python /home/admin/mtr/script_for_cron.py -j datou_current3 -m 20 -a ' -a 3318 ' -s datou_3318 -M 0 -S 0 -U 95,95,120 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 : 669588 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 : ['2733641'] with mtr_portfolio_ids : ['22153537'] and first list_photo_ids : [] new path : /proc/669588/ 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 : 2.954908847808838 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 Wed Apr 9 11:10:30 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 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 : 10372 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-09 11:10:33.809609: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-09 11:10:33.839163: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-09 11:10:33.841430: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fe754000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-09 11:10:33.841465: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-09 11:10:33.845730: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-09 11:10:34.111212: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x33f02de0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-09 11:10:34.111267: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-09 11:10:34.112312: 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 2025-04-09 11:10:34.112642: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 11:10:34.115062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 11:10:34.117410: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 11:10:34.117826: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 11:10:34.120387: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 11:10:34.121630: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 11:10:34.127014: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 11:10:34.128643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 11:10:34.128749: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 11:10:34.129555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 11:10:34.129572: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 11:10:34.129582: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 11:10:34.131411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9607 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. 2025-04-09 11:10:34.465901: 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 2025-04-09 11:10:34.466024: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 11:10:34.466052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 11:10:34.466077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 11:10:34.466102: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 11:10:34.466126: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 11:10:34.466149: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 11:10:34.466173: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 11:10:34.467899: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 11:10:34.469632: 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 2025-04-09 11:10:34.469714: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 11:10:34.469737: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 11:10:34.469759: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 11:10:34.469779: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 11:10:34.469799: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 11:10:34.469819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 11:10:34.469841: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 11:10:34.471567: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 11:10:34.471619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 11:10:34.471631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 11:10:34.471642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 11:10:34.473306: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9607 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 : [] 2025-04-09 11:10:46.341901: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 11:10:46.598761: 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 : 52 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 : 59 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 : 84 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 : 53 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 : 46 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 : 44 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 8.00000 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 : 17 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 : 17 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 : 87 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 : 76 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 : 57 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 : 77 Detection mask done ! Trying to reset tf kernel 670345 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5083 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 finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10151 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 : 0.005718708038330078 nb_pixel_total : 22491 time to create 1 rle with old method : 0.031221628189086914 length of segment : 265 time for calcul the mask position with numpy : 0.005506277084350586 nb_pixel_total : 65645 time to create 1 rle with old method : 0.08848929405212402 length of segment : 308 time for calcul the mask position with numpy : 0.01526021957397461 nb_pixel_total : 241031 time to create 1 rle with new method : 0.017360925674438477 length of segment : 744 time for calcul the mask position with numpy : 0.006440401077270508 nb_pixel_total : 18648 time to create 1 rle with old method : 0.0316624641418457 length of segment : 146 time for calcul the mask position with numpy : 0.007624387741088867 nb_pixel_total : 212788 time to create 1 rle with new method : 0.017642498016357422 length of segment : 572 time for calcul the mask position with numpy : 0.002457857131958008 nb_pixel_total : 20228 time to create 1 rle with old method : 0.027916431427001953 length of segment : 219 time for calcul the mask position with numpy : 0.008457183837890625 nb_pixel_total : 80391 time to create 1 rle with old method : 0.10881638526916504 length of segment : 336 time for calcul the mask position with numpy : 0.0018706321716308594 nb_pixel_total : 93641 time to create 1 rle with old method : 0.1043088436126709 length of segment : 283 time for calcul the mask position with numpy : 0.004904508590698242 nb_pixel_total : 42328 time to create 1 rle with old method : 0.04995083808898926 length of segment : 430 time for calcul the mask position with numpy : 0.001821279525756836 nb_pixel_total : 68169 time to create 1 rle with old method : 0.07734894752502441 length of segment : 274 time for calcul the mask position with numpy : 0.004008293151855469 nb_pixel_total : 26164 time to create 1 rle with old method : 0.03189826011657715 length of segment : 188 time for calcul the mask position with numpy : 0.0056912899017333984 nb_pixel_total : 107902 time to create 1 rle with old method : 0.12245368957519531 length of segment : 283 time for calcul the mask position with numpy : 0.0013179779052734375 nb_pixel_total : 58852 time to create 1 rle with old method : 0.0655522346496582 length of segment : 326 time for calcul the mask position with numpy : 0.0015604496002197266 nb_pixel_total : 15524 time to create 1 rle with old method : 0.017761945724487305 length of segment : 243 time for calcul the mask position with numpy : 0.016240835189819336 nb_pixel_total : 221575 time to create 1 rle with new method : 0.018021821975708008 length of segment : 977 time for calcul the mask position with numpy : 0.002099752426147461 nb_pixel_total : 43898 time to create 1 rle with old method : 0.048241615295410156 length of segment : 382 time for calcul the mask position with numpy : 0.008810281753540039 nb_pixel_total : 95975 time to create 1 rle with old method : 0.113037109375 length of segment : 329 time for calcul the mask position with numpy : 0.11881351470947266 nb_pixel_total : 111822 time to create 1 rle with old method : 0.13084697723388672 length of segment : 486 time for calcul the mask position with numpy : 0.03068852424621582 nb_pixel_total : 118356 time to create 1 rle with old method : 0.13651728630065918 length of segment : 384 time for calcul the mask position with numpy : 0.0002624988555908203 nb_pixel_total : 4452 time to create 1 rle with old method : 0.004945516586303711 length of segment : 74 time for calcul the mask position with numpy : 0.007040500640869141 nb_pixel_total : 21047 time to create 1 rle with old method : 0.0260465145111084 length of segment : 237 time for calcul the mask position with numpy : 0.00460505485534668 nb_pixel_total : 96882 time to create 1 rle with old method : 0.1094367504119873 length of segment : 391 time for calcul the mask position with numpy : 0.016541004180908203 nb_pixel_total : 25917 time to create 1 rle with old method : 0.031646728515625 length of segment : 153 time for calcul the mask position with numpy : 0.02271556854248047 nb_pixel_total : 26020 time to create 1 rle with old method : 0.03201770782470703 length of segment : 194 time for calcul the mask position with numpy : 0.020830154418945312 nb_pixel_total : 155641 time to create 1 rle with new method : 0.011720657348632812 length of segment : 538 time for calcul the mask position with numpy : 0.002978086471557617 nb_pixel_total : 39629 time to create 1 rle with old method : 0.043764591217041016 length of segment : 463 time for calcul the mask position with numpy : 0.0005145072937011719 nb_pixel_total : 27038 time to create 1 rle with old method : 0.03028583526611328 length of segment : 232 time for calcul the mask position with numpy : 0.007592201232910156 nb_pixel_total : 52616 time to create 1 rle with old method : 0.060509443283081055 length of segment : 298 time for calcul the mask position with numpy : 0.016185998916625977 nb_pixel_total : 36550 time to create 1 rle with old method : 0.04387807846069336 length of segment : 187 time for calcul the mask position with numpy : 0.006339311599731445 nb_pixel_total : 78735 time to create 1 rle with old method : 0.0899348258972168 length of segment : 277 time for calcul the mask position with numpy : 0.03256797790527344 nb_pixel_total : 49940 time to create 1 rle with old method : 0.06000328063964844 length of segment : 367 time for calcul the mask position with numpy : 0.014141559600830078 nb_pixel_total : 15020 time to create 1 rle with old method : 0.021314144134521484 length of segment : 154 time for calcul the mask position with numpy : 0.013910770416259766 nb_pixel_total : 32454 time to create 1 rle with old method : 0.04204368591308594 length of segment : 231 time for calcul the mask position with numpy : 0.0009527206420898438 nb_pixel_total : 32604 time to create 1 rle with old method : 0.04224681854248047 length of segment : 336 time for calcul the mask position with numpy : 0.006192445755004883 nb_pixel_total : 69048 time to create 1 rle with old method : 0.09655451774597168 length of segment : 376 time for calcul the mask position with numpy : 0.008343219757080078 nb_pixel_total : 71779 time to create 1 rle with old method : 0.0760037899017334 length of segment : 422 time for calcul the mask position with numpy : 0.004033088684082031 nb_pixel_total : 99973 time to create 1 rle with old method : 0.11517596244812012 length of segment : 420 time for calcul the mask position with numpy : 0.002249479293823242 nb_pixel_total : 47253 time to create 1 rle with old method : 0.051583051681518555 length of segment : 266 time for calcul the mask position with numpy : 0.0063512325286865234 nb_pixel_total : 46705 time to create 1 rle with old method : 0.05819416046142578 length of segment : 209 time for calcul the mask position with numpy : 0.028580427169799805 nb_pixel_total : 37526 time to create 1 rle with old method : 0.045015573501586914 length of segment : 182 time for calcul the mask position with numpy : 0.0007388591766357422 nb_pixel_total : 7447 time to create 1 rle with old method : 0.008230447769165039 length of segment : 101 time for calcul the mask position with numpy : 0.0007326602935791016 nb_pixel_total : 13263 time to create 1 rle with old method : 0.014809846878051758 length of segment : 149 time for calcul the mask position with numpy : 0.043401479721069336 nb_pixel_total : 63151 time to create 1 rle with old method : 0.07205414772033691 length of segment : 387 time for calcul the mask position with numpy : 0.011869192123413086 nb_pixel_total : 26703 time to create 1 rle with old method : 0.030965328216552734 length of segment : 356 time for calcul the mask position with numpy : 0.014830589294433594 nb_pixel_total : 82841 time to create 1 rle with old method : 0.09310770034790039 length of segment : 355 time for calcul the mask position with numpy : 0.0017549991607666016 nb_pixel_total : 26523 time to create 1 rle with old method : 0.02994561195373535 length of segment : 190 time for calcul the mask position with numpy : 0.0009355545043945312 nb_pixel_total : 13590 time to create 1 rle with old method : 0.015055656433105469 length of segment : 209 time for calcul the mask position with numpy : 0.005680084228515625 nb_pixel_total : 104404 time to create 1 rle with old method : 0.12004971504211426 length of segment : 364 time for calcul the mask position with numpy : 0.024383544921875 nb_pixel_total : 199832 time to create 1 rle with new method : 0.013358116149902344 length of segment : 450 time for calcul the mask position with numpy : 0.004144906997680664 nb_pixel_total : 87168 time to create 1 rle with old method : 0.09963655471801758 length of segment : 567 time for calcul the mask position with numpy : 0.010483503341674805 nb_pixel_total : 48765 time to create 1 rle with old method : 0.06017017364501953 length of segment : 276 time for calcul the mask position with numpy : 0.0027022361755371094 nb_pixel_total : 31287 time to create 1 rle with old method : 0.03572893142700195 length of segment : 265 time for calcul the mask position with numpy : 0.004364967346191406 nb_pixel_total : 32876 time to create 1 rle with old method : 0.037397146224975586 length of segment : 245 time for calcul the mask position with numpy : 0.009291887283325195 nb_pixel_total : 44372 time to create 1 rle with old method : 0.07362818717956543 length of segment : 260 time for calcul the mask position with numpy : 0.0010180473327636719 nb_pixel_total : 11131 time to create 1 rle with old method : 0.01534581184387207 length of segment : 172 time for calcul the mask position with numpy : 0.005525112152099609 nb_pixel_total : 22203 time to create 1 rle with old method : 0.029483318328857422 length of segment : 140 time for calcul the mask position with numpy : 0.0043108463287353516 nb_pixel_total : 23500 time to create 1 rle with old method : 0.02840256690979004 length of segment : 289 time for calcul the mask position with numpy : 0.005973339080810547 nb_pixel_total : 64967 time to create 1 rle with old method : 0.08414125442504883 length of segment : 256 time for calcul the mask position with numpy : 0.0030083656311035156 nb_pixel_total : 56883 time to create 1 rle with old method : 0.06537103652954102 length of segment : 341 time for calcul the mask position with numpy : 0.00433802604675293 nb_pixel_total : 26920 time to create 1 rle with old method : 0.03466463088989258 length of segment : 308 time for calcul the mask position with numpy : 0.0019421577453613281 nb_pixel_total : 8161 time to create 1 rle with old method : 0.009064912796020508 length of segment : 141 time for calcul the mask position with numpy : 0.0014314651489257812 nb_pixel_total : 28140 time to create 1 rle with old method : 0.03243088722229004 length of segment : 179 time for calcul the mask position with numpy : 0.0006589889526367188 nb_pixel_total : 14592 time to create 1 rle with old method : 0.01656794548034668 length of segment : 85 time for calcul the mask position with numpy : 0.0015606880187988281 nb_pixel_total : 24832 time to create 1 rle with old method : 0.028214216232299805 length of segment : 188 time for calcul the mask position with numpy : 0.009054422378540039 nb_pixel_total : 142212 time to create 1 rle with old method : 0.16079187393188477 length of segment : 415 time for calcul the mask position with numpy : 0.0007183551788330078 nb_pixel_total : 20962 time to create 1 rle with old method : 0.023845672607421875 length of segment : 168 time for calcul the mask position with numpy : 0.0009746551513671875 nb_pixel_total : 8023 time to create 1 rle with old method : 0.00989675521850586 length of segment : 119 time for calcul the mask position with numpy : 0.004348039627075195 nb_pixel_total : 17360 time to create 1 rle with old method : 0.020848989486694336 length of segment : 292 time for calcul the mask position with numpy : 0.0015411376953125 nb_pixel_total : 25141 time to create 1 rle with old method : 0.02839064598083496 length of segment : 175 time for calcul the mask position with numpy : 0.0007033348083496094 nb_pixel_total : 13657 time to create 1 rle with old method : 0.01600956916809082 length of segment : 218 time for calcul the mask position with numpy : 0.007631540298461914 nb_pixel_total : 127902 time to create 1 rle with old method : 0.14833641052246094 length of segment : 414 time for calcul the mask position with numpy : 0.0026710033416748047 nb_pixel_total : 37878 time to create 1 rle with old method : 0.04433846473693848 length of segment : 192 time for calcul the mask position with numpy : 0.0009293556213378906 nb_pixel_total : 10194 time to create 1 rle with old method : 0.011916399002075195 length of segment : 109 time for calcul the mask position with numpy : 0.007078409194946289 nb_pixel_total : 18309 time to create 1 rle with old method : 0.022908926010131836 length of segment : 156 time for calcul the mask position with numpy : 0.008432388305664062 nb_pixel_total : 23445 time to create 1 rle with old method : 0.028000831604003906 length of segment : 265 time for calcul the mask position with numpy : 0.00410008430480957 nb_pixel_total : 46528 time to create 1 rle with old method : 0.05035114288330078 length of segment : 318 time for calcul the mask position with numpy : 0.005658626556396484 nb_pixel_total : 61858 time to create 1 rle with old method : 0.06798934936523438 length of segment : 305 time for calcul the mask position with numpy : 0.002078533172607422 nb_pixel_total : 18700 time to create 1 rle with old method : 0.020854949951171875 length of segment : 243 time for calcul the mask position with numpy : 0.010840415954589844 nb_pixel_total : 15452 time to create 1 rle with old method : 0.02147984504699707 length of segment : 179 time for calcul the mask position with numpy : 0.010890960693359375 nb_pixel_total : 23662 time to create 1 rle with old method : 0.032094478607177734 length of segment : 283 time for calcul the mask position with numpy : 0.006751537322998047 nb_pixel_total : 56172 time to create 1 rle with old method : 0.06498599052429199 length of segment : 453 time for calcul the mask position with numpy : 0.014820337295532227 nb_pixel_total : 97469 time to create 1 rle with old method : 0.11602234840393066 length of segment : 394 time for calcul the mask position with numpy : 0.04381108283996582 nb_pixel_total : 141680 time to create 1 rle with old method : 0.16175365447998047 length of segment : 664 time for calcul the mask position with numpy : 0.008126258850097656 nb_pixel_total : 18763 time to create 1 rle with old method : 0.022357463836669922 length of segment : 304 time for calcul the mask position with numpy : 0.0023572444915771484 nb_pixel_total : 22817 time to create 1 rle with old method : 0.025542020797729492 length of segment : 285 time for calcul the mask position with numpy : 0.0003962516784667969 nb_pixel_total : 16112 time to create 1 rle with old method : 0.018895864486694336 length of segment : 118 time for calcul the mask position with numpy : 0.0030634403228759766 nb_pixel_total : 11855 time to create 1 rle with old method : 0.01348114013671875 length of segment : 116 time for calcul the mask position with numpy : 0.0003914833068847656 nb_pixel_total : 4920 time to create 1 rle with old method : 0.006049633026123047 length of segment : 72 time for calcul the mask position with numpy : 0.002586841583251953 nb_pixel_total : 31788 time to create 1 rle with old method : 0.03647756576538086 length of segment : 151 time for calcul the mask position with numpy : 0.001644134521484375 nb_pixel_total : 12226 time to create 1 rle with old method : 0.014684915542602539 length of segment : 157 time for calcul the mask position with numpy : 0.0006535053253173828 nb_pixel_total : 8835 time to create 1 rle with old method : 0.00992274284362793 length of segment : 94 time for calcul the mask position with numpy : 0.0056192874908447266 nb_pixel_total : 47459 time to create 1 rle with old method : 0.05389761924743652 length of segment : 354 time for calcul the mask position with numpy : 0.0047607421875 nb_pixel_total : 9267 time to create 1 rle with old method : 0.011478662490844727 length of segment : 117 time for calcul the mask position with numpy : 0.0016932487487792969 nb_pixel_total : 12324 time to create 1 rle with old method : 0.01439666748046875 length of segment : 113 time for calcul the mask position with numpy : 0.00036978721618652344 nb_pixel_total : 4680 time to create 1 rle with old method : 0.005570650100708008 length of segment : 63 time for calcul the mask position with numpy : 0.0006866455078125 nb_pixel_total : 8190 time to create 1 rle with old method : 0.009729146957397461 length of segment : 164 time for calcul the mask position with numpy : 0.0018618106842041016 nb_pixel_total : 20201 time to create 1 rle with old method : 0.022815942764282227 length of segment : 285 time for calcul the mask position with numpy : 0.004538059234619141 nb_pixel_total : 30330 time to create 1 rle with old method : 0.03507542610168457 length of segment : 263 time for calcul the mask position with numpy : 0.0007977485656738281 nb_pixel_total : 14963 time to create 1 rle with old method : 0.017409324645996094 length of segment : 133 time for calcul the mask position with numpy : 0.004914522171020508 nb_pixel_total : 81831 time to create 1 rle with old method : 0.09881591796875 length of segment : 215 time for calcul the mask position with numpy : 0.008481502532958984 nb_pixel_total : 153449 time to create 1 rle with new method : 0.009340763092041016 length of segment : 337 time for calcul the mask position with numpy : 0.0017447471618652344 nb_pixel_total : 18757 time to create 1 rle with old method : 0.0230560302734375 length of segment : 153 time for calcul the mask position with numpy : 0.0007715225219726562 nb_pixel_total : 6895 time to create 1 rle with old method : 0.011704206466674805 length of segment : 93 time for calcul the mask position with numpy : 0.008148431777954102 nb_pixel_total : 81036 time to create 1 rle with old method : 0.10878896713256836 length of segment : 361 time for calcul the mask position with numpy : 0.0036742687225341797 nb_pixel_total : 48611 time to create 1 rle with old method : 0.055887460708618164 length of segment : 223 time for calcul the mask position with numpy : 0.036170005798339844 nb_pixel_total : 292382 time to create 1 rle with new method : 0.03399062156677246 length of segment : 759 time for calcul the mask position with numpy : 0.003490447998046875 nb_pixel_total : 77819 time to create 1 rle with old method : 