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 : 2457968 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 : ['2714309'] with mtr_portfolio_ids : ['21957008'] and first list_photo_ids : [] new path : /proc/2457968/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 13 ; length of list_pids : 13 ; length of list_args : 13 time to download the photos : 2.1622419357299805 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Tue Apr 1 20:20:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-01 20:20:35.475585: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-01 20:20:35.499291: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-01 20:20:35.501457: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0efc000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-01 20:20:35.501512: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-01 20:20:35.505421: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-01 20:20:35.639004: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3496d9c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-01 20:20:35.639052: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-01 20:20:35.640466: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 20:20:35.640837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 20:20:35.643550: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 20:20:35.645979: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 20:20:35.646437: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 20:20:35.649000: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 20:20:35.650172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 20:20:35.654738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 20:20:35.656474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 20:20:35.656544: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 20:20:35.657458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 20:20:35.657476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 20:20:35.657486: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 20:20:35.659195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9879 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-01 20:20:35.932631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 20:20:35.932740: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 20:20:35.932768: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 20:20:35.932794: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 20:20:35.932818: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 20:20:35.932842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 20:20:35.932865: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 20:20:35.932890: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 20:20:35.935138: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 20:20:35.936721: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 20:20:35.936829: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 20:20:35.936858: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 20:20:35.936887: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 20:20:35.936912: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 20:20:35.936932: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 20:20:35.936951: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 20:20:35.936970: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 20:20:35.938224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 20:20:35.938257: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 20:20:35.938266: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 20:20:35.938275: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 20:20:35.939646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9879 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-01 20:20:49.227914: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 20:20:49.414577: 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 : 13 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 : 36 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 : 54 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 : 51 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 : 60 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 : 95 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) None max_time_sub_proc : 3600 parent process len(results) : 13 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 : 7037 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 : 0.013010740280151367 nb_pixel_total : 71375 time to create 1 rle with old method : 0.08171963691711426 length of segment : 385 time for calcul the mask position with numpy : 0.006082057952880859 nb_pixel_total : 21405 time to create 1 rle with old method : 0.02355217933654785 length of segment : 162 time for calcul the mask position with numpy : 0.005269527435302734 nb_pixel_total : 34669 time to create 1 rle with old method : 0.0380399227142334 length of segment : 299 time for calcul the mask position with numpy : 0.0030155181884765625 nb_pixel_total : 9913 time to create 1 rle with old method : 0.014611482620239258 length of segment : 127 time for calcul the mask position with numpy : 0.0014629364013671875 nb_pixel_total : 8298 time to create 1 rle with old method : 0.014750003814697266 length of segment : 101 time for calcul the mask position with numpy : 0.0005979537963867188 nb_pixel_total : 9278 time to create 1 rle with old method : 0.010973930358886719 length of segment : 158 time for calcul the mask position with numpy : 0.005956172943115234 nb_pixel_total : 54354 time to create 1 rle with old method : 0.06394505500793457 length of segment : 272 time for calcul the mask position with numpy : 0.006821393966674805 nb_pixel_total : 14972 time to create 1 rle with old method : 0.01940155029296875 length of segment : 230 time for calcul the mask position with numpy : 0.0012216567993164062 nb_pixel_total : 5509 time to create 1 rle with old method : 0.006396055221557617 length of segment : 75 time for calcul the mask position with numpy : 0.017017841339111328 nb_pixel_total : 40729 time to create 1 rle with old method : 0.050734519958496094 length of segment : 223 time for calcul the mask position with numpy : 0.003780364990234375 nb_pixel_total : 15351 time to create 1 rle with old method : 0.020113468170166016 length of segment : 176 time for calcul the mask position with numpy : 0.0008215904235839844 nb_pixel_total : 12828 time to create 1 rle with old method : 0.015225410461425781 length of segment : 179 time for calcul the mask position with numpy : 0.07399511337280273 nb_pixel_total : 358922 time to create 1 rle with new method : 0.0283963680267334 length of segment : 710 time for calcul the mask position with numpy : 0.0032320022583007812 nb_pixel_total : 18601 time to create 1 rle with old method : 0.021497488021850586 length of segment : 130 time for calcul the mask position with numpy : 0.041372060775756836 nb_pixel_total : 32386 time to create 1 rle with old method : 0.043146371841430664 length of segment : 233 time for calcul the mask position with numpy : 0.03595542907714844 nb_pixel_total : 59137 time to create 1 rle with old method : 0.06822037696838379 length of segment : 384 time for calcul the mask position with numpy : 0.0015072822570800781 nb_pixel_total : 9315 time to create 1 rle with old method : 0.014063835144042969 length of segment : 73 time for calcul the mask position with numpy : 0.015910625457763672 nb_pixel_total : 12833 time to create 1 rle with old method : 0.019980669021606445 length of segment : 138 time for calcul the mask position with numpy : 0.012071609497070312 nb_pixel_total : 48918 time to create 1 rle with old method : 0.06057882308959961 length of segment : 269 time for calcul the mask position with numpy : 0.002924680709838867 nb_pixel_total : 11043 time to create 1 rle with old method : 0.01320338249206543 length of segment : 111 time for calcul the mask position with numpy : 0.019107341766357422 nb_pixel_total : 23846 time to create 1 rle with old method : 0.031945228576660156 length of segment : 163 time for calcul the mask position with numpy : 0.008266210556030273 nb_pixel_total : 13033 time to create 1 rle with old method : 0.01718425750732422 length of segment : 187 time for calcul the mask position with numpy : 0.0018236637115478516 nb_pixel_total : 4688 time to create 1 rle with old method : 0.008410215377807617 length of segment : 159 time for calcul the mask position with numpy : 0.004342794418334961 nb_pixel_total : 21913 time to create 1 rle with old method : 0.03324413299560547 length of segment : 173 time for calcul the mask position with numpy : 0.01744699478149414 nb_pixel_total : 8573 time to create 1 rle with old method : 0.01306009292602539 length of segment : 109 time for calcul the mask position with numpy : 0.05439639091491699 nb_pixel_total : 50827 time to create 1 rle with old method : 0.06247687339782715 length of segment : 381 time for calcul the mask position with numpy : 0.010385990142822266 nb_pixel_total : 13494 time to create 1 rle with old method : 0.021602630615234375 length of segment : 120 time for calcul the mask position with numpy : 0.0009887218475341797 nb_pixel_total : 8566 time to create 1 rle with old method : 0.014698982238769531 length of segment : 103 time for calcul the mask position with numpy : 0.002782583236694336 nb_pixel_total : 10304 time to create 1 rle with old method : 0.015841245651245117 length of segment : 137 time for calcul the mask position with numpy : 0.006421089172363281 nb_pixel_total : 53977 time to create 1 rle with old method : 0.06555938720703125 length of segment : 233 time for calcul the mask position with numpy : 0.023471355438232422 nb_pixel_total : 85785 time to create 1 rle with old method : 0.10233688354492188 length of segment : 469 time for calcul the mask position with numpy : 0.007517099380493164 nb_pixel_total : 7980 time to create 1 rle with old method : 0.011548042297363281 length of segment : 120 time for calcul the mask position with numpy : 0.0017247200012207031 nb_pixel_total : 18636 time to create 1 rle with old method : 0.02171039581298828 length of segment : 241 time for calcul the mask position with numpy : 0.010554075241088867 nb_pixel_total : 10263 time to create 1 rle with old method : 0.018118858337402344 length of segment : 129 time for calcul the mask position with numpy : 0.025145292282104492 nb_pixel_total : 30485 time to create 1 rle with old method : 0.04156136512756348 length of segment : 213 time for calcul the mask position with numpy : 0.047974348068237305 nb_pixel_total : 46305 time to create 1 rle with old method : 0.060065507888793945 length of segment : 283 time for calcul the mask position with numpy : 0.013626813888549805 nb_pixel_total : 25190 time to create 1 rle with old method : 0.03355526924133301 length of segment : 210 time for calcul the mask position with numpy : 0.0005042552947998047 nb_pixel_total : 3608 time to create 1 rle with old method : 0.004252433776855469 length of segment : 75 time for calcul the mask position with numpy : 0.014726638793945312 nb_pixel_total : 24237 time to create 1 rle with old method : 0.03229212760925293 length of segment : 271 time for calcul the mask position with numpy : 0.0008134841918945312 nb_pixel_total : 4723 time to create 1 rle with old method : 0.0056569576263427734 length of segment : 72 time for calcul the mask position with numpy : 0.025507688522338867 nb_pixel_total : 250632 time to create 1 rle with new method : 0.031322479248046875 length of segment : 668 time for calcul the mask position with numpy : 0.00048613548278808594 nb_pixel_total : 19268 time to create 1 rle with old method : 0.022498130798339844 length of segment : 168 time for calcul the mask position with numpy : 0.017548322677612305 nb_pixel_total : 24451 time to create 1 rle with old method : 0.03140544891357422 length of segment : 240 time for calcul the mask position with numpy : 0.014400243759155273 nb_pixel_total : 38580 time to create 1 rle with old method : 0.05242776870727539 length of segment : 258 time for calcul the mask position with numpy : 0.022716999053955078 nb_pixel_total : 18688 time to create 1 rle with old method : 0.02388453483581543 length of segment : 245 time for calcul the mask position with numpy : 0.026308774948120117 nb_pixel_total : 52694 time to create 1 rle with old method : 0.07388067245483398 length of segment : 331 time for calcul the mask position with numpy : 0.013643026351928711 nb_pixel_total : 29917 time to create 1 rle with old method : 0.03764533996582031 length of segment : 281 time for calcul the mask position with numpy : 0.0026297569274902344 nb_pixel_total : 3013 time to create 1 rle with old method : 0.003643035888671875 length of segment : 66 time for calcul the mask position with numpy : 0.00536346435546875 nb_pixel_total : 9117 time to create 1 rle with old method : 0.011321306228637695 length of segment : 87 time for calcul the mask position with numpy : 0.0003426074981689453 nb_pixel_total : 11747 time to create 1 rle with old method : 0.015376091003417969 length of segment : 117 time for calcul the mask position with numpy : 0.0052471160888671875 nb_pixel_total : 47201 time to create 1 rle with old method : 0.06353902816772461 length of segment : 342 time for calcul the mask position with numpy : 0.0007026195526123047 nb_pixel_total : 4231 time to create 1 rle with old method : 0.005877017974853516 length of segment : 44 time for calcul the mask position with numpy : 0.014872550964355469 nb_pixel_total : 20913 time to create 1 rle with old method : 0.03905296325683594 length of segment : 133 time for calcul the mask position with numpy : 0.007109403610229492 nb_pixel_total : 4940 time to create 1 rle with old method : 0.008617162704467773 length of segment : 87 time for calcul the mask position with numpy : 0.0016486644744873047 nb_pixel_total : 10381 time to create 1 rle with old method : 0.014328718185424805 length of segment : 73 time for calcul the mask position with numpy : 0.02351212501525879 nb_pixel_total : 59066 time to create 1 rle with old method : 0.07785940170288086 length of segment : 197 time for calcul the mask position with numpy : 0.010794878005981445 nb_pixel_total : 41274 time to create 1 rle with old method : 0.05732846260070801 length of segment : 245 time for calcul the mask position with numpy : 0.018090009689331055 nb_pixel_total : 24242 time to create 1 rle with old method : 0.03429436683654785 length of segment : 173 time for calcul the mask position with numpy : 0.029297828674316406 nb_pixel_total : 261154 time to create 1 rle with new method : 0.02404642105102539 length of segment : 821 time for calcul the mask position with numpy : 0.00019097328186035156 nb_pixel_total : 5809 time to create 1 rle with old method : 0.0076177120208740234 length of segment : 81 time for calcul the mask position with numpy : 0.011197566986083984 nb_pixel_total : 21923 time to create 1 rle with old method : 0.03097820281982422 length of segment : 256 time for calcul the mask position with numpy : 0.0058057308197021484 nb_pixel_total : 55352 time to create 1 rle with old method : 0.08508753776550293 length of segment : 688 time for calcul the mask position with numpy : 0.00025200843811035156 nb_pixel_total : 7289 time to create 1 rle with old method : 0.008380889892578125 length of segment : 177 time for calcul the mask position with numpy : 0.00015401840209960938 nb_pixel_total : 3877 time to create 1 rle with old method : 0.00458979606628418 length of segment : 81 time for calcul the mask position with numpy : 0.009054183959960938 nb_pixel_total : 284508 time to create 1 rle with new method : 0.0318446159362793 length of segment : 1012 time for calcul the mask position with numpy : 0.0009162425994873047 nb_pixel_total : 7174 time to create 1 rle with old method : 0.013348102569580078 length of segment : 112 time for calcul the mask position with numpy : 0.0068132877349853516 nb_pixel_total : 47288 time to create 1 rle with old method : 0.058463096618652344 length of segment : 464 time for calcul the mask position with numpy : 0.023499011993408203 nb_pixel_total : 97965 time to create 1 rle with old method : 0.11456990242004395 length of segment : 509 time for calcul the mask position with numpy : 0.010781526565551758 nb_pixel_total : 77593 time to create 1 rle with old method : 0.08805131912231445 length of segment : 511 time for calcul the mask position with numpy : 0.023731708526611328 nb_pixel_total : 15833 time to create 1 rle with old method : 0.027616500854492188 length of segment : 169 time for calcul the mask position with numpy : 0.0003414154052734375 nb_pixel_total : 12522 time to create 1 rle with old method : 0.01485896110534668 length of segment : 164 time for calcul the mask position with numpy : 0.03803849220275879 nb_pixel_total : 112834 time to create 1 rle with old method : 0.13248944282531738 length of segment : 511 time for calcul the mask position with numpy : 0.0041654109954833984 nb_pixel_total : 12385 time to create 1 rle with old method : 0.016803503036499023 length of segment : 113 time for calcul the mask position with numpy : 0.0058591365814208984 nb_pixel_total : 35503 time to create 1 rle with old method : 0.04448533058166504 length of segment : 386 time for calcul the mask position with numpy : 0.0003998279571533203 nb_pixel_total : 16083 time to create 1 rle with old method : 0.018338680267333984 length of segment : 160 time for calcul the mask position with numpy : 0.00951528549194336 nb_pixel_total : 42347 time to create 1 rle with old method : 0.048776865005493164 length of segment : 279 time for calcul the mask position with numpy : 0.011822938919067383 nb_pixel_total : 62654 time to create 1 rle with old method : 0.07419610023498535 length of segment : 301 time for calcul the mask position with numpy : 0.00074005126953125 nb_pixel_total : 11963 time to create 1 rle with old method : 0.014001846313476562 length of segment : 135 time for calcul the mask position with numpy : 0.0006000995635986328 nb_pixel_total : 13479 time to create 1 rle with old method : 0.015501976013183594 length of segment : 157 time for calcul the mask position with numpy : 0.0016376972198486328 nb_pixel_total : 11843 time to create 1 rle with old method : 0.013657093048095703 length of segment : 155 time for calcul the mask position with numpy : 0.0032711029052734375 nb_pixel_total : 17277 time to create 1 rle with old method : 0.022632122039794922 length of segment : 156 time for calcul the mask position with numpy : 0.0283205509185791 nb_pixel_total : 148721 time to create 1 rle with old method : 0.16689634323120117 length of segment : 370 time for calcul the mask position with numpy : 0.003431081771850586 nb_pixel_total : 67563 time to create 1 rle with old method : 0.08179354667663574 length of segment : 342 time for calcul the mask position with numpy : 0.00035262107849121094 nb_pixel_total : 15845 time to create 1 rle with old method : 0.01841282844543457 length of segment : 234 time for calcul the mask position with numpy : 0.002597332000732422 nb_pixel_total : 90760 time to create 1 rle with old method : 0.10121631622314453 length of segment : 616 time for calcul the mask position with numpy : 0.001195669174194336 nb_pixel_total : 7984 time to create 1 rle with old method : 0.009315252304077148 length of segment : 100 time for calcul the mask position with numpy : 0.0011584758758544922 nb_pixel_total : 45411 time to create 1 rle with old method : 0.052783966064453125 length of segment : 287 time for calcul the mask position with numpy : 0.002195596694946289 nb_pixel_total : 15918 time to create 1 rle with old method : 0.02303767204284668 length of segment : 204 time for calcul the mask position with numpy : 0.004681587219238281 nb_pixel_total : 100479 time to create 1 rle with old method : 0.11572122573852539 length of segment : 346 time for calcul the mask position with numpy : 0.0011506080627441406 nb_pixel_total : 21946 time to create 1 rle with old method : 0.025800704956054688 length of segment : 156 time for calcul the mask position with numpy : 0.02132868766784668 nb_pixel_total : 214212 time to create 1 rle with new method : 0.030035734176635742 length of segment : 1032 time for calcul the mask position with numpy : 0.0021796226501464844 nb_pixel_total : 15025 time to create 1 rle with old method : 0.020734071731567383 length of segment : 136 time for calcul the mask position with numpy : 0.0009875297546386719 nb_pixel_total : 20043 time to create 1 rle with old method : 0.023855924606323242 length of segment : 162 time for calcul the mask position with numpy : 0.0011639595031738281 nb_pixel_total : 9695 time to create 1 rle with old method : 0.010934829711914062 length of segment : 125 time for calcul the mask position with numpy : 0.0009832382202148438 nb_pixel_total : 14377 time to create 1 rle with old method : 0.016286849975585938 length of segment : 146 time for calcul the mask position with numpy : 0.0003714561462402344 nb_pixel_total : 12677 time to create 1 rle with old method : 0.014686822891235352 length of segment : 111 time for calcul the mask position with numpy : 0.001954317092895508 nb_pixel_total : 20964 time to create 1 rle with old method : 0.024123668670654297 length of segment : 148 time for calcul the mask position with numpy : 0.011444330215454102 nb_pixel_total : 37470 time to create 1 rle with old method : 0.04475593566894531 length of segment : 247 time for calcul the mask position with numpy : 0.007020235061645508 nb_pixel_total : 28073 time to create 1 rle with old method : 0.03348207473754883 length of segment : 211 time for calcul the mask position with numpy : 0.006353139877319336 nb_pixel_total : 55810 time to create 1 rle with old method : 0.09265804290771484 length of segment : 240 time for calcul the mask position with numpy : 0.0022025108337402344 nb_pixel_total : 59401 time to create 1 rle with old method : 0.06624746322631836 length of segment : 386 time for calcul the mask position with numpy : 0.000591278076171875 nb_pixel_total : 16866 time to create 1 rle with old method : 0.018599510192871094 length of segment : 279 time for calcul the mask position with numpy : 0.0011408329010009766 nb_pixel_total : 18923 time to create 1 rle with old method : 0.021938800811767578 length of segment : 184 time for calcul the mask position with numpy : 0.0012161731719970703 nb_pixel_total : 21833 time to create 1 rle with old method : 0.024041414260864258 length of segment : 187 time for calcul the mask position with numpy : 0.0056836605072021484 nb_pixel_total : 16061 time to create 1 rle with old method : 0.02047109603881836 length of segment : 222 time for calcul the mask position with numpy : 0.0010557174682617188 nb_pixel_total : 17709 time to create 1 rle with old method : 0.0289609432220459 length of segment : 134 time for calcul the mask position with numpy : 0.007025718688964844 nb_pixel_total : 119732 time to create 1 rle with old method : 0.13836145401000977 length of segment : 366 time for calcul the mask position with numpy : 0.0017943382263183594 nb_pixel_total : 8537 time to create 1 rle with old method : 0.009527206420898438 length of segment : 168 time for calcul the mask position with numpy : 0.0007925033569335938 nb_pixel_total : 14030 time to create 1 rle with old method : 0.016466617584228516 length of segment : 156 time for calcul the mask position with numpy : 0.0008978843688964844 nb_pixel_total : 20784 time to create 1 rle with old method : 0.023398399353027344 length of segment : 214 time for calcul the mask position with numpy : 0.0006504058837890625 nb_pixel_total : 8205 time to create 1 rle with old method : 0.00956583023071289 length of segment : 129 time for calcul the mask position with numpy : 0.0012547969818115234 nb_pixel_total : 12498 time to create 1 rle with old method : 0.014126300811767578 length of segment : 222 time for calcul the mask position with numpy : 0.0022106170654296875 nb_pixel_total : 29377 time to create 1 rle with old method : 0.03333258628845215 length of segment : 202 time for calcul the mask position with numpy : 0.0031309127807617188 nb_pixel_total : 28662 time to create 1 rle with old method : 0.03248333930969238 length of segment : 201 time for calcul the mask position with numpy : 0.0010144710540771484 nb_pixel_total : 15261 time to create 1 rle with old method : 0.017777681350708008 length of segment : 174 time for calcul the mask position with numpy : 0.0012602806091308594 nb_pixel_total : 13094 time to create 1 rle with old method : 0.01487588882446289 length of segment : 133 time for calcul the mask position with numpy : 0.004369258880615234 nb_pixel_total : 66042 time to create 1 rle with old method : 0.0743398666381836 length of segment : 455 time for calcul the mask position with numpy : 0.00223541259765625 nb_pixel_total : 43046 time to create 1 rle with old method : 0.049408912658691406 length of segment : 244 time for calcul the mask position with numpy : 0.006264686584472656 nb_pixel_total : 26486 time to create 1 rle with old method : 0.03276681900024414 length of segment : 187 time for calcul the mask position with numpy : 0.0005893707275390625 nb_pixel_total : 5366 time to create 1 rle with old method : 0.006475925445556641 length of segment : 101 time for calcul the mask position with numpy : 0.010595321655273438 nb_pixel_total : 63847 time to create 1 rle with old method : 0.08541059494018555 length of segment : 374 time for calcul the mask position with numpy : 0.0005590915679931641 nb_pixel_total : 11110 time to create 1 rle with old method : 0.013228654861450195 length of segment : 94 time for calcul the mask position with numpy : 0.0017681121826171875 nb_pixel_total : 58412 time to create 1 rle with old method : 0.06453657150268555 length of segment : 348 time for calcul the mask position with numpy : 0.07196688652038574 nb_pixel_total : 528166 time to create 1 rle with new method : 0.0726780891418457 length of segment : 1520 time for calcul the mask position with numpy : 0.0017223358154296875 nb_pixel_total : 19481 time to create 1 rle with old method : 0.0243072509765625 length of segment : 313 time for calcul the mask position with numpy : 0.0047266483306884766 nb_pixel_total : 26424 time to create 1 rle with old method : 