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 : 93323 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 : ['2712269'] with mtr_portfolio_ids : ['21941555'] and first list_photo_ids : [] new path : /proc/93323/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 17 ; length of list_pids : 17 ; length of list_args : 17 time to download the photos : 3.0151515007019043 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 10:20:32 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 : 6663 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-01 10:20:35.820119: 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 10:20:35.847143: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-01 10:20:35.849323: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f90e0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-01 10:20:35.849374: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-01 10:20:35.854325: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-01 10:20:36.026168: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x439942f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-01 10:20:36.026230: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-01 10:20:36.027322: 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 10:20:36.027700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 10:20:36.030039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 10:20:36.032344: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 10:20:36.032822: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 10:20:36.035474: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 10:20:36.036850: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 10:20:36.042045: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 10:20:36.043525: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 10:20:36.043656: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 10:20:36.044360: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 10:20:36.044380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 10:20:36.044391: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 10:20:36.046258: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6121 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 10:20:36.522353: 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 10:20:36.522530: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 10:20:36.522623: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 10:20:36.522652: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 10:20:36.522678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 10:20:36.522753: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 10:20:36.522955: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 10:20:36.523075: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 10:20:36.526925: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 10:20:36.528888: 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 10:20:36.529097: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 10:20:36.529137: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 10:20:36.529164: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 10:20:36.529197: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 10:20:36.529223: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 10:20:36.529247: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 10:20:36.529273: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 10:20:36.530995: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 10:20:36.531077: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 10:20:36.531092: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 10:20:36.531104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 10:20:36.532980: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6121 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 10:20:49.222864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 10:20:49.425290: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 10:20:50.862787: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.863471: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.864081: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.864650: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.865228: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.865827: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.866360: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2282521344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.866397: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.867114: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.867135: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.874494: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.874540: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.875119: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.875138: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.881807: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.881874: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.882463: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.882480: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.913926: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.913980: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.914527: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.914542: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.920431: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.920461: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.921024: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.921041: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-01 10:20:50.954722: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.955338: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.957143: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.957691: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:50.999750: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.000320: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.002461: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.003064: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.035434: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.036435: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.038789: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.039809: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.047587: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.048542: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.051080: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.052067: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.063184: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.064227: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.067014: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.068237: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.100840: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.101471: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.102063: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.102647: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.106777: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