 nb_pixel_total : 3741 time to create 1 rle with old method : 0.004123210906982422 time for calcul the mask position with numpy : 0.0064239501953125 nb_pixel_total : 4116 time to create 1 rle with old method : 0.004854440689086914 time for calcul the mask position with numpy : 0.006178379058837891 nb_pixel_total : 1661 time to create 1 rle with old method : 0.0018084049224853516 time for calcul the mask position with numpy : 0.005944013595581055 nb_pixel_total : 99 time to create 1 rle with old method : 0.00013494491577148438 time for calcul the mask position with numpy : 0.007118940353393555 nb_pixel_total : 1907 time to create 1 rle with old method : 0.002125978469848633 time for calcul the mask position with numpy : 0.0060389041900634766 nb_pixel_total : 314 time to create 1 rle with old method : 0.00039958953857421875 time for calcul the mask position with numpy : 0.005983829498291016 nb_pixel_total : 717 time to create 1 rle with old method : 0.00084686279296875 time for calcul the mask position with numpy : 0.006001472473144531 nb_pixel_total : 160 time to create 1 rle with old method : 0.00020885467529296875 time for calcul the mask position with numpy : 0.006272315979003906 nb_pixel_total : 1288 time to create 1 rle with old method : 0.00140380859375 time for calcul the mask position with numpy : 0.006060600280761719 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0013189315795898438 time for calcul the mask position with numpy : 0.005901336669921875 nb_pixel_total : 149 time to create 1 rle with old method : 0.0001888275146484375 time for calcul the mask position with numpy : 0.006295204162597656 nb_pixel_total : 394 time to create 1 rle with old method : 0.0004703998565673828 time for calcul the mask position with numpy : 0.006204843521118164 nb_pixel_total : 651 time to create 1 rle with old method : 0.0007672309875488281 time for calcul the mask position with numpy : 0.006049633026123047 nb_pixel_total : 66 time to create 1 rle with old method : 9.465217590332031e-05 time for calcul the mask position with numpy : 0.00606226921081543 nb_pixel_total : 391 time to create 1 rle with old method : 0.00045943260192871094 time for calcul the mask position with numpy : 0.006098508834838867 nb_pixel_total : 160 time to create 1 rle with old method : 0.00021147727966308594 time for calcul the mask position with numpy : 0.006032228469848633 nb_pixel_total : 476 time to create 1 rle with old method : 0.0006322860717773438 time for calcul the mask position with numpy : 0.006230592727661133 nb_pixel_total : 261 time to create 1 rle with old method : 0.0003108978271484375 time for calcul the mask position with numpy : 0.0065762996673583984 nb_pixel_total : 106478 time to create 1 rle with old method : 0.1138296127319336 time for calcul the mask position with numpy : 0.006281852722167969 nb_pixel_total : 414 time to create 1 rle with old method : 0.0004973411560058594 time for calcul the mask position with numpy : 0.006170034408569336 nb_pixel_total : 404 time to create 1 rle with old method : 0.0005083084106445312 time for calcul the mask position with numpy : 0.006137371063232422 nb_pixel_total : 416 time to create 1 rle with old method : 0.0005102157592773438 time for calcul the mask position with numpy : 0.00623011589050293 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016427040100097656 time for calcul the mask position with numpy : 0.008354425430297852 nb_pixel_total : 169 time to create 1 rle with old method : 0.0002129077911376953 time for calcul the mask position with numpy : 0.006520748138427734 nb_pixel_total : 212 time to create 1 rle with old method : 0.0002753734588623047 time for calcul the mask position with numpy : 0.006819009780883789 nb_pixel_total : 282 time to create 1 rle with old method : 0.0004177093505859375 time for calcul the mask position with numpy : 0.007570028305053711 nb_pixel_total : 19 time to create 1 rle with old method : 7.081031799316406e-05 time for calcul the mask position with numpy : 0.0065381526947021484 nb_pixel_total : 441 time to create 1 rle with old method : 0.0005183219909667969 time for calcul the mask position with numpy : 0.006346702575683594 nb_pixel_total : 383 time to create 1 rle with old method : 0.0004601478576660156 time for calcul the mask position with numpy : 0.006055593490600586 nb_pixel_total : 12 time to create 1 rle with old method : 4.7206878662109375e-05 time for calcul the mask position with numpy : 0.006208181381225586 nb_pixel_total : 1852 time to create 1 rle with old method : 0.002046346664428711 time for calcul the mask position with numpy : 0.006239175796508789 nb_pixel_total : 29 time to create 1 rle with old method : 7.62939453125e-05 time for calcul the mask position with numpy : 0.00879359245300293 nb_pixel_total : 12 time to create 1 rle with old method : 3.600120544433594e-05 time for calcul the mask position with numpy : 0.0060346126556396484 nb_pixel_total : 413 time to create 1 rle with old method : 0.0005464553833007812 time for calcul the mask position with numpy : 0.0061762332916259766 nb_pixel_total : 1221 time to create 1 rle with old method : 0.0014636516571044922 time for calcul the mask position with numpy : 0.007327079772949219 nb_pixel_total : 674 time to create 1 rle with old method : 0.0008323192596435547 time for calcul the mask position with numpy : 0.00637507438659668 nb_pixel_total : 1356 time to create 1 rle with old method : 0.0015871524810791016 time for calcul the mask position with numpy : 0.006347179412841797 nb_pixel_total : 142 time to create 1 rle with old method : 0.0001881122589111328 time for calcul the mask position with numpy : 0.006316423416137695 nb_pixel_total : 229 time to create 1 rle with old method : 0.0002815723419189453 time for calcul the mask position with numpy : 0.006220579147338867 nb_pixel_total : 192 time to create 1 rle with old method : 0.0002465248107910156 time for calcul the mask position with numpy : 0.006013631820678711 nb_pixel_total : 170 time to create 1 rle with old method : 0.00020194053649902344 time for calcul the mask position with numpy : 0.005910158157348633 nb_pixel_total : 1068 time to create 1 rle with old method : 0.0012667179107666016 time for calcul the mask position with numpy : 0.006095170974731445 nb_pixel_total : 89 time to create 1 rle with old method : 0.0001533031463623047 time for calcul the mask position with numpy : 0.006094694137573242 nb_pixel_total : 534 time to create 1 rle with old method : 0.0005772113800048828 time for calcul the mask position with numpy : 0.0061037540435791016 nb_pixel_total : 37 time to create 1 rle with old method : 9.942054748535156e-05 time for calcul the mask position with numpy : 0.006120204925537109 nb_pixel_total : 903 time to create 1 rle with old method : 0.0010380744934082031 time for calcul the mask position with numpy : 0.0062427520751953125 nb_pixel_total : 160 time to create 1 rle with old method : 0.0002040863037109375 time for calcul the mask position with numpy : 0.006226301193237305 nb_pixel_total : 66 time to create 1 rle with old method : 0.00010514259338378906 time for calcul the mask position with numpy : 0.005968809127807617 nb_pixel_total : 544 time to create 1 rle with old method : 0.0006647109985351562 time for calcul the mask position with numpy : 0.0059664249420166016 nb_pixel_total : 713 time to create 1 rle with old method : 0.0007851123809814453 time for calcul the mask position with numpy : 0.006110429763793945 nb_pixel_total : 742 time to create 1 rle with old method : 0.0008451938629150391 time for calcul the mask position with numpy : 0.0061435699462890625 nb_pixel_total : 223 time to create 1 rle with old method : 0.00028896331787109375 time for calcul the mask position with numpy : 0.006105661392211914 nb_pixel_total : 1008 time to create 1 rle with old method : 0.0011453628540039062 time for calcul the mask position with numpy : 0.006241321563720703 nb_pixel_total : 149 time to create 1 rle with old method : 0.00020599365234375 time for calcul the mask position with numpy : 0.008756160736083984 nb_pixel_total : 90 time to create 1 rle with old method : 0.00012445449829101562 time for calcul the mask position with numpy : 0.00615382194519043 nb_pixel_total : 17 time to create 1 rle with old method : 5.698204040527344e-05 time for calcul the mask position with numpy : 0.006136894226074219 nb_pixel_total : 176 time to create 1 rle with old method : 0.0003657341003417969 time for calcul the mask position with numpy : 0.006847381591796875 nb_pixel_total : 433 time to create 1 rle with old method : 0.0005223751068115234 time for calcul the mask position with numpy : 0.00628352165222168 nb_pixel_total : 477 time to create 1 rle with old method : 0.0005602836608886719 time for calcul the mask position with numpy : 0.00610041618347168 nb_pixel_total : 643 time to create 1 rle with old method : 0.000782012939453125 time for calcul the mask position with numpy : 0.0061876773834228516 nb_pixel_total : 7422 time to create 1 rle with old method : 0.008142471313476562 time for calcul the mask position with numpy : 0.006243705749511719 nb_pixel_total : 4752 time to create 1 rle with old method : 0.005482912063598633 time for calcul the mask position with numpy : 0.006238222122192383 nb_pixel_total : 403 time to create 1 rle with old method : 0.0004909038543701172 time for calcul the mask position with numpy : 0.006269216537475586 nb_pixel_total : 1680 time to create 1 rle with old method : 0.0018815994262695312 time for calcul the mask position with numpy : 0.006121397018432617 nb_pixel_total : 88 time to create 1 rle with old method : 0.0001862049102783203 time for calcul the mask position with numpy : 0.006678104400634766 nb_pixel_total : 119 time to create 1 rle with old method : 0.00018668174743652344 time for calcul the mask position with numpy : 0.006475925445556641 nb_pixel_total : 119 time to create 1 rle with old method : 0.00016236305236816406 time for calcul the mask position with numpy : 0.00648045539855957 nb_pixel_total : 1433 time to create 1 rle with old method : 0.0016512870788574219 time for calcul the mask position with numpy : 0.006125450134277344 nb_pixel_total : 806 time to create 1 rle with old method : 0.0009779930114746094 time for calcul the mask position with numpy : 0.0060825347900390625 nb_pixel_total : 238 time to create 1 rle with old method : 0.00029754638671875 time for calcul the mask position with numpy : 0.006159782409667969 nb_pixel_total : 182 time to create 1 rle with old method : 0.00023889541625976562 time for calcul the mask position with numpy : 0.006206989288330078 nb_pixel_total : 302 time to create 1 rle with old method : 0.0003795623779296875 time for calcul the mask position with numpy : 0.006616830825805664 nb_pixel_total : 641 time to create 1 rle with old method : 0.0007092952728271484 time for calcul the mask position with numpy : 0.006292819976806641 nb_pixel_total : 836 time to create 1 rle with old method : 0.0009527206420898438 time for calcul the mask position with numpy : 0.008595705032348633 nb_pixel_total : 980 time to create 1 rle with old method : 0.001150369644165039 time for calcul the mask position with numpy : 0.006212711334228516 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001385211944580078 time for calcul the mask position with numpy : 0.006093025207519531 nb_pixel_total : 375 time to create 1 rle with old method : 0.0004544258117675781 time for calcul the mask position with numpy : 0.006521463394165039 nb_pixel_total : 391 time to create 1 rle with old method : 0.0004818439483642578 time for calcul the mask position with numpy : 0.006598711013793945 nb_pixel_total : 125 time to create 1 rle with old method : 0.00016069412231445312 time for calcul the mask position with numpy : 0.006305694580078125 nb_pixel_total : 3 time to create 1 rle with old method : 2.9802322387695312e-05 time for calcul the mask position with numpy : 0.006382942199707031 nb_pixel_total : 1263 time to create 1 rle with old method : 0.0015246868133544922 time for calcul the mask position with numpy : 0.007367372512817383 nb_pixel_total : 584 time to create 1 rle with old method : 0.0007076263427734375 time for calcul the mask position with numpy : 0.0062601566314697266 nb_pixel_total : 480 time to create 1 rle with old method : 0.0005803108215332031 time for calcul the mask position with numpy : 0.006015300750732422 nb_pixel_total : 303 time to create 1 rle with old method : 0.0003790855407714844 time for calcul the mask position with numpy : 0.006448030471801758 nb_pixel_total : 530 time to create 1 rle with old method : 0.0006365776062011719 time for calcul the mask position with numpy : 0.006197690963745117 nb_pixel_total : 3916 time to create 1 rle with old method : 0.0046427249908447266 time for calcul the mask position with numpy : 0.006110429763793945 nb_pixel_total : 209 time to create 1 rle with old method : 0.00026106834411621094 time for calcul the mask position with numpy : 0.006383180618286133 nb_pixel_total : 507 time to create 1 rle with old method : 0.0006778240203857422 time for calcul the mask position with numpy : 0.006937265396118164 nb_pixel_total : 1338 time to create 1 rle with old method : 0.0015273094177246094 time for calcul the mask position with numpy : 0.006484031677246094 nb_pixel_total : 5 time to create 1 rle with old method : 4.649162292480469e-05 time for calcul the mask position with numpy : 0.006232261657714844 nb_pixel_total : 235 time to create 1 rle with old method : 0.0003838539123535156 time for calcul the mask position with numpy : 0.006183624267578125 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0012938976287841797 time for calcul the mask position with numpy : 0.006403207778930664 nb_pixel_total : 247 time to create 1 rle with old method : 0.0003037452697753906 time for calcul the mask position with numpy : 0.006479978561401367 nb_pixel_total : 570 time to create 1 rle with old method : 0.0006768703460693359 time for calcul the mask position with numpy : 0.00625300407409668 nb_pixel_total : 152 time to create 1 rle with old method : 0.00021266937255859375 create new chi : 1.3350117206573486 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 : 0.0026841163635253906 batch 1 Loaded 143 chid ids of type : 4230 Number RLEs to save : 13754 TO DO : save crop sub photo not yet done ! save time : 0.8026082515716553 nb_obj : 136 nb_hashtags : 8 time to prepare the origin masks : 1.4911961555480957 time for calcul the mask position with numpy : 0.02094745635986328 nb_pixel_total : 1609739 time to create 1 rle with new method : 0.040944814682006836 time for calcul the mask position with numpy : 0.0067746639251708984 nb_pixel_total : 62018 time to create 1 rle with old method : 0.06855368614196777 time for calcul the mask position with numpy : 0.006383657455444336 nb_pixel_total : 316 time to create 1 rle with old method : 0.0003838539123535156 time for calcul the mask position with numpy : 0.006253957748413086 nb_pixel_total : 60 time to create 1 rle with old method : 9.417533874511719e-05 time for calcul the mask position with numpy : 0.006341457366943359 nb_pixel_total : 83 time to create 1 rle with old method : 0.00011920928955078125 time for calcul the mask position with numpy : 0.006279468536376953 nb_pixel_total : 317 time to create 1 rle with old method : 0.00040459632873535156 time for calcul the mask position with numpy : 0.006251811981201172 nb_pixel_total : 400 time to create 1 rle with old method : 0.0005035400390625 time for calcul the mask position with numpy : 0.0065155029296875 nb_pixel_total : 1038 time to create 1 rle with old method : 0.001249074935913086 time for calcul the mask position with numpy : 0.006400346755981445 nb_pixel_total : 511 time to create 1 rle with old method : 0.0006494522094726562 time for calcul the mask position with numpy : 0.006636142730712891 nb_pixel_total : 22 time to create 1 rle with old method : 8.153915405273438e-05 time for calcul the mask position with numpy : 0.006654977798461914 nb_pixel_total : 47 time to create 1 rle with old method : 0.00010085105895996094 time for calcul the mask position with numpy : 0.006907463073730469 nb_pixel_total : 2636 time to create 1 rle with old method : 0.004506587982177734 time for calcul the mask position with numpy : 0.0063283443450927734 nb_pixel_total : 853 time to create 1 rle with old method : 0.0010111331939697266 time for calcul the mask position with numpy : 0.006357908248901367 nb_pixel_total : 818 time to create 1 rle with old method : 0.0010213851928710938 time for calcul the mask position with numpy : 0.008829355239868164 nb_pixel_total : 14529 time to create 1 rle with old method : 0.028759479522705078 time for calcul the mask position with numpy : 0.006921052932739258 nb_pixel_total : 794 time to create 1 rle with old method : 0.0015370845794677734 time for calcul the mask position with numpy : 0.00832676887512207 nb_pixel_total : 1112 time to create 1 rle with old method : 0.001634836196899414 time for calcul the mask position with numpy : 0.007653236389160156 nb_pixel_total : 205 time to create 1 rle with old method : 0.0002560615539550781 time for calcul the mask position with numpy : 0.006223440170288086 nb_pixel_total : 61 time to create 1 rle with old method : 0.00012946128845214844 time for calcul the mask position with numpy : 0.006522655487060547 nb_pixel_total : 1760 time to create 1 rle with old method : 0.002062082290649414 time for calcul the mask position with numpy : 0.0068051815032958984 nb_pixel_total : 2516 time to create 1 rle with old method : 0.0030715465545654297 time for calcul the mask position with numpy : 0.006411552429199219 nb_pixel_total : 1149 time to create 1 rle with old method : 0.0013551712036132812 time for calcul the mask position with numpy : 0.006430149078369141 nb_pixel_total : 577 time to create 1 rle with old method : 0.0007491111755371094 time for calcul the mask position with numpy : 0.006399631500244141 nb_pixel_total : 2079 time to create 1 rle with old method : 0.002419710159301758 time for calcul the mask position with numpy : 0.006356477737426758 nb_pixel_total : 110 time to create 1 rle with old method : 0.00019550323486328125 time for calcul the mask position with numpy : 0.00650334358215332 nb_pixel_total : 9029 time to create 1 rle with old method : 0.010959148406982422 time for calcul the mask position with numpy : 0.006397724151611328 nb_pixel_total : 2 time to create 1 rle with old method : 2.7418136596679688e-05 time for calcul the mask position with numpy : 0.006234884262084961 nb_pixel_total : 576 time to create 1 rle with old method : 0.0006849765777587891 time for calcul the mask position with numpy : 0.006332874298095703 nb_pixel_total : 572 time to create 1 rle with old method : 0.0007033348083496094 time for calcul the mask position with numpy : 0.00635838508605957 nb_pixel_total : 892 time to create 1 rle with old method : 0.0010447502136230469 time for calcul the mask position with numpy : 0.0063898563385009766 nb_pixel_total : 1108 time to create 1 rle with old method : 0.0013151168823242188 time for calcul the mask position with numpy : 0.006432056427001953 nb_pixel_total : 276 time to create 1 rle with old method : 0.0003764629364013672 time for calcul the mask position with numpy : 0.00713658332824707 nb_pixel_total : 151965 time to create 1 rle with new method : 0.02959752082824707 time for calcul the mask position with numpy : 0.006453752517700195 nb_pixel_total : 3185 time to create 1 rle with old method : 0.0037136077880859375 time for calcul the mask position with numpy : 0.006422996520996094 nb_pixel_total : 77 time to create 1 rle with old method : 0.00011467933654785156 time for calcul the mask position with numpy : 0.00624847412109375 nb_pixel_total : 325 time to create 1 rle with old method : 0.0003719329833984375 time for calcul the mask position with numpy : 0.00628352165222168 nb_pixel_total : 73 time to create 1 rle with old method : 0.0001232624053955078 time for calcul the mask position with numpy : 0.006324291229248047 nb_pixel_total : 709 time to create 1 rle with old method : 0.0008225440979003906 time for calcul the mask position with numpy : 0.006242513656616211 nb_pixel_total : 817 time to create 1 rle with old method : 0.0009937286376953125 time for calcul the mask position with numpy : 0.0061228275299072266 nb_pixel_total : 440 time to create 1 rle with old method : 0.0005199909210205078 time for calcul the mask position with numpy : 0.00618743896484375 nb_pixel_total : 528 time to create 1 rle with old method : 0.0006330013275146484 time for calcul the mask position with numpy : 0.006616830825805664 nb_pixel_total : 601 time to create 1 rle with old method : 0.0007357597351074219 time for calcul the mask position with numpy : 0.007345438003540039 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002288818359375 time for calcul the mask position with numpy : 0.006723642349243164 nb_pixel_total : 1377 time to create 1 rle with old method : 0.0016798973083496094 time for calcul the mask position with numpy : 0.006834268569946289 nb_pixel_total : 818 time to create 1 rle with old method : 0.0011484622955322266 time for calcul the mask position with numpy : 0.006408214569091797 nb_pixel_total : 14112 time to create 1 rle with old method : 0.01526188850402832 time for calcul the mask position with numpy : 0.0063860416412353516 nb_pixel_total : 1328 time to create 1 rle with old method : 0.0015060901641845703 time for calcul the mask position with numpy : 0.006328105926513672 nb_pixel_total : 870 time to create 1 rle with old method : 0.0010497570037841797 time for calcul the mask position with numpy : 0.006818056106567383 nb_pixel_total : 212 time to create 1 rle with old method : 0.00026917457580566406 time for calcul the mask position with numpy : 0.006270647048950195 nb_pixel_total : 1380 time to create 1 rle with old method : 0.0017328262329101562 time for calcul the mask position with numpy : 0.006345272064208984 nb_pixel_total : 644 time to create 1 rle with old method : 0.0007550716400146484 time for calcul the mask position with numpy : 0.006270408630371094 nb_pixel_total : 243 time to create 1 rle with old method : 0.0003638267517089844 time for calcul the mask position with numpy : 0.0064296722412109375 nb_pixel_total : 35 time to create 1 rle with old method : 6.175041198730469e-05 time for calcul the mask position with numpy : 0.0062639713287353516 nb_pixel_total : 4918 time to create 1 rle with old method : 0.005541563034057617 time for calcul the mask position with numpy : 0.0065839290618896484 nb_pixel_total : 120 time to create 1 rle with old method : 0.00018215179443359375 time for calcul the mask position with numpy : 0.007627725601196289 nb_pixel_total : 1575 time to create 1 rle with old method : 0.00189208984375 time for calcul the mask position with numpy : 0.006735801696777344 nb_pixel_total : 12 time to create 1 rle with old method : 4.8160552978515625e-05 time for calcul the mask position with numpy : 0.0065746307373046875 nb_pixel_total : 107 time to create 1 rle with old method : 0.00015664100646972656 time for calcul the mask position with numpy : 0.007009029388427734 nb_pixel_total : 1013 time to create 1 rle with old method : 0.0013208389282226562 time for calcul the mask position with numpy : 0.006807565689086914 nb_pixel_total : 764 time to create 1 rle with old method : 0.0009474754333496094 time for calcul the mask position with numpy : 0.007035017013549805 nb_pixel_total : 1388 time to create 1 rle with old method : 0.00183868408203125 time for calcul the mask position with numpy : 0.006738424301147461 nb_pixel_total : 1376 time to create 1 rle with old method : 0.0016553401947021484 time for calcul the mask position with numpy : 0.006821393966674805 nb_pixel_total : 1592 time to create 1 rle with old method : 0.001851797103881836 time for calcul the mask position with numpy : 0.00742340087890625 nb_pixel_total : 683 time to create 1 rle with old method : 0.0008113384246826172 time for calcul the mask position with numpy : 0.006656169891357422 nb_pixel_total : 882 time to create 1 rle with old method : 0.0010530948638916016 time for calcul the mask position with numpy : 0.006429910659790039 nb_pixel_total : 461 time to create 1 rle with old method : 0.0005712509155273438 time for calcul the mask position with numpy : 0.0073964595794677734 nb_pixel_total : 106491 time to create 1 rle with old method : 0.11567378044128418 time for calcul the mask position with numpy : 0.00640869140625 nb_pixel_total : 437 time to create 1 rle with old method : 0.0005240440368652344 time for calcul the mask position with numpy : 0.00610041618347168 nb_pixel_total : 497 time to create 1 rle with old method : 0.0005831718444824219 time for calcul the mask position with numpy : 0.007488250732421875 nb_pixel_total : 418 time to create 1 rle with old method : 0.0005023479461669922 time for calcul the mask position with numpy : 0.006562232971191406 nb_pixel_total : 129 time to create 1 rle with old method : 0.00018453598022460938 time for calcul the mask position with numpy : 0.006558656692504883 nb_pixel_total : 186 time to create 1 rle with old method : 0.00023555755615234375 time for calcul the mask position with numpy : 0.0063593387603759766 nb_pixel_total : 158 time to create 1 rle with old method : 0.00020194053649902344 time for calcul the mask position with numpy : 0.006312370300292969 nb_pixel_total : 289 time to create 1 rle with old method : 0.00033473968505859375 time for calcul the mask position with numpy : 0.006319999694824219 nb_pixel_total : 429 time to create 1 rle with old method : 0.0005412101745605469 time for calcul the mask position with numpy : 0.006838798522949219 nb_pixel_total : 105 time to create 1 rle with old method : 0.00016689300537109375 time for calcul the mask position with numpy : 0.006613969802856445 nb_pixel_total : 388 time to create 1 rle with old method : 0.00047969818115234375 time for calcul the mask position with numpy : 0.008494853973388672 nb_pixel_total : 1797 time to create 1 rle with old method : 0.0021653175354003906 time for calcul the mask position with numpy : 0.0076181888580322266 nb_pixel_total : 5 time to create 1 rle with old method : 3.7670135498046875e-05 time for calcul the mask position with numpy : 0.00634765625 nb_pixel_total : 390 time to create 1 rle with old method : 0.0004649162292480469 time for calcul the mask position with numpy : 0.006481170654296875 nb_pixel_total : 17 time to create 1 rle with old method : 5.650520324707031e-05 time for calcul the mask position with numpy : 0.006911516189575195 nb_pixel_total : 1474 time to create 1 rle with old method : 0.0017800331115722656 time for calcul the mask position with numpy : 0.006821393966674805 nb_pixel_total : 1104 time to create 1 rle with old method : 0.001322031021118164 time for calcul the mask position with numpy : 0.0067212581634521484 nb_pixel_total : 171 time to create 1 rle with old method : 0.00022482872009277344 time for calcul the mask position with numpy : 0.0065267086029052734 