0.02881312370300293 length of segment : 207 time for calcul the mask position with numpy : 0.004948139190673828 nb_pixel_total : 33347 time to create 1 rle with old method : 0.035915374755859375 length of segment : 234 time for calcul the mask position with numpy : 0.0015556812286376953 nb_pixel_total : 10229 time to create 1 rle with old method : 0.011707782745361328 length of segment : 168 time for calcul the mask position with numpy : 0.0007123947143554688 nb_pixel_total : 13040 time to create 1 rle with old method : 0.015144586563110352 length of segment : 98 time for calcul the mask position with numpy : 0.0026993751525878906 nb_pixel_total : 34596 time to create 1 rle with old method : 0.03922319412231445 length of segment : 211 time for calcul the mask position with numpy : 0.0023193359375 nb_pixel_total : 26024 time to create 1 rle with old method : 0.031331539154052734 length of segment : 263 time for calcul the mask position with numpy : 0.0030663013458251953 nb_pixel_total : 32336 time to create 1 rle with old method : 0.046114444732666016 length of segment : 242 time for calcul the mask position with numpy : 0.0003647804260253906 nb_pixel_total : 5683 time to create 1 rle with old method : 0.00652623176574707 length of segment : 75 time for calcul the mask position with numpy : 0.0007793903350830078 nb_pixel_total : 12562 time to create 1 rle with old method : 0.014473676681518555 length of segment : 145 time for calcul the mask position with numpy : 0.0012285709381103516 nb_pixel_total : 14495 time to create 1 rle with old method : 0.01610398292541504 length of segment : 149 time for calcul the mask position with numpy : 0.0009913444519042969 nb_pixel_total : 11144 time to create 1 rle with old method : 0.012983560562133789 length of segment : 113 time for calcul the mask position with numpy : 0.0012302398681640625 nb_pixel_total : 17052 time to create 1 rle with old method : 0.019353151321411133 length of segment : 204 time for calcul the mask position with numpy : 0.0008835792541503906 nb_pixel_total : 11875 time to create 1 rle with old method : 0.013535737991333008 length of segment : 279 time for calcul the mask position with numpy : 0.0020706653594970703 nb_pixel_total : 18996 time to create 1 rle with old method : 0.02211284637451172 length of segment : 200 time for calcul the mask position with numpy : 0.0017974376678466797 nb_pixel_total : 12129 time to create 1 rle with old method : 0.013887405395507812 length of segment : 207 time for calcul the mask position with numpy : 0.0028142929077148438 nb_pixel_total : 50455 time to create 1 rle with old method : 0.06197953224182129 length of segment : 142 time for calcul the mask position with numpy : 0.0009484291076660156 nb_pixel_total : 6112 time to create 1 rle with old method : 0.00710296630859375 length of segment : 82 time for calcul the mask position with numpy : 0.005522727966308594 nb_pixel_total : 70658 time to create 1 rle with old method : 0.07834839820861816 length of segment : 421 time for calcul the mask position with numpy : 0.0007674694061279297 nb_pixel_total : 5804 time to create 1 rle with old method : 0.006704092025756836 length of segment : 80 time for calcul the mask position with numpy : 0.0006651878356933594 nb_pixel_total : 14689 time to create 1 rle with old method : 0.02023482322692871 length of segment : 255 time for calcul the mask position with numpy : 0.002672910690307617 nb_pixel_total : 43797 time to create 1 rle with old method : 0.046347618103027344 length of segment : 327 time for calcul the mask position with numpy : 0.0014662742614746094 nb_pixel_total : 14781 time to create 1 rle with old method : 0.01654672622680664 length of segment : 201 time for calcul the mask position with numpy : 0.01968693733215332 nb_pixel_total : 296078 time to create 1 rle with new method : 0.018298625946044922 length of segment : 758 time for calcul the mask position with numpy : 0.0013761520385742188 nb_pixel_total : 19942 time to create 1 rle with old method : 0.021960735321044922 length of segment : 189 time for calcul the mask position with numpy : 0.0010921955108642578 nb_pixel_total : 14835 time to create 1 rle with old method : 0.017095327377319336 length of segment : 193 time for calcul the mask position with numpy : 0.0018868446350097656 nb_pixel_total : 24638 time to create 1 rle with old method : 0.027443885803222656 length of segment : 303 time for calcul the mask position with numpy : 0.0061228275299072266 nb_pixel_total : 84427 time to create 1 rle with old method : 0.09317851066589355 length of segment : 217 time for calcul the mask position with numpy : 0.0007355213165283203 nb_pixel_total : 20932 time to create 1 rle with old method : 0.023813247680664062 length of segment : 241 time for calcul the mask position with numpy : 0.002774953842163086 nb_pixel_total : 18712 time to create 1 rle with old method : 0.02184438705444336 length of segment : 276 time for calcul the mask position with numpy : 0.0007719993591308594 nb_pixel_total : 9421 time to create 1 rle with old method : 0.010984182357788086 length of segment : 177 time for calcul the mask position with numpy : 0.0008511543273925781 nb_pixel_total : 14521 time to create 1 rle with old method : 0.016071557998657227 length of segment : 147 time for calcul the mask position with numpy : 0.001466512680053711 nb_pixel_total : 28512 time to create 1 rle with old method : 0.03233838081359863 length of segment : 249 time for calcul the mask position with numpy : 0.0016741752624511719 nb_pixel_total : 28761 time to create 1 rle with old method : 0.032037973403930664 length of segment : 211 time for calcul the mask position with numpy : 0.0005557537078857422 nb_pixel_total : 11288 time to create 1 rle with old method : 0.012727022171020508 length of segment : 91 time for calcul the mask position with numpy : 0.0005307197570800781 nb_pixel_total : 11337 time to create 1 rle with old method : 0.012738704681396484 length of segment : 126 time for calcul the mask position with numpy : 0.0003476142883300781 nb_pixel_total : 9857 time to create 1 rle with old method : 0.011276483535766602 length of segment : 121 time for calcul the mask position with numpy : 0.0009009838104248047 nb_pixel_total : 11156 time to create 1 rle with old method : 0.012334823608398438 length of segment : 146 time for calcul the mask position with numpy : 0.0025315284729003906 nb_pixel_total : 37140 time to create 1 rle with old method : 0.04083609580993652 length of segment : 405 time for calcul the mask position with numpy : 0.0035305023193359375 nb_pixel_total : 61122 time to create 1 rle with old method : 0.06548833847045898 length of segment : 296 time for calcul the mask position with numpy : 0.001116037368774414 nb_pixel_total : 15255 time to create 1 rle with old method : 0.01722860336303711 length of segment : 138 time for calcul the mask position with numpy : 0.0004916191101074219 nb_pixel_total : 7541 time to create 1 rle with old method : 0.008966445922851562 length of segment : 245 time for calcul the mask position with numpy : 0.0013480186462402344 nb_pixel_total : 13321 time to create 1 rle with old method : 0.015205621719360352 length of segment : 172 time for calcul the mask position with numpy : 0.00179290771484375 nb_pixel_total : 6603 time to create 1 rle with old method : 0.008018970489501953 length of segment : 263 time for calcul the mask position with numpy : 0.0009405612945556641 nb_pixel_total : 10484 time to create 1 rle with old method : 0.012293577194213867 length of segment : 141 time for calcul the mask position with numpy : 0.0009324550628662109 nb_pixel_total : 19805 time to create 1 rle with old method : 0.023465871810913086 length of segment : 168 time for calcul the mask position with numpy : 0.001505136489868164 nb_pixel_total : 20255 time to create 1 rle with old method : 0.023970842361450195 length of segment : 208 time for calcul the mask position with numpy : 0.00047326087951660156 nb_pixel_total : 21473 time to create 1 rle with old method : 0.024667978286743164 length of segment : 182 time for calcul the mask position with numpy : 0.0008032321929931641 nb_pixel_total : 14432 time to create 1 rle with old method : 0.01661539077758789 length of segment : 123 time for calcul the mask position with numpy : 0.0005538463592529297 nb_pixel_total : 7103 time to create 1 rle with old method : 0.008033275604248047 length of segment : 135 time for calcul the mask position with numpy : 0.0010237693786621094 nb_pixel_total : 9994 time to create 1 rle with old method : 0.011464118957519531 length of segment : 170 time for calcul the mask position with numpy : 0.0018923282623291016 nb_pixel_total : 25348 time to create 1 rle with old method : 0.02844834327697754 length of segment : 198 time for calcul the mask position with numpy : 0.0011506080627441406 nb_pixel_total : 21187 time to create 1 rle with old method : 0.02393341064453125 length of segment : 186 time for calcul the mask position with numpy : 0.0017673969268798828 nb_pixel_total : 28214 time to create 1 rle with old method : 0.03210711479187012 length of segment : 195 time for calcul the mask position with numpy : 0.0007302761077880859 nb_pixel_total : 7167 time to create 1 rle with old method : 0.008531808853149414 length of segment : 137 time for calcul the mask position with numpy : 0.003082752227783203 nb_pixel_total : 30561 time to create 1 rle with old method : 0.034961700439453125 length of segment : 250 time for calcul the mask position with numpy : 0.002707242965698242 nb_pixel_total : 32867 time to create 1 rle with old method : 0.04032135009765625 length of segment : 236 time for calcul the mask position with numpy : 0.0009317398071289062 nb_pixel_total : 7417 time to create 1 rle with old method : 0.008708000183105469 length of segment : 209 time for calcul the mask position with numpy : 0.0008215904235839844 nb_pixel_total : 11627 time to create 1 rle with old method : 0.01324915885925293 length of segment : 286 time for calcul the mask position with numpy : 0.0024406909942626953 nb_pixel_total : 29288 time to create 1 rle with old method : 0.03340482711791992 length of segment : 338 time for calcul the mask position with numpy : 0.0010497570037841797 nb_pixel_total : 14393 time to create 1 rle with old method : 0.016805171966552734 length of segment : 137 time for calcul the mask position with numpy : 0.00116729736328125 nb_pixel_total : 12749 time to create 1 rle with old method : 0.014537334442138672 length of segment : 127 time for calcul the mask position with numpy : 0.0016829967498779297 nb_pixel_total : 23960 time to create 1 rle with old method : 0.027065277099609375 length of segment : 254 time for calcul the mask position with numpy : 0.00213623046875 nb_pixel_total : 31890 time to create 1 rle with old method : 0.037702083587646484 length of segment : 308 time for calcul the mask position with numpy : 0.0010194778442382812 nb_pixel_total : 12064 time to create 1 rle with old method : 0.019669294357299805 length of segment : 109 time for calcul the mask position with numpy : 0.0004253387451171875 nb_pixel_total : 5981 time to create 1 rle with old method : 0.006926298141479492 length of segment : 76 time for calcul the mask position with numpy : 0.0006010532379150391 nb_pixel_total : 9142 time to create 1 rle with old method : 0.010730743408203125 length of segment : 96 time for calcul the mask position with numpy : 0.0006754398345947266 nb_pixel_total : 14663 time to create 1 rle with old method : 0.016917943954467773 length of segment : 148 time for calcul the mask position with numpy : 0.0017547607421875 nb_pixel_total : 24387 time to create 1 rle with old method : 0.027298927307128906 length of segment : 215 time for calcul the mask position with numpy : 0.0008707046508789062 nb_pixel_total : 6199 time to create 1 rle with old method : 0.0070133209228515625 length of segment : 91 time for calcul the mask position with numpy : 0.0011222362518310547 nb_pixel_total : 22584 time to create 1 rle with old method : 0.024158716201782227 length of segment : 268 time for calcul the mask position with numpy : 0.003482341766357422 nb_pixel_total : 40415 time to create 1 rle with old method : 0.04339146614074707 length of segment : 224 time for calcul the mask position with numpy : 0.0012276172637939453 nb_pixel_total : 18386 time to create 1 rle with old method : 0.02041316032409668 length of segment : 206 time for calcul the mask position with numpy : 0.003032207489013672 nb_pixel_total : 40792 time to create 1 rle with old method : 0.06880950927734375 length of segment : 479 time for calcul the mask position with numpy : 0.0006759166717529297 nb_pixel_total : 6738 time to create 1 rle with old method : 0.007936477661132812 length of segment : 91 time for calcul the mask position with numpy : 0.0007421970367431641 nb_pixel_total : 13872 time to create 1 rle with old method : 0.01574087142944336 length of segment : 140 time for calcul the mask position with numpy : 0.005001068115234375 nb_pixel_total : 74467 time to create 1 rle with old method : 0.0816800594329834 length of segment : 314 time for calcul the mask position with numpy : 0.001219034194946289 nb_pixel_total : 18782 time to create 1 rle with old method : 0.020849943161010742 length of segment : 158 time for calcul the mask position with numpy : 0.0012822151184082031 nb_pixel_total : 13554 time to create 1 rle with old method : 0.015506744384765625 length of segment : 223 time for calcul the mask position with numpy : 0.002166748046875 nb_pixel_total : 23698 time to create 1 rle with old method : 0.026714324951171875 length of segment : 318 time for calcul the mask position with numpy : 0.03813624382019043 nb_pixel_total : 558215 time to create 1 rle with new method : 0.635854959487915 length of segment : 1198 time for calcul the mask position with numpy : 0.0008313655853271484 nb_pixel_total : 19080 time to create 1 rle with old method : 0.021964311599731445 length of segment : 199 time for calcul the mask position with numpy : 0.002316713333129883 nb_pixel_total : 47903 time to create 1 rle with old method : 0.055150747299194336 length of segment : 141 time for calcul the mask position with numpy : 0.002753019332885742 nb_pixel_total : 19172 time to create 1 rle with old method : 0.021748781204223633 length of segment : 278 time for calcul the mask position with numpy : 0.0007460117340087891 nb_pixel_total : 6315 time to create 1 rle with old method : 0.007586240768432617 length of segment : 102 time for calcul the mask position with numpy : 0.0015163421630859375 nb_pixel_total : 29266 time to create 1 rle with old method : 0.032666683197021484 length of segment : 244 time for calcul the mask position with numpy : 0.0005295276641845703 nb_pixel_total : 6696 time to create 1 rle with old method : 0.007837533950805664 length of segment : 130 time for calcul the mask position with numpy : 0.0014312267303466797 nb_pixel_total : 20929 time to create 1 rle with old method : 0.033277034759521484 length of segment : 242 time for calcul the mask position with numpy : 0.0012862682342529297 nb_pixel_total : 15192 time to create 1 rle with old method : 0.021570920944213867 length of segment : 159 time for calcul the mask position with numpy : 0.0027818679809570312 nb_pixel_total : 27308 time to create 1 rle with old method : 0.0305325984954834 length of segment : 365 time for calcul the mask position with numpy : 0.0007469654083251953 nb_pixel_total : 12025 time to create 1 rle with old method : 0.013314962387084961 length of segment : 151 time for calcul the mask position with numpy : 0.0024318695068359375 nb_pixel_total : 39507 time to create 1 rle with old method : 0.0439603328704834 length of segment : 278 time for calcul the mask position with numpy : 0.0013341903686523438 nb_pixel_total : 35707 time to create 1 rle with old method : 0.03966093063354492 length of segment : 470 time for calcul the mask position with numpy : 0.0010390281677246094 nb_pixel_total : 18069 time to create 1 rle with old method : 0.020836591720581055 length of segment : 144 time for calcul the mask position with numpy : 0.0010042190551757812 nb_pixel_total : 14873 time to create 1 rle with old method : 0.017360210418701172 length of segment : 148 time for calcul the mask position with numpy : 0.0005896091461181641 nb_pixel_total : 9950 time to create 1 rle with old method : 0.01174020767211914 length of segment : 145 time for calcul the mask position with numpy : 0.0015177726745605469 nb_pixel_total : 32405 time to create 1 rle with old method : 0.0401921272277832 length of segment : 229 time for calcul the mask position with numpy : 0.0029549598693847656 nb_pixel_total : 28174 time to create 1 rle with old method : 0.03292536735534668 length of segment : 354 time for calcul the mask position with numpy : 0.002263784408569336 nb_pixel_total : 30406 time to create 1 rle with old method : 0.034032344818115234 length of segment : 261 time for calcul the mask position with numpy : 0.0005605220794677734 nb_pixel_total : 5815 time to create 1 rle with old method : 0.0068433284759521484 length of segment : 129 time for calcul the mask position with numpy : 0.0004012584686279297 nb_pixel_total : 16349 time to create 1 rle with old method : 0.019275426864624023 length of segment : 148 time for calcul the mask position with numpy : 0.011482715606689453 nb_pixel_total : 295417 time to create 1 rle with new method : 0.017635107040405273 length of segment : 772 time for calcul the mask position with numpy : 0.003937721252441406 nb_pixel_total : 55525 time to create 1 rle with old method : 0.06273198127746582 length of segment : 281 time for calcul the mask position with numpy : 0.0012094974517822266 nb_pixel_total : 14109 time to create 1 rle with old method : 0.016449689865112305 length of segment : 100 time for calcul the mask position with numpy : 0.0013232231140136719 nb_pixel_total : 21930 time to create 1 rle with old method : 0.026312828063964844 length of segment : 163 time for calcul the mask position with numpy : 0.000347137451171875 nb_pixel_total : 4565 time to create 1 rle with old method : 0.005392789840698242 length of segment : 115 time for calcul the mask position with numpy : 0.0008683204650878906 nb_pixel_total : 27413 time to create 1 rle with old method : 0.0316162109375 length of segment : 255 time for calcul the mask position with numpy : 0.0014767646789550781 nb_pixel_total : 25510 time to create 1 rle with old method : 0.030057668685913086 length of segment : 150 time for calcul the mask position with numpy : 0.001707315444946289 nb_pixel_total : 23185 time to create 1 rle with old method : 0.026902198791503906 length of segment : 259 time for calcul the mask position with numpy : 0.0007317066192626953 nb_pixel_total : 14022 time to create 1 rle with old method : 0.016533851623535156 length of segment : 167 time for calcul the mask position with numpy : 0.0015587806701660156 nb_pixel_total : 27770 time to create 1 rle with old method : 0.03265190124511719 length of segment : 201 time for calcul the mask position with numpy : 0.0006468296051025391 nb_pixel_total : 10511 time to create 1 rle with old method : 0.013021230697631836 length of segment : 175 time for calcul the mask position with numpy : 0.008242130279541016 nb_pixel_total : 95042 time to create 1 rle with old method : 0.11029195785522461 length of segment : 297 time for calcul the mask position with numpy : 0.0011699199676513672 nb_pixel_total : 15238 time to create 1 rle with old method : 0.018273353576660156 length of segment : 143 time for calcul the mask position with numpy : 0.0012328624725341797 nb_pixel_total : 9792 time to create 1 rle with old method : 0.011619091033935547 length of segment : 140 time for calcul the mask position with numpy : 0.0009315013885498047 nb_pixel_total : 12581 time to create 1 rle with old method : 0.014978170394897461 length of segment : 135 time for calcul the mask position with numpy : 0.0013051033020019531 nb_pixel_total : 32043 time to create 1 rle with old method : 0.037259578704833984 length of segment : 260 time for calcul the mask position with numpy : 0.002350330352783203 nb_pixel_total : 31849 time to create 1 rle with old method : 0.03920483589172363 length of segment : 238 time for calcul the mask position with numpy : 0.0021486282348632812 nb_pixel_total : 33074 time to create 1 rle with old method : 0.03857111930847168 length of segment : 234 time for calcul the mask position with numpy : 0.0018780231475830078 nb_pixel_total : 34884 time to create 1 rle with old method : 0.039670705795288086 length of segment : 292 time for calcul the mask position with numpy : 0.0022394657135009766 nb_pixel_total : 37889 time to create 1 rle with old method : 0.04391789436340332 length of segment : 437 time for calcul the mask position with numpy : 0.0021212100982666016 nb_pixel_total : 23467 time to create 1 rle with old method : 0.028595685958862305 length of segment : 160 time for calcul the mask position with numpy : 0.0006427764892578125 nb_pixel_total : 6067 time to create 1 rle with old method : 0.007235288619995117 length of segment : 74 time for calcul the mask position with numpy : 0.0019161701202392578 nb_pixel_total : 19955 time to create 1 rle with old method : 0.023183822631835938 length of segment : 214 time for calcul the mask position with numpy : 0.0009279251098632812 nb_pixel_total : 14955 time to create 1 rle with old method : 0.017162084579467773 length of segment : 194 time for calcul the mask position with numpy : 0.0008032321929931641 nb_pixel_total : 20120 time to create 1 rle with old method : 0.024329423904418945 length of segment : 136 time for calcul the mask position with numpy : 0.0011813640594482422 nb_pixel_total : 16484 time to create 1 rle with old method : 0.0193328857421875 length of segment : 141 time for calcul the mask position with numpy : 0.0003383159637451172 nb_pixel_total : 5504 time to create 1 rle with old method : 0.006327390670776367 length of segment : 144 time for calcul the mask position with numpy : 0.00023174285888671875 nb_pixel_total : 4299 time to create 1 rle with old method : 0.004827976226806641 length of segment : 85 time for calcul the mask position with numpy : 0.010365962982177734 nb_pixel_total : 156192 time to create 1 rle with new method : 0.016112804412841797 length of segment : 587 time for calcul the mask position with numpy : 0.001873016357421875 nb_pixel_total : 26298 time to create 1 rle with old method : 0.030529260635375977 length of segment : 248 time for calcul the mask position with numpy : 0.0013170242309570312 nb_pixel_total : 15571 time to create 1 rle with old method : 0.018718242645263672 length of segment : 160 time for calcul the mask position with numpy : 0.0035791397094726562 nb_pixel_total : 40520 time to create 1 rle with old method : 0.05739569664001465 length of segment : 216 time for calcul the mask position with numpy : 0.0027396678924560547 nb_pixel_total : 20613 time to create 1 rle with old method : 0.026715517044067383 length of segment : 284 time for calcul the mask position with numpy : 0.0005769729614257812 nb_pixel_total : 5984 time to create 1 rle with old method : 0.007439851760864258 length of segment : 87 time for calcul the mask position with numpy : 0.0011096000671386719 nb_pixel_total : 11797 time to create 1 rle with old method : 0.014430522918701172 length of segment : 120 time for calcul the mask position with numpy : 0.002519845962524414 nb_pixel_total : 26683 time to create 1 rle with old method : 0.03206682205200195 length of segment : 197 time for calcul the mask position with numpy : 0.0011723041534423828 nb_pixel_total : 9396 time to create 1 rle with old method : 0.011437416076660156 length of segment : 161 time for calcul the mask position with numpy : 0.0017151832580566406 nb_pixel_total : 28185 time to create 1 rle with old method : 0.03161001205444336 length of segment : 272 time for calcul the mask position with numpy : 0.0006375312805175781 nb_pixel_total : 7157 time to create 1 rle with old method : 0.009827136993408203 length of segment : 93 time for calcul the mask position with numpy : 0.0011248588562011719 nb_pixel_total : 21465 time to create 1 rle with old method : 0.025522232055664062 length of segment : 164 time for calcul the mask position with numpy : 0.002043008804321289 nb_pixel_total : 31825 time to create 1 rle with old method : 0.03595685958862305 length of segment : 253 time for calcul the mask position with numpy : 0.0018894672393798828 nb_pixel_total : 32139 time to create 1 rle with old method : 0.037442684173583984 length of segment : 283 time for calcul the mask position with numpy : 0.003175497055053711 nb_pixel_total : 60901 time to create 1 rle with old method : 0.06978487968444824 length of segment : 366 time for calcul the mask position with numpy : 0.0016257762908935547 nb_pixel_total : 36248 time to create 1 rle with old method : 0.04209566116333008 length of segment : 223 time for calcul the mask position with numpy : 0.0004775524139404297 nb_pixel_total : 5921 time to create 1 rle with old method : 0.00701904296875 length of segment : 88 time for calcul the mask position with numpy : 0.0009255409240722656 nb_pixel_total : 12795 time to create 1 rle with old method : 0.015067815780639648 length of segment : 153 time for calcul the mask position with numpy : 0.0044896602630615234 nb_pixel_total : 52743 time to create 1 rle with old method : 0.061521291732788086 length of segment : 307 time for calcul the mask position with numpy : 0.0006937980651855469 nb_pixel_total : 9353 time to create 1 rle with old method : 0.010879993438720703 length of segment : 174 time for calcul the mask position with numpy : 0.003063201904296875 nb_pixel_total : 36118 time to create 1 rle with old method : 0.04061245918273926 length of segment : 270 time for calcul the mask position with numpy : 0.0011281967163085938 nb_pixel_total : 16114 time to create 1 rle with old method : 0.01906108856201172 length of segment : 152 time for calcul the mask position with numpy : 0.00048470497131347656 nb_pixel_total : 6846 time to create 1 rle with old method : 0.008193016052246094 length of segment : 88 time for calcul the mask position with numpy : 0.0009980201721191406 