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/caffe_cuda8_python3/python', '/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', '/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 : 2499418 load datou : 3318 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( 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 : chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? [(photo_id, hashtag_id, hashtag_type, x0, x1, y0, y1, score, seg_temp, polygons), ...] was removed should we ? chemin de la photo was removed should we ? [ (photo_id_loc, hashtag_id, hashtag_type, x0, x1, y0, y1, score, None), ...] was removed should we ? chemin de la photo was removed should we ? id de la photo (peut être local ou global) was removed should we ? chemin de la photo was removed should we ? (x0, y0, x1, y1) was removed should we ? chemin de la photo was removed should we ? donnée sous forme de texte was removed should we ? [ (photo_id, photo_id_loc, hashtag_type, x0, x1, y0, y1, score), ...] was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? id de la photo (peut être local ou global) was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? donnée sous forme de texte was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? chemin de la photo was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? None was removed should we ? donnée sous forme de nombre was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? (photo_id, hashtag_id, score_max) was removed should we ? donnée sous forme de texte was removed should we ? None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? 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)) load thcls load THCL from format json or kwargs add thcl : 2847 in CacheModelConfig load pdts add pdt : 5275 in CacheModelConfig 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 : 3318, datou_cur_ids : ['2711135'] with mtr_portfolio_ids : ['21929818'] and first list_photo_ids : [] new path : /proc/2499418/ 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 ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 15 ; length of list_pids : 15 ; length of list_args : 15 time to download the photos : 3.143826723098755 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 : 10 step1:mask_detect Tue Apr 1 02:40:31 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 : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-01 02:40:34.555837: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-01 02:40:34.583117: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-01 02:40:34.585018: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb7d8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-01 02:40:34.585047: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-01 02:40:34.588699: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-01 02:40:34.830076: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5913950 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-01 02:40:34.830123: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-01 02:40:34.831647: 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 2025-04-01 02:40:34.832040: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 02:40:34.836158: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:40:34.839119: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 02:40:34.839475: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 02:40:34.841943: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 02:40:34.843175: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 02:40:34.848071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 02:40:34.849440: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 02:40:34.849497: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 02:40:34.850260: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 02:40:34.850275: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 02:40:34.850283: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 02:40:34.852037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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. 2025-04-01 02:40:35.227129: 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 2025-04-01 02:40:35.227240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 02:40:35.227271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:40:35.227293: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 02:40:35.227319: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 02:40:35.227341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 02:40:35.227362: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 02:40:35.227381: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 02:40:35.229016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 02:40:35.230385: 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 2025-04-01 02:40:35.230421: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 02:40:35.230444: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:40:35.230463: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 02:40:35.230480: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 02:40:35.230496: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 02:40:35.230517: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 02:40:35.230534: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 02:40:35.232204: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 02:40:35.232240: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 02:40:35.232250: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 02:40:35.232259: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 02:40:35.233680: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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 : [] 2025-04-01 02:40:47.389631: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:40:47.757568: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 15 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 42 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 19 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 49 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 48 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 71 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 45 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 56 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 50 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 49 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 85 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 78 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 71 NEW PHOTO Processing 1 images image shape: (2160, 3264, 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: 3264.00000 nb d'objets trouves : 63 Detection mask done ! Trying to reset tf kernel 2500105 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 236 tf kernel not reseted sub process len(results) : 15 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 15 len(list_Values) 0 process is alive 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 : 5525 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! 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 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.03718376159667969 nb_pixel_total : 17994 time to create 1 rle with old method : 0.024835824966430664 length of segment : 116 time for calcul the mask position with numpy : 0.07998943328857422 nb_pixel_total : 34610 time to create 1 rle with old method : 0.043692827224731445 length of segment : 249 time for calcul the mask position with numpy : 0.018572568893432617 nb_pixel_total : 12748 time to create 1 rle with old method : 0.017454862594604492 length of segment : 135 time for calcul the mask position with numpy : 0.06816673278808594 nb_pixel_total : 23941 time to create 1 rle with old method : 0.028126001358032227 length of segment : 247 time for calcul the mask position with numpy : 0.03495144844055176 nb_pixel_total : 21359 time to create 1 rle with old method : 0.028983592987060547 length of segment : 185 time for calcul the mask position with numpy : 0.06754899024963379 nb_pixel_total : 41546 time to create 1 rle with old method : 0.05079913139343262 length of segment : 228 time for calcul the mask position with numpy : 0.03691554069519043 nb_pixel_total : 14959 time to create 1 rle with old method : 0.021695852279663086 length of segment : 159 time for calcul the mask position with numpy : 0.03214621543884277 nb_pixel_total : 13490 time to create 1 rle with old method : 0.01933598518371582 length of segment : 164 time for calcul the mask position with numpy : 0.04067277908325195 nb_pixel_total : 11279 time to create 1 rle with old method : 0.017953157424926758 length of segment : 128 time for calcul the mask position with numpy : 0.08976268768310547 nb_pixel_total : 75938 time to create 1 rle with old method : 0.1065981388092041 length of segment : 258 time for calcul the mask position with numpy : 0.0496671199798584 nb_pixel_total : 29058 time to create 1 rle with old method : 0.03625893592834473 length of segment : 218 time for calcul the mask position with numpy : 0.00756525993347168 nb_pixel_total : 10658 time to create 1 rle with old method : 0.014616250991821289 length of segment : 156 time for calcul the mask position with numpy : 0.03132033348083496 nb_pixel_total : 14354 time to create 1 rle with old method : 0.021088361740112305 length of segment : 168 time for calcul the mask position with numpy : 0.04756617546081543 nb_pixel_total : 23276 time to create 1 rle with old method : 0.0311892032623291 length of segment : 208 time for calcul the mask position with numpy : 0.12838363647460938 nb_pixel_total : 94812 time to create 1 rle with old method : 0.10985708236694336 length of segment : 569 time for calcul the mask position with numpy : 0.03677821159362793 nb_pixel_total : 29474 time to create 1 rle with old method : 0.03795504570007324 length of segment : 351 time for calcul the mask position with numpy : 0.012391090393066406 nb_pixel_total : 4602 time to create 1 rle with old method : 0.0068035125732421875 length of segment : 68 time for calcul the mask position with numpy : 0.03983902931213379 nb_pixel_total : 18614 time to create 1 rle with old method : 0.023740291595458984 length of segment : 188 time for calcul the mask position with numpy : 0.0295257568359375 nb_pixel_total : 12449 time to create 1 rle with old method : 0.01886153221130371 length of segment : 111 time for calcul the mask position with numpy : 0.002309560775756836 nb_pixel_total : 22033 time to create 1 rle with old method : 0.03181815147399902 length of segment : 202 time for calcul the mask position with numpy : 0.0496220588684082 nb_pixel_total : 68691 time to create 1 rle with old method : 0.10261011123657227 length of segment : 222 time for calcul the mask position with numpy : 0.06560540199279785 nb_pixel_total : 38383 time to create 1 rle with old method : 0.04664802551269531 length of segment : 273 time for calcul the mask position with numpy : 0.06871414184570312 nb_pixel_total : 49271 time to create 1 rle with old method : 0.057361602783203125 length of segment : 282 time for calcul the mask position with numpy : 0.02358269691467285 nb_pixel_total : 11995 time to create 1 rle with old method : 0.017614364624023438 length of segment : 140 time for calcul the mask position with numpy : 0.12617921829223633 nb_pixel_total : 92615 time to create 1 rle with old method : 0.1153252124786377 length of segment : 269 time for calcul the mask position with numpy : 0.08794164657592773 nb_pixel_total : 78070 time to create 1 rle with old method : 0.09740328788757324 length of segment : 504 time for calcul the mask position with numpy : 0.013902664184570312 nb_pixel_total : 9778 time to create 1 rle with old method : 0.014816999435424805 length of segment : 60 time for calcul the mask position with numpy : 0.12513017654418945 nb_pixel_total : 131486 time to create 1 rle with old method : 0.1493377685546875 length of segment : 454 time for calcul the mask position with numpy : 0.03553032875061035 nb_pixel_total : 26512 time to create 1 rle with old method : 0.0317990779876709 length of segment : 163 time for calcul the mask position with numpy : 0.1280684471130371 nb_pixel_total : 216105 time to create 1 rle with new method : 0.01647639274597168 length of segment : 872 time for calcul the mask position with numpy : 0.15480589866638184 nb_pixel_total : 83234 time to create 1 rle with old method : 0.09408354759216309 length of segment : 363 time for calcul the mask position with numpy : 0.1076364517211914 nb_pixel_total : 118444 time to create 1 rle with old method : 0.13136863708496094 length of segment : 457 time for calcul the mask position with numpy : 0.1696474552154541 nb_pixel_total : 78997 time to create 1 rle with old method : 0.0938558578491211 length of segment : 339 time for calcul the mask position with numpy : 0.04842114448547363 nb_pixel_total : 18212 time to create 1 rle with old method : 0.03030252456665039 length of segment : 140 time for calcul the mask position with numpy : 0.07428312301635742 nb_pixel_total : 30505 time to create 1 rle with old method : 0.041939735412597656 length of segment : 215 time for calcul the mask position with numpy : 0.1367342472076416 nb_pixel_total : 73510 time to create 1 rle with old method : 0.08928060531616211 length of segment : 327 time for calcul the mask position with numpy : 0.4411911964416504 nb_pixel_total : 239434 time to create 1 rle with new method : 0.04950261116027832 length of segment : 916 time for calcul the mask position with numpy : 0.05777454376220703 nb_pixel_total : 14492 time to create 1 rle with old method : 0.01669478416442871 length of segment : 137 time for calcul the mask position with numpy : 0.21921420097351074 nb_pixel_total : 108107 time to create 1 rle with old method : 0.12394976615905762 length of segment : 307 time for calcul the mask position with numpy : 0.03863191604614258 nb_pixel_total : 13624 time to create 1 rle with old method : 0.023973464965820312 length of segment : 169 time for calcul the mask position with numpy : 0.04638552665710449 nb_pixel_total : 14689 time to create 1 rle with old method : 0.022792339324951172 length of segment : 183 time for calcul the mask position with numpy : 0.5119192600250244 nb_pixel_total : 418284 time to create 1 rle with new method : 0.06668257713317871 length of segment : 1431 time for calcul the mask position with numpy : 0.000324249267578125 nb_pixel_total : 15951 time to create 1 rle with old method : 0.018618106842041016 length of segment : 139 time for calcul the mask position with numpy : 0.26943039894104004 nb_pixel_total : 167376 time to create 1 rle with new method : 0.011573076248168945 length of segment : 457 time for calcul the mask position with numpy : 0.024600982666015625 nb_pixel_total : 17744 time to create 1 rle with old method : 0.024265766143798828 length of segment : 173 time for calcul the mask position with numpy : 0.005057096481323242 nb_pixel_total : 13789 time to create 1 rle with old method : 0.019772052764892578 length of segment : 114 time for calcul the mask position with numpy : 0.019503116607666016 nb_pixel_total : 63880 time to create 1 rle with old method : 0.08776211738586426 length of segment : 426 time for calcul the mask position with numpy : 0.1171112060546875 nb_pixel_total : 164997 time to create 1 rle with new method : 0.011218070983886719 length of segment : 487 time for calcul the mask position with numpy : 0.004260540008544922 nb_pixel_total : 69113 time to create 1 rle with old method : 0.08055996894836426 length of segment : 431 time for calcul the mask position with numpy : 0.039414167404174805 nb_pixel_total : 17976 time to create 1 rle with old method : 0.022770166397094727 length of segment : 203 time for calcul the mask position with numpy : 0.007436275482177734 nb_pixel_total : 12714 time to create 1 rle with old method : 0.016823768615722656 length of segment : 161 time for calcul the mask position with numpy : 0.24765944480895996 nb_pixel_total : 139861 time to create 1 rle with old method : 0.16244244575500488 length of segment : 578 time for calcul the mask position with numpy : 0.15829849243164062 nb_pixel_total : 16235 time to create 1 rle with old method : 0.024584054946899414 length of segment : 127 time for calcul the mask position with numpy : 2.0342094898223877 nb_pixel_total : 197795 time to create 1 rle with new method : 0.013963937759399414 length of segment : 701 time for calcul the mask position with numpy : 0.06106996536254883 nb_pixel_total : 18055 time to create 1 rle with old method : 0.026149272918701172 length of segment : 196 time for calcul the mask position with numpy : 0.32729625701904297 nb_pixel_total : 32307 time to create 1 rle with old method : 0.04433393478393555 length of segment : 240 time for calcul the mask position with numpy : 0.403139591217041 nb_pixel_total : 91627 time to create 1 rle with old method : 0.10728120803833008 length of segment : 795 time for calcul the mask position with numpy : 0.06651973724365234 nb_pixel_total : 51380 time to create 1 rle with old method : 0.06411457061767578 length of segment : 298 time for calcul the mask position with numpy : 0.48093271255493164 nb_pixel_total : 53133 time to create 1 rle with old method : 0.06428360939025879 length of segment : 233 time for calcul the mask position with numpy : 0.00039315223693847656 nb_pixel_total : 16430 time to create 1 rle with old method : 0.01892852783203125 length of segment : 167 time for calcul the mask position with numpy : 0.2569589614868164 nb_pixel_total : 15066 time to create 1 rle with old method : 0.0220029354095459 length of segment : 153 time for calcul the mask position with numpy : 0.42530298233032227 nb_pixel_total : 22846 time to create 1 rle with old method : 0.03136444091796875 length of segment : 242 time for calcul the mask position with numpy : 0.4611237049102783 nb_pixel_total : 51702 time to create 1 rle with old method : 0.05949974060058594 length of segment : 333 time for calcul the mask position with numpy : 0.013344526290893555 nb_pixel_total : 4455 time to create 1 rle with old method : 0.00928354263305664 length of segment : 39 time for calcul the mask position with numpy : 0.48301243782043457 nb_pixel_total : 90198 time to create 1 rle with old method : 0.10636782646179199 length of segment : 267 time for calcul the mask position with numpy : 1.044961929321289 nb_pixel_total : 449072 time to create 1 rle with new method : 0.039052486419677734 length of segment : 988 time for calcul the mask position with numpy : 0.050089359283447266 nb_pixel_total : 10333 time to create 1 rle with old method : 0.016687393188476562 length of segment : 79 time for calcul the mask position with numpy : 0.29317355155944824 nb_pixel_total : 52543 time to create 1 rle with old method : 0.07142901420593262 length of segment : 237 time for calcul the mask position with numpy : 0.1670527458190918 nb_pixel_total : 25281 time to create 1 rle with old method : 0.03321647644042969 length of segment : 170 time for calcul the mask position with numpy : 0.8881552219390869 nb_pixel_total : 243033 time to create 1 rle with new method : 0.016111373901367188 length of segment : 373 time for calcul the mask position with numpy : 0.3348686695098877 nb_pixel_total : 77446 time to create 1 rle with old method : 0.08956718444824219 length of segment : 407 time for calcul the mask position with numpy : 0.017716407775878906 nb_pixel_total : 13107 time to create 1 rle with old method : 0.019875288009643555 length of segment : 156 time for calcul the mask position with numpy : 0.4923062324523926 nb_pixel_total : 43280 time to create 1 rle with old method : 0.05798935890197754 length of segment : 181 time for calcul the mask position with numpy : 0.29482579231262207 nb_pixel_total : 33515 time to create 1 rle with old method : 0.046633005142211914 length of segment : 284 time for calcul the mask position with numpy : 0.30249834060668945 nb_pixel_total : 86179 time to create 1 rle with old method : 0.10209321975708008 length of segment : 421 time for calcul the mask position with numpy : 0.12640786170959473 nb_pixel_total : 26968 time to create 1 rle with old method : 0.03532099723815918 length of segment : 217 time for calcul the mask position with numpy : 0.20760774612426758 nb_pixel_total : 42632 time to create 1 rle with old method : 0.052275896072387695 length of segment : 288 time for calcul the mask position with numpy : 0.2652885913848877 nb_pixel_total : 52273 time to create 1 rle with old method : 0.06330108642578125 length of segment : 455 time for calcul the mask position with numpy : 0.23723220825195312 nb_pixel_total : 70003 time to create 1 rle with old method : 0.09029221534729004 length of segment : 242 time for calcul the mask position with numpy : 0.16874337196350098 nb_pixel_total : 29584 time to create 1 rle with old method : 0.03739285469055176 length of segment : 300 time for calcul the mask position with numpy : 0.04657316207885742 nb_pixel_total : 10478 time to create 1 rle with old method : 0.01461648941040039 length of segment : 111 time for calcul the mask position with numpy : 0.11841297149658203 nb_pixel_total : 15721 time to create 1 rle with old method : 0.022064685821533203 length of segment : 236 time for calcul the mask position with numpy : 0.20549654960632324 nb_pixel_total : 30179 time to create 1 rle with old method : 0.045517683029174805 length of segment : 327 time for calcul the mask position with numpy : 0.11379075050354004 nb_pixel_total : 22876 time to create 1 rle with old method : 0.030605554580688477 length of segment : 188 time for calcul the mask position with numpy : 0.06660294532775879 nb_pixel_total : 24191 time to create 1 rle with old method : 0.03709721565246582 length of segment : 191 time for calcul the mask position with numpy : 0.4749181270599365 nb_pixel_total : 247706 time to create 1 rle with new method : 0.016539812088012695 length of segment : 333 time for calcul the mask position with numpy : 0.04286766052246094 nb_pixel_total : 9379 time to create 1 rle with old method : 0.015615463256835938 length of segment : 100 time for calcul the mask position with numpy : 0.4674489498138428 nb_pixel_total : 140730 time to create 1 rle with old method : 0.17227625846862793 length of segment : 609 time for calcul the mask position with numpy : 0.05296063423156738 nb_pixel_total : 13606 time to create 1 rle with old method : 0.01897144317626953 length of segment : 128 time for calcul the mask position with numpy : 0.1618061065673828 nb_pixel_total : 37413 time to create 1 rle with old method : 0.0444333553314209 length of segment : 280 time for calcul the mask position with numpy : 0.14049482345581055 nb_pixel_total : 65293 time to create 1 rle with old method : 0.08137941360473633 length of segment : 377 time for calcul the mask position with numpy : 0.047662973403930664 nb_pixel_total : 14745 time to create 1 rle with old method : 0.02211165428161621 length of segment : 168 time for calcul the mask position with numpy : 0.23413872718811035 nb_pixel_total : 51976 time to create 1 rle with old method : 0.06379032135009766 length of segment : 485 time for calcul the mask position with numpy : 0.0781552791595459 nb_pixel_total : 20912 time to create 1 rle with old method : 0.026980161666870117 length of segment : 173 time for calcul the mask position with numpy : 0.01944112777709961 nb_pixel_total : 3295 time to create 1 rle with old method : 0.005794525146484375 length of segment : 73 time for calcul the mask position with numpy : 0.08766293525695801 nb_pixel_total : 38841 time to create 1 rle with old method : 0.04954338073730469 length of segment : 211 time for calcul the mask position with numpy : 0.042730093002319336 nb_pixel_total : 14661 time to create 1 rle with old method : 0.018590688705444336 length of segment : 180 time for calcul the mask position with numpy : 0.012348413467407227 nb_pixel_total : 6847 time to create 1 rle with old method : 0.012579679489135742 length of segment : 145 time for calcul the mask position with numpy : 0.014082908630371094 nb_pixel_total : 17440 time to create 1 rle with old method : 0.025876760482788086 length of segment : 266 time for calcul the mask position with numpy : 0.2757275104522705 nb_pixel_total : 107205 time to create 1 rle with old method : 0.12200117111206055 length of segment : 467 time for calcul the mask position with numpy : 0.08212518692016602 nb_pixel_total : 43502 time to create 1 rle with old method : 0.05363726615905762 length of segment : 298 time for calcul the mask position with numpy : 0.02329254150390625 nb_pixel_total : 3642 time to create 1 rle with old method : 0.00782155990600586 length of segment : 64 time for calcul the mask position with numpy : 0.05633544921875 nb_pixel_total : 26652 time to create 1 rle with old method : 0.03332257270812988 length of segment : 143 time for calcul the mask position with numpy : 0.005223751068115234 nb_pixel_total : 13199 time to create 1 rle with old method : 0.01882648468017578 length of segment : 201 time for calcul the mask position with numpy : 0.030544519424438477 nb_pixel_total : 9055 time to create 1 rle with old method : 0.015242815017700195 length of segment : 91 time for calcul the mask position with numpy : 0.010136127471923828 nb_pixel_total : 8022 time to create 1 rle with old method : 0.012176513671875 length of segment : 137 time for calcul the mask position with numpy : 0.03941655158996582 nb_pixel_total : 27650 time to create 1 rle with old method : 0.0347285270690918 length of segment : 310 time for calcul the mask position with numpy : 0.07619357109069824 nb_pixel_total : 15390 time to create 1 rle with old method : 0.027863502502441406 length of segment : 147 time for