nb_pixel_total : 154 time to create 1 rle with old method : 0.00021696090698242188 time for calcul the mask position with numpy : 0.006590843200683594 nb_pixel_total : 460 time to create 1 rle with old method : 0.0007295608520507812 time for calcul the mask position with numpy : 0.0076138973236083984 nb_pixel_total : 159 time to create 1 rle with old method : 0.0002117156982421875 time for calcul the mask position with numpy : 0.006372690200805664 nb_pixel_total : 556 time to create 1 rle with old method : 0.0008652210235595703 time for calcul the mask position with numpy : 0.006314516067504883 nb_pixel_total : 663 time to create 1 rle with old method : 0.0008528232574462891 time for calcul the mask position with numpy : 0.007349252700805664 nb_pixel_total : 731 time to create 1 rle with old method : 0.0008590221405029297 time for calcul the mask position with numpy : 0.006377458572387695 nb_pixel_total : 208 time to create 1 rle with old method : 0.0002613067626953125 time for calcul the mask position with numpy : 0.006291627883911133 nb_pixel_total : 46 time to create 1 rle with old method : 8.702278137207031e-05 time for calcul the mask position with numpy : 0.007951974868774414 nb_pixel_total : 1046 time to create 1 rle with old method : 0.0012364387512207031 time for calcul the mask position with numpy : 0.006343841552734375 nb_pixel_total : 103 time to create 1 rle with old method : 0.00014710426330566406 time for calcul the mask position with numpy : 0.006352663040161133 nb_pixel_total : 406 time to create 1 rle with old method : 0.0005230903625488281 time for calcul the mask position with numpy : 0.00713038444519043 nb_pixel_total : 339 time to create 1 rle with old method : 0.0004215240478515625 time for calcul the mask position with numpy : 0.007032632827758789 nb_pixel_total : 70 time to create 1 rle with old method : 0.00014209747314453125 time for calcul the mask position with numpy : 0.00808095932006836 nb_pixel_total : 1242 time to create 1 rle with old method : 0.0015287399291992188 time for calcul the mask position with numpy : 0.00931692123413086 nb_pixel_total : 46 time to create 1 rle with old method : 0.00011229515075683594 time for calcul the mask position with numpy : 0.007409095764160156 nb_pixel_total : 937 time to create 1 rle with old method : 0.0011615753173828125 time for calcul the mask position with numpy : 0.006859302520751953 nb_pixel_total : 6793 time to create 1 rle with old method : 0.007718801498413086 time for calcul the mask position with numpy : 0.0061533451080322266 nb_pixel_total : 198 time to create 1 rle with old method : 0.0003466606140136719 time for calcul the mask position with numpy : 0.0070035457611083984 nb_pixel_total : 311 time to create 1 rle with old method : 0.0003936290740966797 time for calcul the mask position with numpy : 0.006278514862060547 nb_pixel_total : 404 time to create 1 rle with old method : 0.0005295276641845703 time for calcul the mask position with numpy : 0.0070078372955322266 nb_pixel_total : 1421 time to create 1 rle with old method : 0.0016787052154541016 time for calcul the mask position with numpy : 0.007160663604736328 nb_pixel_total : 5059 time to create 1 rle with old method : 0.005749940872192383 time for calcul the mask position with numpy : 0.006259441375732422 nb_pixel_total : 187 time to create 1 rle with old method : 0.00026226043701171875 time for calcul the mask position with numpy : 0.006577491760253906 nb_pixel_total : 1180 time to create 1 rle with old method : 0.0013093948364257812 time for calcul the mask position with numpy : 0.006667613983154297 nb_pixel_total : 813 time to create 1 rle with old method : 0.0009655952453613281 time for calcul the mask position with numpy : 0.0067996978759765625 nb_pixel_total : 112 time to create 1 rle with old method : 0.00017213821411132812 time for calcul the mask position with numpy : 0.0070226192474365234 nb_pixel_total : 523 time to create 1 rle with old method : 0.0006260871887207031 time for calcul the mask position with numpy : 0.006949901580810547 nb_pixel_total : 1309 time to create 1 rle with old method : 0.0015854835510253906 time for calcul the mask position with numpy : 0.007691860198974609 nb_pixel_total : 264 time to create 1 rle with old method : 0.0003199577331542969 time for calcul the mask position with numpy : 0.006556510925292969 nb_pixel_total : 271 time to create 1 rle with old method : 0.0003669261932373047 time for calcul the mask position with numpy : 0.006655693054199219 nb_pixel_total : 751 time to create 1 rle with old method : 0.0008976459503173828 time for calcul the mask position with numpy : 0.008107662200927734 nb_pixel_total : 864 time to create 1 rle with old method : 0.0010104179382324219 time for calcul the mask position with numpy : 0.006429195404052734 nb_pixel_total : 141 time to create 1 rle with old method : 0.00019097328186035156 time for calcul the mask position with numpy : 0.0064198970794677734 nb_pixel_total : 436 time to create 1 rle with old method : 0.0005323886871337891 time for calcul the mask position with numpy : 0.007185935974121094 nb_pixel_total : 5511 time to create 1 rle with old method : 0.006758928298950195 time for calcul the mask position with numpy : 0.006491661071777344 nb_pixel_total : 12 time to create 1 rle with old method : 4.506111145019531e-05 time for calcul the mask position with numpy : 0.006301164627075195 nb_pixel_total : 431 time to create 1 rle with old method : 0.0004918575286865234 time for calcul the mask position with numpy : 0.00622868537902832 nb_pixel_total : 141 time to create 1 rle with old method : 0.00018334388732910156 time for calcul the mask position with numpy : 0.0063364505767822266 nb_pixel_total : 15 time to create 1 rle with old method : 5.030632019042969e-05 time for calcul the mask position with numpy : 0.0066449642181396484 nb_pixel_total : 1754 time to create 1 rle with old method : 0.0020704269409179688 time for calcul the mask position with numpy : 0.006686210632324219 nb_pixel_total : 42 time to create 1 rle with old method : 8.845329284667969e-05 time for calcul the mask position with numpy : 0.00628662109375 nb_pixel_total : 872 time to create 1 rle with old method : 0.0010488033294677734 time for calcul the mask position with numpy : 0.006277322769165039 nb_pixel_total : 1051 time to create 1 rle with old method : 0.0012352466583251953 time for calcul the mask position with numpy : 0.0063877105712890625 nb_pixel_total : 128 time to create 1 rle with old method : 0.0001926422119140625 time for calcul the mask position with numpy : 0.006469249725341797 nb_pixel_total : 319 time to create 1 rle with old method : 0.00040221214294433594 time for calcul the mask position with numpy : 0.006737947463989258 nb_pixel_total : 526 time to create 1 rle with old method : 0.0006556510925292969 time for calcul the mask position with numpy : 0.00674748420715332 nb_pixel_total : 5736 time to create 1 rle with old method : 0.0067861080169677734 time for calcul the mask position with numpy : 0.006548643112182617 nb_pixel_total : 553 time to create 1 rle with old method : 0.0006844997406005859 time for calcul the mask position with numpy : 0.00694727897644043 nb_pixel_total : 1219 time to create 1 rle with old method : 0.0014202594757080078 time for calcul the mask position with numpy : 0.006287336349487305 nb_pixel_total : 20 time to create 1 rle with old method : 8.034706115722656e-05 time for calcul the mask position with numpy : 0.006972074508666992 nb_pixel_total : 1115 time to create 1 rle with old method : 0.0013074874877929688 time for calcul the mask position with numpy : 0.00738835334777832 nb_pixel_total : 323 time to create 1 rle with old method : 0.0004017353057861328 time for calcul the mask position with numpy : 0.006464481353759766 nb_pixel_total : 134 time to create 1 rle with old method : 0.00019025802612304688 create new chi : 1.3799304962158203 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 : 0.0036020278930664062 batch 1 Loaded 137 chid ids of type : 4230 Number RLEs to save : 13589 TO DO : save crop sub photo not yet done ! save time : 0.7993452548980713 nb_obj : 146 nb_hashtags : 8 time to prepare the origin masks : 1.4021775722503662 time for calcul the mask position with numpy : 0.07610583305358887 nb_pixel_total : 1786592 time to create 1 rle with new method : 0.16925668716430664 time for calcul the mask position with numpy : 0.008330106735229492 nb_pixel_total : 55090 time to create 1 rle with old method : 0.06025838851928711 time for calcul the mask position with numpy : 0.006636857986450195 nb_pixel_total : 471 time to create 1 rle with old method : 0.0006062984466552734 time for calcul the mask position with numpy : 0.006796121597290039 nb_pixel_total : 46 time to create 1 rle with old method : 0.00012612342834472656 time for calcul the mask position with numpy : 0.007912158966064453 nb_pixel_total : 87 time to create 1 rle with old method : 0.0001392364501953125 time for calcul the mask position with numpy : 0.007283210754394531 nb_pixel_total : 624 time to create 1 rle with old method : 0.0008072853088378906 time for calcul the mask position with numpy : 0.0077321529388427734 nb_pixel_total : 14 time to create 1 rle with old method : 5.602836608886719e-05 time for calcul the mask position with numpy : 0.006819725036621094 nb_pixel_total : 243 time to create 1 rle with old method : 0.0003261566162109375 time for calcul the mask position with numpy : 0.0075130462646484375 nb_pixel_total : 47 time to create 1 rle with old method : 8.0108642578125e-05 time for calcul the mask position with numpy : 0.006696462631225586 nb_pixel_total : 2417 time to create 1 rle with old method : 0.002787351608276367 time for calcul the mask position with numpy : 0.0065801143646240234 nb_pixel_total : 14066 time to create 1 rle with old method : 0.015816450119018555 time for calcul the mask position with numpy : 0.006757974624633789 nb_pixel_total : 1891 time to create 1 rle with old method : 0.002246856689453125 time for calcul the mask position with numpy : 0.006543874740600586 nb_pixel_total : 558 time to create 1 rle with old method : 0.0006935596466064453 time for calcul the mask position with numpy : 0.006432771682739258 nb_pixel_total : 683 time to create 1 rle with old method : 0.0009448528289794922 time for calcul the mask position with numpy : 0.007084846496582031 nb_pixel_total : 199 time to create 1 rle with old method : 0.000255584716796875 time for calcul the mask position with numpy : 0.006427288055419922 nb_pixel_total : 1045 time to create 1 rle with old method : 0.0013210773468017578 time for calcul the mask position with numpy : 0.006645679473876953 nb_pixel_total : 1144 time to create 1 rle with old method : 0.0013384819030761719 time for calcul the mask position with numpy : 0.0069200992584228516 nb_pixel_total : 1789 time to create 1 rle with old method : 0.0020394325256347656 time for calcul the mask position with numpy : 0.007416725158691406 nb_pixel_total : 148 time to create 1 rle with old method : 0.00021719932556152344 time for calcul the mask position with numpy : 0.006442546844482422 nb_pixel_total : 1065 time to create 1 rle with old method : 0.0012624263763427734 time for calcul the mask position with numpy : 0.006442070007324219 nb_pixel_total : 371 time to create 1 rle with old method : 0.0005021095275878906 time for calcul the mask position with numpy : 0.006407976150512695 nb_pixel_total : 1926 time to create 1 rle with old method : 0.002223968505859375 time for calcul the mask position with numpy : 0.006619453430175781 nb_pixel_total : 5531 time to create 1 rle with old method : 0.006340980529785156 time for calcul the mask position with numpy : 0.006747007369995117 nb_pixel_total : 152 time to create 1 rle with old method : 0.00018787384033203125 time for calcul the mask position with numpy : 0.006575345993041992 nb_pixel_total : 103 time to create 1 rle with old method : 0.00015020370483398438 time for calcul the mask position with numpy : 0.007302045822143555 nb_pixel_total : 651 time to create 1 rle with old method : 0.0008478164672851562 time for calcul the mask position with numpy : 0.006886959075927734 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005276203155517578 time for calcul the mask position with numpy : 0.0064525604248046875 nb_pixel_total : 817 time to create 1 rle with old method : 0.0009725093841552734 time for calcul the mask position with numpy : 0.006681203842163086 nb_pixel_total : 1140 time to create 1 rle with old method : 0.001356363296508789 time for calcul the mask position with numpy : 0.007319211959838867 nb_pixel_total : 143 time to create 1 rle with old method : 0.000240325927734375 time for calcul the mask position with numpy : 0.007245540618896484 nb_pixel_total : 144 time to create 1 rle with old method : 0.00020170211791992188 time for calcul the mask position with numpy : 0.006671905517578125 nb_pixel_total : 1978 time to create 1 rle with old method : 0.002498149871826172 time for calcul the mask position with numpy : 0.0065765380859375 nb_pixel_total : 3784 time to create 1 rle with old method : 0.004479646682739258 time for calcul the mask position with numpy : 0.0066258907318115234 nb_pixel_total : 200 time to create 1 rle with old method : 0.0003116130828857422 time for calcul the mask position with numpy : 0.006353139877319336 nb_pixel_total : 2308 time to create 1 rle with old method : 0.002681732177734375 time for calcul the mask position with numpy : 0.006613016128540039 nb_pixel_total : 68 time to create 1 rle with old method : 0.00011086463928222656 time for calcul the mask position with numpy : 0.006661653518676758 nb_pixel_total : 390 time to create 1 rle with old method : 0.00046372413635253906 time for calcul the mask position with numpy : 0.006244182586669922 nb_pixel_total : 2401 time to create 1 rle with old method : 0.0027527809143066406 time for calcul the mask position with numpy : 0.006052970886230469 nb_pixel_total : 728 time to create 1 rle with old method : 0.0008554458618164062 time for calcul the mask position with numpy : 0.007085323333740234 nb_pixel_total : 558 time to create 1 rle with old method : 0.0007421970367431641 time for calcul the mask position with numpy : 0.006311178207397461 nb_pixel_total : 672 time to create 1 rle with old method : 0.0008084774017333984 time for calcul the mask position with numpy : 0.006296396255493164 nb_pixel_total : 761 time to create 1 rle with old method : 0.0008540153503417969 time for calcul the mask position with numpy : 0.006387948989868164 nb_pixel_total : 499 time to create 1 rle with old method : 0.0005979537963867188 time for calcul the mask position with numpy : 0.006350517272949219 nb_pixel_total : 774 time to create 1 rle with old method : 0.0009109973907470703 time for calcul the mask position with numpy : 0.0063436031341552734 nb_pixel_total : 845 time to create 1 rle with old method : 0.0010561943054199219 time for calcul the mask position with numpy : 0.007228851318359375 nb_pixel_total : 625 time to create 1 rle with old method : 0.0007679462432861328 time for calcul the mask position with numpy : 0.0061168670654296875 nb_pixel_total : 168 time to create 1 rle with old method : 0.0002129077911376953 time for calcul the mask position with numpy : 0.008083581924438477 nb_pixel_total : 1203 time to create 1 rle with old method : 0.0013697147369384766 time for calcul the mask position with numpy : 0.006723165512084961 nb_pixel_total : 790 time to create 1 rle with old method : 0.0009748935699462891 time for calcul the mask position with numpy : 0.0069043636322021484 nb_pixel_total : 13960 time to create 1 rle with old method : 0.01513218879699707 time for calcul the mask position with numpy : 0.006820201873779297 nb_pixel_total : 32 time to create 1 rle with old method : 8.916854858398438e-05 time for calcul the mask position with numpy : 0.006899833679199219 nb_pixel_total : 1341 time to create 1 rle with old method : 0.0015506744384765625 time for calcul the mask position with numpy : 0.006722211837768555 nb_pixel_total : 178 time to create 1 rle with old method : 0.000244140625 time for calcul the mask position with numpy : 0.006629228591918945 nb_pixel_total : 2179 time to create 1 rle with old method : 0.0026319026947021484 time for calcul the mask position with numpy : 0.007876396179199219 nb_pixel_total : 1797 time to create 1 rle with old method : 0.0021355152130126953 time for calcul the mask position with numpy : 0.0065195560455322266 nb_pixel_total : 556 time to create 1 rle with old method : 0.0007069110870361328 time for calcul the mask position with numpy : 0.0068817138671875 nb_pixel_total : 1932 time to create 1 rle with old method : 0.0022840499877929688 time for calcul the mask position with numpy : 0.006632328033447266 nb_pixel_total : 10 time to create 1 rle with old method : 5.2928924560546875e-05 time for calcul the mask position with numpy : 0.0066683292388916016 nb_pixel_total : 124 time to create 1 rle with old method : 0.0001850128173828125 time for calcul the mask position with numpy : 0.006992340087890625 nb_pixel_total : 817 time to create 1 rle with old method : 0.0009722709655761719 time for calcul the mask position with numpy : 0.007823944091796875 nb_pixel_total : 1170 time to create 1 rle with old method : 0.0013747215270996094 time for calcul the mask position with numpy : 0.007184028625488281 nb_pixel_total : 1067 time to create 1 rle with old method : 0.0012917518615722656 time for calcul the mask position with numpy : 0.006681203842163086 nb_pixel_total : 1524 time to create 1 rle with old method : 0.001804351806640625 time for calcul the mask position with numpy : 0.006620168685913086 nb_pixel_total : 126 time to create 1 rle with old method : 0.00017213821411132812 time for calcul the mask position with numpy : 0.006306171417236328 nb_pixel_total : 564 time to create 1 rle with old method : 0.0006818771362304688 time for calcul the mask position with numpy : 0.006336212158203125 nb_pixel_total : 65 time to create 1 rle with old method : 0.00010824203491210938 time for calcul the mask position with numpy : 0.006810903549194336 nb_pixel_total : 677 time to create 1 rle with old method : 0.0008521080017089844 time for calcul the mask position with numpy : 0.0076313018798828125 nb_pixel_total : 452 time to create 1 rle with old method : 0.0005886554718017578 time for calcul the mask position with numpy : 0.006437540054321289 nb_pixel_total : 87 time to create 1 rle with old method : 0.00013184547424316406 time for calcul the mask position with numpy : 0.00692439079284668 nb_pixel_total : 105605 time to create 1 rle with old method : 0.11297821998596191 time for calcul the mask position with numpy : 0.0062067508697509766 nb_pixel_total : 400 time to create 1 rle with old method : 0.00048470497131347656 time for calcul the mask position with numpy : 0.006351947784423828 nb_pixel_total : 471 time to create 1 rle with old method : 0.000560760498046875 time for calcul the mask position with numpy : 0.006086826324462891 nb_pixel_total : 133 time to create 1 rle with old method : 0.00017118453979492188 time for calcul the mask position with numpy : 0.0060727596282958984 nb_pixel_total : 77 time to create 1 rle with old method : 0.00010752677917480469 time for calcul the mask position with numpy : 0.006155967712402344 nb_pixel_total : 199 time to create 1 rle with old method : 0.0003101825714111328 time for calcul the mask position with numpy : 0.0064411163330078125 nb_pixel_total : 164 time to create 1 rle with old method : 0.00021767616271972656 time for calcul the mask position with numpy : 0.005988121032714844 nb_pixel_total : 286 time to create 1 rle with old method : 0.00036025047302246094 time for calcul the mask position with numpy : 0.0059642791748046875 nb_pixel_total : 365 time to create 1 rle with old method : 0.00043463706970214844 time for calcul the mask position with numpy : 0.0059299468994140625 nb_pixel_total : 493 time to create 1 rle with old method : 0.0006079673767089844 time for calcul the mask position with numpy : 0.0058460235595703125 nb_pixel_total : 350 time to create 1 rle with old method : 0.0004398822784423828 time for calcul the mask position with numpy : 0.006017446517944336 nb_pixel_total : 7 time to create 1 rle with old method : 4.649162292480469e-05 time for calcul the mask position with numpy : 0.0059010982513427734 nb_pixel_total : 40 time to create 1 rle with old method : 9.322166442871094e-05 time for calcul the mask position with numpy : 0.005945682525634766 nb_pixel_total : 375 time to create 1 rle with old method : 0.0004177093505859375 time for calcul the mask position with numpy : 0.005878448486328125 nb_pixel_total : 1532 time to create 1 rle with old method : 0.0017096996307373047 time for calcul the mask position with numpy : 0.005825042724609375 nb_pixel_total : 18 time to create 1 rle with old method : 0.00012731552124023438 time for calcul the mask position with numpy : 0.005842924118041992 nb_pixel_total : 2047 time to create 1 rle with old method : 0.0023424625396728516 time for calcul the mask position with numpy : 0.005794048309326172 nb_pixel_total : 207 time to create 1 rle with old method : 0.0002295970916748047 time for calcul the mask position with numpy : 0.005809783935546875 nb_pixel_total : 9 time to create 1 rle with old method : 4.267692565917969e-05 time for calcul the mask position with numpy : 0.0059545040130615234 nb_pixel_total : 209 time to create 1 rle with old method : 0.0002484321594238281 time for calcul the mask position with numpy : 0.0059659481048583984 nb_pixel_total : 151 time to create 1 rle with old method : 0.00020194053649902344 time for calcul the mask position with numpy : 0.005827903747558594 nb_pixel_total : 497 time to create 1 rle with old method : 0.0005819797515869141 time for calcul the mask position with numpy : 0.006181240081787109 nb_pixel_total : 180 time to create 1 rle with old method : 0.0002086162567138672 time for calcul the mask position with numpy : 0.005926609039306641 nb_pixel_total : 852 time to create 1 rle with old method : 0.0010030269622802734 time for calcul the mask position with numpy : 0.00597071647644043 nb_pixel_total : 227 time to create 1 rle with old method : 0.00028228759765625 time for calcul the mask position with numpy : 0.006119966506958008 nb_pixel_total : 780 time to create 1 rle with old method : 0.0009319782257080078 time for calcul the mask position with numpy : 0.006463289260864258 nb_pixel_total : 7 time to create 1 rle with old method : 4.839897155761719e-05 time for calcul the mask position with numpy : 0.006330251693725586 nb_pixel_total : 565 time to create 1 rle with old method : 0.0006439685821533203 time for calcul the mask position with numpy : 0.0060994625091552734 nb_pixel_total : 179 time to create 1 rle with old method : 0.0002338886260986328 time for calcul the mask position with numpy : 0.006331443786621094 nb_pixel_total : 44 time to create 1 rle with old method : 0.0001461505889892578 time for calcul the mask position with numpy : 0.006710529327392578 nb_pixel_total : 949 time to create 1 rle with old method : 0.0015823841094970703 time for calcul the mask position with numpy : 0.006499528884887695 nb_pixel_total : 107 time to create 1 rle with old method : 0.0002033710479736328 time for calcul the mask position with numpy : 0.0064928531646728516 nb_pixel_total : 437 time to create 1 rle with old method : 0.0007655620574951172 time for calcul the mask position with numpy : 0.0066530704498291016 nb_pixel_total : 162 time to create 1 rle with old method : 0.0003008842468261719 time for calcul the mask position with numpy : 0.006577253341674805 nb_pixel_total : 480 time to create 1 rle with old method : 0.0008382797241210938 time for calcul the mask position with numpy : 0.006571531295776367 nb_pixel_total : 388 time to create 1 rle with old method : 0.0006885528564453125 time for calcul the mask position with numpy : 0.006608486175537109 nb_pixel_total : 160 time to create 1 rle with old method : 0.0003075599670410156 time for calcul the mask position with numpy : 0.0067903995513916016 nb_pixel_total : 3266 time to create 1 rle with old method : 0.005771160125732422 time for calcul the mask position with numpy : 0.0070955753326416016 nb_pixel_total : 4697 time to create 1 rle with old method : 0.006391763687133789 time for calcul the mask position with numpy : 0.006733417510986328 nb_pixel_total : 851 time to create 1 rle with old method : 0.0009596347808837891 time for calcul the mask position with numpy : 0.006746530532836914 nb_pixel_total : 318 time to create 1 rle with old method : 0.00043201446533203125 time for calcul the mask position with numpy : 0.006505489349365234 nb_pixel_total : 758 time to create 1 rle with old method : 0.0008697509765625 time for calcul the mask position with numpy : 0.00634002685546875 nb_pixel_total : 106 time to create 1 rle with old method : 0.0001785755157470703 time for calcul the mask position with numpy : 0.006535530090332031 nb_pixel_total : 1445 time to create 1 rle with old method : 0.0016338825225830078 time for calcul the mask position with numpy : 0.006239175796508789 nb_pixel_total : 311 time to create 1 rle with old method : 0.0003933906555175781 time for calcul the mask position with numpy : 0.006388425827026367 nb_pixel_total : 4 time to create 1 rle with old method : 4.4345855712890625e-05 time for calcul the mask position with numpy : 0.0065004825592041016 nb_pixel_total : 734 time to create 1 rle with old method : 0.0008723735809326172 time for calcul the mask position with numpy : 0.006356239318847656 nb_pixel_total : 78 time to create 1 rle with old method : 0.00012302398681640625 time for calcul the mask position with numpy : 0.006234407424926758 nb_pixel_total : 525 time to create 1 rle with old method : 0.0006289482116699219 time for calcul the mask position with numpy : 0.006237506866455078 nb_pixel_total : 231 time to create 1 rle with old method : 0.0002918243408203125 time for calcul the mask position with numpy : 0.006329536437988281 nb_pixel_total : 288 time to create 1 rle with old method : 0.00037288665771484375 time for calcul the mask position with numpy : 0.00630950927734375 nb_pixel_total : 1207 time to create 1 rle with old method : 0.0014338493347167969 time for calcul the mask position with numpy : 0.006140708923339844 nb_pixel_total : 864 time to create 1 rle with old method : 0.0010194778442382812 time for calcul the mask position with numpy : 0.006570577621459961 nb_pixel_total : 75 time to create 1 rle with old method : 0.00010251998901367188 time for calcul the mask position with numpy : 0.0063173770904541016 nb_pixel_total : 162 time to create 1 rle with old method : 0.00021004676818847656 time for calcul the mask position with numpy : 0.006524562835693359 nb_pixel_total : 896 time to create 1 rle with old method : 0.0010738372802734375 time for calcul the mask position with numpy : 0.006966590881347656 nb_pixel_total : 143 time to create 1 rle with old method : 0.0001983642578125 time for calcul the mask position with numpy : 0.006819486618041992 nb_pixel_total : 399 time to create 1 rle with old method : 0.0005145072937011719 time for calcul the mask position with numpy : 0.0066394805908203125 nb_pixel_total : 299 time to create 1 rle with old method : 0.0003898143768310547 time for calcul the mask position with numpy : 0.006846904754638672 nb_pixel_total : 120 time to create 1 rle with old method : 0.0002810955047607422 time for calcul the mask position with numpy : 0.0070037841796875 nb_pixel_total : 1342 time to create 1 rle with old method : 0.001596689224243164 time for calcul the mask position with numpy : 0.0069713592529296875 nb_pixel_total : 5 time to create 1 rle with old method : 3.2901763916015625e-05 time for calcul the mask position with numpy : 0.006701946258544922 nb_pixel_total : 760 time to create 1 rle with old method : 0.00091552734375 time for calcul the mask position with numpy : 0.006897449493408203 nb_pixel_total : 1406 time to create 1 rle with old method : 0.0016567707061767578 time for calcul the mask position with numpy : 0.007140398025512695 nb_pixel_total : 321 time to create 1 rle with old method : 0.00040149688720703125 time for calcul the mask position with numpy : 0.006412029266357422 nb_pixel_total : 515 time to create 1 rle with old method : 0.000629425048828125 time for calcul the mask position with numpy : 0.0066127777099609375 nb_pixel_total : 509 time to create 1 rle with old method : 0.0006148815155029297 time for calcul the mask position with numpy : 0.006181478500366211 nb_pixel_total : 216 time to create 1 rle with old method : 0.0002703666687011719 time for calcul the mask position with numpy : 0.0063457489013671875 nb_pixel_total : 591 time to create 1 rle with old method : 0.0007276535034179688 time for calcul the mask position with numpy : 0.008520841598510742 nb_pixel_total : 107 time to create 1 rle with old method : 0.00022101402282714844 time for calcul the mask position with numpy : 0.00838160514831543 nb_pixel_total : 1229 time to create 1 rle with old method : 0.0014142990112304688 time for calcul the mask position with numpy : 0.00840616226196289 nb_pixel_total : 1136 time to create 1 rle with old method : 0.0012590885162353516 time for calcul the mask position with numpy : 0.008431434631347656 nb_pixel_total : 34 time to create 1 rle with old method : 6.890296936035156e-05 time for calcul the mask position with numpy : 0.008501052856445312 nb_pixel_total : 304 time to create 1 rle with old method : 0.0003807544708251953 time for calcul the mask position with numpy : 0.008541584014892578 nb_pixel_total : 379 time to create 1 rle with old method : 0.0004551410675048828 time for calcul the mask position with numpy : 0.00836038589477539 nb_pixel_total : 137 time to create 1 rle with old method : 0.00021457672119140625 time for calcul the mask position with numpy : 0.008359909057617188 nb_pixel_total : 423 time to create 1 rle with old method : 0.0005071163177490234 time for calcul the mask position with numpy : 0.008419036865234375 nb_pixel_total : 211 time to create 1 rle with old method : 0.00024700164794921875 create new chi : 1.566890001296997 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 : 0.002660989761352539 batch 1 Loaded 147 chid ids of type : 4230 Number RLEs to save : 13052 TO DO : save crop sub photo not yet done ! save time : 0.7535126209259033 nb_obj : 150 nb_hashtags : 7 time to prepare the origin masks : 1.4101459980010986 time for calcul the mask position with numpy : 0.1030735969543457 nb_pixel_total : 1785817 time to create 1 rle with new method : 0.09260272979736328 time for calcul the mask position with numpy : 0.006234645843505859 nb_pixel_total : 253 time to create 1 rle with old method : 0.0003116130828857422 time for calcul the mask position with numpy : 0.0060122013092041016 nb_pixel_total : 3 time to create 1 rle with old method : 3.361701965332031e-05 time for calcul the mask position with numpy : 0.006177425384521484 nb_pixel_total : 101 time to create 1 rle with old method : 0.0002009868621826172 time for calcul the mask position with numpy : 0.006068706512451172 nb_pixel_total : 1323 time to create 1 rle with old method : 0.0015535354614257812 time for calcul the mask position with numpy : 0.0060367584228515625 nb_pixel_total : 650 time to create 1 rle with old method : 0.0007808208465576172 time for calcul the mask position with numpy : 0.006201028823852539 nb_pixel_total : 45 time to create 1 rle with old method : 6.628036499023438e-05 time for calcul the mask position with numpy : 0.006244182586669922 nb_pixel_total : 249 time to create 1 rle with old method : 0.00032067298889160156 time for calcul the mask position with numpy : 0.00591278076171875 nb_pixel_total : 194 time to create 1 rle with old method : 0.0002295970916748047 time for calcul the mask position with numpy : 0.006110191345214844 nb_pixel_total : 73 time to create 1 rle with old method : 0.00012350082397460938 time for calcul the mask position with numpy : 0.0061359405517578125 nb_pixel_total : 19 time to create 1 rle with old method : 5.269050598144531e-05 time for calcul the mask position with numpy : 0.0066416263580322266 nb_pixel_total : 57779 time to create 1 rle with old method : 0.06196475028991699 time for calcul the mask position with numpy : 0.005766630172729492 nb_pixel_total : 11 time to create 1 rle with old method : 4.029273986816406e-05 time for calcul the mask position with numpy : 0.006025075912475586 nb_pixel_total : 264 time to create 1 rle with old method : 0.00035452842712402344 time for calcul the mask position with numpy : 0.0058705806732177734 nb_pixel_total : 2373 time to create 1 rle with old method : 0.0025827884674072266 time for calcul the mask position with numpy : 0.005776882171630859 nb_pixel_total : 2 time to create 1 rle with old method : 2.2411346435546875e-05 time for calcul the mask position with numpy : 0.006029605865478516 nb_pixel_total : 170 time to create 1 rle with old method : 0.00020694732666015625 time for calcul the mask position with numpy : 0.005971193313598633 nb_pixel_total : 16728 time to create 1 rle with old method : 0.017397403717041016 time for calcul the mask position with numpy : 0.005736827850341797 nb_pixel_total : 698 time to create 1 rle with old method : 0.0007691383361816406 time for calcul the mask position with numpy : 0.00866389274597168 nb_pixel_total : 71 time to create 1 rle with old method : 0.00011944770812988281 time for calcul the mask position with numpy : 0.01001596450805664 nb_pixel_total : 216 time to create 1 rle with old method : 0.0002675056457519531 time for calcul the mask position with numpy : 0.00987553596496582 nb_pixel_total : 1495 time to create 1 rle with old method : 0.0017080307006835938 time for calcul the mask position with numpy : 0.006047725677490234 nb_pixel_total : 1226 time to create 1 rle with old method : 0.0013699531555175781 time for calcul the mask position with numpy : 0.00600123405456543 nb_pixel_total : 77 time to create 1 rle with old method : 0.0001392364501953125 time for calcul the mask position with numpy : 0.0062520503997802734 nb_pixel_total : 1519 time to create 1 rle with old method : 0.0015993118286132812 time for calcul the mask position with numpy : 0.0061151981353759766 nb_pixel_total : 2625 time to create 1 rle with old method : 0.0029363632202148438 time for calcul the mask position with numpy : 0.0069043636322021484 nb_pixel_total : 1357 time to create 1 rle with old method : 0.002226591110229492 time for calcul the mask position with numpy : 0.0063512325286865234 nb_pixel_total : 6758 time to create 1 rle with old method : 0.007682323455810547 time for calcul the mask position with numpy : 0.007340431213378906 nb_pixel_total : 512 time to create 1 rle with old method : 0.0006511211395263672 time for calcul the mask position with numpy : 0.006556272506713867 nb_pixel_total : 485 time to create 1 rle with old method : 0.0005574226379394531 time for calcul the mask position with numpy : 0.0064046382904052734 nb_pixel_total : 910 time to create 1 rle with old method : 0.0011019706726074219 time for calcul the mask position with numpy : 0.0068111419677734375 nb_pixel_total : 232 time to create 1 rle with old method : 0.00035071372985839844 time for calcul the mask position with numpy : 0.0065572261810302734 nb_pixel_total : 1110 time to create 1 rle with old method : 0.001352548599243164 time for calcul the mask position with numpy : 0.006430387496948242 nb_pixel_total : 1573 time to create 1 rle with old method : 0.002071380615234375 time for calcul the mask position with numpy : 0.006215333938598633 nb_pixel_total : 738 time to create 1 rle with old method : 0.0008656978607177734 time for calcul the mask position with numpy : 0.0065555572509765625 nb_pixel_total : 904 time to create 1 rle with old method : 0.0010821819305419922 time for calcul the mask position with numpy : 0.006492137908935547 nb_pixel_total : 1006 time to create 1 rle with old method : 0.001214742660522461 time for calcul the mask position with numpy : 0.006628990173339844 nb_pixel_total : 1869 time to create 1 rle with old method : 0.0021393299102783203 time for calcul the mask position with numpy : 0.006671428680419922 nb_pixel_total : 1701 time to create 1 rle with old method : 0.0019953250885009766 time for calcul the mask position with numpy : 0.006482124328613281 nb_pixel_total : 168 time to create 1 rle with old method : 0.00022101402282714844 time for calcul the mask position with numpy : 0.0069332122802734375 nb_pixel_total : 63 time to create 1 rle with old method : 0.00011301040649414062 time for calcul the mask position with numpy : 0.010940313339233398 nb_pixel_total : 358 time to create 1 rle with old method : 0.0006427764892578125 time for calcul the mask position with numpy : 0.01041412353515625 nb_pixel_total : 433 time to create 1 rle with old method : 0.0004875659942626953 time for calcul the mask position with numpy : 0.009852886199951172 nb_pixel_total : 17 time to create 1 rle with old method : 4.76837158203125e-05 time for calcul the mask position with numpy : 0.010771036148071289 nb_pixel_total : 622 time to create 1 rle with old method : 0.0007262229919433594 time for calcul the mask position with numpy : 0.009847402572631836 nb_pixel_total : 1134 time to create 1 rle with old method : 0.001280069351196289 time for calcul the mask position with numpy : 0.010772705078125 nb_pixel_total : 334 time to create 1 rle with old method : 0.00044846534729003906 time for calcul the mask position with numpy : 0.009944915771484375 nb_pixel_total : 798 time to create 1 rle with old method : 0.0009596347808837891 time for calcul the mask position with numpy : 0.010179758071899414 nb_pixel_total : 765 time to create 1 rle with old method : 0.0008995532989501953 time for calcul the mask position with numpy : 0.01004648208618164 nb_pixel_total : 150 time to create 1 rle with old method : 0.00018262863159179688 time for calcul the mask position with numpy : 0.00994253158569336 nb_pixel_total : 196 time to create 1 rle with old method : 0.0002548694610595703 time for calcul the mask position with numpy : 0.008061885833740234 nb_pixel_total : 558 time to create 1 rle with old method : 0.0006361007690429688 time for calcul the mask position with numpy : 0.005789756774902344 nb_pixel_total : 788 time to create 1 rle with old method : 0.0008137226104736328 time for calcul the mask position with numpy : 0.005984306335449219 nb_pixel_total : 1436 time to create 1 rle with old method : 0.0016467571258544922 time for calcul the mask position with numpy : 0.0064084529876708984 nb_pixel_total : 321 time to create 1 rle with old method : 0.00037288665771484375 time for calcul the mask position with numpy : 0.006591320037841797 nb_pixel_total : 525 time to create 1 rle with old method : 0.0006434917449951172 time for calcul the mask position with numpy : 0.006158351898193359 nb_pixel_total : 4574 time to create 1 rle with old method : 0.005235910415649414 time for calcul the mask position with numpy : 0.0062408447265625 nb_pixel_total : 108 time to create 1 rle with old method : 0.00014472007751464844 time for calcul the mask position with numpy : 0.0064239501953125 nb_pixel_total : 2099 time to create 1 rle with old method : 0.0023374557495117188 time for calcul the mask position with numpy : 0.006247758865356445 nb_pixel_total : 564 time to create 1 rle with old method : 0.0006725788116455078 time for calcul the mask position with numpy : 0.006483554840087891 nb_pixel_total : 844 time to create 1 rle with old method : 0.001468658447265625 time for calcul the mask position with numpy : 0.008939981460571289 nb_pixel_total : 941 time to create 1 rle with old method : 0.0017135143280029297 time for calcul the mask position with numpy : 0.007265329360961914 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001373291015625 time for calcul the mask position with numpy : 0.0068302154541015625 nb_pixel_total : 15 time to create 1 rle with old method : 5.817413330078125e-05 time for calcul the mask position with numpy : 0.007884502410888672 nb_pixel_total : 1258 time to create 1 rle with old method : 0.0015881061553955078 time for calcul the mask position with numpy : 0.006640195846557617 nb_pixel_total : 1023 time to create 1 rle with old method : 0.0012485980987548828 time for calcul the mask position with numpy : 0.006356716156005859 nb_pixel_total : 1568 time to create 1 rle with old method : 0.0017805099487304688 time for calcul the mask position with numpy : 0.006328582763671875 nb_pixel_total : 394 time to create 1 rle with old method : 0.0004963874816894531 time for calcul the mask position with numpy : 0.00615239143371582 nb_pixel_total : 1448 time to create 1 rle with old method : 0.001764535903930664 time for calcul the mask position with numpy : 0.006268739700317383 nb_pixel_total : 305 time to create 1 rle with old method : 0.0003821849822998047 time for calcul the mask position with numpy : 0.0062220096588134766 nb_pixel_total : 710 time to create 1 rle with old method : 0.0008447170257568359 time for calcul the mask position with numpy : 0.006359577178955078 nb_pixel_total : 858 time to create 1 rle with old method : 0.0010862350463867188 time for calcul the mask position with numpy : 0.006299495697021484 nb_pixel_total : 48 time to create 1 rle with old method : 0.00011444091796875 time for calcul the mask position with numpy : 0.00708460807800293 nb_pixel_total : 455 time to create 1 rle with old method : 0.0005500316619873047 time for calcul the mask position with numpy : 0.006245613098144531 nb_pixel_total : 699 time to create 1 rle with old method : 0.0008652210235595703 time for calcul the mask position with numpy : 0.0062487125396728516 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001423358917236328 time for calcul the mask position with numpy : 0.007467031478881836 nb_pixel_total : 106713 time to create 1 rle with old method : 0.11960220336914062 time for calcul the mask position with numpy : 0.00664067268371582 nb_pixel_total : 452 time to create 1 rle with old method : 0.0005047321319580078 time for calcul the mask position with numpy : 0.0062100887298583984 nb_pixel_total : 116 time to create 1 rle with old method : 0.00016307830810546875 time for calcul the mask position with numpy : 0.006354808807373047 nb_pixel_total : 96 time to create 1 rle with old method : 0.0001285076141357422 time for calcul the mask position with numpy : 0.005980730056762695 nb_pixel_total : 146 time to create 1 rle with old method : 0.00018095970153808594 time for calcul the mask position with numpy : 0.006373882293701172 nb_pixel_total : 173 time to create 1 rle with old method : 0.000225067138671875 time for calcul the mask position with numpy : 0.006245136260986328 nb_pixel_total : 162 time to create 1 rle with old method : 0.0002048015594482422 time for calcul the mask position with numpy : 0.00603938102722168 nb_pixel_total : 277 time to create 1 rle with old method : 0.00034499168395996094 time for calcul the mask position with numpy : 0.006232738494873047 nb_pixel_total : 22 time to create 1 rle with old method : 0.00011873245239257812 time for calcul the mask position with numpy : 0.0062770843505859375 nb_pixel_total : 397 time to create 1 rle with old method : 0.00047779083251953125 time for calcul the mask position with numpy : 0.006634950637817383 nb_pixel_total : 1762 time to create 1 rle with old method : 0.002859354019165039 time for calcul the mask position with numpy : 0.006525516510009766 nb_pixel_total : 343 time to create 1 rle with old method : 0.0004296302795410156 time for calcul the mask position with numpy : 0.0058629512786865234 nb_pixel_total : 185 time to create 1 rle with old method : 0.00024247169494628906 time for calcul the mask position with numpy : 0.00590825080871582 nb_pixel_total : 393 time to create 1 rle with old method : 0.00047850608825683594 time for calcul the mask position with numpy : 0.006238222122192383 nb_pixel_total : 1566 time to create 1 rle with old method : 0.0018413066864013672 time for calcul the mask position with numpy : 0.006621122360229492 nb_pixel_total : 2055 time to create 1 rle with old method : 0.0023026466369628906 time for calcul the mask position with numpy : 0.006975889205932617 nb_pixel_total : 158 time to create 1 rle with old method : 0.0003039836883544922 time for calcul the mask position with numpy : 0.007188320159912109 nb_pixel_total : 213 time to create 1 rle with old method : 0.0003457069396972656 time for calcul the mask position with numpy : 0.006162166595458984 nb_pixel_total : 211 time to create 1 rle with old method : 0.0002467632293701172 time for calcul the mask position with numpy : 0.006079196929931641 nb_pixel_total : 16 time to create 1 rle with old method : 5.173683166503906e-05 time for calcul the mask position with numpy : 0.006479024887084961 nb_pixel_total : 422 time to create 1 rle with old method : 0.0007767677307128906 time for calcul the mask position with numpy : 0.006015777587890625 nb_pixel_total : 827 time to create 1 rle with old method : 0.0009753704071044922 time for calcul the mask position with numpy : 0.006465911865234375 nb_pixel_total : 721 time to create 1 rle with old method : 0.0009295940399169922 time for calcul the mask position with numpy : 0.006634712219238281 nb_pixel_total : 44 time to create 1 rle with old method : 0.00010752677917480469 time for calcul the mask position with numpy : 0.006045818328857422 nb_pixel_total : 162 time to create 1 rle with old method : 0.00021004676818847656 time for calcul the mask position with numpy : 0.0061492919921875 nb_pixel_total : 133 time to create 1 rle with old method : 0.00017523765563964844 time for calcul the mask position with numpy : 0.006165266036987305 nb_pixel_total : 840 time to create 1 rle with old method : 0.0009818077087402344 time for calcul the mask position with numpy : 0.006416797637939453 nb_pixel_total : 135 time to create 1 rle with old method : 0.00018787384033203125 time for calcul the mask position with numpy : 0.00680994987487793 nb_pixel_total : 177 time to create 1 rle with old method : 0.0003561973571777344 time for calcul the mask position with numpy : 0.0062808990478515625 nb_pixel_total : 282 time to create 1 rle with old method : 0.0003535747528076172 time for calcul the mask position with numpy : 0.006183147430419922 nb_pixel_total : 164 time to create 1 rle with old method : 0.0002200603485107422 time for calcul the mask position with numpy : 0.006089448928833008 nb_pixel_total : 2197 time to create 1 rle with old method : 0.003034830093383789 time for calcul the mask position with numpy : 0.007079601287841797 nb_pixel_total : 298 time to create 1 rle with old method : 0.0003788471221923828 time for calcul the mask position with numpy : 0.0059566497802734375 nb_pixel_total : 34 time to create 1 rle with old method : 7.152557373046875e-05 time for calcul the mask position with numpy : 0.0061435699462890625 nb_pixel_total : 182 time to create 1 rle with old method : 0.00029921531677246094 time for calcul the mask position with numpy : 0.006609439849853516 nb_pixel_total : 3058 time to create 1 rle with old method : 0.0042667388916015625 time for calcul the mask position with numpy : 0.006590843200683594 nb_pixel_total : 5232 time to create 1 rle with old method : 0.005919694900512695 time for calcul the mask position with numpy : 0.006327629089355469 nb_pixel_total : 501 time to create 1 rle with old method : 0.0006384849548339844 time for calcul the mask position with numpy : 0.0062487125396728516 nb_pixel_total : 747 time to create 1 rle with old method : 0.0008738040924072266 time for calcul the mask position with numpy : 0.006180763244628906 nb_pixel_total : 723 time to create 1 rle with old method : 0.0008437633514404297 time for calcul the mask position with numpy : 0.006044626235961914 nb_pixel_total : 1613 time to create 1 rle with old method : 0.0019059181213378906 time for calcul the mask position with numpy : 0.006140470504760742 nb_pixel_total : 164 time to create 1 rle with old method : 0.00022554397583007812 time for calcul the mask position with numpy : 0.00658869743347168 nb_pixel_total : 112 time to create 1 rle with old method : 0.00014400482177734375 time for calcul the mask position with numpy : 0.006605863571166992 nb_pixel_total : 753 time to create 1 rle with old method : 0.0008022785186767578 time for calcul the mask position with numpy : 0.006425380706787109 nb_pixel_total : 413 time to create 1 rle with old method : 0.0005130767822265625 time for calcul the mask position with numpy : 0.00638890266418457 nb_pixel_total : 53 time to create 1 rle with old method : 0.00011587142944335938 time for calcul the mask position with numpy : 0.006121397018432617 nb_pixel_total : 1239 time to create 1 rle with old method : 0.0013554096221923828 time for calcul the mask position with numpy : 0.006519317626953125 nb_pixel_total : 283 time to create 1 rle with old method : 0.0004980564117431641 time for calcul the mask position with numpy : 0.006555318832397461 nb_pixel_total : 801 time to create 1 rle with old method : 0.0014374256134033203 time for calcul the mask position with numpy : 0.00655674934387207 nb_pixel_total : 63 time to create 1 rle with old method : 0.0001785755157470703 time for calcul the mask position with numpy : 0.006533622741699219 nb_pixel_total : 993 time to create 1 rle with old method : 0.0016963481903076172 time for calcul the mask position with numpy : 0.006525278091430664 nb_pixel_total : 171 time to create 1 rle with old method : 0.0003097057342529297 time for calcul the mask position with numpy : 0.0065135955810546875 nb_pixel_total : 176 time to create 1 rle with old method : 0.00032258033752441406 time for calcul the mask position with numpy : 0.006541013717651367 nb_pixel_total : 10 time to create 1 rle with old method : 6.866455078125e-05 time for calcul the mask position with numpy : 0.007630586624145508 nb_pixel_total : 419 time to create 1 rle with old method : 0.0004937648773193359 time for calcul the mask position with numpy : 0.006206512451171875 nb_pixel_total : 360 time to create 1 rle with old method : 0.0004456043243408203 time for calcul the mask position with numpy : 0.0061151981353759766 nb_pixel_total : 3308 time to create 1 rle with old method : 0.0037217140197753906 time for calcul the mask position with numpy : 0.007050752639770508 nb_pixel_total : 136 time to create 1 rle with old method : 0.00018024444580078125 time for calcul the mask position with numpy : 0.00599980354309082 nb_pixel_total : 727 time to create 1 rle with old method : 0.0009424686431884766 time for calcul the mask position with numpy : 0.006141185760498047 nb_pixel_total : 1872 time to create 1 rle with old method : 0.002130270004272461 time for calcul the mask position with numpy : 0.006043434143066406 nb_pixel_total : 794 time to create 1 rle with old method : 0.0009133815765380859 time for calcul the mask position with numpy : 0.006817340850830078 nb_pixel_total : 309 time to create 1 rle with old method : 0.0003733634948730469 time for calcul the mask position with numpy : 0.006003379821777344 nb_pixel_total : 575 time to create 1 rle with old method : 0.0006182193756103516 time for calcul the mask position with numpy : 0.005932807922363281 nb_pixel_total : 502 time to create 1 rle with old method : 0.0006191730499267578 time for calcul the mask position with numpy : 0.005829811096191406 nb_pixel_total : 181 time to create 1 rle with old method : 0.00021338462829589844 time for calcul the mask position with numpy : 0.005924224853515625 nb_pixel_total : 794 time to create 1 rle with old method : 0.0010023117065429688 time for calcul the mask position with numpy : 0.006026268005371094 nb_pixel_total : 1355 time to create 1 rle with old method : 0.0015761852264404297 time for calcul the mask position with numpy : 0.006152153015136719 nb_pixel_total : 1 time to create 1 rle with old method : 2.2172927856445312e-05 time for calcul the mask position with numpy : 0.006171703338623047 nb_pixel_total : 355 time to create 1 rle with old method : 0.0003972053527832031 time for calcul the mask position with numpy : 0.005993366241455078 nb_pixel_total : 1145 time to create 1 rle with old method : 0.0013124942779541016 time for calcul the mask position with numpy : 0.006056070327758789 nb_pixel_total : 377 time to create 1 rle with old method : 0.0004177093505859375 time for calcul the mask position with numpy : 0.006090402603149414 nb_pixel_total : 623 time to create 1 rle with old method : 0.0007445812225341797 time for calcul the mask position with numpy : 0.008026838302612305 nb_pixel_total : 247 time to create 1 rle with old method : 0.0002808570861816406 time for calcul the mask position with numpy : 0.008683919906616211 nb_pixel_total : 272 time to create 1 rle with old method : 0.00033664703369140625 time for calcul the mask position with numpy : 0.008206367492675781 nb_pixel_total : 158 time to create 1 rle with old method : 0.000194549560546875 create new chi : 1.5544159412384033 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 : 0.004672527313232422 batch 1 Loaded 151 chid ids of type : 4230 Number RLEs to save : 13406 TO DO : save crop sub photo not yet done ! save time : 0.7414963245391846 nb_obj : 152 nb_hashtags : 8 time to prepare the origin masks : 1.4404003620147705 time for calcul the mask position with numpy : 0.3715839385986328 nb_pixel_total : 1761548 time to create 1 rle with new method : 0.25260257720947266 time for calcul the mask position with numpy : 0.007256269454956055 nb_pixel_total : 66390 time to create 1 rle with old method : 0.07140851020812988 time for calcul the mask position with numpy : 0.006205081939697266 nb_pixel_total : 57 time to create 1 rle with old method : 9.107589721679688e-05 time for calcul the mask position with numpy : 0.006210803985595703 nb_pixel_total : 92 time to create 1 rle with old method : 0.00014281272888183594 time for calcul the mask position