0.08956074714660645 length of segment : 253 time for calcul the mask position with numpy : 0.0031723976135253906 nb_pixel_total : 57964 time to create 1 rle with old method : 0.07032465934753418 length of segment : 321 time for calcul the mask position with numpy : 0.0011398792266845703 nb_pixel_total : 7534 time to create 1 rle with old method : 0.00892186164855957 length of segment : 82 time for calcul the mask position with numpy : 0.0010046958923339844 nb_pixel_total : 29278 time to create 1 rle with old method : 0.0352625846862793 length of segment : 271 time for calcul the mask position with numpy : 0.002142667770385742 nb_pixel_total : 15035 time to create 1 rle with old method : 0.01700282096862793 length of segment : 136 time for calcul the mask position with numpy : 0.006693124771118164 nb_pixel_total : 25628 time to create 1 rle with old method : 0.031668901443481445 length of segment : 216 time for calcul the mask position with numpy : 0.008082389831542969 nb_pixel_total : 39191 time to create 1 rle with old method : 0.05093955993652344 length of segment : 272 time for calcul the mask position with numpy : 0.0021932125091552734 nb_pixel_total : 33959 time to create 1 rle with old method : 0.03929877281188965 length of segment : 381 time for calcul the mask position with numpy : 0.0010395050048828125 nb_pixel_total : 17473 time to create 1 rle with old method : 0.022611379623413086 length of segment : 133 time for calcul the mask position with numpy : 0.005904674530029297 nb_pixel_total : 33833 time to create 1 rle with old method : 0.0479884147644043 length of segment : 483 time for calcul the mask position with numpy : 0.002913236618041992 nb_pixel_total : 42355 time to create 1 rle with old method : 0.04833579063415527 length of segment : 191 time for calcul the mask position with numpy : 0.002735614776611328 nb_pixel_total : 47286 time to create 1 rle with old method : 0.05472373962402344 length of segment : 156 time for calcul the mask position with numpy : 0.07369136810302734 nb_pixel_total : 1045845 time to create 1 rle with new method : 0.3152642250061035 length of segment : 1619 time for calcul the mask position with numpy : 0.015906572341918945 nb_pixel_total : 605412 time to create 1 rle with new method : 0.043123722076416016 length of segment : 1007 time for calcul the mask position with numpy : 0.0007407665252685547 nb_pixel_total : 11635 time to create 1 rle with old method : 0.013340234756469727 length of segment : 137 time for calcul the mask position with numpy : 0.0036804676055908203 nb_pixel_total : 66536 time to create 1 rle with old method : 0.07541894912719727 length of segment : 351 time for calcul the mask position with numpy : 0.001280069351196289 nb_pixel_total : 38133 time to create 1 rle with old method : 0.04297304153442383 length of segment : 170 time for calcul the mask position with numpy : 0.0007143020629882812 nb_pixel_total : 12739 time to create 1 rle with old method : 0.014832496643066406 length of segment : 121 time for calcul the mask position with numpy : 0.0008220672607421875 nb_pixel_total : 27524 time to create 1 rle with old method : 0.0306851863861084 length of segment : 362 time for calcul the mask position with numpy : 0.0027213096618652344 nb_pixel_total : 33503 time to create 1 rle with old method : 0.0379643440246582 length of segment : 295 time for calcul the mask position with numpy : 0.021461963653564453 nb_pixel_total : 116177 time to create 1 rle with old method : 0.1287684440612793 length of segment : 231 time for calcul the mask position with numpy : 0.0014464855194091797 nb_pixel_total : 75630 time to create 1 rle with old method : 0.08541154861450195 length of segment : 301 time for calcul the mask position with numpy : 0.002111673355102539 nb_pixel_total : 51430 time to create 1 rle with old method : 0.05978584289550781 length of segment : 622 time for calcul the mask position with numpy : 0.0009765625 nb_pixel_total : 43074 time to create 1 rle with old method : 0.049612998962402344 length of segment : 307 time for calcul the mask position with numpy : 0.0014934539794921875 nb_pixel_total : 71856 time to create 1 rle with old method : 0.08207988739013672 length of segment : 378 time for calcul the mask position with numpy : 0.002361297607421875 nb_pixel_total : 44965 time to create 1 rle with old method : 0.05058908462524414 length of segment : 251 time for calcul the mask position with numpy : 0.0038182735443115234 nb_pixel_total : 26016 time to create 1 rle with old method : 0.030048370361328125 length of segment : 318 time for calcul the mask position with numpy : 0.0007195472717285156 nb_pixel_total : 12476 time to create 1 rle with old method : 0.01452326774597168 length of segment : 178 time for calcul the mask position with numpy : 0.0013620853424072266 nb_pixel_total : 32686 time to create 1 rle with old method : 0.03713655471801758 length of segment : 201 time for calcul the mask position with numpy : 0.002490520477294922 nb_pixel_total : 49325 time to create 1 rle with old method : 0.05394768714904785 length of segment : 215 time for calcul the mask position with numpy : 0.006087064743041992 nb_pixel_total : 101227 time to create 1 rle with old method : 0.12206196784973145 length of segment : 359 time for calcul the mask position with numpy : 0.003414630889892578 nb_pixel_total : 42568 time to create 1 rle with old method : 0.06243181228637695 length of segment : 304 time for calcul the mask position with numpy : 0.0004134178161621094 nb_pixel_total : 6682 time to create 1 rle with old method : 0.007782697677612305 length of segment : 80 time for calcul the mask position with numpy : 0.0009057521820068359 nb_pixel_total : 12544 time to create 1 rle with old method : 0.014636754989624023 length of segment : 156 time for calcul the mask position with numpy : 0.00099945068359375 nb_pixel_total : 21703 time to create 1 rle with old method : 0.02509617805480957 length of segment : 189 time for calcul the mask position with numpy : 0.004094123840332031 nb_pixel_total : 39700 time to create 1 rle with old method : 0.04592728614807129 length of segment : 521 time for calcul the mask position with numpy : 0.0024404525756835938 nb_pixel_total : 41768 time to create 1 rle with old method : 0.047274112701416016 length of segment : 254 time for calcul the mask position with numpy : 0.000835418701171875 nb_pixel_total : 13331 time to create 1 rle with old method : 0.015324831008911133 length of segment : 154 time for calcul the mask position with numpy : 0.008363485336303711 nb_pixel_total : 158390 time to create 1 rle with new method : 0.014965534210205078 length of segment : 733 time for calcul the mask position with numpy : 0.001901388168334961 nb_pixel_total : 23645 time to create 1 rle with old method : 0.026044368743896484 length of segment : 309 time for calcul the mask position with numpy : 0.0025205612182617188 nb_pixel_total : 53987 time to create 1 rle with old method : 0.06038618087768555 length of segment : 229 time for calcul the mask position with numpy : 0.0005817413330078125 nb_pixel_total : 15018 time to create 1 rle with old method : 0.016829729080200195 length of segment : 156 time for calcul the mask position with numpy : 0.0005092620849609375 nb_pixel_total : 21112 time to create 1 rle with old method : 0.02422356605529785 length of segment : 147 time for calcul the mask position with numpy : 0.0003707408905029297 nb_pixel_total : 9138 time to create 1 rle with old method : 0.01049494743347168 length of segment : 147 time for calcul the mask position with numpy : 0.00018835067749023438 nb_pixel_total : 7688 time to create 1 rle with old method : 0.008622169494628906 length of segment : 123 time for calcul the mask position with numpy : 0.0026662349700927734 nb_pixel_total : 43131 time to create 1 rle with old method : 0.04698824882507324 length of segment : 311 time for calcul the mask position with numpy : 0.0017156600952148438 nb_pixel_total : 41778 time to create 1 rle with old method : 0.05862855911254883 length of segment : 154 time for calcul the mask position with numpy : 0.003725767135620117 nb_pixel_total : 64112 time to create 1 rle with old method : 0.07076597213745117 length of segment : 331 time for calcul the mask position with numpy : 0.0010848045349121094 nb_pixel_total : 32634 time to create 1 rle with old method : 0.03774404525756836 length of segment : 374 time for calcul the mask position with numpy : 0.00044155120849609375 nb_pixel_total : 9906 time to create 1 rle with old method : 0.012041568756103516 length of segment : 139 time for calcul the mask position with numpy : 0.0005636215209960938 nb_pixel_total : 14723 time to create 1 rle with old method : 0.017136812210083008 length of segment : 223 time for calcul the mask position with numpy : 0.0017557144165039062 nb_pixel_total : 32694 time to create 1 rle with old method : 0.03733634948730469 length of segment : 240 time for calcul the mask position with numpy : 0.0005726814270019531 nb_pixel_total : 12521 time to create 1 rle with old method : 0.014742612838745117 length of segment : 160 time for calcul the mask position with numpy : 0.0013892650604248047 nb_pixel_total : 16789 time to create 1 rle with old method : 0.019743919372558594 length of segment : 146 time for calcul the mask position with numpy : 0.002239227294921875 nb_pixel_total : 41255 time to create 1 rle with old method : 0.04793691635131836 length of segment : 239 time for calcul the mask position with numpy : 0.0026526451110839844 nb_pixel_total : 53739 time to create 1 rle with old method : 0.06197810173034668 length of segment : 256 time for calcul the mask position with numpy : 0.004849910736083984 nb_pixel_total : 136232 time to create 1 rle with old method : 0.15683221817016602 length of segment : 226 time for calcul the mask position with numpy : 0.003557443618774414 nb_pixel_total : 48754 time to create 1 rle with old method : 0.055848121643066406 length of segment : 511 time for calcul the mask position with numpy : 0.0007574558258056641 nb_pixel_total : 12291 time to create 1 rle with old method : 0.014392375946044922 length of segment : 267 time for calcul the mask position with numpy : 0.0005958080291748047 nb_pixel_total : 9854 time to create 1 rle with old method : 0.011554718017578125 length of segment : 94 time for calcul the mask position with numpy : 0.0007786750793457031 nb_pixel_total : 20779 time to create 1 rle with old method : 0.024814128875732422 length of segment : 180 time for calcul the mask position with numpy : 0.0016703605651855469 nb_pixel_total : 28931 time to create 1 rle with old method : 0.03278827667236328 length of segment : 334 time for calcul the mask position with numpy : 0.0015797615051269531 nb_pixel_total : 53441 time to create 1 rle with old method : 0.05919933319091797 length of segment : 525 time for calcul the mask position with numpy : 0.00047469139099121094 nb_pixel_total : 6011 time to create 1 rle with old method : 0.007198333740234375 length of segment : 87 time for calcul the mask position with numpy : 0.0048999786376953125 nb_pixel_total : 42176 time to create 1 rle with old method : 0.04836225509643555 length of segment : 369 time for calcul the mask position with numpy : 0.00086212158203125 nb_pixel_total : 13114 time to create 1 rle with old method : 0.01519465446472168 length of segment : 146 time for calcul the mask position with numpy : 0.0021467208862304688 nb_pixel_total : 39917 time to create 1 rle with old method : 0.04941844940185547 length of segment : 242 time for calcul the mask position with numpy : 0.0003123283386230469 nb_pixel_total : 8316 time to create 1 rle with old method : 0.010988473892211914 length of segment : 93 time for calcul the mask position with numpy : 0.0012969970703125 nb_pixel_total : 22872 time to create 1 rle with old method : 0.026837587356567383 length of segment : 174 time for calcul the mask position with numpy : 0.0010600090026855469 nb_pixel_total : 13558 time to create 1 rle with old method : 0.019551753997802734 length of segment : 270 time for calcul the mask position with numpy : 0.002421855926513672 nb_pixel_total : 40606 time to create 1 rle with old method : 0.04597783088684082 length of segment : 227 time for calcul the mask position with numpy : 0.0010693073272705078 nb_pixel_total : 17975 time to create 1 rle with old method : 0.023475170135498047 length of segment : 240 time for calcul the mask position with numpy : 0.0014202594757080078 nb_pixel_total : 21873 time to create 1 rle with old method : 0.02599644660949707 length of segment : 200 time for calcul the mask position with numpy : 0.001310586929321289 nb_pixel_total : 18176 time to create 1 rle with old method : 0.022125720977783203 length of segment : 184 time for calcul the mask position with numpy : 0.007432460784912109 nb_pixel_total : 198265 time to create 1 rle with new method : 0.009545326232910156 length of segment : 577 time for calcul the mask position with numpy : 0.0018463134765625 nb_pixel_total : 33843 time to create 1 rle with old method : 0.04073047637939453 length of segment : 253 time for calcul the mask position with numpy : 0.0014185905456542969 nb_pixel_total : 27933 time to create 1 rle with old method : 0.032146453857421875 length of segment : 226 time for calcul the mask position with numpy : 0.0064525604248046875 nb_pixel_total : 134725 time to create 1 rle with old method : 0.15207815170288086 length of segment : 530 time for calcul the mask position with numpy : 0.011183500289916992 nb_pixel_total : 202325 time to create 1 rle with new method : 0.024019479751586914 length of segment : 689 time for calcul the mask position with numpy : 0.0007150173187255859 nb_pixel_total : 10347 time to create 1 rle with old method : 0.012973546981811523 length of segment : 109 time for calcul the mask position with numpy : 0.0010976791381835938 nb_pixel_total : 24750 time to create 1 rle with old method : 0.028844356536865234 length of segment : 281 time for calcul the mask position with numpy : 0.0007669925689697266 nb_pixel_total : 16878 time to create 1 rle with old method : 0.019367456436157227 length of segment : 164 time for calcul the mask position with numpy : 0.0008323192596435547 nb_pixel_total : 22754 time to create 1 rle with old method : 0.025876760482788086 length of segment : 170 time for calcul the mask position with numpy : 0.0016777515411376953 nb_pixel_total : 52523 time to create 1 rle with old method : 0.05837702751159668 length of segment : 274 time for calcul the mask position with numpy : 0.000293731689453125 nb_pixel_total : 13086 time to create 1 rle with old method : 0.014894485473632812 length of segment : 155 time for calcul the mask position with numpy : 0.0008742809295654297 nb_pixel_total : 13321 time to create 1 rle with old method : 0.014979839324951172 length of segment : 166 time for calcul the mask position with numpy : 0.0007474422454833984 nb_pixel_total : 11675 time to create 1 rle with old method : 0.016890525817871094 length of segment : 156 time for calcul the mask position with numpy : 0.0007352828979492188 nb_pixel_total : 17696 time to create 1 rle with old method : 0.03366518020629883 length of segment : 141 time for calcul the mask position with numpy : 0.0010752677917480469 nb_pixel_total : 25338 time to create 1 rle with old method : 0.028684377670288086 length of segment : 199 time for calcul the mask position with numpy : 0.0007812976837158203 nb_pixel_total : 21516 time to create 1 rle with old method : 0.035482168197631836 length of segment : 141 time for calcul the mask position with numpy : 0.001531839370727539 nb_pixel_total : 40432 time to create 1 rle with old method : 0.04686880111694336 length of segment : 173 time for calcul the mask position with numpy : 0.0013051033020019531 nb_pixel_total : 22956 time to create 1 rle with old method : 0.027197837829589844 length of segment : 215 time for calcul the mask position with numpy : 0.0013217926025390625 nb_pixel_total : 29466 time to create 1 rle with old method : 0.03275775909423828 length of segment : 345 time for calcul the mask position with numpy : 0.0017514228820800781 nb_pixel_total : 44127 time to create 1 rle with old method : 0.050261497497558594 length of segment : 308 time for calcul the mask position with numpy : 0.0006737709045410156 nb_pixel_total : 14247 time to create 1 rle with old method : 0.01650857925415039 length of segment : 156 time for calcul the mask position with numpy : 0.0019392967224121094 nb_pixel_total : 31184 time to create 1 rle with old method : 0.035666465759277344 length of segment : 337 time for calcul the mask position with numpy : 0.0006506443023681641 nb_pixel_total : 23102 time to create 1 rle with old method : 0.026496171951293945 length of segment : 195 time for calcul the mask position with numpy : 0.006926774978637695 nb_pixel_total : 249310 time to create 1 rle with new method : 0.008298397064208984 length of segment : 537 time for calcul the mask position with numpy : 0.0003838539123535156 nb_pixel_total : 7689 time to create 1 rle with old method : 0.008862733840942383 length of segment : 101 time for calcul the mask position with numpy : 0.0018727779388427734 nb_pixel_total : 43453 time to create 1 rle with old method : 0.049733877182006836 length of segment : 389 time for calcul the mask position with numpy : 0.000720977783203125 nb_pixel_total : 18277 time to create 1 rle with old method : 0.020801067352294922 length of segment : 133 time for calcul the mask position with numpy : 0.0015494823455810547 nb_pixel_total : 37902 time to create 1 rle with old method : 0.043469905853271484 length of segment : 184 time for calcul the mask position with numpy : 0.002031564712524414 nb_pixel_total : 57771 time to create 1 rle with old method : 0.06665253639221191 length of segment : 287 time for calcul the mask position with numpy : 0.003062009811401367 nb_pixel_total : 76057 time to create 1 rle with old method : 0.1128396987915039 length of segment : 399 time for calcul the mask position with numpy : 0.0012714862823486328 nb_pixel_total : 21095 time to create 1 rle with old method : 0.02396106719970703 length of segment : 405 time for calcul the mask position with numpy : 0.0009539127349853516 nb_pixel_total : 15726 time to create 1 rle with old method : 0.01781940460205078 length of segment : 272 time for calcul the mask position with numpy : 0.0041751861572265625 nb_pixel_total : 121221 time to create 1 rle with old method : 0.13728761672973633 length of segment : 244 time for calcul the mask position with numpy : 0.0026323795318603516 nb_pixel_total : 56326 time to create 1 rle with old method : 0.06303596496582031 length of segment : 417 time for calcul the mask position with numpy : 0.0007977485656738281 nb_pixel_total : 31561 time to create 1 rle with old method : 0.0353548526763916 length of segment : 274 time for calcul the mask position with numpy : 0.004903078079223633 nb_pixel_total : 35945 time to create 1 rle with old method : 0.04110860824584961 length of segment : 430 time for calcul the mask position with numpy : 0.0006325244903564453 nb_pixel_total : 19420 time to create 1 rle with old method : 0.02199101448059082 length of segment : 251 time for calcul the mask position with numpy : 0.00385284423828125 nb_pixel_total : 115942 time to create 1 rle with old method : 0.1276843547821045 length of segment : 497 time for calcul the mask position with numpy : 0.003081083297729492 nb_pixel_total : 92323 time to create 1 rle with old method : 0.09973263740539551 length of segment : 402 time for calcul the mask position with numpy : 0.0006532669067382812 nb_pixel_total : 10740 time to create 1 rle with old method : 0.012247085571289062 length of segment : 197 time for calcul the mask position with numpy : 0.0006952285766601562 nb_pixel_total : 23903 time to create 1 rle with old method : 0.02725696563720703 length of segment : 221 time for calcul the mask position with numpy : 0.004920482635498047 nb_pixel_total : 102242 time to create 1 rle with old method : 0.11368584632873535 length of segment : 807 time for calcul the mask position with numpy : 0.0025169849395751953 nb_pixel_total : 80093 time to create 1 rle with old method : 0.08866143226623535 length of segment : 300 time for calcul the mask position with numpy : 0.0054852962493896484 nb_pixel_total : 159125 time to create 1 rle with new method : 0.009002685546875 length of segment : 796 time for calcul the mask position with numpy : 0.007361888885498047 nb_pixel_total : 236222 time to create 1 rle with new method : 0.010256528854370117 length of segment : 661 time for calcul the mask position with numpy : 0.0002448558807373047 nb_pixel_total : 4992 time to create 1 rle with old method : 0.005671977996826172 length of segment : 121 time for calcul the mask position with numpy : 0.004863262176513672 nb_pixel_total : 150940 time to create 1 rle with new method : 0.0059778690338134766 length of segment : 559 time for calcul the mask position with numpy : 0.006078958511352539 nb_pixel_total : 237300 time to create 1 rle with new method : 0.0068361759185791016 length of segment : 497 time for calcul the mask position with numpy : 0.0009038448333740234 nb_pixel_total : 26391 time to create 1 rle with old method : 0.029052019119262695 length of segment : 248 time for calcul the mask position with numpy : 0.0007512569427490234 nb_pixel_total : 13408 time to create 1 rle with old method : 0.015491247177124023 length of segment : 214 time for calcul the mask position with numpy : 0.0005033016204833984 nb_pixel_total : 15256 time to create 1 rle with old method : 0.017634153366088867 length of segment : 116 time for calcul the mask position with numpy : 0.0006978511810302734 nb_pixel_total : 17343 time to create 1 rle with old method : 0.020222187042236328 length of segment : 128 time for calcul the mask position with numpy : 0.00619196891784668 nb_pixel_total : 144967 time to create 1 rle with old method : 0.16074275970458984 length of segment : 812 time for calcul the mask position with numpy : 0.02289748191833496 nb_pixel_total : 767830 time to create 1 rle with new method : 0.05895829200744629 length of segment : 968 time for calcul the mask position with numpy : 0.0018317699432373047 nb_pixel_total : 53890 time to create 1 rle with old method : 0.05911087989807129 length of segment : 314 time for calcul the mask position with numpy : 0.00279998779296875 nb_pixel_total : 79944 time to create 1 rle with old method : 0.08660197257995605 length of segment : 300 time for calcul the mask position with numpy : 0.0015439987182617188 nb_pixel_total : 47724 time to create 1 rle with old method : 0.05346965789794922 length of segment : 291 time for calcul the mask position with numpy : 0.0031218528747558594 nb_pixel_total : 74452 time to create 1 rle with old method : 0.10587048530578613 length of segment : 371 time for calcul the mask position with numpy : 0.0004019737243652344 nb_pixel_total : 19570 time to create 1 rle with old method : 0.022307395935058594 length of segment : 157 time for calcul the mask position with numpy : 0.0006356239318847656 nb_pixel_total : 24855 time to create 1 rle with old method : 0.02786421775817871 length of segment : 205 time for calcul the mask position with numpy : 0.0002391338348388672 nb_pixel_total : 4246 time to create 1 rle with old method : 0.004790067672729492 length of segment : 131 time for calcul the mask position with numpy : 0.0006487369537353516 nb_pixel_total : 20168 time to create 1 rle with old method : 0.02261805534362793 length of segment : 638 time for calcul the mask position with