calcul the mask position with numpy : 0.07678532600402832 nb_pixel_total : 23674 time to create 1 rle with old method : 0.03258943557739258 length of segment : 157 time for calcul the mask position with numpy : 0.15491986274719238 nb_pixel_total : 52705 time to create 1 rle with old method : 0.060187578201293945 length of segment : 408 time for calcul the mask position with numpy : 0.03066849708557129 nb_pixel_total : 16300 time to create 1 rle with old method : 0.022859811782836914 length of segment : 162 time for calcul the mask position with numpy : 0.18984055519104004 nb_pixel_total : 84146 time to create 1 rle with old method : 0.09501194953918457 length of segment : 540 time for calcul the mask position with numpy : 0.0752565860748291 nb_pixel_total : 25481 time to create 1 rle with old method : 0.033263444900512695 length of segment : 235 time for calcul the mask position with numpy : 0.08775687217712402 nb_pixel_total : 57078 time to create 1 rle with old method : 0.0708322525024414 length of segment : 247 time for calcul the mask position with numpy : 0.01572728157043457 nb_pixel_total : 12314 time to create 1 rle with old method : 0.014649391174316406 length of segment : 110 time for calcul the mask position with numpy : 0.17552971839904785 nb_pixel_total : 134718 time to create 1 rle with old method : 0.15411901473999023 length of segment : 300 time for calcul the mask position with numpy : 0.01665019989013672 nb_pixel_total : 12286 time to create 1 rle with old method : 0.018245935440063477 length of segment : 136 time for calcul the mask position with numpy : 0.07558107376098633 nb_pixel_total : 15185 time to create 1 rle with old method : 0.02147078514099121 length of segment : 164 time for calcul the mask position with numpy : 0.1849367618560791 nb_pixel_total : 40977 time to create 1 rle with old method : 0.04875349998474121 length of segment : 351 time for calcul the mask position with numpy : 0.14706158638000488 nb_pixel_total : 35941 time to create 1 rle with old method : 0.04972505569458008 length of segment : 222 time for calcul the mask position with numpy : 0.35691237449645996 nb_pixel_total : 213172 time to create 1 rle with new method : 0.012140035629272461 length of segment : 519 time for calcul the mask position with numpy : 0.15556883811950684 nb_pixel_total : 52168 time to create 1 rle with old method : 0.0632779598236084 length of segment : 246 time for calcul the mask position with numpy : 0.22350001335144043 nb_pixel_total : 91046 time to create 1 rle with old method : 0.10850882530212402 length of segment : 279 time for calcul the mask position with numpy : 0.038483381271362305 nb_pixel_total : 6580 time to create 1 rle with old method : 0.011641263961791992 length of segment : 104 time for calcul the mask position with numpy : 0.19704127311706543 nb_pixel_total : 48979 time to create 1 rle with old method : 0.058777570724487305 length of segment : 349 time for calcul the mask position with numpy : 0.11482620239257812 nb_pixel_total : 23301 time to create 1 rle with old method : 0.030362367630004883 length of segment : 203 time for calcul the mask position with numpy : 0.3973679542541504 nb_pixel_total : 182423 time to create 1 rle with new method : 0.012278079986572266 length of segment : 552 time for calcul the mask position with numpy : 0.0877537727355957 nb_pixel_total : 11645 time to create 1 rle with old method : 0.014995336532592773 length of segment : 98 time for calcul the mask position with numpy : 0.09519481658935547 nb_pixel_total : 21859 time to create 1 rle with old method : 0.02950119972229004 length of segment : 236 time for calcul the mask position with numpy : 0.2509467601776123 nb_pixel_total : 133831 time to create 1 rle with old method : 0.15827226638793945 length of segment : 506 time for calcul the mask position with numpy : 0.03270387649536133 nb_pixel_total : 9521 time to create 1 rle with old method : 0.014195442199707031 length of segment : 94 time for calcul the mask position with numpy : 0.039018869400024414 nb_pixel_total : 29418 time to create 1 rle with old method : 0.04482316970825195 length of segment : 177 time for calcul the mask position with numpy : 0.04831552505493164 nb_pixel_total : 42381 time to create 1 rle with old method : 0.05320119857788086 length of segment : 304 time for calcul the mask position with numpy : 0.37273097038269043 nb_pixel_total : 164868 time to create 1 rle with new method : 0.010394573211669922 length of segment : 912 time for calcul the mask position with numpy : 0.027014970779418945 nb_pixel_total : 58484 time to create 1 rle with old method : 0.06804370880126953 length of segment : 269 time for calcul the mask position with numpy : 0.04996371269226074 nb_pixel_total : 7204 time to create 1 rle with old method : 0.008345842361450195 length of segment : 204 time for calcul the mask position with numpy : 0.05355095863342285 nb_pixel_total : 13100 time to create 1 rle with old method : 0.019740819931030273 length of segment : 125 time for calcul the mask position with numpy : 0.013935089111328125 nb_pixel_total : 8471 time to create 1 rle with old method : 0.015541791915893555 length of segment : 121 time for calcul the mask position with numpy : 0.025676965713500977 nb_pixel_total : 17999 time to create 1 rle with old method : 0.02273106575012207 length of segment : 243 time for calcul the mask position with numpy : 0.07554793357849121 nb_pixel_total : 42251 time to create 1 rle with old method : 0.052220821380615234 length of segment : 177 time for calcul the mask position with numpy : 0.04838967323303223 nb_pixel_total : 39679 time to create 1 rle with old method : 0.05126070976257324 length of segment : 326 time for calcul the mask position with numpy : 0.10026097297668457 nb_pixel_total : 53951 time to create 1 rle with old method : 0.06624937057495117 length of segment : 270 time for calcul the mask position with numpy : 0.12356209754943848 nb_pixel_total : 34223 time to create 1 rle with old method : 0.045215606689453125 length of segment : 365 time for calcul the mask position with numpy : 0.04835796356201172 nb_pixel_total : 15302 time to create 1 rle with old method : 0.025818586349487305 length of segment : 121 time for calcul the mask position with numpy : 0.03334403038024902 nb_pixel_total : 12104 time to create 1 rle with old method : 0.01402425765991211 length of segment : 151 time for calcul the mask position with numpy : 0.020560026168823242 nb_pixel_total : 51871 time to create 1 rle with old method : 0.058210134506225586 length of segment : 272 time for calcul the mask position with numpy : 0.03168225288391113 nb_pixel_total : 12749 time to create 1 rle with old method : 0.015093564987182617 length of segment : 94 time for calcul the mask position with numpy : 0.06294870376586914 nb_pixel_total : 36780 time to create 1 rle with old method : 0.04562950134277344 length of segment : 339 time for calcul the mask position with numpy : 0.014140129089355469 nb_pixel_total : 4411 time to create 1 rle with old method : 0.006505012512207031 length of segment : 62 time for calcul the mask position with numpy : 0.10922408103942871 nb_pixel_total : 31439 time to create 1 rle with old method : 0.04689216613769531 length of segment : 208 time for calcul the mask position with numpy : 0.03656172752380371 nb_pixel_total : 14935 time to create 1 rle with old method : 0.02839493751525879 length of segment : 167 time for calcul the mask position with numpy : 0.03248953819274902 nb_pixel_total : 7619 time to create 1 rle with old method : 0.013777732849121094 length of segment : 76 time for calcul the mask position with numpy : 0.05905032157897949 nb_pixel_total : 44412 time to create 1 rle with old method : 0.055200815200805664 length of segment : 333 time for calcul the mask position with numpy : 0.1190652847290039 nb_pixel_total : 85229 time to create 1 rle with old method : 0.09934067726135254 length of segment : 679 time for calcul the mask position with numpy : 0.02418661117553711 nb_pixel_total : 16165 time to create 1 rle with old method : 0.02253580093383789 length of segment : 152 time for calcul the mask position with numpy : 0.06452703475952148 nb_pixel_total : 33478 time to create 1 rle with old method : 0.04239916801452637 length of segment : 261 time for calcul the mask position with numpy : 0.07248640060424805 nb_pixel_total : 26751 time to create 1 rle with old method : 0.03628206253051758 length of segment : 178 time for calcul the mask position with numpy : 0.3634490966796875 nb_pixel_total : 394996 time to create 1 rle with new method : 0.03797769546508789 length of segment : 1016 time for calcul the mask position with numpy : 0.10862565040588379 nb_pixel_total : 64861 time to create 1 rle with old method : 0.07843756675720215 length of segment : 277 time for calcul the mask position with numpy : 0.17588376998901367 nb_pixel_total : 116677 time to create 1 rle with old method : 0.14052772521972656 length of segment : 481 time for calcul the mask position with numpy : 0.021367549896240234 nb_pixel_total : 7836 time to create 1 rle with old method : 0.014892101287841797 length of segment : 99 time for calcul the mask position with numpy : 0.14751076698303223 nb_pixel_total : 87097 time to create 1 rle with old method : 0.13480663299560547 length of segment : 362 time for calcul the mask position with numpy : 0.025919198989868164 nb_pixel_total : 9067 time to create 1 rle with old method : 0.015146732330322266 length of segment : 119 time for calcul the mask position with numpy : 0.3758666515350342 nb_pixel_total : 200545 time to create 1 rle with new method : 0.012350320816040039 length of segment : 621 time for calcul the mask position with numpy : 0.18851590156555176 nb_pixel_total : 93080 time to create 1 rle with old method : 0.10607433319091797 length of segment : 424 time for calcul the mask position with numpy : 0.06081748008728027 nb_pixel_total : 19452 time to create 1 rle with old method : 0.027007102966308594 length of segment : 193 time for calcul the mask position with numpy : 0.04930925369262695 nb_pixel_total : 15911 time to create 1 rle with old method : 0.022197484970092773 length of segment : 266 time for calcul the mask position with numpy : 0.10966634750366211 nb_pixel_total : 43910 time to create 1 rle with old method : 0.05171942710876465 length of segment : 467 time for calcul the mask position with numpy : 0.1052865982055664 nb_pixel_total : 62873 time to create 1 rle with old method : 0.0743875503540039 length of segment : 359 time for calcul the mask position with numpy : 0.0815732479095459 nb_pixel_total : 38478 time to create 1 rle with old method : 0.0467529296875 length of segment : 306 time for calcul the mask position with numpy : 0.25264668464660645 nb_pixel_total : 120173 time to create 1 rle with old method : 0.1616826057434082 length of segment : 932 time for calcul the mask position with numpy : 0.03768014907836914 nb_pixel_total : 33473 time to create 1 rle with old method : 0.04309654235839844 length of segment : 213 time for calcul the mask position with numpy : 0.041101932525634766 nb_pixel_total : 56272 time to create 1 rle with old method : 0.06637310981750488 length of segment : 309 time for calcul the mask position with numpy : 0.0024161338806152344 nb_pixel_total : 22931 time to create 1 rle with old method : 0.02619767189025879 length of segment : 310 time for calcul the mask position with numpy : 0.07941246032714844 nb_pixel_total : 211456 time to create 1 rle with new method : 0.009701251983642578 length of segment : 738 time for calcul the mask position with numpy : 0.017648935317993164 nb_pixel_total : 6328 time to create 1 rle with old method : 0.010318994522094727 length of segment : 119 time for calcul the mask position with numpy : 0.07209014892578125 nb_pixel_total : 39811 time to create 1 rle with old method : 0.048940181732177734 length of segment : 240 time for calcul the mask position with numpy : 0.731696367263794 nb_pixel_total : 823403 time to create 1 rle with new method : 0.09871339797973633 length of segment : 867 time for calcul the mask position with numpy : 0.04658770561218262 nb_pixel_total : 13575 time to create 1 rle with old method : 0.020853042602539062 length of segment : 97 time for calcul the mask position with numpy : 0.007112026214599609 nb_pixel_total : 4984 time to create 1 rle with old method : 0.012215614318847656 length of segment : 78 time for calcul the mask position with numpy : 0.24424433708190918 nb_pixel_total : 199393 time to create 1 rle with new method : 0.008676767349243164 length of segment : 418 time for calcul the mask position with numpy : 0.0826106071472168 nb_pixel_total : 37534 time to create 1 rle with old method : 0.046808481216430664 length of segment : 248 time for calcul the mask position with numpy : 0.05924057960510254 nb_pixel_total : 10957 time to create 1 rle with old method : 0.01801156997680664 length of segment : 171 time for calcul the mask position with numpy : 0.020503997802734375 nb_pixel_total : 10509 time to create 1 rle with old method : 0.012747049331665039 length of segment : 110 time for calcul the mask position with numpy : 0.0752406120300293 nb_pixel_total : 28252 time to create 1 rle with old method : 0.03914332389831543 length of segment : 194 time for calcul the mask position with numpy : 0.3345615863800049 nb_pixel_total : 456929 time to create 1 rle with new method : 0.030894994735717773 length of segment : 792 time for calcul the mask position with numpy : 0.04155588150024414 nb_pixel_total : 18866 time to create 1 rle with old method : 0.025515079498291016 length of segment : 221 time for calcul the mask position with numpy : 0.043030500411987305 nb_pixel_total : 39217 time to create 1 rle with old method : 0.05223989486694336 length of segment : 170 time for calcul the mask position with numpy : 0.05367636680603027 nb_pixel_total : 16890 time to create 1 rle with old method : 0.024518489837646484 length of segment : 225 time for calcul the mask position with numpy : 0.01769399642944336 nb_pixel_total : 10267 time to create 1 rle with old method : 0.01603555679321289 length of segment : 75 time for calcul the mask position with numpy : 0.08985662460327148 nb_pixel_total : 44667 time to create 1 rle with old method : 0.05510544776916504 length of segment : 315 time for calcul the mask position with numpy : 0.0510859489440918 nb_pixel_total : 43432 time to create 1 rle with old method : 0.05056881904602051 length of segment : 319 time for calcul the mask position with numpy : 0.1979067325592041 nb_pixel_total : 527358 time to create 1 rle with new method : 0.05703306198120117 length of segment : 847 time for calcul the mask position with numpy : 0.2467517852783203 nb_pixel_total : 142488 time to create 1 rle with old method : 0.16732382774353027 length of segment : 684 time for calcul the mask position with numpy : 0.019559621810913086 nb_pixel_total : 34224 time to create 1 rle with old method : 0.043582916259765625 length of segment : 308 time for calcul the mask position with numpy : 0.018224000930786133 nb_pixel_total : 20497 time to create 1 rle with old method : 0.02875065803527832 length of segment : 227 time for calcul the mask position with numpy : 0.07087230682373047 nb_pixel_total : 109294 time to create 1 rle with old method : 0.15047192573547363 length of segment : 492 time for calcul the mask position with numpy : 0.003536701202392578 nb_pixel_total : 4097 time to create 1 rle with old method : 0.006061077117919922 length of segment : 98 time for calcul the mask position with numpy : 0.01350712776184082 nb_pixel_total : 19425 time to create 1 rle with old method : 0.037076473236083984 length of segment : 128 time for calcul the mask position with numpy : 0.02343583106994629 nb_pixel_total : 6032 time to create 1 rle with old method : 0.01016378402709961 length of segment : 86 time for calcul the mask position with numpy : 0.004791975021362305 nb_pixel_total : 109424 time to create 1 rle with old method : 0.14179301261901855 length of segment : 539 time for calcul the mask position with numpy : 0.029799222946166992 nb_pixel_total : 6205 time to create 1 rle with old method : 0.01027536392211914 length of segment : 101 time for calcul the mask position with numpy : 0.10110616683959961 nb_pixel_total : 31909 time to create 1 rle with old method : 0.04670238494873047 length of segment : 285 time for calcul the mask position with numpy : 0.03580188751220703 nb_pixel_total : 7813 time to create 1 rle with old method : 0.009322881698608398 length of segment : 115 time for calcul the mask position with numpy : 0.17870664596557617 nb_pixel_total : 84657 time to create 1 rle with old method : 0.103179931640625 length of segment : 385 time for calcul the mask position with numpy : 0.1718432903289795 nb_pixel_total : 72678 time to create 1 rle with old method : 0.0861048698425293 length of segment : 375 time for calcul the mask position with numpy : 0.08989381790161133 nb_pixel_total : 31783 time to create 1 rle with old method : 0.03789949417114258 length of segment : 249 time for calcul the mask position with numpy : 0.0014407634735107422 nb_pixel_total : 9248 time to create 1 rle with old method : 0.011040449142456055 length of segment : 108 time for calcul the mask position with numpy : 0.040110111236572266 nb_pixel_total : 20349 time to create 1 rle with old method : 0.028304576873779297 length of segment : 202 time for calcul the mask position with numpy : 0.03993105888366699 nb_pixel_total : 11733 time to create 1 rle with old method : 0.01876068115234375 length of segment : 168 time for calcul the mask position with numpy : 0.020273923873901367 nb_pixel_total : 5618 time to create 1 rle with old method : 0.010149002075195312 length of segment : 74 time for calcul the mask position with numpy : 0.33676671981811523 nb_pixel_total : 233198 time to create 1 rle with new method : 0.016240596771240234 length of segment : 486 time for calcul the mask position with numpy : 0.12000584602355957 nb_pixel_total : 47112 time to create 1 rle with old method : 0.06290745735168457 length of segment : 352 time for calcul the mask position with numpy : 0.1417405605316162 nb_pixel_total : 26984 time to create 1 rle with old method : 0.035541534423828125 length of segment : 593 time for calcul the mask position with numpy : 0.03136444091796875 nb_pixel_total : 11417 time to create 1 rle with old method : 0.014912605285644531 length of segment : 358 time for calcul the mask position with numpy : 0.33999180793762207 nb_pixel_total : 318702 time to create 1 rle with new method : 0.044412851333618164 length of segment : 703 time for calcul the mask position with numpy : 0.12557029724121094 nb_pixel_total : 55611 time to create 1 rle with old method : 0.10405659675598145 length of segment : 244 time for calcul the mask position with numpy : 0.00024771690368652344 nb_pixel_total : 9302 time to create 1 rle with old method : 0.010828256607055664 length of segment : 191 time for calcul the mask position with numpy : 0.0032584667205810547 nb_pixel_total : 12166 time to create 1 rle with old method : 0.01399374008178711 length of segment : 187 time for calcul the mask position with numpy : 0.022466659545898438 nb_pixel_total : 6310 time to create 1 rle with old method : 0.011505842208862305 length of segment : 115 time for calcul the mask position with numpy : 0.04945802688598633 nb_pixel_total : 27511 time to create 1 rle with old method : 0.03570866584777832 length of segment : 143 time for calcul the mask position with numpy : 0.00015091896057128906 nb_pixel_total : 4833 time to create 1 rle with old method : 0.005714893341064453 length of segment : 94 time for calcul the mask position with numpy : 0.005450010299682617 nb_pixel_total : 11576 time to create 1 rle with old method : 0.01617121696472168 length of segment : 107 time for calcul the mask position with numpy : 0.026273012161254883 nb_pixel_total : 8283 time to create 1 rle with old method : 0.014301776885986328 length of segment : 94 time for calcul the mask position with numpy : 0.0005741119384765625 nb_pixel_total : 11921 time to create 1 rle with old method : 0.013644695281982422 length of segment : 246 time for calcul the mask position with numpy : 0.0042266845703125 nb_pixel_total : 41360 time to create 1 rle with old method : 0.04751920700073242 length of segment : 283 time for calcul the mask position with numpy : 0.0029671192169189453 nb_pixel_total : 50497 time to create 1 rle with old method : 0.05873847007751465 length of segment : 233 time for calcul the mask position with numpy : 0.0008311271667480469 nb_pixel_total : 19832 time to create 1 rle with old method : 0.023319005966186523 length of segment : 134 time for calcul the mask position with numpy : 0.02362060546875 nb_pixel_total : 312292 time to create 1 rle with new method : 0.03005385398864746 length of segment : 1204 time for calcul the mask position with numpy : 0.004586219787597656 nb_pixel_total : 82563 time to create 1 rle with old method : 0.0920872688293457 length of segment : 345 time for calcul the mask position with numpy : 0.004654884338378906 nb_pixel_total : 70734 time to create 1 rle with old method : 0.08293604850769043 length of segment : 240 time for calcul the mask position with numpy : 0.0010521411895751953 nb_pixel_total : 12686 time to create 1 rle with old method : 0.018897056579589844 length of segment : 231 time for calcul the mask position with numpy : 0.0013456344604492188 nb_pixel_total : 26388 time to create 1 rle with old method : 0.040116310119628906 length of segment : 185 time for calcul the mask position with numpy : 0.01576685905456543 nb_pixel_total : 315410 time to create 1 rle with new method : 0.027323484420776367 length of segment : 706 time for calcul the mask position with numpy : 0.0008914470672607422 nb_pixel_total : 13990 time to create 1 rle with old method : 0.016080379486083984 length of segment : 166 time for calcul the mask position with numpy : 0.008200645446777344 nb_pixel_total : 99299 time to create 1 rle with old method : 0.11367917060852051 length of segment : 645 time for calcul the mask position with numpy : 0.0015940666198730469 nb_pixel_total : 36102 time to create 1 rle with old method : 0.055489540100097656 length of segment : 249 time for calcul the mask position with numpy : 0.0026962757110595703 nb_pixel_total : 45892 time to create 1 rle with old method : 0.05266094207763672 length of segment : 302 time for calcul the mask position with numpy : 0.005038022994995117 nb_pixel_total : 49070 time to create 1 rle with old method : 0.06072878837585449 length of segment : 213 time for calcul the mask position with numpy : 0.0005030632019042969 nb_pixel_total : 7921 time to create 1 rle with old method : 0.009531021118164062 length of segment : 156 time for calcul the mask position with numpy : 0.0043332576751708984 nb_pixel_total : 66936 time to create 1 rle with old method : 0.07634973526000977 length of segment : 492 time for calcul the mask position with numpy : 0.0007307529449462891 nb_pixel_total : 25562 time to create 1 rle with old method : 0.030268430709838867 length of segment : 171 time for calcul the mask position with numpy : 0.0009660720825195312 nb_pixel_total : 37620 time to create 1 rle with old method : 0.050073862075805664 length of segment : 235 time for calcul the mask position with numpy : 0.00028514862060546875 nb_pixel_total : 6442 time to create 1 rle with old method : 0.007753133773803711 length of segment : 142 time for calcul the mask position with numpy : 0.0006268024444580078 nb_pixel_total : 11255 time to create 1 rle with old method : 0.013475418090820312 length of segment : 270 time for calcul the mask position with numpy : 0.006395101547241211 nb_pixel_total : 104595 time to create 1 rle with old method : 0.1233208179473877 length of segment : 478 time for calcul the mask position with numpy : 0.0004813671112060547 nb_pixel_total : 21594 time to create 1 rle with old method : 0.024238109588623047 length of segment : 374 time for calcul the mask position with numpy : 0.0006191730499267578 nb_pixel_total : 15096 time to create 1 rle with old method : 0.017415285110473633 length of segment : 173 time for calcul the mask position with numpy : 0.0036737918853759766 nb_pixel_total : 70835 time to create 1 rle with old method : 0.08135128021240234 length of segment : 315 time for calcul the mask position with numpy : 0.022558927536010742 nb_pixel_total : 159201 time to create 1 rle with new method : 0.011669635772705078 length of segment : 516 time for calcul the mask position with numpy : 0.0068607330322265625 nb_pixel_total : 65147 time to create 1 rle with old method : 0.07513546943664551 length of segment : 330 time for calcul the mask position with numpy : 0.030231714248657227 nb_pixel_total : 233925 time to create 1 rle with new method : 0.01979684829711914 length of segment : 587 time for calcul the mask position with numpy : 0.009189128875732422 nb_pixel_total : 63492 time to create 1 rle with old method : 0.07585763931274414 length of segment : 322 time for calcul the mask position with numpy : 0.0036106109619140625 nb_pixel_total : 32158 time to create 1 rle with old method : 0.04589056968688965 length of segment : 290 time for calcul the mask position with numpy : 0.0005681514739990234 nb_pixel_total : 8802 time to create 1 rle with old method : 0.010661840438842773 length of segment : 71 time for calcul the mask position with numpy : 0.0043947696685791016 nb_pixel_total : 62057 time to create 1 rle with old method : 0.06838274002075195 length of segment : 349 time for calcul the mask position with numpy : 0.01041412353515625 nb_pixel_total : 96458 time to create 1 rle with old method : 