.107425: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.125970: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.126604: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.127270: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.127866: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.143172: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.143802: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.144386: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.144966: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.149442: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.150053: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.154890: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.155542: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.168127: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.168744: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.173024: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.173626: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.174208: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.174787: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.175651: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.176243: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.187271: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.187905: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.188513: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.189096: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.189680: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.190262: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.190843: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.191454: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.200950: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.201564: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.208028: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.208637: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.241816: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.241871: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-04-01 10:20:51.242479: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.243088: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.250687: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.251447: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.252189: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.252913: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.262196: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.263200: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.290871: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.291935: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.292945: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.293958: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.298718: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.299783: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.300890: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.301864: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.304568: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.314074: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.315090: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.325288: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.326379: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.327426: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.328420: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.329463: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-04-01 10:20:51.330403: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 17 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 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 : 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 : 45 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 26 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 25 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_imagesNone max_time_sub_proc : 3600 parent process len(results) : 17 len(list_Values) 0 process is alive process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10593 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 : 0.015510082244873047 nb_pixel_total : 11306 time to create 1 rle with old method : 0.020853757858276367 length of segment : 164 time for calcul the mask position with numpy : 0.0007030963897705078 nb_pixel_total : 5559 time to create 1 rle with old method : 0.008868217468261719 length of segment : 106 time for calcul the mask position with numpy : 0.03354167938232422 nb_pixel_total : 41728 time to create 1 rle with old method : 0.07573819160461426 length of segment : 298 time for calcul the mask position with numpy : 0.008254766464233398 nb_pixel_total : 9832 time to create 1 rle with old method : 0.024910688400268555 length of segment : 87 time for calcul the mask position with numpy : 0.042307376861572266 nb_pixel_total : 74973 time to create 1 rle with old method : 0.09213447570800781 length of segment : 331 time for calcul the mask position with numpy : 0.042714595794677734 nb_pixel_total : 58411 time to create 1 rle with old method : 0.06782054901123047 length of segment : 353 time for calcul the mask position with numpy : 0.009428262710571289 nb_pixel_total : 31657 time to create 1 rle with old method : 0.04261445999145508 length of segment : 307 time for calcul the mask position with numpy : 0.0009725093841552734 nb_pixel_total : 13432 time to create 1 rle with old method : 0.015538930892944336 length of segment : 225 time for calcul the mask position with numpy : 0.015917539596557617 nb_pixel_total : 23787 time to create 1 rle with old method : 0.030874252319335938 length of segment : 173 time for calcul the mask position with numpy : 0.007843494415283203 nb_pixel_total : 23546 time to create 1 rle with old method : 0.03309917449951172 length of segment : 199 time for calcul the mask position with numpy : 0.013020992279052734 nb_pixel_total : 11271 time to create 1 rle with old method : 0.018025875091552734 length of segment : 146 time for calcul the mask position with numpy : 0.01983046531677246 nb_pixel_total : 33652 time to create 1 rle with old method : 0.0401918888092041 length of segment : 255 time for calcul the mask position with numpy : 0.002876758575439453 nb_pixel_total : 10085 time to create 1 rle with old method : 0.011992454528808594 length of segment : 105 time for calcul the mask position with numpy : 0.003953695297241211 nb_pixel_total : 34655 time to create 1 rle with old method : 0.046202659606933594 length of segment : 373 time for calcul the mask position with numpy : 0.0015125274658203125 nb_pixel_total : 15802 time to create 1 rle with old method : 0.018727779388427734 length of segment : 149 time for calcul the mask position with numpy : 0.0014760494232177734 nb_pixel_total : 4435 time to create 1 rle with old method : 0.006750345230102539 length of segment : 86 time for calcul the mask position with numpy : 0.005957365036010742 nb_pixel_total : 45660 time to create 1 rle with old method : 