numpy : 0.0008826255798339844 nb_pixel_total : 35858 time to create 1 rle with old method : 0.0394287109375 length of segment : 202 time for calcul the mask position with numpy : 0.0015659332275390625 nb_pixel_total : 46083 time to create 1 rle with old method : 0.05040860176086426 length of segment : 387 time for calcul the mask position with numpy : 0.0008423328399658203 nb_pixel_total : 56586 time to create 1 rle with old method : 0.06096053123474121 length of segment : 307 time for calcul the mask position with numpy : 0.005204200744628906 nb_pixel_total : 160360 time to create 1 rle with new method : 0.00858163833618164 length of segment : 760 time for calcul the mask position with numpy : 0.0017020702362060547 nb_pixel_total : 99226 time to create 1 rle with old method : 0.11523938179016113 length of segment : 231 time for calcul the mask position with numpy : 0.00018405914306640625 nb_pixel_total : 7367 time to create 1 rle with old method : 0.008544921875 length of segment : 104 time for calcul the mask position with numpy : 0.0003414154052734375 nb_pixel_total : 8574 time to create 1 rle with old method : 0.010023355484008789 length of segment : 124 time for calcul the mask position with numpy : 0.0005712509155273438 nb_pixel_total : 19738 time to create 1 rle with old method : 0.023480653762817383 length of segment : 146 time for calcul the mask position with numpy : 0.0009090900421142578 nb_pixel_total : 21950 time to create 1 rle with old method : 0.026488065719604492 length of segment : 198 time for calcul the mask position with numpy : 0.0005242824554443359 nb_pixel_total : 14948 time to create 1 rle with old method : 0.017525672912597656 length of segment : 140 time for calcul the mask position with numpy : 0.0010602474212646484 nb_pixel_total : 21713 time to create 1 rle with old method : 0.024758577346801758 length of segment : 373 time for calcul the mask position with numpy : 0.0005652904510498047 nb_pixel_total : 26976 time to create 1 rle with old method : 0.03072047233581543 length of segment : 154 time for calcul the mask position with numpy : 0.0003743171691894531 nb_pixel_total : 10932 time to create 1 rle with old method : 0.012814521789550781 length of segment : 94 time for calcul the mask position with numpy : 0.001367330551147461 nb_pixel_total : 49666 time to create 1 rle with old method : 0.05525016784667969 length of segment : 319 time for calcul the mask position with numpy : 0.00730586051940918 nb_pixel_total : 361522 time to create 1 rle with new method : 0.13720965385437012 length of segment : 765 time for calcul the mask position with numpy : 0.0008485317230224609 nb_pixel_total : 17450 time to create 1 rle with old method : 0.027415037155151367 length of segment : 207 time for calcul the mask position with numpy : 0.00019073486328125 nb_pixel_total : 4943 time to create 1 rle with old method : 0.005624055862426758 length of segment : 74 time for calcul the mask position with numpy : 0.0009014606475830078 nb_pixel_total : 26821 time to create 1 rle with old method : 0.029005050659179688 length of segment : 294 time for calcul the mask position with numpy : 0.00017499923706054688 nb_pixel_total : 4249 time to create 1 rle with old method : 0.004900217056274414 length of segment : 57 time for calcul the mask position with numpy : 0.004035472869873047 nb_pixel_total : 199111 time to create 1 rle with new method : 0.00814509391784668 length of segment : 853 time for calcul the mask position with numpy : 0.000614166259765625 nb_pixel_total : 19726 time to create 1 rle with old method : 0.021385669708251953 length of segment : 202 time for calcul the mask position with numpy : 0.0008461475372314453 nb_pixel_total : 22582 time to create 1 rle with old method : 0.02556300163269043 length of segment : 232 time for calcul the mask position with numpy : 0.0007216930389404297 nb_pixel_total : 19515 time to create 1 rle with old method : 0.022373676300048828 length of segment : 189 time for calcul the mask position with numpy : 0.02158951759338379 nb_pixel_total : 1096823 time to create 1 rle with new method : 0.06913065910339355 length of segment : 1181 time for calcul the mask position with numpy : 0.0005471706390380859 nb_pixel_total : 19180 time to create 1 rle with old method : 0.020815134048461914 length of segment : 145 time for calcul the mask position with numpy : 0.0007040500640869141 nb_pixel_total : 22732 time to create 1 rle with old method : 0.024600744247436523 length of segment : 251 time for calcul the mask position with numpy : 0.00016260147094726562 nb_pixel_total : 3643 time to create 1 rle with old method : 0.004364490509033203 length of segment : 57 time for calcul the mask position with numpy : 0.0003592967987060547 nb_pixel_total : 9874 time to create 1 rle with old method : 0.010917186737060547 length of segment : 126 time for calcul the mask position with numpy : 0.005648136138916016 nb_pixel_total : 298286 time to create 1 rle with new method : 0.01032257080078125 length of segment : 576 time for calcul the mask position with numpy : 0.0007653236389160156 nb_pixel_total : 38240 time to create 1 rle with old method : 0.04362058639526367 length of segment : 269 time for calcul the mask position with numpy : 0.007889509201049805 nb_pixel_total : 115517 time to create 1 rle with old method : 0.12807416915893555 length of segment : 350 time for calcul the mask position with numpy : 0.004927396774291992 nb_pixel_total : 76858 time to create 1 rle with old method : 0.0835580825805664 length of segment : 494 time for calcul the mask position with numpy : 0.0026133060455322266 nb_pixel_total : 33918 time to create 1 rle with old method : 0.03705024719238281 length of segment : 329 time for calcul the mask position with numpy : 0.0016026496887207031 nb_pixel_total : 29491 time to create 1 rle with old method : 0.032892465591430664 length of segment : 184 time for calcul the mask position with numpy : 0.004744768142700195 nb_pixel_total : 122121 time to create 1 rle with old method : 0.13536429405212402 length of segment : 287 time for calcul the mask position with numpy : 0.007588386535644531 nb_pixel_total : 109404 time to create 1 rle with old method : 0.12000584602355957 length of segment : 537 time for calcul the mask position with numpy : 0.0009543895721435547 nb_pixel_total : 15243 time to create 1 rle with old method : 0.017651081085205078 length of segment : 128 time for calcul the mask position with numpy : 0.0015811920166015625 nb_pixel_total : 18838 time to create 1 rle with old method : 0.021395444869995117 length of segment : 225 time for calcul the mask position with numpy : 0.00973963737487793 nb_pixel_total : 208024 time to create 1 rle with new method : 0.009908914566040039 length of segment : 535 time for calcul the mask position with numpy : 0.0024552345275878906 nb_pixel_total : 37681 time to create 1 rle with old method : 0.04175829887390137 length of segment : 215 time for calcul the mask position with numpy : 0.0043141841888427734 nb_pixel_total : 79364 time to create 1 rle with old method : 0.08913516998291016 length of segment : 338 time for calcul the mask position with numpy : 0.009564638137817383 nb_pixel_total : 155460 time to create 1 rle with new method : 0.009764671325683594 length of segment : 777 time for calcul the mask position with numpy : 0.007263660430908203 nb_pixel_total : 90108 time to create 1 rle with old method : 0.10019707679748535 length of segment : 446 time for calcul the mask position with numpy : 0.0020112991333007812 nb_pixel_total : 35234 time to create 1 rle with old method : 0.03856801986694336 length of segment : 262 time for calcul the mask position with numpy : 0.0033028125762939453 nb_pixel_total : 37884 time to create 1 rle with old method : 0.057762861251831055 length of segment : 381 time for calcul the mask position with numpy : 0.007791996002197266 nb_pixel_total : 100018 time to create 1 rle with old method : 0.11324024200439453 length of segment : 410 time for calcul the mask position with numpy : 0.000579833984375 nb_pixel_total : 11503 time to create 1 rle with old method : 0.013463258743286133 length of segment : 147 time for calcul the mask position with numpy : 0.003538370132446289 nb_pixel_total : 46073 time to create 1 rle with old method : 0.07013463973999023 length of segment : 267 time for calcul the mask position with numpy : 0.000850677490234375 nb_pixel_total : 16859 time to create 1 rle with old method : 0.01971125602722168 length of segment : 189 time for calcul the mask position with numpy : 0.0024034976959228516 nb_pixel_total : 36200 time to create 1 rle with old method : 0.042093753814697266 length of segment : 315 time for calcul the mask position with numpy : 0.0015416145324707031 nb_pixel_total : 24538 time to create 1 rle with old method : 0.029038667678833008 length of segment : 153 time for calcul the mask position with numpy : 0.0009145736694335938 nb_pixel_total : 14570 time to create 1 rle with old method : 0.016844749450683594 length of segment : 229 time for calcul the mask position with numpy : 0.0008866786956787109 nb_pixel_total : 15416 time to create 1 rle with old method : 0.01788187026977539 length of segment : 160 time for calcul the mask position with numpy : 0.0014142990112304688 nb_pixel_total : 22802 time to create 1 rle with old method : 0.026580810546875 length of segment : 287 time for calcul the mask position with numpy : 0.0008006095886230469 nb_pixel_total : 14942 time to create 1 rle with old method : 0.017235994338989258 length of segment : 167 time for calcul the mask position with numpy : 0.0015048980712890625 nb_pixel_total : 21218 time to create 1 rle with old method : 0.025061845779418945 length of segment : 245 time for calcul the mask position with numpy : 0.0023984909057617188 nb_pixel_total : 28823 time to create 1 rle with old method : 0.03295111656188965 length of segment : 335 time for calcul the mask position with numpy : 0.003180980682373047 nb_pixel_total : 64912 time to create 1 rle with old method : 0.07303309440612793 length of segment : 288 time for calcul the mask position with numpy : 0.007199287414550781 nb_pixel_total : 124351 time to create 1 rle with old method : 0.1417698860168457 length of segment : 375 time for calcul the mask position with numpy : 0.0012357234954833984 nb_pixel_total : 10814 time to create 1 rle with old method : 0.018484830856323242 length of segment : 127 time for calcul the mask position with numpy : 0.0012428760528564453 nb_pixel_total : 17328 time to create 1 rle with old method : 0.020153045654296875 length of segment : 174 time for calcul the mask position with numpy : 0.003567934036254883 nb_pixel_total : 37843 time to create 1 rle with old method : 0.04333353042602539 length of segment : 374 time for calcul the mask position with numpy : 0.005498409271240234 nb_pixel_total : 73422 time to create 1 rle with old method : 0.08116960525512695 length of segment : 384 time for calcul the mask position with numpy : 0.0012969970703125 nb_pixel_total : 12832 time to create 1 rle with old method : 0.015102148056030273 length of segment : 213 time for calcul the mask position with numpy : 0.0017943382263183594 nb_pixel_total : 27993 time to create 1 rle with old method : 0.03222179412841797 length of segment : 181 time for calcul the mask position with numpy : 0.0022454261779785156 nb_pixel_total : 43060 time to create 1 rle with old method : 0.04947352409362793 length of segment : 240 time for calcul the mask position with numpy : 0.0003414154052734375 nb_pixel_total : 5400 time to create 1 rle with old method : 0.006613492965698242 length of segment : 65 time for calcul the mask position with numpy : 0.0009856224060058594 nb_pixel_total : 10488 time to create 1 rle with old method : 0.012570858001708984 length of segment : 139 time for calcul the mask position with numpy : 0.0005688667297363281 nb_pixel_total : 9316 time to create 1 rle with old method : 0.010971784591674805 length of segment : 83 time for calcul the mask position with numpy : 0.00027370452880859375 nb_pixel_total : 7325 time to create 1 rle with old method : 0.008931159973144531 length of segment : 134 time for calcul the mask position with numpy : 0.0005321502685546875 nb_pixel_total : 10842 time to create 1 rle with old method : 0.01233673095703125 length of segment : 117 time for calcul the mask position with numpy : 0.0006771087646484375 nb_pixel_total : 17600 time to create 1 rle with old method : 0.019890546798706055 length of segment : 150 time for calcul the mask position with numpy : 0.005007505416870117 nb_pixel_total : 90280 time to create 1 rle with old method : 0.09853529930114746 length of segment : 440 time for calcul the mask position with numpy : 0.0028600692749023438 nb_pixel_total : 47110 time to create 1 rle with old method : 0.05125904083251953 length of segment : 265 time for calcul the mask position with numpy : 0.00312042236328125 nb_pixel_total : 46673 time to create 1 rle with old method : 0.05226492881774902 length of segment : 244 time for calcul the mask position with numpy : 0.0013706684112548828 nb_pixel_total : 22760 time to create 1 rle with old method : 0.025177478790283203 length of segment : 215 time for calcul the mask position with numpy : 0.0014026165008544922 nb_pixel_total : 28501 time to create 1 rle with old method : 0.03154563903808594 length of segment : 148 time for calcul the mask position with numpy : 0.001998424530029297 nb_pixel_total : 15008 time to create 1 rle with old method : 0.016970157623291016 length of segment : 231 time for calcul the mask position with numpy : 0.0007495880126953125 nb_pixel_total : 19607 time to create 1 rle with old method : 0.02075815200805664 length of segment : 174 time for calcul the mask position with numpy : 0.0016295909881591797 nb_pixel_total : 28211 time to create 1 rle with old method : 0.03085017204284668 length of segment : 282 time for calcul the mask position with numpy : 0.01105046272277832 nb_pixel_total : 141736 time to create 1 rle with old method : 0.1547224521636963 length of segment : 418 time for calcul the mask position with numpy : 0.0037941932678222656 nb_pixel_total : 58866 time to create 1 rle with old method : 0.06477141380310059 length of segment : 417 time for calcul the mask position with numpy : 0.0017290115356445312 nb_pixel_total : 33318 time to create 1 rle with old method : 0.037268638610839844 length of segment : 141 time for calcul the mask position with numpy : 0.0014951229095458984 nb_pixel_total : 33931 time to create 1 rle with old method : 0.037418365478515625 length of segment : 232 time for calcul the mask position with numpy : 0.0011992454528808594 nb_pixel_total : 20609 time to create 1 rle with old method : 0.023089170455932617 length of segment : 199 time for calcul the mask position with numpy : 0.0015006065368652344 nb_pixel_total : 22203 time to create 1 rle with old method : 0.025508880615234375 length of segment : 260 time for calcul the mask position with numpy : 0.0013644695281982422 nb_pixel_total : 19962 time to create 1 rle with old method : 0.0225522518157959 length of segment : 201 time for calcul the mask position with numpy : 0.0031385421752929688 nb_pixel_total : 44461 time to create 1 rle with old method : 0.04886651039123535 length of segment : 277 time for calcul the mask position with numpy : 0.025882959365844727 nb_pixel_total : 506105 time to create 1 rle with new method : 0.18149662017822266 length of segment : 916 time for calcul the mask position with numpy : 0.002369403839111328 nb_pixel_total : 65465 time to create 1 rle with old method : 0.07453441619873047 length of segment : 214 time for calcul the mask position with numpy : 0.007950782775878906 nb_pixel_total : 116556 time to create 1 rle with old method : 0.12881112098693848 length of segment : 485 time for calcul the mask position with numpy : 0.0022270679473876953 nb_pixel_total : 64554 time to create 1 rle with old method : 0.0790104866027832 length of segment : 347 time for calcul the mask position with numpy : 0.0035517215728759766 nb_pixel_total : 46739 time to create 1 rle with old method : 0.06593036651611328 length of segment : 257 time for calcul the mask position with numpy : 0.0025124549865722656 nb_pixel_total : 43272 time to create 1 rle with old method : 0.04880332946777344 length of segment : 239 time for calcul the mask position with numpy : 0.0009131431579589844 nb_pixel_total : 13392 time to create 1 rle with old method : 0.022067785263061523 length of segment : 150 time for calcul the mask position with numpy : 0.0020737648010253906 nb_pixel_total : 37063 time to create 1 rle with old method : 0.04799485206604004 length of segment : 270 time for calcul the mask position with numpy : 0.0006289482116699219 nb_pixel_total : 9253 time to create 1 rle with old method : 0.010604143142700195 length of segment : 101 time for calcul the mask position with numpy : 0.0021533966064453125 nb_pixel_total : 40970 time to create 1 rle with old method : 0.045731544494628906 length of segment : 481 time for calcul the mask position with numpy : 0.00043702125549316406 nb_pixel_total : 11434 time to create 1 rle with old method : 0.01310586929321289 length of segment : 237 time for calcul the mask position with numpy : 0.0003101825714111328 nb_pixel_total : 12571 time to create 1 rle with old method : 0.014487743377685547 length of segment : 113 time for calcul the mask position with numpy : 0.0012881755828857422 nb_pixel_total : 46343 time to create 1 rle with old method : 0.05134701728820801 length of segment : 208 time for calcul the mask position with numpy : 0.0002110004425048828 nb_pixel_total : 6523 time to create 1 rle with old method : 0.007751941680908203 length of segment : 101 time for calcul the mask position with numpy : 0.008388280868530273 nb_pixel_total : 64178 time to create 1 rle with old method : 0.07286763191223145 length of segment : 591 time for calcul the mask position with numpy : 0.0011103153228759766 nb_pixel_total : 24473 time to create 1 rle with old method : 0.027972936630249023 length of segment : 197 time for calcul the mask position with numpy : 0.0018012523651123047 nb_pixel_total : 35478 time to create 1 rle with old method : 0.04094362258911133 length of segment : 303 time for calcul the mask position with numpy : 0.005137920379638672 nb_pixel_total : 63336 time to create 1 rle with old method : 0.07254505157470703 length of segment : 442 time for calcul the mask position with numpy : 0.0017359256744384766 nb_pixel_total : 40015 time to create 1 rle with old method : 0.04567146301269531 length of segment : 601 time for calcul the mask position with numpy : 0.0007996559143066406 nb_pixel_total : 16555 time to create 1 rle with old method : 0.017926692962646484 length of segment : 201 time for calcul the mask position with numpy : 0.0010738372802734375 nb_pixel_total : 31275 time to create 1 rle with old method : 0.03451275825500488 length of segment : 336 time for calcul the mask position with numpy : 0.002871274948120117 nb_pixel_total : 86425 time to create 1 rle with old method : 0.09929895401000977 length of segment : 270 time for calcul the mask position with numpy : 0.0023033618927001953 nb_pixel_total : 31516 time to create 1 rle with old method : 0.05034160614013672 length of segment : 341 time for calcul the mask position with numpy : 0.0027806758880615234 nb_pixel_total : 37263 time to create 1 rle with old method : 0.04254484176635742 length of segment : 591 time for calcul the mask position with numpy : 0.0012295246124267578 nb_pixel_total : 17075 time to create 1 rle with old method : 0.02006673812866211 length of segment : 185 time for calcul the mask position with numpy : 0.0021610260009765625 nb_pixel_total : 40520 time to create 1 rle with old method : 0.05011320114135742 length of segment : 295 time for calcul the mask position with numpy : 0.001371145248413086 nb_pixel_total : 32083 time to create 1 rle with old method : 0.03722214698791504 length of segment : 132 time for calcul the mask position with numpy : 0.0017247200012207031 nb_pixel_total : 26654 time to create 1 rle with old method : 0.030897855758666992 length of segment : 191 time for calcul the mask position with numpy : 0.001390695571899414 nb_pixel_total : 37170 time to create 1 rle with old method : 0.042447566986083984 length of segment : 163 time for calcul the mask position with numpy : 0.004770517349243164 nb_pixel_total : 77012 time to create 1 rle with old method : 0.10526108741760254 length of segment : 715 time for calcul the mask position with numpy : 0.00063323974609375 nb_pixel_total : 10907 time to create 1 rle with old method : 0.012386798858642578 length of segment : 111 time for calcul the mask position with numpy : 0.0019016265869140625 nb_pixel_total : 40491 time to create 1 rle with old method : 0.04585433006286621 length of segment : 321 time for calcul the mask position with numpy : 0.003127574920654297 nb_pixel_total : 133771 time to create 1 rle with old method : 0.15062403678894043 length of segment : 617 time for calcul the mask position with numpy : 0.0005674362182617188 nb_pixel_total : 13546 time to create 1 rle with old method : 0.01695394515991211 length of segment : 88 time for calcul the mask position with numpy : 0.004051923751831055 nb_pixel_total : 84472 time to create 1 rle with old method : 0.09477877616882324 length of segment : 344 time for calcul the mask position with numpy : 0.002093791961669922 nb_pixel_total : 33319 time to create 1 rle with old method : 0.03797745704650879 length of segment : 336 time for calcul the mask position with numpy : 0.001897573471069336 nb_pixel_total : 27004 time to create 1 rle with old method : 0.03275179862976074 length of segment : 259 time for calcul the mask position with numpy : 0.0012440681457519531 nb_pixel_total : 23957 time to create 1 rle with old method : 0.027466535568237305 length of segment : 211 time for calcul the mask position with numpy : 0.0005397796630859375 nb_pixel_total : 5115 time to create 1 rle with old method : 0.006145954132080078 length of segment : 90 time for calcul the mask position with numpy : 0.00024080276489257812 nb_pixel_total : 3046 time to create 1 rle with old method : 0.003563404083251953 length of segment : 58 time for calcul the mask position with numpy : 0.0018911361694335938 nb_pixel_total : 35369 time to create 1 rle with old method : 0.0406641960144043 length of segment : 259 time for calcul the mask position with numpy : 0.0021626949310302734 nb_pixel_total : 42238 time to create 1 rle with old method : 0.048274993896484375 length of segment : 262 time for calcul the mask position with numpy : 0.0018188953399658203 nb_pixel_total : 42210 time to create 1 rle with old method : 0.047574758529663086 length of segment : 202 time for calcul the mask position with numpy : 0.002644777297973633 nb_pixel_total : 66002 time to create 1 rle with old method : 0.07462453842163086 length of segment : 232 time for calcul the mask position with numpy : 0.0007700920104980469 nb_pixel_total : 12820 time to create 1 rle with old method : 0.015012264251708984 length of segment : 115 time for calcul the mask position with numpy : 0.0014913082122802734 nb_pixel_total : 11523 time to create 1 rle with old method : 0.013675212860107422 length of segment : 253 time for calcul the mask position with numpy : 0.002313375473022461 nb_pixel_total : 41090 time to create 1 rle with old method : 0.04874014854431152 length of segment : 241 time for calcul the mask position with numpy : 0.0007505416870117188 nb_pixel_total : 9967 time to create 1 rle with old method : 0.011504650115966797 length of segment : 186 time for calcul the mask position with numpy : 0.0007207393646240234 nb_pixel_total : 12095 time to create 1 rle with old method : 0.013746023178100586 length of segment : 195 time for calcul the mask position with numpy : 0.0004134178161621094 nb_pixel_total : 9644 time to create 1 rle with old method : 0.011424541473388672 length of segment : 128 time for calcul the mask position with numpy : 0.0009219646453857422 nb_pixel_total : 21582 time to create 1 rle with old method : 0.024882793426513672 length of segment : 154 time for calcul the mask position with numpy : 0.0005035400390625 nb_pixel_total : 8517 time to create 1 rle with old method : 0.009900569915771484 length of segment : 110 time for calcul the mask position with numpy : 0.0063266754150390625 nb_pixel_total : 162583 time to create 1 rle with new method : 0.00665283203125 length of segment : 313 time for calcul the mask position with numpy : 0.0006976127624511719 nb_pixel_total : 17345 time to create 1 rle with old method : 0.01964282989501953 length of segment : 173 time for calcul the mask position with numpy : 0.0029790401458740234 nb_pixel_total : 68653 time to create 1 rle with old method : 0.0759429931640625 length of segment : 372 time for calcul the mask position with numpy : 0.0006709098815917969 nb_pixel_total : 11213 time to create 1 rle with old method : 0.013550281524658203 length of segment : 100 time for calcul the mask position with numpy : 0.001760721206665039 nb_pixel_total : 31398 time to create 1 rle with old method : 0.035597801208496094 length of segment : 328 time for calcul the mask position with numpy : 0.0007939338684082031 nb_pixel_total : 16741 time to create 1 rle with old method : 0.0198361873626709 length of segment : 100 time for calcul the mask position with numpy : 0.0008006095886230469 nb_pixel_total : 14486 time to create 1 rle with old method : 0.02300572395324707 length of segment : 129 time for calcul the mask position with numpy : 0.005804300308227539 nb_pixel_total : 86668 time to create 1 rle with old method : 0.10875797271728516 length of segment : 341 time for calcul the mask position with numpy : 0.00103759765625 nb_pixel_total : 18918 time to create 1 rle with old method : 0.021700620651245117 length of segment : 129 time for calcul the mask position with numpy : 0.0010421276092529297 nb_pixel_total : 12113 time to create 1 rle with old method : 0.013634204864501953 length of segment : 185 time for calcul the mask position with numpy : 0.0043103694915771484 nb_pixel_total : 48360 time to create 1 rle with old method : 0.0538175106048584 length of segment : 320 time for calcul the mask position with numpy : 0.001003265380859375 nb_pixel_total : 12372 time to create 1 rle with old method : 0.013962745666503906 length of segment : 199 time for calcul the mask position with numpy : 0.0011148452758789062 nb_pixel_total : 16412 time to create 1 rle with old method : 0.019036293029785156 length of segment : 110 time for calcul the mask position with numpy : 0.002544403076171875 nb_pixel_total : 37358 time to create 1 rle with old method : 0.04298973083496094 length of segment : 300 time for calcul the mask position with numpy : 0.009676933288574219 nb_pixel_total : 196430 time to create 1 rle with new method : 0.011304378509521484 length of segment : 503 time for calcul the mask position with numpy : 0.0019588470458984375 nb_pixel_total : 36449 time to create 1 rle with old method : 0.04186367988586426 length of segment : 201 time for calcul the mask position with numpy : 0.0014884471893310547 nb_pixel_total : 16507 time to create 1 rle with old method : 0.01851654052734375 length of segment : 202 time for calcul the mask position with numpy : 0.0016357898712158203 nb_pixel_total : 21804 time to create 1 rle with old method : 0.024610042572021484 length of segment : 149 time for calcul the mask position with numpy : 0.0034193992614746094 nb_pixel_total : 45780 time to create 1 rle with old method : 0.050993919372558594 length of segment : 273 time for calcul the mask position with numpy : 0.0032455921173095703 nb_pixel_total : 33502 time to create 1 rle with old method : 0.03898000717163086 length of segment : 407 time for calcul the mask position with numpy : 0.001814126968383789 nb_pixel_total : 32905 time to create 1 rle with old method : 0.03777122497558594 length of segment : 168 time for calcul the mask position with numpy : 0.0022857189178466797 nb_pixel_total : 34598 time to create 1 rle with old method : 0.03905320167541504 length of segment : 225 time for calcul the mask position with numpy : 0.009354352951049805 nb_pixel_total : 194975 time to create 1 rle with new method : 0.009126663208007812 length of segment : 630 time for calcul the mask position with numpy : 0.0011076927185058594 nb_pixel_total : 14182 time to create 1 rle with old method : 0.01658773422241211 length of segment : 132 time for calcul the mask position with numpy : 0.003419160842895508 nb_pixel_total : 53275 time to create 1 rle with old method : 0.061879634857177734 length of segment : 323 time for calcul the mask position with numpy : 0.0033807754516601562 nb_pixel_total : 54662 time to create 1 rle with old method : 0.06136727333068848 length of segment : 537 time for calcul the mask position with numpy : 0.0013375282287597656 nb_pixel_total : 19704 time to create 1 rle with old method : 0.022217988967895508 length of segment : 115 time for calcul the mask position with numpy : 0.0014450550079345703 nb_pixel_total : 19471 time to create 1 rle with old method : 0.02224564552307129 length of segment : 164 time for calcul the mask position with numpy : 0.0021581649780273438 nb_pixel_total : 43204 time to create 1 rle with old method : 0.04886460304260254 length of segment : 282 time for calcul the mask position with numpy : 0.0018858909606933594 nb_pixel_total : 28105 time to create 1 rle with old method : 0.031922340393066406 length of segment : 409 time for calcul the mask position with numpy : 0.0032281875610351562 nb_pixel_total : 64838 time to create 1 rle with old method : 0.07247018814086914 length of segment : 287 time for calcul the mask position with numpy : 0.0004901885986328125 nb_pixel_total : 9829 time to create 1 rle with old method : 0.011152267456054688 length of segment : 195 time for calcul the mask position with numpy : 0.0009815692901611328 nb_pixel_total : 14904 time to create 1 rle with old method : 0.01723194122314453 length of segment : 172 time for calcul the mask position with numpy : 0.001394033432006836 nb_pixel_total : 30123 time to create 1 rle with old method : 0.03466916084289551 length of segment : 212 time for calcul the mask position with numpy : 0.0006182193756103516 nb_pixel_total : 8308 time to create 1 rle with old method : 0.009572744369506836 length of segment : 131 time for calcul the mask position with numpy : 0.00041675567626953125 nb_pixel_total : 7944 time to create 1 rle with old method : 0.009258031845092773 length of segment : 97 time for calcul the mask position with numpy : 0.0006458759307861328 nb_pixel_total : 19563 time to create 1 rle with old method : 0.02302384376525879 length of segment : 164 time for calcul the mask position with numpy : 0.0006556510925292969 nb_pixel_total : 13472 time to create 1 rle with old method : 0.015531063079833984 length of segment : 118 time for calcul the mask position with numpy : 0.0006372928619384766 nb_pixel_total : 17851 time to create 1 rle with old method : 0.020775318145751953 length of segment : 153 time for calcul the mask position with numpy : 0.002973794937133789 nb_pixel_total : 52943 time to create 1 rle with old method : 0.059732675552368164 length of segment : 403 time for calcul the mask position with numpy : 0.0052471160888671875 nb_pixel_total : 83294 time to create 1 rle with old method : 0.09702825546264648 length of segment : 280 time for calcul the mask position with numpy : 0.0004413127899169922 nb_pixel_total : 9992 time to create 1 rle with old method : 0.011467933654785156 length of segment : 121 time for calcul the mask position with numpy : 0.003760814666748047 nb_pixel_total : 59156 time to create 1 rle with old method : 0.06607699394226074 length of segment : 304 time for calcul the mask position with numpy : 0.0002887248992919922 nb_pixel_total : 6702 time to create 1 rle with old method : 0.008013248443603516 length of segment : 62 time spent for convertir_results : 38.76793932914734 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 426 chid ids of type : 3594 Number RLEs to save : 120303 save missing photos in datou_result : time spend for datou_step_exec : 181.26965832710266 time spend to save output : 10.941753149032593 total time spend for step 1 : 192.21141147613525 step2:crop_condition Wed Apr 9 11:13:43 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 426 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 ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 314 About to insert : list_path_to_insert length 314 new photo from crops ! About to upload 314 photos upload in portfolio : 3736932 init cache_photo without model_param we have 314 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744190095_669588 we have uploaded 314 photos in the portfolio 3736932 time of upload the photos Elapsed time : 79.18660545349121 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 ! map_result returned by crop_photo_return_map_crop : length : 60 About to insert : list_path_to_insert length 60 new photo from crops ! About to upload 60 photos upload in portfolio : 3736932 init cache_photo without model_param we have 60 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744190197_669588 we have uploaded 60 photos in the portfolio 3736932 time of upload the photos Elapsed time : 14.459468364715576 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 ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744190214_669588 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.7557022571563721 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 ! map_result returned by crop_photo_return_map_crop : length : 31 About to insert : list_path_to_insert length 31 new photo from crops ! About to upload 31 photos upload in portfolio : 3736932 init cache_photo without model_param we have 31 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744190232_669588 we have uploaded 31 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.20215654373169 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 ! 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 : 12 About to insert : list_path_to_insert length 12 new photo from crops ! About to upload 12 photos upload in portfolio : 3736932 init cache_photo without model_param we have 12 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744190246_669588 we have uploaded 12 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.8360958099365234 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 ! 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 : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744190253_669588 we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.554499626159668 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 ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744190257_669588 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.9326248168945312 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 [1350453033, 1350452961, 1350452938, 1350452909, 1350254131, 1350254126, 1350254122, 1350254118, 1350254115, 1350254103, 1350254053, 1350254048, 1350254043, 1350254039, 1350254036] Looping around the photos to save general results len do output : 426 /1350740443Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740444Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740446Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740447Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740448Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740449Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740450Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740451Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740452Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740453Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740454Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740455Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740456Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740457Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740458Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740459Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740460Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740461Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740462Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740463Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740464Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740465Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740485Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740508Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740532Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740595Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350740855Didn't 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/1350741021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741031Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350741054Didn'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, '2733641') ('3318', '22153537', '1350453033', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452961', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452938', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452909', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254131', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254126', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254122', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254118', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254115', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254103', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254053', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254048', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254043', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254039', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254036', None, None, None, None, None, '2733641') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1293 time used for this insertion : 0.05689382553100586 save_final save missing photos in datou_result : time spend for datou_step_exec : 234.58220028877258 time spend to save output : 0.0682668685913086 total time spend for step 2 : 234.6504671573639 step3:rle_unique_nms_with_priority Wed Apr 9 11:17:37 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 426 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 17 nb_hashtags : 4 time to prepare the origin masks : 4.951776504516602 time for calcul the mask position with numpy : 0.592132568359375 nb_pixel_total : 5658123 time to create 1 rle with new method : 0.556384801864624 time for calcul the mask position with numpy : 0.028820514678955078 nb_pixel_total : 95975 time to create 1 rle with old method : 0.10959100723266602 time for calcul the mask position with numpy : 0.020603418350219727 nb_pixel_total : 2445 time to create 1 rle with old method : 0.0029196739196777344 time for calcul the mask position with numpy : 0.022657155990600586 nb_pixel_total : 220541 time to create 1 rle with new method : 0.5851800441741943 time for calcul the mask position with numpy : 0.022825241088867188 nb_pixel_total : 15524 time to create 1 rle with old method : 0.017278671264648438 time for calcul the mask position with numpy : 0.02441859245300293 nb_pixel_total : 58852 time to create 1 rle with old method : 0.06729459762573242 time for calcul the mask position with numpy : 0.023465394973754883 nb_pixel_total : 107902 time to create 1 rle with old method : 0.12164616584777832 time for calcul the mask position with numpy : 0.022304296493530273 nb_pixel_total : 26164 time to create 1 rle with old method : 0.02983713150024414 time for calcul the mask position with numpy : 0.023263931274414062 nb_pixel_total : 68169 time to create 1 rle with old method : 0.0796043872833252 time for calcul the mask position with numpy : 0.021239042282104492 nb_pixel_total : 41832 time to create 1 rle with old method : 0.05022120475769043 time for calcul the mask position with numpy : 0.02483367919921875 nb_pixel_total : 93641 time to create 1 rle with old method : 0.12698149681091309 time for calcul the mask position with numpy : 0.02490544319152832 nb_pixel_total : 80241 time to create 1 rle with old method : 0.08786678314208984 time for calcul the mask position with numpy : 0.02168440818786621 nb_pixel_total : 20228 time to create 1 rle with old method : 0.022647857666015625 time for calcul the mask position with numpy : 0.024822235107421875 nb_pixel_total : 212788 time to create 1 rle with new method : 0.5785489082336426 time for calcul the mask position with numpy : 0.0224916934967041 nb_pixel_total : 18648 time to create 1 rle with old method : 0.02063441276550293 time for calcul the mask position with numpy : 0.02288508415222168 nb_pixel_total : 241031 time to create 1 rle with new method : 0.5389225482940674 time for calcul the mask position with numpy : 0.022301435470581055 nb_pixel_total : 65645 time to create 1 rle with old method : 0.07268285751342773 time for calcul the mask position with numpy : 0.022869348526000977 nb_pixel_total : 22491 time to create 1 rle with old method : 0.0248260498046875 create new chi : 4.181412935256958 time to delete rle : 0.019626617431640625 batch 1 Loaded 35 chid ids of type : 3594 ++++++++++++++++++++++Number RLEs to save : 14278 TO DO : save crop sub photo not yet done ! save time : 1.0269060134887695 nb_obj : 27 nb_hashtags : 5 time to prepare the origin masks : 3.91275954246521 time for calcul the mask position with numpy : 0.39020609855651855 nb_pixel_total : 5672612 time to create 1 rle with new method : 0.6013033390045166 time for calcul the mask position with numpy : 0.029242753982543945 nb_pixel_total : 47253 time to create 1 rle with old method : 0.05383753776550293 time for calcul the mask position with numpy : 0.028877735137939453 nb_pixel_total : 25907 time to create 1 rle with old method : 0.03220033645629883 time for calcul the mask position with numpy : 0.030202388763427734 nb_pixel_total : 46705 time to create 1 rle with old method : 0.05672883987426758 time for calcul the mask position with numpy : 0.028302669525146484 nb_pixel_total : 13231 time to create 1 rle with old method : 0.014333009719848633 time for calcul the mask position with numpy : 0.028686046600341797 nb_pixel_total : 118356 time to create 1 rle with old method : 0.12873578071594238 time for calcul the mask position with numpy : 0.02831268310546875 nb_pixel_total : 21047 time to create 1 rle with old method : 0.022965431213378906 time for calcul the mask position with numpy : 0.028073787689208984 nb_pixel_total : 36550 time to create 1 rle with old method : 0.03899240493774414 time for calcul the mask position with numpy : 0.02728271484375 nb_pixel_total : 25917 time to create 1 rle with old method : 0.02825617790222168 time for calcul the mask position with numpy : 0.02811431884765625 nb_pixel_total : 48634 time to create 1 rle with old method : 0.052610158920288086 time for calcul the mask position with numpy : 0.027118682861328125 nb_pixel_total : 15020 time to create 1 rle with old method : 0.015932798385620117 time for calcul the mask position with numpy : 0.02779388427734375 nb_pixel_total : 111822 time to create 1 rle with old method : 0.11942625045776367 time for calcul the mask position with numpy : 0.03004169464111328 nb_pixel_total : 37526 time to create 1 rle with old method : 0.04334712028503418 time for calcul the mask position with numpy : 0.030304908752441406 nb_pixel_total : 26020 time to create 1 rle with old method : 0.04111218452453613 time for calcul the mask position with numpy : 0.03275465965270996 nb_pixel_total : 32454 time to create 1 rle with old method : 0.03742575645446777 time for calcul the mask position with numpy : 0.02837538719177246 nb_pixel_total : 63151 time to create 1 rle with old method : 0.06685805320739746 time for calcul the mask position with numpy : 0.02927994728088379 nb_pixel_total : 52616 time to create 1 rle with old method : 0.06529641151428223 time for calcul the mask position with numpy : 0.028363704681396484 nb_pixel_total : 78735 time to create 1 rle with old method : 0.08504772186279297 time for calcul the mask position with numpy : 0.02860736846923828 nb_pixel_total : 96882 time to create 1 rle with old method : 0.10465669631958008 time for calcul the mask position with numpy : 0.028101205825805664 nb_pixel_total : 99973 time to create 1 rle with old method : 0.10959768295288086 time for calcul the mask position with numpy : 0.028412580490112305 nb_pixel_total : 32469 time to create 1 rle with old method : 0.03552508354187012 time for calcul the mask position with numpy : 0.0301363468170166 nb_pixel_total : 155641 time to create 1 rle with new method : 0.3732898235321045 time for calcul the mask position with numpy : 0.028239727020263672 nb_pixel_total : 39629 time to create 1 rle with old method : 0.04307866096496582 time for calcul the mask position with numpy : 0.027485370635986328 nb_pixel_total : 218 time to create 1 rle with old method : 0.00036716461181640625 time for calcul the mask position with numpy : 0.027169466018676758 nb_pixel_total : 71779 time to create 1 rle with old method : 0.07534027099609375 time for calcul the mask position with numpy : 0.02850651741027832 nb_pixel_total : 69048 time to create 1 rle with old method : 0.07838296890258789 time for calcul the mask position with numpy : 0.028610706329345703 nb_pixel_total : 4452 time to create 1 rle with old method : 0.004921913146972656 time for calcul the mask position with numpy : 0.027981281280517578 nb_pixel_total : 6593 time to create 1 rle with old method : 0.0071201324462890625 create new chi : 3.5571272373199463 time to delete rle : 0.0022656917572021484 batch 1 Loaded 55 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17102 TO DO : save crop sub photo not yet done ! save time : 1.0316298007965088 nb_obj : 27 nb_hashtags : 4 time to prepare the origin masks : 3.7381296157836914 time for calcul the mask position with numpy : 0.2899465560913086 nb_pixel_total : 5754079 time to create 1 rle with new method : 1.091282606124878 time for calcul the mask position with numpy : 0.03021097183227539 nb_pixel_total : 23500 time to create 1 rle with old method : 0.026946067810058594 time for calcul the mask position with numpy : 0.029725313186645508 nb_pixel_total : 87168 time to create 1 rle with old method : 0.10788273811340332 time for calcul the mask position with numpy : 0.03381752967834473 nb_pixel_total : 56883 time to create 1 rle with old method : 0.0690925121307373 time for calcul the mask position with numpy : 0.03085947036743164 nb_pixel_total : 25141 time to create 1 rle with old method : 0.028530359268188477 time for calcul the mask position with numpy : 0.029213428497314453 nb_pixel_total : 20962 time to create 1 rle with old method : 0.02373027801513672 time for calcul the mask position with numpy : 0.029488563537597656 nb_pixel_total : 142212 time to create 1 rle with old method : 0.15551543235778809 time for calcul the mask position with numpy : 0.029359817504882812 nb_pixel_total : 104404 time to create 1 rle with old method : 0.11539149284362793 