0.11710691452026367 length of segment : 342 time for calcul the mask position with numpy : 0.003300189971923828 nb_pixel_total : 32055 time to create 1 rle with old method : 0.03548145294189453 length of segment : 174 time for calcul the mask position with numpy : 0.0029828548431396484 nb_pixel_total : 14402 time to create 1 rle with old method : 0.016228914260864258 length of segment : 152 time for calcul the mask position with numpy : 0.00577545166015625 nb_pixel_total : 25520 time to create 1 rle with old method : 0.029726743698120117 length of segment : 327 time for calcul the mask position with numpy : 0.01289820671081543 nb_pixel_total : 109864 time to create 1 rle with old method : 0.12388229370117188 length of segment : 474 time for calcul the mask position with numpy : 0.0010859966278076172 nb_pixel_total : 17812 time to create 1 rle with old method : 0.020429611206054688 length of segment : 181 time for calcul the mask position with numpy : 0.0013394355773925781 nb_pixel_total : 12820 time to create 1 rle with old method : 0.014943361282348633 length of segment : 222 time for calcul the mask position with numpy : 0.0048100948333740234 nb_pixel_total : 71687 time to create 1 rle with old method : 0.0808866024017334 length of segment : 234 time for calcul the mask position with numpy : 0.003515005111694336 nb_pixel_total : 22520 time to create 1 rle with old method : 0.025722742080688477 length of segment : 224 time for calcul the mask position with numpy : 0.004864692687988281 nb_pixel_total : 8254 time to create 1 rle with old method : 0.009741067886352539 length of segment : 119 time for calcul the mask position with numpy : 0.0018606185913085938 nb_pixel_total : 31105 time to create 1 rle with old method : 0.035506248474121094 length of segment : 211 time for calcul the mask position with numpy : 0.0012607574462890625 nb_pixel_total : 16706 time to create 1 rle with old method : 0.019409894943237305 length of segment : 198 time for calcul the mask position with numpy : 0.006963968276977539 nb_pixel_total : 60046 time to create 1 rle with old method : 0.07396411895751953 length of segment : 456 time for calcul the mask position with numpy : 0.011178970336914062 nb_pixel_total : 21069 time to create 1 rle with old method : 0.02961111068725586 length of segment : 116 time for calcul the mask position with numpy : 0.0010294914245605469 nb_pixel_total : 28059 time to create 1 rle with old method : 0.032814979553222656 length of segment : 224 time for calcul the mask position with numpy : 0.0011570453643798828 nb_pixel_total : 28797 time to create 1 rle with old method : 0.03738713264465332 length of segment : 219 time for calcul the mask position with numpy : 0.0025186538696289062 nb_pixel_total : 47796 time to create 1 rle with old method : 0.05382847785949707 length of segment : 396 time for calcul the mask position with numpy : 0.010988712310791016 nb_pixel_total : 60809 time to create 1 rle with old method : 0.07581472396850586 length of segment : 282 time for calcul the mask position with numpy : 0.011748552322387695 nb_pixel_total : 95681 time to create 1 rle with old method : 0.1151885986328125 length of segment : 330 time for calcul the mask position with numpy : 0.004669189453125 nb_pixel_total : 71780 time to create 1 rle with old method : 0.0819544792175293 length of segment : 255 time for calcul the mask position with numpy : 0.003598928451538086 nb_pixel_total : 34317 time to create 1 rle with old method : 0.039699554443359375 length of segment : 188 time for calcul the mask position with numpy : 0.0003314018249511719 nb_pixel_total : 9798 time to create 1 rle with old method : 0.01175999641418457 length of segment : 197 time for calcul the mask position with numpy : 0.004762172698974609 nb_pixel_total : 73949 time to create 1 rle with old method : 0.08557844161987305 length of segment : 528 time for calcul the mask position with numpy : 0.0038776397705078125 nb_pixel_total : 39043 time to create 1 rle with old method : 0.04531526565551758 length of segment : 207 time for calcul the mask position with numpy : 0.0022559165954589844 nb_pixel_total : 25256 time to create 1 rle with old method : 0.02951955795288086 length of segment : 204 time for calcul the mask position with numpy : 0.0041353702545166016 nb_pixel_total : 35085 time to create 1 rle with old method : 0.04084515571594238 length of segment : 200 time for calcul the mask position with numpy : 0.000993967056274414 nb_pixel_total : 9870 time to create 1 rle with old method : 0.011632204055786133 length of segment : 97 time for calcul the mask position with numpy : 0.0007381439208984375 nb_pixel_total : 11166 time to create 1 rle with old method : 0.013733386993408203 length of segment : 84 time for calcul the mask position with numpy : 0.0031011104583740234 nb_pixel_total : 18679 time to create 1 rle with old method : 0.022106409072875977 length of segment : 155 time for calcul the mask position with numpy : 0.0007104873657226562 nb_pixel_total : 8444 time to create 1 rle with old method : 0.010096073150634766 length of segment : 105 time for calcul the mask position with numpy : 0.0034754276275634766 nb_pixel_total : 23757 time to create 1 rle with old method : 0.02767324447631836 length of segment : 188 time for calcul the mask position with numpy : 0.0019989013671875 nb_pixel_total : 21991 time to create 1 rle with old method : 0.02599024772644043 length of segment : 114 time for calcul the mask position with numpy : 0.0048639774322509766 nb_pixel_total : 16169 time to create 1 rle with old method : 0.01858973503112793 length of segment : 278 time for calcul the mask position with numpy : 0.0007770061492919922 nb_pixel_total : 39442 time to create 1 rle with old method : 0.045067548751831055 length of segment : 177 time for calcul the mask position with numpy : 0.02534341812133789 nb_pixel_total : 106605 time to create 1 rle with old method : 0.12225079536437988 length of segment : 289 time for calcul the mask position with numpy : 0.013616085052490234 nb_pixel_total : 95094 time to create 1 rle with old method : 0.13267230987548828 length of segment : 317 time for calcul the mask position with numpy : 0.0017349720001220703 nb_pixel_total : 15329 time to create 1 rle with old method : 0.018079280853271484 length of segment : 144 time for calcul the mask position with numpy : 0.0030393600463867188 nb_pixel_total : 29262 time to create 1 rle with old method : 0.03341197967529297 length of segment : 315 time for calcul the mask position with numpy : 0.0056955814361572266 nb_pixel_total : 89676 time to create 1 rle with old method : 0.10283231735229492 length of segment : 401 time for calcul the mask position with numpy : 0.001519918441772461 nb_pixel_total : 21947 time to create 1 rle with old method : 0.025516748428344727 length of segment : 214 time for calcul the mask position with numpy : 0.0013647079467773438 nb_pixel_total : 16136 time to create 1 rle with old method : 0.019348621368408203 length of segment : 211 time for calcul the mask position with numpy : 0.0008392333984375 nb_pixel_total : 9484 time to create 1 rle with old method : 0.01274728775024414 length of segment : 115 time for calcul the mask position with numpy : 0.001354217529296875 nb_pixel_total : 16886 time to create 1 rle with old method : 0.01986074447631836 length of segment : 194 time for calcul the mask position with numpy : 0.026181459426879883 nb_pixel_total : 297558 time to create 1 rle with new method : 0.033120155334472656 length of segment : 956 time for calcul the mask position with numpy : 0.0012345314025878906 nb_pixel_total : 43186 time to create 1 rle with old method : 0.050377607345581055 length of segment : 411 time for calcul the mask position with numpy : 0.0033273696899414062 nb_pixel_total : 40328 time to create 1 rle with old method : 0.04893326759338379 length of segment : 270 time for calcul the mask position with numpy : 0.0051860809326171875 nb_pixel_total : 76380 time to create 1 rle with old method : 0.08636736869812012 length of segment : 329 time for calcul the mask position with numpy : 0.0026183128356933594 nb_pixel_total : 25288 time to create 1 rle with old method : 0.02991485595703125 length of segment : 244 time for calcul the mask position with numpy : 0.0007872581481933594 nb_pixel_total : 12381 time to create 1 rle with old method : 0.014738798141479492 length of segment : 150 time for calcul the mask position with numpy : 0.0020513534545898438 nb_pixel_total : 32622 time to create 1 rle with old method : 0.03796958923339844 length of segment : 233 time for calcul the mask position with numpy : 0.0013391971588134766 nb_pixel_total : 14383 time to create 1 rle with old method : 0.017073869705200195 length of segment : 199 time for calcul the mask position with numpy : 0.001688241958618164 nb_pixel_total : 27606 time to create 1 rle with old method : 0.032317161560058594 length of segment : 144 time for calcul the mask position with numpy : 0.010019540786743164 nb_pixel_total : 177135 time to create 1 rle with new method : 0.020583629608154297 length of segment : 437 time for calcul the mask position with numpy : 0.0018956661224365234 nb_pixel_total : 17781 time to create 1 rle with old method : 0.021084308624267578 length of segment : 330 time for calcul the mask position with numpy : 0.0007946491241455078 nb_pixel_total : 20505 time to create 1 rle with old method : 0.02437305450439453 length of segment : 196 time for calcul the mask position with numpy : 0.0012352466583251953 nb_pixel_total : 14771 time to create 1 rle with old method : 0.0178220272064209 length of segment : 139 time for calcul the mask position with numpy : 0.0019960403442382812 nb_pixel_total : 38712 time to create 1 rle with old method : 0.045172691345214844 length of segment : 297 time for calcul the mask position with numpy : 0.003365755081176758 nb_pixel_total : 37519 time to create 1 rle with old method : 0.0486600399017334 length of segment : 191 time for calcul the mask position with numpy : 0.0011093616485595703 nb_pixel_total : 12308 time to create 1 rle with old method : 0.11261796951293945 length of segment : 107 time for calcul the mask position with numpy : 0.0023365020751953125 nb_pixel_total : 18012 time to create 1 rle with old method : 0.08616781234741211 length of segment : 339 time for calcul the mask position with numpy : 0.001561880111694336 nb_pixel_total : 17034 time to create 1 rle with old method : 0.020277023315429688 length of segment : 220 time for calcul the mask position with numpy : 0.012157440185546875 nb_pixel_total : 170017 time to create 1 rle with new method : 0.016817331314086914 length of segment : 727 time for calcul the mask position with numpy : 0.002357006072998047 nb_pixel_total : 28578 time to create 1 rle with old method : 0.0332028865814209 length of segment : 270 time for calcul the mask position with numpy : 0.0006489753723144531 nb_pixel_total : 8713 time to create 1 rle with old method : 0.010574579238891602 length of segment : 78 time for calcul the mask position with numpy : 0.0008447170257568359 nb_pixel_total : 8501 time to create 1 rle with old method : 0.010130643844604492 length of segment : 175 time for calcul the mask position with numpy : 0.007383108139038086 nb_pixel_total : 103644 time to create 1 rle with old method : 0.11617660522460938 length of segment : 447 time for calcul the mask position with numpy : 0.0022726058959960938 nb_pixel_total : 36309 time to create 1 rle with old method : 0.0410463809967041 length of segment : 354 time for calcul the mask position with numpy : 0.0023834705352783203 nb_pixel_total : 38395 time to create 1 rle with old method : 0.04576754570007324 length of segment : 249 time for calcul the mask position with numpy : 0.01624441146850586 nb_pixel_total : 193345 time to create 1 rle with new method : 0.019138336181640625 length of segment : 822 time for calcul the mask position with numpy : 0.002049684524536133 nb_pixel_total : 28726 time to create 1 rle with old method : 0.03654170036315918 length of segment : 119 time for calcul the mask position with numpy : 0.0009222030639648438 nb_pixel_total : 7770 time to create 1 rle with old method : 0.011671304702758789 length of segment : 110 time for calcul the mask position with numpy : 0.010974645614624023 nb_pixel_total : 121320 time to create 1 rle with old method : 0.16159415245056152 length of segment : 613 time for calcul the mask position with numpy : 0.0018532276153564453 nb_pixel_total : 26920 time to create 1 rle with old method : 0.0313112735748291 length of segment : 185 time for calcul the mask position with numpy : 0.0010955333709716797 nb_pixel_total : 13518 time to create 1 rle with old method : 0.016021251678466797 length of segment : 143 time for calcul the mask position with numpy : 0.0004661083221435547 nb_pixel_total : 5559 time to create 1 rle with old method : 0.0067026615142822266 length of segment : 109 time for calcul the mask position with numpy : 0.004341602325439453 nb_pixel_total : 36577 time to create 1 rle with old method : 0.04980134963989258 length of segment : 395 time for calcul the mask position with numpy : 0.010424375534057617 nb_pixel_total : 8844 time to create 1 rle with old method : 0.010579824447631836 length of segment : 135 time for calcul the mask position with numpy : 0.005317211151123047 nb_pixel_total : 40342 time to create 1 rle with old method : 0.05468869209289551 length of segment : 260 time for calcul the mask position with numpy : 0.0014231204986572266 nb_pixel_total : 20816 time to create 1 rle with old method : 0.024390220642089844 length of segment : 228 time for calcul the mask position with numpy : 0.0013356208801269531 nb_pixel_total : 19947 time to create 1 rle with old method : 0.024002552032470703 length of segment : 162 time for calcul the mask position with numpy : 0.004848480224609375 nb_pixel_total : 169432 time to create 1 rle with new method : 0.010224580764770508 length of segment : 646 time for calcul the mask position with numpy : 0.0004878044128417969 nb_pixel_total : 6711 time to create 1 rle with old method : 0.008022546768188477 length of segment : 64 time for calcul the mask position with numpy : 0.0001785755157470703 nb_pixel_total : 5267 time to create 1 rle with old method : 0.006172895431518555 length of segment : 112 time for calcul the mask position with numpy : 0.001191854476928711 nb_pixel_total : 14911 time to create 1 rle with old method : 0.017796039581298828 length of segment : 132 time for calcul the mask position with numpy : 0.028509140014648438 nb_pixel_total : 498236 time to create 1 rle with new method : 0.06381058692932129 length of segment : 567 time for calcul the mask position with numpy : 0.0009810924530029297 nb_pixel_total : 11108 time to create 1 rle with old method : 0.012914419174194336 length of segment : 171 time for calcul the mask position with numpy : 0.0062983036041259766 nb_pixel_total : 89565 time to create 1 rle with old method : 0.10294437408447266 length of segment : 423 time for calcul the mask position with numpy : 0.0022003650665283203 nb_pixel_total : 45709 time to create 1 rle with old method : 0.053894996643066406 length of segment : 151 time for calcul the mask position with numpy : 0.0006871223449707031 nb_pixel_total : 11517 time to create 1 rle with old method : 0.013692140579223633 length of segment : 122 time for calcul the mask position with numpy : 0.0005156993865966797 nb_pixel_total : 6528 time to create 1 rle with old method : 0.007915496826171875 length of segment : 120 time for calcul the mask position with numpy : 0.013185501098632812 nb_pixel_total : 133060 time to create 1 rle with old method : 0.15844035148620605 length of segment : 855 time for calcul the mask position with numpy : 0.0016930103302001953 nb_pixel_total : 23113 time to create 1 rle with old method : 0.028556108474731445 length of segment : 181 time for calcul the mask position with numpy : 0.0006062984466552734 nb_pixel_total : 8972 time to create 1 rle with old method : 0.01314854621887207 length of segment : 140 time for calcul the mask position with numpy : 0.0007143020629882812 nb_pixel_total : 27910 time to create 1 rle with old method : 0.03523969650268555 length of segment : 309 time for calcul the mask position with numpy : 0.0014278888702392578 nb_pixel_total : 11047 time to create 1 rle with old method : 0.013069868087768555 length of segment : 170 time for calcul the mask position with numpy : 0.001199960708618164 nb_pixel_total : 11998 time to create 1 rle with old method : 0.014503240585327148 length of segment : 117 time for calcul the mask position with numpy : 0.0006077289581298828 nb_pixel_total : 9539 time to create 1 rle with old method : 0.012858867645263672 length of segment : 68 time for calcul the mask position with numpy : 0.019254684448242188 nb_pixel_total : 269564 time to create 1 rle with new method : 0.02716231346130371 length of segment : 783 time for calcul the mask position with numpy : 0.0042531490325927734 nb_pixel_total : 45522 time to create 1 rle with old method : 0.05562472343444824 length of segment : 407 time for calcul the mask position with numpy : 0.0019044876098632812 nb_pixel_total : 21533 time to create 1 rle with old method : 0.026795625686645508 length of segment : 313 time for calcul the mask position with numpy : 0.0008373260498046875 nb_pixel_total : 23526 time to create 1 rle with old method : 0.0307157039642334 length of segment : 333 time for calcul the mask position with numpy : 0.0018990039825439453 nb_pixel_total : 33368 time to create 1 rle with old method : 0.0543370246887207 length of segment : 162 time for calcul the mask position with numpy : 0.0015778541564941406 nb_pixel_total : 9860 time to create 1 rle with old method : 0.01181173324584961 length of segment : 286 time for calcul the mask position with numpy : 0.0005199909210205078 nb_pixel_total : 9572 time to create 1 rle with old method : 0.011360883712768555 length of segment : 89 time for calcul the mask position with numpy : 0.00016498565673828125 nb_pixel_total : 6530 time to create 1 rle with old method : 0.008128166198730469 length of segment : 72 time for calcul the mask position with numpy : 0.0011742115020751953 nb_pixel_total : 15883 time to create 1 rle with old method : 0.018863201141357422 length of segment : 121 time for calcul the mask position with numpy : 0.0010447502136230469 nb_pixel_total : 19856 time to create 1 rle with old method : 0.02670574188232422 length of segment : 171 time for calcul the mask position with numpy : 0.0012674331665039062 nb_pixel_total : 23672 time to create 1 rle with old method : 0.02749013900756836 length of segment : 318 time for calcul the mask position with numpy : 0.0010428428649902344 nb_pixel_total : 17606 time to create 1 rle with old method : 0.021091461181640625 length of segment : 123 time for calcul the mask position with numpy : 0.00424647331237793 nb_pixel_total : 95226 time to create 1 rle with old method : 0.11453509330749512 length of segment : 392 time for calcul the mask position with numpy : 0.0003635883331298828 nb_pixel_total : 14657 time to create 1 rle with old method : 0.017145395278930664 length of segment : 138 time for calcul the mask position with numpy : 0.0012171268463134766 nb_pixel_total : 26367 time to create 1 rle with old method : 0.030737638473510742 length of segment : 300 time for calcul the mask position with numpy : 0.005061149597167969 nb_pixel_total : 94329 time to create 1 rle with old method : 0.10801386833190918 length of segment : 362 time for calcul the mask position with numpy : 0.001794576644897461 nb_pixel_total : 31245 time to create 1 rle with old method : 0.03542685508728027 length of segment : 221 time for calcul the mask position with numpy : 0.009100198745727539 nb_pixel_total : 135620 time to create 1 rle with old method : 0.1783580780029297 length of segment : 510 time for calcul the mask position with numpy : 0.03089118003845215 nb_pixel_total : 462403 time to create 1 rle with new method : 0.03876519203186035 length of segment : 688 time for calcul the mask position with numpy : 0.0009157657623291016 nb_pixel_total : 11418 time to create 1 rle with old method : 0.01325845718383789 length of segment : 169 time for calcul the mask position with numpy : 0.0006463527679443359 nb_pixel_total : 11419 time to create 1 rle with old method : 0.013740062713623047 length of segment : 118 time for calcul the mask position with numpy : 0.004456520080566406 nb_pixel_total : 73720 time to create 1 rle with old method : 0.08510231971740723 length of segment : 458 time for calcul the mask position with numpy : 0.0022554397583007812 nb_pixel_total : 45926 time to create 1 rle with old method : 0.05314970016479492 length of segment : 152 time for calcul the mask position with numpy : 0.0007762908935546875 nb_pixel_total : 25417 time to create 1 rle with old method : 0.03285861015319824 length of segment : 194 time for calcul the mask position with numpy : 0.0006518363952636719 nb_pixel_total : 29074 time to create 1 rle with old method : 0.03333616256713867 length of segment : 207 time for calcul the mask position with numpy : 0.0011959075927734375 nb_pixel_total : 21463 time to create 1 rle with old method : 0.0246431827545166 length of segment : 159 time for calcul the mask position with numpy : 0.0023953914642333984 nb_pixel_total : 23799 time to create 1 rle with old method : 0.028273582458496094 length of segment : 269 time for calcul the mask position with numpy : 0.00016832351684570312 nb_pixel_total : 6906 time to create 1 rle with old method : 0.008628368377685547 length of segment : 66 time for calcul the mask position with numpy : 0.000713348388671875 nb_pixel_total : 11069 time to create 1 rle with old method : 0.013670921325683594 length of segment : 112 time for calcul the mask position with numpy : 0.005720615386962891 nb_pixel_total : 98839 time to create 1 rle with old method : 0.1121375560760498 length of segment : 451 time for calcul the mask position with numpy : 0.0005326271057128906 nb_pixel_total : 9565 time to create 1 rle with old method : 0.011873245239257812 length of segment : 68 time for calcul the mask position with numpy : 0.009760618209838867 nb_pixel_total : 122692 time to create 1 rle with old method : 0.15013670921325684 length of segment : 408 time for calcul the mask position with numpy : 0.01145792007446289 nb_pixel_total : 202316 time to create 1 rle with new method : 0.014409780502319336 length of segment : 795 time for calcul the mask position with numpy : 0.001081705093383789 nb_pixel_total : 13877 time to create 1 rle with old method : 0.015959501266479492 length of segment : 194 time for calcul the mask position with numpy : 0.0005931854248046875 nb_pixel_total : 9237 time to create 1 rle with old method : 0.010847806930541992 length of segment : 132 time for calcul the mask position with numpy : 0.0009140968322753906 nb_pixel_total : 18014 time to create 1 rle with old method : 0.020910978317260742 length of segment : 127 time for calcul the mask position with numpy : 0.00144195556640625 nb_pixel_total : 19435 time to create 1 rle with old method : 0.022882938385009766 length of segment : 196 time for calcul the mask position with numpy : 0.001321554183959961 nb_pixel_total : 11643 time to create 1 rle with old method : 0.0138092041015625 length of segment : 204 time for calcul the mask position with numpy : 0.0012459754943847656 nb_pixel_total : 17635 time to create 1 rle with old method : 0.020583391189575195 length of segment : 277 time for calcul the mask position with numpy : 0.0011675357818603516 nb_pixel_total : 32905 time to create 1 rle with old method : 0.03914308547973633 length of segment : 207 time for calcul the mask position with numpy : 0.007981538772583008 nb_pixel_total : 188939 time to create 1 rle with new method : 0.007785797119140625 length of segment : 459 time for calcul the mask position with numpy : 0.0011200904846191406 nb_pixel_total : 20225 time to create 1 rle with old method : 0.02313852310180664 length of segment : 161 time for calcul the mask position with numpy : 0.00036597251892089844 nb_pixel_total : 5566 time to create 1 rle with old method : 0.0067021846771240234 length of segment : 80 time for calcul the mask position with numpy : 0.0009784698486328125 nb_pixel_total : 16518 time to create 1 rle with old method : 0.019962310791015625 length of segment : 120 time for calcul the mask position with numpy : 0.0011446475982666016 nb_pixel_total : 17816 time to create 1 rle with old method : 0.0210115909576416 length of segment : 128 time for calcul the mask position with numpy : 0.002575397491455078 nb_pixel_total : 36763 time to create 1 rle with old method : 0.04323530197143555 length of segment : 323 time for calcul the mask position with numpy : 0.002766847610473633 nb_pixel_total : 61016 time to create 1 rle with old method : 0.07338476181030273 length of segment : 355 time for calcul the mask position with numpy : 0.0007076263427734375 nb_pixel_total : 9195 time to create 1 rle with old method : 0.01080942153930664 length of segment : 112 time for calcul the mask position with numpy : 0.023545503616333008 nb_pixel_total : 504856 time to create 1 rle with new method : 0.11246156692504883 length of segment : 574 time for calcul the mask position with numpy : 0.0006580352783203125 nb_pixel_total : 8491 time to create 1 rle with old method : 0.010046243667602539 length of segment : 161 time for calcul the mask position with numpy : 0.001390695571899414 nb_pixel_total : 21245 time to create 1 rle with old method : 0.024129867553710938 length of segment : 176 time for calcul the mask position with numpy : 0.0007569789886474609 nb_pixel_total : 14955 