0.053026676177978516 length of segment : 398 time for calcul the mask position with numpy : 0.005244731903076172 nb_pixel_total : 53929 time to create 1 rle with old method : 0.07243943214416504 length of segment : 328 time for calcul the mask position with numpy : 0.0009407997131347656 nb_pixel_total : 5806 time to create 1 rle with old method : 0.006924629211425781 length of segment : 106 time for calcul the mask position with numpy : 0.02126765251159668 nb_pixel_total : 34784 time to create 1 rle with old method : 0.04491615295410156 length of segment : 218 time for calcul the mask position with numpy : 0.0022034645080566406 nb_pixel_total : 15684 time to create 1 rle with old method : 0.02363729476928711 length of segment : 112 time for calcul the mask position with numpy : 0.007676362991333008 nb_pixel_total : 10924 time to create 1 rle with old method : 0.022100448608398438 length of segment : 112 time for calcul the mask position with numpy : 0.006148099899291992 nb_pixel_total : 20112 time to create 1 rle with old method : 0.025340795516967773 length of segment : 201 time for calcul the mask position with numpy : 0.00032138824462890625 nb_pixel_total : 9730 time to create 1 rle with old method : 0.011450529098510742 length of segment : 139 time for calcul the mask position with numpy : 0.002504110336303711 nb_pixel_total : 25669 time to create 1 rle with old method : 0.02956414222717285 length of segment : 134 time for calcul the mask position with numpy : 0.020554304122924805 nb_pixel_total : 26443 time to create 1 rle with old method : 0.03383278846740723 length of segment : 287 time for calcul the mask position with numpy : 0.0019953250885009766 nb_pixel_total : 17209 time to create 1 rle with old method : 0.02464151382446289 length of segment : 246 time for calcul the mask position with numpy : 0.008940696716308594 nb_pixel_total : 44570 time to create 1 rle with old method : 0.052961111068725586 length of segment : 277 time for calcul the mask position with numpy : 0.02830672264099121 nb_pixel_total : 82517 time to create 1 rle with old method : 0.12460613250732422 length of segment : 671 time for calcul the mask position with numpy : 0.0004763603210449219 nb_pixel_total : 7809 time to create 1 rle with old method : 0.009223699569702148 length of segment : 83 time for calcul the mask position with numpy : 0.020901918411254883 nb_pixel_total : 34041 time to create 1 rle with old method : 0.043788909912109375 length of segment : 202 time for calcul the mask position with numpy : 0.0012764930725097656 nb_pixel_total : 10612 time to create 1 rle with old method : 0.012715816497802734 length of segment : 108 time for calcul the mask position with numpy : 0.0015981197357177734 nb_pixel_total : 7112 time to create 1 rle with old method : 0.009423494338989258 length of segment : 146 time for calcul the mask position with numpy : 0.009009599685668945 nb_pixel_total : 19602 time to create 1 rle with old method : 0.027866125106811523 length of segment : 168 time for calcul the mask position with numpy : 0.001573801040649414 nb_pixel_total : 10121 time to create 1 rle with old method : 0.012630462646484375 length of segment : 87 time for calcul the mask position with numpy : 0.0016918182373046875 nb_pixel_total : 7145 time to create 1 rle with old method : 0.008640289306640625 length of segment : 94 time for calcul the mask position with numpy : 0.0027709007263183594 nb_pixel_total : 26013 time to create 1 rle with old method : 0.03057241439819336 length of segment : 287 time for calcul the mask position with numpy : 0.012423992156982422 nb_pixel_total : 49446 time to create 1 rle with old method : 0.08339953422546387 length of segment : 265 time for calcul the mask position with numpy : 0.021562814712524414 nb_pixel_total : 47966 time to create 1 rle with old method : 0.058478593826293945 length of segment : 259 time for calcul the mask position with numpy : 0.015747547149658203 nb_pixel_total : 123247 time to create 1 rle with old method : 0.1500396728515625 length of segment : 494 time for calcul the mask position with numpy : 0.00099945068359375 nb_pixel_total : 10046 time to create 1 rle with old method : 0.016924381256103516 length of segment : 115 time for calcul the mask position with numpy : 0.0022826194763183594 nb_pixel_total : 19676 time to create 1 rle with old method : 0.028564929962158203 length of segment : 137 time for calcul the mask position with numpy : 0.0008399486541748047 nb_pixel_total : 9358 time to create 1 rle with old method : 0.011001348495483398 length of segment : 118 time for calcul the mask position with numpy : 0.0014843940734863281 nb_pixel_total : 18513 time to create 1 rle with old method : 0.02144479751586914 length of segment : 179 time for calcul the mask position with numpy : 0.0004668235778808594 nb_pixel_total : 5711 time to create 1 rle with old method : 0.0069882869720458984 length of segment : 66 time for calcul the mask position with numpy : 0.001680135726928711 nb_pixel_total : 27049 time to create 1 rle with old method : 0.03119945526123047 length of segment : 250 time for calcul the mask position with numpy : 0.0010745525360107422 nb_pixel_total : 13599 time to create 1 rle with old method : 0.020941734313964844 length of segment : 115 time for calcul the mask position with numpy : 0.004630088806152344 nb_pixel_total : 29270 time to create 1 rle with old method : 0.03549075126647949 length of segment : 217 time for calcul the mask position with numpy : 0.013611793518066406 nb_pixel_total : 63825 time to create 1 rle with old method : 0.07697248458862305 length of segment : 250 time for calcul the mask position with numpy : 0.00024080276489257812 nb_pixel_total : 6696 time to create 1 rle with old method : 0.008097171783447266 length of segment : 84 time for calcul the mask position with numpy : 0.0007417201995849609 nb_pixel_total : 10799 time to create 1 rle with old method : 0.017071962356567383 length of segment : 129 time for calcul the mask position with numpy : 0.0006754398345947266 nb_pixel_total : 3798 time to create 1 rle with old method : 0.004689216613769531 length of segment : 63 time for calcul the mask position with numpy : 0.0010075569152832031 nb_pixel_total : 14392 time to create 1 rle with old method : 0.01673579216003418 length of segment : 131 time for calcul the mask position with numpy : 0.0004818439483642578 nb_pixel_total : 8226 time to create 1 rle with old method : 0.009873151779174805 length of segment : 116 time for calcul the mask position with numpy : 0.00036978721618652344 nb_pixel_total : 12593 time to create 1 rle with old method : 0.014860153198242188 length of segment : 183 time for calcul the mask position with numpy : 0.004714250564575195 nb_pixel_total : 22546 time to create 1 rle with old method : 0.027695655822753906 length of segment : 191 time for calcul the mask position with numpy : 0.003033161163330078 nb_pixel_total : 29657 time to create 1 rle with old method : 0.03598594665527344 length of segment : 367 time for calcul the mask position with numpy : 0.0007741451263427734 nb_pixel_total : 12928 time to create 1 rle