time for calcul the mask position with numpy : 0.028223276138305664 nb_pixel_total : 13590 time to create 1 rle with old method : 0.01522064208984375 time for calcul the mask position with numpy : 0.028277873992919922 nb_pixel_total : 31287 time to create 1 rle with old method : 0.03459572792053223 time for calcul the mask position with numpy : 0.030033111572265625 nb_pixel_total : 199832 time to create 1 rle with new method : 0.6929020881652832 time for calcul the mask position with numpy : 0.02928757667541504 nb_pixel_total : 82841 time to create 1 rle with old method : 0.09353876113891602 time for calcul the mask position with numpy : 0.02957606315612793 nb_pixel_total : 64967 time to create 1 rle with old method : 0.07246232032775879 time for calcul the mask position with numpy : 0.029510021209716797 nb_pixel_total : 48765 time to create 1 rle with old method : 0.05474710464477539 time for calcul the mask position with numpy : 0.03249502182006836 nb_pixel_total : 115819 time to create 1 rle with old method : 0.1459951400756836 time for calcul the mask position with numpy : 0.029217958450317383 nb_pixel_total : 26920 time to create 1 rle with old method : 0.030919551849365234 time for calcul the mask position with numpy : 0.029458999633789062 nb_pixel_total : 17360 time to create 1 rle with old method : 0.020166397094726562 time for calcul the mask position with numpy : 0.02892279624938965 nb_pixel_total : 32876 time to create 1 rle with old method : 0.037744998931884766 time for calcul the mask position with numpy : 0.03377866744995117 nb_pixel_total : 8161 time to create 1 rle with old method : 0.00938725471496582 time for calcul the mask position with numpy : 0.03116011619567871 nb_pixel_total : 22203 time to create 1 rle with old method : 0.032499074935913086 time for calcul the mask position with numpy : 0.02967381477355957 nb_pixel_total : 26523 time to create 1 rle with old method : 0.03047466278076172 time for calcul the mask position with numpy : 0.029990673065185547 nb_pixel_total : 44372 time to create 1 rle with old method : 0.05455517768859863 time for calcul the mask position with numpy : 0.02930903434753418 nb_pixel_total : 13657 time to create 1 rle with old method : 0.015991687774658203 time for calcul the mask position with numpy : 0.0314786434173584 nb_pixel_total : 24832 time to create 1 rle with old method : 0.03248262405395508 time for calcul the mask position with numpy : 0.035156965255737305 nb_pixel_total : 8023 time to create 1 rle with old method : 0.01012730598449707 time for calcul the mask position with numpy : 0.0343317985534668 nb_pixel_total : 28140 time to create 1 rle with old method : 0.03226757049560547 time for calcul the mask position with numpy : 0.03175020217895508 nb_pixel_total : 11131 time to create 1 rle with old method : 0.013201475143432617 time for calcul the mask position with numpy : 0.030356168746948242 nb_pixel_total : 14592 time to create 1 rle with old method : 0.018613338470458984 create new chi : 4.2420594692230225 time to delete rle : 0.0034554004669189453 batch 1 Loaded 55 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++Number RLEs to save : 16177 TO DO : save crop sub photo not yet done ! save time : 1.1365160942077637 nb_obj : 34 nb_hashtags : 3 time to prepare the origin masks : 4.00646448135376 time for calcul the mask position with numpy : 0.5401716232299805 nb_pixel_total : 5851933 time to create 1 rle with new method : 0.5809979438781738 time for calcul the mask position with numpy : 0.028668880462646484 nb_pixel_total : 4680 time to create 1 rle with old method : 0.005372762680053711 time for calcul the mask position with numpy : 0.028879404067993164 nb_pixel_total : 81831 time to create 1 rle with old method : 0.09356570243835449 time for calcul the mask position with numpy : 0.029134511947631836 nb_pixel_total : 30330 time to create 1 rle with old method : 0.0355222225189209 time for calcul the mask position with numpy : 0.03097701072692871 nb_pixel_total : 56172 time to create 1 rle with old method : 0.0663154125213623 time for calcul the mask position with numpy : 0.02970147132873535 nb_pixel_total : 81036 time to create 1 rle with old method : 0.09322166442871094 time for calcul the mask position with numpy : 0.029459476470947266 nb_pixel_total : 128000 time to create 1 rle with old method : 0.1439189910888672 time for calcul the mask position with numpy : 0.02925729751586914 nb_pixel_total : 15452 time to create 1 rle with old method : 0.01735091209411621 time for calcul the mask position with numpy : 0.027885913848876953 nb_pixel_total : 9267 time to create 1 rle with old method : 0.01021122932434082 time for calcul the mask position with numpy : 0.02880382537841797 nb_pixel_total : 23445 time to create 1 rle with old method : 0.02664470672607422 time for calcul the mask position with numpy : 0.02955460548400879 nb_pixel_total : 11855 time to create 1 rle with old method : 0.01366424560546875 time for calcul the mask position with numpy : 0.029744625091552734 nb_pixel_total : 7655 time to create 1 rle with old method : 0.008955955505371094 time for calcul the mask position with numpy : 0.030102014541625977 nb_pixel_total : 23662 time to create 1 rle with old method : 0.03795504570007324 time for calcul the mask position with numpy : 0.03332066535949707 nb_pixel_total : 18309 time to create 1 rle with old method : 0.021289825439453125 time for calcul the mask position with numpy : 0.029574871063232422 nb_pixel_total : 153449 time to create 1 rle with new method : 0.7096912860870361 time for calcul the mask position with numpy : 0.0330500602722168 nb_pixel_total : 20201 time to create 1 rle with old method : 0.030185461044311523 time for calcul the mask position with numpy : 0.02868199348449707 nb_pixel_total : 47459 time to create 1 rle with old method : 0.05315375328063965 time for calcul the mask position with numpy : 0.029032230377197266 nb_pixel_total : 31788 time to create 1 rle with old method : 0.035704851150512695 time for calcul the mask position with numpy : 0.02947211265563965 nb_pixel_total : 18757 time to create 1 rle with old method : 0.02182769775390625 time for calcul the mask position with numpy : 0.029282093048095703 nb_pixel_total : 14963 time to create 1 rle with old method : 0.01725006103515625 time for calcul the mask position with numpy : 0.029167652130126953 nb_pixel_total : 8835 time to create 1 rle with old method : 0.010324239730834961 time for calcul the mask position with numpy : 0.029440879821777344 nb_pixel_total : 18700 time to create 1 rle with old method : 0.021744251251220703 time for calcul the mask position with numpy : 0.030736207962036133 nb_pixel_total : 12226 time to create 1 rle with old method : 0.013963460922241211 time for calcul the mask position with numpy : 0.028464794158935547 nb_pixel_total : 4920 time to create 1 rle with old method : 0.0055370330810546875 time for calcul the mask position with numpy : 0.029088973999023438 nb_pixel_total : 8190 time to create 1 rle with old method : 0.009042024612426758 time for calcul the mask position with numpy : 0.02987527847290039 nb_pixel_total : 6895 time to create 1 rle with old method : 0.007794380187988281 time for calcul the mask position with numpy : 0.029251575469970703 nb_pixel_total : 97469 time to create 1 rle with old method : 0.10825610160827637 time for calcul the mask position with numpy : 0.02926349639892578 nb_pixel_total : 37878 time to create 1 rle with old method : 0.044094085693359375 time for calcul the mask position with numpy : 0.02907729148864746 nb_pixel_total : 46528 time to create 1 rle with old method : 0.05308866500854492 time for calcul the mask position with numpy : 0.028116464614868164 nb_pixel_total : 18763 time to create 1 rle with old method : 0.021778106689453125 time for calcul the mask position with numpy : 0.02815532684326172 nb_pixel_total : 22817 time to create 1 rle with old method : 0.02616119384765625 time for calcul the mask position with numpy : 0.02834343910217285 nb_pixel_total : 48611 time to create 1 rle with old method : 0.0538935661315918 time for calcul the mask position with numpy : 0.028199195861816406 nb_pixel_total : 61858 time to create 1 rle with old method : 0.0689551830291748 time for calcul the mask position with numpy : 0.02886676788330078 nb_pixel_total : 10194 time to create 1 rle with old method : 0.01155233383178711 time for calcul the mask position with numpy : 0.029320716857910156 nb_pixel_total : 16112 time to create 1 rle with old method : 0.02636551856994629 create new chi : 4.1114020347595215 time to delete rle : 0.00345611572265625 batch 1 Loaded 69 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17428 TO DO : save crop sub photo not yet done ! save time : 1.1934988498687744 nb_obj : 30 nb_hashtags : 4 time to prepare the origin masks : 5.033378601074219 time for calcul the mask position with numpy : 0.23013997077941895 nb_pixel_total : 4112599 time to create 1 rle with new method : 0.7340941429138184 time for calcul the mask position with numpy : 0.02884697914123535 nb_pixel_total : 75486 time to create 1 rle with old method : 0.08282995223999023 time for calcul the mask position with numpy : 0.041678428649902344 nb_pixel_total : 605371 time to create 1 rle with new method : 0.49948811531066895 time for calcul the mask position with numpy : 0.030544042587280273 nb_pixel_total : 77819 time to create 1 rle with old method : 0.08871722221374512 time for calcul the mask position with numpy : 0.029044389724731445 nb_pixel_total : 3348 time to create 1 rle with old method : 0.003968238830566406 time for calcul the mask position with numpy : 0.02850508689880371 nb_pixel_total : 22948 time to create 1 rle with old method : 0.025835514068603516 time for calcul the mask position with numpy : 0.050470590591430664 nb_pixel_total : 1004190 time to create 1 rle with new method : 0.874434232711792 time for calcul the mask position with numpy : 0.02926039695739746 nb_pixel_total : 33913 time to create 1 rle with old method : 0.03713512420654297 time for calcul the mask position with numpy : 0.02837681770324707 nb_pixel_total : 12739 time to create 1 rle with old method : 0.013811588287353516 time for calcul the mask position with numpy : 0.02913045883178711 nb_pixel_total : 11635 time to create 1 rle with old method : 0.013103246688842773 time for calcul the mask position with numpy : 0.02960658073425293 nb_pixel_total : 17473 time to create 1 rle with old method : 0.019572973251342773 time for calcul the mask position with numpy : 0.02895212173461914 nb_pixel_total : 43074 time to create 1 rle with old method : 0.04794788360595703 time for calcul the mask position with numpy : 0.02909564971923828 nb_pixel_total : 65739 time to create 1 rle with old method : 0.07355785369873047 time for calcul the mask position with numpy : 0.029266357421875 nb_pixel_total : 69467 time to create 1 rle with old method : 0.07749438285827637 time for calcul the mask position with numpy : 0.028560638427734375 nb_pixel_total : 47286 time to create 1 rle with old method : 0.050837039947509766 time for calcul the mask position with numpy : 0.028753042221069336 nb_pixel_total : 29278 time to create 1 rle with old method : 0.0318760871887207 time for calcul the mask position with numpy : 0.02812814712524414 nb_pixel_total : 38133 time to create 1 rle with old method : 0.058614492416381836 time for calcul the mask position with numpy : 0.03283190727233887 nb_pixel_total : 32686 time to create 1 rle with old method : 0.035891056060791016 time for calcul the mask position with numpy : 0.02821803092956543 nb_pixel_total : 12476 time to create 1 rle with old method : 0.013590097427368164 time for calcul the mask position with numpy : 0.028840065002441406 nb_pixel_total : 57964 time to create 1 rle with old method : 0.0640408992767334 time for calcul the mask position with numpy : 0.029004812240600586 nb_pixel_total : 44965 time to create 1 rle with old method : 0.05034637451171875 time for calcul the mask position with numpy : 0.029089927673339844 nb_pixel_total : 33503 time to create 1 rle with old method : 0.038056135177612305 time for calcul the mask position with numpy : 0.030245065689086914 nb_pixel_total : 292382 time to create 1 rle with new method : 0.5963735580444336 time for calcul the mask position with numpy : 0.02913689613342285 nb_pixel_total : 42355 time to create 1 rle with old method : 0.04706144332885742 time for calcul the mask position with numpy : 0.03176307678222656 nb_pixel_total : 33833 time to create 1 rle with old method : 0.03796195983886719 time for calcul the mask position with numpy : 0.028722286224365234 nb_pixel_total : 25628 time to create 1 rle with old method : 0.029192447662353516 time for calcul the mask position with numpy : 0.029056549072265625 nb_pixel_total : 7534 time to create 1 rle with old method : 0.008669137954711914 time for calcul the mask position with numpy : 0.02942490577697754 nb_pixel_total : 39191 time to create 1 rle with old method : 0.04375910758972168 time for calcul the mask position with numpy : 0.029063701629638672 nb_pixel_total : 116177 time to create 1 rle with old method : 0.1266191005706787 time for calcul the mask position with numpy : 0.02892303466796875 nb_pixel_total : 26013 time to create 1 rle with old method : 0.029231786727905273 time for calcul the mask position with numpy : 0.030568838119506836 nb_pixel_total : 15035 time to create 1 rle with old method : 0.016709089279174805 create new chi : 5.120688438415527 time to delete rle : 0.005115509033203125 batch 1 Loaded 61 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21585 TO DO : save crop sub photo not yet done ! save time : 1.670196771621704 nb_obj : 43 nb_hashtags : 5 time to prepare the origin masks : 3.9923248291015625 time for calcul the mask position with numpy : 1.1746034622192383 nb_pixel_total : 5650897 time to create 1 rle with new method : 0.7605044841766357 time for calcul the mask position with numpy : 0.028691768646240234 nb_pixel_total : 9854 time to create 1 rle with old method : 0.01127171516418457 time for calcul the mask position with numpy : 0.02913522720336914 nb_pixel_total : 20779 time to create 1 rle with old method : 0.027089595794677734 time for calcul the mask position with numpy : 0.031011581420898438 nb_pixel_total : 101227 time to create 1 rle with old method : 0.11301350593566895 time for calcul the mask position with numpy : 0.0286405086517334 nb_pixel_total : 1832 time to create 1 rle with old method : 0.0022153854370117188 time for calcul the mask position with numpy : 0.029062747955322266 nb_pixel_total : 23645 time to create 1 rle with old method : 0.027132749557495117 time for calcul the mask position with numpy : 0.029158353805541992 nb_pixel_total : 6011 time to create 1 rle with old method : 0.006824970245361328 time for calcul the mask position with numpy : 0.03445553779602051 nb_pixel_total : 41255 time to create 1 rle with old method : 0.04631233215332031 time for calcul the mask position with numpy : 0.0315399169921875 nb_pixel_total : 32694 time to create 1 rle with old method : 0.04657459259033203 time for calcul the mask position with numpy : 0.03434038162231445 nb_pixel_total : 39661 time to create 1 rle with old method : 0.044075965881347656 time for calcul the mask position with numpy : 0.028459548950195312 nb_pixel_total : 12544 time to create 1 rle with old method : 0.01395416259765625 time for calcul the mask position with numpy : 0.028360605239868164 nb_pixel_total : 6961 time to create 1 rle with old method : 0.007821798324584961 time for calcul the mask position with numpy : 0.02898120880126953 nb_pixel_total : 7937 time to create 1 rle with old method : 0.009102582931518555 time for calcul the mask position with numpy : 0.028421640396118164 nb_pixel_total : 12521 time to create 1 rle with old method : 0.014058589935302734 time for calcul the mask position with numpy : 0.02822136878967285 nb_pixel_total : 15018 time to create 1 rle with old method : 0.017007112503051758 time for calcul the mask position with numpy : 0.029239654541015625 nb_pixel_total : 53987 time to create 1 rle with old method : 0.05994153022766113 time for calcul the mask position with numpy : 0.02866840362548828 nb_pixel_total : 16789 time to create 1 rle with old method : 0.018742084503173828 time for calcul the mask position with numpy : 0.028757333755493164 nb_pixel_total : 41778 time to create 1 rle with old method : 0.046475887298583984 time for calcul the mask position with numpy : 0.027962684631347656 nb_pixel_total : 49325 time to create 1 rle with old method : 0.05373740196228027 time for calcul the mask position with numpy : 0.028873443603515625 nb_pixel_total : 12291 time to create 1 rle with old method : 0.013386011123657227 time for calcul the mask position with numpy : 0.027880430221557617 nb_pixel_total : 43131 time to create 1 rle with old method : 0.046767473220825195 time for calcul the mask position with numpy : 0.028738737106323242 nb_pixel_total : 53739 time to create 1 rle with old method : 0.05880308151245117 time for calcul the mask position with numpy : 0.027977705001831055 nb_pixel_total : 22872 time to create 1 rle with old method : 0.025043487548828125 time for calcul the mask position with numpy : 0.028206586837768555 nb_pixel_total : 9138 time to create 1 rle with old method : 0.009403705596923828 time for calcul the mask position with numpy : 0.027711868286132812 nb_pixel_total : 53441 time to create 1 rle with old method : 0.056714534759521484 time for calcul the mask position with numpy : 0.027027368545532227 nb_pixel_total : 13558 time to create 1 rle with old method : 0.014533519744873047 time for calcul the mask position with numpy : 0.027842998504638672 nb_pixel_total : 114615 time to create 1 rle with old method : 0.12198233604431152 time for calcul the mask position with numpy : 0.028116703033447266 nb_pixel_total : 21703 time to create 1 rle with old method : 0.024227142333984375 time for calcul the mask position with numpy : 0.02798914909362793 nb_pixel_total : 21112 time to create 1 rle with old method : 0.0230100154876709 time for calcul the mask position with numpy : 0.028192758560180664 nb_pixel_total : 1729 time to create 1 rle with old method : 0.0020945072174072266 time for calcul the mask position with numpy : 0.028992652893066406 nb_pixel_total : 40606 time to create 1 rle with old method : 0.045079708099365234 time for calcul the mask position with numpy : 0.028378725051879883 nb_pixel_total : 64112 time to create 1 rle with old method : 0.07002401351928711 time for calcul the mask position with numpy : 0.027507781982421875 nb_pixel_total : 41768 time to create 1 rle with old method : 0.043624162673950195 time for calcul the mask position with numpy : 0.027713537216186523 nb_pixel_total : 13114 time to create 1 rle with old method : 0.01393890380859375 time for calcul the mask position with numpy : 0.026834964752197266 nb_pixel_total : 43097 time to create 1 rle with old method : 0.04548525810241699 time for calcul the mask position with numpy : 0.0273895263671875 nb_pixel_total : 13331 time to create 1 rle with old method : 0.014089584350585938 time for calcul the mask position with numpy : 0.02681589126586914 nb_pixel_total : 28931 time to create 1 rle with old method : 0.03085470199584961 time for calcul the mask position with numpy : 0.02750706672668457 nb_pixel_total : 6682 time to create 1 rle with old method : 0.007040262222290039 time for calcul the mask position with numpy : 0.02738642692565918 nb_pixel_total : 42176 time to create 1 rle with old method : 0.043703317642211914 time for calcul the mask position with numpy : 0.026376724243164062 nb_pixel_total : 39917 time to create 1 rle with old method : 0.04158735275268555 time for calcul the mask position with numpy : 0.027426958084106445 nb_pixel_total : 17975 time to create 1 rle with old method : 0.019627809524536133 time for calcul the mask position with numpy : 0.028127670288085938 nb_pixel_total : 42568 time to create 1 rle with old method : 0.045906782150268555 time for calcul the mask position with numpy : 0.028574466705322266 nb_pixel_total : 7687 time to create 1 rle with old method : 0.008565902709960938 time for calcul the mask position with numpy : 0.029009342193603516 nb_pixel_total : 136232 time to create 1 rle with old method : 0.14909625053405762 create new chi : 4.7416791915893555 time to delete rle : 0.0031585693359375 batch 1 Loaded 87 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21785 TO DO : save crop sub photo not yet done ! save time : 1.2825803756713867 nb_obj : 25 nb_hashtags : 4 time to prepare the origin masks : 8.689306020736694 time for calcul the mask position with numpy : 0.688122034072876 nb_pixel_total : 6011271 time to create 1 rle with new method : 0.7308287620544434 time for calcul the mask position with numpy : 0.02576899528503418 nb_pixel_total : 1516 time to create 1 rle with old method : 0.0019447803497314453 time for calcul the mask position with numpy : 0.021962404251098633 nb_pixel_total : 31184 time to create 1 rle with old method : 0.049918413162231445 time for calcul the mask position with numpy : 0.02258753776550293 nb_pixel_total : 14247 time to create 1 rle with old method : 0.018387794494628906 time for calcul the mask position with numpy : 0.02071380615234375 nb_pixel_total : 44127 time to create 1 rle with old method : 0.047881364822387695 time for calcul the mask position with numpy : 0.021667957305908203 nb_pixel_total : 20040 time to create 1 rle with old method : 0.02263665199279785 time for calcul the mask position with numpy : 0.023077964782714844 nb_pixel_total : 22956 time to create 1 rle with old method : 0.025823354721069336 time for calcul the mask position with numpy : 0.022003650665283203 nb_pixel_total : 40432 time to create 1 rle with old method : 0.04391336441040039 time for calcul the mask position with numpy : 0.0215756893157959 nb_pixel_total : 21516 time to create 1 rle with old method : 0.023390531539916992 time for calcul the mask position with numpy : 0.02126622200012207 nb_pixel_total : 25338 time to create 1 rle with old method : 0.028116464614868164 time for calcul the mask position with numpy : 0.021305322647094727 nb_pixel_total : 17696 time to create 1 rle with old method : 0.019597768783569336 time for calcul the mask position with numpy : 0.021288394927978516 nb_pixel_total : 11675 time to create 1 rle with old method : 0.013033866882324219 time for calcul the mask position with numpy : 0.02195262908935547 nb_pixel_total : 13321 time to create 1 rle with old method : 0.014978885650634766 time for calcul the mask position with numpy : 0.021172285079956055 nb_pixel_total : 10529 time to create 1 rle with old method : 0.011336565017700195 time for calcul the mask position with numpy : 0.02222752571105957 nb_pixel_total : 52523 time to create 1 rle with old method : 0.05750465393066406 time for calcul the mask position with numpy : 0.022650718688964844 nb_pixel_total : 22754 time to create 1 rle with old method : 0.0321507453918457 time for calcul the mask position with numpy : 0.023070096969604492 nb_pixel_total : 16878 time to create 1 rle with old method : 0.018939971923828125 time for calcul the mask position with numpy : 0.021153926849365234 nb_pixel_total : 24750 time to create 1 rle with old method : 0.027623414993286133 time for calcul the mask position with numpy : 0.022748470306396484 nb_pixel_total : 10347 time to create 1 rle with old method : 0.011651039123535156 time for calcul the mask position with numpy : 0.02242875099182129 nb_pixel_total : 202325 time to create 1 rle