time to create 1 rle with old method : 0.01729559898376465 length of segment : 113 time for calcul the mask position with numpy : 0.0018053054809570312 nb_pixel_total : 25341 time to create 1 rle with old method : 0.029139995574951172 length of segment : 160 time for calcul the mask position with numpy : 0.00548100471496582 nb_pixel_total : 99108 time to create 1 rle with old method : 0.11043977737426758 length of segment : 487 time for calcul the mask position with numpy : 0.00074005126953125 nb_pixel_total : 11138 time to create 1 rle with old method : 0.013356924057006836 length of segment : 113 time for calcul the mask position with numpy : 0.0007612705230712891 nb_pixel_total : 9073 time to create 1 rle with old method : 0.01059722900390625 length of segment : 140 time for calcul the mask position with numpy : 0.00830221176147461 nb_pixel_total : 173505 time to create 1 rle with new method : 0.011317729949951172 length of segment : 882 time for calcul the mask position with numpy : 0.0007236003875732422 nb_pixel_total : 11542 time to create 1 rle with old method : 0.013606786727905273 length of segment : 102 time for calcul the mask position with numpy : 0.0005421638488769531 nb_pixel_total : 8873 time to create 1 rle with old method : 0.01043701171875 length of segment : 86 time for calcul the mask position with numpy : 0.00034356117248535156 nb_pixel_total : 16273 time to create 1 rle with old method : 0.01866912841796875 length of segment : 136 time for calcul the mask position with numpy : 0.006273508071899414 nb_pixel_total : 158713 time to create 1 rle with new method : 0.006365060806274414 length of segment : 445 time for calcul the mask position with numpy : 0.0015616416931152344 nb_pixel_total : 35301 time to create 1 rle with old method : 0.040929317474365234 length of segment : 134 time for calcul the mask position with numpy : 0.0002734661102294922 nb_pixel_total : 10930 time to create 1 rle with old method : 0.014146566390991211 length of segment : 127 time for calcul the mask position with numpy : 0.004987478256225586 nb_pixel_total : 62855 time to create 1 rle with old method : 0.09527182579040527 length of segment : 691 time for calcul the mask position with numpy : 0.001956939697265625 nb_pixel_total : 37031 time to create 1 rle with old method : 0.04251885414123535 length of segment : 232 time for calcul the mask position with numpy : 0.011818647384643555 nb_pixel_total : 249103 time to create 1 rle with new method : 0.01287698745727539 length of segment : 640 time for calcul the mask position with numpy : 0.0006108283996582031 nb_pixel_total : 11007 time to create 1 rle with old method : 0.012930154800415039 length of segment : 110 time for calcul the mask position with numpy : 0.001661539077758789 nb_pixel_total : 48815 time to create 1 rle with old method : 0.0570065975189209 length of segment : 275 time for calcul the mask position with numpy : 0.0005931854248046875 nb_pixel_total : 19320 time to create 1 rle with old method : 0.02244424819946289 length of segment : 174 time for calcul the mask position with numpy : 0.0014705657958984375 nb_pixel_total : 22180 time to create 1 rle with old method : 0.025468111038208008 length of segment : 295 time for calcul the mask position with numpy : 0.0018591880798339844 nb_pixel_total : 36291 time to create 1 rle with old method : 0.04481816291809082 length of segment : 255 time for calcul the mask position with numpy : 0.000982522964477539 nb_pixel_total : 15838 time to create 1 rle with old method : 0.018758535385131836 length of segment : 300 time for calcul the mask position with numpy : 0.00090789794921875 nb_pixel_total : 8663 time to create 1 rle with old method : 0.010170936584472656 length of segment : 124 time for calcul the mask position with numpy : 0.0007240772247314453 nb_pixel_total : 12913 time to create 1 rle with old method : 0.015455007553100586 length of segment : 184 time for calcul the mask position with numpy : 0.0004172325134277344 nb_pixel_total : 6072 time to create 1 rle with old method : 0.007013082504272461 length of segment : 74 time for calcul the mask position with numpy : 0.00037598609924316406 nb_pixel_total : 10119 time to create 1 rle with old method : 0.012266397476196289 length of segment : 72 time for calcul the mask position with numpy : 0.0007226467132568359 nb_pixel_total : 32872 time to create 1 rle with old method : 0.03837299346923828 length of segment : 175 time for calcul the mask position with numpy : 0.0019271373748779297 nb_pixel_total : 53973 time to create 1 rle with old method : 0.06317329406738281 length of segment : 365 time for calcul the mask position with numpy : 0.0018384456634521484 nb_pixel_total : 26128 time to create 1 rle with old method : 0.029587984085083008 length of segment : 263 time for calcul the mask position with numpy : 0.0011875629425048828 nb_pixel_total : 21413 time to create 1 rle with old method : 0.024864673614501953 length of segment : 199 time for calcul the mask position with numpy : 0.002771615982055664 nb_pixel_total : 37951 time to create 1 rle with old method : 0.04465627670288086 length of segment : 325 time for calcul the mask position with numpy : 0.002160787582397461 nb_pixel_total : 24173 time to create 1 rle with old method : 0.03183460235595703 length of segment : 297 time spent for convertir_results : 67.6006076335907 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 770 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 121829 save missing photos in datou_result : time spend for datou_step_exec : 303.76217579841614 time spend to save output : 11.5408775806427 total time spend for step 1 : 315.30305337905884 step2:crop_condition Tue Apr 1 02:45:47 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 ! 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 Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 15 ! batch 1 Loaded 770 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 426 About to insert : list_path_to_insert length 426 new photo from crops ! About to upload 426 photos upload in portfolio : 3736932 init cache_photo without model_param we have 426 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468408_2499418 we have uploaded 426 photos in the portfolio 3736932 time of upload the photos Elapsed time : 130.97671508789062 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 181 About to insert : list_path_to_insert length 181 new photo from crops ! About to upload 181 photos upload in portfolio : 3736932 init cache_photo without model_param we have 181 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468580_2499418 we have uploaded 181 photos in the portfolio 3736932 time of upload the photos Elapsed time : 79.68915724754333 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 9 About to insert : list_path_to_insert length 9 new photo from crops ! About to upload 9 photos upload in portfolio : 3736932 init cache_photo without model_param we have 9 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468664_2499418 we have uploaded 9 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.4317069053649902 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 59 About to insert : list_path_to_insert length 59 new photo from crops ! About to upload 59 photos upload in portfolio : 3736932 init cache_photo without model_param we have 59 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468684_2499418 we have uploaded 59 photos in the portfolio 3736932 time of upload the photos Elapsed time : 31.801345109939575 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 18 About to insert : list_path_to_insert length 18 new photo from crops ! About to upload 18 photos upload in portfolio : 3736932 init cache_photo without model_param we have 18 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468720_2499418 we have uploaded 18 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.898188591003418 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468732_2499418 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.5375771522521973 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 24 About to insert : list_path_to_insert length 24 new photo from crops ! About to upload 24 photos upload in portfolio : 3736932 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743468741_2499418 we have uploaded 24 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.179277658462524 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349150976, 1349145716, 1349145691, 1349145687, 1348990554, 1348990549, 1348990539, 1348990537, 1348990534, 1348990500, 1348990497, 1348990491, 1348990489, 1348990485, 1348990483] Looping around the photos to save general results len do output : 720 /1349180333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180334Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180341Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180356Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180371Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180385Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180387Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180390Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180391Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180398Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180401Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180408Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180409Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180412Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180413Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180414Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180416Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180417Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180418Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180420Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180421Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180422Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180423Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180424Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180425Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180426Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180427Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180428Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180429Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180430Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180431Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180432Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180433Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180434Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180435Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180436Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180437Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180438Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180439Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180440Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180442Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180443Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180445Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180446Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180447Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180448Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180449Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180450Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180451Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180452Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180453Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180454Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180455Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180456Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180457Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180458Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180459Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180460Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180461Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180462Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180463Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180464Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180465Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180508Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180532Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180595Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180645Didn't retrieve data .Didn't retrieve 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1349180698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180723Didn't retrieve data 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retrieve data . /1349180763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180836Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1349180878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180883Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180894Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180903Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180905Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180930Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1349180947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180950Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180952Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180963Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180965Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180967Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180969Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180973Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349180998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1349181025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181056Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181057Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181059Didn't retrieve data .Didn't retrieve 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181281Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181286Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349181288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349150976', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145716', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145691', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145687', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990554', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990549', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990539', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990537', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990534', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990500', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990497', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990491', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990489', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990485', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990483', None, None, None, None, None, '2711135') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 2175 time used for this insertion : 0.10450315475463867 save_final save missing photos in datou_result : time spend for datou_step_exec : 402.7763364315033 time spend to save output : 0.2871394157409668 total time spend for step 2 : 403.06347584724426 step3:rle_unique_nms_with_priority Tue Apr 1 02:52:30 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 expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 770 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 21 nb_hashtags : 2 time to prepare the origin masks : 8.507303237915039 time for calcul the mask position with numpy : 0.6378576755523682 nb_pixel_total : 6459971 time to create 1 rle with new method : 0.588892936706543 time for calcul the mask position with numpy : 0.031434059143066406 nb_pixel_total : 68691 time to create 1 rle with old method : 0.07993173599243164 time for calcul the mask position with numpy : 0.034479618072509766 nb_pixel_total : 16417 time to create 1 rle with old method : 0.018536806106567383 time for calcul the mask position with numpy : 0.02160191535949707 nb_pixel_total : 12449 time to create 1 rle with old method : 0.01409006118774414 time for calcul the mask position with numpy : 0.021970272064208984 nb_pixel_total : 18614 time to create 1 rle with old method : 0.020845413208007812 time for calcul the mask position with numpy : 0.020634889602661133 nb_pixel_total : 4602 time to create 1 rle with old method : 0.005238056182861328 time for calcul the mask position with numpy : 0.021273136138916016 nb_pixel_total : 29474 time to create 1 rle with old method : 0.03277134895324707 time for calcul the mask position with numpy : 0.021222591400146484 nb_pixel_total : 94812 time to create 1 rle with old method : 0.11256003379821777 time for calcul the mask position with numpy : 0.0220639705657959 nb_pixel_total : 23276 time to create 1 rle with old method : 0.027794361114501953 time for calcul the mask position with numpy : 0.021778345108032227 nb_pixel_total : 14354 time to create 1 rle with old method : 0.016207218170166016 time for calcul the mask position with numpy : 0.02104353904724121 nb_pixel_total : 10658 time to create 1 rle with old method : 0.012083292007446289 time for calcul the mask position with numpy : 0.021222352981567383 nb_pixel_total : 29058 time to create 1 rle with old method : 0.032396554946899414 time for calcul the mask position with numpy : 0.021107912063598633 nb_pixel_total : 75938 time to create 1 rle with old method : 0.08525395393371582 time for calcul the mask position with numpy : 0.021837949752807617 nb_pixel_total : 11279 time to create 1 rle with old method : 0.012629985809326172 time for calcul the mask position with numpy : 0.021146059036254883 nb_pixel_total : 13490 time to create 1 rle with old method : 0.015088796615600586 time for calcul the mask position with numpy : 0.020933866500854492 nb_pixel_total : 14959 time to create 1 rle with old method : 0.016898393630981445 time for calcul the mask position with numpy : 0.021503925323486328 nb_pixel_total : 41546 time to create 1 rle with old method : 0.04730081558227539 time for calcul the mask position with numpy : 0.021707534790039062 nb_pixel_total : 21359 time to create 1 rle with old method : 0.02587127685546875 time for calcul the mask position with numpy : 0.021734237670898438 nb_pixel_total : 23941 time to create 1 rle with old method : 0.026985645294189453 time for calcul the mask position with numpy : 0.02312302589416504 nb_pixel_total : 12748 time to create 1 rle with old method : 0.01611328125 time for calcul the mask position with numpy : 0.021103858947753906 nb_pixel_total : 34610 time to create 1 rle with old method : 0.03867936134338379 time for calcul the mask position with numpy : 0.021728992462158203 nb_pixel_total : 17994 time to create 1 rle with old method : 0.020273923873901367 create new chi : 2.4172916412353516 time to delete rle : 0.057489633560180664 batch 1 Loaded 43 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 10711 TO DO : save crop sub photo not yet done ! save time : 0.8483583927154541 nb_obj : 9 nb_hashtags : 2 time to prepare the origin masks : 2.3216264247894287 time for calcul the mask position with numpy : 0.7465565204620361 nb_pixel_total : 6396030 time to create 1 rle with new method : 0.6415083408355713 time for calcul the mask position with numpy : 0.02293705940246582 nb_pixel_total : 216100 time to create 1 rle with new method : 0.5040402412414551 time for calcul the mask position with numpy : 0.020994901657104492 nb_pixel_total : 26512 time to create 1 rle with old method : 0.02990555763244629 time for calcul the mask position with numpy : 0.021499156951904297 nb_pixel_total : 131486 time to create 1 rle with old method : 0.14779210090637207 time for calcul the mask position with numpy : 0.02109074592590332 nb_pixel_total : 9778 time to create 1 rle with old method : 0.01121377944946289 time for calcul the mask position with numpy : 0.022974252700805664 nb_pixel_total : 78070 time to create 1 rle with old method : 0.08763599395751953 time for calcul the mask position with numpy : 0.023177385330200195 nb_pixel_total : 92615 time to create 1 rle with old method : 0.11422896385192871 time for calcul the mask position with numpy : 0.021541357040405273 nb_pixel_total : 11995 time to create 1 rle with old method : 0.013618946075439453 time for calcul the mask position with numpy : 0.024603605270385742 nb_pixel_total : 49271 time to create 1 rle with old method : 0.054466962814331055 time for calcul the mask position with numpy : 0.0218198299407959 nb_pixel_total : 38383 time to create 1 rle with old method : 0.04266858100891113 create new chi : 2.667881488800049 time to delete rle : 0.001344442367553711 batch 1 Loaded 19 chid ids of type : 3594 +++++++++Number RLEs to save : 8179 TO DO : save crop sub photo not yet done ! save time : 0.6561172008514404 nb_obj : 21 nb_hashtags : 3 time to prepare the origin masks : 7.676540851593018 time for calcul the mask position with numpy : 0.24046897888183594 nb_pixel_total : 5396328 time to create 1 rle with new method : 0.6831188201904297 time for calcul the mask position with numpy : 0.03266143798828125 nb_pixel_total : 12714 time to create 1 rle with old method : 0.015618324279785156 time for calcul the mask position with numpy : 0.022829532623291016 nb_pixel_total : 17976 time to create 1 rle with old method : 0.02207183837890625 time for calcul the mask position with numpy : 0.02230978012084961 nb_pixel_total : 5909 time to create 1 rle with old method : 0.007520437240600586 time for calcul the mask position with numpy : 0.02332329750061035 nb_pixel_total : 164997 time to create 1 rle with new method : 0.6875379085540771 time for calcul the mask position with numpy : 0.021296977996826172 nb_pixel_total : 63880 time to create 1 rle with old method : 0.07133221626281738 time for calcul the mask position with numpy : 0.020650386810302734 nb_pixel_total : 13789 time to create 1 rle with old method : 0.01560211181640625 time for calcul the mask position with numpy : 0.020908594131469727 nb_pixel_total : 17744 time to create 1 rle with old method : 0.02004265785217285 time for calcul the mask position with numpy : 0.021784543991088867 nb_pixel_total : 167376 time to create 1 rle with new method : 0.3725855350494385 time for calcul the mask position with numpy : 0.021109580993652344 nb_pixel_total : 55 time to create 1 rle with old method : 0.0002040863037109375 time for calcul the mask position with numpy : 0.027688026428222656 nb_pixel_total : 396224 time to create 1 rle with new method : 0.3479125499725342 time for calcul the mask position with numpy : 0.021358013153076172 nb_pixel_total : 14689 time to create 1 rle with old method : 0.0165407657623291 time for calcul the mask position with numpy : 0.02158355712890625 nb_pixel_total : 13624 time to create 1 rle with old method : 0.015448331832885742 time for calcul the mask position with numpy : 0.02244853973388672 nb_pixel_total : 108107 time to create 1 rle with old method : 0.11975336074829102 time for calcul the mask position with numpy : 0.02041339874267578 nb_pixel_total : 14492 time to create 1 rle with old method : 0.015668869018554688 time for calcul the mask position with numpy : 0.022152185440063477 nb_pixel_total : 239434 time to create 1 rle with new method : 0.547468900680542 time for calcul the mask position with numpy : 0.021602392196655273 nb_pixel_total : 73510 time to create 1 rle with old method : 0.08359360694885254 time for calcul the mask position with numpy : 0.0213620662689209 nb_pixel_total : 30505 time to create 1 rle with old method : 0.03428292274475098 time for calcul the mask position with numpy : 0.02052903175354004 nb_pixel_total : 18212 time to create 1 rle with old method : 0.020489931106567383 time for calcul the mask position with numpy : 0.021123647689819336 nb_pixel_total : 78997 time to create 1 rle with old method : 0.08772993087768555 time for calcul the mask position with numpy : 0.021391868591308594 nb_pixel_total : 118444 time to create 1 rle with old method : 0.13395094871520996 time for calcul the mask position with numpy : 0.02134561538696289 nb_pixel_total : 83234 time to create 1 rle with old method : 0.0935359001159668 create new chi : 4.25246524810791 time to delete rle : 0.0024492740631103516 batch 1 Loaded 43 chid ids of type : 3594 ++++++++++++++++++++++++++++Number RLEs to save : 16365 TO DO : save crop sub photo not yet done ! save time : 1.0750808715820312 nb_obj : 21 nb_hashtags : 6 time to prepare the origin masks : 10.467178106307983 time for calcul the mask position with numpy : 0.3173563480377197 nb_pixel_total : 5394701 time to create 1 rle with new method : 0.3183314800262451 time for calcul the mask position with numpy : 0.022104263305664062 nb_pixel_total : 13107 time to create 1 rle with old method : 0.014660120010375977 time for calcul the mask position with numpy : 0.022519826889038086 nb_pixel_total : 77344 time to create 1 rle with old method : 0.0856781005859375 time for calcul the mask position with numpy : 0.021827220916748047 nb_pixel_total : 243024 time to create 1 rle with new method : 0.3304474353790283 time for calcul the mask position with numpy : 0.021647214889526367 nb_pixel_total : 25281 time to create 1 rle with old method : 0.0287473201751709 time for calcul the mask position with numpy : 0.021916627883911133 nb_pixel_total : 52543 time to create 1 rle with old method : 0.061603546142578125 time for calcul the mask position with