with old method : 0.021221160888671875 length of segment : 168 time for calcul the mask position with numpy : 0.0007205009460449219 nb_pixel_total : 30329 time to create 1 rle with old method : 0.034891366958618164 length of segment : 198 time for calcul the mask position with numpy : 0.010699272155761719 nb_pixel_total : 59136 time to create 1 rle with old method : 0.07053279876708984 length of segment : 380 time for calcul the mask position with numpy : 0.0008258819580078125 nb_pixel_total : 9456 time to create 1 rle with old method : 0.011348962783813477 length of segment : 118 time for calcul the mask position with numpy : 0.0015494823455810547 nb_pixel_total : 7625 time to create 1 rle with old method : 0.009023189544677734 length of segment : 123 time for calcul the mask position with numpy : 0.0001742839813232422 nb_pixel_total : 6007 time to create 1 rle with old method : 0.0072176456451416016 length of segment : 100 time for calcul the mask position with numpy : 0.00043201446533203125 nb_pixel_total : 10350 time to create 1 rle with old method : 0.012635231018066406 length of segment : 147 time for calcul the mask position with numpy : 0.01652693748474121 nb_pixel_total : 16110 time to create 1 rle with old method : 0.02644491195678711 length of segment : 146 time for calcul the mask position with numpy : 0.001005411148071289 nb_pixel_total : 24201 time to create 1 rle with old method : 0.03480720520019531 length of segment : 193 time for calcul the mask position with numpy : 0.007090091705322266 nb_pixel_total : 29961 time to create 1 rle with old method : 0.03706240653991699 length of segment : 176 time for calcul the mask position with numpy : 0.002910614013671875 nb_pixel_total : 91380 time to create 1 rle with old method : 0.10773539543151855 length of segment : 277 time for calcul the mask position with numpy : 0.002214193344116211 nb_pixel_total : 20443 time to create 1 rle with old method : 0.023005247116088867 length of segment : 202 time for calcul the mask position with numpy : 0.0008366107940673828 nb_pixel_total : 10043 time to create 1 rle with old method : 0.017542362213134766 length of segment : 106 time for calcul the mask position with numpy : 0.0006594657897949219 nb_pixel_total : 11844 time to create 1 rle with old method : 0.014225006103515625 length of segment : 120 time for calcul the mask position with numpy : 0.004280567169189453 nb_pixel_total : 35109 time to create 1 rle with old method : 0.04008078575134277 length of segment : 303 time for calcul the mask position with numpy : 0.003200054168701172 nb_pixel_total : 33127 time to create 1 rle with old method : 0.04091358184814453 length of segment : 274 time for calcul the mask position with numpy : 0.0005791187286376953 nb_pixel_total : 15727 time to create 1 rle with old method : 0.018547773361206055 length of segment : 169 time for calcul the mask position with numpy : 0.02793145179748535 nb_pixel_total : 30116 time to create 1 rle with old method : 0.039954423904418945 length of segment : 247 time for calcul the mask position with numpy : 0.0007612705230712891 nb_pixel_total : 8596 time to create 1 rle with old method : 0.010086774826049805 length of segment : 120 time for calcul the mask position with numpy : 0.012285470962524414 nb_pixel_total : 12354 time to create 1 rle with old method : 0.0199277400970459 length of segment : 119 time for calcul the mask position with numpy : 0.00180816650390625 nb_pixel_total : 20455 time to create 1 rle with old method : 0.024291276931762695 length of segment : 269 time for calcul the mask position with numpy : 0.004218101501464844 nb_pixel_total : 34849 time to create 1 rle with old method : 0.039643049240112305 length of segment : 181 time for calcul the mask position with numpy : 0.001382589340209961 nb_pixel_total : 7024 time to create 1 rle with old method : 0.008496522903442383 length of segment : 73 time for calcul the mask position with numpy : 0.0016264915466308594 nb_pixel_total : 20002 time to create 1 rle with old method : 0.025564908981323242 length of segment : 172 time for calcul the mask position with numpy : 0.003131866455078125 nb_pixel_total : 33002 time to create 1 rle with old method : 0.04462289810180664 length of segment : 208 time for calcul the mask position with numpy : 0.004099845886230469 nb_pixel_total : 16891 time to create 1 rle with old method : 0.022415637969970703 length of segment : 174 time for calcul the mask position with numpy : 0.004326343536376953 nb_pixel_total : 35899 time to create 1 rle with old method : 0.07073354721069336 length of segment : 210 time for calcul the mask position with numpy : 0.0017650127410888672 nb_pixel_total : 12779 time to create 1 rle with old method : 0.026894569396972656 length of segment : 123 time for calcul the mask position with numpy : 0.001399993896484375 nb_pixel_total : 6501 time to create 1 rle with old method : 0.00832223892211914 length of segment : 72 time for calcul the mask position with numpy : 0.0008833408355712891 nb_pixel_total : 5748 time to create 1 rle with old method : 0.01050567626953125 length of segment : 127 time for calcul the mask position with numpy : 0.006067514419555664 nb_pixel_total : 31190 time to create 1 rle with old method : 0.037194013595581055 length of segment : 269 time for calcul the mask position with numpy : 0.0007412433624267578 nb_pixel_total : 10234 time to create 1 rle with old method : 0.01238107681274414 length of segment : 127 time for calcul the mask position with numpy : 0.004802703857421875 nb_pixel_total : 44605 time to create 1 rle with old method : 0.052164554595947266 length of segment : 256 time for calcul the mask position with numpy : 0.002386331558227539 nb_pixel_total : 11395 time to create 1 rle with old method : 0.01666259765625 length of segment : 95 time for calcul the mask position with numpy : 0.0004723072052001953 nb_pixel_total : 4096 time to create 1 rle with old method : 0.005662679672241211 length of segment : 56 time for calcul the mask position with numpy : 0.0055141448974609375 nb_pixel_total : 60241 time to create 1 rle with old method : 0.06877636909484863 length of segment : 406 time for calcul the mask position with numpy : 0.0012717247009277344 nb_pixel_total : 15032 time to create 1 rle with old method : 0.01749897003173828 length of segment : 138 time for calcul the mask position with numpy : 0.00030422210693359375 nb_pixel_total : 5028 time to create 1 rle with old method : 0.006319999694824219 length of segment : 88 time for calcul the mask position with numpy : 0.0035552978515625 nb_pixel_total : 33645 time to create 1 rle with old method : 0.04402732849121094 length of segment : 208 time for calcul the mask position with numpy : 0.0006451606750488281 nb_pixel_total : 4401 time to create 1 rle with old method : 0.00545048713684082 length of segment : 72 time for calcul the mask position with numpy : 0.0005598068237304688 nb_pixel_total : 5647 time to create 1 rle with old method : 0.006997823715209961 length of segment : 76 time for calcul the mask position with numpy : 0.0010569095611572266 nb_pixel_total : 10528 time to create 