with new method : 0.37221312522888184 time for calcul the mask position with numpy : 0.025856733322143555 nb_pixel_total : 134725 time to create 1 rle with old method : 0.15150165557861328 time for calcul the mask position with numpy : 0.021892070770263672 nb_pixel_total : 27933 time to create 1 rle with old method : 0.030992984771728516 time for calcul the mask position with numpy : 0.023642778396606445 nb_pixel_total : 33843 time to create 1 rle with old method : 0.03699660301208496 time for calcul the mask position with numpy : 0.022510766983032227 nb_pixel_total : 198265 time to create 1 rle with new method : 0.8237764835357666 time for calcul the mask position with numpy : 0.023432493209838867 nb_pixel_total : 18176 time to create 1 rle with old method : 0.020467281341552734 time for calcul the mask position with numpy : 0.023437976837158203 nb_pixel_total : 21873 time to create 1 rle with old method : 0.02454972267150879 create new chi : 3.9923410415649414 time to delete rle : 0.002295970916748047 batch 1 Loaded 51 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++Number RLEs to save : 14546 TO DO : save crop sub photo not yet done ! save time : 0.8984582424163818 nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 8.220001935958862 time for calcul the mask position with numpy : 0.21466636657714844 nb_pixel_total : 5489057 time to create 1 rle with new method : 1.3492159843444824 time for calcul the mask position with numpy : 0.01985311508178711 nb_pixel_total : 4992 time to create 1 rle with old method : 0.0052297115325927734 time for calcul the mask position with numpy : 0.027980566024780273 nb_pixel_total : 235963 time to create 1 rle with new method : 0.6369307041168213 time for calcul the mask position with numpy : 0.03487801551818848 nb_pixel_total : 103232 time to create 1 rle with old method : 0.11305427551269531 time for calcul the mask position with numpy : 0.03729867935180664 nb_pixel_total : 80093 time to create 1 rle with old method : 0.08714747428894043 time for calcul the mask position with numpy : 0.0254058837890625 nb_pixel_total : 102242 time to create 1 rle with old method : 0.10947513580322266 time for calcul the mask position with numpy : 0.021062374114990234 nb_pixel_total : 23903 time to create 1 rle with old method : 0.026532411575317383 time for calcul the mask position with numpy : 0.02132868766784668 nb_pixel_total : 10740 time to create 1 rle with old method : 0.01186370849609375 time for calcul the mask position with numpy : 0.022051334381103516 nb_pixel_total : 92323 time to create 1 rle with old method : 0.10065150260925293 time for calcul the mask position with numpy : 0.02236008644104004 nb_pixel_total : 115942 time to create 1 rle with old method : 0.12745165824890137 time for calcul the mask position with numpy : 0.021073341369628906 nb_pixel_total : 19420 time to create 1 rle with old method : 0.020475149154663086 time for calcul the mask position with numpy : 0.020326852798461914 nb_pixel_total : 35945 time to create 1 rle with old method : 0.03987836837768555 time for calcul the mask position with numpy : 0.02063918113708496 nb_pixel_total : 31561 time to create 1 rle with old method : 0.03407907485961914 time for calcul the mask position with numpy : 0.021200180053710938 nb_pixel_total : 56326 time to create 1 rle with old method : 0.07525229454040527 time for calcul the mask position with numpy : 0.02492523193359375 nb_pixel_total : 121221 time to create 1 rle with old method : 0.13288474082946777 time for calcul the mask position with numpy : 0.020806312561035156 nb_pixel_total : 15726 time to create 1 rle with old method : 0.016314029693603516 time for calcul the mask position with numpy : 0.020632266998291016 nb_pixel_total : 21095 time to create 1 rle with old method : 0.02396416664123535 time for calcul the mask position with numpy : 0.021774768829345703 nb_pixel_total : 76057 time to create 1 rle with old method : 0.08305239677429199 time for calcul the mask position with numpy : 0.02156853675842285 nb_pixel_total : 57771 time to create 1 rle with old method : 0.06441378593444824 time for calcul the mask position with numpy : 0.025170564651489258 nb_pixel_total : 37902 time to create 1 rle with old method : 0.061768293380737305 time for calcul the mask position with numpy : 0.02342963218688965 nb_pixel_total : 18277 time to create 1 rle with old method : 0.022039175033569336 time for calcul the mask position with numpy : 0.022383928298950195 nb_pixel_total : 43453 time to create 1 rle with old method : 0.04872417449951172 time for calcul the mask position with numpy : 0.02364659309387207 nb_pixel_total : 7689 time to create 1 rle with old method : 0.008570671081542969 time for calcul the mask position with numpy : 0.025026798248291016 nb_pixel_total : 249310 time to create 1 rle with new method : 0.3652787208557129 create new chi : 4.405207395553589 time to delete rle : 0.0028378963470458984 batch 1 Loaded 47 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++Number RLEs to save : 18271 TO DO : save crop sub photo not yet done ! save time : 1.0644636154174805 nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 8.58474349975586 time for calcul the mask position with numpy : 0.3085448741912842 nb_pixel_total : 5139240 time to create 1 rle with new method : 0.888463020324707 time for calcul the mask position with numpy : 0.02063131332397461 nb_pixel_total : 19738 time to create 1 rle with old method : 0.023425817489624023 time for calcul the mask position with numpy : 0.020976781845092773 nb_pixel_total : 8574 time to create 1 rle with old method : 0.009665966033935547 time for calcul the mask position with numpy : 0.021959543228149414 nb_pixel_total : 2511 time to create 1 rle with old method : 0.0029745101928710938 time for calcul the mask position with numpy : 0.021731138229370117 nb_pixel_total : 13193 time to create 1 rle with old method : 0.015196084976196289 time for calcul the mask position with numpy : 0.024041175842285156 nb_pixel_total : 106503 time to create 1 rle with old method : 0.14026427268981934 time for calcul the mask position with numpy : 0.021030426025390625 nb_pixel_total : 146 time to create 1 rle with old method : 0.00019121170043945312 time for calcul the mask position with numpy : 0.021162748336791992 nb_pixel_total : 46083 time to create 1 rle with old method : 0.048305511474609375 time for calcul the mask position with numpy : 0.021176815032958984 nb_pixel_total : 35858 time to create 1 rle with old method : 0.03776884078979492 time for calcul the mask position with numpy : 0.020236730575561523 nb_pixel_total : 20168 time to create 1 rle with old method : 0.021876811981201172 time for calcul the mask position with numpy : 0.021957874298095703 nb_pixel_total : 4246 time to create 1 rle with old method : 0.004642486572265625 time for calcul the mask position with numpy : 0.021228313446044922 nb_pixel_total : 24855 time to create 1 rle with old method : 0.027102231979370117 time for calcul the mask position with numpy : 0.021548748016357422 nb_pixel_total : 74452 time to create 1 rle with old method : 0.09328341484069824 time for calcul the mask position with numpy : 0.020435810089111328 nb_pixel_total : 47724 time to create 1 rle with old method : 0.052506208419799805 time for calcul the mask position with numpy : 0.023696422576904297 nb_pixel_total : 79944 time to create 1 rle with old method : 0.09457802772521973 time for calcul the mask position with numpy : 0.022661924362182617 nb_pixel_total : 53570 time to create 1 rle with old method : 0.058828115463256836 time for calcul the mask position with numpy : 0.025871992111206055 nb_pixel_total : 767830 time to create 1 rle with new method : 0.6092700958251953 time for calcul the mask position with numpy : 0.023133039474487305 nb_pixel_total : 144967 time to create 1 rle with old method : 0.1603386402130127 time for calcul the mask position with numpy : 0.02185535430908203 nb_pixel_total : 17343 time to create 1 rle with old method : 0.01901078224182129 time for calcul the mask position with numpy : 0.022269248962402344 nb_pixel_total : 15256 time to create 1 rle with old method : 0.017049789428710938 time for calcul the mask position with numpy : 0.021497488021850586 nb_pixel_total : 13408 time to create 1 rle with old method : 0.014633417129516602 time for calcul the mask position with numpy : 0.021227121353149414 nb_pixel_total : 26391 time to create 1 rle with old method : 0.02977275848388672 time for calcul the mask position with numpy : 0.022397994995117188 nb_pixel_total : 237300 time to create 1 rle with new method : 0.6637787818908691 time for calcul the mask position with numpy : 0.022642850875854492 nb_pixel_total : 150940 time to create 1 rle with new method : 0.6442606449127197 create new chi : 4.5953545570373535 time to delete rle : 0.003743410110473633 batch 1 Loaded 48 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16823 TO DO : save crop sub photo not yet done ! save time : 1.0174260139465332 nb_obj : 12 nb_hashtags : 2 time to prepare the origin masks : 3.925020456314087 time for calcul the mask position with numpy : 0.5585989952087402 nb_pixel_total : 6320857 time to create 1 rle with new method : 0.49218201637268066 time for calcul the mask position with numpy : 0.02269148826599121 nb_pixel_total : 197910 time to create 1 rle with new method : 0.7026016712188721 time for calcul the mask position with numpy : 0.019964218139648438 nb_pixel_total : 4249 time to create 1 rle with old method : 0.004462003707885742 time for calcul the mask position with numpy : 0.02058696746826172 nb_pixel_total : 26821 time to create 1 rle with old method : 0.028898239135742188 time for calcul the mask position with numpy : 0.020214080810546875 nb_pixel_total : 4943 time to create 1 rle with old method : 0.005202054977416992 time for calcul the mask position with numpy : 0.02110147476196289 nb_pixel_total : 17450 time to create 1 rle with old method : 0.0186920166015625 time for calcul the mask position with numpy : 0.022446870803833008 nb_pixel_total : 331825 time to create 1 rle with new method : 0.5050251483917236 time for calcul the mask position with numpy : 0.020607948303222656 nb_pixel_total : 49666 time to create 1 rle with old method : 0.053586483001708984 time for calcul the mask position with numpy : 0.020036697387695312 nb_pixel_total : 10932 time to create 1 rle with old method : 0.012232780456542969 time for calcul the mask position with numpy : 0.020383119583129883 nb_pixel_total : 26976 time to create 1 rle with old method : 0.028861522674560547 time for calcul the mask position with numpy : 0.02177715301513672 nb_pixel_total : 21713 time to create 1 rle with old method : 0.025058984756469727 time for calcul the mask position with numpy : 0.021952390670776367 nb_pixel_total : 14948 time to create 1 rle with old method : 0.016481637954711914 time for calcul the mask position with numpy : 0.02141404151916504 nb_pixel_total : 21950 time to create 1 rle with old method : 0.02452564239501953 create new chi : 2.81142258644104 time to delete rle : 0.0013222694396972656 batch 1 Loaded 25 chid ids of type : 3594 +++++++++++++++Number RLEs to save : 8828 TO DO : save crop sub photo not yet done ! save time : 0.8229820728302002 nb_obj : 10 nb_hashtags : 3 time to prepare the origin masks : 3.8247339725494385 time for calcul the mask position with numpy : 0.29926466941833496 nb_pixel_total : 5499730 time to create 1 rle with new method : 0.8980605602264404 time for calcul the mask position with numpy : 0.020929813385009766 nb_pixel_total : 38149 time to create 1 rle with old method : 0.041619300842285156 time for calcul the mask position with numpy : 0.02326345443725586 nb_pixel_total : 298286 time to create 1 rle with new method : 0.6314010620117188 time for calcul the mask position with numpy : 0.02176356315612793 nb_pixel_total : 9874 time to create 1 rle with old method : 0.010750532150268555 time for calcul the mask position with numpy : 0.02069878578186035 nb_pixel_total : 3643 time to create 1 rle with old method : 0.004126310348510742 time for calcul the mask position with numpy : 0.02175760269165039 nb_pixel_total : 22732 time to create 1 rle with old method : 0.026311635971069336 time for calcul the mask position with numpy : 0.020901918411254883 nb_pixel_total : 19180 time to create 1 rle with old method : 0.02099776268005371 time for calcul the mask position with numpy : 0.032935380935668945 nb_pixel_total : 1096823 time to create 1 rle with new method : 0.6509506702423096 time for calcul the mask position with numpy : 0.02062535285949707 nb_pixel_total : 19515 time to create 1 rle with old method : 0.022000789642333984 time for calcul the mask position with numpy : 0.02073502540588379 nb_pixel_total : 22582 time to create 1 rle with old method : 0.025270938873291016 time for calcul the mask position with numpy : 0.021734952926635742 nb_pixel_total : 19726 time to create 1 rle with old method : 0.021804332733154297 create new chi : 2.9560606479644775 time to delete rle : 0.0011799335479736328 batch 1 Loaded 21 chid ids of type : 3594 +++++++++++Number RLEs to save : 8593 TO DO : save crop sub photo not yet done ! save time : 1.0137529373168945 nb_obj : 51 nb_hashtags : 4 time to prepare the origin masks : 4.695476293563843 time for calcul the mask position with numpy : 0.34992146492004395 nb_pixel_total : 4631071 time to create 1 rle with new method : 0.7885229587554932 time for calcul the mask position with numpy : 0.03796792030334473 nb_pixel_total : 17600 time to create 1 rle with old method : 0.020457029342651367 time for calcul the mask position with numpy : 0.030312538146972656 nb_pixel_total : 122121 time to create 1 rle with old method : 0.14057636260986328 time for calcul the mask position with numpy : 0.029605388641357422 nb_pixel_total : 10488 time to create 1 rle with old method : 0.011384010314941406 time for calcul the mask position with numpy : 0.02859783172607422 nb_pixel_total : 15243 time to create 1 rle with old method : 0.016966581344604492 time for calcul the mask position with numpy : 0.031081199645996094 nb_pixel_total : 16228 time to create 1 rle with old method : 0.0204465389251709 time for calcul the mask position with numpy : 0.030002355575561523 nb_pixel_total : 4666 time to create 1 rle with old method : 0.0051038265228271484 time for calcul the mask position with numpy : 0.028296709060668945 nb_pixel_total : 17287 time to create 1 rle with old method : 0.01905512809753418 time for calcul the mask position with numpy : 0.027530193328857422 nb_pixel_total : 33918 time to create 1 rle with old method : 0.048187255859375 time for calcul the mask position with numpy : 0.03336358070373535 nb_pixel_total : 76858 time to create 1 rle with old method : 0.08471465110778809 time for calcul the mask position with numpy : 0.0300137996673584 nb_pixel_total : 37884 time to create 1 rle with old method : 0.046648263931274414 time for calcul the mask position with numpy : 0.028272628784179688 nb_pixel_total : 28823 time to create 1 rle with old method : 0.031087160110473633 time for calcul the mask position with numpy : 0.030924558639526367 nb_pixel_total : 109404 time to create 1 rle with old method : 0.1328723430633545 time for calcul the mask position with numpy : 0.030031442642211914 nb_pixel_total : 15008 time to create 1 rle with old method : 0.01687145233154297 time for calcul the mask position with numpy : 0.028572559356689453 nb_pixel_total : 14382 time to create 1 rle with old method : 0.016234397888183594 time for calcul the mask position with numpy : 0.030689239501953125 nb_pixel_total : 115517 time to create 1 rle with old method : 0.13890528678894043 time for calcul the mask position with numpy : 0.028723478317260742 nb_pixel_total : 22802 time to create 1 rle with old method : 0.02550363540649414 time for calcul the mask position with numpy : 0.02956414222717285 nb_pixel_total : 47110 time to create 1 rle with old method : 0.053106069564819336 time for calcul the mask position with numpy : 0.029465913772583008 nb_pixel_total : 46673 time to create 1 rle with old method : 0.05231618881225586 time for calcul the mask position with numpy : 0.0284121036529541 nb_pixel_total : 27993 time to create 1 rle with old method : 0.03135371208190918 time for calcul the mask position with numpy : 0.030692338943481445 nb_pixel_total : 155460 time to create 1 rle with new method : 0.324368953704834 time for calcul the mask position with numpy : 0.027521848678588867 nb_pixel_total : 24538 time to create 1 rle with old method : 0.025958538055419922 time for calcul the mask position with numpy : 0.027212142944335938 nb_pixel_total : 5400 time to create 1 rle with old method : 0.006265163421630859 time for calcul the mask position with numpy : 0.028163433074951172 nb_pixel_total : 43060 time to create 1 rle with old method : 0.04658651351928711 time for calcul the mask position with numpy : 0.02783942222595215 nb_pixel_total : 46073 time to create 1 rle with old method : 0.0506291389465332 time for calcul the mask position with numpy : 0.02834343910217285 nb_pixel_total : 15416 time to create 1 rle with old method : 0.017354726791381836 time for calcul the mask position with numpy : 0.030864238739013672 nb_pixel_total : 28501 time to create 1 rle with old method : 0.0457158088684082 time for calcul the mask position with numpy : 0.03339076042175293 nb_pixel_total : 90280 time to create 1 rle with old method : 0.09930062294006348 time for calcul the mask position with numpy : 0.02983546257019043 nb_pixel_total : 90108 time to create 1 rle with old method : 0.0983731746673584 time for calcul the mask position with numpy : 0.029556751251220703 nb_pixel_total : 37843 time to create 1 rle with old method : 0.04137563705444336 time for calcul the mask position with numpy : 0.028946638107299805 nb_pixel_total : 64912 time to create 1 rle with old method : 0.07132577896118164 time for calcul the mask position with numpy : 0.028641223907470703 nb_pixel_total : 29491 time to create 1 rle with old method : 0.032112836837768555 time for calcul the mask position with numpy : 0.02833271026611328 nb_pixel_total : 18838 time to create 1 rle with old method : 0.021051883697509766 time for calcul the mask position with numpy : 0.028406381607055664 nb_pixel_total : 12832 time to create 1 rle with old method : 0.014504671096801758 time for calcul the mask position with numpy : 0.02893829345703125 nb_pixel_total : 21218 time to create 1 rle with old method : 0.024933576583862305 time for calcul the mask position with numpy : 0.02896881103515625 nb_pixel_total : 79364 time to create 1 rle with old method : 0.08937382698059082 time for calcul the mask position with numpy : 0.028558015823364258 nb_pixel_total : 28211 time to create 1 rle with old method : 0.031546831130981445 time for calcul the mask position with numpy : 0.02958083152770996 nb_pixel_total : 37681 time to create 1 rle with old method : 0.04342794418334961 time for calcul the mask position with numpy : 0.028509140014648438 nb_pixel_total : 99964 time to create 1 rle with old method : 0.11233019828796387 time for calcul the mask position with numpy : 0.030300617218017578 nb_pixel_total : 3374 time to create 1 rle with old method : 0.003904581069946289 time for calcul the mask position with numpy : 0.028290271759033203 nb_pixel_total : 22760 time to create 1 rle with old method : 0.024412155151367188 time for calcul the mask position with numpy : 0.028136491775512695 nb_pixel_total : 10842 time to create 1 rle with old method : 0.011846065521240234 time for calcul the mask position with numpy : 0.028554439544677734 nb_pixel_total : 35234 time to create 1 rle with old method : 0.038777828216552734 time for calcul the mask position with numpy : 0.028954505920410156 nb_pixel_total : 10814 time to create 1 rle with old method : 0.012265443801879883 time for calcul the mask position with numpy : 0.028942584991455078 nb_pixel_total : 14942 time to create 1 rle with old method : 0.01696014404296875 time for calcul the mask position with numpy : 0.02871108055114746 nb_pixel_total : 36200 time to create 1 rle with old method : 0.03986072540283203 time for calcul the mask position with numpy : 0.028268098831176758 nb_pixel_total : 19037 time to create 1 rle with old method : 0.02779984474182129 time for calcul the mask position with numpy : 0.03473806381225586 nb_pixel_total : 208024 time to create 1 rle with new method : 0.3768444061279297 time for calcul the mask position with numpy : 0.02925896644592285 nb_pixel_total : 73344 time to create 1 rle with old method : 0.08031797409057617 time for calcul the mask position with numpy : 0.029105424880981445 nb_pixel_total : 124351 time to create 1 rle with old method : 0.1318502426147461 time for calcul the mask position with numpy : 0.028313875198364258 nb_pixel_total : 141736 time to create 1 rle with old method : 0.15660595893859863 time for calcul the mask position with numpy : 0.027520418167114258 nb_pixel_total : 9316 time to create 1 rle with old method : 0.009624004364013672 create new chi : 5.760090589523315 time to delete rle : 0.006999015808105469 batch 1 Loaded 103 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29380 TO DO : save crop sub photo not yet done ! save time : 1.8099768161773682 nb_obj : 42 nb_hashtags : 5 time to prepare the origin masks : 4.227996587753296 time for calcul the mask position with numpy : 0.38579392433166504 nb_pixel_total : 4977474 time to create 1 rle with new method : 1.2931852340698242 time for calcul the mask position with numpy : 0.028077125549316406 nb_pixel_total : 10907 time to create 1 rle with old method : 0.011946439743041992 time for calcul the mask position with numpy : 0.02701258659362793 nb_pixel_total : 32083 time to create 1 rle with old method : 0.03520703315734863 time for calcul the mask position with numpy : 0.02845597267150879 nb_pixel_total : 33318 time to create 1 rle with old method : 0.036659955978393555 time for calcul the mask position with numpy : 0.02823638916015625 nb_pixel_total : 77012 time to create 1 rle with old method : 0.08357763290405273 time for calcul the mask position with numpy : 0.029042482376098633 nb_pixel_total : 63336 time to create 1 rle with old method : 0.07008671760559082 time for calcul the mask position with numpy : 0.03182864189147949 nb_pixel_total : 506105 time to create 1 rle with new method : 0.6105096340179443 time for calcul the mask position with numpy : 0.0285184383392334 nb_pixel_total : 9253 time to create 1 rle with old method : 0.010267019271850586 time for calcul the mask position with numpy : 0.028818607330322266 nb_pixel_total : 17075 time to create 1 rle with old method : 0.019083499908447266 time for calcul the mask position with numpy : 0.02857518196105957 nb_pixel_total : 43272 time to create 1 rle with old method : 0.047866106033325195 time for calcul the mask position with numpy : 0.028573036193847656 nb_pixel_total : 64178 time to create 1 rle with old method : 0.07203149795532227 time for calcul the mask position with numpy : 0.027914047241210938 nb_pixel_total : 31516 time to create 1 rle with old method : 0.034360647201538086 time for calcul the mask position with numpy : 0.02751946449279785 nb_pixel_total : 26654 time to create 1 rle with old method : 0.02963852882385254 time for calcul the mask position with numpy : 0.0278780460357666 nb_pixel_total : 22203 time to create 1 rle with old method : 0.02343273162841797 time for calcul the mask position with numpy : 0.031630754470825195 nb_pixel_total : 46739 time to create 1 rle with old method : 0.06710934638977051 time for