with numpy : 0.006366729736328125 nb_pixel_total : 204 time to create 1 rle with old method : 0.0004076957702636719 time for calcul the mask position with numpy : 0.006493806838989258 nb_pixel_total : 343 time to create 1 rle with old method : 0.000446319580078125 time for calcul the mask position with numpy : 0.0062961578369140625 nb_pixel_total : 229 time to create 1 rle with old method : 0.0003654956817626953 time for calcul the mask position with numpy : 0.006276369094848633 nb_pixel_total : 4 time to create 1 rle with old method : 2.7894973754882812e-05 time for calcul the mask position with numpy : 0.006140470504760742 nb_pixel_total : 2 time to create 1 rle with old method : 2.2411346435546875e-05 time for calcul the mask position with numpy : 0.006262540817260742 nb_pixel_total : 321 time to create 1 rle with old method : 0.0004324913024902344 time for calcul the mask position with numpy : 0.006244659423828125 nb_pixel_total : 2455 time to create 1 rle with old method : 0.0028214454650878906 time for calcul the mask position with numpy : 0.0066225528717041016 nb_pixel_total : 99 time to create 1 rle with old method : 0.00014519691467285156 time for calcul the mask position with numpy : 0.006192207336425781 nb_pixel_total : 851 time to create 1 rle with old method : 0.0010144710540771484 time for calcul the mask position with numpy : 0.00659942626953125 nb_pixel_total : 6095 time to create 1 rle with old method : 0.008198261260986328 time for calcul the mask position with numpy : 0.00641322135925293 nb_pixel_total : 257 time to create 1 rle with old method : 0.00030994415283203125 time for calcul the mask position with numpy : 0.006198883056640625 nb_pixel_total : 747 time to create 1 rle with old method : 0.0008990764617919922 time for calcul the mask position with numpy : 0.006197690963745117 nb_pixel_total : 74 time to create 1 rle with old method : 0.00013184547424316406 time for calcul the mask position with numpy : 0.0061397552490234375 nb_pixel_total : 104 time to create 1 rle with old method : 0.00014352798461914062 time for calcul the mask position with numpy : 0.0062062740325927734 nb_pixel_total : 1149 time to create 1 rle with old method : 0.0013697147369384766 time for calcul the mask position with numpy : 0.007822036743164062 nb_pixel_total : 1636 time to create 1 rle with old method : 0.0019192695617675781 time for calcul the mask position with numpy : 0.006169319152832031 nb_pixel_total : 1127 time to create 1 rle with old method : 0.0013039112091064453 time for calcul the mask position with numpy : 0.0062351226806640625 nb_pixel_total : 4193 time to create 1 rle with old method : 0.004838228225708008 time for calcul the mask position with numpy : 0.0060961246490478516 nb_pixel_total : 11353 time to create 1 rle with old method : 0.012646675109863281 time for calcul the mask position with numpy : 0.006035804748535156 nb_pixel_total : 149 time to create 1 rle with old method : 0.00018167495727539062 time for calcul the mask position with numpy : 0.006043434143066406 nb_pixel_total : 504 time to create 1 rle with old method : 0.0005886554718017578 time for calcul the mask position with numpy : 0.006112813949584961 nb_pixel_total : 479 time to create 1 rle with old method : 0.0005517005920410156 time for calcul the mask position with numpy : 0.006179094314575195 nb_pixel_total : 941 time to create 1 rle with old method : 0.0010075569152832031 time for calcul the mask position with numpy : 0.006019115447998047 nb_pixel_total : 1490 time to create 1 rle with old method : 0.0016667842864990234 time for calcul the mask position with numpy : 0.0061724185943603516 nb_pixel_total : 102 time to create 1 rle with old method : 0.00022149085998535156 time for calcul the mask position with numpy : 0.0060274600982666016 nb_pixel_total : 8815 time to create 1 rle with old method : 0.010056257247924805 time for calcul the mask position with numpy : 0.006250858306884766 nb_pixel_total : 3317 time to create 1 rle with old method : 0.004081249237060547 time for calcul the mask position with numpy : 0.006469249725341797 nb_pixel_total : 1058 time to create 1 rle with old method : 0.0012478828430175781 time for calcul the mask position with numpy : 0.0065097808837890625 nb_pixel_total : 73 time to create 1 rle with old method : 0.0001125335693359375 time for calcul the mask position with numpy : 0.006320953369140625 nb_pixel_total : 473 time to create 1 rle with old method : 0.0008232593536376953 time for calcul the mask position with numpy : 0.008122444152832031 nb_pixel_total : 456 time to create 1 rle with old method : 0.0008056163787841797 time for calcul the mask position with numpy : 0.007581949234008789 nb_pixel_total : 1783 time to create 1 rle with old method : 0.002121448516845703 time for calcul the mask position with numpy : 0.0071887969970703125 nb_pixel_total : 137 time to create 1 rle with old method : 0.00018525123596191406 time for calcul the mask position with numpy : 0.006404399871826172 nb_pixel_total : 815 time to create 1 rle with old method : 0.0009906291961669922 time for calcul the mask position with numpy : 0.006805896759033203 nb_pixel_total : 154 time to create 1 rle with old method : 0.0001938343048095703 time for calcul the mask position with numpy : 0.006455183029174805 nb_pixel_total : 90 time to create 1 rle with old method : 0.00020551681518554688 time for calcul the mask position with numpy : 0.006442070007324219 nb_pixel_total : 142 time to create 1 rle with old method : 0.00018477439880371094 time for calcul the mask position with numpy : 0.00598597526550293 nb_pixel_total : 529 time to create 1 rle with old method : 0.0006785392761230469 time for calcul the mask position with numpy : 0.0059621334075927734 nb_pixel_total : 68 time to create 1 rle with old method : 0.0001468658447265625 time for calcul the mask position with numpy : 0.006573915481567383 nb_pixel_total : 10580 time to create 1 rle with old method : 0.012529611587524414 time for calcul the mask position with numpy : 0.006217479705810547 nb_pixel_total : 1366 time to create 1 rle with old method : 0.0016391277313232422 time for calcul the mask position with numpy : 0.006110668182373047 nb_pixel_total : 804 time to create 1 rle with old method : 0.0009293556213378906 time for calcul the mask position with numpy : 0.006055116653442383 nb_pixel_total : 1475 time to create 1 rle with old method : 0.0016965866088867188 time for calcul the mask position with numpy : 0.006169319152832031 nb_pixel_total : 357 time to create 1 rle with old method : 0.00045418739318847656 time for calcul the mask position with numpy : 0.006319284439086914 nb_pixel_total : 3856 time to create 1 rle with old method : 0.004883766174316406 time for calcul the mask position with numpy : 0.006364107131958008 nb_pixel_total : 1831 time to create 1 rle with old method : 0.0026946067810058594 time for calcul the mask position with numpy : 0.006098508834838867 nb_pixel_total : 491 time to create 1 rle with old method : 0.0006020069122314453 time for calcul the mask position with numpy : 0.005854606628417969 nb_pixel_total : 684 time to create 1 rle with old method : 0.0007641315460205078 time for calcul the mask position with numpy : 0.006100893020629883 nb_pixel_total : 367 time to create 1 rle with old method : 0.0004584789276123047 time for calcul the mask position with numpy : 0.0059773921966552734 nb_pixel_total : 38 time to create 1 rle with old method : 9.989738464355469e-05 time for calcul the mask position with numpy : 0.006020784378051758 nb_pixel_total : 3192 time to create 1 rle with old method : 0.003687143325805664 time for calcul the mask position with numpy : 0.005991697311401367 nb_pixel_total : 97 time to create 1 rle with old method : 0.00013256072998046875 time for calcul the mask position with numpy : 0.00664520263671875 nb_pixel_total : 799 time to create 1 rle with old method : 0.0008633136749267578 time for calcul the mask position with numpy : 0.005820512771606445 nb_pixel_total : 816 time to create 1 rle with old method : 0.0009877681732177734 time for calcul the mask position with numpy : 0.006018400192260742 nb_pixel_total : 72 time to create 1 rle with old method : 0.00010132789611816406 time for calcul the mask position with numpy : 0.0060880184173583984 nb_pixel_total : 1491 time to create 1 rle with old method : 0.0016248226165771484 time for calcul the mask position with numpy : 0.006697416305541992 nb_pixel_total : 1079 time to create 1 rle with old method : 0.001280069351196289 time for calcul the mask position with numpy : 0.0061528682708740234 nb_pixel_total : 96 time to create 1 rle with old method : 0.0001399517059326172 time for calcul the mask position with numpy : 0.006235599517822266 nb_pixel_total : 91 time to create 1 rle with old method : 0.00016999244689941406 time for calcul the mask position with numpy : 0.006509065628051758 nb_pixel_total : 1627 time to create 1 rle with old method : 0.0018908977508544922 time for calcul the mask position with numpy : 0.006045103073120117 nb_pixel_total : 419 time to create 1 rle with old method : 0.0004513263702392578 time for calcul the mask position with numpy : 0.006064176559448242 nb_pixel_total : 1595 time to create 1 rle with old method : 0.0018720626831054688 time for calcul the mask position with numpy : 0.006518840789794922 nb_pixel_total : 35 time to create 1 rle with old method : 7.677078247070312e-05 time for calcul the mask position with numpy : 0.0064275264739990234 nb_pixel_total : 938 time to create 1 rle with old method : 0.00106048583984375 time for calcul the mask position with numpy : 0.00649571418762207 nb_pixel_total : 491 time to create 1 rle with old method : 0.0005862712860107422 time for calcul the mask position with numpy : 0.00653386116027832 nb_pixel_total : 1021 time to create 1 rle with old method : 0.0012128353118896484 time for calcul the mask position with numpy : 0.0064182281494140625 nb_pixel_total : 101 time to create 1 rle with old method : 0.0001342296600341797 time for calcul the mask position with numpy : 0.006833314895629883 nb_pixel_total : 106532 time to create 1 rle with old method : 0.1470472812652588 time for calcul the mask position with numpy : 0.006403684616088867 nb_pixel_total : 399 time to create 1 rle with old method : 0.0007240772247314453 time for calcul the mask position with numpy : 0.006486654281616211 nb_pixel_total : 93 time to create 1 rle with old method : 0.00013709068298339844 time for calcul the mask position with numpy : 0.0062389373779296875 nb_pixel_total : 93 time to create 1 rle with old method : 0.00013446807861328125 time for calcul the mask position with numpy : 0.006430149078369141 nb_pixel_total : 162 time to create 1 rle with old method : 0.00019240379333496094 time for calcul the mask position with numpy : 0.006297588348388672 nb_pixel_total : 298 time to create 1 rle with old method : 0.0003688335418701172 time for calcul the mask position with numpy : 0.006197214126586914 nb_pixel_total : 160 time to create 1 rle with old method : 0.00020885467529296875 time for calcul the mask position with numpy : 0.006203413009643555 nb_pixel_total : 668 time to create 1 rle with old method : 0.0007765293121337891 time for calcul the mask position with numpy : 0.006242275238037109 nb_pixel_total : 403 time to create 1 rle with old method : 0.0004677772521972656 time for calcul the mask position with numpy : 0.006231546401977539 nb_pixel_total : 17 time to create 1 rle with old method : 4.458427429199219e-05 time for calcul the mask position with numpy : 0.0060939788818359375 nb_pixel_total : 1947 time to create 1 rle with old method : 0.002031087875366211 time for calcul the mask position with numpy : 0.00597691535949707 nb_pixel_total : 39 time to create 1 rle with old method : 8.20159912109375e-05 time for calcul the mask position with numpy : 0.006276130676269531 nb_pixel_total : 102 time to create 1 rle with old method : 0.00017595291137695312 time for calcul the mask position with numpy : 0.006257057189941406 nb_pixel_total : 348 time to create 1 rle with old method : 0.00041413307189941406 time for calcul the mask position with numpy : 0.006342887878417969 nb_pixel_total : 1168 time to create 1 rle with old method : 0.001379251480102539 time for calcul the mask position with numpy : 0.0063173770904541016 nb_pixel_total : 10 time to create 1 rle with old method : 4.029273986816406e-05 time for calcul the mask position with numpy : 0.0062444210052490234 nb_pixel_total : 410 time to create 1 rle with old method : 0.0004913806915283203 time for calcul the mask position with numpy : 0.006281852722167969 nb_pixel_total : 29 time to create 1 rle with old method : 7.367134094238281e-05 time for calcul the mask position with numpy : 0.006582736968994141 nb_pixel_total : 174 time to create 1 rle with old method : 0.00023365020751953125 time for calcul the mask position with numpy : 0.006394863128662109 nb_pixel_total : 153 time to create 1 rle with old method : 0.0002086162567138672 time for calcul the mask position with numpy : 0.006190061569213867 nb_pixel_total : 108 time to create 1 rle with old method : 0.00015044212341308594 time for calcul the mask position with numpy : 0.006225109100341797 nb_pixel_total : 19 time to create 1 rle with old method : 5.125999450683594e-05 time for calcul the mask position with numpy : 0.006243705749511719 nb_pixel_total : 226 time to create 1 rle with old method : 0.00027561187744140625 time for calcul the mask position with numpy : 0.006256103515625 nb_pixel_total : 214 time to create 1 rle with old method : 0.0002639293670654297 time for calcul the mask position with numpy : 0.006079912185668945 nb_pixel_total : 953 time to create 1 rle with old method : 0.0011212825775146484 time for calcul the mask position with numpy : 0.0061588287353515625 nb_pixel_total : 157 time to create 1 rle with old method : 0.00026106834411621094 time for calcul the mask position with numpy : 0.0059375762939453125 nb_pixel_total : 913 time to create 1 rle with old method : 0.0010690689086914062 time for calcul the mask position with numpy : 0.006025552749633789 nb_pixel_total : 57 time to create 1 rle with old method : 0.0001163482666015625 time for calcul the mask position with numpy : 0.006203889846801758 nb_pixel_total : 488 time to create 1 rle with old method : 0.0005655288696289062 time for calcul the mask position with numpy : 0.006451129913330078 nb_pixel_total : 886 time to create 1 rle with old method : 0.0009963512420654297 time for calcul the mask position with numpy : 0.006128549575805664 nb_pixel_total : 176 time to create 1 rle with old method : 0.0002193450927734375 time for calcul the mask position with numpy : 0.006002902984619141 nb_pixel_total : 153 time to create 1 rle with old method : 0.00018358230590820312 time for calcul the mask position with numpy : 0.0062541961669921875 nb_pixel_total : 624 time to create 1 rle with old method : 0.0007405281066894531 time for calcul the mask position with numpy : 0.006299018859863281 nb_pixel_total : 996 time to create 1 rle with old method : 0.0011603832244873047 time for calcul the mask position with numpy : 0.006392955780029297 nb_pixel_total : 131 time to create 1 rle with old method : 0.00018930435180664062 time for calcul the mask position with numpy : 0.005976438522338867 nb_pixel_total : 755 time to create 1 rle with old method : 0.0008635520935058594 time for calcul the mask position with numpy : 0.0060617923736572266 nb_pixel_total : 5 time to create 1 rle with old method : 5.0067901611328125e-05 time for calcul the mask position with numpy : 0.006090879440307617 nb_pixel_total : 516 time to create 1 rle with old method : 0.0005979537963867188 time for calcul the mask position with numpy : 0.006254911422729492 nb_pixel_total : 166 time to create 1 rle with old method : 0.0002880096435546875 time for calcul the mask position with numpy : 0.0066967010498046875 nb_pixel_total : 42 time to create 1 rle with old method : 9.34600830078125e-05 time for calcul the mask position with numpy : 0.006128549575805664 nb_pixel_total : 887 time to create 1 rle with old method : 0.0010554790496826172 time for calcul the mask position with numpy : 0.006406545639038086 nb_pixel_total : 95 time to create 1 rle with old method : 0.00013327598571777344 time for calcul the mask position with numpy : 0.006866455078125 nb_pixel_total : 161 time to create 1 rle with old method : 0.0003533363342285156 time for calcul the mask position with numpy : 0.0063364505767822266 nb_pixel_total : 2222 time to create 1 rle with old method : 0.0025620460510253906 time for calcul the mask position with numpy : 0.006378650665283203 nb_pixel_total : 365 time to create 1 rle with old method : 0.0004677772521972656 time for calcul the mask position with numpy : 0.006018400192260742 nb_pixel_total : 61 time to create 1 rle with old method : 0.000110626220703125 time for calcul the mask position with numpy : 0.005954265594482422 nb_pixel_total : 2488 time to create 1 rle with old method : 0.0029184818267822266 time for calcul the mask position with numpy : 0.005978584289550781 nb_pixel_total : 807 time to create 1 rle with old method : 0.0009849071502685547 time for calcul the mask position with numpy : 0.006287336349487305 nb_pixel_total : 3436 time to create 1 rle with old method : 0.0038623809814453125 time for calcul the mask position with numpy : 0.0060961246490478516 nb_pixel_total : 597 time to create 1 rle with old method : 0.0007045269012451172 time for calcul the mask position with numpy : 0.0059282779693603516 nb_pixel_total : 2 time to create 1 rle with old method : 3.337860107421875e-05 time for calcul the mask position with numpy : 0.006099224090576172 nb_pixel_total : 1691 time to create 1 rle with old method : 0.0018093585968017578 time for calcul the mask position with numpy : 0.005916118621826172 nb_pixel_total : 26 time to create 1 rle with old method : 5.364418029785156e-05 time for calcul the mask position with numpy : 0.005948543548583984 nb_pixel_total : 905 time to create 1 rle with old method : 0.0011222362518310547 time for calcul the mask position with numpy : 0.0061380863189697266 nb_pixel_total : 573 time to create 1 rle with old method : 0.0006167888641357422 time for calcul the mask position with numpy : 0.00604557991027832 nb_pixel_total : 1196 time to create 1 rle with old method : 0.0014185905456542969 time for calcul the mask position with numpy : 0.005999326705932617 nb_pixel_total : 12 time to create 1 rle with old method : 5.7220458984375e-05 time for calcul the mask position with numpy : 0.005719423294067383 nb_pixel_total : 286 time to create 1 rle with old method : 0.0003273487091064453 time for calcul the mask position with numpy : 0.009459495544433594 nb_pixel_total : 70 time to create 1 rle with old method : 0.00010275840759277344 time for calcul the mask position with numpy : 0.005971431732177734 nb_pixel_total : 916 time to create 1 rle with old method : 0.0010423660278320312 time for calcul the mask position with numpy : 0.005846500396728516 nb_pixel_total : 136 time to create 1 rle with old method : 0.00017762184143066406 time for calcul the mask position with numpy : 0.005955696105957031 nb_pixel_total : 441 time to create 1 rle with old method : 0.0005338191986083984 time for calcul the mask position with numpy : 0.005823850631713867 nb_pixel_total : 4818 time to create 1 rle with old method : 0.005203962326049805 time for calcul the mask position with numpy : 0.0058917999267578125 nb_pixel_total : 348 time to create 1 rle with old method : 0.00041866302490234375 time for calcul the mask position with numpy : 0.0061206817626953125 nb_pixel_total : 165 time to create 1 rle with old method : 0.0002346038818359375 time for calcul the mask position with numpy : 0.005937814712524414 nb_pixel_total : 137 time to create 1 rle with old method : 0.00016760826110839844 time for calcul the mask position with numpy : 0.005781412124633789 nb_pixel_total : 722 time to create 1 rle with old method : 0.0008523464202880859 time for calcul the mask position with numpy : 0.006047487258911133 nb_pixel_total : 952 time to create 1 rle with old method : 0.00116729736328125 time for calcul the mask position with numpy : 0.005794048309326172 nb_pixel_total : 295 time to create 1 rle with old method : 0.0003256797790527344 time for calcul the mask position with numpy : 0.005688190460205078 nb_pixel_total : 707 time to create 1 rle with old method : 0.0007755756378173828 time for calcul the mask position with numpy : 0.0057544708251953125 nb_pixel_total : 491 time to create 1 rle with old method : 0.0006003379821777344 time for calcul the mask position with numpy : 0.006184101104736328 nb_pixel_total : 5407 time to create 1 rle with old method : 0.00597071647644043 time for calcul the mask position with numpy : 0.0060117244720458984 nb_pixel_total : 509 time to create 1 rle with old method : 0.0006210803985595703 time for calcul the mask position with numpy : 0.006060361862182617 nb_pixel_total : 1251 time to create 1 rle with old method : 0.001407623291015625 time for calcul the mask position with numpy : 0.005968809127807617 nb_pixel_total : 1170 time to create 1 rle with old method : 0.0013453960418701172 time for calcul the mask position with numpy : 0.0060272216796875 nb_pixel_total : 79 time to create 1 rle with old method : 0.00011396408081054688 time for calcul the mask position with numpy : 0.00616908073425293 nb_pixel_total : 159 time to create 1 rle with old method : 0.0001881122589111328 time for calcul the mask position with numpy : 0.006043672561645508 nb_pixel_total : 252 time to create 1 rle with old method : 0.00037360191345214844 time for calcul the mask position with numpy : 0.006027936935424805 nb_pixel_total : 322 time to create 1 rle with old method : 0.00039076805114746094 time for calcul the mask position with numpy : 0.00842595100402832 nb_pixel_total : 551 time to create 1 rle with old method : 0.0006873607635498047 time for calcul the mask position with numpy : 0.010209321975708008 nb_pixel_total : 144 time to create 1 rle with old method : 0.00029349327087402344 time for calcul the mask position with numpy : 0.011040210723876953 nb_pixel_total : 244 time to create 1 rle with old method : 0.0004706382751464844 create new chi : 1.9869890213012695 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 : 0.0037376880645751953 batch 1 Loaded 153 chid ids of type : 4230 Number RLEs to save : 14016 TO DO : save crop sub photo not yet done ! save time : 0.823249340057373 nb_obj : 141 nb_hashtags : 7 time to prepare the origin masks : 1.3369369506835938 time for calcul the mask position with numpy : 0.026883602142333984 nb_pixel_total : 1849812 time to create 1 rle with new method : 0.051807403564453125 time for calcul the mask position with numpy : 0.006371736526489258 nb_pixel_total : 313 time to create 1 rle with old method : 0.0003895759582519531 time for calcul the mask position with numpy : 0.006287574768066406 nb_pixel_total : 54 time to create 1 rle with old method : 0.00010156631469726562 time for calcul the mask position with numpy : 0.006464958190917969 nb_pixel_total : 7 time to create 1 rle with old method : 4.315376281738281e-05 time for calcul the mask position with numpy : 0.006639719009399414 nb_pixel_total : 1449 time to create 1 rle with old method : 0.0017080307006835938 time for calcul the mask position with numpy : 0.006773471832275391 nb_pixel_total : 191 time to create 1 rle with old method : 0.000244140625 time for calcul the mask position with numpy : 0.006608486175537109 nb_pixel_total : 42 time to create 1 rle with old method : 7.43865966796875e-05 time for calcul the mask position with numpy : 0.006616353988647461 nb_pixel_total : 38 time to create 1 rle with old method : 7.987022399902344e-05 time for calcul the mask position with numpy : 0.0064890384674072266 nb_pixel_total : 439 time to create 1 rle with old method : 0.0005807876586914062 time for calcul the mask position with numpy : 0.006711006164550781 nb_pixel_total : 79 time to create 1 rle with old method : 0.0001277923583984375 time for calcul the mask position with numpy : 0.006400108337402344 nb_pixel_total : 58 time to create 1 rle with old method : 0.00010752677917480469 time for calcul the mask position with numpy : 0.0065860748291015625 nb_pixel_total : 65 time to create 1 rle with old method : 0.00011777877807617188 time for calcul the mask position with numpy : 0.0064127445220947266 nb_pixel_total : 2428 time to create 1 rle with old method : 0.002847433090209961 time for calcul the mask position with numpy : 0.006633758544921875 nb_pixel_total : 154 time to create 1 rle with old method : 0.00020265579223632812 time for calcul the mask position with numpy : 0.0064928531646728516 nb_pixel_total : 899 time to create 1 rle with old method : 0.0010166168212890625 time for calcul the mask position with numpy : 0.006453990936279297 nb_pixel_total : 1755 time to create 1 rle with old method : 0.002123117446899414 time for calcul the mask position with numpy : 0.006684541702270508 nb_pixel_total : 895 time to create 1 rle with old method : 0.0010886192321777344 time for calcul the mask position with numpy : 0.006529569625854492 nb_pixel_total : 50 time to create 1 rle with old method : 0.00010085105895996094 time for calcul the mask position with numpy : 0.0066492557525634766 nb_pixel_total : 203 time to create 1 rle with old method : 0.0002646446228027344 time for calcul the mask position with numpy : 0.006569862365722656 nb_pixel_total : 1357 time to create 1 rle with old method : 0.0015597343444824219 time for calcul the mask position with numpy : 0.0066030025482177734 nb_pixel_total : 664 time to create 1 rle with old method : 0.0007951259613037109 time for calcul the mask position with numpy : 0.006419181823730469 nb_pixel_total : 2048 time to create 1 rle with old method : 0.0024416446685791016 time for calcul the mask position with numpy : 0.006292104721069336 nb_pixel_total : 1425 time to create 1 rle with old method : 0.001676321029663086 time for calcul the mask position with numpy : 0.006036281585693359 nb_pixel_total : 3890 time to create 1 rle with old method : 0.004349946975708008 time for calcul the mask position with numpy : 0.006243228912353516 nb_pixel_total : 196 time to create 1 rle with old method : 0.00023412704467773438 time for calcul the mask position with numpy : 0.006200313568115234 nb_pixel_total : 10546 time to create 1 rle with old method : 0.011558294296264648 time for calcul the mask position with numpy : 0.00610804557800293 nb_pixel_total : 85 time to create 1 rle with old method : 0.0001289844512939453 time for calcul the mask position with numpy : 0.006342887878417969 nb_pixel_total : 496 time to create 1 rle with old method : 0.0005953311920166016 time for calcul the mask position with numpy : 0.006116390228271484 nb_pixel_total : 507 time to create 1 rle with old method : 0.0006673336029052734 time for calcul the mask position with numpy : 0.006145000457763672 nb_pixel_total : 886 time to