1 rle with old method : 0.013194084167480469 length of segment : 137 time for calcul the mask position with numpy : 0.03528547286987305 nb_pixel_total : 50832 time to create 1 rle with old method : 0.06397294998168945 length of segment : 342 time for calcul the mask position with numpy : 0.002618551254272461 nb_pixel_total : 32684 time to create 1 rle with old method : 0.0394587516784668 length of segment : 171 time for calcul the mask position with numpy : 0.0008046627044677734 nb_pixel_total : 11565 time to create 1 rle with old method : 0.015013933181762695 length of segment : 95 time for calcul the mask position with numpy : 0.0007343292236328125 nb_pixel_total : 10638 time to create 1 rle with old method : 0.012953519821166992 length of segment : 117 time for calcul the mask position with numpy : 0.0036389827728271484 nb_pixel_total : 47257 time to create 1 rle with old method : 0.05661749839782715 length of segment : 229 time for calcul the mask position with numpy : 0.0004954338073730469 nb_pixel_total : 5999 time to create 1 rle with old method : 0.007499217987060547 length of segment : 79 time for calcul the mask position with numpy : 0.003371000289916992 nb_pixel_total : 41631 time to create 1 rle with old method : 0.04862165451049805 length of segment : 228 time for calcul the mask position with numpy : 0.0008199214935302734 nb_pixel_total : 5820 time to create 1 rle with old method : 0.009945392608642578 length of segment : 103 time for calcul the mask position with numpy : 0.002867460250854492 nb_pixel_total : 28456 time to create 1 rle with old method : 0.04036116600036621 length of segment : 198 time for calcul the mask position with numpy : 0.00179290771484375 nb_pixel_total : 12773 time to create 1 rle with old method : 0.015239715576171875 length of segment : 115 time for calcul the mask position with numpy : 0.0013685226440429688 nb_pixel_total : 19575 time to create 1 rle with old method : 0.022607803344726562 length of segment : 243 time for calcul the mask position with numpy : 0.002941608428955078 nb_pixel_total : 38418 time to create 1 rle with old method : 0.04304337501525879 length of segment : 367 time for calcul the mask position with numpy : 0.0009806156158447266 nb_pixel_total : 11410 time to create 1 rle with old method : 0.013143062591552734 length of segment : 116 time for calcul the mask position with numpy : 0.0008256435394287109 nb_pixel_total : 5111 time to create 1 rle with old method : 0.006069183349609375 length of segment : 75 time for calcul the mask position with numpy : 0.0038423538208007812 nb_pixel_total : 11777 time to create 1 rle with old method : 0.01585984230041504 length of segment : 116 time for calcul the mask position with numpy : 0.004224538803100586 nb_pixel_total : 33862 time to create 1 rle with old method : 0.04385638236999512 length of segment : 289 time for calcul the mask position with numpy : 0.00026917457580566406 nb_pixel_total : 2651 time to create 1 rle with old method : 0.003076791763305664 length of segment : 60 time for calcul the mask position with numpy : 0.0017695426940917969 nb_pixel_total : 16310 time to create 1 rle with old method : 0.0188295841217041 length of segment : 163 time for calcul the mask position with numpy : 0.0041692256927490234 nb_pixel_total : 18816 time to create 1 rle with old method : 0.02386331558227539 length of segment : 178 time for calcul the mask position with numpy : 0.0008361339569091797 nb_pixel_total : 13625 time to create 1 rle with old method : 0.01534724235534668 length of segment : 129 time for calcul the mask position with numpy : 0.0010178089141845703 nb_pixel_total : 12906 time to create 1 rle with old method : 0.015529155731201172 length of segment : 137 time for calcul the mask position with numpy : 0.01562809944152832 nb_pixel_total : 100204 time to create 1 rle with old method : 0.11444616317749023 length of segment : 587 time for calcul the mask position with numpy : 0.002470731735229492 nb_pixel_total : 20664 time to create 1 rle with old method : 0.025196075439453125 length of segment : 202 time for calcul the mask position with numpy : 0.0019330978393554688 nb_pixel_total : 23377 time to create 1 rle with old method : 0.028964996337890625 length of segment : 212 time for calcul the mask position with numpy : 0.004510164260864258 nb_pixel_total : 47715 time to create 1 rle with old method : 0.07986807823181152 length of segment : 314 time for calcul the mask position with numpy : 0.0067598819732666016 nb_pixel_total : 107429 time to create 1 rle with old method : 0.11969232559204102 length of segment : 384 time for calcul the mask position with numpy : 0.0042536258697509766 nb_pixel_total : 89556 time to create 1 rle with old method : 0.10068154335021973 length of segment : 342 time for calcul the mask position with numpy : 0.009553909301757812 nb_pixel_total : 58399 time to create 1 rle with old method : 0.06901741027832031 length of segment : 379 time for calcul the mask position with numpy : 0.0014693737030029297 nb_pixel_total : 31882 time to create 1 rle with old method : 0.05227375030517578 length of segment : 244 time for calcul the mask position with numpy : 0.0003464221954345703 nb_pixel_total : 7407 time to create 1 rle with old method : 0.008872747421264648 length of segment : 94 time for calcul the mask position with numpy : 0.018522977828979492 nb_pixel_total : 191983 time to create 1 rle with new method : 0.01422262191772461 length of segment : 556 time for calcul the mask position with numpy : 0.005873680114746094 nb_pixel_total : 59161 time to create 1 rle with old method : 0.06793427467346191 length of segment : 464 time for calcul the mask position with numpy : 0.005625009536743164 nb_pixel_total : 104339 time to create 1 rle with old method : 0.11836743354797363 length of segment : 371 time for calcul the mask position with numpy : 0.0006313323974609375 nb_pixel_total : 11543 time to create 1 rle with old method : 0.013418197631835938 length of segment : 132 time for calcul the mask position with numpy : 0.0004673004150390625 nb_pixel_total : 5509 time to create 1 rle with old method : 0.006677150726318359 length of segment : 112 time for calcul the mask position with numpy : 0.0007004737854003906 nb_pixel_total : 15348 time to create 1 rle with old method : 0.019153118133544922 length of segment : 206 time for calcul the mask position with numpy : 0.004453420639038086 nb_pixel_total : 92851 time to create 1 rle with old method : 0.10551881790161133 length of segment : 333 time for calcul the mask position with numpy : 0.00040030479431152344 nb_pixel_total : 16577 time to create 1 rle with old method : 0.019475698471069336 length of segment : 164 time for calcul the mask position with numpy : 0.004023075103759766 nb_pixel_total : 75210 time to create 1 rle with old method : 0.08735132217407227 length of segment : 202 time for calcul the mask position with numpy : 0.002543926239013672 nb_pixel_total : 21679 time to create 1 rle with old method : 0.03782463073730469 length of segment : 161 time for calcul the mask position with numpy : 0.0015599727630615234 nb_pixel_total : 