calcul the mask position with numpy : 0.027638912200927734 nb_pixel_total : 9958 time to create 1 rle with old method : 0.010678529739379883 time for calcul the mask position with numpy : 0.027827024459838867 nb_pixel_total : 20609 time to create 1 rle with old method : 0.0227358341217041 time for calcul the mask position with numpy : 0.028478622436523438 nb_pixel_total : 35478 time to create 1 rle with old method : 0.038651227951049805 time for calcul the mask position with numpy : 0.028015613555908203 nb_pixel_total : 58866 time to create 1 rle with old method : 0.06286191940307617 time for calcul the mask position with numpy : 0.027988910675048828 nb_pixel_total : 10235 time to create 1 rle with old method : 0.011626720428466797 time for calcul the mask position with numpy : 0.027869462966918945 nb_pixel_total : 6523 time to create 1 rle with old method : 0.0072629451751708984 time for calcul the mask position with numpy : 0.028223514556884766 nb_pixel_total : 37170 time to create 1 rle with old method : 0.0404057502746582 time for calcul the mask position with numpy : 0.028413772583007812 nb_pixel_total : 116556 time to create 1 rle with old method : 0.12508440017700195 time for calcul the mask position with numpy : 0.028200149536132812 nb_pixel_total : 12571 time to create 1 rle with old method : 0.01374053955078125 time for calcul the mask position with numpy : 0.02747821807861328 nb_pixel_total : 31258 time to create 1 rle with old method : 0.03334832191467285 time for calcul the mask position with numpy : 0.028337955474853516 nb_pixel_total : 84462 time to create 1 rle with old method : 0.08857393264770508 time for calcul the mask position with numpy : 0.028280019760131836 nb_pixel_total : 44461 time to create 1 rle with old method : 0.04786491394042969 time for calcul the mask position with numpy : 0.0284268856048584 nb_pixel_total : 19962 time to create 1 rle with old method : 0.022756338119506836 time for calcul the mask position with numpy : 0.029447078704833984 nb_pixel_total : 64554 time to create 1 rle with old method : 0.07175111770629883 time for calcul the mask position with numpy : 0.02917933464050293 nb_pixel_total : 37263 time to create 1 rle with old method : 0.04247331619262695 time for calcul the mask position with numpy : 0.02930140495300293 nb_pixel_total : 65465 time to create 1 rle with old method : 0.07933902740478516 time for calcul the mask position with numpy : 0.02939629554748535 nb_pixel_total : 40970 time to create 1 rle with old method : 0.04612994194030762 time for calcul the mask position with numpy : 0.029002666473388672 nb_pixel_total : 37063 time to create 1 rle with old method : 0.04162907600402832 time for calcul the mask position with numpy : 0.02886795997619629 nb_pixel_total : 40491 time to create 1 rle with old method : 0.044423818588256836 time for calcul the mask position with numpy : 0.028226613998413086 nb_pixel_total : 24473 time to create 1 rle with old method : 0.026831626892089844 time for calcul the mask position with numpy : 0.029918909072875977 nb_pixel_total : 40520 time to create 1 rle with old method : 0.044861555099487305 time for calcul the mask position with numpy : 0.028722047805786133 nb_pixel_total : 40015 time to create 1 rle with old method : 0.04418659210205078 time for calcul the mask position with numpy : 0.028176307678222656 nb_pixel_total : 33931 time to create 1 rle with old method : 0.036855220794677734 time for calcul the mask position with numpy : 0.028069257736206055 nb_pixel_total : 16555 time to create 1 rle with old method : 0.018032550811767578 time for calcul the mask position with numpy : 0.028289318084716797 nb_pixel_total : 86425 time to create 1 rle with old method : 0.09509491920471191 time for calcul the mask position with numpy : 0.03280210494995117 nb_pixel_total : 46343 time to create 1 rle with old method : 0.05205535888671875 time for calcul the mask position with numpy : 0.05171322822570801 nb_pixel_total : 13392 time to create 1 rle with old method : 0.029031038284301758 time for calcul the mask position with numpy : 0.029780149459838867 nb_pixel_total : 13546 time to create 1 rle with old method : 0.015891075134277344 create new chi : 5.332834720611572 time to delete rle : 0.004516124725341797 batch 1 Loaded 85 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26924 TO DO : save crop sub photo not yet done ! save time : 1.7597665786743164 nb_obj : 24 nb_hashtags : 3 time to prepare the origin masks : 8.005664587020874 time for calcul the mask position with numpy : 0.7130694389343262 nb_pixel_total : 6322323 time to create 1 rle with new method : 0.7072567939758301 time for calcul the mask position with numpy : 0.03999781608581543 nb_pixel_total : 14486 time to create 1 rle with old method : 0.0233762264251709 time for calcul the mask position with numpy : 0.03625845909118652 nb_pixel_total : 16741 time to create 1 rle with old method : 0.01854562759399414 time for calcul the mask position with numpy : 0.033847808837890625 nb_pixel_total : 31398 time to create 1 rle with old method : 0.034718990325927734 time for calcul the mask position with numpy : 0.03389859199523926 nb_pixel_total : 11213 time to create 1 rle with old method : 0.01224517822265625 time for calcul the mask position with numpy : 0.03801751136779785 nb_pixel_total : 68653 time to create 1 rle with old method : 0.09167861938476562 time for calcul the mask position with numpy : 0.03435969352722168 nb_pixel_total : 17345 time to create 1 rle with old method : 0.019559144973754883 time for calcul the mask position with numpy : 0.03552103042602539 nb_pixel_total : 162583 time to create 1 rle with new method : 0.6437044143676758 time for calcul the mask position with numpy : 0.03413987159729004 nb_pixel_total : 8517 time to create 1 rle with old method : 0.009390592575073242 time for calcul the mask position with numpy : 0.033255577087402344 nb_pixel_total : 21582 time to create 1 rle with old method : 0.02553725242614746 time for calcul the mask position with numpy : 0.03487515449523926 nb_pixel_total : 9644 time to create 1 rle with old method : 0.010893106460571289 time for calcul the mask position with numpy : 0.03505444526672363 nb_pixel_total : 12095 time to create 1 rle with old method : 0.013223648071289062 time for calcul the mask position with numpy : 0.02602243423461914 nb_pixel_total : 9967 time to create 1 rle with old method : 0.011228561401367188 time for calcul the mask position with numpy : 0.024405956268310547 nb_pixel_total : 41090 time to create 1 rle with old method : 0.045500993728637695 time for calcul the mask position with numpy : 0.021723508834838867 nb_pixel_total : 11523 time to create 1 rle with old method : 0.012675762176513672 time for calcul the mask position with numpy : 0.021518707275390625 nb_pixel_total : 12820 time to create 1 rle with old method : 0.014064311981201172 time for calcul the mask position with numpy : 0.022295713424682617 nb_pixel_total : 66002 time to create 1 rle with old method : 0.07250666618347168 time for calcul the mask position with numpy : 0.02613687515258789 nb_pixel_total : 42210 time to create 1 rle with old method : 0.04480576515197754 time for calcul the mask position with numpy : 0.020493507385253906 nb_pixel_total : 42238 time to create 1 rle with old method : 0.04384493827819824 time for calcul the mask position with numpy : 0.021311044692993164 nb_pixel_total : 35369 time to create 1 rle with old method : 0.03906726837158203 time for calcul the mask position with numpy : 0.03148961067199707 nb_pixel_total : 3046 time to create 1 rle with old method : 0.0035238265991210938 time for calcul the mask position with numpy : 0.03278183937072754 nb_pixel_total : 5115 time to create 1 rle with old method : 0.005469560623168945 time for calcul the mask position with numpy : 0.03203701972961426 nb_pixel_total : 23957 time to create 1 rle with old method : 0.024671077728271484 time for calcul the mask position with numpy : 0.03329825401306152 nb_pixel_total : 27004 time to create 1 rle with old method : 0.029675960540771484 time for calcul the mask position with numpy : 0.032763004302978516 nb_pixel_total : 33319 time to create 1 rle with old method : 0.036183834075927734 create new chi : 3.4984803199768066 time to delete rle : 0.0017306804656982422 batch 1 Loaded 49 chid ids of type : 3594 +++++++++++++++++++++++++++++++++Number RLEs to save : 11772 TO DO : save crop sub photo not yet done ! save time : 0.7645359039306641 nb_obj : 37 nb_hashtags : 4 time to prepare the origin masks : 4.105749130249023 time for calcul the mask position with numpy : 0.7080099582672119 nb_pixel_total : 5576286 time to create 1 rle with new method : 0.5401871204376221 time for calcul the mask position with numpy : 0.03087162971496582 nb_pixel_total : 86668 time to create 1 rle with old method : 0.09470176696777344 time for calcul the mask position with numpy : 0.027869462966918945 nb_pixel_total : 16412 time to create 1 rle with old method : 0.017596721649169922 time for calcul the mask position with numpy : 0.028768539428710938 nb_pixel_total : 196430 time to create 1 rle with new method : 0.5754034519195557 time for calcul the mask position with numpy : 0.032105445861816406 nb_pixel_total : 194975 time to create 1 rle with new method : 0.3318476676940918 time for calcul the mask position with numpy : 0.03275132179260254 nb_pixel_total : 14904 time to create 1 rle with old method : 0.024278640747070312 time for calcul the mask position with numpy : 0.029009103775024414 nb_pixel_total : 12113 time to create 1 rle with old method : 0.01317596435546875 time for calcul the mask position with numpy : 0.027301549911499023 nb_pixel_total : 16507 time to create 1 rle with old method : 0.017711877822875977 time for calcul the mask position with numpy : 0.027584552764892578 nb_pixel_total : 9829 time to create 1 rle with old method : 0.010670900344848633 time for calcul the mask position with numpy : 0.027003049850463867 nb_pixel_total : 19563 time to create 1 rle with old method : 0.020682334899902344 time for calcul the mask position with numpy : 0.027870893478393555 nb_pixel_total : 8308 time to create 1 rle with old method : 0.009001016616821289 time for calcul the mask position with numpy : 0.027777433395385742 nb_pixel_total : 33502 time to create 1 rle with old method : 0.045174598693847656 time for calcul the mask position with numpy : 0.03670334815979004 nb_pixel_total : 52910 time to create 1 rle with old method : 0.05866646766662598 time for calcul the mask position with numpy : 0.028806447982788086 nb_pixel_total : 62152 time to create 1 rle with old method : 0.06744098663330078 time for calcul the mask position with numpy : 0.027751922607421875 nb_pixel_total : 13472 time to create 1 rle with old method : 0.014764785766601562 time for calcul the mask position with numpy : 0.028153657913208008 nb_pixel_total : 18918 time to create 1 rle with old method : 0.02064990997314453 time for calcul the mask position with numpy : 0.027821779251098633 nb_pixel_total : 17851 time to create 1 rle with old method : 0.019133567810058594 time for calcul the mask position with numpy : 0.027518033981323242 nb_pixel_total : 14182 time to create 1 rle with old method : 0.015258312225341797 time for calcul the mask position with numpy : 0.027439117431640625 nb_pixel_total : 19704 time to create 1 rle with old method : 0.021300315856933594 time for calcul the mask position with numpy : 0.027475357055664062 nb_pixel_total : 48360 time to create 1 rle with old method : 0.05241036415100098 time for calcul the mask position with numpy : 0.02818012237548828 nb_pixel_total : 83294 time to create 1 rle with old method : 0.08961272239685059 time for calcul the mask position with numpy : 0.028462886810302734 nb_pixel_total : 37358 time to create 1 rle with old method : 0.04004812240600586 time for calcul the mask position with numpy : 0.026631593704223633 nb_pixel_total : 34598 time to create 1 rle with old method : 0.03548026084899902 time for calcul the mask position with numpy : 0.026426076889038086 nb_pixel_total : 19471 time to create 1 rle with old method : 0.021116971969604492 time for calcul the mask position with numpy : 0.02842545509338379 nb_pixel_total : 32905 time to create 1 rle with old method : 0.0368502140045166 time for calcul the mask position with numpy : 0.0329592227935791 nb_pixel_total : 43204 time to create 1 rle with old method : 0.04900479316711426 time for calcul the mask position with numpy : 0.02806234359741211 nb_pixel_total : 28105 time to create 1 rle with old method : 0.031830549240112305 time for calcul the mask position with numpy : 0.029767751693725586 nb_pixel_total : 30123 time to create 1 rle with old method : 0.0324406623840332 time for calcul the mask position with numpy : 0.02879047393798828 nb_pixel_total : 36449 time to create 1 rle with old method : 0.040390968322753906 time for calcul the mask position with numpy : 0.028788328170776367 nb_pixel_total : 53275 time to create 1 rle with old method : 0.05881786346435547 time for calcul the mask position with numpy : 0.028558969497680664 nb_pixel_total : 9992 time to create 1 rle with old method : 0.010768890380859375 time for calcul the mask position with numpy : 0.0287020206451416 nb_pixel_total : 54662 time to create 1 rle with old method : 0.05954170227050781 time for calcul the mask position with numpy : 0.028629779815673828 nb_pixel_total : 59156 time to create 1 rle with old method : 0.06324124336242676 time for calcul the mask position with numpy : 0.028005123138427734 nb_pixel_total : 45780 time to create 1 rle with old method : 0.05013680458068848 time for calcul the mask position with numpy : 0.028594970703125 nb_pixel_total : 21804 time to create 1 rle with old method : 0.023827552795410156 time for calcul the mask position with numpy : 0.02837824821472168 nb_pixel_total : 12372 time to create 1 rle with old method : 0.013397216796875 time for calcul the mask position with numpy : 0.028225183486938477 nb_pixel_total : 7944 time to create 1 rle with old method : 0.008823633193969727 time for calcul the mask position with numpy : 0.02799057960510254 nb_pixel_total : 6702 time to create 1 rle with old method : 0.0073320865631103516 create new chi : 4.498520612716675 time to delete rle : 0.003386974334716797 batch 1 Loaded 75 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19982 TO DO : save crop sub photo not yet done ! save time : 1.7692506313323975 map_output_result : {1350453033: (0.0, 'Should be the crop_list due to order', 0), 1350452961: (0.0, 'Should be the crop_list due to order', 0), 1350452938: (0.0, 'Should be the crop_list due to order', 0), 1350452909: (0.0, 'Should be the crop_list due to order', 0), 1350254131: (0.0, 'Should be the crop_list due to order', 0), 1350254126: (0.0, 'Should be the crop_list due to order', 0), 1350254122: (0.0, 'Should be the crop_list due to order', 0), 1350254118: (0.0, 'Should be the crop_list due to order', 0), 1350254115: (0.0, 'Should be the crop_list due to order', 0), 1350254103: (0.0, 'Should be the crop_list due to order', 0), 1350254053: (0.0, 'Should be the crop_list due to order', 0), 1350254048: (0.0, 'Should be the crop_list due to order', 0), 1350254043: (0.0, 'Should be the crop_list due to order', 0), 1350254039: (0.0, 'Should be the crop_list due to order', 0), 1350254036: (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 [1350453033, 1350452961, 1350452938, 1350452909, 1350254131, 1350254126, 1350254122, 1350254118, 1350254115, 1350254103, 1350254053, 1350254048, 1350254043, 1350254039, 1350254036] Looping around the photos to save general results len do output : 15 /1350453033.Didn't retrieve data . /1350452961.Didn't retrieve data . /1350452938.Didn't retrieve data . /1350452909.Didn't retrieve data . /1350254131.Didn't retrieve data . /1350254126.Didn't retrieve data . /1350254122.Didn't retrieve data . /1350254118.Didn't retrieve data . /1350254115.Didn't retrieve data . /1350254103.Didn't retrieve data . /1350254053.Didn't retrieve data . /1350254048.Didn't retrieve data . /1350254043.Didn't retrieve data . /1350254039.Didn't retrieve data . /1350254036.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, '2733641') ('3318', '22153537', '1350453033', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452961', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452938', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452909', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254131', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254126', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254122', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254118', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254115', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254103', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254053', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254048', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254043', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254039', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254036', None, None, None, None, None, '2733641') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 45 time used for this insertion : 0.013101816177368164 save_final save missing photos in datou_result : time spend for datou_step_exec : 163.85983204841614 time spend to save output : 0.01367044448852539 total time spend for step 3 : 163.87350249290466 step4:ventilate_hashtags_in_portfolio Wed Apr 9 11:20:21 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 : 22153537 get user id for portfolio 22153537 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22153537 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_fonce','autre','background','mal_croppe','papier','flou','pehd','metal','carton','environnement','pet_clair')) 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`=22153537 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_fonce','autre','background','mal_croppe','papier','flou','pehd','metal','carton','environnement','pet_clair')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") 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`=22153537 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_fonce','autre','background','mal_croppe','papier','flou','pehd','metal','carton','environnement','pet_clair')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22159204,22159205,22159206,22159207,22159208,22159209,22159210,22159211,22159212,22159213,22159214?tags=pet_fonce,autre,background,mal_croppe,papier,flou,pehd,metal,carton,environnement,pet_clair Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350453033, 1350452961, 1350452938, 1350452909, 1350254131, 1350254126, 1350254122, 1350254118, 1350254115, 1350254103, 1350254053, 1350254048, 1350254043, 1350254039, 1350254036] Looping around the photos to save general results len do output : 1 /22153537. 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, '2733641') ('3318', '22153537', '1350453033', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452961', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452938', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452909', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254131', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254126', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254122', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254118', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254115', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254103', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254053', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254048', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254043', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254039', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254036', None, None, None, None, None, '2733641') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.017700910568237305 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.8424663543701172 time spend to save output : 0.01813793182373047 total time spend for step 4 : 1.8606042861938477 step5:final Wed Apr 9 11:20:23 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 : {1350453033: ('0.21830035100459932',), 1350452961: ('0.21830035100459932',), 1350452938: ('0.21830035100459932',), 1350452909: ('0.21830035100459932',), 1350254131: ('0.21830035100459932',), 1350254126: ('0.21830035100459932',), 1350254122: ('0.21830035100459932',), 1350254118: ('0.21830035100459932',), 1350254115: ('0.21830035100459932',), 1350254103: ('0.21830035100459932',), 1350254053: ('0.21830035100459932',), 1350254048: ('0.21830035100459932',), 1350254043: ('0.21830035100459932',), 1350254039: ('0.21830035100459932',), 1350254036: ('0.21830035100459932',)} new output for save of step final : {1350453033: ('0.21830035100459932',), 1350452961: ('0.21830035100459932',), 1350452938: ('0.21830035100459932',), 1350452909: ('0.21830035100459932',), 1350254131: ('0.21830035100459932',), 1350254126: ('0.21830035100459932',), 1350254122: ('0.21830035100459932',), 1350254118: ('0.21830035100459932',), 1350254115: ('0.21830035100459932',), 1350254103: ('0.21830035100459932',), 1350254053: ('0.21830035100459932',), 1350254048: ('0.21830035100459932',), 1350254043: ('0.21830035100459932',), 1350254039: ('0.21830035100459932',), 1350254036: ('0.21830035100459932',)} [1350453033, 1350452961, 1350452938, 1350452909, 1350254131, 1350254126, 1350254122, 1350254118, 1350254115, 1350254103, 1350254053, 1350254048, 1350254043, 1350254039, 1350254036] Looping around the photos to save general results len do output : 15 /1350453033.Didn't retrieve data . /1350452961.Didn't retrieve data . /1350452938.Didn't retrieve data . /1350452909.Didn't retrieve data . /1350254131.Didn't retrieve data . /1350254126.Didn't retrieve data . /1350254122.Didn't retrieve data . /1350254118.Didn't retrieve data . /1350254115.Didn't retrieve data . /1350254103.Didn't retrieve data . /1350254053.Didn't retrieve data . /1350254048.Didn't retrieve data . /1350254043.Didn't retrieve data . /1350254039.Didn't retrieve data . /1350254036.