create 1 rle with old method : 0.0009529590606689453 time for calcul the mask position with numpy : 0.0062525272369384766 nb_pixel_total : 340 time to create 1 rle with old method : 0.0004124641418457031 time for calcul the mask position with numpy : 0.006021738052368164 nb_pixel_total : 1604 time to create 1 rle with old method : 0.0018420219421386719 time for calcul the mask position with numpy : 0.006117820739746094 nb_pixel_total : 82 time to create 1 rle with old method : 0.0001659393310546875 time for calcul the mask position with numpy : 0.006267070770263672 nb_pixel_total : 5984 time to create 1 rle with old method : 0.006842851638793945 time for calcul the mask position with numpy : 0.00616145133972168 nb_pixel_total : 191 time to create 1 rle with old method : 0.00024366378784179688 time for calcul the mask position with numpy : 0.006375551223754883 nb_pixel_total : 4187 time to create 1 rle with old method : 0.004819631576538086 time for calcul the mask position with numpy : 0.006090641021728516 nb_pixel_total : 739 time to create 1 rle with old method : 0.0008966922760009766 time for calcul the mask position with numpy : 0.006316184997558594 nb_pixel_total : 1390 time to create 1 rle with old method : 0.0016326904296875 time for calcul the mask position with numpy : 0.005908489227294922 nb_pixel_total : 66 time to create 1 rle with old method : 9.036064147949219e-05 time for calcul the mask position with numpy : 0.006148576736450195 nb_pixel_total : 739 time to create 1 rle with old method : 0.0009872913360595703 time for calcul the mask position with numpy : 0.0062541961669921875 nb_pixel_total : 551 time to create 1 rle with old method : 0.0006649494171142578 time for calcul the mask position with numpy : 0.006081104278564453 nb_pixel_total : 1109 time to create 1 rle with old method : 0.001314401626586914 time for calcul the mask position with numpy : 0.005866050720214844 nb_pixel_total : 577 time to create 1 rle with old method : 0.0006406307220458984 time for calcul the mask position with numpy : 0.00598454475402832 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002243518829345703 time for calcul the mask position with numpy : 0.005942821502685547 nb_pixel_total : 33 time to create 1 rle with old method : 7.557868957519531e-05 time for calcul the mask position with numpy : 0.005997896194458008 nb_pixel_total : 149 time to create 1 rle with old method : 0.000213623046875 time for calcul the mask position with numpy : 0.006058454513549805 nb_pixel_total : 490 time to create 1 rle with old method : 0.0006000995635986328 time for calcul the mask position with numpy : 0.006167888641357422 nb_pixel_total : 1326 time to create 1 rle with old method : 0.0016131401062011719 time for calcul the mask position with numpy : 0.006123542785644531 nb_pixel_total : 806 time to create 1 rle with old method : 0.0009424686431884766 time for calcul the mask position with numpy : 0.007948875427246094 nb_pixel_total : 43 time to create 1 rle with old method : 8.463859558105469e-05 time for calcul the mask position with numpy : 0.006430387496948242 nb_pixel_total : 1292 time to create 1 rle with old method : 0.0014717578887939453 time for calcul the mask position with numpy : 0.006352424621582031 nb_pixel_total : 55 time to create 1 rle with old method : 0.00010728836059570312 time for calcul the mask position with numpy : 0.0062732696533203125 nb_pixel_total : 3175 time to create 1 rle with old method : 0.003641843795776367 time for calcul the mask position with numpy : 0.006297588348388672 nb_pixel_total : 1732 time to create 1 rle with old method : 0.0020608901977539062 time for calcul the mask position with numpy : 0.006389141082763672 nb_pixel_total : 300 time to create 1 rle with old method : 0.00037169456481933594 time for calcul the mask position with numpy : 0.006349086761474609 nb_pixel_total : 577 time to create 1 rle with old method : 0.0006489753723144531 time for calcul the mask position with numpy : 0.006354093551635742 nb_pixel_total : 351 time to create 1 rle with old method : 0.00042700767517089844 time for calcul the mask position with numpy : 0.006330728530883789 nb_pixel_total : 676 time to create 1 rle with old method : 0.0008382797241210938 time for calcul the mask position with numpy : 0.006392478942871094 nb_pixel_total : 104 time to create 1 rle with old method : 0.0001404285430908203 time for calcul the mask position with numpy : 0.0062787532806396484 nb_pixel_total : 10 time to create 1 rle with old method : 3.314018249511719e-05 time for calcul the mask position with numpy : 0.00627589225769043 nb_pixel_total : 955 time to create 1 rle with old method : 0.0011103153228759766 time for calcul the mask position with numpy : 0.006257295608520508 nb_pixel_total : 730 time to create 1 rle with old method : 0.0007696151733398438 time for calcul the mask position with numpy : 0.005987882614135742 nb_pixel_total : 1931 time to create 1 rle with old method : 0.0021011829376220703 time for calcul the mask position with numpy : 0.006131410598754883 nb_pixel_total : 1265 time to create 1 rle with old method : 0.001436471939086914 time for calcul the mask position with numpy : 0.006041049957275391 nb_pixel_total : 16 time to create 1 rle with old method : 8.511543273925781e-05 time for calcul the mask position with numpy : 0.006228923797607422 nb_pixel_total : 1686 time to create 1 rle with old method : 0.0019383430480957031 time for calcul the mask position with numpy : 0.006216764450073242 nb_pixel_total : 270 time to create 1 rle with old method : 0.00034546852111816406 time for calcul the mask position with numpy : 0.006286144256591797 nb_pixel_total : 501 time to create 1 rle with old method : 0.0006070137023925781 time for calcul the mask position with numpy : 0.006476879119873047 nb_pixel_total : 614 time to create 1 rle with old method : 0.0007510185241699219 time for calcul the mask position with numpy : 0.006290912628173828 nb_pixel_total : 86 time to create 1 rle with old method : 0.00013303756713867188 time for calcul the mask position with numpy : 0.006818056106567383 nb_pixel_total : 106172 time to create 1 rle with old method : 0.11036825180053711 time for calcul the mask position with numpy : 0.0059473514556884766 nb_pixel_total : 405 time to create 1 rle with old method : 0.00047087669372558594 time for calcul the mask position with numpy : 0.005789518356323242 nb_pixel_total : 538 time to create 1 rle with old method : 0.0005948543548583984 time for calcul the mask position with numpy : 0.005925655364990234 nb_pixel_total : 99 time to create 1 rle with old method : 0.0001385211944580078 time for calcul the mask position with numpy : 0.005881071090698242 nb_pixel_total : 175 time to create 1 rle with old method : 0.0002257823944091797 time for calcul the mask position with numpy : 0.00579071044921875 nb_pixel_total : 298 time to create 1 rle with old method : 0.000347137451171875 time for calcul the mask position with numpy : 0.005824565887451172 nb_pixel_total : 135 time to create 1 rle with old method : 0.00016736984252929688 time for calcul the mask position with numpy : 0.005749702453613281 nb_pixel_total : 326 time to create 1 rle with old method : 0.00034999847412109375 time for calcul the mask position with numpy : 0.006144046783447266 nb_pixel_total : 2249 time to create 1 rle with old method : 0.0024394989013671875 time for calcul the mask position with numpy : 0.00576019287109375 nb_pixel_total : 15 time to create 1 rle with old method : 5.650520324707031e-05 time for calcul the mask position with numpy : 0.005872011184692383 nb_pixel_total : 388 time to create 1 rle with old method : 0.0005161762237548828 time for calcul the mask position with numpy : 0.005756378173828125 nb_pixel_total : 193 time to create 1 rle with old method : 0.0002543926239013672 time for calcul the mask position with numpy : 0.005963325500488281 nb_pixel_total : 1533 time to create 1 rle with old method : 0.0017962455749511719 time for calcul the mask position with numpy : 0.006049394607543945 nb_pixel_total : 446 time to create 1 rle with old method : 0.0005381107330322266 time for calcul the mask position with numpy : 0.005603790283203125 nb_pixel_total : 15 time to create 1 rle with old method : 5.53131103515625e-05 time for calcul the mask position with numpy : 0.005742073059082031 nb_pixel_total : 1734 time to create 1 rle with old method : 0.001861572265625 time for calcul the mask position with numpy : 0.00584721565246582 nb_pixel_total : 28 time to create 1 rle with old method : 5.4836273193359375e-05 time for calcul the mask position with numpy : 0.005681514739990234 nb_pixel_total : 1426 time to create 1 rle with old method : 0.001528024673461914 time for calcul the mask position with numpy : 0.005711555480957031 nb_pixel_total : 257 time to create 1 rle with old method : 0.00030231475830078125 time for calcul the mask position with numpy : 0.0059778690338134766 nb_pixel_total : 165 time to create 1 rle with old method : 0.00020313262939453125 time for calcul the mask position with numpy : 0.005925178527832031 nb_pixel_total : 187 time to create 1 rle with old method : 0.00022268295288085938 time for calcul the mask position with numpy : 0.005784273147583008 nb_pixel_total : 912 time to create 1 rle with old method : 0.0010461807250976562 time for calcul the mask position with numpy : 0.0057544708251953125 nb_pixel_total : 957 time to create 1 rle with old method : 0.001100778579711914 time for calcul the mask position with numpy : 0.005772829055786133 nb_pixel_total : 409 time to create 1 rle with old method : 0.0004634857177734375 time for calcul the mask position with numpy : 0.005820751190185547 nb_pixel_total : 693 time to create 1 rle with old method : 0.0008256435394287109 time for calcul the mask position with numpy : 0.005781888961791992 nb_pixel_total : 2070 time to create 1 rle with old method : 0.0023946762084960938 time for calcul the mask position with numpy : 0.0059773921966552734 nb_pixel_total : 950 time to create 1 rle with old method : 0.0010876655578613281 time for calcul the mask position with numpy : 0.0059201717376708984 nb_pixel_total : 170 time to create 1 rle with old method : 0.00019741058349609375 time for calcul the mask position with numpy : 0.005873441696166992 nb_pixel_total : 94 time to create 1 rle with old method : 0.0001308917999267578 time for calcul the mask position with numpy : 0.006011009216308594 nb_pixel_total : 672 time to create 1 rle with old method : 0.0009415149688720703 time for calcul the mask position with numpy : 0.006046295166015625 nb_pixel_total : 13 time to create 1 rle with old method : 6.008148193359375e-05 time for calcul the mask position with numpy : 0.0060498714447021484 nb_pixel_total : 184 time to create 1 rle with old method : 0.0002384185791015625 time for calcul the mask position with numpy : 0.0062808990478515625 nb_pixel_total : 58 time to create 1 rle with old method : 0.00011587142944335938 time for calcul the mask position with numpy : 0.006539821624755859 nb_pixel_total : 137 time to create 1 rle with old method : 0.00022792816162109375 time for calcul the mask position with numpy : 0.006027936935424805 nb_pixel_total : 1741 time to create 1 rle with old method : 0.002040863037109375 time for calcul the mask position with numpy : 0.006091594696044922 nb_pixel_total : 454 time to create 1 rle with old method : 0.0005502700805664062 time for calcul the mask position with numpy : 0.006446361541748047 nb_pixel_total : 457 time to create 1 rle with old method : 0.0005567073822021484 time for calcul the mask position with numpy : 0.006141185760498047 nb_pixel_total : 3199 time to create 1 rle with old method : 0.0035886764526367188 time for calcul the mask position with numpy : 0.006544828414916992 nb_pixel_total : 4654 time to create 1 rle with old method : 0.007739067077636719 time for calcul the mask position with numpy : 0.006781101226806641 nb_pixel_total : 811 time to create 1 rle with old method : 0.001314401626586914 time for calcul the mask position with numpy : 0.006508350372314453 nb_pixel_total : 438 time to create 1 rle with old method : 0.0007257461547851562 time for calcul the mask position with numpy : 0.006589651107788086 nb_pixel_total : 716 time to create 1 rle with old method : 0.0011951923370361328 time for calcul the mask position with numpy : 0.006543397903442383 nb_pixel_total : 1627 time to create 1 rle with old method : 0.0026674270629882812 time for calcul the mask position with numpy : 0.006480693817138672 nb_pixel_total : 20 time to create 1 rle with old method : 8.177757263183594e-05 time for calcul the mask position with numpy : 0.006536006927490234 nb_pixel_total : 36 time to create 1 rle with old method : 0.00012373924255371094 time for calcul the mask position with numpy : 0.006596803665161133 nb_pixel_total : 967 time to create 1 rle with old method : 0.0016455650329589844 time for calcul the mask position with numpy : 0.006604909896850586 nb_pixel_total : 553 time to create 1 rle with old method : 0.0009748935699462891 time for calcul the mask position with numpy : 0.00650334358215332 nb_pixel_total : 226 time to create 1 rle with old method : 0.0004124641418457031 time for calcul the mask position with numpy : 0.0064868927001953125 nb_pixel_total : 285 time to create 1 rle with old method : 0.0005109310150146484 time for calcul the mask position with numpy : 0.0063855648040771484 nb_pixel_total : 612 time to create 1 rle with old method : 0.0007443428039550781 time for calcul the mask position with numpy : 0.0059909820556640625 nb_pixel_total : 801 time to create 1 rle with old method : 0.0008938312530517578 time for calcul the mask position with numpy : 0.006037712097167969 nb_pixel_total : 992 time to create 1 rle with old method : 0.0011775493621826172 time for calcul the mask position with numpy : 0.006087779998779297 nb_pixel_total : 124 time to create 1 rle with old method : 0.00015807151794433594 time for calcul the mask position with numpy : 0.0061185359954833984 nb_pixel_total : 387 time to create 1 rle with old method : 0.00047206878662109375 time for calcul the mask position with numpy : 0.005881786346435547 nb_pixel_total : 3906 time to create 1 rle with old method : 0.0044367313385009766 time for calcul the mask position with numpy : 0.00600886344909668 nb_pixel_total : 220 time to create 1 rle with old method : 0.0002510547637939453 time for calcul the mask position with numpy : 0.0059814453125 nb_pixel_total : 148 time to create 1 rle with old method : 0.0001811981201171875 time for calcul the mask position with numpy : 0.006032705307006836 nb_pixel_total : 1201 time to create 1 rle with old method : 0.0013289451599121094 time for calcul the mask position with numpy : 0.005917549133300781 nb_pixel_total : 46 time to create 1 rle with old method : 8.940696716308594e-05 time for calcul the mask position with numpy : 0.0058858394622802734 nb_pixel_total : 1114 time to create 1 rle with old method : 0.0013146400451660156 time for calcul the mask position with numpy : 0.0059130191802978516 nb_pixel_total : 1287 time to create 1 rle with old method : 0.0015177726745605469 time for calcul the mask position with numpy : 0.005870342254638672 nb_pixel_total : 619 time to create 1 rle with old method : 0.0007059574127197266 time for calcul the mask position with numpy : 0.0058820247650146484 nb_pixel_total : 305 time to create 1 rle with old method : 0.0003781318664550781 time for calcul the mask position with numpy : 0.00583195686340332 nb_pixel_total : 503 time to create 1 rle with old method : 0.0005774497985839844 time for calcul the mask position with numpy : 0.005910634994506836 nb_pixel_total : 184 time to create 1 rle with old method : 0.00022268295288085938 time for calcul the mask position with numpy : 0.006029844284057617 nb_pixel_total : 522 time to create 1 rle with old method : 0.0006160736083984375 time for calcul the mask position with numpy : 0.006065845489501953 nb_pixel_total : 1224 time to create 1 rle with old method : 0.0014548301696777344 time for calcul the mask position with numpy : 0.005972385406494141 nb_pixel_total : 44 time to create 1 rle with old method : 8.487701416015625e-05 time for calcul the mask position with numpy : 0.006129026412963867 nb_pixel_total : 342 time to create 1 rle with old method : 0.0004184246063232422 time for calcul the mask position with numpy : 0.005856752395629883 nb_pixel_total : 7 time to create 1 rle with old method : 3.504753112792969e-05 time for calcul the mask position with numpy : 0.006058692932128906 nb_pixel_total : 629 time to create 1 rle with old method : 0.0007317066192626953 time for calcul the mask position with numpy : 0.006023406982421875 nb_pixel_total : 158 time to create 1 rle with old method : 0.00019979476928710938 create new chi : 1.2091662883758545 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 : 0.002489328384399414 batch 1 Loaded 142 chid ids of type : 4230 Number RLEs to save : 12545 TO DO : save crop sub photo not yet done ! save time : 0.7400968074798584 map_output_result : {1332937882: (0.0, 'Should be the crop_list due to order', 0.0), 1332937879: (0.0, 'Should be the crop_list due to order', 0.0), 1332937852: (0.0, 'Should be the crop_list due to order', 0.0), 1332937850: (0.0, 'Should be the crop_list due to order', 0.0), 1332937845: (0.0, 'Should be the crop_list due to order', 0.0), 1332937839: (0.0, 'Should be the crop_list due to order', 0.0), 1332937831: (0.0, 'Should be the crop_list due to order', 0.0), 1332937826: (0.0, 'Should be the crop_list due to order', 0.0), 1332937795: (0.0, 'Should be the crop_list due to order', 0.0), 1332937790: (0.0, 'Should be the crop_list due to order', 0.0), 1332937783: (0.0, 'Should be the crop_list due to order', 0.0), 1332937779: (0.0, 'Should be the crop_list due to order', 0.0), 1332937775: (0.0, 'Should be the crop_list due to order', 0.0), 1332937771: (0.0, 'Should be the crop_list due to order', 0.0), 1332937745: (0.0, 'Should be the crop_list due to order', 0.0), 1332937740: (0.0, 'Should be the crop_list due to order', 0.0), 1332937735: (0.0, 'Should be the crop_list due to order', 0.0), 1332937728: (0.0, 'Should be the crop_list due to order', 0.0), 1332937720: (0.0, 'Should be the crop_list due to order', 0.0), 1332937713: (0.0, 'Should be the crop_list due to order', 0.0), 1332937694: (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 [1332937882, 1332937879, 1332937852, 1332937850, 1332937845, 1332937839, 1332937831, 1332937826, 1332937795, 1332937790, 1332937783, 1332937779, 1332937775, 1332937771, 1332937745, 1332937740, 1332937735, 1332937728, 1332937720, 1332937713, 1332937694] Looping around the photos to save general results len do output : 21 /1332937882.Didn't retrieve data . /1332937879.Didn't retrieve data . /1332937852.Didn't retrieve data . /1332937850.Didn't retrieve data . /1332937845.Didn't retrieve data . /1332937839.Didn't retrieve data . /1332937831.Didn't retrieve data . /1332937826.Didn't retrieve data . /1332937795.Didn't retrieve data . /1332937790.Didn't retrieve data . /1332937783.Didn't retrieve data . /1332937779.Didn't retrieve data . /1332937775.Didn't retrieve data . /1332937771.Didn't retrieve data . /1332937745.Didn't retrieve data . /1332937740.Didn't retrieve data . /1332937735.Didn't retrieve data . /1332937728.Didn't retrieve data . /1332937720.Didn't retrieve data . /1332937713.Didn't retrieve data . /1332937694.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, '2529450') ('4234', None, '1332937882', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937879', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937852', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937850', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937845', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937839', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937831', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937826', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937795', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937790', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937783', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937779', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937775', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937771', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937745', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937740', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937735', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937728', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937720', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937713', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937694', None, None, None, None, None, '2529450') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 63 time used for this insertion : 0.020995378494262695 save_final save missing photos in datou_result : time spend for datou_step_exec : 89.58694052696228 time spend to save output : 0.021743059158325195 total time spend for step 4 : 89.6086835861206 step5:crop_condition Wed Feb 12 08:54:24 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 3146 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 ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! 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 : 1153 About to insert : list_path_to_insert length 1153 new photo from crops ! About to upload 1153 photos upload in portfolio : 4869462 init cache_photo without model_param we have 1153 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739346881_2481130 we have uploaded 1153 photos in the portfolio 4869462 time of upload the photos Elapsed time : 252.06032991409302 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 ! Next one ! 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 : 350 About to insert : list_path_to_insert length 350 new photo from crops ! About to upload 350 photos upload in portfolio : 4869462 init cache_photo without model_param we have 350 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739347139_2481130 we have uploaded 350 photos in the portfolio 4869462 time of upload the photos Elapsed time : 91.71255564689636 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 ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! 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 : 25 About to insert : list_path_to_insert length 25 new photo from crops ! About to upload 25 photos upload in portfolio : 4869462 init cache_photo without model_param we have 25 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739347232_2481130 we have uploaded 25 photos in the portfolio 4869462 time of upload the photos Elapsed time : 8.809885501861572 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 ! map_result returned by crop_photo_return_map_crop : length : 188 About to insert : list_path_to_insert length 188 new photo from crops ! About to upload 188 photos upload in portfolio : 4869462 init cache_photo without model_param we have 188 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739347244_2481130 we have uploaded 188 photos in the portfolio 4869462 time of upload the photos Elapsed time : 40.95667886734009 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 ! map_result returned by crop_photo_return_map_crop : length : 381 About to insert : list_path_to_insert length 381 new photo from crops ! About to upload 381 photos upload in portfolio : 4869462 init cache_photo without model_param we have 381 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739347292_2481130 we have uploaded 381 photos in the portfolio 4869462 time of upload the photos Elapsed time : 90.79479050636292 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 ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! Next one ! 