16698 time to create 1 rle with old method : 0.019689083099365234 length of segment : 207 time for calcul the mask position with numpy : 0.00020384788513183594 nb_pixel_total : 7601 time to create 1 rle with old method : 0.009311914443969727 length of segment : 80 time for calcul the mask position with numpy : 0.0008146762847900391 nb_pixel_total : 26087 time to create 1 rle with old method : 0.03044271469116211 length of segment : 271 time for calcul the mask position with numpy : 0.0032498836517333984 nb_pixel_total : 43522 time to create 1 rle with old method : 0.05153632164001465 length of segment : 298 time for calcul the mask position with numpy : 0.0013477802276611328 nb_pixel_total : 13685 time to create 1 rle with old method : 0.01586008071899414 length of segment : 181 time for calcul the mask position with numpy : 0.0006144046783447266 nb_pixel_total : 15984 time to create 1 rle with old method : 0.019113779067993164 length of segment : 141 time for calcul the mask position with numpy : 0.0005943775177001953 nb_pixel_total : 13898 time to create 1 rle with old method : 0.016623735427856445 length of segment : 113 time for calcul the mask position with numpy : 0.002209186553955078 nb_pixel_total : 13234 time to create 1 rle with old method : 0.022295236587524414 length of segment : 75 time for calcul the mask position with numpy : 0.0007627010345458984 nb_pixel_total : 16620 time to create 1 rle with old method : 0.024215221405029297 length of segment : 113 time for calcul the mask position with numpy : 0.0009057521820068359 nb_pixel_total : 30146 time to create 1 rle with old method : 0.035938262939453125 length of segment : 193 time for calcul the mask position with numpy : 0.002033710479736328 nb_pixel_total : 28835 time to create 1 rle with old method : 0.03798270225524902 length of segment : 217 time for calcul the mask position with numpy : 0.0003628730773925781 nb_pixel_total : 17671 time to create 1 rle with old method : 0.021160602569580078 length of segment : 86 time for calcul the mask position with numpy : 0.0013926029205322266 nb_pixel_total : 19054 time to create 1 rle with old method : 0.02252674102783203 length of segment : 112 time for calcul the mask position with numpy : 0.00023818016052246094 nb_pixel_total : 10750 time to create 1 rle with old method : 0.013562679290771484 length of segment : 99 time for calcul the mask position with numpy : 0.0010533332824707031 nb_pixel_total : 28602 time to create 1 rle with old method : 0.03537392616271973 length of segment : 154 time for calcul the mask position with numpy : 0.00044798851013183594 nb_pixel_total : 13530 time to create 1 rle with old method : 0.01648879051208496 length of segment : 150 time for calcul the mask position with numpy : 0.0013995170593261719 nb_pixel_total : 18753 time to create 1 rle with old method : 0.027616500854492188 length of segment : 150 time for calcul the mask position with numpy : 0.0013344287872314453 nb_pixel_total : 49544 time to create 1 rle with old method : 0.05819058418273926 length of segment : 272 time for calcul the mask position with numpy : 0.0005180835723876953 nb_pixel_total : 19267 time to create 1 rle with old method : 0.0232999324798584 length of segment : 127 time for calcul the mask position with numpy : 0.0008997917175292969 nb_pixel_total : 17134 time to create 1 rle with old method : 0.029004573822021484 length of segment : 168 time for calcul the mask position with numpy : 0.0019130706787109375 nb_pixel_total : 56896 time to create 1 rle with old method : 0.06444454193115234 length of segment : 461 time for calcul the mask position with numpy : 0.0004439353942871094 nb_pixel_total : 23608 time to create 1 rle with old method : 0.02919292449951172 length of segment : 99 time for calcul the mask position with numpy : 0.002808094024658203 nb_pixel_total : 62405 time to create 1 rle with old method : 0.07726263999938965 length of segment : 346 time for calcul the mask position with numpy : 0.0007364749908447266 nb_pixel_total : 31153 time to create 1 rle with old method : 0.03548431396484375 length of segment : 159 time for calcul the mask position with numpy : 0.000690460205078125 nb_pixel_total : 30681 time to create 1 rle with old method : 0.03578782081604004 length of segment : 125 time for calcul the mask position with numpy : 0.0021877288818359375 nb_pixel_total : 104666 time to create 1 rle with old method : 0.138031005859375 length of segment : 352 time for calcul the mask position with numpy : 0.0005412101745605469 nb_pixel_total : 13773 time to create 1 rle with old method : 0.0160675048828125 length of segment : 200 time for calcul the mask position with numpy : 0.005285739898681641 nb_pixel_total : 27837 time to create 1 rle with old method : 0.03370928764343262 length of segment : 196 time for calcul the mask position with numpy : 0.01397705078125 nb_pixel_total : 60924 time to create 1 rle with old method : 0.0766141414642334 length of segment : 402 time for calcul the mask position with numpy : 0.002746105194091797 nb_pixel_total : 18565 time to create 1 rle with old method : 0.02424335479736328 length of segment : 126 time for calcul the mask position with numpy : 0.0024340152740478516 nb_pixel_total : 14564 time to create 1 rle with old method : 0.025495052337646484 length of segment : 133 time for calcul the mask position with numpy : 0.004662036895751953 nb_pixel_total : 122307 time to create 1 rle with old method : 0.15504121780395508 length of segment : 397 time for calcul the mask position with numpy : 0.004763603210449219 nb_pixel_total : 22903 time to create 1 rle with old method : 0.03443408012390137 length of segment : 146 time for calcul the mask position with numpy : 0.0008292198181152344 nb_pixel_total : 19805 time to create 1 rle with old method : 0.02514791488647461 length of segment : 297 time for calcul the mask position with numpy : 0.003644227981567383 nb_pixel_total : 113371 time to create 1 rle with old method : 0.13396096229553223 length of segment : 437 time for calcul the mask position with numpy : 0.004457235336303711 nb_pixel_total : 9624 time to create 1 rle with old method : 0.011486530303955078 length of segment : 130 time for calcul the mask position with numpy : 0.0003933906555175781 nb_pixel_total : 18593 time to create 1 rle with old method : 0.0228269100189209 length of segment : 206 time for calcul the mask position with numpy : 0.006876707077026367 nb_pixel_total : 174211 time to create 1 rle with new method : 0.009217023849487305 length of segment : 434 time for calcul the mask position with numpy : 0.0012214183807373047 nb_pixel_total : 17575 time to create 1 rle with old method : 0.03447604179382324 length of segment : 155 time for calcul the mask position with numpy : 0.002318143844604492 nb_pixel_total : 20980 time to create 1 rle with old method : 0.024428844451904297 length of segment : 258 time for calcul the mask position with numpy : 0.002774477005004883 nb_pixel_total : 29570 time to create 1 rle with old method : 0.033789873123168945 length of segment : 188 time for calcul the mask position with numpy : 0.00464940071105957 