nb_pixel_total : 14220 time to create 1 rle with old method : 0.0170290470123291 length of segment : 120 time for calcul the mask position with numpy : 0.0015711784362792969 nb_pixel_total : 22304 time to create 1 rle with old method : 0.025824785232543945 length of segment : 144 time for calcul the mask position with numpy : 0.0009684562683105469 nb_pixel_total : 17063 time to create 1 rle with old method : 0.019369840621948242 length of segment : 196 time for calcul the mask position with numpy : 0.0025908946990966797 nb_pixel_total : 30997 time to create 1 rle with old method : 0.035086870193481445 length of segment : 411 time for calcul the mask position with numpy : 0.0015721321105957031 nb_pixel_total : 16265 time to create 1 rle with old method : 0.01899409294128418 length of segment : 187 time for calcul the mask position with numpy : 0.0007293224334716797 nb_pixel_total : 4717 time to create 1 rle with old method : 0.0055217742919921875 length of segment : 111 time for calcul the mask position with numpy : 0.015332698822021484 nb_pixel_total : 302252 time to create 1 rle with new method : 0.016405820846557617 length of segment : 685 time for calcul the mask position with numpy : 0.0006167888641357422 nb_pixel_total : 12940 time to create 1 rle with old method : 0.014595508575439453 length of segment : 223 time for calcul the mask position with numpy : 0.0005702972412109375 nb_pixel_total : 16233 time to create 1 rle with old method : 0.01826643943786621 length of segment : 222 time for calcul the mask position with numpy : 0.001970529556274414 nb_pixel_total : 31052 time to create 1 rle with old method : 0.035063982009887695 length of segment : 243 time for calcul the mask position with numpy : 0.0017457008361816406 nb_pixel_total : 24715 time to create 1 rle with old method : 0.02759861946105957 length of segment : 210 time for calcul the mask position with numpy : 0.0015735626220703125 nb_pixel_total : 14812 time to create 1 rle with old method : 0.016533374786376953 length of segment : 335 time for calcul the mask position with numpy : 0.0007240772247314453 nb_pixel_total : 13174 time to create 1 rle with old method : 0.017589807510375977 length of segment : 138 time for calcul the mask position with numpy : 0.0011134147644042969 nb_pixel_total : 14816 time to create 1 rle with old method : 0.01656174659729004 length of segment : 141 time for calcul the mask position with numpy : 0.0009167194366455078 nb_pixel_total : 11559 time to create 1 rle with old method : 0.013082742691040039 length of segment : 131 time for calcul the mask position with numpy : 0.0016124248504638672 nb_pixel_total : 25609 time to create 1 rle with old method : 0.02954578399658203 length of segment : 229 time for calcul the mask position with numpy : 0.004879951477050781 nb_pixel_total : 49262 time to create 1 rle with old method : 0.06243610382080078 length of segment : 375 time for calcul the mask position with numpy : 0.0016393661499023438 nb_pixel_total : 12329 time to create 1 rle with old method : 0.014719963073730469 length of segment : 195 time for calcul the mask position with numpy : 0.00018405914306640625 nb_pixel_total : 6203 time to create 1 rle with old method : 0.00727081298828125 length of segment : 129 time for calcul the mask position with numpy : 0.0011134147644042969 nb_pixel_total : 11724 time to create 1 rle with old method : 0.01374506950378418 length of segment : 156 time for calcul the mask position with numpy : 0.0006265640258789062 nb_pixel_total : 8219 time to create 1 rle with old method : 0.009981393814086914 length of segment : 113 time for calcul the mask position with numpy : 0.0007410049438476562 nb_pixel_total : 13059 time to create 1 rle with old method : 0.015228509902954102 length of segment : 171 time for calcul the mask position with numpy : 0.00028133392333984375 nb_pixel_total : 8632 time to create 1 rle with old method : 0.010233879089355469 length of segment : 97 time for calcul the mask position with numpy : 0.0017504692077636719 nb_pixel_total : 28751 time to create 1 rle with old method : 0.03314948081970215 length of segment : 188 time for calcul the mask position with numpy : 0.0025556087493896484 nb_pixel_total : 48071 time to create 1 rle with old method : 0.055258989334106445 length of segment : 389 time for calcul the mask position with numpy : 0.000926971435546875 nb_pixel_total : 11660 time to create 1 rle with old method : 0.014094352722167969 length of segment : 123 time for calcul the mask position with numpy : 0.0018362998962402344 nb_pixel_total : 25917 time to create 1 rle with old method : 0.02979564666748047 length of segment : 241 time for calcul the mask position with numpy : 0.0008585453033447266 nb_pixel_total : 9653 time to create 1 rle with old method : 0.011531352996826172 length of segment : 143 time for calcul the mask position with numpy : 0.0044939517974853516 nb_pixel_total : 59626 time to create 1 rle with old method : 0.07184314727783203 length of segment : 271 time for calcul the mask position with numpy : 0.04058504104614258 nb_pixel_total : 597320 time to create 1 rle with new method : 0.10021805763244629 length of segment : 1270 time for calcul the mask position with numpy : 0.0037441253662109375 nb_pixel_total : 61505 time to create 1 rle with old method : 0.0719146728515625 length of segment : 334 time for calcul the mask position with numpy : 0.000993490219116211 nb_pixel_total : 13280 time to create 1 rle with old method : 0.015733957290649414 length of segment : 177 time for calcul the mask position with numpy : 0.0007474422454833984 nb_pixel_total : 8353 time to create 1 rle with old method : 0.010070323944091797 length of segment : 140 time for calcul the mask position with numpy : 0.0011539459228515625 nb_pixel_total : 18804 time to create 1 rle with old method : 0.023508071899414062 length of segment : 170 time for calcul the mask position with numpy : 0.0002982616424560547 nb_pixel_total : 1740 time to create 1 rle with old method : 0.0021219253540039062 length of segment : 69 time for calcul the mask position with numpy : 0.002930879592895508 nb_pixel_total : 15720 time to create 1 rle with old method : 0.01924872398376465 length of segment : 118 time for calcul the mask position with numpy : 0.011369466781616211 nb_pixel_total : 76738 time to create 1 rle with old method : 0.09379863739013672 length of segment : 339 time for calcul the mask position with numpy : 0.0006480216979980469 nb_pixel_total : 20470 time to create 1 rle with old method : 0.026524782180786133 length of segment : 260 time for calcul the mask position with numpy : 0.0004916191101074219 nb_pixel_total : 11168 time to create 1 rle with old method : 0.01403355598449707 length of segment : 107 time for calcul the mask position with numpy : 0.0008220672607421875 nb_pixel_total : 7284 time to create 1 rle with old method : 0.008849143981933594 length of segment : 125 time for calcul the mask position with numpy : 0.0005304813385009766 nb_pixel_total : 10551 time to create 1 rle with old method : 0.013283014297485352 length of segment : 67 time for calcul the mask position with numpy : 0.0015518665313720703 nb_pixel_total : 27598 time to create 1 rle with old method : 0.033823251724243164 length of segment : 179 time for calcul the mask position with numpy : 0.022920608520507812 nb_pixel_total : 493837 time to create 1 rle with new method : 0.4046919345855713 length of segment : 1101 time for calcul the mask position with numpy : 0.012531042098999023 nb_pixel_total : 228631 time to create 1 rle with new method : 0.021610498428344727 length of segment : 605 time for calcul the mask position with numpy : 0.004170656204223633 nb_pixel_total : 100570 time to create 1 rle with old method : 0.11409902572631836 length of segment : 480 time for calcul the mask position with numpy : 0.008524656295776367 nb_pixel_total : 134892 time to create 1 rle with old method : 0.16224384307861328 length of segment : 575 time for calcul the mask position with numpy : 0.0016777515411376953 nb_pixel_total : 38119 time to create 1 rle with old method : 0.054410457611083984 length of segment : 145 time for calcul the mask position with numpy : 0.0005254745483398438 nb_pixel_total : 12531 time to create 1 rle with old method : 0.01461338996887207 length of segment : 189 time for calcul the mask position with numpy : 0.0007679462432861328 nb_pixel_total : 40213 time to create 1 rle with old method : 0.04602980613708496 length of segment : 227 time for calcul the mask position with numpy : 0.0019795894622802734 nb_pixel_total : 48038 time to create 1 rle with old method : 0.057944297790527344 length of segment : 237 time for calcul the mask position with numpy : 0.0010347366333007812 nb_pixel_total : 56572 time to create 1 rle with old method : 0.06524205207824707 length of segment : 281 time for calcul the mask position with numpy : 0.0024750232696533203 nb_pixel_total : 64959 time to create 1 rle with old method : 0.07393884658813477 length of segment : 352 time for calcul the mask position with numpy : 0.007611751556396484 nb_pixel_total : 204754 time to create 1 rle with new method : 0.010755300521850586 length of segment : 364 time for calcul the mask position with numpy : 0.0015711784362792969 nb_pixel_total : 18938 time to create 1 rle with old method : 0.02167510986328125 length of segment : 481 time for calcul the mask position with numpy : 0.0007891654968261719 nb_pixel_total : 18419 time to create 1 rle with old method : 0.021361351013183594 length of segment : 161 time for calcul the mask position with numpy : 0.000499725341796875 nb_pixel_total : 12493 time to create 1 rle with old method : 0.01444864273071289 length of segment : 167 time for calcul the mask position with numpy : 0.005712032318115234 nb_pixel_total : 196431 time to create 1 rle with new method : 0.00948786735534668 length of segment : 686 time for calcul the mask position with numpy : 0.0005688667297363281 nb_pixel_total : 20085 time to create 1 rle with old method : 0.02377915382385254 length of segment : 190 time for calcul the mask position with numpy : 0.0007090568542480469 nb_pixel_total : 18343 time to create 1 rle with old method : 0.021600723266601562 length of segment : 175 time for calcul the mask position with numpy : 0.0035963058471679688 nb_pixel_total : 66742 time to create 1 rle with old method : 0.07584428787231445 length of segment : 306 time for calcul the mask position with numpy : 0.0018460750579833984 nb_pixel_total : 41147 time to create 1 rle with old method : 0.04837822914123535 length of segment : 185 time for calcul the mask position with numpy : 0.0018167495727539062 nb_pixel_total : 38610 time to create 1 rle with old method : 0.04430246353149414 length of segment : 330 time for calcul the mask position with numpy : 0.0020194053649902344 nb_pixel_total : 32413 time to create 1 rle with old method : 0.03763127326965332 length of segment : 231 time for calcul the mask position with numpy : 0.0011563301086425781 nb_pixel_total : 28783 time to create 1 rle with old method : 0.032605886459350586 length of segment : 159 time for calcul the mask position with numpy : 0.0023033618927001953 nb_pixel_total : 48011 time to create 1 rle with old method : 0.057569026947021484 length of segment : 301 time for calcul the mask position with numpy : 0.004593610763549805 nb_pixel_total : 117967 time to create 1 rle with old method : 0.13333630561828613 length of segment : 520 time for calcul the mask position with numpy : 0.0007016658782958984 nb_pixel_total : 14520 time to create 1 rle with old method : 0.0167086124420166 length of segment : 124 time for calcul the mask position with numpy : 0.0019567012786865234 nb_pixel_total : 46736 time to create 1 rle with old method : 0.054573774337768555 length of segment : 314 time for calcul the mask position with numpy : 0.007875919342041016 nb_pixel_total : 136328 time to create 1 rle with old method : 0.15060877799987793 length of segment : 597 time for calcul the mask position with numpy : 0.002816915512084961 nb_pixel_total : 96816 time to create 1 rle with old method : 0.11074137687683105 length of segment : 239 time for calcul the mask position with numpy : 0.007012605667114258 nb_pixel_total : 238630 time to create 1 rle with new method : 0.010944604873657227 length of segment : 474 time for calcul the mask position with numpy : 0.001199960708618164 nb_pixel_total : 31141 time to create 1 rle with old method : 0.0355226993560791 length of segment : 164 time for calcul the mask position with numpy : 0.0008559226989746094 nb_pixel_total : 30549 time to create 1 rle with old method : 0.034114837646484375 length of segment : 224 time for calcul the mask position with numpy : 0.019005537033081055 nb_pixel_total : 533686 time to create 1 rle with new method : 0.04916691780090332 length of segment : 928 time for calcul the mask position with numpy : 0.0020384788513183594 nb_pixel_total : 76425 time to create 1 rle with old method : 0.09176874160766602 length of segment : 598 time for calcul the mask position with numpy : 0.0013165473937988281 nb_pixel_total : 49720 time to create 1 rle with old method : 0.05573868751525879 length of segment : 321 time for calcul the mask position with numpy : 0.003154277801513672 nb_pixel_total : 82745 time to create 1 rle with old method : 0.09432077407836914 length of segment : 512 time for calcul the mask position with numpy : 0.0017664432525634766 nb_pixel_total : 40208 time to create 1 rle with old method : 0.04553985595703125 length of segment : 322 time for calcul the mask position with numpy : 0.0010170936584472656 nb_pixel_total : 35910 time to create 1 rle with old method : 0.04137134552001953 length of segment : 141 time for calcul the mask position with numpy : 0.0002167224884033203 nb_pixel_total : 12611 time to create 1 rle with old method : 0.014883279800415039 length of segment : 127 time for calcul the mask position with numpy : 0.024022579193115234 nb_pixel_total : 624374 time to create 1 rle with new method : 0.24811291694641113 length of segment : 900 time for calcul the mask position with numpy : 0.003730297088623047 nb_pixel_total : 73526 time to create 1 rle with old method : 0.07928633689880371 length of segment : 682 time for calcul the mask position with numpy : 0.002275228500366211 nb_pixel_total : 60810 time to create 1 rle with old method : 0.06783604621887207 length of segment : 425 time for calcul the mask position with numpy : 0.0011191368103027344 nb_pixel_total : 46122 time to create 1 rle with old method : 0.051636457443237305 length of segment : 221 time for calcul the mask position with numpy : 0.00019311904907226562 nb_pixel_total : 6386 time to create 1 rle with old method : 0.007259845733642578 length of segment : 119 time for calcul the mask position with numpy : 0.0005450248718261719 nb_pixel_total : 30864 time to create 1 rle with old method : 0.03631997108459473 length of segment : 233 time for calcul the mask position with numpy : 0.00024819374084472656 nb_pixel_total : 9616 time to create 1 rle with old method : 0.011301040649414062 length of segment : 140 time for calcul the mask position with numpy : 0.01752448081970215 nb_pixel_total : 556553 time to create 1 rle with new method : 0.04184889793395996 length of segment : 997 time for calcul the mask position with numpy : 0.0026044845581054688 nb_pixel_total : 48814 time to create 1 rle with old method : 0.05326652526855469 length of segment : 428 time for calcul the mask position with numpy : 0.013959169387817383 nb_pixel_total : 360084 time to create 1 rle with new method : 0.03676915168762207 length of segment : 1147 time for calcul the mask position with numpy : 0.0007755756378173828 nb_pixel_total : 16459 time to create 1 rle with old method : 0.01858043670654297 length of segment : 209 time for calcul the mask position with numpy : 0.0023772716522216797 nb_pixel_total : 55361 time to create 1 rle with old method : 0.06024360656738281 length of segment : 338 time for calcul the mask position with numpy : 0.0008454322814941406 nb_pixel_total : 16238 time to create 1 rle with old method : 0.017693042755126953 length of segment : 139 time for calcul the mask position with numpy : 0.005904197692871094 nb_pixel_total : 124912 time to create 1 rle with old method : 0.13728094100952148 length of segment : 814 time for calcul the mask position with numpy : 0.0003006458282470703 nb_pixel_total : 5849 time to create 1 rle with old method : 0.006493091583251953 length of segment : 92 time for calcul the mask position with numpy : 0.0010581016540527344 nb_pixel_total : 48607 time to create 1 rle with old method : 0.051242828369140625 length of segment : 204 time for calcul the mask position with numpy : 0.00072479248046875 nb_pixel_total : 24240 time to create 1 rle with old method : 0.026127338409423828 length of segment : 254 time for calcul the mask position with numpy : 0.0012812614440917969 nb_pixel_total : 50009 time to create 1 rle with old method : 0.05726337432861328 length of segment : 174 time for calcul the mask position with numpy : 0.001016855239868164 nb_pixel_total : 24739 time to create 1 rle with old method : 0.029321908950805664 length of segment : 219 time for calcul the mask position with numpy : 0.0013725757598876953 nb_pixel_total : 32688 time to create 1 rle with old method : 0.03832674026489258 length of segment : 205 time for calcul the mask position with numpy : 0.0022912025451660156 nb_pixel_total : 120741 time to create 1 rle with old method : 0.13692474365234375 length of segment : 511 time for calcul the mask position with numpy : 0.003087759017944336 nb_pixel_total : 180163 time to create 1 rle with new method : 0.010039329528808594 length of segment : 764 time for calcul the mask position with numpy : 0.002944469451904297 nb_pixel_total : 75655 time to create 1 rle with old method : 0.09315943717956543 length of segment : 496 time for calcul the mask position with numpy : 0.0010843276977539062 nb_pixel_total : 46765 time to create 1 rle with old method : 0.05906200408935547 length of segment : 180 time for calcul the mask position with numpy : 0.002678394317626953 nb_pixel_total : 112895 time to create 1 rle with old method : 0.12996864318847656 length of segment : 305 time for calcul the mask position with numpy : 0.0008544921875 nb_pixel_total : 25121 time to create 1 rle with old method : 0.04090452194213867 length of segment : 170 time for calcul the mask position with numpy : 0.00042057037353515625 nb_pixel_total : 13697 time to create 1 rle with old method : 0.016118526458740234 length of segment : 111 time for calcul the mask position with numpy : 0.0037093162536621094 nb_pixel_total : 102824 time to create 1 rle with old method : 0.11581850051879883 length of segment : 502 time for calcul the mask position with numpy : 0.0026700496673583984 nb_pixel_total : 79850 time to create 1 rle with old method : 0.08892011642456055 length of segment : 317 time for calcul the mask position with numpy : 0.0063190460205078125 nb_pixel_total : 208772 time to create 1 rle with new method : 0.010146379470825195 length of segment : 481 time for calcul the mask position with numpy : 0.008482694625854492 nb_pixel_total : 315834 time to create 1 rle with new method : 0.013948678970336914 length of segment : 807 time for calcul the mask position with numpy : 0.008639335632324219 nb_pixel_total : 214235 time to create 1 rle with new method : 0.0167539119720459 length of segment : 548 time for calcul the mask position with numpy : 0.004499912261962891 nb_pixel_total : 87954 time to create 1 rle with old method : 0.09616303443908691 length of segment : 546 time for calcul the mask position with numpy : 0.0005714893341064453 nb_pixel_total : 13568 time to create 1 rle with old method : 0.016677141189575195 length of segment : 138 time for calcul the mask position with numpy : 0.017116069793701172 nb_pixel_total : 546770 time to create 1 rle with new method : 0.0467534065246582 length of segment : 953 time for calcul the mask position with numpy : 0.0010509490966796875 nb_pixel_total : 27338 time to create 1 rle with old method : 0.03025507926940918 length of segment : 192 time for calcul the mask position with numpy : 0.0020673274993896484 nb_pixel_total : 43969 time to create 1 rle with old method : 0.055632829666137695 length of segment : 175 time for calcul the mask position with numpy : 0.005548000335693359 nb_pixel_total : 117184 time to create 1 rle with old method : 0.12319087982177734 length of segment : 616 time for calcul the mask position with numpy : 0.0027017593383789062 nb_pixel_total : 92175 time to create 1 rle with old method : 0.0969095230102539 length of segment : 484 time for calcul the mask position with numpy : 0.0029397010803222656 nb_pixel_total : 34639 time to create 1 rle with old method : 0.03679990768432617 length of segment : 575 time for calcul the mask position with numpy : 0.0004892349243164062 nb_pixel_total : 28908 time to create 1 rle with old method : 0.03055405616760254 length of segment : 259 time for calcul the mask position with numpy : 0.0003635883331298828 nb_pixel_total : 6742 time to create 1 rle with old method : 0.008010149002075195 length of segment : 99 time for calcul the mask position with numpy : 0.0012919902801513672 nb_pixel_total : 24359 time to create 1 rle with old method : 0.02718377113342285 length of segment : 231 time for calcul the mask position with numpy : 0.004647493362426758 nb_pixel_total : 109404 time to create 1 rle with old method : 0.1193857192993164 length of segment : 639 time for calcul the mask position with numpy : 0.0008740425109863281 nb_pixel_total : 26495 time to create 1 rle with old method : 0.029306650161743164 length of segment : 179 time for calcul the mask position with numpy : 0.0004165172576904297 nb_pixel_total : 9153 time to create 1 rle with old method : 0.010091781616210938 length of segment : 153 time for calcul the mask position with numpy : 0.0041027069091796875 nb_pixel_total : 113960 time to create 1 rle with old method : 0.12167882919311523 length of segment : 805 time for calcul the mask position with numpy : 0.002609729766845703 nb_pixel_total : 50146 time to create 1 rle with old method : 0.055315494537353516 length of segment : 401 time for calcul the mask position with numpy : 0.0027985572814941406 nb_pixel_total : 115827 time to create 1 rle with old method : 0.15151548385620117 length of segment : 249 time for calcul the mask position with numpy : 0.005239248275756836 nb_pixel_total : 89425 time to create 1 rle with old method : 0.09595680236816406 length of segment : 557 time for calcul the mask position with numpy : 0.001842498779296875 nb_pixel_total : 47000 time to create 1 rle with old method : 0.05063319206237793 length of segment : 258 time for calcul the mask position with numpy : 0.0026624202728271484 nb_pixel_total : 69879 time to create 1 rle with old method : 0.07727241516113281 length of segment : 229 time for calcul the mask position with numpy : 0.00036978721618652344 nb_pixel_total : 7727 time to create 1 rle with old method : 0.008828401565551758 length of segment : 113 time for calcul the mask position with numpy : 0.0009555816650390625 nb_pixel_total : 38420 time to create 1 rle with old method : 0.0417330265045166 length of segment : 283 time for calcul the mask position with numpy : 0.012326240539550781 nb_pixel_total : 286929 time to create 1 rle with new method : 0.04816150665283203 length of segment : 1063 time for calcul the mask position with numpy : 0.0002701282501220703 nb_pixel_total : 7552 time to create 1 rle with old method : 0.008361101150512695 length of segment : 143 time for calcul the mask position with numpy : 0.0012807846069335938 nb_pixel_total : 19388 time to create 1 rle with old method : 0.022542715072631836 length of segment : 218 time for calcul the mask position with numpy : 0.0006451606750488281 nb_pixel_total : 9615 time to create 1 rle with old method : 0.01116800308227539 length of segment : 84 time for calcul the mask position with numpy : 0.0010075569152832031 nb_pixel_total : 16158 time to create 1 rle with old method : 0.019044160842895508 length of segment : 163 time for calcul the mask position with numpy : 0.0022079944610595703 nb_pixel_total : 25737 time to create 1 rle with old method : 0.03037738800048828 length of segment : 251 time for calcul the mask position with numpy : 0.0008594989776611328 nb_pixel_total : 11906 time to create 1 rle with old method : 0.014516353607177734 length of segment : 146 time for calcul the mask position with numpy : 0.0010716915130615234 nb_pixel_total : 16929 time to create 1 rle with old method : 0.020197391510009766 length of segment : 182 time for calcul the mask position with numpy : 0.001188516616821289 nb_pixel_total : 19061 time to create 1 rle with old method : 0.022797346115112305 length of segment : 145 time for calcul the mask position with numpy : 0.009598016738891602 nb_pixel_total : 116051 time to create 1 rle with old method : 0.12664484977722168 length of segment : 557 time for calcul the mask position with numpy : 0.0028252601623535156 nb_pixel_total : 32903 time to create 1 rle with old method : 0.037052154541015625 length of segment : 539 time for calcul the mask position with numpy : 0.0013065338134765625 nb_pixel_total : 22890 time to create 1 rle with old method : 0.02602863311767578 length of segment : 203 time for calcul the mask position with numpy : 0.002151966094970703 nb_pixel_total : 24247 time to create 1 rle with old method : 0.02946758270263672 length of segment : 281 time for calcul the mask position with numpy : 0.001194000244140625 nb_pixel_total : 19097 time to create 1 rle with old method : 0.03148221969604492 length of segment : 256 time for calcul the mask position with numpy : 0.0027344226837158203 nb_pixel_total : 49942 time to create 1 rle with old method : 0.05426025390625 length of segment : 255 time for calcul the mask position with numpy : 0.0012586116790771484 nb_pixel_total : 20746 time to create 1 rle with old method : 0.02363300323486328 length of segment : 262 time for calcul the mask position with numpy : 0.0006313323974609375 nb_pixel_total : 11111 time to create 1 rle with old method : 0.012484312057495117 length of segment : 89 