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 : 23 About to insert : list_path_to_insert length 23 new photo from crops ! About to upload 23 photos upload in portfolio : 4869462 init cache_photo without model_param we have 23 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1739347384_2481130 we have uploaded 23 photos in the portfolio 4869462 time of upload the photos Elapsed time : 4.605098724365234 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 delete rles for these photos Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1332937882, 1332937879, 1332937852, 1332937850, 1332937845, 1332937839, 1332937831, 1332937826, 1332937795, 1332937790, 1332937783, 1332937779, 1332937775, 1332937771, 1332937745, 1332937740, 1332937735, 1332937728, 1332937720, 1332937713, 1332937694] Looping around the photos to save general results len do output : 2120 /1337042005Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042007Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042015Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042031Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042040Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1337042043Didn't retrieve data 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('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937879', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937852', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937850', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937845', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937839', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937831', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937826', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937795', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937790', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937783', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937779', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937775', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937771', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937745', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937740', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937735', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937728', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937720', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937713', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937694', None, None, None, None, None, '2529450') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6381 time used for this insertion : 0.30359530448913574 save_final save missing photos in datou_result : time spend for datou_step_exec : 526.9058783054352 time spend to save output : 0.4682753086090088 total time spend for step 5 : 527.3741536140442 step6:thcl Wed Feb 12 09:03:11 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 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 : 0.1491851806640625 time to convert the images to numpy array : 0.6770124435424805 time to import caffe and check if the image exist : 0.13487625122070312 time to convert the images to numpy array : 0.6978278160095215 time to import caffe and check if the image exist : 0.14788484573364258 time to convert the images to numpy array : 0.6915187835693359 time to import caffe and check if the image exist : 0.14538097381591797 time to convert the images to numpy array : 0.6981673240661621 time to import caffe and check if the image exist : 0.16286349296569824 time to convert the images to numpy array : 0.6864800453186035 time to import caffe and check if the image exist : 0.15711188316345215 time to convert the images to numpy array : 0.6941249370574951 time to import caffe and check if the image exist : 0.16062521934509277 time to convert the images to numpy array : 0.6988234519958496 time to import caffe and check if the image exist : 0.14018869400024414 time to convert the images to numpy array : 0.7220418453216553 time to import caffe and check if the image exist : 0.14635205268859863 time to convert the images to numpy array : 0.7170627117156982 time to import caffe and check if the image exist : 0.17796564102172852 time to convert the images to numpy array : 0.6853542327880859 total time to convert the images to numpy array : 1.2095131874084473 list photo_ids error: [] list photo_ids correct : [1337042005, 1337042006, 1337042007, 1337042008, 1337042009, 1337042010, 1337042011, 1337042012, 1337042013, 1337042014, 1337042015, 1337042016, 1337042017, 1337042018, 1337042019, 1337042020, 1337042021, 1337042022, 1337042023, 1337042024, 1337042025, 1337042026, 1337042027, 1337042028, 1337042029, 1337042030, 1337042031, 1337042032, 1337042033, 1337042034, 1337042035, 1337042036, 1337042037, 1337042038, 1337042039, 1337042040, 1337042041, 1337042042, 1337042043, 1337042044, 1337042045, 1337042046, 1337042047, 1337042048, 1337042049, 1337042050, 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1337044762, 1337044763, 1337044764, 1337044765, 1337044767, 1337044768, 1337044769, 1337044770, 1337044771, 1337044772, 1337044773, 1337044774, 1337044775, 1337044776, 1337044777, 1337044778, 1337044779, 1337044780, 1337044781, 1337044782, 1337044783, 1337044784, 1337044785, 1337044786, 1337044787, 1337044788, 1337044789, 1337043966, 1337043967, 1337043968, 1337043969, 1337043970, 1337043971, 1337043972, 1337043973, 1337043974, 1337043975, 1337043976, 1337043977, 1337043978, 1337043979, 1337043980, 1337043982, 1337043983, 1337043984, 1337043985, 1337043991, 1337043992, 1337043993, 1337043997, 1337043998, 1337043999, 1337044000, 1337044001, 1337044002, 1337044003, 1337044004, 1337044005, 1337044006, 1337044007, 1337044008, 1337044009, 1337044010, 1337044011, 1337044012, 1337044013, 1337044014, 1337044015, 1337044016, 1337044017, 1337044018, 1337044083, 1337044084, 1337044085, 1337044087, 1337044088, 1337044089, 1337044090, 1337044091, 1337044092, 1337044093, 1337044094, 1337044095, 1337044096, 1337044097, 1337044098, 1337044099, 1337044100, 1337044101, 1337044102, 1337044103, 1337044104, 1337044105, 1337044106, 1337044107, 1337044108, 1337044109, 1337044110, 1337044111, 1337044112, 1337044113, 1337044114, 1337044115, 1337044116, 1337044117, 1337044119, 1337044120, 1337044121, 1337044122, 1337044123, 1337044124, 1337044125, 1337044126, 1337044127, 1337044128, 1337044129, 1337044130, 1337044131, 1337044132, 1337044133, 1337044134, 1337044135, 1337044136, 1337044137, 1337044138, 1337044139, 1337044140, 1337044141, 1337044142, 1337044143, 1337044144, 1337044145, 1337044146, 1337044147, 1337044148, 1337044149, 1337044150, 1337044151, 1337044152, 1337044153, 1337044154, 1337044155, 1337044156, 1337044157, 1337044158, 1337044159, 1337044160, 1337044161, 1337044162, 1337044163, 1337044164, 1337044165, 1337044166, 1337044167, 1337044168, 1337044169, 1337044170, 1337044171, 1337044172, 1337044173, 1337044174, 1337044175, 1337044176, 1337044177, 1337044178, 1337044180, 1337044181, 1337044182, 1337044183, 1337044184, 1337044185, 1337044186, 1337044187, 1337044188, 1337044189, 1337044190, 1337044191, 1337044192, 1337044193, 1337044194, 1337044195, 1337044196, 1337044197, 1337044198, 1337044199, 1337044200, 1337044201, 1337044202, 1337044203, 1337044204, 1337044205, 1337044206, 1337044207, 1337044208, 1337044209, 1337044210, 1337044211, 1337044212, 1337044213, 1337044214, 1337044215, 1337044216, 1337044217, 1337044218, 1337044219, 1337044220, 1337044221, 1337044222, 1337044223, 1337044225, 1337044226, 1337044227, 1337044228, 1337044229, 1337044230, 1337044231, 1337044232, 1337044233, 1337044234, 1337044235, 1337044236, 1337044237, 1337044238, 1337044239, 1337044240, 1337044241, 1337044242, 1337044243, 1337044244, 1337044245, 1337044246, 1337044247, 1337044248, 1337044249, 1337044250, 1337044251, 1337044252, 1337044253, 1337044254, 1337042866, 1337042867, 1337042868, 1337042869, 1337042870, 1337042871, 1337042872, 1337042873, 1337042874, 1337042875, 1337042876, 1337042877, 1337042878, 1337042879, 1337042880, 1337042881, 1337042882, 1337042883, 1337042884, 1337042885, 1337042886, 1337042890, 1337042893, 1337042897, 1337042901, 1337042905, 1337042909, 1337042913, 1337042917, 1337042921, 1337042925, 1337042929, 1337042935, 1337042939, 1337042942, 1337042946, 1337042950, 1337042954, 1337042958, 1337042962, 1337042966, 1337042970, 1337042974, 1337042978, 1337042982, 1337042986, 1337042990, 1337042994, 1337042998, 1337043002, 1337043007, 1337043010, 1337043014, 1337043018, 1337043022, 1337043026, 1337043030, 1337043034, 1337043038, 1337043042, 1337043046, 1337043050, 1337043054, 1337043059, 1337043063, 1337043067, 1337043071, 1337043075, 1337043079, 1337043083, 1337043087, 1337043091, 1337043095, 1337043099, 1337043103, 1337043107, 1337043111, 1337043115, 1337043119, 1337043123, 1337043128, 1337043132, 1337043136, 1337043140, 1337043144, 1337043148, 1337043151, 1337043155, 1337043159, 1337043163, 1337043167, 1337043171, 1337043175, 1337043178, 1337043182, 1337043186, 1337043190, 1337043194, 1337043198, 1337043202, 1337043206, 1337043210, 1337043214, 1337043218, 1337043222, 1337043226, 1337043230, 1337043234, 1337043238, 1337043242, 1337043246, 1337043250, 1337043254, 1337043258, 1337043263, 1337043266, 1337043269, 1337043270, 1337043271, 1337043272, 1337043273, 1337043274, 1337043275, 1337043276, 1337043277, 1337043278, 1337043279, 1337043280, 1337043281, 1337043282, 1337043283, 1337043284, 1337043285, 1337043286, 1337043287, 1337043288, 1337043289, 1337043290, 1337043292, 1337043293, 1337043294, 1337043295, 1337043296, 1337043297, 1337043298, 1337043299, 1337043301, 1337043302, 1337043303, 1337043304, 1337043305, 1337043306, 1337043307, 1337043308, 1337043309, 1337043310, 1337043311, 1337043312, 1337043313, 1337043314, 1337043315, 1337043316, 1337043317, 1337043318, 1337043319, 1337043320, 1337043321, 1337043322, 1337043323, 1337043324, 1337043325, 1337043326, 1337043327, 1337043328, 1337043329, 1337043330, 1337043331, 1337043332, 1337043333, 1337043334, 1337043335, 1337043336, 1337043337, 1337043338, 1337043339, 1337043340, 1337043341, 1337043342, 1337043343, 1337043344, 1337043345, 1337043346, 1337043347, 1337043348, 1337043349, 1337043350, 1337043351, 1337043352, 1337043353, 1337043354, 1337043355, 1337043356, 1337043357, 1337043358, 1337043359, 1337043360, 1337043361, 1337043362, 1337043363, 1337043364, 1337043365, 1337043366, 1337043744, 1337043745, 1337043746, 1337043747, 1337043748, 1337043749, 1337043750, 1337043751, 1337043752, 1337043753, 1337043754, 1337043755, 1337043756, 1337043757, 1337043758, 1337043759, 1337043760, 1337043761, 1337043762, 1337043763, 1337043764, 1337043765, 1337043766, 1337043767, 1337043768, 1337043769, 1337043770, 1337043771, 1337043772, 1337043773, 1337043774, 1337043775, 1337043776, 1337043777, 1337043778, 1337043779, 1337043780, 1337043781, 1337043782, 1337043783, 1337043784, 1337043785, 1337043786, 1337043787, 1337043789, 1337043790, 1337043791, 1337043793, 1337043794, 1337043795, 1337043796, 1337043797, 1337043798, 1337043799, 1337043800, 1337043801, 1337043802, 1337043805, 1337043807, 1337043808, 1337043809, 1337043810, 1337043811, 1337043812, 1337043813, 1337043814, 1337043815, 1337043816, 1337043817, 1337043818, 1337043819, 1337043820, 1337043821, 1337043822, 1337043823, 1337043824, 1337043825, 1337043826, 1337043827, 1337043828, 1337043829, 1337043830, 1337043831, 1337043832, 1337043833, 1337043834, 1337043835, 1337043836, 1337043837, 1337043838, 1337043839, 1337043840, 1337043841, 1337043842, 1337043843, 1337043844, 1337043845, 1337043846, 1337043847, 1337043848, 1337043849, 1337043850, 1337043851, 1337043852, 1337043853, 1337043854, 1337043855, 1337043856, 1337043857, 1337043858, 1337043860, 1337043861, 1337043862, 1337043863, 1337043864, 1337043865, 1337043866, 1337043867, 1337043868, 1337043869, 1337043870, 1337043871, 1337043872, 1337043873, 1337043874, 1337043875, 1337043876, 1337043877, 1337043878, 1337043879, 1337043880, 1337043882, 1337043883, 1337043884, 1337043885, 1337043886, 1337043887, 1337043888, 1337043889, 1337043890, 1337043891, 1337043892, 1337043894, 1337043895, 1337043896, 1337043897, 1337043898, 1337043899, 1337043900, 1337043901, 1337043902, 1337043903, 1337043905, 1337043906, 1337043907, 1337043908, 1337043909, 1337043910, 1337043911, 1337043912, 1337043913, 1337043914, 1337043915, 1337043916, 1337043917, 1337043918, 1337043919, 1337043920, 1337043921, 1337043922, 1337043923, 1337043924, 1337043925, 1337043926, 1337043927, 1337043928, 1337043929, 1337043930, 1337043931, 1337043932, 1337043933, 1337043934, 1337043935, 1337043936, 1337043937, 1337043938, 1337043939, 1337043940, 1337043941, 1337043942, 1337043943, 1337043944, 1337043945, 1337043946, 1337043947, 1337043948, 1337043949, 1337043950, 1337043951, 1337043952, 1337043954, 1337043955, 1337043956, 1337043957, 1337043958, 1337043959, 1337043960, 1337043961, 1337043962, 1337043963, 1337043964, 1337043965] number of photos to traite : 2120 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 : 10774 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 : 10555 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 10.36319375038147 time used to do the prediction : 6.9136879444122314 save descriptor for thcl : 3237 time to traite the descriptors : 12.82267427444458 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 3 To insert : 1337042005 To insert : 1337042006 To insert : 1337042007 To insert : 1337042008 To insert : 1337042009 To insert : 1337042010 To insert : 1337042011 To insert : 1337042012 To insert : 1337042013 To insert : 1337042014 To insert : 1337042015 To insert : 1337042016 To insert : 1337042017 To insert : 1337042018 To insert : 1337042019 To insert : 1337042020 To insert : 1337042021 To insert : 1337042022 To insert : 1337042023 To insert : 1337042024 To insert : 1337042025 To insert : 1337042026 To insert : 1337042027 To insert : 1337042028 To insert : 1337042029 To insert : 1337042030 To insert : 1337042031 To insert : 1337042032 To insert : 1337042033 To insert : 1337042034 To insert : 1337042035 To insert : 1337042036 To insert : 1337042037 To insert : 1337042038 To insert : 1337042039 To insert : 1337042040 To insert : 1337042041 To insert : 1337042042 To insert : 1337042043 To insert : 1337042044 To insert : 1337042045 To insert : 1337042046 To insert : 1337042047 To insert : 1337042048 To insert : 1337042049 To insert : 1337042050 To insert : 1337042051 To insert : 1337042052 To insert : 1337042053 To insert : 1337042054 To insert : 1337042055 To insert : 1337042056 To insert : 1337042058 To insert : 1337042059 To insert : 1337042060 To insert : 1337042061 To insert : 1337042062 To insert : 1337042063 To insert : 1337042064 To insert : 1337042065 To insert : 1337042066 To insert : 1337042067 To insert : 1337042068 To insert : 1337042069 To insert : 1337042070 To insert : 1337042071 To insert : 1337042072 To insert : 1337042073 To insert : 1337042074 To insert : 1337042075 To insert : 1337042076 To insert : 1337042077 To insert : 1337042078 To insert : 1337042079 To insert : 1337042080 To insert : 1337042081 To insert : 1337042082 To insert : 1337042083 To insert : 1337042084 To insert : 1337042085 To insert : 1337042086 To insert : 1337042087 To insert : 1337042088 To insert : 1337042089 To insert : 1337042090 To insert : 1337042091 To insert : 1337042092 To insert : 1337042093 To insert : 1337042094 To insert : 1337042095 To insert : 1337042096 To insert : 1337042097 To insert : 1337042098 To insert : 1337042099 To insert : 1337042100 To insert : 1337042101 To insert : 1337042102 To insert : 1337042103 To insert : 1337042104 To insert : 1337042105 To insert : 1337042106 To insert : 1337042107 To insert : 1337042108 To insert : 1337042109 To insert : 1337042110 To insert : 1337042111 To insert : 1337042112 To insert : 1337042113 To insert : 1337042114 To insert : 1337042115 To insert : 1337042116 To insert : 1337042117 To insert : 1337042118 To insert : 1337042119 To insert : 1337042120 To insert : 1337042121 To insert : 1337042122 To insert : 1337042123 To insert : 1337042124 To insert : 1337042125 To insert : 1337042126 To insert : 1337042127 To insert : 1337042128 To insert : 1337042129 To insert : 1337042130 To insert : 1337042131 To insert : 1337042132 To insert : 1337042133 To insert : 1337042134 To insert : 1337042135 To insert : 1337042136 To insert : 1337042137 To insert : 1337042138 To insert : 1337042139 To insert : 1337042140 To insert : 1337042141 To insert : 1337042142 To insert : 1337042144 To insert : 1337042145 To insert : 1337042146 To insert : 1337042147 To insert : 1337042148 To insert : 1337042149 To insert : 1337042150 To insert : 1337042151 To insert : 1337042152 To insert : 1337042153 To insert : 1337042154 To insert : 1337042155 To insert : 1337042156 To insert : 1337042157 To insert : 1337042158 To insert : 1337042159 To insert : 1337042160 To insert : 1337042161 To insert : 1337042162 To insert : 1337042163 To insert : 1337042164 To insert : 1337042165 To insert : 1337042166 To insert : 1337042167 To insert : 1337042168 To insert : 1337042169 To insert : 1337042170 To insert : 1337042171 To insert : 1337042173 To insert : 1337042174 To insert : 1337042175 To insert : 1337042176 To insert : 1337042177 To insert : 1337042178 To insert : 1337042179 To insert : 1337042180 To insert : 1337042181 To insert : 1337042182 To insert : 1337042183 To insert : 1337042184 To insert : 1337042185 To insert : 1337042186 To insert : 1337042187 To insert : 1337042188 To insert : 1337042189 To insert : 1337042190 To insert : 1337042191 To insert : 1337042192 To insert : 1337042193 To insert : 1337042194 To insert : 1337042195 To insert : 1337042196 To insert : 1337042197 To insert : 1337042198 To insert : 1337042199 To insert : 1337042200 To insert : 1337042201 To insert : 1337042202 To insert : 1337042203 To insert : 1337042204 To insert : 1337042205 To insert : 1337042206 To insert : 1337042207 To insert : 1337042208 To insert : 1337042209 To insert : 1337042210 To insert : 1337042211 To insert : 1337042212 To insert : 1337042213 To insert : 1337042214 To insert : 1337042215 To insert : 1337042216 To insert : 1337042217 To insert : 1337042218 To insert : 1337042219 To insert : 1337043367 To insert : 1337043368 To insert : 1337043369 To insert : 1337043370 To insert : 1337043371 To insert : 1337043372 To insert : 1337043373 To insert : 1337043374 To insert : 1337043375 To insert : 1337043376 To insert : 1337043377 To insert : 1337043378 To insert : 1337043379 To insert : 1337043380 To insert : 1337043381 To insert : 1337043382 To insert : 1337043383 To insert : 1337043384 To insert : 1337043385 To insert : 1337043386 To insert : 1337043387 To insert : 1337043388 To insert : 1337043389 To insert : 1337043391 To insert : 1337043392 To insert : 1337043394 To insert : 1337043395 To insert : 1337043396 To insert : 1337043397 To insert : 1337043398 To insert : 1337043399 To insert : 1337043400 To insert : 1337043401 To insert : 1337043402 To insert : 1337043403 To insert : 1337043404 To insert : 1337043405 To insert : 1337043406 To insert : 1337043407 To insert : 1337043408 To insert : 1337043409 To insert : 1337043410 To insert : 1337043411 To insert : 1337043412 To insert : 1337043413 To insert : 1337043414 To insert : 1337043415 To insert : 1337043416 To insert : 1337043417 To insert : 1337043418 To insert : 1337043419 To insert : 1337043420 To insert : 1337043421 To insert : 1337043422 To insert : 1337043423 To insert : 1337043424 To insert : 1337043425 To insert : 1337043427 To insert : 1337043429 To insert : 1337043431 To insert : 1337043433 To insert : 1337043435 To insert : 1337043436 To insert : 1337043437 To insert : 1337043438 To insert : 1337043439 To insert : 1337043440 To insert : 1337043441 To insert : 1337043442 To insert : 1337043443 To insert : 1337043444 To insert : 1337043445 To insert : 1337043446 To insert : 1337043447 To insert : 1337043448 To insert : 1337043449 To insert : 1337043450 To insert : 1337043451 To insert : 1337043452 To insert : 1337043453 To insert : 1337043454 To insert : 1337043455 To insert : 1337043456 To insert : 1337043457 To insert : 1337043458 To insert : 1337043459 To insert : 1337043460 To insert : 1337043461 To insert : 1337043462 To insert : 1337043463 To insert : 1337043464 To insert : 1337043465 To insert : 1337043466 To insert : 1337043604 To insert : 1337043605 To insert : 1337043606 To insert : 1337043607 To insert : 1337043608 To insert : 1337043609 To insert : 1337043610 To insert : 1337043611 To insert : 1337043612 To insert : 1337043613 To insert : 1337043614 To insert : 1337043615 To insert : 1337043616 To insert : 1337043617 To insert : 1337043618 To insert : 1337043619 To insert : 1337043620 To insert : 1337043621 To insert : 1337043622 To insert : 1337043623 To insert : 1337043624 To insert : 1337043625 To insert : 1337043626 To insert : 1337043627 To insert : 1337043628 To insert : 1337043629 To insert : 1337043630 To insert : 1337043631 To insert : 1337043632 To insert : 1337043633 To insert : 1337043634 To insert : 1337043635 To insert : 1337043636 To insert : 1337043637 To insert : 1337043638 To insert : 1337043639 To insert : 1337043640 To insert : 1337043641 To insert : 1337043642 To insert : 1337043643 To insert : 1337043644 To insert : 1337043645 To insert : 1337043646 To insert : 1337043647 To insert : 1337043648 To insert : 1337043649 To insert : 1337043650 To insert : 1337043651 To insert : 1337043652 To insert : 1337043653 To insert : 1337043654 To insert : 1337043655 To insert : 1337043656 To insert : 1337043657 To insert : 1337043658 To insert : 1337043659 To insert : 1337043660 To insert : 1337043661 To insert : 1337043662 To insert : 1337043663 To insert : 1337043664 To insert : 1337043665 To insert : 1337043666 To insert : 1337043667 To insert : 1337043668 To insert : 1337043669 To insert : 1337043670 To insert : 1337043673 To insert : 1337043674 To insert : 1337043675 To insert : 1337043676 To insert : 1337043679 To insert : 1337043680 To insert : 1337043682 To insert : 1337043683 To insert : 1337043684 To insert : 1337043685 To insert : 1337043686 To insert : 1337043687 To insert : 1337043688 To insert : 1337043689 To insert : 1337043690 To insert : 1337043691 To insert : 1337043692 To insert : 1337043693 To insert : 1337043694 To insert : 1337043695 To insert : 1337043697 To insert : 1337043698 To insert : 1337043699 To insert : 1337043700 To insert : 1337043701 To insert : 1337043702 To insert : 1337043703 To insert : 1337043704 To insert : 1337043705 To insert : 1337043706 To insert : 1337043707 To insert : 1337043708 To insert : 1337043709 To insert : 1337043710 To insert : 1337043711 To insert : 1337043715 To insert : 1337043716 To insert : 1337043717 To insert : 1337043718 To insert : 1337043719 To insert : 1337043720 To insert : 1337043721 To insert : 1337043723 To insert : 1337043726 To insert : 1337043728 To insert : 1337043730 To insert : 1337043732 To insert : 1337043734 To insert : 1337043736 To insert : 1337043738 To insert : 1337043740 To insert : 1337043742 To insert : 1337042650 To insert : 1337042651 To insert : 1337042652 To insert : 1337042653 To insert : 1337042654 To insert : 1337042655 To insert : 1337042656 To insert : 1337042657 To insert : 1337042658 To insert : 1337042659 To insert : 1337042660 To insert : 1337042661 To insert : 1337042662 To insert : 1337042663 To insert : 1337042664 To insert : 1337042665 To insert : 1337042666 To insert : 1337042667 To insert : 1337042668 To insert : 1337042669 To insert : 1337042670 To insert : 1337042671 To insert : 1337042672 To insert : 1337042673 To insert : 1337042674 To insert : 1337042675 To insert : 1337042676 To insert : 1337042677 To insert : 1337042678 To insert : 1337042679 To insert : 1337042680 To insert : 1337042681 To insert : 1337042682 To insert : 1337042683 To insert : 1337042684 To insert : 1337042685 To insert : 1337042686 To insert : 1337042687 To insert : 1337042688 To insert : 1337042689 To insert : 1337042690 To insert : 1337042691 To insert : 1337042692 To insert : 1337042693 To insert : 1337042694 To insert : 1337042695 To insert : 1337042696 To insert : 1337042697 To insert : 1337042698 To insert : 1337042699 To insert : 1337042700 To insert : 1337042701 To insert : 1337042702 To insert : 1337042703 To insert : 1337042704 To insert : 1337042705 To insert : 1337042706 To insert : 1337042707 To insert : 1337042709 To insert : 1337042710 To insert : 1337042711 To insert : 1337042712 To insert : 1337042713 To insert : 1337042714 To insert : 1337042715 To insert : 1337042716 To insert : 1337042717 To insert : 1337042718 To insert : 1337042719 To insert : 1337042720 To insert : 1337042721 To insert : 1337042722 To insert : 1337042723 To insert : 1337042724 To insert : 1337042725 To insert : 1337042726 To insert : 1337042727 To insert : 1337042728 To insert : 1337042729 To insert : 1337042730 To insert : 1337042731 To insert : 1337042732 To insert : 1337042733 To insert : 1337042734 To insert : 1337042735 To insert : 1337042736 To insert : 1337042737 To insert : 1337042738 To insert : 1337042739 To insert : 1337042740 To insert : 1337042741 To insert : 1337042742 To insert : 1337042743 To insert : 1337042744 To insert : 1337042745 To insert : 1337042746 To insert : 1337042747 To insert : 1337042748 To insert : 1337042749 To insert : 1337042750 To insert : 1337042751 To insert : 1337042752 To insert : 1337042753 To insert : 1337042754 To insert : 1337042755 To insert : 1337042756 To insert : 1337042757 To insert : 1337042758 To insert : 1337042759 To insert : 1337042760 To insert : 1337042761 To insert : 1337042762 To insert : 1337042763 To insert : 1337042764 To insert : 1337042765 To insert : 1337042766 To insert : 1337042767 To insert : 1337042768 To insert : 1337042769 To insert : 1337042770 To insert : 1337042771 To insert : 1337042772 To insert : 1337042773 To insert : 1337042774 To insert : 1337042775 To insert : 1337042776 To insert : 1337042777 To insert : 1337042778 To insert : 1337042779 To insert : 1337042780 To insert : 1337042781 To insert : 1337042782 To insert : 1337042783 To insert : 1337042784 To insert : 1337042785 To insert : 1337042786 To insert : 1337042787 To insert : 1337042788 To insert : 1337042789 To insert : 1337042790 To insert : 1337042791 To insert : 1337042792 To insert : 1337042793 To insert : 1337042794 To insert : 1337042795 To insert : 1337042796 To insert : 1337042797 To insert : 1337042798 To insert : 1337042799 To insert : 1337042800 To insert : 1337042801 To insert : 1337042802 To insert : 1337042803 To insert : 1337042804 To insert : 1337042805 To insert : 1337042806 To insert : 1337042807 To insert : 1337042809 To insert : 1337042810 To insert : 1337042811 To insert : 1337042812 To insert : 1337042814 To insert : 1337042815 To insert : 1337042816 To insert : 1337042817 To insert : 1337042818 To insert : 1337042819 To insert : 1337042820 To insert : 1337042821 To insert : 1337042822 To insert : 1337042823 To insert : 1337042824 To insert : 1337042825 To insert : 1337042826 To insert : 1337042827 To insert : 1337042828 To insert : 1337042829 To insert : 1337042830 To insert : 1337042831 To insert : 1337042832 To insert : 1337042833 To insert : 1337042834 To insert : 1337042835 To insert : 1337042836 To insert : 1337042837 To insert : 1337042838 To insert : 1337042839 To insert : 1337042840 To insert : 1337042841 To insert : 1337042842 To insert : 1337042843 To insert : 1337042844 To insert : 1337042845 To insert : 1337042847 To insert : 1337042848 To insert : 1337042849 To insert : 1337042850 To insert : 1337042851 To insert : 1337042852 To insert : 1337042853 To insert : 1337042854 To insert : 1337042855 To insert : 1337042856 To insert : 1337042857 To insert : 1337042858 To insert : 1337042859 To insert : 1337042860 To insert : 1337042861 To insert : 1337042862 To insert : 1337042863 To insert : 1337042864 To insert : 1337042865 To insert : 1337044255 To insert : 1337044256 To insert : 1337044257 To insert : 1337044258 To insert : 1337044259 To insert : 1337044260 To insert : 1337044261 To insert : 1337044262 To insert : 1337044263 To insert : 1337044264 To insert : 1337044265 To insert : 1337044267 To insert : 1337044268 To insert : 1337044269 To insert : 1337044270 To insert : 1337044271 To insert : 1337044272 To insert : 1337044273 To insert : 1337044274 To insert : 1337044275 To insert : 1337044376 To insert : 1337044377 To insert : 1337044378 To insert : 1337044379 To insert : 1337044380 To insert : 1337044381 To insert : 1337044382 To insert : 1337044383 To insert : 1337044384 To insert : 1337044385 To insert : 1337044386 To insert : 1337044387 To insert : 1337044388 To insert : 1337044389 To insert : 1337044390 To insert : 1337044391 To insert : 1337044392 To insert : 1337044393 To insert : 1337044394 To insert : 1337044395 To insert : 1337044398 To insert : 1337044400 To insert : 1337044401 To insert : 1337044402 To insert : 1337044403 To insert : 1337044404 To insert : 1337044405 To insert : 1337044406 To insert : 1337044407 To insert : 1337044408 To insert : 1337044409 To insert : 1337044410 To insert : 1337044411 To insert : 1337044412 To insert : 1337044413 To insert : 1337044414 To insert : 1337044415 To insert : 1337044416 To insert : 1337044417 To insert : 1337044418 To insert : 1337044419 To insert : 1337044420 To insert : 1337044421 To insert : 1337044422 To insert : 1337044423 To insert : 1337044424 To insert : 1337044425 To insert : 1337044426 To insert : 1337044427 To insert : 1337044428 To insert : 1337044429 To insert : 1337044430 To insert : 1337044431 To insert : 1337044432 To insert : 1337044433 To insert : 1337044434 To insert : 1337044435 To insert : 1337044436 To insert : 1337044437 To insert : 1337044438 To insert : 1337044439 To insert : 1337044440 To insert : 1337044441 To insert : 1337044442 To insert : 1337044443 To insert : 1337044444 To insert : 1337044445 To insert : 1337044446 To insert : 1337044447 To insert : 1337044448 To insert : 1337044449 To insert : 1337044450 To insert : 1337044451 To insert : 1337044452 To insert : 1337044453 To insert : 1337044454 To insert : 1337044455 To insert : 1337044456 To insert : 1337044457 To insert : 1337044458 To insert : 1337044459 To insert : 1337044461 To insert : 1337044462 To