numpy : 0.02062702178955078 nb_pixel_total : 10333 time to create 1 rle with old method : 0.011601448059082031 time for calcul the mask position with numpy : 0.02461695671081543 nb_pixel_total : 447495 time to create 1 rle with new method : 0.3180046081542969 time for calcul the mask position with numpy : 0.021910667419433594 nb_pixel_total : 90198 time to create 1 rle with old method : 0.10063385963439941 time for calcul the mask position with numpy : 0.02187657356262207 nb_pixel_total : 4455 time to create 1 rle with old method : 0.005120754241943359 time for calcul the mask position with numpy : 0.021733760833740234 nb_pixel_total : 51702 time to create 1 rle with old method : 0.05803036689758301 time for calcul the mask position with numpy : 0.021528244018554688 nb_pixel_total : 22846 time to create 1 rle with old method : 0.02592921257019043 time for calcul the mask position with numpy : 0.021258115768432617 nb_pixel_total : 15066 time to create 1 rle with old method : 0.017030954360961914 time for calcul the mask position with numpy : 0.02144622802734375 nb_pixel_total : 1924 time to create 1 rle with old method : 0.002241373062133789 time for calcul the mask position with numpy : 0.02133035659790039 nb_pixel_total : 53133 time to create 1 rle with old method : 0.059464454650878906 time for calcul the mask position with numpy : 0.021075725555419922 nb_pixel_total : 51380 time to create 1 rle with old method : 0.05746197700500488 time for calcul the mask position with numpy : 0.02138996124267578 nb_pixel_total : 91455 time to create 1 rle with old method : 0.1029355525970459 time for calcul the mask position with numpy : 0.020731210708618164 nb_pixel_total : 32307 time to create 1 rle with old method : 0.03522944450378418 time for calcul the mask position with numpy : 0.019931316375732422 nb_pixel_total : 18055 time to create 1 rle with old method : 0.019704818725585938 time for calcul the mask position with numpy : 0.021671772003173828 nb_pixel_total : 197795 time to create 1 rle with new method : 0.3288702964782715 time for calcul the mask position with numpy : 0.022950172424316406 nb_pixel_total : 16235 time to create 1 rle with old method : 0.0184171199798584 time for calcul the mask position with numpy : 0.022899627685546875 nb_pixel_total : 139861 time to create 1 rle with old method : 0.15891194343566895 create new chi : 3.032994270324707 time to delete rle : 0.002385854721069336 batch 1 Loaded 43 chid ids of type : 3594 +++++++++++++++++++++++++++Number RLEs to save : 15271 TO DO : save crop sub photo not yet done ! save time : 1.446892261505127 nb_obj : 46 nb_hashtags : 5 time to prepare the origin masks : 4.442329168319702 time for calcul the mask position with numpy : 0.647956132888794 nb_pixel_total : 5195062 time to create 1 rle with new method : 0.34522461891174316 time for calcul the mask position with numpy : 0.02865314483642578 nb_pixel_total : 29584 time to create 1 rle with old method : 0.03314375877380371 time for calcul the mask position with numpy : 0.03268575668334961 nb_pixel_total : 247706 time to create 1 rle with new method : 0.40979766845703125 time for calcul the mask position with numpy : 0.0289003849029541 nb_pixel_total : 14745 time to create 1 rle with old method : 0.016591787338256836 time for calcul the mask position with numpy : 0.028887033462524414 nb_pixel_total : 3295 time to create 1 rle with old method : 0.0037322044372558594 time for calcul the mask position with numpy : 0.028946876525878906 nb_pixel_total : 70003 time to create 1 rle with old method : 0.07816171646118164 time for calcul the mask position with numpy : 0.02876901626586914 nb_pixel_total : 9379 time to create 1 rle with old method : 0.010667562484741211 time for calcul the mask position with numpy : 0.02913212776184082 nb_pixel_total : 107205 time to create 1 rle with old method : 0.11910080909729004 time for calcul the mask position with numpy : 0.03040289878845215 nb_pixel_total : 27650 time to create 1 rle with old method : 0.044873952865600586 time for calcul the mask position with numpy : 0.03480076789855957 nb_pixel_total : 43280 time to create 1 rle with old method : 0.05574202537536621 time for calcul the mask position with numpy : 0.030376911163330078 nb_pixel_total : 134718 time to create 1 rle with old method : 0.15135788917541504 time for calcul the mask position with numpy : 0.02936530113220215 nb_pixel_total : 42632 time to create 1 rle with old method : 0.04808354377746582 time for calcul the mask position with numpy : 0.02979111671447754 nb_pixel_total : 84146 time to create 1 rle with old method : 0.09581255912780762 time for calcul the mask position with numpy : 0.02973651885986328 nb_pixel_total : 13199 time to create 1 rle with old method : 0.01535177230834961 time for calcul the mask position with numpy : 0.032073974609375 nb_pixel_total : 52273 time to create 1 rle with old method : 0.06606292724609375 time for calcul the mask position with numpy : 0.031006813049316406 nb_pixel_total : 25481 time to create 1 rle with old method : 0.030665159225463867 time for calcul the mask position with numpy : 0.029693603515625 nb_pixel_total : 12286 time to create 1 rle with old method : 0.013920068740844727 time for calcul the mask position with numpy : 0.02970743179321289 nb_pixel_total : 23674 time to create 1 rle with old method : 0.026652812957763672 time for calcul the mask position with numpy : 0.02890801429748535 nb_pixel_total : 22876 time to create 1 rle with old method : 0.025753498077392578 time for calcul the mask position with numpy : 0.030829668045043945 nb_pixel_total : 15390 time to create 1 rle with old method : 0.017430543899536133 time for calcul the mask position with numpy : 0.03415989875793457 nb_pixel_total : 15185 time to create 1 rle with old method : 0.017047643661499023 time for calcul the mask position with numpy : 0.029112577438354492 nb_pixel_total : 40111 time to create 1 rle with old method : 0.0445711612701416 time for calcul the mask position with numpy : 0.028901338577270508 nb_pixel_total : 30179 time to create 1 rle with old method : 0.03380393981933594 time for calcul the mask position with numpy : 0.029065370559692383 nb_pixel_total : 51976 time to create 1 rle with old method : 0.05855989456176758 time for calcul the mask position with numpy : 0.029336214065551758 nb_pixel_total : 15721 time to create 1 rle with old method : 0.017678260803222656 time for calcul the mask position with numpy : 0.029199838638305664 nb_pixel_total : 26652 time to create 1 rle with old method : 0.030019521713256836 time for calcul the mask position with numpy : 0.029245853424072266 nb_pixel_total : 52705 time to create 1 rle with old method : 0.0601956844329834 time for calcul the mask position with numpy : 0.031183719635009766 nb_pixel_total : 13606 time to create 1 rle with old method : 0.015353202819824219 time for calcul the mask position with numpy : 0.0295717716217041 nb_pixel_total : 86179 time to create 1 rle with old method : 0.11936020851135254 time for calcul the mask position with numpy : 0.029857873916625977 nb_pixel_total : 10478 time to create 1 rle with old method : 0.011866331100463867 time for calcul the mask position with numpy : 0.029213428497314453 nb_pixel_total : 33515 time to create 1 rle with old method : 0.037401676177978516 time for calcul the mask position with numpy : 0.02907276153564453 nb_pixel_total : 16300 time to create 1 rle with old method : 0.01849818229675293 time for calcul the mask position with numpy : 0.029229164123535156 nb_pixel_total : 65293 time to create 1 rle with old method : 0.07295894622802734 time for calcul the mask position with numpy : 0.029123783111572266 nb_pixel_total : 37413 time to create 1 rle with old method : 0.041588783264160156 time for calcul the mask position with numpy : 0.02917337417602539 nb_pixel_total : 24191 time to create 1 rle with old method : 0.027008056640625 time for calcul the mask position with numpy : 0.02985095977783203 nb_pixel_total : 140730 time to create 1 rle with old method : 0.15729546546936035 time for calcul the mask position with numpy : 0.029273271560668945 nb_pixel_total : 17086 time to create 1 rle with old method : 0.020681142807006836 time for calcul the mask position with numpy : 0.02895975112915039 nb_pixel_total : 38841 time to create 1 rle with old method : 0.04370260238647461 time for calcul the mask position with numpy : 0.029149532318115234 nb_pixel_total : 14661 time to create 1 rle with old method : 0.018001079559326172 time for calcul the mask position with numpy : 0.03391623497009277 nb_pixel_total : 26968 time to create 1 rle with old method : 0.030333757400512695 time for calcul the mask position with numpy : 0.029376983642578125 nb_pixel_total : 20912 time to create 1 rle with old method : 0.023747682571411133 time for calcul the mask position with numpy : 0.03017592430114746 nb_pixel_total : 57078 time to create 1 rle with old method : 0.06383252143859863 time for calcul the mask position with numpy : 0.029333114624023438 nb_pixel_total : 8022 time to create 1 rle with old method : 0.00921320915222168 time for calcul the mask position with numpy : 0.0316927433013916 nb_pixel_total : 6847 time to create 1 rle with old method : 0.008470535278320312 time for calcul the mask position with numpy : 0.031037092208862305 nb_pixel_total : 12310 time to create 1 rle with old method : 0.015246391296386719 time for calcul the mask position with numpy : 0.030954599380493164 nb_pixel_total : 9055 time to create 1 rle with old method : 0.011188507080078125 time for calcul the mask position with numpy : 0.031137704849243164 nb_pixel_total : 3642 time to create 1 rle with old method : 0.0045626163482666016 create new chi : 4.7095606327056885 time to delete rle : 0.0047724246978759766 batch 1 Loaded 93 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24573 TO DO : save crop sub photo not yet done ! save time : 3.475835084915161 nb_obj : 37 nb_hashtags : 4 time to prepare the origin masks : 4.344563007354736 time for calcul the mask position with numpy : 0.4470400810241699 nb_pixel_total : 5335223 time to create 1 rle with new method : 0.983644962310791 time for calcul the mask position with numpy : 0.029259443283081055 nb_pixel_total : 7619 time to create 1 rle with old method : 0.008674383163452148 time for calcul the mask position with numpy : 0.0293731689453125 nb_pixel_total : 40977 time to create 1 rle with old method : 0.04554486274719238 time for calcul the mask position with numpy : 0.028856277465820312 nb_pixel_total : 7204 time to create 1 rle with old method : 0.008054494857788086 time for calcul the mask position with numpy : 0.028541088104248047 nb_pixel_total : 14935 time to create 1 rle with old method : 0.016426563262939453 time for calcul the mask position with numpy : 0.030726194381713867 nb_pixel_total : 35941 time to create 1 rle with old method : 0.04022836685180664 time for calcul the mask position with numpy : 0.028905153274536133 nb_pixel_total : 12104 time to create 1 rle with old method : 0.01360011100769043 time for calcul the mask position with numpy : 0.030799388885498047 nb_pixel_total : 213172 time to create 1 rle with new method : 0.6294381618499756 time for calcul the mask position with numpy : 0.027631521224975586 nb_pixel_total : 48965 time to create 1 rle with old method : 0.05170392990112305 time for calcul the mask position with numpy : 0.027324676513671875 nb_pixel_total : 164868 time to create 1 rle with new method : 0.444288969039917 time for calcul the mask position with numpy : 0.029127836227416992 nb_pixel_total : 44391 time to create 1 rle with old method : 0.05218362808227539 time for calcul the mask position with numpy : 0.029211759567260742 nb_pixel_total : 42381 time to create 1 rle with old method : 0.047286272048950195 time for calcul the mask position with numpy : 0.029497861862182617 nb_pixel_total : 133831 time to create 1 rle with old method : 0.14876008033752441 time for calcul the mask position with numpy : 0.0340118408203125 nb_pixel_total : 51871 time to create 1 rle with old method : 0.05816960334777832 time for calcul the mask position with numpy : 0.02890944480895996 nb_pixel_total : 34223 time to create 1 rle with old method : 0.03821134567260742 time for calcul the mask position with numpy : 0.029452800750732422 nb_pixel_total : 91046 time to create 1 rle with old method : 0.10333490371704102 time for calcul the mask position with numpy : 0.029232501983642578 nb_pixel_total : 85229 time to create 1 rle with old method : 0.09514904022216797 time for calcul the mask position with numpy : 0.028899192810058594 nb_pixel_total : 36780 time to create 1 rle with old method : 0.04124808311462402 time for calcul the mask position with numpy : 0.029171466827392578 nb_pixel_total : 17999 time to create 1 rle with old method : 0.02200794219970703 time for calcul the mask position with numpy : 0.0299990177154541 nb_pixel_total : 58484 time to create 1 rle with old method : 0.0650632381439209 time for calcul the mask position with numpy : 0.029711246490478516 nb_pixel_total : 182423 time to create 1 rle with new method : 0.3661031723022461 time for calcul the mask position with numpy : 0.029114246368408203 nb_pixel_total : 31439 time to create 1 rle with old method : 0.04721856117248535 time for calcul the mask position with numpy : 0.03287959098815918 nb_pixel_total : 15302 time to create 1 rle with old method : 0.01887822151184082 time for calcul the mask position with numpy : 0.02905106544494629 nb_pixel_total : 14914 time to create 1 rle with old method : 0.016736507415771484 time for calcul the mask position with numpy : 0.029160022735595703 nb_pixel_total : 29418 time to create 1 rle with old method : 0.032835960388183594 time for calcul the mask position with numpy : 0.02906346321105957 nb_pixel_total : 23301 time to create 1 rle with old method : 0.025834083557128906 time for calcul the mask position with numpy : 0.0290982723236084 nb_pixel_total : 4411 time to create 1 rle with old method : 0.0050318241119384766 time for calcul the mask position with numpy : 0.029746294021606445 nb_pixel_total : 39679 time to create 1 rle with old method : 0.048951148986816406 time for calcul the mask position with numpy : 0.028972387313842773 nb_pixel_total : 8471 time to create 1 rle with old method : 0.00948476791381836 time for calcul the mask position with numpy : 0.029252290725708008 nb_pixel_total : 52168 time to create 1 rle with old method : 0.058621883392333984 time for calcul the mask position with numpy : 0.028981447219848633 nb_pixel_total : 21859 time to create 1 rle with old method : 0.02460503578186035 time for calcul the mask position with numpy : 0.029190540313720703 nb_pixel_total : 53951 time to create 1 rle with old method : 0.06088733673095703 time for calcul the mask position with numpy : 0.03011798858642578 nb_pixel_total : 42066 time to create 1 rle with old method : 0.047128915786743164 time for calcul the mask position with numpy : 0.02897787094116211 nb_pixel_total : 13100 time to create 1 rle with old method : 0.014539718627929688 time for calcul the mask position with numpy : 0.029014110565185547 nb_pixel_total : 6580 time to create 1 rle with old method : 0.007457256317138672 time for calcul the mask position with numpy : 0.02925419807434082 nb_pixel_total : 11645 time to create 1 rle with old method : 0.013157129287719727 time for calcul the mask position with numpy : 0.02982783317565918 nb_pixel_total : 9521 time to create 1 rle with old method : 0.010672569274902344 time for calcul the mask position with numpy : 0.02897191047668457 nb_pixel_total : 12749 time to create 1 rle with old method : 0.014393091201782227 create new chi : 5.396687030792236 time to delete rle : 0.003304719924926758 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21832 TO DO : save crop sub photo not yet done ! save time : 1.806992769241333 nb_obj : 22 nb_hashtags : 4 time to prepare the origin masks : 4.295169830322266 time for calcul the mask position with numpy : 0.8782389163970947 nb_pixel_total : 5361617 time to create 1 rle with new method : 0.7944746017456055 time for calcul the mask position with numpy : 0.02869439125061035 nb_pixel_total : 7836 time to create 1 rle with old method : 0.008850336074829102 time for calcul the mask position with numpy : 0.028696775436401367 nb_pixel_total : 33478 time to create 1 rle with old method : 0.03759026527404785 time for calcul the mask position with numpy : 0.02874279022216797 nb_pixel_total : 6328 time to create 1 rle with old method : 0.007063388824462891 time for calcul the mask position with numpy : 0.0289304256439209 nb_pixel_total : 64861 time to create 1 rle with old method : 0.0726618766784668 time for calcul the mask position with numpy : 0.030832290649414062 nb_pixel_total : 394996 time to create 1 rle with new method : 0.46587443351745605 time for calcul the mask position with numpy : 0.029058218002319336 nb_pixel_total : 87097 time to create 1 rle with old method : 0.09732651710510254 time for calcul the mask position with numpy : 0.029111862182617188 nb_pixel_total : 62873 time to create 1 rle with old method : 0.07020211219787598 time for calcul the mask position with numpy : 0.030019283294677734 nb_pixel_total : 211456 time to create 1 rle with new method : 0.9161872863769531 time for calcul the mask position with numpy : 0.028927326202392578 nb_pixel_total : 38478 time to create 1 rle with old method : 0.04584360122680664 time for calcul the mask position with numpy : 0.030651330947875977 nb_pixel_total : 200545 time to create 1 rle with new method : 0.5736403465270996 time for calcul the mask position with numpy : 0.028266429901123047 nb_pixel_total : 43910 time to create 1 rle with old method : 0.04877758026123047 time for calcul the mask position with numpy : 0.027640581130981445 nb_pixel_total : 19452 time to create 1 rle with old method : 0.02051711082458496 time for calcul the mask position with numpy : 0.02754688262939453 nb_pixel_total : 93080 time to create 1 rle with old method : 0.09828925132751465 time for calcul the mask position with numpy : 0.028751611709594727 nb_pixel_total : 116677 time to create 1 rle with old method : 0.12958574295043945 time for calcul the mask position with numpy : 0.028520822525024414 nb_pixel_total : 56272 time to create 1 rle with old method : 0.06114315986633301 time for calcul the mask position with numpy : 0.028898954391479492 nb_pixel_total : 26751 time to create 1 rle with old method : 0.029964923858642578 time for calcul the mask position with numpy : 0.02936244010925293 nb_pixel_total : 120173 time to create 1 rle with old method : 0.13623666763305664 time for calcul the mask position with numpy : 0.029137372970581055 nb_pixel_total : 9067 time to create 1 rle with old method : 0.010216236114501953 time for calcul the mask position with numpy : 0.03074336051940918 nb_pixel_total : 33473 time to create 1 rle with old method : 0.03742575645446777 time for calcul the mask position with numpy : 0.02883744239807129 nb_pixel_total : 6098 time to create 1 rle with old method : 0.0069255828857421875 time for calcul the mask position with numpy : 0.028041839599609375 nb_pixel_total : 39811 time to create 1 rle with old method : 0.04298543930053711 time for calcul the mask position with numpy : 0.028974294662475586 nb_pixel_total : 15911 time to create 1 rle with old method : 0.01785421371459961 create new chi : 5.348074197769165 time to delete rle : 0.0027048587799072266 batch 1 Loaded 45 chid ids of type : 3594 ++++++++++++++++++++++++++++++++Number RLEs to save : 18438 TO DO : save crop sub photo not yet done ! save time : 4.0596678256988525 nb_obj : 24 nb_hashtags : 5 time to prepare the origin masks : 4.900602340698242 time for calcul the mask position with numpy : 0.1900022029876709 nb_pixel_total : 4429067 time to create 1 rle with new method : 0.6667630672454834 time for calcul the mask position with numpy : 0.029204845428466797 nb_pixel_total : 28252 time to create 1 rle with old method : 0.03172016143798828 time for calcul the mask position with numpy : 0.029633045196533203 nb_pixel_total : 37534 time to create 1 rle with old method : 0.04218888282775879 time for calcul the mask position with numpy : 0.02902078628540039 nb_pixel_total : 19425 time to create 1 rle with old method : 0.02179241180419922 time for calcul the mask position with numpy : 0.028859615325927734 nb_pixel_total : 10509 time to create 1 rle with old method : 0.011839866638183594 time for calcul the mask position with numpy : 0.03149867057800293 nb_pixel_total : 456929 time to create 1 rle with new method : 0.8246681690216064 time for calcul the mask position with numpy : 0.02961421012878418 nb_pixel_total : 199393 time to create 1 rle with new method : 0.3386039733886719 time for calcul the mask position with numpy : 0.028971433639526367 nb_pixel_total : 43432 time to create 1 rle with old method : 0.048384666442871094 time for calcul the mask position with numpy : 0.028931379318237305 nb_pixel_total : 44667 time to create 1 rle with old method : 0.04995369911193848 time for calcul the mask position with numpy : 0.028699159622192383 nb_pixel_total : 10957 time to create 1 rle with old method : 0.012495279312133789 time for calcul the mask position with numpy : 0.02871847152709961 nb_pixel_total : 20497 time to create 1 rle with old method : 0.022917509078979492 time for calcul the mask position with numpy : 0.03221011161804199 nb_pixel_total : 527358 time to create 1 rle with new method : 0.3218517303466797 time for calcul the mask position with numpy : 0.02890944480895996 nb_pixel_total : 3357 time to create 1 rle with old method : 0.004172801971435547 time for calcul the mask position with numpy : 0.029245376586914062 nb_pixel_total : 104820 time to create 1 rle with old method : 0.12263369560241699 time for calcul the mask position with numpy : 0.03051471710205078 nb_pixel_total : 142488 time to create 1 rle with old method : 0.17421507835388184 time for calcul the mask position with numpy : 0.029433250427246094 nb_pixel_total : 39217 time to create 1 rle with old method : 0.04881787300109863 time for calcul the mask position with numpy : 0.04987287521362305 nb_pixel_total : 823403 time to create 1 rle with new method : 0.3577544689178467 time for calcul the mask position with numpy : 0.028069019317626953 nb_pixel_total : 6032 time to create 1 rle with old method : 0.006630420684814453 time for calcul the mask position with numpy : 0.028392314910888672 nb_pixel_total : 34224 time to create 1 rle with old method : 0.03653120994567871 time for calcul the mask position with numpy : 0.027927875518798828 nb_pixel_total : 16890 time to create 1 rle with old method : 0.018984556198120117 time for calcul the mask position with numpy : 0.028998851776123047 nb_pixel_total : 4097 time to create 1 rle with old method : 0.004692792892456055 time for calcul the mask position with numpy : 0.03232288360595703 nb_pixel_total : 18866 time to create 1 rle with old method : 0.021233081817626953 time for calcul the mask position with numpy : 0.028890609741210938 nb_pixel_total : 4984 time to create 1 rle with old method : 0.005579710006713867 time for calcul the mask position with numpy : 0.029829978942871094 nb_pixel_total : 13575 time to create 1 rle with old method : 0.02309703826904297 time for calcul the mask position with numpy : 0.034517765045166016 nb_pixel_total : 10267 time to create 1 rle with old method : 0.01537775993347168 create new chi : 4.273714780807495 time to delete rle : 0.0025789737701416016 batch 1 Loaded 49 chid ids of type : 3594 ++++++++++++++++++++++++++++++Number RLEs to save : 16618 TO DO : save crop sub photo not yet done ! save time : 1.1001598834991455 nb_obj : 25 nb_hashtags : 3 time to prepare the origin masks : 4.22356104850769 time for calcul the mask position with numpy : 0.2491903305053711 nb_pixel_total : 5989874 time to create 1 rle with new method : 0.5814976692199707 time for calcul the mask position with numpy : 0.02913212776184082 nb_pixel_total : 8283 time to create 1 rle with old method : 0.009476900100708008 time for calcul the mask position with numpy : 0.02907085418701172 nb_pixel_total : 6205 time to create 1 rle with old method : 0.007772207260131836 time for calcul the mask position with numpy : 0.02901148796081543 nb_pixel_total : 27511 time to create 1 rle with old method : 0.0311582088470459 time for calcul the mask position with numpy : 0.02904057502746582 nb_pixel_total : 20349 time to create 1 rle with old method : 0.023059368133544922 time for calcul the mask position with numpy : 0.030403852462768555 nb_pixel_total : 9279 time to create 1 rle with old method : 0.01060628890991211 time for calcul the mask position with numpy : 0.029063701629638672 nb_pixel_total : 26984 time to create 1 rle with old method : 0.030619144439697266 time for calcul the mask position with numpy : 0.029286861419677734 nb_pixel_total : 11733 time to create 1 rle with old method : 0.013277530670166016 time for calcul the mask position with numpy : 0.029429197311401367 nb_pixel_total : 84657 time to create 1 rle with old method : 0.09428977966308594 time for calcul the mask position with numpy : 0.0288236141204834 nb_pixel_total : 9248 time to create 1 rle with old method : 0.010508537292480469 time for calcul the mask position with numpy : 0.028720378875732422 nb_pixel_total : 12165 time to create 1 rle with old method : 0.01370859146118164 time for calcul the mask position with numpy : 0.02880239486694336 nb_pixel_total : 11417 time to create 1 rle with old method : 0.013129711151123047 time for calcul the mask position with numpy : 0.028894662857055664 nb_pixel_total : 8370 time to create 1 rle with old method : 0.009478569030761719 time for calcul