nb_pixel_total : 19647 time to create 1 rle with old method : 0.02583479881286621 length of segment : 128 time for calcul the mask position with numpy : 0.01581859588623047 nb_pixel_total : 99228 time to create 1 rle with old method : 0.13172054290771484 length of segment : 455 time for calcul the mask position with numpy : 0.004555463790893555 nb_pixel_total : 81694 time to create 1 rle with old method : 0.09226250648498535 length of segment : 253 time for calcul the mask position with numpy : 0.004530906677246094 nb_pixel_total : 29304 time to create 1 rle with old method : 0.03376960754394531 length of segment : 398 time for calcul the mask position with numpy : 0.05113410949707031 nb_pixel_total : 391665 time to create 1 rle with new method : 0.05829977989196777 length of segment : 885 time for calcul the mask position with numpy : 0.0011665821075439453 nb_pixel_total : 5582 time to create 1 rle with old method : 0.006655216217041016 length of segment : 81 time for calcul the mask position with numpy : 0.00040984153747558594 nb_pixel_total : 6898 time to create 1 rle with old method : 0.008222103118896484 length of segment : 87 time for calcul the mask position with numpy : 0.04182291030883789 nb_pixel_total : 379009 time to create 1 rle with new method : 0.23727011680603027 length of segment : 977 time for calcul the mask position with numpy : 0.0002777576446533203 nb_pixel_total : 11922 time to create 1 rle with old method : 0.013845205307006836 length of segment : 142 time for calcul the mask position with numpy : 0.0008804798126220703 nb_pixel_total : 5612 time to create 1 rle with old method : 0.00661468505859375 length of segment : 121 time for calcul the mask position with numpy : 0.0005199909210205078 nb_pixel_total : 22326 time to create 1 rle with old method : 0.02634406089782715 length of segment : 237 time for calcul the mask position with numpy : 0.0001704692840576172 nb_pixel_total : 2997 time to create 1 rle with old method : 0.007998943328857422 length of segment : 73 time for calcul the mask position with numpy : 0.0016639232635498047 nb_pixel_total : 31191 time to create 1 rle with old method : 0.0380859375 length of segment : 219 time for calcul the mask position with numpy : 0.001130819320678711 nb_pixel_total : 17072 time to create 1 rle with old method : 0.025046825408935547 length of segment : 229 time for calcul the mask position with numpy : 0.0028777122497558594 nb_pixel_total : 5084 time to create 1 rle with old method : 0.008693456649780273 length of segment : 158 time for calcul the mask position with numpy : 0.00023245811462402344 nb_pixel_total : 6837 time to create 1 rle with old method : 0.010717153549194336 length of segment : 122 time for calcul the mask position with numpy : 0.0007109642028808594 nb_pixel_total : 27378 time to create 1 rle with old method : 0.03151297569274902 length of segment : 160 time for calcul the mask position with numpy : 0.0005497932434082031 nb_pixel_total : 25915 time to create 1 rle with old method : 0.030036211013793945 length of segment : 156 time for calcul the mask position with numpy : 0.0006127357482910156 nb_pixel_total : 9440 time to create 1 rle with old method : 0.011505603790283203 length of segment : 189 time for calcul the mask position with numpy : 0.0004787445068359375 nb_pixel_total : 6144 time to create 1 rle with old method : 0.007395267486572266 length of segment : 93 time for calcul the mask position with numpy : 0.0020208358764648438 nb_pixel_total : 64727 time to create 1 rle with old method : 0.07434773445129395 length of segment : 326 time for calcul the mask position with numpy : 0.0008482933044433594 nb_pixel_total : 38759 time to create 1 rle with old method : 0.0441889762878418 length of segment : 388 time for calcul the mask position with numpy : 0.00040984153747558594 nb_pixel_total : 15421 time to create 1 rle with old method : 0.018324613571166992 length of segment : 188 time for calcul the mask position with numpy : 0.006775617599487305 nb_pixel_total : 180537 time to create 1 rle with new method : 0.008698701858520508 length of segment : 516 time for calcul the mask position with numpy : 0.00251007080078125 nb_pixel_total : 59766 time to create 1 rle with old method : 0.06757855415344238 length of segment : 233 time for calcul the mask position with numpy : 0.0008077621459960938 nb_pixel_total : 13877 time to create 1 rle with old method : 0.016204833984375 length of segment : 115 time for calcul the mask position with numpy : 0.0009305477142333984 nb_pixel_total : 11905 time to create 1 rle with old method : 0.014084577560424805 length of segment : 127 time for calcul the mask position with numpy : 0.0008883476257324219 nb_pixel_total : 19810 time to create 1 rle with old method : 0.022783994674682617 length of segment : 146 time for calcul the mask position with numpy : 0.000782012939453125 nb_pixel_total : 14981 time to create 1 rle with old method : 0.017389774322509766 length of segment : 151 time for calcul the mask position with numpy : 0.0005381107330322266 nb_pixel_total : 14337 time to create 1 rle with old method : 0.016834497451782227 length of segment : 135 time for calcul the mask position with numpy : 0.007237434387207031 nb_pixel_total : 202444 time to create 1 rle with new method : 0.010000944137573242 length of segment : 538 time for calcul the mask position with numpy : 0.0004220008850097656 nb_pixel_total : 22145 time to create 1 rle with old method : 0.026114702224731445 length of segment : 182 time for calcul the mask position with numpy : 0.001863241195678711 nb_pixel_total : 37165 time to create 1 rle with old method : 0.04464077949523926 length of segment : 194 time for calcul the mask position with numpy : 0.0007390975952148438 nb_pixel_total : 31483 time to create 1 rle with old method : 0.03525042533874512 length of segment : 322 time for calcul the mask position with numpy : 0.0008509159088134766 nb_pixel_total : 23721 time to create 1 rle with old method : 0.0273897647857666 length of segment : 148 time for calcul the mask position with numpy : 0.0010671615600585938 nb_pixel_total : 13465 time to create 1 rle with old method : 0.015679359436035156 length of segment : 128 time for calcul the mask position with numpy : 0.0007405281066894531 nb_pixel_total : 16839 time to create 1 rle with old method : 0.02047443389892578 length of segment : 249 time for calcul the mask position with numpy : 0.0029795169830322266 nb_pixel_total : 70175 time to create 1 rle with old method : 0.0791616439819336 length of segment : 348 time for calcul the mask position with numpy : 0.0011415481567382812 nb_pixel_total : 21600 time to create 1 rle with old method : 0.024281978607177734 length of segment : 211 time for calcul the mask position with numpy : 0.0002818107604980469 nb_pixel_total : 5955 time to create 1 rle with old method : 0.007195472717285156 length of segment : 81 time for calcul the mask position with numpy : 0.007771492004394531 nb_pixel_total : 185532 time to create 1 rle with new method : 0.0099945068359375 length of segment : 536 time for calcul the mask position with numpy : 