insert : 1337044463 To insert : 1337044464 To insert : 1337044465 To insert : 1337044466 To insert : 1337044467 To insert : 1337044468 To insert : 1337044469 To insert : 1337044470 To insert : 1337044471 To insert : 1337044472 To insert : 1337044473 To insert : 1337044475 To insert : 1337044476 To insert : 1337044477 To insert : 1337044478 To insert : 1337044479 To insert : 1337044480 To insert : 1337044481 To insert : 1337044482 To insert : 1337044483 To insert : 1337044484 To insert : 1337044485 To insert : 1337044486 To insert : 1337044487 To insert : 1337044488 To insert : 1337044489 To insert : 1337044490 To insert : 1337044491 To insert : 1337044492 To insert : 1337044493 To insert : 1337044494 To insert : 1337044495 To insert : 1337044496 To insert : 1337044497 To insert : 1337044498 To insert : 1337044499 To insert : 1337044500 To insert : 1337044501 To insert : 1337044502 To insert : 1337044503 To insert : 1337044504 To insert : 1337044505 To insert : 1337044506 To insert : 1337044507 To insert : 1337044508 To insert : 1337044509 To insert : 1337044510 To insert : 1337044511 To insert : 1337044512 To insert : 1337044513 To insert : 1337044514 To insert : 1337044515 To insert : 1337044516 To insert : 1337044518 To insert : 1337044519 To insert : 1337044520 To insert : 1337044521 To insert : 1337044522 To insert : 1337044523 To insert : 1337044524 To insert : 1337044525 To insert : 1337044526 To insert : 1337044527 To insert : 1337044528 To insert : 1337044529 To insert : 1337044530 To insert : 1337044531 To insert : 1337044532 To insert : 1337044533 To insert : 1337044534 To insert : 1337044535 To insert : 1337044536 To insert : 1337044537 To insert : 1337044538 To insert : 1337044539 To insert : 1337044540 To insert : 1337044542 To insert : 1337044543 To insert : 1337044544 To insert : 1337044545 To insert : 1337044546 To insert : 1337044547 To insert : 1337044548 To insert : 1337044549 To insert : 1337044550 To insert : 1337044551 To insert : 1337044552 To insert : 1337044553 To insert : 1337044554 To insert : 1337044555 To insert : 1337044556 To insert : 1337044557 To insert : 1337044558 To insert : 1337044559 To insert : 1337044560 To insert : 1337044561 To insert : 1337044562 To insert : 1337044563 To insert : 1337044564 To insert : 1337044565 To insert : 1337044566 To insert : 1337044567 To insert : 1337044568 To insert : 1337044569 To insert : 1337044570 To insert : 1337044571 To insert : 1337044572 To insert : 1337044574 To insert : 1337044575 To insert : 1337042220 To insert : 1337042221 To insert : 1337042222 To insert : 1337042223 To insert : 1337042224 To insert : 1337042225 To insert : 1337042226 To insert : 1337042227 To insert : 1337042228 To insert : 1337042229 To insert : 1337042230 To insert : 1337042231 To insert : 1337042232 To insert : 1337042233 To insert : 1337042234 To insert : 1337042235 To insert : 1337042236 To insert : 1337042237 To insert : 1337042238 To insert : 1337042239 To insert : 1337042240 To insert : 1337042241 To insert : 1337042242 To insert : 1337042243 To insert : 1337042244 To insert : 1337042245 To insert : 1337042246 To insert : 1337042247 To insert : 1337042248 To insert : 1337042249 To insert : 1337042250 To insert : 1337042251 To insert : 1337042252 To insert : 1337042253 To insert : 1337042254 To insert : 1337042255 To insert : 1337042256 To insert : 1337042257 To insert : 1337042258 To insert : 1337042259 To insert : 1337042260 To insert : 1337042261 To insert : 1337042262 To insert : 1337042263 To insert : 1337042264 To insert : 1337042265 To insert : 1337042266 To insert : 1337042267 To insert : 1337042269 To insert : 1337042270 To insert : 1337042271 To insert : 1337042272 To insert : 1337042273 To insert : 1337042274 To insert : 1337042275 To insert : 1337042276 To insert : 1337042277 To insert : 1337042278 To insert : 1337042279 To insert : 1337042280 To insert : 1337042281 To insert : 1337042282 To insert : 1337042283 To insert : 1337042284 To insert : 1337042285 To insert : 1337042286 To insert : 1337042287 To insert : 1337042288 To insert : 1337042289 To insert : 1337042290 To insert : 1337042291 To insert : 1337042292 To insert : 1337042293 To insert : 1337042294 To insert : 1337042295 To insert : 1337042296 To insert : 1337042297 To insert : 1337042298 To insert : 1337042299 To insert : 1337042300 To insert : 1337042301 To insert : 1337042302 To insert : 1337042303 To insert : 1337042304 To insert : 1337042305 To insert : 1337042306 To insert : 1337042307 To insert : 1337042308 To insert : 1337042309 To insert : 1337042310 To insert : 1337042311 To insert : 1337042312 To insert : 1337042313 To insert : 1337042314 To insert : 1337042315 To insert : 1337042316 To insert : 1337042317 To insert : 1337042318 To insert : 1337042319 To insert : 1337042320 To insert : 1337042321 To insert : 1337042322 To insert : 1337042323 To insert : 1337042324 To insert : 1337042325 To insert : 1337042326 To insert : 1337042327 To insert : 1337042328 To insert : 1337042329 To insert : 1337042330 To insert : 1337042331 To insert : 1337042332 To insert : 1337042333 To insert : 1337042334 To insert : 1337042335 To insert : 1337042336 To insert : 1337042337 To insert : 1337042338 To insert : 1337042339 To insert : 1337042340 To insert : 1337042341 To insert : 1337042342 To insert : 1337042343 To insert : 1337042344 To insert : 1337042345 To insert : 1337042346 To insert : 1337042347 To insert : 1337042348 To insert : 1337042349 To insert : 1337042350 To insert : 1337042351 To insert : 1337042352 To insert : 1337042353 To insert : 1337042354 To insert : 1337042356 To insert : 1337042357 To insert : 1337042358 To insert : 1337042359 To insert : 1337042360 To insert : 1337042361 To insert : 1337042362 To insert : 1337042363 To insert : 1337042364 To insert : 1337042365 To insert : 1337042366 To insert : 1337042367 To insert : 1337042368 To insert : 1337042369 To insert : 1337042370 To insert : 1337042371 To insert : 1337042372 To insert : 1337042373 To insert : 1337042374 To insert : 1337042375 To insert : 1337042376 To insert : 1337042377 To insert : 1337042378 To insert : 1337042379 To insert : 1337042380 To insert : 1337042381 To insert : 1337042382 To insert : 1337042383 To insert : 1337042384 To insert : 1337042385 To insert : 1337042386 To insert : 1337042387 To insert : 1337042388 To insert : 1337042389 To insert : 1337042390 To insert : 1337042391 To insert : 1337042392 To insert : 1337042393 To insert : 1337042394 To insert : 1337042395 To insert : 1337042396 To insert : 1337042397 To insert : 1337042398 To insert : 1337042399 To insert : 1337042400 To insert : 1337042401 To insert : 1337042402 To insert : 1337042403 To insert : 1337042404 To insert : 1337042405 To insert : 1337042406 To insert : 1337042407 To insert : 1337042408 To insert : 1337042409 To insert : 1337042410 To insert : 1337042411 To insert : 1337042412 To insert : 1337042413 To insert : 1337042414 To insert : 1337042415 To insert : 1337042416 To insert : 1337042417 To insert : 1337042418 To insert : 1337042419 To insert : 1337042420 To insert : 1337042421 To insert : 1337042422 To insert : 1337042423 To insert : 1337042424 To insert : 1337042425 To insert : 1337042426 To insert : 1337042427 To insert : 1337042428 To insert : 1337042429 To insert : 1337042430 To insert : 1337042431 To insert : 1337042432 To insert : 1337042433 To insert : 1337042434 To insert : 1337042435 To insert : 1337042436 To insert : 1337042437 To insert : 1337042438 To insert : 1337042439 To insert : 1337042440 To insert : 1337042441 To insert : 1337042442 To insert : 1337042443 To insert : 1337042444 To insert : 1337042446 To insert : 1337042447 To insert : 1337042448 To insert : 1337042449 To insert : 1337042450 To insert : 1337042451 To insert : 1337042452 To insert : 1337042453 To insert : 1337042454 To insert : 1337042455 To insert : 1337042456 To insert : 1337042457 To insert : 1337042458 To insert : 1337042459 To insert : 1337042460 To insert : 1337042461 To insert : 1337042462 To insert : 1337042463 To insert : 1337042464 To insert : 1337042465 To insert : 1337042466 To insert : 1337042467 To insert : 1337042468 To insert : 1337042469 To insert : 1337042470 To insert : 1337042471 To insert : 1337042472 To insert : 1337042473 To insert : 1337042474 To insert : 1337042475 To insert : 1337042476 To insert : 1337042477 To insert : 1337042478 To insert : 1337042479 To insert : 1337042480 To insert : 1337042481 To insert : 1337042482 To insert : 1337042483 To insert : 1337042484 To insert : 1337042485 To insert : 1337042486 To insert : 1337042487 To insert : 1337042488 To insert : 1337042489 To insert : 1337042490 To insert : 1337042491 To insert : 1337042492 To insert : 1337042493 To insert : 1337042494 To insert : 1337042495 To insert : 1337042496 To insert : 1337042497 To insert : 1337042498 To insert : 1337042499 To insert : 1337042500 To insert : 1337042501 To insert : 1337042502 To insert : 1337042503 To insert : 1337042504 To insert : 1337042505 To insert : 1337042506 To insert : 1337042507 To insert : 1337042508 To insert : 1337042509 To insert : 1337042510 To insert : 1337042511 To insert : 1337042512 To insert : 1337042513 To insert : 1337042514 To insert : 1337042515 To insert : 1337042516 To insert : 1337042517 To insert : 1337042518 To insert : 1337042519 To insert : 1337042520 To insert : 1337042521 To insert : 1337042522 To insert : 1337042523 To insert : 1337042524 To insert : 1337042525 To insert : 1337042526 To insert : 1337042527 To insert : 1337042528 To insert : 1337042529 To insert : 1337042530 To insert : 1337042531 To insert : 1337042532 To insert : 1337042533 To insert : 1337042534 To insert : 1337042535 To insert : 1337042536 To insert : 1337042538 To insert : 1337042539 To insert : 1337042540 To insert : 1337042541 To insert : 1337042542 To insert : 1337042543 To insert : 1337042544 To insert : 1337042545 To insert : 1337042546 To insert : 1337042547 To insert : 1337042548 To insert : 1337042549 To insert : 1337042550 To insert : 1337042551 To insert : 1337042552 To insert : 1337042553 To insert : 1337042554 To insert : 1337042555 To insert : 1337042556 To insert : 1337042557 To insert : 1337042558 To insert : 1337042559 To insert : 1337042560 To insert : 1337042561 To insert : 1337042562 To insert : 1337042563 To insert : 1337042564 To insert : 1337042565 To insert : 1337042566 To insert : 1337042567 To insert : 1337042568 To insert : 1337042569 To insert : 1337042570 To insert : 1337042571 To insert : 1337042572 To insert : 1337042573 To insert : 1337042574 To insert : 1337042575 To insert : 1337042576 To insert : 1337042577 To insert : 1337042578 To insert : 1337042579 To insert : 1337042580 To insert : 1337042581 To insert : 1337042582 To insert : 1337042583 To insert : 1337042584 To insert : 1337042585 To insert : 1337042586 To insert : 1337042587 To insert : 1337042588 To insert : 1337042589 To insert : 1337042590 To insert : 1337042591 To insert : 1337042592 To insert : 1337042593 To insert : 1337042594 To insert : 1337042595 To insert : 1337042596 To insert : 1337042597 To insert : 1337042598 To insert : 1337042599 To insert : 1337042600 To insert : 1337042601 To insert : 1337042602 To insert : 1337042603 To insert : 1337042604 To insert : 1337042605 To insert : 1337042606 To insert : 1337042607 To insert : 1337042608 To insert : 1337042609 To insert : 1337042610 To insert : 1337042612 To insert : 1337042613 To insert : 1337042614 To insert : 1337042615 To insert : 1337042616 To insert : 1337042617 To insert : 1337042618 To insert : 1337042619 To insert : 1337042620 To insert : 1337042621 To insert : 1337042622 To insert : 1337042623 To insert : 1337042624 To insert : 1337042625 To insert : 1337042626 To insert : 1337042627 To insert : 1337042628 To insert : 1337042630 To insert : 1337042631 To insert : 1337042632 To insert : 1337042633 To insert : 1337042634 To insert : 1337042635 To insert : 1337042636 To insert : 1337042637 To insert : 1337042638 To insert : 1337042639 To insert : 1337042640 To insert : 1337042641 To insert : 1337042642 To insert : 1337042643 To insert : 1337042644 To insert : 1337042645 To insert : 1337042646 To insert : 1337042647 To insert : 1337042648 To insert : 1337042649 To insert : 1337044576 To insert : 1337044577 To insert : 1337044578 To insert : 1337044579 To insert : 1337044580 To insert : 1337044581 To insert : 1337044582 To insert : 1337044583 To insert : 1337044584 To insert : 1337044585 To insert : 1337044586 To insert : 1337044587 To insert : 1337044588 To insert : 1337044589 To insert : 1337044590 To insert : 1337044591 To insert : 1337044592 To insert : 1337044593 To insert : 1337044594 To insert : 1337044595 To insert : 1337044596 To insert : 1337044597 To insert : 1337044598 To insert : 1337044599 To insert : 1337044600 To insert : 1337044601 To insert : 1337044602 To insert : 1337044603 To insert : 1337044604 To insert : 1337044605 To insert : 1337044606 To insert : 1337044607 To insert : 1337044608 To insert : 1337044609 To insert : 1337044610 To insert : 1337044611 To insert : 1337044612 To insert : 1337044613 To insert : 1337044614 To insert : 1337044615 To insert : 1337044616 To insert : 1337044617 To insert : 1337044618 To insert : 1337044619 To insert : 1337044620 To insert : 1337044621 To insert : 1337044622 To insert : 1337044623 To insert : 1337044624 To insert : 1337044625 To insert : 1337044626 To insert : 1337044627 To insert : 1337044628 To insert : 1337044629 To insert : 1337044630 To insert : 1337044631 To insert : 1337044632 To insert : 1337044633 To insert : 1337044634 To insert : 1337044635 To insert : 1337044636 To insert : 1337044637 To insert : 1337044638 To insert : 1337044639 To insert : 1337044640 To insert : 1337044641 To insert : 1337044642 To insert : 1337044643 To insert : 1337044644 To insert : 1337044645 To insert : 1337044646 To insert : 1337044647 To insert : 1337044648 To insert : 1337044649 To insert : 1337044650 To insert : 1337044651 To insert : 1337044652 To insert : 1337044653 To insert : 1337044654 To insert : 1337044656 To insert : 1337044657 To insert : 1337044658 To insert : 1337044659 To insert : 1337044660 To insert : 1337044661 To insert : 1337044662 To insert : 1337044663 To insert : 1337044664 To insert : 1337044665 To insert : 1337044666 To insert : 1337044667 To insert : 1337044668 To insert : 1337044669 To insert : 1337044670 To insert : 1337044671 To insert : 1337044672 To insert : 1337044673 To insert : 1337044674 To insert : 1337044675 To insert : 1337044676 To insert : 1337044677 To insert : 1337044678 To insert : 1337044679 To insert : 1337044680 To insert : 1337044681 To insert : 1337044682 To insert : 1337044683 To insert : 1337044684 To insert : 1337044685 To insert : 1337044686 To insert : 1337044687 To insert : 1337044688 To insert : 1337044689 To insert : 1337044690 To insert : 1337044691 To insert : 1337044692 To insert : 1337044693 To insert : 1337044694 To insert : 1337044695 To insert : 1337044696 To insert : 1337044697 To insert : 1337044698 To insert : 1337044699 To insert : 1337044700 To insert : 1337044701 To insert : 1337044702 To insert : 1337044703 To insert : 1337044704 To insert : 1337044705 To insert : 1337044706 To insert : 1337044707 To insert : 1337044708 To insert : 1337044709 To insert : 1337044710 To insert : 1337044711 To insert : 1337044712 To insert : 1337044713 To insert : 1337044714 To insert : 1337044715 To insert : 1337044716 To insert : 1337044717 To insert : 1337044718 To insert : 1337044719 To insert : 1337044720 To insert : 1337044721 To insert : 1337044722 To insert : 1337044723 To insert : 1337044724 To insert : 1337044725 To insert : 1337044726 To insert : 1337044727 To insert : 1337044728 To insert : 1337044729 To insert : 1337044730 To insert : 1337044731 To insert : 1337044732 To insert : 1337044733 To insert : 1337044734 To insert : 1337044735 To insert : 1337044736 To insert : 1337044737 To insert : 1337044738 To insert : 1337044739 To insert : 1337044740 To insert : 1337044741 To insert : 1337044742 To insert : 1337044743 To insert : 1337044744 To insert : 1337044745 To insert : 1337044746 To insert : 1337044747 To insert : 1337044748 To insert : 1337044749 To insert : 1337044750 To insert : 1337044751 To insert : 1337044752 To insert : 1337044753 To insert : 1337044754 To insert : 1337044755 To insert : 1337044756 To insert : 1337044757 To insert : 1337044758 To insert : 1337044759 To insert : 1337044760 To insert : 1337044761 To insert : 1337044762 To insert : 1337044763 To insert : 1337044764 To insert : 1337044765 To insert : 1337044767 To insert : 1337044768 To insert : 1337044769 To insert : 1337044770 To insert : 1337044771 To insert : 1337044772 To insert : 1337044773 To insert : 1337044774 To insert : 1337044775 To insert : 1337044776 To insert : 1337044777 To insert : 1337044778 To insert : 1337044779 To insert : 1337044780 To insert : 1337044781 To insert : 1337044782 To insert : 1337044783 To insert : 1337044784 To insert : 1337044785 To insert : 1337044786 To insert : 1337044787 To insert : 1337044788 To insert : 1337044789 To insert : 1337043966 To insert : 1337043967 To insert : 1337043968 To insert : 1337043969 To insert : 1337043970 To insert : 1337043971 To insert : 1337043972 To insert : 1337043973 To insert : 1337043974 To insert : 1337043975 To insert : 1337043976 To insert : 1337043977 To insert : 1337043978 To insert : 1337043979 To insert : 1337043980 To insert : 1337043982 To insert : 1337043983 To insert : 1337043984 To insert : 1337043985 To insert : 1337043991 To insert : 1337043992 To insert : 1337043993 To insert : 1337043997 To insert : 1337043998 To insert : 1337043999 To insert : 1337044000 To insert : 1337044001 To insert : 1337044002 To insert : 1337044003 To insert : 1337044004 To insert : 1337044005 To insert : 1337044006 To insert : 1337044007 To insert : 1337044008 To insert : 1337044009 To insert : 1337044010 To insert : 1337044011 To insert : 1337044012 To insert : 1337044013 To insert : 1337044014 To insert : 1337044015 To insert : 1337044016 To insert : 1337044017 To insert : 1337044018 To insert : 1337044083 To insert : 1337044084 To insert : 1337044085 To insert : 1337044087 To insert : 1337044088 To insert : 1337044089 To insert : 1337044090 To insert : 1337044091 To insert : 1337044092 To insert : 1337044093 To insert : 1337044094 To insert : 1337044095 To insert : 1337044096 To insert : 1337044097 To insert : 1337044098 To insert : 1337044099 To insert : 1337044100 To insert : 1337044101 To insert : 1337044102 To insert : 1337044103 To insert : 1337044104 To insert : 1337044105 To insert : 1337044106 To insert : 1337044107 To insert : 1337044108 To insert : 1337044109 To insert : 1337044110 To insert : 1337044111 To insert : 1337044112 To insert : 1337044113 To insert : 1337044114 To insert : 1337044115 To insert : 1337044116 To insert : 1337044117 To insert : 1337044119 To insert : 1337044120 To insert : 1337044121 To insert : 1337044122 To insert : 1337044123 To insert : 1337044124 To insert : 1337044125 To insert : 1337044126 To insert : 1337044127 To insert : 1337044128 To insert : 1337044129 To insert : 1337044130 To insert : 1337044131 To insert : 1337044132 To insert : 1337044133 To insert : 1337044134 To insert : 1337044135 To insert : 1337044136 To insert : 1337044137 To insert : 1337044138 To insert : 1337044139 To insert : 1337044140 To insert : 1337044141 To insert : 1337044142 To insert : 1337044143 To insert : 1337044144 To insert : 1337044145 To insert : 1337044146 To insert : 1337044147 To insert : 1337044148 To insert : 1337044149 To insert : 1337044150 To insert : 1337044151 To insert : 1337044152 To insert : 1337044153 To insert : 1337044154 To insert : 1337044155 To insert : 1337044156 To insert : 1337044157 To insert : 1337044158 To insert : 1337044159 To insert : 1337044160 To insert : 1337044161 To insert : 1337044162 To insert : 1337044163 To insert : 1337044164 To insert : 1337044165 To insert : 1337044166 To insert : 1337044167 To insert : 1337044168 To insert : 1337044169 To insert : 1337044170 To insert : 1337044171 To insert : 1337044172 To insert : 1337044173 To insert : 1337044174 To insert : 1337044175 To insert : 1337044176 To insert : 1337044177 To insert : 1337044178 To insert : 1337044180 To insert : 1337044181 To insert : 1337044182 To insert : 1337044183 To insert : 1337044184 To insert : 1337044185 To insert : 1337044186 To insert : 1337044187 To insert : 1337044188 To insert : 1337044189 To insert : 1337044190 To insert : 1337044191 To insert : 1337044192 To insert : 1337044193 To insert : 1337044194 To insert : 1337044195 To insert : 1337044196 To insert : 1337044197 To insert : 1337044198 To insert : 1337044199 To insert : 1337044200 To insert : 1337044201 To insert : 1337044202 To insert : 1337044203 To insert : 1337044204 To insert : 1337044205 To insert : 1337044206 To insert : 1337044207 To insert : 1337044208 To insert : 1337044209 To insert : 1337044210 To insert : 1337044211 To insert : 1337044212 To insert : 1337044213 To insert : 1337044214 To insert : 1337044215 To insert : 1337044216 To insert : 1337044217 To insert : 1337044218 To insert : 1337044219 To insert : 1337044220 To insert : 1337044221 To insert : 1337044222 To insert : 1337044223 To insert : 1337044225 To insert : 1337044226 To insert : 1337044227 To insert : 1337044228 To insert : 1337044229 To insert : 1337044230 To insert : 1337044231 To insert : 1337044232 To insert : 1337044233 To insert : 1337044234 To insert : 1337044235 To insert : 1337044236 To insert : 1337044237 To insert : 1337044238 To insert : 1337044239 To insert : 1337044240 To insert : 1337044241 To insert : 1337044242 To insert : 1337044243 To insert : 1337044244 To insert : 1337044245 To insert : 1337044246 To insert : 1337044247 To insert : 1337044248 To insert : 1337044249 To insert : 1337044250 To insert : 1337044251 To insert : 1337044252 To insert : 1337044253 To insert : 1337044254 To insert : 1337042866 To insert : 1337042867 To insert : 1337042868 To insert : 1337042869 To insert : 1337042870 To insert : 1337042871 To insert : 1337042872 To insert : 1337042873 To insert : 1337042874 To insert : 1337042875 To insert : 1337042876 To insert : 1337042877 To insert : 1337042878 To insert : 1337042879 To insert : 1337042880 To insert : 1337042881 To insert : 1337042882 To insert : 1337042883 To insert : 1337042884 To insert : 1337042885 To insert : 1337042886 To insert : 1337042890 To insert : 1337042893 To insert : 1337042897 To insert : 1337042901 To insert : 1337042905 To insert : 1337042909 To insert : 1337042913 To insert : 1337042917 To insert : 1337042921 To insert : 1337042925 To insert : 1337042929 To insert : 1337042935 To insert : 1337042939 To insert : 1337042942 To insert : 1337042946 To insert : 1337042950 To insert : 1337042954 To insert : 1337042958 To insert : 1337042962 To insert : 1337042966 To insert : 1337042970 To insert : 1337042974 To insert : 1337042978 To insert : 1337042982 To insert : 1337042986 To insert : 1337042990 To insert : 1337042994 To insert : 1337042998 To insert : 1337043002 To insert : 1337043007 To insert : 1337043010 To insert : 1337043014 To insert : 1337043018 To insert : 1337043022 To insert : 1337043026 To insert : 1337043030 To insert : 1337043034 To insert : 1337043038 To insert : 1337043042 To insert : 1337043046 To insert : 1337043050 To insert : 1337043054 To insert : 1337043059 To insert : 1337043063 To insert : 1337043067 To insert : 1337043071 To insert : 1337043075 To insert : 1337043079 To insert : 1337043083 To insert : 1337043087 To insert : 1337043091 To insert : 1337043095 To insert : 1337043099 To insert : 1337043103 To insert : 1337043107 To insert : 1337043111 To insert : 1337043115 To insert : 1337043119 To insert : 1337043123 To insert : 1337043128 To insert : 1337043132 To insert : 1337043136 To insert : 1337043140 To insert : 1337043144 To insert : 1337043148 To insert : 1337043151 To insert : 1337043155 To insert : 1337043159 To insert : 1337043163 To insert : 1337043167 To insert : 1337043171 To insert : 1337043175 To insert : 1337043178 To insert : 1337043182 To insert : 1337043186 To insert : 1337043190 To insert : 1337043194 To insert : 1337043198 To insert : 1337043202 To insert : 1337043206 To insert : 1337043210 To insert : 1337043214 To insert : 1337043218 To insert : 1337043222 To insert : 1337043226 To insert : 1337043230 To insert : 1337043234 To insert : 1337043238 To insert : 1337043242 To insert : 1337043246 To insert : 1337043250 To insert : 1337043254 To insert : 1337043258 To insert : 1337043263 To insert : 1337043266 To insert : 1337043269 To insert : 1337043270 To insert : 1337043271 To insert : 1337043272 To insert : 1337043273 To insert : 1337043274 To insert : 1337043275 To insert : 1337043276 To insert : 1337043277 To insert : 1337043278 To insert : 1337043279 To insert : 1337043280 To insert : 1337043281 To insert : 1337043282 To insert : 1337043283 To insert : 1337043284 To insert : 1337043285 To insert : 1337043286 To insert : 1337043287 To insert : 1337043288 To insert : 1337043289 To insert : 1337043290 To insert : 1337043292 To insert : 1337043293 To insert : 1337043294 To insert : 1337043295 To insert : 1337043296 To insert : 1337043297 To insert : 1337043298 To insert : 1337043299 To insert : 1337043301 To insert : 1337043302 To insert : 1337043303 To insert : 1337043304 To insert : 1337043305 To insert : 1337043306 To insert : 1337043307 To insert : 1337043308 To insert : 1337043309 To insert : 1337043310 To insert : 1337043311 To insert : 1337043312 To insert : 1337043313 To insert : 1337043314 To insert : 1337043315 To insert : 1337043316 To insert : 1337043317 To insert : 1337043318 To insert : 1337043319 To insert : 1337043320 To insert : 1337043321 To insert : 1337043322 To insert : 1337043323 To insert : 1337043324 To insert : 1337043325 To insert : 1337043326 To insert : 1337043327 To insert : 1337043328 To insert : 1337043329 To insert : 1337043330 To insert : 1337043331 To insert : 1337043332 To insert : 1337043333 To insert : 1337043334 To insert : 1337043335 To insert : 1337043336 To insert : 1337043337 To insert : 1337043338 To insert : 1337043339 To insert : 1337043340 To insert : 1337043341 To insert : 1337043342 To insert : 1337043343 To insert : 1337043344 To insert : 1337043345 To insert : 1337043346 To insert : 1337043347 To insert : 1337043348 To insert : 1337043349 To insert : 1337043350 To insert : 1337043351 To insert : 1337043352 To insert : 1337043353 To insert : 1337043354 To insert : 1337043355 To insert : 1337043356 To insert : 1337043357 To insert : 1337043358 To insert : 1337043359 To insert : 1337043360 To insert : 1337043361 To insert : 1337043362 To insert : 1337043363 To insert : 1337043364 To insert : 1337043365 To insert : 1337043366 To insert : 1337043744 To insert : 1337043745 To insert : 1337043746 To insert : 1337043747 To insert : 1337043748 To insert : 1337043749 To insert : 1337043750 To insert : 1337043751 To insert : 1337043752 To insert : 1337043753 To insert : 1337043754 