time for calcul the mask position with numpy : 0.004103899002075195 nb_pixel_total : 62846 time to create 1 rle with old method : 0.06987833976745605 length of segment : 256 time for calcul the mask position with numpy : 0.001280069351196289 nb_pixel_total : 21995 time to create 1 rle with old method : 0.025648832321166992 length of segment : 112 time for calcul the mask position with numpy : 0.0010526180267333984 nb_pixel_total : 15187 time to create 1 rle with old method : 0.017963409423828125 length of segment : 164 time for calcul the mask position with numpy : 0.0019397735595703125 nb_pixel_total : 16706 time to create 1 rle with old method : 0.019120454788208008 length of segment : 287 time for calcul the mask position with numpy : 0.0022804737091064453 nb_pixel_total : 25744 time to create 1 rle with old method : 0.028817415237426758 length of segment : 279 time for calcul the mask position with numpy : 0.002126932144165039 nb_pixel_total : 26989 time to create 1 rle with old method : 0.03004741668701172 length of segment : 293 time for calcul the mask position with numpy : 0.0043599605560302734 nb_pixel_total : 36350 time to create 1 rle with old method : 0.041236162185668945 length of segment : 343 time for calcul the mask position with numpy : 0.002012968063354492 nb_pixel_total : 24907 time to create 1 rle with old method : 0.03075432777404785 length of segment : 195 time for calcul the mask position with numpy : 0.0026950836181640625 nb_pixel_total : 26718 time to create 1 rle with old method : 0.030597448348999023 length of segment : 343 time for calcul the mask position with numpy : 0.002455472946166992 nb_pixel_total : 30137 time to create 1 rle with old method : 0.0345003604888916 length of segment : 242 time for calcul the mask position with numpy : 0.0032739639282226562 nb_pixel_total : 41721 time to create 1 rle with old method : 0.046889305114746094 length of segment : 332 time for calcul the mask position with numpy : 0.0022172927856445312 nb_pixel_total : 34127 time to create 1 rle with old method : 0.03836679458618164 length of segment : 304 time for calcul the mask position with numpy : 0.0009927749633789062 nb_pixel_total : 15872 time to create 1 rle with old method : 0.01851630210876465 length of segment : 116 time for calcul the mask position with numpy : 0.0006353855133056641 nb_pixel_total : 7146 time to create 1 rle with old method : 0.008240222930908203 length of segment : 99 time for calcul the mask position with numpy : 0.0006537437438964844 nb_pixel_total : 9255 time to create 1 rle with old method : 0.010691165924072266 length of segment : 140 time for calcul the mask position with numpy : 0.0008344650268554688 nb_pixel_total : 14785 time to create 1 rle with old method : 0.016742944717407227 length of segment : 139 time for calcul the mask position with numpy : 0.0005257129669189453 nb_pixel_total : 13661 time to create 1 rle with old method : 0.015814781188964844 length of segment : 205 time for calcul the mask position with numpy : 0.008728265762329102 nb_pixel_total : 77018 time to create 1 rle with old method : 0.08577489852905273 length of segment : 486 time for calcul the mask position with numpy : 0.0011565685272216797 nb_pixel_total : 13901 time to create 1 rle with old method : 0.016227006912231445 length of segment : 270 time for calcul the mask position with numpy : 0.0015172958374023438 nb_pixel_total : 18506 time to create 1 rle with old method : 0.02119922637939453 length of segment : 135 time for calcul the mask position with numpy : 0.00043082237243652344 nb_pixel_total : 6524 time to create 1 rle with old method : 0.007604122161865234 length of segment : 62 time for calcul the mask position with numpy : 0.0005428791046142578 nb_pixel_total : 12620 time to create 1 rle with old method : 0.014623165130615234 length of segment : 112 time for calcul the mask position with numpy : 0.002034425735473633 nb_pixel_total : 36954 time to create 1 rle with old method : 0.04107093811035156 length of segment : 224 time for calcul the mask position with numpy : 0.0008668899536132812 nb_pixel_total : 10133 time to create 1 rle with old method : 0.01124119758605957 length of segment : 219 time for calcul the mask position with numpy : 0.005501747131347656 nb_pixel_total : 88934 time to create 1 rle with old method : 0.09717845916748047 length of segment : 406 time for calcul the mask position with numpy : 0.0013475418090820312 nb_pixel_total : 24285 time to create 1 rle with old method : 0.02838754653930664 length of segment : 172 time for calcul the mask position with numpy : 0.0038595199584960938 nb_pixel_total : 74765 time to create 1 rle with old method : 0.08275151252746582 length of segment : 290 time for calcul the mask position with numpy : 0.001422882080078125 nb_pixel_total : 17384 time to create 1 rle with old method : 0.01943659782409668 length of segment : 235 time for calcul the mask position with numpy : 0.00026607513427734375 nb_pixel_total : 3558 time to create 1 rle with old method : 0.00440216064453125 length of segment : 84 time for calcul the mask position with numpy : 0.0019252300262451172 nb_pixel_total : 29062 time to create 1 rle with old method : 0.0334320068359375 length of segment : 347 time for calcul the mask position with numpy : 0.0021059513092041016 nb_pixel_total : 33205 time to create 1 rle with old method : 0.03754281997680664 length of segment : 275 time for calcul the mask position with numpy : 0.002948284149169922 nb_pixel_total : 19211 time to create 1 rle with old method : 0.02223515510559082 length of segment : 229 time for calcul the mask position with numpy : 0.0008556842803955078 nb_pixel_total : 16607 time to create 1 rle with old method : 0.0187835693359375 length of segment : 181 time for calcul the mask position with numpy : 0.0007257461547851562 nb_pixel_total : 10943 time to create 1 rle with old method : 0.012243270874023438 length of segment : 160 time for calcul the mask position with numpy : 0.00016760826110839844 nb_pixel_total : 4635 time to create 1 rle with old method : 0.005293369293212891 length of segment : 95 time for calcul the mask position with numpy : 0.003957271575927734 nb_pixel_total : 33796 time to create 1 rle with old method : 0.0374140739440918 length of segment : 314 time for calcul the mask position with numpy : 0.0006468296051025391 nb_pixel_total : 16355 time to create 1 rle with old method : 0.017918109893798828 length of segment : 154 time for calcul the mask position with numpy : 0.00045299530029296875 nb_pixel_total : 6440 time to create 1 rle with old method : 0.007111549377441406 length of segment : 98 time for calcul the mask position with numpy : 0.0005691051483154297 nb_pixel_total : 13017 time to create 1 rle with old method : 0.014122962951660156 length of segment : 110 time for calcul the mask position with numpy : 0.001965045928955078 nb_pixel_total : 28104 time to create 1 rle with old method : 0.029932260513305664 length of segment : 183 time for calcul the mask position with numpy : 0.0015277862548828125 nb_pixel_total : 28301 time to create 1 rle with old method : 0.030783891677856445 length of segment : 181 time for calcul the mask position with numpy : 0.0012867450714111328 nb_pixel_total : 24773 time to create 1 rle with old method : 0.02690887451171875 length of segment : 186 time spent for convertir_results : 37.950490951538086 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 469 chid ids of type : 3594 Number RLEs to save : 122841 save missing photos in datou_result : time spend for datou_step_exec : 188.69304585456848 time spend to save output : 13.414458990097046 total time spend for step 1 : 202.10750484466553 step2:crop_condition Tue Apr 1 20:23:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 13 ! batch 1 Loaded 469 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 ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 354 About to insert : list_path_to_insert length 354 new photo from crops ! About to upload 354 photos upload in portfolio : 3736932 init cache_photo without model_param we have 354 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743531897_2457968 we have uploaded 354 photos in the portfolio 3736932 time of upload the photos Elapsed time : 100.38143944740295 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 77 About to insert : list_path_to_insert length 77 new photo from crops ! About to upload 77 photos upload in portfolio : 3736932 init cache_photo without model_param we have 77 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743532027_2457968 we have uploaded 77 photos in the portfolio 3736932 time of upload the photos Elapsed time : 33.59642457962036 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 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 ! map_result returned by crop_photo_return_map_crop : length : 22 About to insert : list_path_to_insert length 22 new photo from crops ! About to upload 22 photos upload in portfolio : 3736932 init cache_photo without model_param we have 22 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743532074_2457968 we have uploaded 22 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.992757558822632 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 ! 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 : 15 About to insert : list_path_to_insert length 15 new photo from crops ! About to upload 15 photos upload in portfolio : 3736932 init cache_photo without model_param we have 15 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743532086_2457968 we have uploaded 15 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.28221321105957 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 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 ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743532093_2457968 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6110975742340088 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349321881, 1349321862, 1349321823, 1349321792, 1349321645, 1349321623, 1349321620, 1349321618, 1349321615, 1349321609, 1349321585, 1349321576, 1349321571] Looping around the photos to save general results len do output : 469 /1349337678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337789Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337883Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337894Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337903Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337905Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337906Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337907Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337909Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337917Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349337929Didn't retrieve data .Didn't retrieve data .Didn't 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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, '2714309') ('3318', '21957008', '1349321881', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321862', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321823', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321792', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321645', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321623', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321620', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321618', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321615', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321609', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321585', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321576', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321571', None, None, None, None, None, '2714309') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1420 time used for this insertion : 0.06078052520751953 save_final save missing photos in datou_result : time spend for datou_step_exec : 259.35364055633545 time spend to save output : 0.07038712501525879 total time spend for step 2 : 259.4240276813507 step3:rle_unique_nms_with_priority Tue Apr 1 20:28:14 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 469 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 13 nb_hashtags : 2 time to prepare the origin masks : 3.8784613609313965 time for calcul the mask position with numpy : 0.5974512100219727 nb_pixel_total : 6392637 time to create 1 rle with new method : 0.5226798057556152 time for calcul the mask position with numpy : 0.02301192283630371 nb_pixel_total : 358922 time to create 1 rle with new method : 0.48350071907043457 time for calcul the mask position with numpy : 0.02533698081970215 nb_pixel_total : 12828 time to create 1 rle with old method : 0.01841568946838379 time for calcul the mask position with numpy : 0.030537128448486328 nb_pixel_total : 15351 time to create 1 rle with old method : 0.017993450164794922 time for calcul the mask position with numpy : 0.022672414779663086 nb_pixel_total : 40729 time to create 1 rle with old method : 0.04477071762084961 time for calcul the mask position with numpy : 0.022339344024658203 nb_pixel_total : 5509 time to create 1 rle with old method : 0.006287336349487305 time for calcul the mask position with numpy : 0.02236771583557129 nb_pixel_total : 14972 time to create 1 rle with old method : 0.016674280166625977 time for calcul the mask position with numpy : 0.023592472076416016 nb_pixel_total : 54354 time to create 1 rle with old method : 0.06081223487854004 time for calcul the mask position with numpy : 0.022531986236572266 nb_pixel_total : 9278 time to create 1 rle with old method : 0.01036381721496582 time for calcul the mask position with numpy : 0.022557735443115234 nb_pixel_total : 8298 time to create 1 rle with old method : 0.009309053421020508 time for calcul the mask position with numpy : 0.02144169807434082 nb_pixel_total : 9913 time to create 1 rle with old method : 0.011243581771850586 time for calcul the mask position with numpy : 0.020399093627929688 nb_pixel_total : 34669 time to create 1 rle with old method : 0.041188955307006836 time for calcul the mask position with numpy : 0.021665573120117188 nb_pixel_total : 21405 time to create 1 rle with old method : 0.02351212501525879 time for calcul the mask position with numpy : 0.021490097045898438 nb_pixel_total : 71375 time to create 1 rle with old method : 0.08335638046264648 create new chi : 2.303868055343628 time to delete rle : 0.022789478302001953 batch 1 Loaded 27 chid ids of type : 3594 ++++++++++++++++++Number RLEs to save : 8354 TO DO : save crop sub photo not yet done ! save time : 0.5577690601348877 nb_obj : 27 nb_hashtags : 2 time to prepare the origin masks : 3.453388214111328 time for calcul the mask position with numpy : 0.35681891441345215 nb_pixel_total : 6397826 time to create 1 rle with new method : 2.1365108489990234 time for calcul the mask position with numpy : 0.028652667999267578 nb_pixel_total : 4688 time to create 1 rle with old method : 0.005428791046142578 time for calcul the mask position with numpy : 0.02893209457397461 nb_pixel_total : 4723 time to create 1 rle with old method : 0.005320549011230469 time for calcul the mask position with numpy : 0.02883744239807129 nb_pixel_total : 13033 time to create 1 rle with old method : 0.014456033706665039 time for calcul the mask position with numpy : 0.028804302215576172 nb_pixel_total : 10304 time to create 1 rle with old method : 0.011821985244750977 time for calcul the mask position with numpy : 0.02924370765686035 nb_pixel_total : 44575 time to create 1 rle with old method : 0.050862789154052734 time for calcul the mask position with numpy : 0.028008222579956055 nb_pixel_total : 8573 time to create 1 rle with old method : 0.009648323059082031 time for calcul the mask position with numpy : 0.028718233108520508 nb_pixel_total : 32386 time to create 1 rle with old method : 0.03668403625488281 time for calcul the mask position with numpy : 0.02892446517944336 nb_pixel_total : 46305 time to create 1 rle with old method : 0.05173182487487793 time for calcul the mask position with numpy : 0.0289461612701416 nb_pixel_total : 24237 time to create 1 rle with old method : 0.027502059936523438 time for calcul the mask position with numpy : 0.02899312973022461 nb_pixel_total : 25190 time to create 1 rle with old method : 0.028208494186401367 time for calcul the mask position with numpy : 0.028131484985351562 nb_pixel_total : 12833 time to create 1 rle with old method : 0.013991594314575195 time for calcul the mask position with numpy : 0.027452468872070312 nb_pixel_total : 23846 time to create 1 rle with old method : 0.02584385871887207 time for calcul the mask position with numpy : 0.027931928634643555 nb_pixel_total : 7980 time to create 1 rle with old method : 0.008877992630004883 time for calcul the mask position with numpy : 0.033898115158081055 nb_pixel_total : 21913 time to create 1 rle with old method : 0.027189254760742188 time for calcul the mask position with numpy : 0.02919149398803711 nb_pixel_total : 48918 time to create 1 rle with old method : 0.05476880073547363 time for calcul the mask position with numpy : 0.028556346893310547 nb_pixel_total : 8566 time to create 1 rle with old method : 0.00948786735534668 time for calcul the mask position with numpy : 0.02819037437438965 nb_pixel_total : 59137 time to create 1 rle with old method : 0.06398582458496094 time for calcul the mask position with numpy : 0.028006315231323242 nb_pixel_total : 11043 time to create 1 rle with old method : 0.012049198150634766 time for calcul the mask position with numpy : 0.027599811553955078 nb_pixel_total : 13494 time to create 1 rle with old method : 0.014669656753540039 time for calcul the mask position with numpy : 0.027848005294799805 nb_pixel_total : 10263 time to create 1 rle with old method : 0.011421918869018555 time for calcul the mask position with numpy : 0.028132915496826172 nb_pixel_total : 30485 time to create 1 rle with old method : 0.033849239349365234 time for calcul the mask position with numpy : 0.02768707275390625 nb_pixel_total : 18601 time to create 1 rle with old method : 0.019718408584594727 time for calcul the mask position with numpy : 0.027637481689453125 nb_pixel_total : 18636 time to create 1 rle with old method : 0.020114898681640625 time for calcul the mask position with numpy : 0.027613162994384766 nb_pixel_total : 85785 time to create 1 rle with old method : 0.08702969551086426 time for calcul the mask position with numpy : 0.027291059494018555 nb_pixel_total : 3608 time to create 1 rle with old method : 0.004116058349609375 time for calcul the mask position with numpy : 0.028029680252075195 nb_pixel_total : 53977 time to create 1 rle with old method : 0.05810999870300293 time for calcul the mask position with numpy : 0.027832508087158203 nb_pixel_total : 9315 time to create 1 rle with old method : 0.009896039962768555 create new chi : 4.020575284957886 time to delete rle : 0.0017333030700683594 batch 1 Loaded 55 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 12388 TO DO : save crop sub photo not yet done ! save time : 1.565854787826538 nb_obj : 29 nb_hashtags : 3 time to prepare the origin masks : 3.8829174041748047 time for calcul the mask position with numpy : 2.194854736328125 nb_pixel_total : 5774919 time to create 1 rle with new method : 0.7518982887268066 time for calcul the mask position with numpy : 0.027271032333374023 nb_pixel_total : 9117 time to create 1 rle with old method : 0.010326385498046875 time for calcul the mask position with numpy : 0.028740406036376953 nb_pixel_total : 18688 time to create 1 rle with old method : 0.025185346603393555 time for calcul the mask position with numpy : 0.03706097602844238 nb_pixel_total : 52692 time to create 1 rle with old method : 0.06817364692687988 time for calcul the mask position with numpy : 0.02953815460205078 nb_pixel_total : 3013 time to create 1 rle with old method : 0.0034880638122558594 time for calcul the mask position with numpy : 0.029013395309448242 nb_pixel_total : 24242 time to create 1 rle with old method : 0.02717447280883789 time for calcul the mask position with numpy : 0.02918529510498047 nb_pixel_total : 97965 time to create 1 rle with old method : 0.1080925464630127 time for calcul the mask position with numpy : 0.028589248657226562 nb_pixel_total : 4940 time to create 1 rle with old method : 0.00551915168762207 time for calcul the mask position with numpy : 0.028836727142333984 nb_pixel_total : 24451 time to create 1 rle with old method : 0.027600526809692383 time for calcul the mask position with numpy : 0.03086996078491211 nb_pixel_total : 250632 time to create 1 rle with new method : 0.7477595806121826 time for calcul the mask position with numpy : 0.028842687606811523 nb_pixel_total : 7289 time to create 1 rle with old method : 0.008145809173583984 time for calcul the mask position with numpy : 0.033519744873046875 nb_pixel_total : 26342 time to create 1 rle with old method : 0.030010461807250977 time for calcul the mask position with numpy : 0.03235793113708496 nb_pixel_total : 5809 time to create 1 rle with old method : 0.006445169448852539 time for calcul the mask position with numpy : 0.02977728843688965 nb_pixel_total : 260594 time to create 1 rle with new method : 0.7015132904052734 time for calcul the mask position with numpy : 0.029323339462280273 nb_pixel_total : 19268 time to create 1 rle with old method : 0.021351337432861328 time for calcul the mask position with numpy : 0.02884507179260254 nb_pixel_total : 77593 time to create 1 rle with old method : 0.08700037002563477 time for calcul the mask position with numpy : 0.029140233993530273 nb_pixel_total : 41274 time to create 1 rle with old method : 0.0462949275970459 time for calcul the mask position with numpy : 0.029004573822021484 nb_pixel_total : 38580 time to create 1 rle with old method : 0.0426335334777832 time for calcul the mask position with numpy : 0.02909994125366211 nb_pixel_total : 21923 time to create 1 rle with old method : 0.024346351623535156 time for calcul the mask position with numpy : 0.028812646865844727 nb_pixel_total : 29917 time to create 1 rle with old method : 0.03362727165222168 time for calcul the mask position with numpy : 0.028755903244018555 nb_pixel_total : 4143 time to create 1 rle with old method : 0.0048580169677734375 time for calcul the mask position with numpy : 0.03153800964355469 nb_pixel_total : 7174 time to create 1 rle with old method : 0.00801539421081543 time for calcul the mask position with numpy : 0.02907276153564453 nb_pixel_total : 55352 time to create 1 rle with old method : 0.06566309928894043 time for calcul the mask position with numpy : 0.028714418411254883 nb_pixel_total : 11747 time to create 1 rle with old method : 0.01296234130859375 time for calcul the mask position with numpy : 0.02847123146057129 nb_pixel_total : 47201 time to create 1 rle with old method : 0.05220341682434082 time for calcul the mask position with numpy : 0.02867603302001953 nb_pixel_total : 4231 time to create 1 rle with old method : 0.00460362434387207 time for calcul the mask position with numpy : 0.028441905975341797 nb_pixel_total : 47288 time to create 1 rle with old method : 0.05196952819824219 time for calcul the mask position with numpy : 0.02890300750732422 nb_pixel_total : 3877 time to create 1 rle with old method : 0.004431009292602539 time for calcul the mask position with numpy : 0.029320478439331055 nb_pixel_total : 59066 time to create 1 rle with old method : 0.06557226181030273 time for calcul the mask position with numpy : 0.029230833053588867 nb_pixel_total : 20913 time to create 1 rle with old method : 0.03351092338562012 create new chi : 6.219327449798584 time to delete rle : 0.003559112548828125 batch 1 Loaded 59 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18463 TO DO : save crop sub photo not yet done ! save time : 1.3651180267333984 nb_obj : 33 nb_hashtags : 4 time to prepare the origin masks : 3.835503101348877 time for calcul the mask position with numpy : 1.0201287269592285 nb_pixel_total : 5734400 time to create 1 rle with new method : 0.3463120460510254 time for calcul the mask position with numpy : 0.028635025024414062 nb_pixel_total : 15833 time to create 1 rle with old method : 0.018547773361206055 time for calcul the mask position with numpy : 0.029383182525634766 nb_pixel_total : 148721 time to create 1 rle with old method : 0.19055509567260742 time for calcul the mask position with numpy : 0.02896714210510254 nb_pixel_total : 20964 time to create 1 rle with old method : 0.023654937744140625 time for calcul the mask position with numpy : 0.02870941162109375 nb_pixel_total : 35503 time to create 1 rle with old method : 0.040447235107421875 time for calcul the mask position with numpy : 0.02978348731994629 nb_pixel_total : 112834 time to create 1 rle with old method : 0.12603473663330078 time for calcul the mask position with numpy : 0.028932571411132812 nb_pixel_total : 11843 time to create 1 rle with old method : 0.015840768814086914 time for calcul the mask position with numpy : 0.028914928436279297 nb_pixel_total : 37470 time to create 1 rle with old method : 0.042324066162109375 time for calcul the mask position with numpy : 0.029309988021850586 nb_pixel_total : 42347 time to create 1 rle with old method : 0.04618716239929199 time for calcul the mask position with numpy : 0.028519868850708008 nb_pixel_total : 12385 time to create 1 rle with old method : 0.013743162155151367 time for calcul the mask position with numpy : 0.02867269515991211 nb_pixel_total : 14377 time to create 1 rle with old method : 0.01658940315246582 time for calcul the mask position with numpy : 0.02836132049560547 nb_pixel_total : 9695 time to create 1 rle with old method : 0.010782957077026367 time for calcul the mask position with numpy : 0.028499364852905273 nb_pixel_total : 13479 time to create 1 rle with old method : 0.015070915222167969 time for calcul the mask position with numpy : 0.02733755111694336 nb_pixel_total : 62654 time to create 1 rle with old method : 0.06691932678222656 time for calcul the mask position with numpy : 0.028075218200683594 nb_pixel_total : 55810 time to create 1 rle with old method : 0.059760332107543945 time for calcul the mask position with numpy : 0.0291290283203125 