the mask position with numpy : 0.028705596923828125 nb_pixel_total : 31909 time to create 1 rle with old method : 0.03583264350891113 time for calcul the mask position with numpy : 0.028967618942260742 nb_pixel_total : 7813 time to create 1 rle with old method : 0.008875131607055664 time for calcul the mask position with numpy : 0.028832197189331055 nb_pixel_total : 562 time to create 1 rle with old method : 0.0008192062377929688 time for calcul the mask position with numpy : 0.02912425994873047 nb_pixel_total : 72678 time to create 1 rle with old method : 0.08032083511352539 time for calcul the mask position with numpy : 0.032822608947753906 nb_pixel_total : 318702 time to create 1 rle with new method : 0.5040905475616455 time for calcul the mask position with numpy : 0.030760765075683594 nb_pixel_total : 55611 time to create 1 rle with old method : 0.07284855842590332 time for calcul the mask position with numpy : 0.03093099594116211 nb_pixel_total : 47112 time to create 1 rle with old method : 0.053453922271728516 time for calcul the mask position with numpy : 0.03306865692138672 nb_pixel_total : 2258 time to create 1 rle with old method : 0.00469970703125 time for calcul the mask position with numpy : 0.03473711013793945 nb_pixel_total : 232262 time to create 1 rle with new method : 0.5503756999969482 time for calcul the mask position with numpy : 0.028981447219848633 nb_pixel_total : 5618 time to create 1 rle with old method : 0.0064241886138916016 time for calcul the mask position with numpy : 0.02903580665588379 nb_pixel_total : 31783 time to create 1 rle with old method : 0.03593587875366211 time for calcul the mask position with numpy : 0.029057979583740234 nb_pixel_total : 6310 time to create 1 rle with old method : 0.007231473922729492 time for calcul the mask position with numpy : 0.03263688087463379 nb_pixel_total : 11547 time to create 1 rle with old method : 0.019870519638061523 create new chi : 3.2997922897338867 time to delete rle : 0.0029783248901367188 batch 1 Loaded 54 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++Number RLEs to save : 14247 TO DO : save crop sub photo not yet done ! save time : 1.0163905620574951 nb_obj : 53 nb_hashtags : 4 time to prepare the origin masks : 12.872533082962036 time for calcul the mask position with numpy : 0.2751426696777344 nb_pixel_total : 5069194 time to create 1 rle with new method : 0.5533289909362793 time for calcul the mask position with numpy : 0.02897500991821289 nb_pixel_total : 11255 time to create 1 rle with old method : 0.012764930725097656 time for calcul the mask position with numpy : 0.028977632522583008 nb_pixel_total : 1346 time to create 1 rle with old method : 0.0017406940460205078 time for calcul the mask position with numpy : 0.02902698516845703 nb_pixel_total : 2226 time to create 1 rle with old method : 0.002765655517578125 time for calcul the mask position with numpy : 0.02921295166015625 nb_pixel_total : 34106 time to create 1 rle with old method : 0.03992104530334473 time for calcul the mask position with numpy : 0.02926349639892578 nb_pixel_total : 6442 time to create 1 rle with old method : 0.007344961166381836 time for calcul the mask position with numpy : 0.028957128524780273 nb_pixel_total : 456 time to create 1 rle with old method : 0.0007376670837402344 time for calcul the mask position with numpy : 0.029166698455810547 nb_pixel_total : 140 time to create 1 rle with old method : 0.00033783912658691406 time for calcul the mask position with numpy : 0.029388427734375 nb_pixel_total : 25506 time to create 1 rle with old method : 0.028844594955444336 time for calcul the mask position with numpy : 0.02985095977783203 nb_pixel_total : 95323 time to create 1 rle with old method : 0.1079263687133789 time for calcul the mask position with numpy : 0.029120206832885742 nb_pixel_total : 6852 time to create 1 rle with old method : 0.008035659790039062 time for calcul the mask position with numpy : 0.037403106689453125 nb_pixel_total : 99299 time to create 1 rle with old method : 0.11866021156311035 time for calcul the mask position with numpy : 0.032207489013671875 nb_pixel_total : 15522 time to create 1 rle with old method : 0.017746686935424805 time for calcul the mask position with numpy : 0.03314828872680664 nb_pixel_total : 297114 time to create 1 rle with new method : 0.3934659957885742 time for calcul the mask position with numpy : 0.033873558044433594 nb_pixel_total : 344 time to create 1 rle with old method : 0.0008029937744140625 time for calcul the mask position with numpy : 0.033466339111328125 nb_pixel_total : 30781 time to create 1 rle with old method : 0.03763222694396973 time for calcul the mask position with numpy : 0.040543556213378906 nb_pixel_total : 16229 time to create 1 rle with old method : 0.03223371505737305 time for calcul the mask position with numpy : 0.02939772605895996 nb_pixel_total : 1081 time to create 1 rle with old method : 0.0014197826385498047 time for calcul the mask position with numpy : 0.02937793731689453 nb_pixel_total : 19537 time to create 1 rle with old method : 0.02301502227783203 time for calcul the mask position with numpy : 0.039842844009399414 nb_pixel_total : 1082 time to create 1 rle with old method : 0.0014278888702392578 time for calcul the mask position with numpy : 0.028937816619873047 nb_pixel_total : 1943 time to create 1 rle with old method : 0.002593517303466797 time for calcul the mask position with numpy : 0.029226303100585938 nb_pixel_total : 856 time to create 1 rle with old method : 0.0013980865478515625 time for calcul the mask position with numpy : 0.028997421264648438 nb_pixel_total : 25799 time to create 1 rle with old method : 0.029278039932250977 time for calcul the mask position with numpy : 0.03016805648803711 nb_pixel_total : 52002 time to create 1 rle with old method : 0.060395240783691406 time for calcul the mask position with numpy : 0.028896570205688477 nb_pixel_total : 35781 time to create 1 rle with old method : 0.04068136215209961 time for calcul the mask position with numpy : 0.028722047805786133 nb_pixel_total : 21850 time to create 1 rle with old method : 0.024571895599365234 time for calcul the mask position with numpy : 0.028652429580688477 nb_pixel_total : 1755 time to create 1 rle with old method : 0.002675294876098633 time for calcul the mask position with numpy : 0.02905440330505371 nb_pixel_total : 81988 time to create 1 rle with old method : 0.09163260459899902 time for calcul the mask position with numpy : 0.029268980026245117 nb_pixel_total : 131753 time to create 1 rle with old method : 0.1464848518371582 time for calcul the mask position with numpy : 0.02881932258605957 nb_pixel_total : 13990 time to create 1 rle with old method : 0.015697956085205078 time for calcul the mask position with numpy : 0.028842449188232422 nb_pixel_total : 64 time to create 1 rle with old method : 0.00018858909606933594 time for calcul the mask position with numpy : 0.028725862503051758 nb_pixel_total : 1291 time to create 1 rle with old method : 0.0018610954284667969 time for calcul the mask position with numpy : 0.029169082641601562 nb_pixel_total : 49070 time to create 1 rle with old method : 0.05503106117248535 time for calcul the mask position with numpy : 0.028931856155395508 nb_pixel_total : 9072 time to create 1 rle with old method : 0.010734796524047852 time for calcul the mask position with numpy : 0.030327558517456055 nb_pixel_total : 315410 time to create 1 rle with new method : 0.3861510753631592 time for calcul the mask position with numpy : 0.028981685638427734 nb_pixel_total : 464 time to create 1 rle with old method : 0.0006668567657470703 time for calcul the mask position with numpy : 0.029523372650146484 nb_pixel_total : 12686 time to create 1 rle with old method : 0.014338254928588867 time for calcul the mask position with numpy : 0.029428482055664062 nb_pixel_total : 7953 time to create 1 rle with old method : 0.011338949203491211 time for calcul the mask position with numpy : 0.031109094619750977 nb_pixel_total : 81 time to create 1 rle with old method : 0.000278472900390625 time for calcul the mask position with numpy : 0.04183220863342285 nb_pixel_total : 70835 time to create 1 rle with old method : 0.08069205284118652 time for calcul the mask position with numpy : 0.028743267059326172 nb_pixel_total : 407 time to create 1 rle with old method : 0.0007123947143554688 time for calcul the mask position with numpy : 0.028775691986083984 nb_pixel_total : 37484 time to create 1 rle with old method : 0.04213213920593262 time for calcul the mask position with numpy : 0.031041622161865234 nb_pixel_total : 304 time to create 1 rle with old method : 0.0005724430084228516 time for calcul the mask position with numpy : 0.03112030029296875 nb_pixel_total : 5257 time to create 1 rle with old method : 0.0067098140716552734 time for calcul the mask position with numpy : 0.029607534408569336 nb_pixel_total : 67038 time to create 1 rle with old method : 0.07887506484985352 time for calcul the mask position with numpy : 0.029142141342163086 nb_pixel_total : 17168 time to create 1 rle with old method : 0.020141124725341797 time for calcul the mask position with numpy : 0.03026103973388672 nb_pixel_total : 67 time to create 1 rle with old method : 0.0002765655517578125 time for calcul the mask position with numpy : 0.03215932846069336 nb_pixel_total : 117996 time to create 1 rle with old method : 0.14046263694763184 time for calcul the mask position with numpy : 0.029395341873168945 nb_pixel_total : 104595 time to create 1 rle with old method : 0.11792612075805664 time for calcul the mask position with numpy : 0.02876877784729004 nb_pixel_total : 733 time to create 1 rle with old method : 0.0010077953338623047 time for calcul the mask position with numpy : 0.030526161193847656 nb_pixel_total : 41360 time to create 1 rle with old method : 0.04655051231384277 time for calcul the mask position with numpy : 0.02905750274658203 nb_pixel_total : 70734 time to create 1 rle with old method : 0.07962417602539062 time for calcul the mask position with numpy : 0.029135942459106445 nb_pixel_total : 983 time to create 1 rle with old method : 0.0016088485717773438 time for calcul the mask position with numpy : 0.02910447120666504 nb_pixel_total : 17636 time to create 1 rle with old method : 0.020177602767944336 create new chi : 4.904564142227173 time to delete rle : 0.006053447723388672 batch 1 Loaded 128 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27212 TO DO : save crop sub photo not yet done ! save time : 1.9264848232269287 nb_obj : 45 nb_hashtags : 5 time to prepare the origin masks : 4.704023361206055 time for calcul the mask position with numpy : 0.18707871437072754 nb_pixel_total : 5039380 time to create 1 rle with new method : 0.47672080993652344 time for calcul the mask position with numpy : 0.02942490577697754 nb_pixel_total : 34317 time to create 1 rle with old method : 0.03816938400268555 time for calcul the mask position with numpy : 0.029404878616333008 nb_pixel_total : 11166 time to create 1 rle with old method : 0.012627840042114258 time for calcul the mask position with numpy : 0.029593229293823242 nb_pixel_total : 159201 time to create 1 rle with new method : 0.33105039596557617 time for calcul the mask position with numpy : 0.029307842254638672 nb_pixel_total : 9798 time to create 1 rle with old method : 0.011034488677978516 time for calcul the mask position with numpy : 0.031820058822631836 nb_pixel_total : 230408 time to create 1 rle with new method : 0.4844179153442383 time for calcul the mask position with numpy : 0.02974700927734375 nb_pixel_total : 23757 time to create 1 rle with old method : 0.026859045028686523 time for calcul the mask position with numpy : 0.030034780502319336 nb_pixel_total : 95094 time to create 1 rle with old method : 0.11640477180480957 time for calcul the mask position with numpy : 0.030437231063842773 nb_pixel_total : 63492 time to create 1 rle with old method : 0.07322311401367188 time for calcul the mask position with numpy : 0.029187917709350586 nb_pixel_total : 22520 time to create 1 rle with old method : 0.025288105010986328 time for calcul the mask position with numpy : 0.03036332130432129 nb_pixel_total : 96458 time to create 1 rle with old method : 0.1080009937286377 time for calcul the mask position with numpy : 0.02999281883239746 nb_pixel_total : 71687 time to create 1 rle with old method : 0.08010649681091309 time for calcul the mask position with numpy : 0.0290830135345459 nb_pixel_total : 16706 time to create 1 rle with old method : 0.018957853317260742 time for calcul the mask position with numpy : 0.029326677322387695 nb_pixel_total : 47796 time to create 1 rle with old method : 0.053461551666259766 time for calcul the mask position with numpy : 0.029621601104736328 nb_pixel_total : 17808 time to create 1 rle with old method : 0.019814014434814453 time for calcul the mask position with numpy : 0.02912735939025879 nb_pixel_total : 28059 time to create 1 rle with old method : 0.031612396240234375 time for calcul the mask position with numpy : 0.02910590171813965 nb_pixel_total : 28797 time to create 1 rle with old method : 0.03215599060058594 time for calcul the mask position with numpy : 0.029282093048095703 nb_pixel_total : 71780 time to create 1 rle with old method : 0.08010196685791016 time for calcul the mask position with numpy : 0.029002904891967773 nb_pixel_total : 31105 time to create 1 rle with old method : 0.03486752510070801 time for calcul the mask position with numpy : 0.029140949249267578 nb_pixel_total : 95681 time to create 1 rle with old method : 0.1078031063079834 time for calcul the mask position with numpy : 0.028900623321533203 nb_pixel_total : 8802 time to create 1 rle with old method : 0.01007390022277832 time for calcul the mask position with numpy : 0.028830289840698242 nb_pixel_total : 14402 time to create 1 rle with old method : 0.016146421432495117 time for calcul the mask position with numpy : 0.028934001922607422 nb_pixel_total : 35085 time to create 1 rle with old method : 0.04272937774658203 time for calcul the mask position with numpy : 0.02872323989868164 nb_pixel_total : 5496 time to create 1 rle with old method : 0.006440401077270508 time for calcul the mask position with numpy : 0.028754234313964844 nb_pixel_total : 15329 time to create 1 rle with old method : 0.01719832420349121 time for calcul the mask position with numpy : 0.028868675231933594 nb_pixel_total : 18679 time to create 1 rle with old method : 0.020872831344604492 time for calcul the mask position with numpy : 0.02925419807434082 nb_pixel_total : 109864 time to create 1 rle with old method : 0.12294268608093262 time for calcul the mask position with numpy : 0.028853654861450195 nb_pixel_total : 46842 time to create 1 rle with old method : 0.0522005558013916 time for calcul the mask position with numpy : 0.028896808624267578 nb_pixel_total : 12820 time to create 1 rle with old method : 0.014431238174438477 time for calcul the mask position with numpy : 0.02884817123413086 nb_pixel_total : 25520 time to create 1 rle with old method : 0.02992105484008789 time for calcul the mask position with numpy : 0.029736042022705078 nb_pixel_total : 65147 time to create 1 rle with old method : 0.07628846168518066 time for calcul the mask position with numpy : 0.029074430465698242 nb_pixel_total : 32055 time to create 1 rle with old method : 0.03793525695800781 time for calcul the mask position with numpy : 0.02949976921081543 nb_pixel_total : 32158 time to create 1 rle with old method : 0.03799319267272949 time for calcul the mask position with numpy : 0.029757261276245117 nb_pixel_total : 39043 time to create 1 rle with old method : 0.05229544639587402 time for calcul the mask position with numpy : 0.0336611270904541 nb_pixel_total : 73949 time to create 1 rle with old method : 0.08492922782897949 time for calcul the mask position with numpy : 0.029034852981567383 nb_pixel_total : 62057 time to create 1 rle with old method : 0.06982636451721191 time for calcul the mask position with numpy : 0.02890300750732422 nb_pixel_total : 9870 time to create 1 rle with old method : 0.01104283332824707 time for calcul the mask position with numpy : 0.0289151668548584 nb_pixel_total : 25256 time to create 1 rle with old method : 0.02822399139404297 time for calcul the mask position with numpy : 0.02887248992919922 nb_pixel_total : 8254 time to create 1 rle with old method : 0.009350299835205078 time for calcul the mask position with numpy : 0.0292966365814209 nb_pixel_total : 106605 time to create 1 rle with old method : 0.13101840019226074 time for calcul the mask position with numpy : 0.03299832344055176 nb_pixel_total : 75 time to create 1 rle with old method : 0.0006682872772216797 time for calcul the mask position with numpy : 0.030834197998046875 nb_pixel_total : 16169 time to create 1 rle with old method : 0.020630598068237305 time for calcul the mask position with numpy : 0.03025650978088379 nb_pixel_total : 60809 time to create 1 rle with old method : 0.06754374504089355 time for calcul the mask position with numpy : 0.028980016708374023 nb_pixel_total : 8444 time to create 1 rle with old method : 0.00951838493347168 time for calcul the mask position with numpy : 0.029021501541137695 nb_pixel_total : 1431 time to create 1 rle with old method : 0.0018763542175292969 time for calcul the mask position with numpy : 0.028902530670166016 nb_pixel_total : 21069 time to create 1 rle with old method : 0.023876428604125977 create new chi : 4.754275321960449 time to delete rle : 0.0038444995880126953 batch 1 Loaded 92 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23731 TO DO : save crop sub photo not yet done ! save time : 1.638359546661377 nb_obj : 51 nb_hashtags : 5 time to prepare the origin masks : 5.4213807582855225 time for calcul the mask position with numpy : 0.33399176597595215 nb_pixel_total : 5023313 time to create 1 rle with new method : 0.5559298992156982 time for calcul the mask position with numpy : 0.02825641632080078 nb_pixel_total : 6711 time to create 1 rle with old method : 0.007730722427368164 time for calcul the mask position with numpy : 0.028242111206054688 nb_pixel_total : 17034 time to create 1 rle with old method : 0.01931285858154297 time for calcul the mask position with numpy : 0.029151439666748047 nb_pixel_total : 8713 time to create 1 rle with old method : 0.010712623596191406 time for calcul the mask position with numpy : 0.029913663864135742 nb_pixel_total : 27606 time to create 1 rle with old method : 0.03243136405944824 time for calcul the mask position with numpy : 0.02887248992919922 nb_pixel_total : 29262 time to create 1 rle with old method : 0.046723365783691406 time for calcul the mask position with numpy : 0.032567739486694336 nb_pixel_total : 293159 time to create 1 rle with new method : 0.4284207820892334 time for calcul the mask position with numpy : 0.029047250747680664 nb_pixel_total : 17781 time to create 1 rle with old method : 0.020132064819335938 time for calcul the mask position with numpy : 0.030164718627929688 nb_pixel_total : 193345 time to create 1 rle with new method : 0.4098343849182129 time for calcul the mask position with numpy : 0.028896331787109375 nb_pixel_total : 18012 time to create 1 rle with old method : 0.020206689834594727 time for calcul the mask position with numpy : 0.026413440704345703 nb_pixel_total : 8844 time to create 1 rle with old method : 0.009026527404785156 time for calcul the mask position with numpy : 0.027524948120117188 nb_pixel_total : 89676 time to create 1 rle with old method : 0.09677410125732422 time for calcul the mask position with numpy : 0.03174591064453125 nb_pixel_total : 8501 time to create 1 rle with old method : 0.009732961654663086 time for calcul the mask position with numpy : 0.028998374938964844 nb_pixel_total : 10097 time to create 1 rle with old method : 0.011595726013183594 time for calcul the mask position with numpy : 0.029033660888671875 nb_pixel_total : 14911 time to create 1 rle with old method : 0.016887903213500977 time for calcul the mask position with numpy : 0.02919292449951172 nb_pixel_total : 20413 time to create 1 rle with old method : 0.022801637649536133 time for calcul the mask position with numpy : 0.028947830200195312 nb_pixel_total : 36577 time to create 1 rle with old method : 0.04133439064025879 time for calcul the mask position with numpy : 0.03093719482421875 nb_pixel_total : 170017 time to create 1 rle with new method : 0.38510799407958984 time for calcul the mask position with numpy : 0.028913021087646484 nb_pixel_total : 25288 time to create 1 rle with old method : 0.028522729873657227 time for calcul the mask position with numpy : 0.02883315086364746 nb_pixel_total : 4383 time to create 1 rle with old method : 0.005575418472290039 time for calcul the mask position with numpy : 0.02886962890625 nb_pixel_total : 12308 time to create 1 rle with old method : 0.014217853546142578 time for calcul the mask position with numpy : 0.0299222469329834 nb_pixel_total : 177135 time to create 1 rle with new method : 0.3929288387298584 time for calcul the mask position with numpy : 0.028946876525878906 nb_pixel_total : 16136 time to create 1 rle with old method : 0.018241405487060547 time for calcul the mask position with numpy : 0.028995752334594727 nb_pixel_total : 3821 time to create 1 rle with old method : 0.004337787628173828 time for calcul the mask position with numpy : 0.029923439025878906 nb_pixel_total : 103644 time to create 1 rle with old method : 0.11670351028442383 time for calcul the mask position with numpy : 0.029098987579345703 nb_pixel_total : 37519 time to create 1 rle with old method : 0.04216146469116211 time for calcul the mask position with numpy : 0.030219078063964844 nb_pixel_total : 32622 time to create 1 rle with old method : 0.03762030601501465 time for calcul the mask position with numpy : 0.03224968910217285 nb_pixel_total : 19947 time to create 1 rle with old method : 0.022558212280273438 time for calcul the mask position with numpy : 0.028992652893066406 nb_pixel_total : 36309 time to create 1 rle with old method : 0.04105019569396973 time for calcul the mask position with numpy : 0.029095172882080078 nb_pixel_total : 14383 time to create 1 rle with old method : 0.016450881958007812 time for calcul the mask position with numpy : 0.03032994270324707 nb_pixel_total : 121320 time to create 1 rle with old method : 0.14046049118041992 time for calcul the mask position with numpy : 0.029294729232788086 nb_pixel_total : 28578 time to create 1 rle with old method : 0.031995534896850586 time for calcul the mask position with numpy : 0.028792619705200195 nb_pixel_total : 799 time to create 1 rle with old method : 0.0013248920440673828 time for calcul the mask position with numpy : 0.029016971588134766 nb_pixel_total : 27974 time to create 1 rle with old method : 0.031645774841308594 time for calcul the mask position with numpy : 0.029355764389038086 nb_pixel_total : 76380 time to create 1 rle with old method : 0.0946810245513916 time for calcul the mask position with numpy : 0.03291797637939453 nb_pixel_total : 21947 time to create 1 rle with old method : 0.030726194381713867 time for calcul the mask position with numpy : 0.029074668884277344 nb_pixel_total : 38694 time to create 1 rle with old method : 0.04334139823913574 time for calcul the mask position with numpy : 0.029169559478759766 nb_pixel_total : 16886 time to create 1 rle with old method : 0.01877903938293457 time for calcul the mask position with numpy : 0.028940200805664062 nb_pixel_total : 12103 time to create 1 rle with old method : 0.013631105422973633 time for calcul the mask position with numpy : 0.029063701629638672 nb_pixel_total : 38395 time to create 1 rle with old method : 0.043201446533203125 time for calcul the mask position with numpy : 0.028916120529174805 nb_pixel_total : 12381 time to create 1 rle with old method : 0.01394510269165039 time for calcul the mask position with numpy : 0.028997182846069336 nb_pixel_total : 78 time to create 1 rle with old method : 0.000240325927734375 time for calcul the mask position with numpy : 0.02953338623046875 nb_pixel_total : 26920 time to create 1 rle with old method : 0.029933452606201172 time for calcul the mask position with numpy : 0.029001235961914062 nb_pixel_total : 20816 time to create 1 rle with old method : 0.023296594619750977 time for calcul the mask position with numpy : 0.0291750431060791 nb_pixel_total : 7770 time to create 1 rle with old method : 0.00892329216003418 time for calcul the mask position with numpy : 0.02916240692138672 nb_pixel_total : 3266 time to create 1 rle with old method : 0.004026651382446289 time for calcul the mask position with numpy : 0.029386520385742188 nb_pixel_total : 37896 time to create 1 rle with old method : 0.04232430458068848 time for calcul the mask position with numpy : 0.030397415161132812 nb_pixel_total : 13518 time to create 1 rle with old method : 0.022355318069458008 time for calcul the mask position with numpy : 0.03410649299621582 nb_pixel_total : 14771 time to create 1 rle with old method : 0.02760004997253418 time for calcul the mask position with numpy : 0.03918337821960449 nb_pixel_total : 9484 time to create 1 rle with old method : 0.010680437088012695 time for calcul the mask position with numpy : 0.02947211265563965 nb_pixel_total : 37238 time to create 1 rle with old method : 0.04223489761352539 time for calcul the mask position with numpy : 0.029392480850219727 nb_pixel_total : 5559 time to create 1 rle with old method : 0.009192466735839844 create new chi : 5.549282073974609 time to delete rle : 0.006757259368896484 batch 1 Loaded 109 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27246 TO DO : save crop