To insert : 1337043755 To insert : 1337043756 To insert : 1337043757 To insert : 1337043758 To insert : 1337043759 To insert : 1337043760 To insert : 1337043761 To insert : 1337043762 To insert : 1337043763 To insert : 1337043764 To insert : 1337043765 To insert : 1337043766 To insert : 1337043767 To insert : 1337043768 To insert : 1337043769 To insert : 1337043770 To insert : 1337043771 To insert : 1337043772 To insert : 1337043773 To insert : 1337043774 To insert : 1337043775 To insert : 1337043776 To insert : 1337043777 To insert : 1337043778 To insert : 1337043779 To insert : 1337043780 To insert : 1337043781 To insert : 1337043782 To insert : 1337043783 To insert : 1337043784 To insert : 1337043785 To insert : 1337043786 To insert : 1337043787 To insert : 1337043789 To insert : 1337043790 To insert : 1337043791 To insert : 1337043793 To insert : 1337043794 To insert : 1337043795 To insert : 1337043796 To insert : 1337043797 To insert : 1337043798 To insert : 1337043799 To insert : 1337043800 To insert : 1337043801 To insert : 1337043802 To insert : 1337043805 To insert : 1337043807 To insert : 1337043808 To insert : 1337043809 To insert : 1337043810 To insert : 1337043811 To insert : 1337043812 To insert : 1337043813 To insert : 1337043814 To insert : 1337043815 To insert : 1337043816 To insert : 1337043817 To insert : 1337043818 To insert : 1337043819 To insert : 1337043820 To insert : 1337043821 To insert : 1337043822 To insert : 1337043823 To insert : 1337043824 To insert : 1337043825 To insert : 1337043826 To insert : 1337043827 To insert : 1337043828 To insert : 1337043829 To insert : 1337043830 To insert : 1337043831 To insert : 1337043832 To insert : 1337043833 To insert : 1337043834 To insert : 1337043835 To insert : 1337043836 To insert : 1337043837 To insert : 1337043838 To insert : 1337043839 To insert : 1337043840 To insert : 1337043841 To insert : 1337043842 To insert : 1337043843 To insert : 1337043844 To insert : 1337043845 To insert : 1337043846 To insert : 1337043847 To insert : 1337043848 To insert : 1337043849 To insert : 1337043850 To insert : 1337043851 To insert : 1337043852 To insert : 1337043853 To insert : 1337043854 To insert : 1337043855 To insert : 1337043856 To insert : 1337043857 To insert : 1337043858 To insert : 1337043860 To insert : 1337043861 To insert : 1337043862 To insert : 1337043863 To insert : 1337043864 To insert : 1337043865 To insert : 1337043866 To insert : 1337043867 To insert : 1337043868 To insert : 1337043869 To insert : 1337043870 To insert : 1337043871 To insert : 1337043872 To insert : 1337043873 To insert : 1337043874 To insert : 1337043875 To insert : 1337043876 To insert : 1337043877 To insert : 1337043878 To insert : 1337043879 To insert : 1337043880 To insert : 1337043882 To insert : 1337043883 To insert : 1337043884 To insert : 1337043885 To insert : 1337043886 To insert : 1337043887 To insert : 1337043888 To insert : 1337043889 To insert : 1337043890 To insert : 1337043891 To insert : 1337043892 To insert : 1337043894 To insert : 1337043895 To insert : 1337043896 To insert : 1337043897 To insert : 1337043898 To insert : 1337043899 To insert : 1337043900 To insert : 1337043901 To insert : 1337043902 To insert : 1337043903 To insert : 1337043905 To insert : 1337043906 To insert : 1337043907 To insert : 1337043908 To insert : 1337043909 To insert : 1337043910 To insert : 1337043911 To insert : 1337043912 To insert : 1337043913 To insert : 1337043914 To insert : 1337043915 To insert : 1337043916 To insert : 1337043917 To insert : 1337043918 To insert : 1337043919 To insert : 1337043920 To insert : 1337043921 To insert : 1337043922 To insert : 1337043923 To insert : 1337043924 To insert : 1337043925 To insert : 1337043926 To insert : 1337043927 To insert : 1337043928 To insert : 1337043929 To insert : 1337043930 To insert : 1337043931 To insert : 1337043932 To insert : 1337043933 To insert : 1337043934 To insert : 1337043935 To insert : 1337043936 To insert : 1337043937 To insert : 1337043938 To insert : 1337043939 To insert : 1337043940 To insert : 1337043941 To insert : 1337043942 To insert : 1337043943 To insert : 1337043944 To insert : 1337043945 To insert : 1337043946 To insert : 1337043947 To insert : 1337043948 To insert : 1337043949 To insert : 1337043950 To insert : 1337043951 To insert : 1337043952 To insert : 1337043954 To insert : 1337043955 To insert : 1337043956 To insert : 1337043957 To insert : 1337043958 To insert : 1337043959 To insert : 1337043960 To insert : 1337043961 To insert : 1337043962 To insert : 1337043963 To insert : 1337043964 To insert : 1337043965 time to insert the descriptors : 359.127064704895 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 : 3311 time used for this insertion : 0.21779274940490723 save missing photos in datou_result : time spend for datou_step_exec : 395.0542335510254 time spend to save output : 0.32805657386779785 total time spend for step 6 : 395.3822901248932 step7:ventilate_hashtags_in_portfolio Wed Feb 12 09:09:47 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 : 20070508 get user id for portfolio 20070508 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 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','flou','PET_fonce','papier','PEHD','Papier','environnement','refus','mal_croppe','autre','pehd','Carton','pet_fonce','metal','PET_clair','pet_clair','Tetrapak','Film_plastique')) AND mptpi.`min_score`=0.6 To do 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`=20070508 AND mptpi.`type`=4231 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','flou','PET_fonce','papier','PEHD','Papier','environnement','refus','mal_croppe','autre','pehd','Carton','pet_fonce','metal','PET_clair','pet_clair','Tetrapak','Film_plastique')) AND mptpi.`min_score`=0.6 To do lien utilise dans velours : https://www.fotonower.com/velours/20160766,20160779,20160768,20160769,20160770,20160781,20160777,20160776,20160774,20160775,20160778,20160780,20160782?tags=carton,pet_fonce,autre,film_plastique,environnement,pet_clair,pehd,papier,mal_croppe,flou,tetrapak,metal,refus&datou_id_consolidate=4235&port_consolidate=20070508 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1332937882, 1332937879, 1332937852, 1332937850, 1332937845, 1332937839, 1332937831, 1332937826, 1332937795, 1332937790, 1332937783, 1332937779, 1332937775, 1332937771, 1332937745, 1332937740, 1332937735, 1332937728, 1332937720, 1332937713, 1332937694] Looping around the photos to save general results len do output : 1 /20070508. 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, '2529450') ('4234', None, '1332937882', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937879', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937852', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937850', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937845', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937839', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937831', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937826', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937795', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937790', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937783', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937779', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937775', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937771', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937745', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937740', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937735', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937728', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937720', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937713', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937694', None, None, None, None, None, '2529450') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 22 time used for this insertion : 0.014793157577514648 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.800358772277832 time spend to save output : 0.015125274658203125 total time spend for step 7 : 1.8154840469360352 step8:final Wed Feb 12 09:09:48 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 : {1332937882: ('0.23416400311747604',), 1332937879: ('0.23416400311747604',), 1332937852: ('0.23416400311747604',), 1332937850: ('0.23416400311747604',), 1332937845: ('0.23416400311747604',), 1332937839: ('0.23416400311747604',), 1332937831: ('0.23416400311747604',), 1332937826: ('0.23416400311747604',), 1332937795: ('0.23416400311747604',), 1332937790: ('0.23416400311747604',), 1332937783: ('0.23416400311747604',), 1332937779: ('0.23416400311747604',), 1332937775: ('0.23416400311747604',), 1332937771: ('0.23416400311747604',), 1332937745: ('0.23416400311747604',), 1332937740: ('0.23416400311747604',), 1332937735: ('0.23416400311747604',), 1332937728: ('0.23416400311747604',), 1332937720: ('0.23416400311747604',), 1332937713: ('0.23416400311747604',), 1332937694: ('0.23416400311747604',)} new output for save of step final : {1332937882: ('0.23416400311747604',), 1332937879: ('0.23416400311747604',), 1332937852: ('0.23416400311747604',), 1332937850: ('0.23416400311747604',), 1332937845: ('0.23416400311747604',), 1332937839: ('0.23416400311747604',), 1332937831: ('0.23416400311747604',), 1332937826: ('0.23416400311747604',), 1332937795: ('0.23416400311747604',), 1332937790: ('0.23416400311747604',), 1332937783: ('0.23416400311747604',), 1332937779: ('0.23416400311747604',), 1332937775: ('0.23416400311747604',), 1332937771: ('0.23416400311747604',), 1332937745: ('0.23416400311747604',), 1332937740: ('0.23416400311747604',), 1332937735: ('0.23416400311747604',), 1332937728: ('0.23416400311747604',), 1332937720: ('0.23416400311747604',), 1332937713: ('0.23416400311747604',), 1332937694: ('0.23416400311747604',)} [1332937882, 1332937879, 1332937852, 1332937850, 1332937845, 1332937839, 1332937831, 1332937826, 1332937795, 1332937790, 1332937783, 1332937779, 1332937775, 1332937771, 1332937745, 1332937740, 1332937735, 1332937728, 1332937720, 1332937713, 1332937694] Looping around the photos to save general results len do output : 21 /1332937882.Didn't retrieve data . /1332937879.Didn't retrieve data . /1332937852.Didn't retrieve data . /1332937850.Didn't retrieve data . /1332937845.Didn't retrieve data . /1332937839.Didn't retrieve data . /1332937831.Didn't retrieve data . /1332937826.Didn't retrieve data . /1332937795.Didn't retrieve data . /1332937790.Didn't retrieve data . /1332937783.Didn't retrieve data . /1332937779.Didn't retrieve data . /1332937775.Didn't retrieve data . /1332937771.Didn't retrieve data . /1332937745.Didn't retrieve data . /1332937740.Didn't retrieve data . /1332937735.Didn't retrieve data . /1332937728.Didn't retrieve data . /1332937720.Didn't retrieve data . /1332937713.Didn't retrieve data . /1332937694.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, '2529450') ('4234', None, '1332937882', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937879', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937852', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937850', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937845', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937839', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937831', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937826', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937795', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937790', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937783', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937779', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937775', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937771', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937745', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937740', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937735', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937728', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937720', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937713', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937694', None, None, None, None, None, '2529450') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 63 time used for this insertion : 0.015654563903808594 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.20794057846069336 time spend to save output : 0.016602277755737305 total time spend for step 8 : 0.22454285621643066 step9:velours_tree Wed Feb 12 09:09:49 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 : 20160766,20160779,20160768,20160769,20160770,20160781,20160777,20160776,20160774,20160775,20160778,20160780,20160782 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=20160766 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=20160779 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=20160768 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=20160769 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=20160770 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=20160781 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=20160777 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=20160776 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=20160774 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=20160775 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=20160778 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=20160780 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=20160782 ORDER BY ph.size desc - Loading descriptors... Size : 2048 len(descriptors) : 5000 Compute structured hierarchical clustering... ward : AgglomerativeClustering(n_clusters=6) ward.labels_ : [2 0 5 ... 2 3 3] Elapsed time: 17.05837893486023 graph_id used : 77459 - Beta version, working pretty good on 11-5-16 ! too many photos (11147 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 : 485.5460596084595 time spend to save output : 0.01856708526611328 total time spend for step 9 : 485.5646266937256 step10:send_mail_cod Wed Feb 12 09:17:54 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_P20070508_12-02-2025_09_17_54.pdf 20160748 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 .imagette201607481739348274 20160761 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette201607611739348276 20160750 change filename to text 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.change filename to text .change filename to text .imagette201607631739348279 20160759 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 .imagette201607591739348281 20160758 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 .imagette201607581739348282 20160756 imagette201607561739348283 20160757 imagette201607571739348283 20160760 imagette201607601739348283 20160762 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 .imagette201607621739348283 20160764 imagette201607641739348285 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20070508 and hashtag_type = 4230 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20160766,20160779,20160768,20160769,20160770,20160781,20160777,20160776,20160774,20160775,20160778,20160780,20160782?tags=carton,pet_fonce,autre,film_plastique,environnement,pet_clair,pehd,papier,mal_croppe,flou,tetrapak,metal,refus&datou_id_consolidate=4235&port_consolidate=20070508 your option no_mail is active, we will not send the real mail to your client args[1332937882] : ((1332937882, -7.779968145687247, 492609224), (1332937882, -0.5369432714639465, 501862349), '0.23416400311747604') apple ((1332937882, -7.779968145687247, 492609224), (1332937882, -0.5369432714639465, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937879] : ((1332937879, -7.775083382821967, 492609224), (1332937879, -0.5397309109828685, 501862349), '0.23416400311747604') apple ((1332937879, -7.775083382821967, 492609224), (1332937879, -0.5397309109828685, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937852] : ((1332937852, -7.76211971613327, 492609224), (1332937852, -0.542148035046544, 501862349), '0.23416400311747604') apple ((1332937852, -7.76211971613327, 492609224), (1332937852, -0.542148035046544, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937850] : ((1332937850, -7.753704476939944, 492609224), (1332937850, -0.5444932292589707, 501862349), '0.23416400311747604') apple ((1332937850, -7.753704476939944, 492609224), (1332937850, -0.5444932292589707, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937845] : ((1332937845, -7.757662503865591, 492609224), (1332937845, -0.547691056811317, 501862349), '0.23416400311747604') apple ((1332937845, -7.757662503865591, 492609224), (1332937845, -0.547691056811317, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937839] : ((1332937839, -7.75355035884945, 492609224), (1332937839, -0.5463844320156003, 501862349), '0.23416400311747604') apple ((1332937839, -7.75355035884945, 492609224), (1332937839, -0.5463844320156003, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937831] : ((1332937831, -7.749716586537498, 492609224), (1332937831, -0.548568565303427, 501862349), '0.23416400311747604') apple ((1332937831, -7.749716586537498, 492609224), (1332937831, -0.548568565303427, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937826] : ((1332937826, -7.735172358592695, 492609224), (1332937826, -0.546911916537217, 501862349), '0.23416400311747604') apple ((1332937826, -7.735172358592695, 492609224), (1332937826, -0.546911916537217, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937795] : ((1332937795, -7.723696918767472, 492609224), (1332937795, -0.5455971562651993, 501862349), '0.23416400311747604') apple ((1332937795, -7.723696918767472, 492609224), (1332937795, -0.5455971562651993, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937790] : ((1332937790, -7.741549851799454, 492609224), (1332937790, -0.5435512362511584, 501862349), '0.23416400311747604') apple ((1332937790, -7.741549851799454, 492609224), (1332937790, -0.5435512362511584, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937783] : ((1332937783, -7.736430657661612, 492609224), (1332937783, -0.516730128545772, 501862349), '0.23416400311747604') apple ((1332937783, -7.736430657661612, 492609224), (1332937783, -0.516730128545772, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937779] : ((1332937779, -7.770792002591815, 492609224), (1332937779, -0.5270538632471413, 501862349), '0.23416400311747604') apple ((1332937779, -7.770792002591815, 492609224), (1332937779, -0.5270538632471413, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937775] : ((1332937775, -7.843782170196549, 492609224), (1332937775, -0.5442840014919453, 501862349), '0.23416400311747604') apple ((1332937775, -7.843782170196549, 492609224), (1332937775, -0.5442840014919453, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937771] : ((1332937771, -7.8468820751613455, 492609224), (1332937771, -0.5307438925409103, 501862349), '0.23416400311747604') apple ((1332937771, -7.8468820751613455, 492609224), (1332937771, -0.5307438925409103, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937745] : ((1332937745, -7.827786240339442, 492609224), (1332937745, -0.46971903839082985, 496442774), '0.23416400311747604') apple ((1332937745, -7.827786240339442, 492609224), (1332937745, -0.46971903839082985, 496442774), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937740] : ((1332937740, -7.830637711339109, 492609224), (1332937740, -0.4557009557046219, 496442774), '0.23416400311747604') apple ((1332937740, -7.830637711339109, 492609224), (1332937740, -0.4557009557046219, 496442774), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937735] : ((1332937735, -7.8365095951384705, 492609224), (1332937735, -0.49316898787397573, 501862349), '0.23416400311747604') apple ((1332937735, -7.8365095951384705, 492609224), (1332937735, -0.49316898787397573, 501862349), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937728] : ((1332937728, -7.873750553107825, 492609224), (1332937728, -0.3953951585215586, 496442774), '0.23416400311747604') apple ((1332937728, -7.873750553107825, 492609224), (1332937728, -0.3953951585215586, 496442774), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937720] : ((1332937720, -7.918271148765961, 492609224), (1332937720, -0.3510700262425428, 496442774), '0.23416400311747604') apple ((1332937720, -7.918271148765961, 492609224), (1332937720, -0.3510700262425428, 496442774), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937713] : ((1332937713, -7.922149427565916, 492609224), (1332937713, -0.3781762854192857, 496442774), '0.23416400311747604') apple ((1332937713, -7.922149427565916, 492609224), (1332937713, -0.3781762854192857, 496442774), '0.23416400311747604') We are sending mail with results at cod@fotonower.com args[1332937694] : ((1332937694, -7.92305057161556, 492609224), (1332937694, -0.38579144870284565, 496442774), '0.23416400311747604') apple ((1332937694, -7.92305057161556, 492609224), (1332937694, -0.38579144870284565, 496442774), '0.23416400311747604') We are sending mail with results at cod@fotonower.com refus_total : 0.23416400311747604 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=20070508 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_P20070508_12-02-2025_09_17_54.pdf results_Qualipapia_P20070508_12-02-2025_09_17_54.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20070508_12-02-2025_09_17_54.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','20070508','results_Qualipapia_P20070508_12-02-2025_09_17_54.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Qualipapia_P20070508_12-02-2025_09_17_54.pdf','pdf','','0.36','0.23416400311747604') Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1332937882, 1332937879, 1332937852, 1332937850, 1332937845, 1332937839, 1332937831, 1332937826, 1332937795, 1332937790, 1332937783, 1332937779, 1332937775, 1332937771, 1332937745, 1332937740, 1332937735, 1332937728, 1332937720, 1332937713, 1332937694] 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, '2529450') ('4234', None, '1332937882', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937879', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937852', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937850', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937845', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937839', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937831', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937826', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937795', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937790', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937783', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937779', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937775', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937771', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937745', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937740', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937735', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937728', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937720', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937713', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937694', None, None, None, None, None, '2529450') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.015635251998901367 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.389377355575562 time spend to save output : 0.01961803436279297 total time spend for step 10 : 13.408995389938354 step11:split_time_score Wed Feb 12 09:18:08 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', 123),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 30012025 20070508 Nombre de photos uploadées : 123 / 23040 (0%) 30012025 20070508 Nombre de photos taguées (types de déchets): 123 / 123 (100%) 30012025 20070508 Nombre de photos taguées (volume) : 0 / 123 (0%) elapsed_time : load_data_split_time_score 5.9604644775390625e-06 elapsed_time : order_list_meta_photo_and_scores 1.7642974853515625e-05 ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.007249593734741211 elapsed_time : insert_dashboard_record_day_entry 0.0237119197845459 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_12-02-2025_09_17_54.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_11-02-2025_12_00_21.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_11-02-2025_11_56_05.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_11-02-2025_13_37_13.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_12-02-2025_00_59_38.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_11-02-2025_12_39_25.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': 123, 'nb_taggue_class': 123, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1332937882, 1332937879, 1332937852, 1332937850, 1332937845, 1332937839, 1332937831, 1332937826, 1332937795, 1332937790, 1332937783, 1332937779, 1332937775, 1332937771, 1332937745, 1332937740, 1332937735, 1332937728, 1332937720, 1332937713, 1332937694] Looping around the photos to save general results len do output : 1 /20070508Didn'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, '2529450') ('4234', None, '1332937882', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937879', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937852', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937850', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937845', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937839', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937831', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937826', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937795', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937790', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937783', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937779', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937775', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937771', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937745', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937740', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937735', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937728', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937720', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937713', None, None, None, None, None, '2529450') ('4234', None, None, None, None, None, None, None, '2529450') ('4234', None, '1332937694', None, None, None, None, None, '2529450') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 22 time used for this insertion : 0.014978647232055664 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.542762041091919 time spend to save output : 0.015327692031860352 total time spend for step 11 : 12.55808973312378 caffe_path_current : About to save ! 2 After save, about to update current ! update_current_state 282.37user 127.63system 27:46.78elapsed 24%CPU (0avgtext+0avgdata 3325072maxresident)k 756264inputs+141640outputs (22756major+6583620minor)pagefaults 0swaps