nb_pixel_total : 210172 time to create 1 rle with new method : 0.5993366241455078 time for calcul the mask position with numpy : 0.03324460983276367 nb_pixel_total : 750 time to create 1 rle with old method : 0.0010137557983398438 time for calcul the mask position with numpy : 0.03255438804626465 nb_pixel_total : 7984 time to create 1 rle with old method : 0.00917363166809082 time for calcul the mask position with numpy : 0.029575347900390625 nb_pixel_total : 11471 time to create 1 rle with old method : 0.014014959335327148 time for calcul the mask position with numpy : 0.02960824966430664 nb_pixel_total : 15918 time to create 1 rle with old method : 0.020777225494384766 time for calcul the mask position with numpy : 0.02965378761291504 nb_pixel_total : 15082 time to create 1 rle with old method : 0.01704239845275879 time for calcul the mask position with numpy : 0.029139280319213867 nb_pixel_total : 67563 time to create 1 rle with old method : 0.07551693916320801 time for calcul the mask position with numpy : 0.028929471969604492 nb_pixel_total : 15025 time to create 1 rle with old method : 0.017270565032958984 time for calcul the mask position with numpy : 0.02903890609741211 nb_pixel_total : 12522 time to create 1 rle with old method : 0.01412820816040039 time for calcul the mask position with numpy : 0.029408693313598633 nb_pixel_total : 45411 time to create 1 rle with old method : 0.05008125305175781 time for calcul the mask position with numpy : 0.028237104415893555 nb_pixel_total : 11963 time to create 1 rle with old method : 0.013504266738891602 time for calcul the mask position with numpy : 0.028934955596923828 nb_pixel_total : 15845 time to create 1 rle with old method : 0.018131732940673828 time for calcul the mask position with numpy : 0.028861284255981445 nb_pixel_total : 20007 time to create 1 rle with old method : 0.022023677825927734 time for calcul the mask position with numpy : 0.03182387351989746 nb_pixel_total : 100479 time to create 1 rle with old method : 0.1212911605834961 time for calcul the mask position with numpy : 0.027801990509033203 nb_pixel_total : 90760 time to create 1 rle with old method : 0.09839344024658203 time for calcul the mask position with numpy : 0.034406423568725586 nb_pixel_total : 19609 time to create 1 rle with old method : 0.021543264389038086 time for calcul the mask position with numpy : 0.03297543525695801 nb_pixel_total : 16083 time to create 1 rle with old method : 0.025851964950561523 time for calcul the mask position with numpy : 0.03312063217163086 nb_pixel_total : 28004 time to create 1 rle with old method : 0.031230926513671875 time for calcul the mask position with numpy : 0.02994251251220703 nb_pixel_total : 17277 time to create 1 rle with old method : 0.018857717514038086 create new chi : 4.280792951583862 time to delete rle : 0.003111124038696289 batch 1 Loaded 67 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18348 TO DO : save crop sub photo not yet done ! save time : 1.1255104541778564 nb_obj : 22 nb_hashtags : 3 time to prepare the origin masks : 6.434339284896851 time for calcul the mask position with numpy : 0.3075900077819824 nb_pixel_total : 6076630 time to create 1 rle with new method : 0.3140411376953125 time for calcul the mask position with numpy : 0.023041725158691406 nb_pixel_total : 411152 time to create 1 rle with new method : 0.31392359733581543 time for calcul the mask position with numpy : 0.020808696746826172 nb_pixel_total : 1855 time to create 1 rle with old method : 0.002353668212890625 time for calcul the mask position with numpy : 0.021248340606689453 nb_pixel_total : 11110 time to create 1 rle with old method : 0.01251983642578125 time for calcul the mask position with numpy : 0.021968841552734375 nb_pixel_total : 63847 time to create 1 rle with old method : 0.07135868072509766 time for calcul the mask position with numpy : 0.021528244018554688 nb_pixel_total : 5366 time to create 1 rle with old method : 0.006094932556152344 time for calcul the mask position with numpy : 0.021895647048950195 nb_pixel_total : 26486 time to create 1 rle with old method : 0.029800891876220703 time for calcul the mask position with numpy : 0.021074295043945312 nb_pixel_total : 43046 time to create 1 rle with old method : 0.04578280448913574 time for calcul the mask position with numpy : 0.020590543746948242 nb_pixel_total : 66042 time to create 1 rle with old method : 0.07275271415710449 time for calcul the mask position with numpy : 0.02364969253540039 nb_pixel_total : 13094 time to create 1 rle with old method : 0.015636444091796875 time for calcul the mask position with numpy : 0.023496150970458984 nb_pixel_total : 15261 time to create 1 rle with old method : 0.037052154541015625 time for calcul the mask position with numpy : 0.025156259536743164 nb_pixel_total : 28662 time to create 1 rle with old method : 0.032013654708862305 time for calcul the mask position with numpy : 0.022552967071533203 nb_pixel_total : 29377 time to create 1 rle with old method : 0.03314518928527832 time for calcul the mask position with numpy : 0.02171182632446289 nb_pixel_total : 12498 time to create 1 rle with old method : 0.014084815979003906 time for calcul the mask position with numpy : 0.021422386169433594 nb_pixel_total : 8205 time to create 1 rle with old method : 0.009189367294311523 time for calcul the mask position with numpy : 0.020491361618041992 nb_pixel_total : 20784 time to create 1 rle with old method : 0.023253917694091797 time for calcul the mask position with numpy : 0.021485090255737305 nb_pixel_total : 14030 time to create 1 rle with old method : 0.01585555076599121 time for calcul the mask position with numpy : 0.02129960060119629 nb_pixel_total : 8537 time to create 1 rle with old method : 0.009894609451293945 time for calcul the mask position with numpy : 0.022391796112060547 nb_pixel_total : 119732 time to create 1 rle with old method : 0.13452577590942383 time for calcul the mask position with numpy : 0.021648645401000977 nb_pixel_total : 17709 time to create 1 rle with old method : 0.0242764949798584 time for calcul the mask position with numpy : 0.022487640380859375 nb_pixel_total : 16061 time to create 1 rle with old method : 0.018491506576538086 time for calcul the mask position with numpy : 0.0221710205078125 nb_pixel_total : 21833 time to create 1 rle with old method : 0.02489304542541504 time for calcul the mask position with numpy : 0.02130293846130371 nb_pixel_total : 18923 time to create 1 rle with old method : 0.021408557891845703 create new chi : 2.1277506351470947 time to delete rle : 0.0019457340240478516 batch 1 Loaded 45 chid ids of type : 3594 +++++++++++++++++++++++++++++Number RLEs to save : 13684 TO DO : save crop sub photo not yet done ! save time : 0.8496227264404297 nb_obj : 50 nb_hashtags : 3 time to prepare the origin masks : 3.9752469062805176 time for calcul the mask position with numpy : 1.187199354171753 nb_pixel_total : 5742160 time to create 1 rle with new method : 0.5316934585571289 time for calcul the mask position with numpy : 0.029665231704711914 nb_pixel_total : 50455 time to create 1 rle with old method : 0.05643510818481445 time for calcul the mask position with numpy : 0.02941131591796875 nb_pixel_total : 17052 time to create 1 rle with old method : 0.02239847183227539 time for calcul the mask position with numpy : 0.033191680908203125 nb_pixel_total : 24638 time to create 1 rle with old method : 0.030732393264770508 time for calcul the mask position with numpy : 0.029221057891845703 nb_pixel_total : 11337 time to create 1 rle with old method : 0.013447284698486328 time for calcul the mask position with numpy : 0.02943110466003418 nb_pixel_total : 6832 time to create 1 rle with old method : 0.007811546325683594 time for calcul the mask position with numpy : 0.029276609420776367 nb_pixel_total : 14495 time to create 1 rle with old method : 0.017283916473388672 time for calcul the mask position with numpy : 0.029336214065551758 nb_pixel_total : 33347 time to create 1 rle with old method : 0.03965926170349121 time for calcul the mask position with numpy : 0.03185105323791504 nb_pixel_total : 296078 time to create 1 rle with new method : 0.429379940032959 time for calcul the mask position with numpy : 0.029163837432861328 nb_pixel_total : 13625 time to create 1 rle with old method : 0.015012979507446289 time for calcul the mask position with numpy : 0.027843713760375977 nb_pixel_total : 387 time to create 1 rle with old method : 0.0005905628204345703 time for calcul the mask position with numpy : 0.027674436569213867 nb_pixel_total : 32336 time to create 1 rle with old method : 0.035080671310424805 time for calcul the mask position with numpy : 0.028023958206176758 nb_pixel_total : 26424 time to create 1 rle with old method : 0.028374433517456055 time for calcul the mask position with numpy : 0.028402090072631836 nb_pixel_total : 10229 time to create 1 rle with old method : 0.011032342910766602 time for calcul the mask position with numpy : 0.02845144271850586 nb_pixel_total : 6112 time to create 1 rle with old method : 0.006943702697753906 time for calcul the mask position with numpy : 0.028752565383911133 nb_pixel_total : 11875 time to create 1 rle with old method : 0.013456344604492188 time for calcul the mask position with numpy : 0.028095483779907227 nb_pixel_total : 11156 time to create 1 rle with old method : 0.012159585952758789 time for calcul the mask position with numpy : 0.02795243263244629 nb_pixel_total : 19805 time to create 1 rle with old method : 0.02135157585144043 time for calcul the mask position with numpy : 0.028863906860351562 nb_pixel_total : 70658 time to create 1 rle with old method : 0.08680105209350586 time for calcul the mask position with numpy : 0.02768087387084961 nb_pixel_total : 34596 time to create 1 rle with old method : 0.03592801094055176 time for calcul the mask position with numpy : 0.027524232864379883 nb_pixel_total : 5804 time to create 1 rle with old method : 0.0062024593353271484 time for calcul the mask position with numpy : 0.027676820755004883 nb_pixel_total : 28761 time to create 1 rle with old method : 0.030218124389648438 time for calcul the mask position with numpy : 0.02675008773803711 nb_pixel_total : 14781 time to create 1 rle with old method : 0.015588760375976562 time for calcul the mask position with numpy : 0.026827096939086914 nb_pixel_total : 6432 time to create 1 rle with old method : 0.007029294967651367 time for calcul the mask position with numpy : 0.027454614639282227 nb_pixel_total : 12562 time to create 1 rle with old method : 0.013053417205810547 time for calcul the mask position with numpy : 0.026990890502929688 nb_pixel_total : 10484 time to create 1 rle with old method : 0.011293172836303711 time for calcul the mask position with numpy : 0.036245107650756836 nb_pixel_total : 20255 time to create 1 rle with old method : 0.031938791275024414 time for calcul the mask position with numpy : 0.03168296813964844 nb_pixel_total : 18996 time to create 1 rle with old method : 0.02130270004272461 time for calcul the mask position with numpy : 0.02911543846130371 nb_pixel_total : 15255 time to create 1 rle with old method : 0.017818927764892578 time for calcul the mask position with numpy : 0.032669782638549805 nb_pixel_total : 43797 time to create 1 rle with old method : 0.05195164680480957 time for calcul the mask position with numpy : 0.031958580017089844 nb_pixel_total : 19942 time to create 1 rle with old method : 0.022269010543823242 time for calcul the mask position with numpy : 0.029618263244628906 nb_pixel_total : 14432 time to create 1 rle with old method : 0.017864227294921875 time for calcul the mask position with numpy : 0.029128074645996094 nb_pixel_total : 26024 time to create 1 rle with old method : 0.030120372772216797 time for calcul the mask position with numpy : 0.041165828704833984 nb_pixel_total : 14521 time to create 1 rle with old method : 0.0162656307220459 time for calcul the mask position with numpy : 0.02924823760986328 nb_pixel_total : 13321 time to create 1 rle with old method : 0.014136075973510742 time for calcul the mask position with numpy : 0.028578996658325195 nb_pixel_total : 18712 time to create 1 rle with old method : 0.02112293243408203 time for calcul the mask position with numpy : 0.0294802188873291 nb_pixel_total : 61122 time to create 1 rle with old method : 0.09315919876098633 time for calcul the mask position with numpy : 0.028037548065185547 nb_pixel_total : 37140 time to create 1 rle with old method : 0.04041457176208496 time for calcul the mask position with numpy : 0.0287625789642334 nb_pixel_total : 9421 time to create 1 rle with old method : 0.01058816909790039 time for calcul the mask position with numpy : 0.02904987335205078 nb_pixel_total : 84427 time to create 1 rle with old method : 0.09097027778625488 time for calcul the mask position with numpy : 0.02831101417541504 nb_pixel_total : 12129 time to create 1 rle with old method : 0.015270471572875977 time for calcul the mask position with numpy : 0.032103776931762695 nb_pixel_total : 7382 time to create 1 rle with old method : 0.008281946182250977 time for calcul the mask position with numpy : 0.028520822525024414 nb_pixel_total : 11144 time to create 1 rle with old method : 0.012221813201904297 time for calcul the mask position with numpy : 0.028188467025756836 nb_pixel_total : 19481 time to create 1 rle with old method : 0.021703720092773438 time for calcul the mask position with numpy : 0.028590679168701172 nb_pixel_total : 7103 time to create 1 rle with old method : 0.007791042327880859 time for calcul the mask position with numpy : 0.028296709060668945 nb_pixel_total : 28512 time to create 1 rle with old method : 0.03176474571228027 time for calcul the mask position with numpy : 0.028476715087890625 nb_pixel_total : 14835 time to create 1 rle with old method : 0.016515254974365234 time for calcul the mask position with numpy : 0.029221773147583008 nb_pixel_total : 9857 time to create 1 rle with old method : 0.010704755783081055 time for calcul the mask position with numpy : 0.028389453887939453 nb_pixel_total : 13040 time to create 1 rle with old method : 0.01497793197631836 time for calcul the mask position with numpy : 0.03136634826660156 nb_pixel_total : 11288 time to create 1 rle with old method : 0.012437820434570312 time for calcul the mask position with numpy : 0.028405427932739258 nb_pixel_total : 5683 time to create 1 rle with old method : 0.006357908248901367 create new chi : 4.856152296066284 time to delete rle : 0.0034322738647460938 batch 1 Loaded 101 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22471 TO DO : save crop sub photo not yet done ! save time : 1.3620598316192627 nb_obj : 68 nb_hashtags : 3 time to prepare the origin masks : 4.562601804733276 time for calcul the mask position with numpy : 0.19372224807739258 nb_pixel_total : 4760597 time to create 1 rle with new method : 0.8421087265014648 time for calcul the mask position with numpy : 0.02823925018310547 nb_pixel_total : 47903 time to create 1 rle with old method : 0.05146527290344238 time for calcul the mask position with numpy : 0.02936100959777832 nb_pixel_total : 18386 time to create 1 rle with old method : 0.029730796813964844 time for calcul the mask position with numpy : 0.03272676467895508 nb_pixel_total : 12581 time to create 1 rle with old method : 0.02137470245361328 time for calcul the mask position with numpy : 0.0323028564453125 nb_pixel_total : 28174 time to create 1 rle with old method : 0.03104424476623535 time for calcul the mask position with numpy : 0.02907729148864746 nb_pixel_total : 32867 time to create 1 rle with old method : 0.03678107261657715 time for calcul the mask position with numpy : 0.030272960662841797 nb_pixel_total : 273868 time to create 1 rle with new method : 0.372600793838501 time for calcul the mask position with numpy : 0.02742147445678711 nb_pixel_total : 29266 time to create 1 rle with old method : 0.03132009506225586 time for calcul the mask position with numpy : 0.02814459800720215 nb_pixel_total : 25348 time to create 1 rle with old method : 0.02777266502380371 time for calcul the mask position with numpy : 0.02716350555419922 nb_pixel_total : 9994 time to create 1 rle with old method : 0.010747909545898438 time for calcul the mask position with numpy : 0.027594566345214844 nb_pixel_total : 6738 time to create 1 rle with old method : 0.007295131683349609 time for calcul the mask position with numpy : 0.02768254280090332 nb_pixel_total : 11627 time to create 1 rle with old method : 0.012712240219116211 time for calcul the mask position with numpy : 0.027985334396362305 nb_pixel_total : 28214 time to create 1 rle with old method : 0.03151106834411621 time for calcul the mask position with numpy : 0.028717517852783203 nb_pixel_total : 6199 time to create 1 rle with old method : 0.00700068473815918 time for calcul the mask position with numpy : 0.0288851261138916 nb_pixel_total : 13554 time to create 1 rle with old method : 0.015213251113891602 time for calcul the mask position with numpy : 0.031115293502807617 nb_pixel_total : 558215 time to create 1 rle with new method : 0.7559528350830078 time for calcul the mask position with numpy : 0.027927160263061523 nb_pixel_total : 21187 time to create 1 rle with old method : 0.023164749145507812 time for calcul the mask position with numpy : 0.027632951736450195 nb_pixel_total : 24387 time to create 1 rle with old method : 0.026749849319458008 time for calcul the mask position with numpy : 0.027840137481689453 nb_pixel_total : 15238 time to create 1 rle with old method : 0.016710758209228516 time for calcul the mask position with numpy : 0.027273178100585938 nb_pixel_total : 12025 time to create 1 rle with old method : 0.012735128402709961 time for calcul the mask position with numpy : 0.026767969131469727 nb_pixel_total : 39507 time to create 1 rle with old method : 0.04146385192871094 time for calcul the mask position with numpy : 0.02796626091003418 nb_pixel_total : 9792 time to create 1 rle with old method : 0.010500907897949219 time for calcul the mask position with numpy : 0.027975082397460938 nb_pixel_total : 14109 time to create 1 rle with old method : 0.015439271926879883 time for calcul the mask position with numpy : 0.028120756149291992 nb_pixel_total : 10511 time to create 1 rle with old method : 0.011416435241699219 time for calcul the mask position with numpy : 0.028064966201782227 nb_pixel_total : 30991 time to create 1 rle with old method : 0.03334164619445801 time for calcul the mask position with numpy : 0.028734207153320312 nb_pixel_total : 23185 time to create 1 rle with old method : 0.026146411895751953 time for calcul the mask position with numpy : 0.028030872344970703 nb_pixel_total : 31849 time to create 1 rle with old method : 0.03474831581115723 time for calcul the mask position with numpy : 0.027942657470703125 nb_pixel_total : 30561 time to create 1 rle with old method : 0.03719592094421387 time for calcul the mask position with numpy : 0.028391361236572266 nb_pixel_total : 13872 time to create 1 rle with old method : 0.015477657318115234 time for calcul the mask position with numpy : 0.028165817260742188 nb_pixel_total : 19080 time to create 1 rle with old method : 0.02124333381652832 time for calcul the mask position with numpy : 0.028001785278320312 nb_pixel_total : 19172 time to create 1 rle with old method : 0.021096467971801758 time for calcul the mask position with numpy : 0.02784109115600586 nb_pixel_total : 5815 time to create 1 rle with old method : 0.006289243698120117 time for calcul the mask position with numpy : 0.027286767959594727 nb_pixel_total : 74467 time to create 1 rle with old method : 0.07939863204956055 time for calcul the mask position with numpy : 0.02843618392944336 nb_pixel_total : 15192 time to create 1 rle with old method : 0.0164492130279541 time for calcul the mask position with numpy : 0.026591062545776367 nb_pixel_total : 30406 time to create 1 rle with old method : 0.03255414962768555 time for calcul the mask position with numpy : 0.027895450592041016 nb_pixel_total : 27413 time to create 1 rle with old method : 0.029820919036865234 time for calcul the mask position with numpy : 0.027001619338989258 nb_pixel_total : 13984 time to create 1 rle with old method : 0.01500701904296875 time for calcul the mask position with numpy : 0.02764606475830078 nb_pixel_total : 14873 time to create 1 rle with old method : 0.016162395477294922 time for calcul the mask position with numpy : 0.02808690071105957 nb_pixel_total : 21930 time to create 1 rle with old method : 0.024512767791748047 time for calcul the mask position with numpy : 0.03280353546142578 nb_pixel_total : 23960 time to create 1 rle with old method : 0.038608551025390625 time for calcul the mask position with numpy : 0.028836727142333984 nb_pixel_total : 55525 time to create 1 rle with old method : 0.05958819389343262 time for calcul the mask position with numpy : 0.02760910987854004 nb_pixel_total : 6682 time to create 1 rle with old method : 0.0071353912353515625 time for calcul the mask position with numpy : 0.0276181697845459 nb_pixel_total : 9142 time to create 1 rle with old method : 0.010005474090576172 time for calcul the mask position with numpy : 0.028310298919677734 nb_pixel_total : 20929 time to create 1 rle with old method : 0.02253866195678711 time for calcul the mask position with numpy : 0.027521133422851562 nb_pixel_total : 14663 time to create 1 rle with old method : 0.01565384864807129 time for calcul the mask position with numpy : 0.02826666831970215 nb_pixel_total : 12064 time to create 1 rle with old method : 0.013162374496459961 time for calcul the mask position with numpy : 0.028000593185424805 nb_pixel_total : 4565 time to create 1 rle with old method : 0.0051114559173583984 time for calcul the mask position with numpy : 0.027859210968017578 nb_pixel_total : 18069 time to create 1 rle with old method : 0.01954936981201172 time for calcul the mask position with numpy : 0.027965068817138672 nb_pixel_total : 34761 time to create 1 rle with old method : 0.038834571838378906 time for calcul the mask position with numpy : 0.028082847595214844 nb_pixel_total : 40415 time to create 1 rle with old method : 0.04357337951660156 time for calcul the mask position with numpy : 0.02778458595275879 nb_pixel_total : 31866 time to create 1 rle with old method : 0.034856319427490234 time for calcul the mask position with numpy : 0.02927255630493164 nb_pixel_total : 23698 time to create 1 rle with old method : 0.026751279830932617 time for calcul the mask position with numpy : 0.02810811996459961 nb_pixel_total : 595 time to create 1 rle with old method : 0.0007970333099365234 time for calcul the mask position with numpy : 0.0281984806060791 nb_pixel_total : 95042 time to create 1 rle with old method : 0.10178542137145996 time for calcul the mask position with numpy : 0.028660297393798828 nb_pixel_total : 22584 time to create 1 rle with old method : 0.025347232818603516 time for calcul the mask position with numpy : 0.03005242347717285 nb_pixel_total : 29225 time to create 1 rle with old method : 0.03270411491394043 time for calcul the mask position with numpy : 0.029001951217651367 nb_pixel_total : 6315 time to create 1 rle with old method : 0.007133007049560547 time for calcul the mask position with numpy : 0.02900552749633789 nb_pixel_total : 25483 time to create 1 rle with old method : 0.0284883975982666 time for calcul the mask position with numpy : 0.028813838958740234 nb_pixel_total : 7417 time to create 1 rle with old method : 0.008452177047729492 time for calcul the mask position with numpy : 0.02834033966064453 nb_pixel_total : 7167 time to create 1 rle with old method : 0.008195638656616211 time for calcul the mask position with numpy : 0.028905868530273438 nb_pixel_total : 12749 time to create 1 rle with old method : 0.014432430267333984 time for calcul the mask position with numpy : 0.028969287872314453 nb_pixel_total : 29456 time to create 1 rle with old method : 0.03297758102416992 time for calcul the mask position with numpy : 0.028749942779541016 nb_pixel_total : 9950 time to create 1 rle with old method : 0.01111292839050293 time for calcul the mask position with numpy : 0.028224468231201172 nb_pixel_total : 32405 time to create 1 rle with old method : 0.036881446838378906 time for calcul the mask position with numpy : 0.028348922729492188 nb_pixel_total : 18782 time to create 1 rle with old method : 0.022345542907714844 time for calcul the mask position with numpy : 0.02885293960571289 nb_pixel_total : 27770 time to create 1 rle with old method : 0.03136157989501953 time for calcul the mask position with numpy : 0.028899192810058594 nb_pixel_total : 14393 time to create 1 rle with old method : 0.022998332977294922 time for calcul the mask position with numpy : 0.03288912773132324 nb_pixel_total : 25510 time to create 1 rle with old method : 0.0339510440826416 time for calcul the mask position with numpy : 0.028538227081298828 nb_pixel_total : 5981 time to create 1 rle with old method : 0.0068089962005615234 create new chi : 5.823001146316528 time to delete rle : 0.004969358444213867 batch 1 Loaded 137 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 32122 TO DO : save crop sub photo not yet done ! save time : 1.8977341651916504 nb_obj : 76 nb_hashtags : 2 time to prepare the origin masks : 4.773208379745483 time for calcul the mask position with numpy : 0.2059323787689209 nb_pixel_total : 4515885 time to create 1 rle with new method : 0.34222912788391113 time for calcul the mask position with numpy : 0.028789281845092773 nb_pixel_total : 11559 time to create 1 rle with old method : 0.012988805770874023 time for calcul the mask position with numpy : 0.029133319854736328 nb_pixel_total : 13280 time to create 1 rle with old method : 0.014961004257202148 time for calcul the mask position with numpy : 0.028683185577392578 nb_pixel_total : 15571 time to create 1 rle with old method : 0.017017602920532227 time for calcul the mask position with numpy : 0.0282900333404541 nb_pixel_total : 33074 time to create 1 rle with old method : 0.03630876541137695 time for calcul the mask position with numpy : 0.028914928436279297 nb_pixel_total : 8219 time to create 1 rle with old method : 