sub photo not yet done ! save time : 4.130509853363037 nb_obj : 34 nb_hashtags : 5 time to prepare the origin masks : 4.977167129516602 time for calcul the mask position with numpy : 0.3652830123901367 nb_pixel_total : 5267107 time to create 1 rle with new method : 0.5854151248931885 time for calcul the mask position with numpy : 0.028898239135742188 nb_pixel_total : 45709 time to create 1 rle with old method : 0.0512998104095459 time for calcul the mask position with numpy : 0.02898406982421875 nb_pixel_total : 15883 time to create 1 rle with old method : 0.017885446548461914 time for calcul the mask position with numpy : 0.029932498931884766 nb_pixel_total : 269564 time to create 1 rle with new method : 0.3698604106903076 time for calcul the mask position with numpy : 0.028850317001342773 nb_pixel_total : 14657 time to create 1 rle with old method : 0.016406774520874023 time for calcul the mask position with numpy : 0.0327296257019043 nb_pixel_total : 33368 time to create 1 rle with old method : 0.05345344543457031 time for calcul the mask position with numpy : 0.028151750564575195 nb_pixel_total : 9572 time to create 1 rle with old method : 0.010756254196166992 time for calcul the mask position with numpy : 0.02850484848022461 nb_pixel_total : 1863 time to create 1 rle with old method : 0.0025031566619873047 time for calcul the mask position with numpy : 0.029021501541137695 nb_pixel_total : 94329 time to create 1 rle with old method : 0.10558819770812988 time for calcul the mask position with numpy : 0.02878427505493164 nb_pixel_total : 11517 time to create 1 rle with old method : 0.01295924186706543 time for calcul the mask position with numpy : 0.02870941162109375 nb_pixel_total : 19856 time to create 1 rle with old method : 0.02237987518310547 time for calcul the mask position with numpy : 0.028957605361938477 nb_pixel_total : 11108 time to create 1 rle with old method : 0.012422561645507812 time for calcul the mask position with numpy : 0.02857804298400879 nb_pixel_total : 11047 time to create 1 rle with old method : 0.012409687042236328 time for calcul the mask position with numpy : 0.028853654861450195 nb_pixel_total : 11998 time to create 1 rle with old method : 0.013539791107177734 time for calcul the mask position with numpy : 0.028612375259399414 nb_pixel_total : 9860 time to create 1 rle with old method : 0.011055469512939453 time for calcul the mask position with numpy : 0.02923440933227539 nb_pixel_total : 133060 time to create 1 rle with old method : 0.14817094802856445 time for calcul the mask position with numpy : 0.029040098190307617 nb_pixel_total : 95226 time to create 1 rle with old method : 0.10656237602233887 time for calcul the mask position with numpy : 0.03214240074157715 nb_pixel_total : 26367 time to create 1 rle with old method : 0.029619932174682617 time for calcul the mask position with numpy : 0.02870035171508789 nb_pixel_total : 8972 time to create 1 rle with old method : 0.010117530822753906 time for calcul the mask position with numpy : 0.028661727905273438 nb_pixel_total : 6528 time to create 1 rle with old method : 0.0073394775390625 time for calcul the mask position with numpy : 0.02874612808227539 nb_pixel_total : 23672 time to create 1 rle with old method : 0.026461362838745117 time for calcul the mask position with numpy : 0.02870941162109375 nb_pixel_total : 31245 time to create 1 rle with old method : 0.035144805908203125 time for calcul the mask position with numpy : 0.028723955154418945 nb_pixel_total : 21533 time to create 1 rle with old method : 0.024148941040039062 time for calcul the mask position with numpy : 0.028795719146728516 nb_pixel_total : 17393 time to create 1 rle with old method : 0.019657373428344727 time for calcul the mask position with numpy : 0.028688430786132812 nb_pixel_total : 347 time to create 1 rle with old method : 0.0006501674652099609 time for calcul the mask position with numpy : 0.029267072677612305 nb_pixel_total : 135620 time to create 1 rle with old method : 0.15129303932189941 time for calcul the mask position with numpy : 0.03145003318786621 nb_pixel_total : 498236 time to create 1 rle with new method : 0.6101179122924805 time for calcul the mask position with numpy : 0.02886056900024414 nb_pixel_total : 637 time to create 1 rle with old method : 0.0011229515075683594 time for calcul the mask position with numpy : 0.029224395751953125 nb_pixel_total : 89685 time to create 1 rle with old method : 0.10020160675048828 time for calcul the mask position with numpy : 0.029022216796875 nb_pixel_total : 23113 time to create 1 rle with old method : 0.025940418243408203 time for calcul the mask position with numpy : 0.029939651489257812 nb_pixel_total : 45522 time to create 1 rle with old method : 0.05729818344116211 time for calcul the mask position with numpy : 0.02926802635192871 nb_pixel_total : 26051 time to create 1 rle with old method : 0.029336214065551758 time for calcul the mask position with numpy : 0.028819561004638672 nb_pixel_total : 23526 time to create 1 rle with old method : 0.026441097259521484 time for calcul the mask position with numpy : 0.02901172637939453 nb_pixel_total : 6530 time to create 1 rle with old method : 0.007343769073486328 time for calcul the mask position with numpy : 0.028856754302978516 nb_pixel_total : 9539 time to create 1 rle with old method : 0.010803699493408203 create new chi : 4.162183523178101 time to delete rle : 0.0030553340911865234 batch 1 Loaded 73 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19929 TO DO : save crop sub photo not yet done ! save time : 1.3374273777008057 nb_obj : 30 nb_hashtags : 5 time to prepare the origin masks : 4.148179054260254 time for calcul the mask position with numpy : 0.2741215229034424 nb_pixel_total : 5416169 time to create 1 rle with new method : 0.4946115016937256 time for calcul the mask position with numpy : 0.028157472610473633 nb_pixel_total : 45926 time to create 1 rle with old method : 0.05030536651611328 time for calcul the mask position with numpy : 0.02799391746520996 nb_pixel_total : 16518 time to create 1 rle with old method : 0.018244028091430664 time for calcul the mask position with numpy : 0.028304576873779297 nb_pixel_total : 18014 time to create 1 rle with old method : 0.01997852325439453 time for calcul the mask position with numpy : 0.02856755256652832 nb_pixel_total : 98839 time to create 1 rle with old method : 0.10861587524414062 time for calcul the mask position with numpy : 0.028293609619140625 nb_pixel_total : 11419 time to create 1 rle with old method : 0.012612342834472656 time for calcul the mask position with numpy : 0.027947425842285156 nb_pixel_total : 11418 time to create 1 rle with old method : 0.012421131134033203 time for calcul the mask position with numpy : 0.027853965759277344 nb_pixel_total : 11643 time to create 1 rle with old method : 0.012767553329467773 time for calcul the mask position with numpy : 0.028353214263916016 nb_pixel_total : 11069 time to create 1 rle with old method : 0.012412071228027344 time for calcul the mask position with numpy : 0.028057336807250977 nb_pixel_total : 17635 time to create 1 rle with old method : 0.019432783126831055 time for calcul the mask position with numpy : 0.027893543243408203 nb_pixel_total : 9237 time to create 1 rle with old method : 0.01023101806640625 time for calcul the mask position with numpy : 0.029337644577026367 nb_pixel_total : 188939 time to create 1 rle with new method : 0.3842954635620117 time for calcul the mask position with numpy : 0.029338598251342773 nb_pixel_total : 20225 time to create 1 rle with old method : 0.02313852310180664 time for calcul the mask position with numpy : 0.030608177185058594 nb_pixel_total : 202316 time to create 1 rle with new method : 0.340348482131958 time for calcul the mask position with numpy : 0.028266191482543945 nb_pixel_total : 61016 time to create 1 rle with old method : 0.0668492317199707 time for calcul the mask position with numpy : 0.027689456939697266 nb_pixel_total : 9195 time to create 1 rle with old method : 0.09383773803710938 time for calcul the mask position with numpy : 0.027408123016357422 nb_pixel_total : 5566 time to create 1 rle with old method : 0.006248950958251953 time for calcul the mask position with numpy : 0.027956008911132812 nb_pixel_total : 13877 time to create 1 rle with old method : 0.015163183212280273 time for calcul the mask position with numpy : 0.028473377227783203 nb_pixel_total : 36763 time to create 1 rle with old method : 0.04047536849975586 time for calcul the mask position with numpy : 0.028125286102294922 nb_pixel_total : 17816 time to create 1 rle with old method : 0.019663572311401367 time for calcul the mask position with numpy : 0.028223752975463867 nb_pixel_total : 19435 time to create 1 rle with old method : 0.021242856979370117 time for calcul the mask position with numpy : 0.0285646915435791 nb_pixel_total : 122692 time to create 1 rle with old method : 0.13433122634887695 time for calcul the mask position with numpy : 0.030234336853027344 nb_pixel_total : 462403 time to create 1 rle with new method : 0.3207573890686035 time for calcul the mask position with numpy : 0.028307676315307617 nb_pixel_total : 32905 time to create 1 rle with old method : 0.03584122657775879 time for calcul the mask position with numpy : 0.028103351593017578 nb_pixel_total : 25344 time to create 1 rle with old method : 0.028537988662719727 time for calcul the mask position with numpy : 0.02829265594482422 nb_pixel_total : 73720 time to create 1 rle with old method : 0.0809929370880127 time for calcul the mask position with numpy : 0.028303146362304688 nb_pixel_total : 21463 time to create 1 rle with old method : 0.02332472801208496 time for calcul the mask position with numpy : 0.028348207473754883 nb_pixel_total : 28408 time to create 1 rle with old method : 0.0311734676361084 time for calcul the mask position with numpy : 0.02831554412841797 nb_pixel_total : 23799 time to create 1 rle with old method : 0.02554607391357422 time for calcul the mask position with numpy : 0.027424097061157227 nb_pixel_total : 6906 time to create 1 rle with old method : 0.0077838897705078125 time for calcul the mask position with numpy : 0.028105974197387695 nb_pixel_total : 9565 time to create 1 rle with old method : 0.010496377944946289 create new chi : 3.7039546966552734 time to delete rle : 0.0024242401123046875 batch 1 Loaded 62 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16819 TO DO : save crop sub photo not yet done ! save time : 1.1369349956512451 nb_obj : 36 nb_hashtags : 5 time to prepare the origin masks : 5.404966831207275 time for calcul the mask position with numpy : 0.4972538948059082 nb_pixel_total : 5201885 time to create 1 rle with new method : 0.4473757743835449 time for calcul the mask position with numpy : 0.02945709228515625 nb_pixel_total : 35301 time to create 1 rle with old method : 0.040692806243896484 time for calcul the mask position with numpy : 0.030628442764282227 nb_pixel_total : 11542 time to create 1 rle with old method : 0.019069910049438477 time for calcul the mask position with numpy : 0.03284645080566406 nb_pixel_total : 6072 time to create 1 rle with old method : 0.008133411407470703 time for calcul the mask position with numpy : 0.030153274536132812 nb_pixel_total : 19320 time to create 1 rle with old method : 0.023147106170654297 time for calcul the mask position with numpy : 0.03181648254394531 nb_pixel_total : 249103 time to create 1 rle with new method : 0.49916505813598633 time for calcul the mask position with numpy : 0.030035972595214844 nb_pixel_total : 8873 time to create 1 rle with old method : 0.010184049606323242 time for calcul the mask position with numpy : 0.029247283935546875 nb_pixel_total : 11007 time to create 1 rle with old method : 0.013170003890991211 time for calcul the mask position with numpy : 0.02993321418762207 nb_pixel_total : 8491 time to create 1 rle with old method : 0.009621143341064453 time for calcul the mask position with numpy : 0.029366016387939453 nb_pixel_total : 53973 time to create 1 rle with old method : 0.06183004379272461 time for calcul the mask position with numpy : 0.030321836471557617 nb_pixel_total : 9073 time to create 1 rle with old method : 0.012787103652954102 time for calcul the mask position with numpy : 0.02923274040222168 nb_pixel_total : 23904 time to create 1 rle with old method : 0.02720165252685547 time for calcul the mask position with numpy : 0.029598236083984375 nb_pixel_total : 11138 time to create 1 rle with old method : 0.012819051742553711 time for calcul the mask position with numpy : 0.02917933464050293 nb_pixel_total : 8663 time to create 1 rle with old method : 0.009787797927856445 time for calcul the mask position with numpy : 0.03050851821899414 nb_pixel_total : 21245 time to create 1 rle with old method : 0.024773597717285156 time for calcul the mask position with numpy : 0.039932966232299805 nb_pixel_total : 173505 time to create 1 rle with new method : 0.43544507026672363 time for calcul the mask position with numpy : 0.02862691879272461 nb_pixel_total : 158713 time to create 1 rle with new method : 0.3228013515472412 time for calcul the mask position with numpy : 0.028078079223632812 nb_pixel_total : 4398 time to create 1 rle with old method : 0.005379676818847656 time for calcul the mask position with numpy : 0.028252363204956055 nb_pixel_total : 62855 time to create 1 rle with old method : 0.06719541549682617 time for calcul the mask position with numpy : 0.02704000473022461 nb_pixel_total : 22180 time to create 1 rle with old method : 0.023477554321289062 time for calcul the mask position with numpy : 0.02861499786376953 nb_pixel_total : 37951 time to create 1 rle with old method : 0.04260563850402832 time for calcul the mask position with numpy : 0.02793121337890625 nb_pixel_total : 36291 time to create 1 rle with old method : 0.03839921951293945 time for calcul the mask position with numpy : 0.026909351348876953 nb_pixel_total : 26128 time to create 1 rle with old method : 0.027732372283935547 time for calcul the mask position with numpy : 0.026930570602416992 nb_pixel_total : 37031 time to create 1 rle with old method : 0.03907442092895508 time for calcul the mask position with numpy : 0.02687239646911621 nb_pixel_total : 21413 time to create 1 rle with old method : 0.022356271743774414 time for calcul the mask position with numpy : 0.02737736701965332 nb_pixel_total : 12913 time to create 1 rle with old method : 0.014195442199707031 time for calcul the mask position with numpy : 0.030466079711914062 nb_pixel_total : 504856 time to create 1 rle with new method : 0.3996403217315674 time for calcul the mask position with numpy : 0.0289304256439209 nb_pixel_total : 32872 time to create 1 rle with old method : 0.03696298599243164 time for calcul the mask position with numpy : 0.028815746307373047 nb_pixel_total : 15838 time to create 1 rle with old method : 0.018140316009521484 time for calcul the mask position with numpy : 0.028684377670288086 nb_pixel_total : 1892 time to create 1 rle with old method : 0.0022385120391845703 time for calcul the mask position with numpy : 0.0290529727935791 nb_pixel_total : 99108 time to create 1 rle with old method : 0.11104989051818848 time for calcul the mask position with numpy : 0.029278993606567383 nb_pixel_total : 25341 time to create 1 rle with old method : 0.02843952178955078 time for calcul the mask position with numpy : 0.028974533081054688 nb_pixel_total : 14955 time to create 1 rle with old method : 0.016828536987304688 time for calcul the mask position with numpy : 0.02892923355102539 nb_pixel_total : 48815 time to create 1 rle with old method : 0.05442166328430176 time for calcul the mask position with numpy : 0.028895854949951172 nb_pixel_total : 12546 time to create 1 rle with old method : 0.014117240905761719 time for calcul the mask position with numpy : 0.029024124145507812 nb_pixel_total : 10930 time to create 1 rle with old method : 0.012469053268432617 time for calcul the mask position with numpy : 0.02908635139465332 nb_pixel_total : 10119 time to create 1 rle with old method : 0.011493206024169922 create new chi : 4.654138565063477 time to delete rle : 0.003285646438598633 batch 1 Loaded 77 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20750 TO DO : save crop sub photo not yet done ! save time : 3.7626311779022217 map_output_result : {1349150976: (0.0, 'Should be the crop_list due to order', 0), 1349145716: (0.0, 'Should be the crop_list due to order', 0), 1349145691: (0.0, 'Should be the crop_list due to order', 0), 1349145687: (0.0, 'Should be the crop_list due to order', 0), 1348990554: (0.0, 'Should be the crop_list due to order', 0), 1348990549: (0.0, 'Should be the crop_list due to order', 0), 1348990539: (0.0, 'Should be the crop_list due to order', 0), 1348990537: (0.0, 'Should be the crop_list due to order', 0), 1348990534: (0.0, 'Should be the crop_list due to order', 0), 1348990500: (0.0, 'Should be the crop_list due to order', 0), 1348990497: (0.0, 'Should be the crop_list due to order', 0), 1348990491: (0.0, 'Should be the crop_list due to order', 0), 1348990489: (0.0, 'Should be the crop_list due to order', 0), 1348990485: (0.0, 'Should be the crop_list due to order', 0), 1348990483: (0.0, 'Should be the crop_list due to order', 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 [1349150976, 1349145716, 1349145691, 1349145687, 1348990554, 1348990549, 1348990539, 1348990537, 1348990534, 1348990500, 1348990497, 1348990491, 1348990489, 1348990485, 1348990483] Looping around the photos to save general results len do output : 15 /1349150976.Didn't retrieve data . /1349145716.Didn't retrieve data . /1349145691.Didn't retrieve data . /1349145687.Didn't retrieve data . /1348990554.Didn't retrieve data . /1348990549.Didn't retrieve data . /1348990539.Didn't retrieve data . /1348990537.Didn't retrieve data . /1348990534.Didn't retrieve data . /1348990500.Didn't retrieve data . /1348990497.Didn't retrieve data . /1348990491.Didn't retrieve data . /1348990489.Didn't retrieve data . /1348990485.Didn't retrieve data . /1348990483.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 ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349150976', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145716', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145691', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145687', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990554', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990549', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990539', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990537', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990534', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990500', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990497', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990491', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990489', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990485', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990483', None, None, None, None, None, '2711135') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 45 time used for this insertion : 0.015217065811157227 save_final save missing photos in datou_result : time spend for datou_step_exec : 184.13490796089172 time spend to save output : 0.03746652603149414 total time spend for step 3 : 184.17237448692322 step4:ventilate_hashtags_in_portfolio Tue Apr 1 02:55:34 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 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 : 21929818 get user id for portfolio 21929818 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929818 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','carton','pet_fonce','autre','background','papier','mal_croppe','pehd','pet_clair','metal','flou')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929818 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','carton','pet_fonce','autre','background','papier','mal_croppe','pehd','pet_clair','metal','flou')) AND mptpi.`min_score`=0.5 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`=21929818 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','carton','pet_fonce','autre','background','papier','mal_croppe','pehd','pet_clair','metal','flou')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21931550,21931551,21931552,21931553,21931554,21931555,21931556,21931557,21931558,21931559,21931560?tags=environnement,carton,pet_fonce,autre,background,papier,mal_croppe,pehd,pet_clair,metal,flou Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349150976, 1349145716, 1349145691, 1349145687, 1348990554, 1348990549, 1348990539, 1348990537, 1348990534, 1348990500, 1348990497, 1348990491, 1348990489, 1348990485, 1348990483] Looping around the photos to save general results len do output : 1 /21929818. 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 ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349150976', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145716', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145691', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145687', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990554', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990549', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990539', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990537', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990534', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990500', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990497', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990491', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990489', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990485', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990483', None, None, None, None, None, '2711135') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.5128564834594727 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.3014180660247803 time spend to save output : 0.5153594017028809 total time spend for step 4 : 1.8167774677276611 step5:final Tue Apr 1 02:55:36 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 : {1349150976: ('0.23430577304224173',), 1349145716: ('0.23430577304224173',), 1349145691: ('0.23430577304224173',), 1349145687: ('0.23430577304224173',), 1348990554: ('0.23430577304224173',), 1348990549: ('0.23430577304224173',), 1348990539: ('0.23430577304224173',), 1348990537: ('0.23430577304224173',), 1348990534: ('0.23430577304224173',), 1348990500: ('0.23430577304224173',), 1348990497: ('0.23430577304224173',), 1348990491: ('0.23430577304224173',), 1348990489: ('0.23430577304224173',), 1348990485: ('0.23430577304224173',), 1348990483: ('0.23430577304224173',)} new output for save of step final : {1349150976: ('0.23430577304224173',), 1349145716: ('0.23430577304224173',), 1349145691: ('0.23430577304224173',), 1349145687: ('0.23430577304224173',), 1348990554: ('0.23430577304224173',), 1348990549: ('0.23430577304224173',), 1348990539: ('0.23430577304224173',), 1348990537: ('0.23430577304224173',), 1348990534: ('0.23430577304224173',), 1348990500: ('0.23430577304224173',), 1348990497: ('0.23430577304224173',), 1348990491: ('0.23430577304224173',), 1348990489: ('0.23430577304224173',), 1348990485: ('0.23430577304224173',), 1348990483: ('0.23430577304224173',)} [1349150976, 1349145716, 1349145691, 1349145687, 1348990554, 1348990549, 1348990539, 1348990537, 1348990534, 1348990500, 1348990497, 1348990491, 1348990489, 1348990485, 1348990483] Looping around the photos to save general results len do output : 15 /1349150976.Didn't retrieve data . /1349145716.Didn't retrieve data . /1349145691.Didn't retrieve data . /1349145687.Didn't retrieve data . /1348990554.Didn't retrieve data . /1348990549.Didn't retrieve data . /1348990539.Didn't retrieve data . /1348990537.Didn't retrieve data . /1348990534.Didn't retrieve data . /1348990500.Didn't retrieve data . /1348990497.Didn't retrieve data . /1348990491.Didn't retrieve data . /1348990489.Didn't retrieve data . /1348990485.Didn't retrieve data . /1348990483.