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, '2733641') ('3318', '22153537', '1350453033', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452961', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452938', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452909', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254131', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254126', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254122', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254118', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254115', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254103', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254053', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254048', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254043', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254039', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254036', None, None, None, None, None, '2733641') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 45 time used for this insertion : 0.13154268264770508 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10868597030639648 time spend to save output : 0.1322319507598877 total time spend for step 5 : 0.24091792106628418 step6:blur_detection Wed Apr 9 11:20:23 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/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549.jpg resize: (2160, 3264) 1350453033 -3.7922144381546543 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f.jpg resize: (2160, 3264) 1350452961 -6.948706092987704 treat image : temp/1744189827_669588_1350452938_9f4b22faaf1827e9ca4bb3c0a3f7dde4.jpg resize: (2160, 3264) 1350452938 -4.613006749961581 treat image : temp/1744189827_669588_1350452909_2b5359afb43b40d1d3a45619244ee491.jpg resize: (2160, 3264) 1350452909 -5.468158235627563 treat image : temp/1744189827_669588_1350254131_d8697704fe47c2c86f3c1c9e8eba972c.jpg resize: (2160, 3264) 1350254131 -3.4666837611387793 treat image : temp/1744189827_669588_1350254126_118455aafd9ad69af706c5cf6690dd25.jpg resize: (2160, 3264) 1350254126 -2.827379528581538 treat image : temp/1744189827_669588_1350254122_113c8900067cec2f98f0e9e90fc84797.jpg resize: (2160, 3264) 1350254122 -2.785851521643483 treat image : temp/1744189827_669588_1350254118_8aba4e044ddc25d8525dfa9718bcdc2a.jpg resize: (2160, 3264) 1350254118 -4.864281760540138 treat image : temp/1744189827_669588_1350254115_caea1dda18c366d210a38048acfbad4d.jpg resize: (2160, 3264) 1350254115 -5.165522211772307 treat image : temp/1744189827_669588_1350254103_952cd75404374b86dc1bf43383499268.jpg resize: (2160, 3264) 1350254103 -0.9487547331573666 treat image : temp/1744189827_669588_1350254053_be2101b48eb8def21e56a0647db66b68.jpg resize: (2160, 3264) 1350254053 -2.0191616815473936 treat image : temp/1744189827_669588_1350254048_490ed070208025c9540299a50935fb71.jpg resize: (2160, 3264) 1350254048 -3.8680767847407727 treat image : temp/1744189827_669588_1350254043_92ef07fb69d191335492b42efcc8780b.jpg resize: (2160, 3264) 1350254043 -4.032795464583227 treat image : temp/1744189827_669588_1350254039_224dfb126b87187c6c6c43b6480935a9.jpg resize: (2160, 3264) 1350254039 -3.977484698358868 treat image : temp/1744189827_669588_1350254036_d6adff56721ade72de1168160f5a19ce.jpg resize: (2160, 3264) 1350254036 -4.511060654142492 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336976_0.png resize: (961, 441) 1350740443 -1.0882488218663076 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336975_0.png resize: (229, 134) 1350740444 -2.5737692579519003 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336972_0.png resize: (188, 207) 1350740446 -1.8569060713437286 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336974_0.png resize: (325, 270) 1350740447 -2.7978453528067 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336965_0.png resize: (145, 220) 1350740448 -2.1302472287261978 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336973_0.png resize: (268, 471) 1350740449 -2.851588682716548 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336969_0.png resize: (283, 475) 1350740450 -3.268574705836965 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336963_0.png resize: (250, 444) 1350740451 -2.5439352622453626 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336964_0.png resize: (439, 832) 1350740452 -3.259871920947862 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336971_0.png resize: (223, 559) 1350740453 -2.096745989357989 treat image : temp/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549_rle_crop_3751336962_0.png resize: (264, 106) 1350740454 -1.5992873897647661 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336990_0.png resize: (183, 229) 1350740455 -1.5351911268039125 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336997_0.png resize: (403, 299) 1350740456 -2.503906580147938 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336979_0.png resize: (379, 565) 1350740457 -3.670168443865245 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336992_0.png resize: (334, 249) 1350740458 -2.047743714581529 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336984_0.png resize: (153, 265) 1350740459 -2.2007750348260897 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336983_0.png resize: (375, 360) 1350740460 -4.508797616507216 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336999_0.png resize: (256, 279) 1350740461 -3.17739264017152 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336998_0.png resize: (418, 411) 1350740462 -4.493003734423304 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751337000_0.png resize: (209, 314) 1350740463 -4.75383008589769 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751337004_0.png resize: (317, 308) 1350740464 -4.606120882847411 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751337005_0.png resize: (392, 249) 1350740465 -2.1402160712645353 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336982_0.png resize: (187, 170) 1350740466 -4.049073263283197 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336980_0.png resize: (384, 443) 1350740467 -5.504172947342491 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336987_0.png resize: (324, 341) 1350740468 -4.130417437461523 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336996_0.png resize: (355, 347) 1350740469 -4.21606366391068 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336989_0.png resize: (298, 277) 1350740470 -5.115469561594308 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751337003_0.png resize: (149, 131) 1350740471 -3.536136459039265 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336986_0.png resize: (508, 448) 1350740472 -4.034654873621621 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336988_0.png resize: (231, 161) 1350740473 -1.9002438714625303 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336995_0.png resize: (225, 249) 1350740474 -4.383911973925141 treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751336981_0.png resize: (74, 72) 1350740475 -1.122533967078891 treat image : temp/1744189827_669588_1350452938_9f4b22faaf1827e9ca4bb3c0a3f7dde4_rle_crop_3751337011_0.png resize: (566, 188) 1350740476 -0.4417853898700085 treat image : temp/1744189827_669588_1350452938_9f4b22faaf1827e9ca4bb3c0a3f7dde4_rle_crop_3751337009_0.png resize: (364, 378) 1350740477 -1.8205687220677413 treat image : temp/1744189827_669588_1350452938_9f4b22faaf1827e9ca4bb3c0a3f7dde4_rle_crop_3751337014_0.png resize: (245, 211) 1350740478 -1.3949254275742529 treat image : 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temp/1744189827_669588_1350254043_92ef07fb69d191335492b42efcc8780b_rle_crop_3751337304_0.png resize: (188, 310) 1350741054 -2.8146215146905083 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 : 441 time used for this insertion : 0.03129458427429199 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 441 time used for this insertion : 0.17181777954101562 save missing photos in datou_result : time spend for datou_step_exec : 67.52311682701111 time spend to save output : 0.20988178253173828 total time spend for step 6 : 67.73299860954285 step7:brightness Wed Apr 9 11:21:31 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 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/1744189827_669588_1350453033_adda3dab4379884c83542da30c032549.jpg treat image : temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f.jpg treat image : temp/1744189827_669588_1350452938_9f4b22faaf1827e9ca4bb3c0a3f7dde4.jpg treat image : temp/1744189827_669588_1350452909_2b5359afb43b40d1d3a45619244ee491.jpg treat image : temp/1744189827_669588_1350254131_d8697704fe47c2c86f3c1c9e8eba972c.jpg treat image : temp/1744189827_669588_1350254126_118455aafd9ad69af706c5cf6690dd25.jpg treat image : temp/1744189827_669588_1350254122_113c8900067cec2f98f0e9e90fc84797.jpg treat image : temp/1744189827_669588_1350254118_8aba4e044ddc25d8525dfa9718bcdc2a.jpg treat image : temp/1744189827_669588_1350254115_caea1dda18c366d210a38048acfbad4d.jpg treat image : temp/1744189827_669588_1350254103_952cd75404374b86dc1bf43383499268.jpg treat image : 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temp/1744189827_669588_1350452961_4c3ad80330dc8e6dc72e163d8dec6f5f_rle_crop_3751337002_0.png treat image : temp/1744189827_669588_1350254131_d8697704fe47c2c86f3c1c9e8eba972c_rle_crop_3751337096_0.png treat image : temp/1744189827_669588_1350254126_118455aafd9ad69af706c5cf6690dd25_rle_crop_3751337138_0.png treat image : temp/1744189827_669588_1350254048_490ed070208025c9540299a50935fb71_rle_crop_3751337277_0.png treat image : temp/1744189827_669588_1350254036_d6adff56721ade72de1168160f5a19ce_rle_crop_3751337371_0.png treat image : temp/1744189827_669588_1350254122_113c8900067cec2f98f0e9e90fc84797_rle_crop_3751337155_0.png treat image : temp/1744189827_669588_1350254043_92ef07fb69d191335492b42efcc8780b_rle_crop_3751337304_0.png 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 : 441 time used for this insertion : 0.03125786781311035 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 441 time used for this insertion : 0.07486653327941895 save missing photos in datou_result : time spend for datou_step_exec : 16.19196391105652 time spend to save output : 0.11276006698608398 total time spend for step 7 : 16.304723978042603 step8:velours_tree Wed Apr 9 11:21:47 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 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 : 0.31734800338745117 time spend to save output : 4.8160552978515625e-05 total time spend for step 8 : 0.3173961639404297 step9:send_mail_cod Wed Apr 9 11:21:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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_P22153537_09-04-2025_11_21_48.pdf 22159204 change filename to text .change filename to text .imagette221592041744190508 22159205 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 .imagette221592051744190508 22159206 imagette221592061744190508 22159207 imagette221592071744190508 22159208 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 .imagette221592081744190508 22159209 imagette221592091744190510 22159210 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette221592101744190510 22159211 change filename to text .change filename to text .imagette221592111744190510 22159212 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 .imagette221592121744190511 22159214 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 .imagette221592141744190512 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22153537 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22159204,22159205,22159206,22159207,22159208,22159209,22159210,22159211,22159212,22159213,22159214?tags=pet_fonce,autre,background,mal_croppe,papier,flou,pehd,metal,carton,environnement,pet_clair args[1350453033] : ((1350453033, -3.7922144381546543, 492609224), (1350453033, 0.11118052578233172, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350452961] : ((1350452961, -6.948706092987704, 492609224), (1350452961, -0.06772742383969972, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350452938] : ((1350452938, -4.613006749961581, 492609224), (1350452938, -0.013929801269871174, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350452909] : ((1350452909, -5.468158235627563, 492609224), (1350452909, 0.06571776632211913, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254131] : ((1350254131, -3.4666837611387793, 492609224), (1350254131, 0.39184647401985223, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254126] : ((1350254126, -2.827379528581538, 492609224), (1350254126, -0.07407467478842565, 496442774), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254122] : ((1350254122, -2.785851521643483, 492609224), (1350254122, -0.03222992424382347, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254118] : ((1350254118, -4.864281760540138, 492609224), (1350254118, -0.14575309867579087, 496442774), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254115] : ((1350254115, -5.165522211772307, 492609224), (1350254115, -0.13148427771848492, 496442774), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254103] : ((1350254103, -0.9487547331573666, 492688767), (1350254103, 0.035458384851562795, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254053] : ((1350254053, -2.0191616815473936, 492609224), (1350254053, 0.33452092706385167, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254048] : ((1350254048, -3.8680767847407727, 492609224), (1350254048, -0.012697955965894177, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254043] : ((1350254043, -4.032795464583227, 492609224), (1350254043, 0.11043277357974762, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254039] : ((1350254039, -3.977484698358868, 492609224), (1350254039, -0.1152435588323975, 496442774), '0.21830035100459932') We are sending mail with results at report@fotonower.com args[1350254036] : ((1350254036, -4.511060654142492, 492609224), (1350254036, 0.0009196010787762339, 2107752395), '0.21830035100459932') We are sending mail with results at report@fotonower.com refus_total : 0.21830035100459932 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=22153537 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350453033,1350254036,1350254048,1350452909,1350452938,1350452961,1350254039,1350254043,1350254053,1350254103,1350254115,1350254118,1350254122,1350254126,1350254131) Found this number of photos: 15 begin to download photo : 1350453033 begin to download photo : 1350452938 begin to download photo : 1350254053 begin to download photo : 1350254122 download finish for photo 1350453033 begin to download photo : 1350254036 download finish for photo 1350254053 begin to download photo : 1350254103 download finish for photo 1350452938 begin to download photo : 1350452961 download finish for photo 1350254122 begin to download photo : 1350254126 download finish for photo 1350254036 begin to download photo : 1350254048 download finish for photo 1350254126 begin to download photo : 1350254131 download finish for photo 1350254103 begin to download photo : 1350254115 download finish for photo 1350452961 begin to download photo : 1350254039 download finish for photo 1350254048 begin to download photo : 1350452909 download finish for photo 1350254131 download finish for photo 1350254039 begin to download photo : 1350254043 download finish for photo 1350254043 download finish for photo 1350452909 download finish for photo 1350254115 begin to download photo : 1350254118 download finish for photo 1350254118 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153537_09-04-2025_11_21_48.pdf results_Auto_P22153537_09-04-2025_11_21_48.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153537_09-04-2025_11_21_48.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','22153537','results_Auto_P22153537_09-04-2025_11_21_48.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153537_09-04-2025_11_21_48.pdf','pdf','','1.33','0.21830035100459932') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22153537

https://www.fotonower.com/image?json=false&list_photos_id=1350453033
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350452961
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350452938
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350452909
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254131
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254126
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254122
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254118
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254115
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254103
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254053
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254048
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254043
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254039
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350254036
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 21.83%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22159204?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22159205?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22159208?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22159210?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22159211?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22159212?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22159214?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153537_09-04-2025_11_21_48.pdf.

Lien vers velours :https://www.fotonower.com/velours/22159204,22159205,22159206,22159207,22159208,22159209,22159210,22159211,22159212,22159213,22159214?tags=pet_fonce,autre,background,mal_croppe,papier,flou,pehd,metal,carton,environnement,pet_clair.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 09:21:58 GMT Content-Length: 0 Connection: close X-Message-Id: zbFnUH7BRkiE6sxjr1n_ww 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 [1350453033, 1350452961, 1350452938, 1350452909, 1350254131, 1350254126, 1350254122, 1350254118, 1350254115, 1350254103, 1350254053, 1350254048, 1350254043, 1350254039, 1350254036] 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, '2733641') ('3318', '22153537', '1350453033', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452961', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452938', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452909', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254131', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254126', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254122', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254118', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254115', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254103', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254053', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254048', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254043', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254039', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254036', None, None, None, None, None, '2733641') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 15 time used for this insertion : 0.013974189758300781 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.599713802337646 time spend to save output : 0.014252901077270508 total time spend for step 9 : 10.613966703414917 step10:split_time_score Wed Apr 9 11:21:58 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'}] (('09', 15),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 07042025 22153537 Nombre de photos uploadées : 15 / 23040 (0%) 07042025 22153537 Nombre de photos taguées (types de déchets): 0 / 15 (0%) 07042025 22153537 Nombre de photos taguées (volume) : 0 / 15 (0%) elapsed_time : load_data_split_time_score 2.86102294921875e-06 elapsed_time : order_list_meta_photo_and_scores 6.9141387939453125e-06 ??????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0007462501525878906 elapsed_time : insert_dashboard_record_day_entry 0.02226734161376953 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153527 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153533 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153536 order by id desc limit 1 Qualite : 0.21830035100459932 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153537_09-04-2025_11_21_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153537 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153537 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153567 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153572 order by id desc limit 1 Qualite : 0.21942126162450581 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153573_09-04-2025_11_09_25.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153573 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153573 AND mptpi.`type`=3594 To do Qualite : 0.21058722823620202 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153575_09-04-2025_11_12_06.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153575 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153575 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153579 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153585 order by id desc limit 1 Qualite : 0.1808327092411039 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153590_09-04-2025_11_04_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153590 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153590 AND mptpi.`type`=3594 To do Qualite : 0.2245747095134351 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153594_09-04-2025_10_39_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153594 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153594 AND mptpi.`type`=3594 To do Qualite : 0.18817508340141612 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153599_09-04-2025_10_31_18.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153599 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153599 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153631 order by id desc limit 1 Qualite : 0.2387647538497724 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153644_09-04-2025_10_11_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153644 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153644 AND mptpi.`type`=3594 To do Qualite : 0.20050398026424382 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153647_09-04-2025_10_19_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153647 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153647 AND mptpi.`type`=3594 To do Qualite : 0.16284528966389794 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153651_09-04-2025_09_56_05.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153651 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153651 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153655 order by id desc limit 1 Qualite : 0.13395730297527286 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22153656_09-04-2025_09_56_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22153656 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=22153656 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'07042025': {'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 [1350453033, 1350452961, 1350452938, 1350452909, 1350254131, 1350254126, 1350254122, 1350254118, 1350254115, 1350254103, 1350254053, 1350254048, 1350254043, 1350254039, 1350254036] Looping around the photos to save general results len do output : 1 /22153537Didn'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, '2733641') ('3318', '22153537', '1350453033', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452961', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452938', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350452909', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254131', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254126', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254122', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254118', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254115', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254103', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254053', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254048', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254043', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254039', None, None, None, None, None, '2733641') ('3318', None, None, None, None, None, None, None, '2733641') ('3318', '22153537', '1350254036', None, None, None, None, None, '2733641') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 16 time used for this insertion : 0.015001773834228516 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.048466920852661 time spend to save output : 0.015229940414428711 total time spend for step 10 : 9.06369686126709 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 365.37user 194.10system 11:42.59elapsed 79%CPU (0avgtext+0avgdata 9316936maxresident)k 2489224inputs+303144outputs (79382major+31293030minor)pagefaults 0swaps