0.00932002067565918 time for calcul the mask position with numpy : 0.03101325035095215 nb_pixel_total : 302242 time to create 1 rle with new method : 0.3516988754272461 time for calcul the mask position with numpy : 0.02924656867980957 nb_pixel_total : 24715 time to create 1 rle with old method : 0.027726411819458008 time for calcul the mask position with numpy : 0.02923417091369629 nb_pixel_total : 28751 time to create 1 rle with old method : 0.03282666206359863 time for calcul the mask position with numpy : 0.03577876091003418 nb_pixel_total : 26683 time to create 1 rle with old method : 0.030876874923706055 time for calcul the mask position with numpy : 0.02897334098815918 nb_pixel_total : 28185 time to create 1 rle with old method : 0.03243660926818848 time for calcul the mask position with numpy : 0.029009342193603516 nb_pixel_total : 9396 time to create 1 rle with old method : 0.01053166389465332 time for calcul the mask position with numpy : 0.029064655303955078 nb_pixel_total : 6067 time to create 1 rle with old method : 0.0068817138671875 time for calcul the mask position with numpy : 0.029068470001220703 nb_pixel_total : 9353 time to create 1 rle with old method : 0.010531187057495117 time for calcul the mask position with numpy : 0.029023170471191406 nb_pixel_total : 17063 time to create 1 rle with old method : 0.019299983978271484 time for calcul the mask position with numpy : 0.029513835906982422 nb_pixel_total : 9653 time to create 1 rle with old method : 0.013476133346557617 time for calcul the mask position with numpy : 0.02918529510498047 nb_pixel_total : 18804 time to create 1 rle with old method : 0.02286672592163086 time for calcul the mask position with numpy : 0.02893352508544922 nb_pixel_total : 40520 time to create 1 rle with old method : 0.05242466926574707 time for calcul the mask position with numpy : 0.03619265556335449 nb_pixel_total : 23467 time to create 1 rle with old method : 0.02519392967224121 time for calcul the mask position with numpy : 0.028294086456298828 nb_pixel_total : 5984 time to create 1 rle with old method : 0.0066907405853271484 time for calcul the mask position with numpy : 0.028011560440063477 nb_pixel_total : 12593 time to create 1 rle with old method : 0.013689041137695312 time for calcul the mask position with numpy : 0.029187917709350586 nb_pixel_total : 156192 time to create 1 rle with new method : 0.707122802734375 time for calcul the mask position with numpy : 0.03354072570800781 nb_pixel_total : 595032 time to create 1 rle with new method : 0.5479183197021484 time for calcul the mask position with numpy : 0.029177188873291016 nb_pixel_total : 759 time to create 1 rle with old method : 0.0009427070617675781 time for calcul the mask position with numpy : 0.029068708419799805 nb_pixel_total : 19955 time to create 1 rle with old method : 0.02233147621154785 time for calcul the mask position with numpy : 0.028902053833007812 nb_pixel_total : 16484 time to create 1 rle with old method : 0.01854848861694336 time for calcul the mask position with numpy : 0.028928041458129883 nb_pixel_total : 12795 time to create 1 rle with old method : 0.014316082000732422 time for calcul the mask position with numpy : 0.028954505920410156 nb_pixel_total : 13059 time to create 1 rle with old method : 0.01459813117980957 time for calcul the mask position with numpy : 0.028804779052734375 nb_pixel_total : 25609 time to create 1 rle with old method : 0.02829885482788086 time for calcul the mask position with numpy : 0.028942346572875977 nb_pixel_total : 4299 time to create 1 rle with old method : 0.004826545715332031 time for calcul the mask position with numpy : 0.028204917907714844 nb_pixel_total : 10619 time to create 1 rle with old method : 0.012132406234741211 time for calcul the mask position with numpy : 0.03285574913024902 nb_pixel_total : 6133 time to create 1 rle with old method : 0.006761789321899414 time for calcul the mask position with numpy : 0.02892327308654785 nb_pixel_total : 48 time to create 1 rle with old method : 0.00014734268188476562 time for calcul the mask position with numpy : 0.029381990432739258 nb_pixel_total : 49262 time to create 1 rle with old method : 0.055230140686035156 time for calcul the mask position with numpy : 0.02914595603942871 nb_pixel_total : 36118 time to create 1 rle with old method : 0.04043889045715332 time for calcul the mask position with numpy : 0.028902292251586914 nb_pixel_total : 16114 time to create 1 rle with old method : 0.017629623413085938 time for calcul the mask position with numpy : 0.028737783432006836 nb_pixel_total : 20613 time to create 1 rle with old method : 0.023248910903930664 time for calcul the mask position with numpy : 0.02923893928527832 nb_pixel_total : 61505 time to create 1 rle with old method : 0.06878995895385742 time for calcul the mask position with numpy : 0.02903270721435547 nb_pixel_total : 14812 time to create 1 rle with old method : 0.018967628479003906 time for calcul the mask position with numpy : 0.033008575439453125 nb_pixel_total : 14955 time to create 1 rle with old method : 0.02386617660522461 time for calcul the mask position with numpy : 0.032236337661743164 nb_pixel_total : 16265 time to create 1 rle with old method : 0.01831221580505371 time for calcul the mask position with numpy : 0.028805971145629883 nb_pixel_total : 11660 time to create 1 rle with old method : 0.01314544677734375 time for calcul the mask position with numpy : 0.028873205184936523 nb_pixel_total : 5504 time to create 1 rle with old method : 0.006316661834716797 time for calcul the mask position with numpy : 0.028888463973999023 nb_pixel_total : 4717 time to create 1 rle with old method : 0.005433320999145508 time for calcul the mask position with numpy : 0.02897191047668457 nb_pixel_total : 25917 time to create 1 rle with old method : 0.02924823760986328 time for calcul the mask position with numpy : 0.029010295867919922 nb_pixel_total : 34884 time to create 1 rle with old method : 0.03931856155395508 time for calcul the mask position with numpy : 0.029241323471069336 nb_pixel_total : 26298 time to create 1 rle with old method : 0.02985548973083496 time for calcul the mask position with numpy : 0.029256582260131836 nb_pixel_total : 31052 time to create 1 rle with old method : 0.034441471099853516 time for calcul the mask position with numpy : 0.028037309646606445 nb_pixel_total : 20120 time to create 1 rle with old method : 0.02244281768798828 time for calcul the mask position with numpy : 0.028789520263671875 nb_pixel_total : 1740 time to create 1 rle with old method : 0.0019884109497070312 time for calcul the mask position with numpy : 0.03012871742248535 nb_pixel_total : 59626 time to create 1 rle with old method : 0.06654810905456543 time for calcul the mask position with numpy : 0.029117107391357422 nb_pixel_total : 30997 time to create 1 rle with old method : 0.036415815353393555 time for calcul the mask position with numpy : 0.028862476348876953 nb_pixel_total : 10502 time to create 1 rle with old method : 0.011775970458984375 time for calcul the mask position with numpy : 0.02887892723083496 nb_pixel_total : 9026 time to create 1 rle with old method : 0.010137081146240234 time for calcul the mask position with numpy : 0.028715848922729492 nb_pixel_total : 18753 time to create 1 rle with old method : 0.020776987075805664 time for calcul the mask position with numpy : 0.027934789657592773 nb_pixel_total : 7157 time to create 1 rle with old method : 0.008040904998779297 time for calcul the mask position with numpy : 0.03224825859069824 nb_pixel_total : 7284 time to create 1 rle with old method : 0.011831998825073242 time for calcul the mask position with numpy : 0.032767295837402344 nb_pixel_total : 5921 time to create 1 rle with old method : 0.009690284729003906 time for calcul the mask position with numpy : 0.028923749923706055 nb_pixel_total : 52578 time to create 1 rle with old method : 0.05843830108642578 time for calcul the mask position with numpy : 0.02895951271057129 nb_pixel_total : 76738 time to create 1 rle with old method : 0.08600974082946777 time for calcul the mask position with numpy : 0.02892589569091797 nb_pixel_total : 13174 time to create 1 rle with old method : 0.014905452728271484 time for calcul the mask position with numpy : 0.028818130493164062 nb_pixel_total : 37889 time to create 1 rle with old method : 0.042482614517211914 time for calcul the mask position with numpy : 0.028863906860351562 nb_pixel_total : 32139 time to create 1 rle with old method : 0.03595900535583496 time for calcul the mask position with numpy : 0.02867412567138672 nb_pixel_total : 12329 time to create 1 rle with old method : 0.01387166976928711 time for calcul the mask position with numpy : 0.028852224349975586 nb_pixel_total : 60901 time to create 1 rle with old method : 0.06773614883422852 time for calcul the mask position with numpy : 0.02876758575439453 nb_pixel_total : 11797 time to create 1 rle with old method : 0.013323783874511719 time for calcul the mask position with numpy : 0.028664350509643555 nb_pixel_total : 8353 time to create 1 rle with old method : 0.010309219360351562 time for calcul the mask position with numpy : 0.028859853744506836 nb_pixel_total : 36248 time to create 1 rle with old method : 0.03992509841918945 time for calcul the mask position with numpy : 0.02883172035217285 nb_pixel_total : 21465 time to create 1 rle with old method : 0.02423381805419922 time for calcul the mask position with numpy : 0.028796911239624023 nb_pixel_total : 27598 time to create 1 rle with old method : 0.03095722198486328 time for calcul the mask position with numpy : 0.02873086929321289 nb_pixel_total : 11724 time to create 1 rle with old method : 0.013265132904052734 time for calcul the mask position with numpy : 0.02866077423095703 nb_pixel_total : 22304 time to create 1 rle with old method : 0.025028705596923828 time for calcul the mask position with numpy : 0.02859187126159668 nb_pixel_total : 15720 time to create 1 rle with old method : 0.01798844337463379 time for calcul the mask position with numpy : 0.028790950775146484 nb_pixel_total : 14816 time to create 1 rle with old method : 0.016547203063964844 time for calcul the mask position with numpy : 0.0290987491607666 nb_pixel_total : 14220 time to create 1 rle with old method : 0.016959428787231445 time for calcul the mask position with numpy : 0.03499865531921387 nb_pixel_total : 6846 time to create 1 rle with old method : 0.007907390594482422 time for calcul the mask position with numpy : 0.028559446334838867 nb_pixel_total : 10551 time to create 1 rle with old method : 0.011853218078613281 create new chi : 6.191538095474243 time to delete rle : 0.0052068233489990234 batch 1 Loaded 153 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 33814 TO DO : save crop sub photo not yet done ! save time : 2.0155837535858154 nb_obj : 22 nb_hashtags : 4 time to prepare the origin masks : 7.545363187789917 time for calcul the mask position with numpy : 0.36782407760620117 nb_pixel_total : 5137394 time to create 1 rle with new method : 0.4826815128326416 time for calcul the mask position with numpy : 0.03432464599609375 nb_pixel_total : 28783 time to create 1 rle with old method : 0.03268766403198242 time for calcul the mask position with numpy : 0.03351426124572754 nb_pixel_total : 32413 time to create 1 rle with old method : 0.03570222854614258 time for calcul the mask position with numpy : 0.03850531578063965 nb_pixel_total : 38285 time to create 1 rle with old method : 0.041719675064086914 time for calcul the mask position with numpy : 0.034049272537231445 nb_pixel_total : 41147 time to create 1 rle with old method : 0.04591965675354004 time for calcul the mask position with numpy : 0.03426623344421387 nb_pixel_total : 66742 time to create 1 rle with old method : 0.07268452644348145 time for calcul the mask position with numpy : 0.03417468070983887 nb_pixel_total : 18343 time to create 1 rle with old method : 0.020009279251098633 time for calcul the mask position with numpy : 0.037244319915771484 nb_pixel_total : 17736 time to create 1 rle with old method : 0.01981806755065918 time for calcul the mask position with numpy : 0.04249858856201172 nb_pixel_total : 196431 time to create 1 rle with new method : 0.5971200466156006 time for calcul the mask position with numpy : 0.03403902053833008 nb_pixel_total : 12493 time to create 1 rle with old method : 0.013331174850463867 time for calcul the mask position with numpy : 0.03309774398803711 nb_pixel_total : 18419 time to create 1 rle with old method : 0.0195465087890625 time for calcul the mask position with numpy : 0.0328977108001709 nb_pixel_total : 18938 time to create 1 rle with old method : 0.021195411682128906 time for calcul the mask position with numpy : 0.035447120666503906 nb_pixel_total : 204754 time to create 1 rle with new method : 0.559619665145874 time for calcul the mask position with numpy : 0.035048723220825195 nb_pixel_total : 64959 time to create 1 rle with old method : 0.08065509796142578 time for calcul the mask position with numpy : 0.03627276420593262 nb_pixel_total : 56572 time to create 1 rle with old method : 0.06527137756347656 time for calcul the mask position with numpy : 0.03458452224731445 nb_pixel_total : 48038 time to create 1 rle with old method : 0.06224822998046875 time for calcul the mask position with numpy : 0.03280043601989746 nb_pixel_total : 40213 time to create 1 rle with old method : 0.04963254928588867 time for calcul the mask position with numpy : 0.03685712814331055 nb_pixel_total : 12531 time to create 1 rle with old method : 0.01859879493713379 time for calcul the mask position with numpy : 0.03272223472595215 nb_pixel_total : 38119 time to create 1 rle with old method : 0.04183173179626465 time for calcul the mask position with numpy : 0.031585693359375 nb_pixel_total : 134892 time to create 1 rle with old method : 0.15262436866760254 time for calcul the mask position with numpy : 0.03567337989807129 nb_pixel_total : 100570 time to create 1 rle with old method : 0.10943841934204102 time for calcul the mask position with numpy : 0.03171586990356445 nb_pixel_total : 228631 time to create 1 rle with new method : 0.5537738800048828 time for calcul the mask position with numpy : 0.04070878028869629 nb_pixel_total : 493837 time to create 1 rle with new method : 0.4609246253967285 create new chi : 4.82087516784668 time to delete rle : 0.004476785659790039 batch 1 Loaded 45 chid ids of type : 3594 +++++++++++++++++++++++++++++++Number RLEs to save : 17257 TO DO : save crop sub photo not yet done ! save time : 1.283958911895752 nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 7.552016019821167 time for calcul the mask position with numpy : 0.19229793548583984 nb_pixel_total : 4649209 time to create 1 rle with new method : 0.44069933891296387 time for calcul the mask position with numpy : 0.020761728286743164 nb_pixel_total : 9616 time to create 1 rle with old method : 0.010770797729492188 time for calcul the mask position with numpy : 0.021509408950805664 nb_pixel_total : 1551 time to create 1 rle with old method : 0.00196075439453125 time for calcul the mask position with numpy : 0.02196669578552246 nb_pixel_total : 6386 time to create 1 rle with old method : 0.007223606109619141 time for calcul the mask position with numpy : 0.02156829833984375 nb_pixel_total : 46122 time to create 1 rle with old method : 0.051198482513427734 time for calcul the mask position with numpy : 0.02142953872680664 nb_pixel_total : 60810 time to create 1 rle with old method : 0.06778311729431152 time for calcul the mask position with numpy : 0.02579665184020996 nb_pixel_total : 73526 time to create 1 rle with old method : 0.08121418952941895 time for calcul the mask position with numpy : 0.02770256996154785 nb_pixel_total : 624374 time to create 1 rle with new method : 0.45752596855163574 time for calcul the mask position with numpy : 0.021535396575927734 nb_pixel_total : 146 time to create 1 rle with old method : 0.00030803680419921875 time for calcul the mask position with numpy : 0.022588491439819336 nb_pixel_total : 35910 time to create 1 rle with old method : 0.041079044342041016 time for calcul the mask position with numpy : 0.021358728408813477 nb_pixel_total : 40208 time to create 1 rle with old method : 0.04500842094421387 time for calcul the mask position with numpy : 0.020980358123779297 nb_pixel_total : 82745 time to create 1 rle with old method : 0.09132599830627441 time for calcul the mask position with numpy : 0.021740198135375977 nb_pixel_total : 49700 time to create 1 rle with old method : 0.05431795120239258 time for calcul the mask position with numpy : 0.021876811981201172 nb_pixel_total : 75553 time to create 1 rle with old method : 0.10775184631347656 time for calcul the mask position with numpy : 0.0614781379699707 nb_pixel_total : 533686 time to create 1 rle with new method : 0.8560571670532227 time for calcul the mask position with numpy : 0.03442192077636719 nb_pixel_total : 30549 time to create 1 rle with old method : 0.03474235534667969 time for calcul the mask position with numpy : 0.032662153244018555 nb_pixel_total : 31141 time to create 1 rle with old method : 0.03592348098754883 time for calcul the mask position with numpy : 0.033661842346191406 nb_pixel_total : 238630 time to create 1 rle with new method : 0.5459158420562744 time for calcul the mask position with numpy : 0.03282880783081055 nb_pixel_total : 96816 time to create 1 rle with old method : 0.1070098876953125 time for calcul the mask position with numpy : 0.024584531784057617 nb_pixel_total : 136328 time to create 1 rle with old method : 0.15152406692504883 time for calcul the mask position with numpy : 0.021955013275146484 nb_pixel_total : 46736 time to create 1 rle with old method : 0.052478790283203125 time for calcul the mask position with numpy : 0.021711349487304688 nb_pixel_total : 14520 time to create 1 rle with old method : 0.016301631927490234 time for calcul the mask position with numpy : 0.02252483367919922 nb_pixel_total : 117967 time to create 1 rle with old method : 0.13389921188354492 time for calcul the mask position with numpy : 0.02176690101623535 nb_pixel_total : 48011 time to create 1 rle with old method : 0.05346560478210449 create new chi : 4.347328424453735 time to delete rle : 0.0030448436737060547 batch 1 Loaded 47 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++Number RLEs to save : 18843 TO DO : save crop sub photo not yet done ! save time : 1.3446824550628662 nb_obj : 26 nb_hashtags : 4 time to prepare the origin masks : 5.059110641479492 time for calcul the mask position with numpy : 0.42858028411865234 nb_pixel_total : 4400028 time to create 1 rle with new method : 1.001711368560791 time for calcul the mask position with numpy : 0.028815746307373047 nb_pixel_total : 626 time to create 1 rle with old method : 0.001039743423461914 time for calcul the mask position with numpy : 0.028181076049804688 nb_pixel_total : 25121 time to create 1 rle with old method : 0.027932167053222656 time for calcul the mask position with numpy : 0.029012680053710938 nb_pixel_total : 5849 time to create 1 rle with old method : 0.006502866744995117 time for calcul the mask position with numpy : 0.028675079345703125 nb_pixel_total : 50009 time to create 1 rle with old method : 0.05433011054992676 time for calcul the mask position with numpy : 0.028654813766479492 nb_pixel_total : 48814 time to create 1 rle with old method : 0.05424213409423828 time for calcul the mask position with numpy : 0.0300748348236084 nb_pixel_total : 124912 time to create 1 rle with old method : 0.1499650478363037 time for calcul the mask position with numpy : 0.02906632423400879 nb_pixel_total : 16459 time to create 1 rle with old method : 0.018397092819213867 time for calcul the mask position with numpy : 0.028604984283447266 nb_pixel_total : 48607 time to create 1 rle with old method : 0.0537722110748291 time for calcul the mask position with numpy : 0.029053211212158203 nb_pixel_total : 32688 time to create 1 rle with old method : 0.036621809005737305 time for calcul the mask position with numpy : 0.028801679611206055 nb_pixel_total : 102824 time to create 1 rle with old method : 0.11445164680480957 time for calcul the mask position with numpy : 0.029148340225219727 nb_pixel_total : 53879 time to create 1 rle with old method : 0.060210466384887695 time for calcul the mask position with numpy : 0.02894735336303711 nb_pixel_total : 24240 time to create 1 rle with old method : 0.02702641487121582 time for calcul the mask position with numpy : 0.02904677391052246 nb_pixel_total : 13697 time to create 1 rle with old method : 0.01753997802734375 time for calcul the mask position with numpy : 0.03176593780517578 nb_pixel_total : 208772 time to create 1 rle with new method : 0.42733025550842285 time for calcul the mask position with numpy : 0.028668642044067383 nb_pixel_total : 10901 time to create 1 rle with old method : 0.012949705123901367 time for calcul the mask position with numpy : 0.029074907302856445 nb_pixel_total : 24739 time to create 1 rle with old method : 0.027601242065429688 time for calcul the mask position with numpy : 0.029439687728881836 nb_pixel_total : 16238 time to create 1 rle with old method : 0.018793821334838867 time for calcul the mask position with numpy : 0.029233694076538086 nb_pixel_total : 55361 time to create 1 rle with old method : 0.08241152763366699 time for calcul the mask position with numpy : 0.032143592834472656 nb_pixel_total : 87954 time to create 1 rle with old method : 0.10048580169677734 time for calcul the mask position with numpy : 0.03156280517578125 nb_pixel_total : 120342 time to create 1 rle with old method : 0.13402533531188965 time for calcul the mask position with numpy : 0.035631656646728516 nb_pixel_total : 556553 time to create 1 rle with new method : 0.5414369106292725 time for calcul the mask position with numpy : 0.030557632446289062 nb_pixel_total : 360084 time to create 1 rle with new method : 0.40476131439208984 time for calcul the mask position with numpy : 0.03267502784729004 nb_pixel_total : 312482 time to create 1 rle with new method : 0.2833542823791504 time for calcul the mask position with numpy : 0.028673410415649414 nb_pixel_total : 157928 time to create 1 rle with new method : 0.3860054016113281 time for calcul the mask position with numpy : 0.028638601303100586 nb_pixel_total : 79850 time to create 1 rle with old method : 0.08821773529052734 time for calcul the mask position with numpy : 0.02907705307006836 nb_pixel_total : 111283 time to create 1 rle with old method : 0.12292337417602539 create new chi : 5.609729766845703 time to delete rle : 0.005291938781738281 batch 1 Loaded 53 chid ids of type : 3594 +++++++++++++++++++++++++++++++Number RLEs to save : 22354 TO DO : save crop sub photo not yet done ! save time : 1.380889892578125 nb_obj : 23 nb_hashtags : 3 time to prepare the origin masks : 6.1542158126831055 time for calcul the mask position with numpy : 0.47625112533569336 nb_pixel_total : 5192771 time to create 1 rle with new method : 0.6038715839385986 time for calcul the mask position with numpy : 0.03428792953491211 nb_pixel_total : 5842 time to create 1 rle with old method : 0.00652623176574707 time for calcul the mask position with numpy : 0.037131547927856445 nb_pixel_total : 239197 time to create 1 rle with new method : 0.7143645286560059 time for calcul the mask position with numpy : 0.034494876861572266 nb_pixel_total : 38420 time to create 1 rle with old method : 0.04354691505432129 time for calcul the mask position with numpy : 0.03375434875488281 nb_pixel_total : 7727 time to create 1 rle with old method : 0.00836491584777832 time for calcul the mask position with numpy : 0.024151325225830078 nb_pixel_total : 69879 time to create 1 rle with old method : 0.08066439628601074 time for calcul the mask position with numpy : 0.02182602882385254 nb_pixel_total : 47000 time to create 1 rle with old method : 0.051732778549194336 time for calcul the mask position with numpy : 0.022933244705200195 nb_pixel_total : 89425 time to create 1 rle with old method : 0.1016230583190918 time for calcul the mask position with numpy : 0.02331686019897461 nb_pixel_total : 115825 time to create 1 rle with old method : 0.46094441413879395 time for calcul the mask position with numpy : 0.03020024299621582 nb_pixel_total : 50146 time to create 1 rle with old method : 0.09990572929382324 time for calcul the mask position with numpy : 0.05720806121826172 nb_pixel_total : 113960 time to create 1 rle with old method : 0.21538257598876953 time for calcul the mask position with numpy : 0.05355644226074219 nb_pixel_total : 9153 time to create 1 rle with old method : 0.017024993896484375 time for calcul the mask position with numpy : 0.05645155906677246 nb_pixel_total : 26495 time to create 1 rle with old method : 0.04889702796936035 time for calcul the mask position with numpy : 0.05212664604187012 nb_pixel_total : 109046 time to create 1 rle with old method : 0.20697999000549316 time for calcul the mask position with numpy : 0.04703402519226074 nb_pixel_total : 24359 time to create 1 rle with old method : 0.03278040885925293 time for calcul the mask position with numpy : 0.03833508491516113 nb_pixel_total : 6742 time to create 1 rle with old method : 0.007635593414306641 time for calcul the mask position with numpy : 0.038015127182006836 nb_pixel_total : 28610 time to create 1 rle with old method : 0.044713735580444336 time for calcul the mask position with numpy : 0.047852277755737305 nb_pixel_total : 34639 time to create 1 rle with old method : 0.062398672103881836 time for calcul the mask position with numpy : 0.0501558780670166 nb_pixel_total : 92175 time to create 1 rle with old method : 0.11977434158325195 time for calcul the mask position with numpy : 0.04228520393371582 nb_pixel_total : 117184 time to create 1 rle with old method : 0.12933945655822754 time for calcul the mask position with numpy : 0.03421974182128906 nb_pixel_total : 43969 time to create 1 rle with old method : 0.04836916923522949 time for calcul the mask position with numpy : 0.032599687576293945 nb_pixel_total : 27338 time to create 1 rle with old method : 0.030120134353637695 time for calcul the mask position