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 ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349150976', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145716', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145691', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145687', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990554', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990549', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990539', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990537', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990534', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990500', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990497', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990491', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990489', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990485', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990483', None, None, None, None, None, '2711135') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 45 time used for this insertion : 0.01736593246459961 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.258925199508667 time spend to save output : 0.02593398094177246 total time spend for step 5 : 0.28485918045043945 step6:blur_detection Tue Apr 1 02:55:36 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 inside step blur_detection methode: ratio et variance treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910.jpg resize: (2160, 3264) 1349150976 -2.3156349730607104 treat image : temp/1743468028_2499418_1349145716_8a2b4679f1238463d1ed5dda95c5b89f.jpg resize: (2160, 3264) 1349145716 -2.84889808009275 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509.jpg resize: (2160, 3264) 1349145691 -4.188974681327141 treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351.jpg resize: (2160, 3264) 1349145687 -3.422053868810591 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb.jpg resize: (2160, 3264) 1348990554 -2.895521610743893 treat image : temp/1743468028_2499418_1348990549_2c8f475ea76bbc3fffcecb5038245fa0.jpg resize: (2160, 3264) 1348990549 -6.850502936631432 treat image : temp/1743468028_2499418_1348990539_a203803e8853ba5eca5146b1f16782f7.jpg resize: (2160, 3264) 1348990539 -2.2137888323413533 treat image : temp/1743468028_2499418_1348990537_10bf83ae3eff34b8b32377edce98abb7.jpg resize: (2160, 3264) 1348990537 -1.4589583807610245 treat image : temp/1743468028_2499418_1348990534_e6497ce888aa9a502a1ac6e8d36ea99e.jpg resize: (2160, 3264) 1348990534 -4.613687148254605 treat image : temp/1743468028_2499418_1348990500_c280cf2dafd51805f709fa76ad159412.jpg resize: (2160, 3264) 1348990500 -1.7534022582668907 treat image : temp/1743468028_2499418_1348990497_0c91ce08c990b4dc468c03d6655ad3c1.jpg resize: (2160, 3264) 1348990497 -3.673913890628008 treat image : temp/1743468028_2499418_1348990491_7af62e2ea42f6b5eb2be44e3800d9734.jpg resize: (2160, 3264) 1348990491 -4.423212212338163 treat image : temp/1743468028_2499418_1348990489_171ab9fac68f2234003a5214126876fc.jpg resize: (2160, 3264) 1348990489 -6.392847451019325 treat image : temp/1743468028_2499418_1348990485_7cb6035d49e77dec811ac7f0574ddb49.jpg resize: (2160, 3264) 1348990485 -6.389391067384178 treat image : temp/1743468028_2499418_1348990483_a47c23bb0594c8709fd88623819498ed.jpg resize: (2160, 3264) 1348990483 -3.879775280536989 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157594_0.png resize: (208, 142) 1349180333 -0.9530590082431138 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157584_0.png resize: (245, 153) 1349180334 -2.986632513474088 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157599_0.png resize: (110, 196) 1349180335 -2.034957387101038 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157597_0.png resize: (67, 102) 1349180336 -1.9860005481372203 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157582_0.png resize: (249, 266) 1349180337 -0.44519057420820873 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157585_0.png resize: (174, 209) 1349180338 -1.9706978404516635 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157592_0.png resize: (154, 86) 1349180339 -0.48376130028560205 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157589_0.png resize: (114, 135) 1349180340 -0.47823183900459665 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157596_0.png resize: (247, 247) 1349180341 -1.4076190203072854 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157583_0.png resize: (96, 244) 1349180342 -1.9723298042841333 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157587_0.png resize: (159, 164) 1349180343 -0.29732566061130544 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157593_0.png resize: (167, 118) 1349180344 -2.092664423993002 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157595_0.png resize: (521, 354) 1349180345 -2.8295328498904437 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157600_0.png resize: (201, 142) 1349180346 -1.2061738314546797 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157586_0.png resize: (223, 241) 1349180347 -1.4610527573418335 treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910_rle_crop_3742157588_0.png resize: (164, 160) 1349180348 -2.1036305029232993 treat image : temp/1743468028_2499418_1349145716_8a2b4679f1238463d1ed5dda95c5b89f_rle_crop_3742157602_0.png resize: (261, 285) 1349180349 -0.973313425499767 treat image : temp/1743468028_2499418_1349145716_8a2b4679f1238463d1ed5dda95c5b89f_rle_crop_3742157606_0.png resize: (474, 416) 1349180350 -2.369184078867585 treat image : temp/1743468028_2499418_1349145716_8a2b4679f1238463d1ed5dda95c5b89f_rle_crop_3742157603_0.png resize: (282, 288) 1349180351 -0.9474506914888322 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157631_0.png resize: (153, 109) 1349180352 -2.1240077569441054 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157619_0.png resize: (306, 489) 1349180353 -0.9893057330290388 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157613_0.png resize: (337, 366) 1349180354 -1.5046309694898412 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157618_0.png resize: (112, 203) 1349180355 -2.8886170321263442 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157611_0.png resize: (358, 423) 1349180356 -2.2158875039988843 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157621_0.png resize: (183, 129) 1349180357 -1.3614176169237144 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157612_0.png resize: (257, 856) 1349180358 -3.7937666451262855 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157627_0.png resize: (409, 328) 1349180359 -3.330584345074223 treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509_rle_crop_3742157623_0.png resize: (135, 168) 1349180360 -1.91504506767068 treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351_rle_crop_3742157637_0.png resize: (584, 254) 1349180361 -3.3518627040982842 treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351_rle_crop_3742157632_0.png resize: (561, 364) 1349180362 -2.923197271263729 treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351_rle_crop_3742157640_0.png resize: (167, 165) 1349180364 -2.1255700980644128 treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351_rle_crop_3742157646_0.png resize: (983, 626) 1349180365 -2.5904217158889127 treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351_rle_crop_3742157647_0.png resize: (76, 182) 1349180366 -2.081858904631772 treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351_rle_crop_3742157638_0.png resize: (269, 288) 1349180367 -3.6639083840139413 treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351_rle_crop_3742157635_0.png resize: (196, 180) 1349180368 -1.1093850496290927 treat image : 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temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157677_0.png resize: (119, 199) 1349180381 -2.6686445781657624 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157654_0.png resize: (281, 157) 1349180382 -1.609896144928854 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157686_0.png resize: (137, 85) 1349180383 -1.8734979569221886 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157660_0.png resize: (299, 129) 1349180384 -2.856756793035345 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157678_0.png resize: (135, 88) 1349180385 -2.359172448925377 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157683_0.png resize: (129, 267) 1349180386 -2.6358083971731068 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157695_0.png resize: (110, 161) 1349180387 -1.409027634238561 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157656_0.png resize: (194, 235) 1349180388 -2.60130244490186 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157688_0.png resize: (138, 190) 1349180389 -0.9358253652958586 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157684_0.png resize: (199, 93) 1349180390 -0.768360069908349 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157680_0.png resize: (467, 340) 1349180391 -0.47354926550368376 treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb_rle_crop_3742157672_0.png resize: (168, 113) 1349180392 -0.5618476530463127 treat image : 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temp/1743468028_2499418_1348990483_a47c23bb0594c8709fd88623819498ed_bib_crop_3741007425_0.jpg resize: (130, 352) 1349181288 2.177614778208954 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 : 735 time used for this insertion : 0.2140507698059082 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 735 time used for this insertion : 0.1442091464996338 save missing photos in datou_result : time spend for datou_step_exec : 72.86392021179199 time spend to save output : 0.3712747097015381 total time spend for step 6 : 73.23519492149353 step7:brightness Tue Apr 1 02:56:49 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 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 inside step calcul brightness treat image : temp/1743468028_2499418_1349150976_14061b289eec7df457f05063e3037910.jpg treat image : temp/1743468028_2499418_1349145716_8a2b4679f1238463d1ed5dda95c5b89f.jpg treat image : temp/1743468028_2499418_1349145691_2d5c267216cf3a9477aa3c015f2b5509.jpg treat image : temp/1743468028_2499418_1349145687_f26292ab3e053a9fffd87f2b10837351.jpg treat image : temp/1743468028_2499418_1348990554_a06d0f00552012f8267fc89b808346cb.jpg treat image : temp/1743468028_2499418_1348990549_2c8f475ea76bbc3fffcecb5038245fa0.jpg treat image : temp/1743468028_2499418_1348990539_a203803e8853ba5eca5146b1f16782f7.jpg treat image : temp/1743468028_2499418_1348990537_10bf83ae3eff34b8b32377edce98abb7.jpg treat image : temp/1743468028_2499418_1348990534_e6497ce888aa9a502a1ac6e8d36ea99e.jpg treat image : temp/1743468028_2499418_1348990500_c280cf2dafd51805f709fa76ad159412.jpg treat image : 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temp/1743468028_2499418_1348990485_7cb6035d49e77dec811ac7f0574ddb49_rle_crop_3741002128_0.png treat image : temp/1743468028_2499418_1348990485_7cb6035d49e77dec811ac7f0574ddb49_bib_crop_3741007317_0.jpg treat image : temp/1743468028_2499418_1348990483_a47c23bb0594c8709fd88623819498ed_rle_crop_3741002149_0.png treat image : temp/1743468028_2499418_1348990483_a47c23bb0594c8709fd88623819498ed_bib_crop_3741007425_0.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 : 735 time used for this insertion : 0.15267372131347656 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 735 time used for this insertion : 0.1204376220703125 save missing photos in datou_result : time spend for datou_step_exec : 21.136962890625 time spend to save output : 0.28031373023986816 total time spend for step 7 : 21.417276620864868 step8:velours_tree Tue Apr 1 02:57:11 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 can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 2.6985912322998047 time spend to save output : 4.3392181396484375e-05 total time spend for step 8 : 2.698634624481201 step9:send_mail_cod Tue Apr 1 02:57:13 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 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 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/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P21929818_01-04-2025_02_57_13.pdf 21931551 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 .imagette219315511743469033 21931552 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 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.change filename to text .change filename to text .change filename to text .change filename to text .imagette219315581743469043 21931559 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 .imagette219315591743469044 21931560 imagette219315601743469045 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21929818 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21931550,21931551,21931552,21931553,21931554,21931555,21931556,21931557,21931558,21931559,21931560?tags=environnement,carton,pet_fonce,autre,background,papier,mal_croppe,pehd,pet_clair,metal,flou args[1349150976] : ((1349150976, -2.3156349730607104, 492609224), (1349150976, -0.3083268826535306, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1349145716] : ((1349145716, -2.84889808009275, 492609224), (1349145716, -0.25123617535040216, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1349145691] : ((1349145691, -4.188974681327141, 492609224), (1349145691, 0.1969263256526011, 2107752395), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1349145687] : ((1349145687, -3.422053868810591, 492609224), (1349145687, -0.5683375471808169, 501862349), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990554] : ((1348990554, -2.895521610743893, 492609224), (1348990554, -0.04314333542632656, 2107752395), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990549] : ((1348990549, -6.850502936631432, 492609224), (1348990549, -0.25010335924606175, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990539] : ((1348990539, -2.2137888323413533, 492609224), (1348990539, -0.12152050852656021, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990537] : ((1348990537, -1.4589583807610245, 492688767), (1348990537, -0.18346976053556271, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990534] : ((1348990534, -4.613687148254605, 492609224), (1348990534, -0.4992459202673671, 501862349), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990500] : ((1348990500, -1.7534022582668907, 492688767), (1348990500, -0.22892760983571678, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990497] : ((1348990497, -3.673913890628008, 492609224), (1348990497, -0.0976659775463837, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990491] : ((1348990491, -4.423212212338163, 492609224), (1348990491, -0.38221662050331767, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990489] : ((1348990489, -6.392847451019325, 492609224), (1348990489, -0.2654417225381973, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990485] : ((1348990485, -6.389391067384178, 492609224), (1348990485, -0.25701017776498736, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com args[1348990483] : ((1348990483, -3.879775280536989, 492609224), (1348990483, -0.2749059130279966, 496442774), '0.23430577304224173') We are sending mail with results at report@fotonower.com refus_total : 0.23430577304224173 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=21929818 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1348990483,1348990500,1349145687,1349145691,1348990485,1348990489,1348990491,1348990497,1348990534,1348990537,1348990539,1348990549,1348990554,1349145716,1349150976) Found this number of photos: 15 begin to download photo : 1348990483 begin to download photo : 1348990485 begin to download photo : 1348990534 begin to download photo : 1348990554 download finish for photo 1348990485 begin to download photo : 1348990489 download finish for photo 1348990554 begin to download photo : 1349145716 download finish for photo 1348990534 begin to download photo : 1348990537 download finish for photo 1348990483 begin to download photo : 1348990500 download finish for photo 1349145716 begin to download photo : 1349150976 download finish for photo 1348990489 begin to download photo : 1348990491 download finish for photo 1348990537 begin to download photo : 1348990539 download finish for photo 1349150976 download finish for photo 1348990500 begin to download photo : 1349145687 download finish for photo 1348990539 begin to download photo : 1348990549 download finish for photo 1348990491 begin to download photo : 1348990497 download finish for photo 1349145687 begin to download photo : 1349145691 download finish for photo 1348990549 download finish for photo 1348990497 download finish for photo 1349145691 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929818_01-04-2025_02_57_13.pdf results_Auto_P21929818_01-04-2025_02_57_13.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929818_01-04-2025_02_57_13.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3318','21929818','results_Auto_P21929818_01-04-2025_02_57_13.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929818_01-04-2025_02_57_13.pdf','pdf','','1.12','0.23430577304224173') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21929818

https://www.fotonower.com/image?json=false&list_photos_id=1349150976
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349145716
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349145691
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349145687
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990554
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990549
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990539
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990537
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990534
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990500
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990497
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990491
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990489
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990485
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1348990483
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 23.43%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/21931551?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21931552?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21931553?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/21931555?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/21931557?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21931558?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21931559?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929818_01-04-2025_02_57_13.pdf.

Lien vers velours :https://www.fotonower.com/velours/21931550,21931551,21931552,21931553,21931554,21931555,21931556,21931557,21931558,21931559,21931560?tags=environnement,carton,pet_fonce,autre,background,papier,mal_croppe,pehd,pet_clair,metal,flou.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 01 Apr 2025 00:57:29 GMT Content-Length: 0 Connection: close X-Message-Id: TRyrIFJoQn6od1M7vZhcPQ Access-Control-Allow-Origin: https://sendgrid.api-docs.io Access-Control-Allow-Methods: POST Access-Control-Allow-Headers: Authorization, Content-Type, On-behalf-of, x-sg-elas-acl Access-Control-Max-Age: 600 X-No-CORS-Reason: https://sendgrid.com/docs/Classroom/Basics/API/cors.html Strict-Transport-Security: max-age=31536000; includeSubDomains Content-Security-Policy: frame-ancestors 'none' Cache-Control: no-cache X-Content-Type-Options: no-sniff Referrer-Policy: strict-origin-when-cross-origin Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : send_mail_cod we use saveGeneral [1349150976, 1349145716, 1349145691, 1349145687, 1348990554, 1348990549, 1348990539, 1348990537, 1348990534, 1348990500, 1348990497, 1348990491, 1348990489, 1348990485, 1348990483] 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 ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349150976', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145716', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145691', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145687', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990554', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990549', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990539', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990537', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990534', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990500', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990497', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990491', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990489', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990485', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990483', None, None, None, None, None, '2711135') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.2701265811920166 save_final save missing photos in datou_result : time spend for datou_step_exec : 16.04016876220703 time spend to save output : 0.27049970626831055 total time spend for step 9 : 16.310668468475342 step10:split_time_score Tue Apr 1 02:57:30 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 ! complete output_args for input 1 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': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] 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'}] (('12', 15),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 31032025 21929818 Nombre de photos uploadées : 15 / 23040 (0%) 31032025 21929818 Nombre de photos taguées (types de déchets): 0 / 15 (0%) 31032025 21929818 Nombre de photos taguées (volume) : 0 / 15 (0%) elapsed_time : load_data_split_time_score 7.867813110351562e-06 elapsed_time : order_list_meta_photo_and_scores 2.0742416381835938e-05 ??????????????? elapsed_time : fill_and_build_computed_from_old_data 0.00087738037109375 elapsed_time : insert_dashboard_record_day_entry 0.022683143615722656 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 : 0.14630428184005087 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925661_31-03-2025_22_59_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925661 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21925661 AND mptpi.`type`=3594 To do Qualite : 0.14145216719895026 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925662_31-03-2025_22_51_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925662 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21925662 AND mptpi.`type`=3594 To do Qualite : 0.0939584877250109 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21905169_31-03-2025_11_54_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21905169 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21905169 AND mptpi.`type`=3726 To do Qualite : 0.24895207748060239 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929800_01-04-2025_02_11_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929800 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929800 AND mptpi.`type`=3594 To do Qualite : 0.23430577304224173 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929818_01-04-2025_02_57_13.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929818 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929818 AND mptpi.`type`=3594 To do Qualite : 0.083542372459225 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930826_01-04-2025_02_21_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930826 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21930826 AND mptpi.`type`=3726 To do Qualite : 0.22413743184629167 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930834_01-04-2025_02_49_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930834 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21930834 AND mptpi.`type`=3594 To do Qualite : 0.18642559428022043 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930836_01-04-2025_02_34_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930836 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21930836 AND mptpi.`type`=3594 To do Qualite : 0.2305784432354592 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929822_01-04-2025_01_34_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929822 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929822 AND mptpi.`type`=3594 To do Qualite : 0.06655992376993317 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929825_01-04-2025_01_30_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929825 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929825 AND mptpi.`type`=3726 To do Qualite : 0.22924124322132222 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21926965_31-03-2025_23_29_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21926965 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21926965 AND mptpi.`type`=3594 To do Qualite : 0.2189041275706411 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925669_31-03-2025_22_45_27.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925669 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21925669 AND mptpi.`type`=3594 To do Qualite : 0.18545522031874095 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925670_31-03-2025_22_36_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925670 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21925670 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'31032025': {'nb_upload': 15, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1349150976, 1349145716, 1349145691, 1349145687, 1348990554, 1348990549, 1348990539, 1348990537, 1348990534, 1348990500, 1348990497, 1348990491, 1348990489, 1348990485, 1348990483] Looping around the photos to save general results len do output : 1 /21929818Didn'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 ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349150976', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145716', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145691', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1349145687', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990554', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990549', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990539', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990537', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990534', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990500', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990497', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990491', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990489', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990485', None, None, None, None, None, '2711135') ('3318', None, None, None, None, None, None, None, '2711135') ('3318', '21929818', '1348990483', None, None, None, None, None, '2711135') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.015924453735351562 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.997100830078125 time spend to save output : 0.016152143478393555 total time spend for step 10 : 1.0132529735565186 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 15 set_done_treatment 425.70user 209.90system 17:05.48elapsed 61%CPU (0avgtext+0avgdata 6750264maxresident)k 19706144inputs+320464outputs (553744major+34135093minor)pagefaults 0swaps