with numpy : 0.03957509994506836 nb_pixel_total : 546770 time to create 1 rle with new method : 1.086961030960083 time for calcul the mask position with numpy : 0.03409767150878906 nb_pixel_total : 13568 time to create 1 rle with old method : 0.014974117279052734 create new chi : 5.681066274642944 time to delete rle : 0.005620241165161133 batch 1 Loaded 47 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++Number RLEs to save : 18997 TO DO : save crop sub photo not yet done ! save time : 1.2726218700408936 nb_obj : 57 nb_hashtags : 4 time to prepare the origin masks : 4.35560941696167 time for calcul the mask position with numpy : 0.5296375751495361 nb_pixel_total : 5568610 time to create 1 rle with new method : 0.9804537296295166 time for calcul the mask position with numpy : 0.029341459274291992 nb_pixel_total : 36954 time to create 1 rle with old method : 0.0410151481628418 time for calcul the mask position with numpy : 0.028860807418823242 nb_pixel_total : 6524 time to create 1 rle with old method : 0.007323741912841797 time for calcul the mask position with numpy : 0.028985977172851562 nb_pixel_total : 29062 time to create 1 rle with old method : 0.03246498107910156 time for calcul the mask position with numpy : 0.029124736785888672 nb_pixel_total : 25744 time to create 1 rle with old method : 0.028853416442871094 time for calcul the mask position with numpy : 0.029082059860229492 nb_pixel_total : 32903 time to create 1 rle with old method : 0.0370485782623291 time for calcul the mask position with numpy : 0.02918267250061035 nb_pixel_total : 21738 time to create 1 rle with old method : 0.026661157608032227 time for calcul the mask position with numpy : 0.029036521911621094 nb_pixel_total : 15872 time to create 1 rle with old method : 0.01790165901184082 time for calcul the mask position with numpy : 0.0285642147064209 nb_pixel_total : 10133 time to create 1 rle with old method : 0.011299610137939453 time for calcul the mask position with numpy : 0.02865457534790039 nb_pixel_total : 20064 time to create 1 rle with old method : 0.022543907165527344 time for calcul the mask position with numpy : 0.028765439987182617 nb_pixel_total : 25737 time to create 1 rle with old method : 0.028076887130737305 time for calcul the mask position with numpy : 0.02858448028564453 nb_pixel_total : 11111 time to create 1 rle with old method : 0.012217044830322266 time for calcul the mask position with numpy : 0.028347492218017578 nb_pixel_total : 24735 time to create 1 rle with old method : 0.0275576114654541 time for calcul the mask position with numpy : 0.028964519500732422 nb_pixel_total : 22890 time to create 1 rle with old method : 0.025435686111450195 time for calcul the mask position with numpy : 0.027396202087402344 nb_pixel_total : 19097 time to create 1 rle with old method : 0.02071094512939453 time for calcul the mask position with numpy : 0.028165817260742188 nb_pixel_total : 16158 time to create 1 rle with old method : 0.017459392547607422 time for calcul the mask position with numpy : 0.027618885040283203 nb_pixel_total : 24285 time to create 1 rle with old method : 0.026376724243164062 time for calcul the mask position with numpy : 0.026567697525024414 nb_pixel_total : 19211 time to create 1 rle with old method : 0.02117633819580078 time for calcul the mask position with numpy : 0.02886366844177246 nb_pixel_total : 12137 time to create 1 rle with old method : 0.012510538101196289 time for calcul the mask position with numpy : 0.027347803115844727 nb_pixel_total : 116051 time to create 1 rle with old method : 0.12345385551452637 time for calcul the mask position with numpy : 0.027344942092895508 nb_pixel_total : 28104 time to create 1 rle with old method : 0.03669905662536621 time for calcul the mask position with numpy : 0.0327610969543457 nb_pixel_total : 6440 time to create 1 rle with old method : 0.010403633117675781 time for calcul the mask position with numpy : 0.03200054168701172 nb_pixel_total : 19388 time to create 1 rle with old method : 0.021679401397705078 time for calcul the mask position with numpy : 0.028456687927246094 nb_pixel_total : 24907 time to create 1 rle with old method : 0.026799917221069336 time for calcul the mask position with numpy : 0.027407169342041016 nb_pixel_total : 33205 time to create 1 rle with old method : 0.03560233116149902 time for calcul the mask position with numpy : 0.027511119842529297 nb_pixel_total : 41721 time to create 1 rle with old method : 0.04516410827636719 time for calcul the mask position with numpy : 0.028046131134033203 nb_pixel_total : 16586 time to create 1 rle with old method : 0.01846027374267578 time for calcul the mask position with numpy : 0.02864694595336914 nb_pixel_total : 13901 time to create 1 rle with old method : 0.01518702507019043 time for calcul the mask position with numpy : 0.02873396873474121 nb_pixel_total : 74765 time to create 1 rle with old method : 0.08301782608032227 time for calcul the mask position with numpy : 0.028862714767456055 nb_pixel_total : 7146 time to create 1 rle with old method : 0.008076906204223633 time for calcul the mask position with numpy : 0.028326749801635742 nb_pixel_total : 34127 time to create 1 rle with old method : 0.03812146186828613 time for calcul the mask position with numpy : 0.028276681900024414 nb_pixel_total : 30137 time to create 1 rle with old method : 0.0332794189453125 time for calcul the mask position with numpy : 0.029050111770629883 nb_pixel_total : 62846 time to create 1 rle with old method : 0.06752991676330566 time for calcul the mask position with numpy : 0.027335405349731445 nb_pixel_total : 36350 time to create 1 rle with old method : 0.03983712196350098 time for calcul the mask position with numpy : 0.028211116790771484 nb_pixel_total : 21995 time to create 1 rle with old method : 0.023997783660888672 time for calcul the mask position with numpy : 0.027765989303588867 nb_pixel_total : 16355 time to create 1 rle with old method : 0.017975807189941406 time for calcul the mask position with numpy : 0.027846813201904297 nb_pixel_total : 49942 time to create 1 rle with old method : 0.053133249282836914 time for calcul the mask position with numpy : 0.027571439743041992 nb_pixel_total : 24247 time to create 1 rle with old method : 0.02667236328125 time for calcul the mask position with numpy : 0.028924942016601562 nb_pixel_total : 33796 time to create 1 rle with old method : 0.037149906158447266 time for calcul the mask position with numpy : 0.027872323989868164 nb_pixel_total : 16706 time to create 1 rle with old method : 0.01797771453857422 time for calcul the mask position with numpy : 0.02716994285583496 nb_pixel_total : 19061 time to create 1 rle with old method : 0.020824909210205078 time for calcul the mask position with numpy : 0.02876734733581543 nb_pixel_total : 9615 time to create 1 rle with old method : 0.010817527770996094 time for calcul the mask position with numpy : 0.028704166412353516 nb_pixel_total : 12620 time to create 1 rle with old method : 0.01412057876586914 time for calcul the mask position with numpy : 0.0286867618560791 nb_pixel_total : 4635 time to create 1 rle with old method : 0.005243539810180664 time for calcul the mask position with numpy : 0.02921462059020996 nb_pixel_total : 88934 time to create 1 rle with old method : 0.09975361824035645 time for calcul the mask position with numpy : 0.028919219970703125 nb_pixel_total : 14785 time to create 1 rle with old method : 0.016189098358154297 time for calcul the mask position with numpy : 0.028662681579589844 nb_pixel_total : 76935 time to create 1 rle with old method : 0.08384823799133301 time for calcul the mask position with numpy : 0.02733135223388672 nb_pixel_total : 28301 time to create 1 rle with old method : 0.02949690818786621 time for calcul the mask position with numpy : 0.026532411575317383 nb_pixel_total : 15187 time to create 1 rle with old method : 0.015972375869750977 time for calcul the mask position with numpy : 0.027279376983642578 nb_pixel_total : 18506 time to create 1 rle with old method : 0.020506620407104492 time for calcul the mask position with numpy : 0.027899503707885742 nb_pixel_total : 16929 time to create 1 rle with old method : 0.01906275749206543 time for calcul the mask position with numpy : 0.029266834259033203 nb_pixel_total : 17384 time to create 1 rle with old method : 0.021405935287475586 time for calcul the mask position with numpy : 0.02896738052368164 nb_pixel_total : 26989 time to create 1 rle with old method : 0.030083417892456055 time for calcul the mask position with numpy : 0.02875828742980957 nb_pixel_total : 11906 time to create 1 rle with old method : 0.01295161247253418 time for calcul the mask position with numpy : 0.028586387634277344 nb_pixel_total : 9255 time to create 1 rle with old method : 0.010123252868652344 time for calcul the mask position with numpy : 0.028052330017089844 nb_pixel_total : 10943 time to create 1 rle with old method : 0.011933326721191406 time for calcul the mask position with numpy : 0.02854013442993164 nb_pixel_total : 13017 time to create 1 rle with old method : 0.014596939086914062 time for calcul the mask position with numpy : 0.02834773063659668 nb_pixel_total : 3558 time to create 1 rle with old method : 0.003969669342041016 create new chi : 4.808547496795654 time to delete rle : 0.004393815994262695 batch 1 Loaded 115 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27101 TO DO : save crop sub photo not yet done ! save time : 1.6397347450256348 map_output_result : {1349321881: (0.0, 'Should be the crop_list due to order', 0), 1349321862: (0.0, 'Should be the crop_list due to order', 0), 1349321823: (0.0, 'Should be the crop_list due to order', 0), 1349321792: (0.0, 'Should be the crop_list due to order', 0), 1349321645: (0.0, 'Should be the crop_list due to order', 0), 1349321623: (0.0, 'Should be the crop_list due to order', 0), 1349321620: (0.0, 'Should be the crop_list due to order', 0), 1349321618: (0.0, 'Should be the crop_list due to order', 0), 1349321615: (0.0, 'Should be the crop_list due to order', 0), 1349321609: (0.0, 'Should be the crop_list due to order', 0), 1349321585: (0.0, 'Should be the crop_list due to order', 0), 1349321576: (0.0, 'Should be the crop_list due to order', 0), 1349321571: (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 [1349321881, 1349321862, 1349321823, 1349321792, 1349321645, 1349321623, 1349321620, 1349321618, 1349321615, 1349321609, 1349321585, 1349321576, 1349321571] Looping around the photos to save general results len do output : 13 /1349321881.Didn't retrieve data . /1349321862.Didn't retrieve data . /1349321823.Didn't retrieve data . /1349321792.Didn't retrieve data . /1349321645.Didn't retrieve data . /1349321623.Didn't retrieve data . /1349321620.Didn't retrieve data . /1349321618.Didn't retrieve data . /1349321615.Didn't retrieve data . /1349321609.Didn't retrieve data . /1349321585.Didn't retrieve data . /1349321576.Didn't retrieve data . /1349321571.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, '2714309') ('3318', '21957008', '1349321881', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321862', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321823', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321792', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321645', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321623', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321620', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321618', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321615', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321609', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321585', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321576', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321571', None, None, None, None, None, '2714309') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 39 time used for this insertion : 0.017719030380249023 save_final save missing photos in datou_result : time spend for datou_step_exec : 146.19092321395874 time spend to save output : 0.018337249755859375 total time spend for step 3 : 146.2092604637146 step4:ventilate_hashtags_in_portfolio Tue Apr 1 20:30:40 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 : 21957008 get user id for portfolio 21957008 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21957008 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','carton','pet_clair','environnement','flou','pet_fonce','papier','mal_croppe','pehd','metal','autre')) 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`=21957008 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','carton','pet_clair','environnement','flou','pet_fonce','papier','mal_croppe','pehd','metal','autre')) 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`=21957008 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','carton','pet_clair','environnement','flou','pet_fonce','papier','mal_croppe','pehd','metal','autre')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21957492,21957493,21957494,21957495,21957496,21957497,21957498,21957499,21957500,21957501,21957502?tags=background,carton,pet_clair,environnement,flou,pet_fonce,papier,mal_croppe,pehd,metal,autre Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349321881, 1349321862, 1349321823, 1349321792, 1349321645, 1349321623, 1349321620, 1349321618, 1349321615, 1349321609, 1349321585, 1349321576, 1349321571] Looping around the photos to save general results len do output : 1 /21957008. 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, '2714309') ('3318', '21957008', '1349321881', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321862', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321823', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321792', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321645', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321623', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321620', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321618', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321615', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321609', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321585', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321576', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321571', None, None, None, None, None, '2714309') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 14 time used for this insertion : 0.01398158073425293 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.662980318069458 time spend to save output : 0.014278650283813477 total time spend for step 4 : 1.6772589683532715 step5:final Tue Apr 1 20:30:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! Inside saveOutput : final : False verbose : 0 original output for save of step final : {1349321881: ('0.2325076767708508',), 1349321862: ('0.2325076767708508',), 1349321823: ('0.2325076767708508',), 1349321792: ('0.2325076767708508',), 1349321645: ('0.2325076767708508',), 1349321623: ('0.2325076767708508',), 1349321620: ('0.2325076767708508',), 1349321618: ('0.2325076767708508',), 1349321615: ('0.2325076767708508',), 1349321609: ('0.2325076767708508',), 1349321585: ('0.2325076767708508',), 1349321576: ('0.2325076767708508',), 1349321571: ('0.2325076767708508',)} new output for save of step final : {1349321881: ('0.2325076767708508',), 1349321862: ('0.2325076767708508',), 1349321823: ('0.2325076767708508',), 1349321792: ('0.2325076767708508',), 1349321645: ('0.2325076767708508',), 1349321623: ('0.2325076767708508',), 1349321620: ('0.2325076767708508',), 1349321618: ('0.2325076767708508',), 1349321615: ('0.2325076767708508',), 1349321609: ('0.2325076767708508',), 1349321585: ('0.2325076767708508',), 1349321576: ('0.2325076767708508',), 1349321571: ('0.2325076767708508',)} [1349321881, 1349321862, 1349321823, 1349321792, 1349321645, 1349321623, 1349321620, 1349321618, 1349321615, 1349321609, 1349321585, 1349321576, 1349321571] Looping around the photos to save general results len do output : 13 /1349321881.Didn't retrieve data . /1349321862.Didn't retrieve data . /1349321823.Didn't retrieve data . /1349321792.Didn't retrieve data . /1349321645.Didn't retrieve data . /1349321623.Didn't retrieve data . /1349321620.Didn't retrieve data . /1349321618.Didn't retrieve data . /1349321615.Didn't retrieve data . /1349321609.Didn't retrieve data . /1349321585.Didn't retrieve data . /1349321576.Didn't retrieve data . /1349321571.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, '2714309') ('3318', '21957008', '1349321881', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321862', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321823', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321792', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321645', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321623', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321620', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321618', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321615', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321609', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321585', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321576', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321571', None, None, None, None, None, '2714309') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 39 time used for this insertion : 0.013550519943237305 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10657382011413574 time spend to save output : 0.01423025131225586 total time spend for step 5 : 0.1208040714263916 step6:blur_detection Tue Apr 1 20:30:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03.jpg resize: (2160, 3264) 1349321881 -2.8065608112180094 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b.jpg resize: (2160, 3264) 1349321862 -2.96413558933239 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45.jpg resize: (2160, 3264) 1349321823 -3.7553194033892097 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f.jpg resize: (2160, 3264) 1349321792 -3.9375012835623355 treat image : temp/1743531630_2457968_1349321645_f90a70ea86819d89e37cab674c9930bb.jpg resize: (2160, 3264) 1349321645 -4.777347099481951 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89.jpg resize: (2160, 3264) 1349321623 -5.724497800098617 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7.jpg resize: (2160, 3264) 1349321620 -6.780193275748375 treat image : temp/1743531630_2457968_1349321618_dba6dd45d58f7ac38a01ab5dec4b85b8.jpg resize: (2160, 3264) 1349321618 -6.180569501928341 treat image : temp/1743531630_2457968_1349321615_9d819cf217ed7cffe32234c557c26eb5.jpg resize: (2160, 3264) 1349321615 -3.822066598000655 treat image : temp/1743531630_2457968_1349321609_a9dcbe9e0e384ee4d7cda4c87042e9d3.jpg resize: (2160, 3264) 1349321609 -6.136195531297705 treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001.jpg resize: (2160, 3264) 1349321585 -6.282354905467842 treat image : temp/1743531630_2457968_1349321576_88385e4cf15f6a08fe019fb6d4739189.jpg resize: (2160, 3264) 1349321576 -4.414339466864757 treat image : temp/1743531630_2457968_1349321571_5ae431b789c2066e3910ab5c509367ba.jpg resize: (2160, 3264) 1349321571 -4.807614244809561 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097120_0.png resize: (186, 129) 1349337678 -1.981988107281493 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097118_0.png resize: (102, 129) 1349337679 -2.8121479415033908 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097122_0.png resize: (223, 274) 1349337680 -0.8216176123941283 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097125_0.png resize: (710, 727) 1349337682 -2.4078892179516607 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097115_0.png resize: (293, 180) 1349337683 -1.4892798745515659 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097117_0.png resize: (101, 116) 1349337684 -0.04985811312026823 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097116_0.png resize: (99, 162) 1349337685 -1.7848201228467322 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097119_0.png resize: (227, 346) 1349337686 -2.2863794887895117 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097123_0.png resize: (139, 183) 1349337687 -1.1225861927760623 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097114_0.png resize: (146, 254) 1349337688 -2.1691503790313194 treat image : temp/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03_rle_crop_3743097121_0.png resize: (74, 84) 1349337689 -0.7427727410182562 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097147_0.png resize: (211, 191) 1349337690 -1.869601246042957 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097132_0.png resize: (97, 163) 1349337691 -3.112288727594765 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097146_0.png resize: (127, 120) 1349337692 -1.6950634334423231 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097140_0.png resize: (103, 138) 1349337693 -2.4655447132064565 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097144_0.png resize: (120, 142) 1349337694 -2.6338941013355646 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097136_0.png resize: (173, 156) 1349337695 -0.04660185171218692 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097126_0.png resize: (130, 208) 1349337696 -2.890370115610901 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097127_0.png resize: (204, 239) 1349337697 -2.0812575561942532 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097145_0.png resize: (169, 161) 1349337698 -3.0173273908602476 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097130_0.png resize: (137, 151) 1349337699 -2.4513360393072046 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097143_0.png resize: (469, 307) 1349337700 -2.4190326761788605 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097129_0.png resize: (70, 172) 1349337701 -1.194218640919993 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097128_0.png resize: (380, 263) 1349337702 -1.931195147268792 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097134_0.png resize: (187, 94) 1349337703 -1.1047904716030499 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097138_0.png resize: (285, 311) 1349337704 -1.2864294996121508 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097149_0.png resize: (210, 165) 1349337705 -2.6574639863617957 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097142_0.png resize: (217, 332) 1349337706 -3.375656771307423 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097131_0.png resize: (269, 309) 1349337707 -2.0341759496090024 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097150_0.png resize: (69, 73) 1349337708 -1.8297619981540927 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097137_0.png resize: (109, 110) 1349337709 -0.4815097149150772 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097152_0.png resize: (72, 85) 1349337710 -1.5286925736595949 treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b_rle_crop_3743097135_0.png resize: (108, 192) 1349337711 -2.4583254050094343 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097177_0.png resize: (771, 625) 1349337712 -2.028838782664257 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097160_0.png resize: (65, 58) 1349337713 -0.039656739000144006 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097174_0.png resize: (527, 212) 1349337714 -2.870977299592193 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097157_0.png resize: (245, 114) 1349337715 -2.286623120395856 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097170_0.png resize: (166, 222) 1349337716 -1.3708483535845815 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097161_0.png resize: (87, 240) 1349337717 -2.7804674285618307 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097163_0.png resize: (267, 323) 1349337718 -1.74138073071911 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097155_0.png resize: (223, 223) 1349337719 -1.733782629783889 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097168_0.png resize: (193, 428) 1349337720 -1.8152598454891904 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097158_0.png resize: (301, 279) 1349337721 -2.421799870897696 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097176_0.png resize: (78, 86) 1349337723 -2.3035996318486367 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097162_0.png resize: (117, 150) 1349337724 -0.8883662929391645 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097165_0.png resize: (133, 224) 1349337725 -1.859180311902522 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097164_0.png resize: (44, 118) 1349337726 -0.19865801201802857 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097166_0.png resize: (86, 73) 1349337727 -0.5637494284925171 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097173_0.png resize: (248, 125) 1349337728 -1.4304031367671108 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097179_0.png resize: (314, 311) 1349337729 -2.5885897512739358 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097159_0.png resize: (209, 214) 1349337730 -2.998608282204952 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097167_0.png resize: (66, 205) 1349337731 -1.3188479658999477 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097178_0.png resize: (95, 125) 1349337732 -1.7168399743308458 treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45_rle_crop_3743097175_0.png resize: (175, 65) 1349337733 -2.515218930150203 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097187_0.png resize: (156, 155) 1349337734 -2.228451760860107 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097190_0.png resize: (172, 176) 1349337736 -1.9627816285152222 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097196_0.png resize: (230, 88) 1349337737 -1.9612981058223649 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097182_0.png resize: (169, 126) 1349337739 -0.6704563333622607 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097199_0.png resize: (261, 315) 1349337740 -2.0975398177185562 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097189_0.png resize: (281, 404) 1349337741 -2.3153373767939143 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097202_0.png resize: (154, 213) 1349337742 -3.0654878432989094 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097197_0.png resize: (385, 522) 1349337743 -3.136057356138146 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097208_0.png resize: (111, 157) 1349337744 -0.8466082277079436 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097210_0.png resize: (246, 222) 1349337745 -3.9010847283969134 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097191_0.png resize: (149, 116) 1349337746 -2.040343205234203 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097203_0.png resize: (582, 710) 1349337747 -3.951937078480143 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097207_0.png resize: (113, 165) 1349337748 -4.586791725591107 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097206_0.png resize: (125, 113) 1349337749 -2.1372632332673924 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097213_0.png resize: (371, 381) 1349337750 -2.8513421157709637 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097198_0.png resize: (100, 105) 1349337751 0.5410272310131826 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097186_0.png resize: (219, 426) 1349337752 -1.8629394189790422 treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097193_0.png resize: (155, 142) 1349337753 -0.8157896920251981 treat image : 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temp/1743531630_2457968_1349321645_f90a70ea86819d89e37cab674c9930bb_rle_crop_3743097220_0.png resize: (168, 57) 1349337761 3.4829966599660502 treat image : temp/1743531630_2457968_1349321645_f90a70ea86819d89e37cab674c9930bb_rle_crop_3743097215_0.png resize: (155, 214) 1349337762 -2.674413077808418 treat image : temp/1743531630_2457968_1349321645_f90a70ea86819d89e37cab674c9930bb_rle_crop_3743097230_0.png resize: (202, 299) 1349337763 -2.014445653052348 treat image : temp/1743531630_2457968_1349321645_f90a70ea86819d89e37cab674c9930bb_rle_crop_3743097236_0.png resize: (965, 878) 1349337764 -3.5516626071220467 treat image : temp/1743531630_2457968_1349321645_f90a70ea86819d89e37cab674c9930bb_rle_crop_3743097219_0.png resize: (338, 661) 1349337765 -2.7530922684837935 treat image : temp/1743531630_2457968_1349321645_f90a70ea86819d89e37cab674c9930bb_rle_crop_3743097216_0.png resize: (185, 169) 1349337766 -1.727000113884352 treat image : 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temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097246_0.png resize: (126, 129) 1349337785 -3.6735468468092742 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097264_0.png resize: (212, 505) 1349337786 -3.46491469226871 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097247_0.png resize: (149, 146) 1349337787 -2.2054819008453648 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097282_0.png resize: (168, 183) 1349337788 -3.1140741470231696 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097239_0.png resize: (234, 263) 1349337789 -2.6795017211704657 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097251_0.png resize: (179, 161) 1349337790 -3.8414727206195187 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097266_0.png resize: (265, 244) 1349337791 -3.532966100602058 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097258_0.png resize: (326, 207) 1349337792 -4.39387805029084 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097270_0.png resize: (164, 227) 1349337793 -3.4919183349830387 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097244_0.png resize: (242, 183) 1349337794 -3.466167670299171 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097277_0.png resize: (138, 152) 1349337795 -2.7750826135124163 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097276_0.png resize: (288, 329) 1349337796 -4.858947673908341 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097274_0.png resize: (146, 136) 1349337797 -3.8119106598199877 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097281_0.png resize: (141, 115) 1349337798 -2.0851563392711756 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097243_0.png resize: (232, 198) 1349337799 -3.3204910829252223 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097245_0.png resize: (73, 89) 1349337800 -0.9261940300672079 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097237_0.png resize: (255, 145) 1349337802 -3.4625986317755424 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097241_0.png resize: (98, 171) 1349337803 -4.292023315669821 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097278_0.png resize: (146, 171) 1349337804 -4.266727564545506 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097284_0.png resize: (148, 207) 1349337805 -1.5095640440368145 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097252_0.png resize: (215, 208) 1349337806 -3.350898655473668 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097267_0.png resize: (135, 151) 1349337807 -3.915003603794136 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097271_0.png resize: (88, 144) 1349337808 -2.93524400597838 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097265_0.png resize: (218, 160) 1349337809 -3.413695925291962 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097273_0.png resize: (119, 108) 1349337810 -2.9381750957592643 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097248_0.png resize: (110, 176) 1349337811 -2.4612113181090893 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097269_0.png resize: (203, 190) 1349337812 -4.818483477679141 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097279_0.png resize: (170, 179) 1349337813 -0.937961664743887 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097250_0.png resize: (172, 125) 1349337814 -1.8256216346667584 treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89_rle_crop_3743097255_0.png resize: (258, 478) 1349337815 -4.381926874524981 treat image : 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temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097328_0.png resize: (252, 270) 1349337867 -4.807583389395285 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097307_0.png resize: (263, 127) 1349337868 -3.9763191210830877 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097296_0.png resize: (206, 249) 1349337869 -4.410950608469328 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097323_0.png resize: (108, 101) 1349337870 -3.034000223613541 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097353_0.png resize: (233, 190) 1349337871 -5.968920765370021 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097342_0.png resize: (113, 71) 1349337872 -5.547153584461056 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097325_0.png resize: (159, 116) 1349337873 -4.633979861583492 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097308_0.png resize: (188, 397) 1349337874 -4.88487018917644 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097337_0.png resize: (105, 221) 1349337875 -3.279334608135344 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097303_0.png resize: (94, 126) 1349337876 -3.9888419812290588 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097336_0.png resize: (129, 70) 1349337877 -3.5743812127851013 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097335_0.png resize: (290, 195) 1349337878 -0.9691895696521471 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097312_0.png resize: (140, 122) 1349337879 -5.4393324010950215 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097291_0.png resize: (136, 72) 1349337880 -4.185383143761469 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097348_0.png resize: (175, 90) 1349337881 -5.48573642798841 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097288_0.png resize: (195, 233) 1349337882 1.6729002300891302 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097315_0.png resize: (222, 110) 1349337883 -3.069985482000293 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097345_0.png resize: (182, 174) 1349337885 -4.768152765280262 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097327_0.png resize: (150, 114) 1349337886 -4.5877469794558445 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097326_0.png resize: (265, 216) 1349337887 -4.018963416857447 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097294_0.png resize: (112, 145) 1349337888 -4.503330420793158 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097330_0.png resize: (141, 153) 1349337889 -3.1187403214190317 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097340_0.png resize: (94, 208) 1349337890 -5.0449504382862145 treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7_rle_crop_3743097309_0.png resize: (205, 102) 1349337891 -3.257411292547624 treat image : temp/1743531630_2457968_1349321618_dba6dd45d58f7ac38a01ab5dec4b85b8_rle_crop_3743097409_0.png resize: (85, 140) 1349337892 -2.7777412577955825 treat image : temp/1743531630_2457968_1349321618_dba6dd45d58f7ac38a01ab5dec4b85b8_rle_crop_3743097375_0.png resize: (247, 167) 1349337893 -2.6262934756839713 treat image : temp/1743531630_2457968_1349321618_dba6dd45d58f7ac38a01ab5dec4b85b8_rle_crop_3743097428_0.png resize: (122, 88) 1349337894 -3.375661462112769 treat image : temp/1743531630_2457968_1349321618_dba6dd45d58f7ac38a01ab5dec4b85b8_rle_crop_3743097394_0.png resize: (106, 65) 1349337895 -1.163687461615085 treat image : temp/1743531630_2457968_1349321618_dba6dd45d58f7ac38a01ab5dec4b85b8_rle_crop_3743097358_0.png resize: (156, 311) 1349337896 -3.1851544669432523 treat image : temp/1743531630_2457968_1349321618_dba6dd45d58f7ac38a01ab5dec4b85b8_rle_crop_3743097396_0.png resize: (219, 110) 1349337897 -2.9908566071065974 treat image : 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temp/1743531630_2457968_1349321615_9d819cf217ed7cffe32234c557c26eb5_rle_crop_3743097435_0.png resize: (144, 349) 1349338406 -1.6729934551019412 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 : 482 time used for this insertion : 0.034699440002441406 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 482 time used for this insertion : 0.09613370895385742 save missing photos in datou_result : time spend for datou_step_exec : 62.969685316085815 time spend to save output : 0.13713526725769043 total time spend for step 6 : 63.106820583343506 step7:brightness Tue Apr 1 20:31:45 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/1743531630_2457968_1349321881_31a2185f7bd682e2e536414c0d7e0a03.jpg treat image : temp/1743531630_2457968_1349321862_88c9f0364cb1eb6b03931b874e70296b.jpg treat image : temp/1743531630_2457968_1349321823_1c86f0d289182a7e25a73502603caa45.jpg treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f.jpg treat image : temp/1743531630_2457968_1349321645_f90a70ea86819d89e37cab674c9930bb.jpg treat image : temp/1743531630_2457968_1349321623_b40d5bfadc23c79d7f3b6247e808bb89.jpg treat image : temp/1743531630_2457968_1349321620_9474515728e6ad1cdafb3f65a88820b7.jpg treat image : temp/1743531630_2457968_1349321618_dba6dd45d58f7ac38a01ab5dec4b85b8.jpg treat image : temp/1743531630_2457968_1349321615_9d819cf217ed7cffe32234c557c26eb5.jpg treat image : temp/1743531630_2457968_1349321609_a9dcbe9e0e384ee4d7cda4c87042e9d3.jpg treat image : 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temp/1743531630_2457968_1349321609_a9dcbe9e0e384ee4d7cda4c87042e9d3_rle_crop_3743097459_0.png treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001_rle_crop_3743097499_0.png treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001_rle_crop_3743097497_0.png treat image : temp/1743531630_2457968_1349321571_5ae431b789c2066e3910ab5c509367ba_rle_crop_3743097564_0.png treat image : temp/1743531630_2457968_1349321571_5ae431b789c2066e3910ab5c509367ba_rle_crop_3743097540_0.png treat image : temp/1743531630_2457968_1349321792_0fc5457717b39237b00cb498e20a3a1f_rle_crop_3743097211_0.png treat image : temp/1743531630_2457968_1349321609_a9dcbe9e0e384ee4d7cda4c87042e9d3_rle_crop_3743097453_0.png treat image : temp/1743531630_2457968_1349321609_a9dcbe9e0e384ee4d7cda4c87042e9d3_rle_crop_3743097461_0.png treat image : temp/1743531630_2457968_1349321609_a9dcbe9e0e384ee4d7cda4c87042e9d3_rle_crop_3743097468_0.png treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001_rle_crop_3743097493_0.png treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001_rle_crop_3743097482_0.png treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001_rle_crop_3743097498_0.png treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001_rle_crop_3743097492_0.png treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001_rle_crop_3743097484_0.png treat image : temp/1743531630_2457968_1349321585_69c854af5118f262affccf580aab8001_rle_crop_3743097494_0.png treat image : temp/1743531630_2457968_1349321576_88385e4cf15f6a08fe019fb6d4739189_rle_crop_3743097517_0.png treat image : temp/1743531630_2457968_1349321576_88385e4cf15f6a08fe019fb6d4739189_rle_crop_3743097520_0.png treat image : temp/1743531630_2457968_1349321576_88385e4cf15f6a08fe019fb6d4739189_rle_crop_3743097513_0.png treat image : temp/1743531630_2457968_1349321576_88385e4cf15f6a08fe019fb6d4739189_rle_crop_3743097516_0.png treat image : temp/1743531630_2457968_1349321571_5ae431b789c2066e3910ab5c509367ba_rle_crop_3743097566_0.png treat image : temp/1743531630_2457968_1349321615_9d819cf217ed7cffe32234c557c26eb5_rle_crop_3743097435_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 : 482 time used for this insertion : 0.028171539306640625 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 482 time used for this insertion : 0.07693099975585938 save missing photos in datou_result : time spend for datou_step_exec : 16.084335327148438 time spend to save output : 0.11197161674499512 total time spend for step 7 : 16.196306943893433 step8:velours_tree Tue Apr 1 20:32:01 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 : 0.18211054801940918 time spend to save output : 6.842613220214844e-05 total time spend for step 8 : 0.18217897415161133 step9:send_mail_cod Tue Apr 1 20:32:01 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_P21957008_01-04-2025_20_32_01.pdf 21957492 imagette219574921743532321 21957493 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 .imagette219574931743532321 21957494 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 .imagette219574941743532323 21957496 imagette219574961743532325 21957497 change filename to text .imagette219574971743532325 21957498 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 .imagette219574981743532325 21957499 imagette219574991743532326 21957500 imagette219575001743532326 21957501 imagette219575011743532326 21957502 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 .imagette219575021743532326 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21957008 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21957492,21957493,21957494,21957495,21957496,21957497,21957498,21957499,21957500,21957501,21957502?tags=background,carton,pet_clair,environnement,flou,pet_fonce,papier,mal_croppe,pehd,metal,autre args[1349321881] : ((1349321881, -2.8065608112180094, 492609224), (1349321881, -0.29855038559950614, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321862] : ((1349321862, -2.96413558933239, 492609224), (1349321862, -0.2958342916619656, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321823] : ((1349321823, -3.7553194033892097, 492609224), (1349321823, -0.1609742897754076, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321792] : ((1349321792, -3.9375012835623355, 492609224), (1349321792, -0.24297570307493715, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321645] : ((1349321645, -4.777347099481951, 492609224), (1349321645, -0.026138852765970076, 2107752395), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321623] : ((1349321623, -5.724497800098617, 492609224), (1349321623, 0.1666323446648296, 2107752395), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321620] : ((1349321620, -6.780193275748375, 492609224), (1349321620, -0.10759650903623169, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321618] : ((1349321618, -6.180569501928341, 492609224), (1349321618, -0.08534454680980491, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321615] : ((1349321615, -3.822066598000655, 492609224), (1349321615, -0.4126199618277501, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321609] : ((1349321609, -6.136195531297705, 492609224), (1349321609, -0.42073706333167926, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321585] : ((1349321585, -6.282354905467842, 492609224), (1349321585, -0.3447507231294212, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321576] : ((1349321576, -4.414339466864757, 492609224), (1349321576, -0.32074372569093, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com args[1349321571] : ((1349321571, -4.807614244809561, 492609224), (1349321571, -0.11857821965468507, 496442774), '0.2325076767708508') We are sending mail with results at report@fotonower.com refus_total : 0.2325076767708508 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=21957008 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349321576,1349321585,1349321615,1349321609,1349321618,1349321620,1349321623,1349321571,1349321645,1349321792,1349321823,1349321862,1349321881) Found this number of photos: 13 begin to download photo : 1349321576 begin to download photo : 1349321609 begin to download photo : 1349321623 begin to download photo : 1349321792 begin to download photo : 1349321881 download finish for photo 1349321792 begin to download photo : 1349321823 download finish for photo 1349321609 begin to download photo : 1349321618 download finish for photo 1349321881 download finish for photo 1349321623 begin to download photo : 1349321571 download finish for photo 1349321576 begin to download photo : 1349321585 download finish for photo 1349321823 begin to download photo : 1349321862 download finish for photo 1349321618 begin to download photo : 1349321620 download finish for photo 1349321585 begin to download photo : 1349321615 download finish for photo 1349321571 begin to download photo : 1349321645 download finish for photo 1349321620 download finish for photo 1349321615 download finish for photo 1349321862 download finish for photo 1349321645 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21957008_01-04-2025_20_32_01.pdf results_Auto_P21957008_01-04-2025_20_32_01.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21957008_01-04-2025_20_32_01.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','21957008','results_Auto_P21957008_01-04-2025_20_32_01.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21957008_01-04-2025_20_32_01.pdf','pdf','','1.33','0.2325076767708508') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21957008

https://www.fotonower.com/image?json=false&list_photos_id=1349321881
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321862
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321823
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321792
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321645
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321623
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321620
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321618
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321615
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321609
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321585
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321576
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349321571
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 23.25%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/21957493?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21957494?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21957497?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/21957498?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21957502?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21957008_01-04-2025_20_32_01.pdf.

Lien vers velours :https://www.fotonower.com/velours/21957492,21957493,21957494,21957495,21957496,21957497,21957498,21957499,21957500,21957501,21957502?tags=background,carton,pet_clair,environnement,flou,pet_fonce,papier,mal_croppe,pehd,metal,autre.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 01 Apr 2025 18:32:11 GMT Content-Length: 0 Connection: close X-Message-Id: bziREevbQh2xS8DhMSOtuw 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 [1349321881, 1349321862, 1349321823, 1349321792, 1349321645, 1349321623, 1349321620, 1349321618, 1349321615, 1349321609, 1349321585, 1349321576, 1349321571] 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, '2714309') ('3318', '21957008', '1349321881', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321862', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321823', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321792', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321645', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321623', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321620', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321618', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321615', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321609', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321585', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321576', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321571', None, None, None, None, None, '2714309') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.016054868698120117 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.111589908599854 time spend to save output : 0.016322612762451172 total time spend for step 9 : 10.127912521362305 step10:split_time_score Tue Apr 1 20:32:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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'}] (('18', 13),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 01042025 21957008 Nombre de photos uploadées : 13 / 23040 (0%) 01042025 21957008 Nombre de photos taguées (types de déchets): 0 / 13 (0%) 01042025 21957008 Nombre de photos taguées (volume) : 0 / 13 (0%) elapsed_time : load_data_split_time_score 2.6226043701171875e-06 elapsed_time : order_list_meta_photo_and_scores 6.198883056640625e-06 ????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0009088516235351562 elapsed_time : insert_dashboard_record_day_entry 0.025072097778320312 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.19774587565145885 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21941555_01-04-2025_10_34_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21941555 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21941555 AND mptpi.`type`=3594 To do Qualite : 0.10205312159586055 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21944149 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21944149 AND mptpi.`type`=3594 To do Qualite : 0.06586668547792947 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21943744_01-04-2025_12_02_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21943744 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21943744 AND mptpi.`type`=3726 To do Qualite : 0.12565741349965417 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21948989_01-04-2025_16_21_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21948989 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21948989 AND mptpi.`type`=3726 To do Qualite : 0.24201299057431616 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21951749_01-04-2025_19_56_11.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21951749 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21951749 AND mptpi.`type`=3594 To do Qualite : 0.19322973402323884 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21951755_01-04-2025_18_08_54.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21951755 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21951755 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21956986 order by id desc limit 1 Qualite : 0.0620171856580458 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21956987_01-04-2025_20_24_39.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21956987 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21956987 AND mptpi.`type`=3726 To do Qualite : 0.2325076767708508 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21957008_01-04-2025_20_32_01.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21957008 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21957008 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'01042025': {'nb_upload': 13, '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 [1349321881, 1349321862, 1349321823, 1349321792, 1349321645, 1349321623, 1349321620, 1349321618, 1349321615, 1349321609, 1349321585, 1349321576, 1349321571] Looping around the photos to save general results len do output : 1 /21957008Didn'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, '2714309') ('3318', '21957008', '1349321881', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321862', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321823', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321792', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321645', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321623', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321620', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321618', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321615', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321609', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321585', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321576', None, None, None, None, None, '2714309') ('3318', None, None, None, None, None, None, None, '2714309') ('3318', '21957008', '1349321571', None, None, None, None, None, '2714309') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 14 time used for this insertion : 0.013872623443603516 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.294985055923462 time spend to save output : 0.014045476913452148 total time spend for step 10 : 2.309030532836914 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 13 set_done_treatment 346.19user 183.62system 11:48.43elapsed 74%CPU (0avgtext+0avgdata 9002704maxresident)k 2342560inputs+314584outputs (78753major+33326240minor)pagefaults 0swaps