0.0074481964111328125 nb_pixel_total : 172788 time to create 1 rle with new method : 0.01075434684753418 length of segment : 522 time for calcul the mask position with numpy : 0.00029850006103515625 nb_pixel_total : 7327 time to create 1 rle with old method : 0.008907079696655273 length of segment : 69 time for calcul the mask position with numpy : 0.0007271766662597656 nb_pixel_total : 27803 time to create 1 rle with old method : 0.03190922737121582 length of segment : 191 time for calcul the mask position with numpy : 0.0015668869018554688 nb_pixel_total : 10620 time to create 1 rle with old method : 0.012609243392944336 length of segment : 105 time for calcul the mask position with numpy : 0.0029926300048828125 nb_pixel_total : 65656 time to create 1 rle with old method : 0.08707809448242188 length of segment : 332 time for calcul the mask position with numpy : 0.003011465072631836 nb_pixel_total : 14826 time to create 1 rle with old method : 0.020448684692382812 length of segment : 175 time for calcul the mask position with numpy : 0.0005085468292236328 nb_pixel_total : 11510 time to create 1 rle with old method : 0.013481855392456055 length of segment : 108 time for calcul the mask position with numpy : 0.0009684562683105469 nb_pixel_total : 24939 time to create 1 rle with old method : 0.028682231903076172 length of segment : 252 time for calcul the mask position with numpy : 0.0022025108337402344 nb_pixel_total : 28550 time to create 1 rle with old method : 0.03307199478149414 length of segment : 211 time for calcul the mask position with numpy : 0.0036163330078125 nb_pixel_total : 70636 time to create 1 rle with old method : 0.07972455024719238 length of segment : 306 time for calcul the mask position with numpy : 0.0005350112915039062 nb_pixel_total : 10300 time to create 1 rle with old method : 0.011802911758422852 length of segment : 131 time for calcul the mask position with numpy : 0.0004611015319824219 nb_pixel_total : 13489 time to create 1 rle with old method : 0.016203880310058594 length of segment : 118 time for calcul the mask position with numpy : 0.0023190975189208984 nb_pixel_total : 38493 time to create 1 rle with old method : 0.047072649002075195 length of segment : 249 time for calcul the mask position with numpy : 0.0025408267974853516 nb_pixel_total : 38925 time to create 1 rle with old method : 0.047104835510253906 length of segment : 220 time for calcul the mask position with numpy : 0.0004737377166748047 nb_pixel_total : 10139 time to create 1 rle with old method : 0.01254892349243164 length of segment : 133 time for calcul the mask position with numpy : 0.001505136489868164 nb_pixel_total : 35219 time to create 1 rle with old method : 0.040328025817871094 length of segment : 277 time for calcul the mask position with numpy : 0.00040531158447265625 nb_pixel_total : 6765 time to create 1 rle with old method : 0.008774042129516602 length of segment : 88 time for calcul the mask position with numpy : 0.0013239383697509766 nb_pixel_total : 23666 time to create 1 rle with old method : 0.03144192695617676 length of segment : 195 time for calcul the mask position with numpy : 0.0011603832244873047 nb_pixel_total : 30447 time to create 1 rle with old method : 0.04710841178894043 length of segment : 226 time for calcul the mask position with numpy : 0.0004296302795410156 nb_pixel_total : 9944 time to create 1 rle with old method : 0.013514518737792969 length of segment : 109 time for calcul the mask position with numpy : 0.01063394546508789 nb_pixel_total : 105363 time to create 1 rle with old method : 0.12378382682800293 length of segment : 854 time for calcul the mask position with numpy : 0.0004215240478515625 nb_pixel_total : 6406 time to create 1 rle with old method : 0.007860660552978516 length of segment : 92 time for calcul the mask position with numpy : 0.0003345012664794922 nb_pixel_total : 5249 time to create 1 rle with old method : 0.006457805633544922 length of segment : 129 time for calcul the mask position with numpy : 0.0006895065307617188 nb_pixel_total : 13873 time to create 1 rle with old method : 0.01695728302001953 length of segment : 151 time for calcul the mask position with numpy : 0.0003037452697753906 nb_pixel_total : 2659 time to create 1 rle with old method : 0.003302335739135742 length of segment : 71 time for calcul the mask position with numpy : 0.0025191307067871094 nb_pixel_total : 15245 time to create 1 rle with old method : 0.017983675003051758 length of segment : 192 time for calcul the mask position with numpy : 0.00095367431640625 nb_pixel_total : 16368 time to create 1 rle with old method : 0.02030491828918457 length of segment : 169 time for calcul the mask position with numpy : 0.0011875629425048828 nb_pixel_total : 31301 time to create 1 rle with old method : 0.036345720291137695 length of segment : 226 time for calcul the mask position with numpy : 0.0035698413848876953 nb_pixel_total : 92541 time to create 1 rle with old method : 0.11073637008666992 length of segment : 279 time for calcul the mask position with numpy : 0.0006372928619384766 nb_pixel_total : 19330 time to create 1 rle with old method : 0.02237534523010254 length of segment : 126 time for calcul the mask position with numpy : 0.0008404254913330078 nb_pixel_total : 15184 time to create 1 rle with old method : 0.017607450485229492 length of segment : 186 time for calcul the mask position with numpy : 0.003842592239379883 nb_pixel_total : 51839 time to create 1 rle with old method : 0.061322689056396484 length of segment : 397 time for calcul the mask position with numpy : 0.006817817687988281 nb_pixel_total : 106306 time to create 1 rle with old method : 0.12135434150695801 length of segment : 312 time for calcul the mask position with numpy : 0.0007617473602294922 nb_pixel_total : 16728 time to create 1 rle with old method : 0.01992201805114746 length of segment : 206 time for calcul the mask position with numpy : 0.0008332729339599609 nb_pixel_total : 20835 time to create 1 rle with old method : 0.024005651473999023 length of segment : 186 time for calcul the mask position with numpy : 0.0020301342010498047 nb_pixel_total : 27779 time to create 1 rle with old method : 0.03170585632324219 length of segment : 253 time for calcul the mask position with numpy : 0.002218961715698242 nb_pixel_total : 17041 time to create 1 rle with old method : 0.018750667572021484 length of segment : 226 time for calcul the mask position with numpy : 0.0005078315734863281 nb_pixel_total : 11343 time to create 1 rle with old method : 0.013188838958740234 length of segment : 154 time for calcul the mask position with numpy : 0.0010890960693359375 nb_pixel_total : 26292 time to create 1 rle with old method : 0.02908039093017578 length of segment : 247 time for calcul the mask position with numpy : 0.0011363029479980469 nb_pixel_total : 27138 time to create 1 rle with old method : 0.0385127067565918 length of segment : 196 time for calcul the mask position with numpy : 0.0008699893951416016 nb_pixel_total : 14264 time to create 1 rle with old method : 0.024100780487060547 length of segment : 145 time for calcul the mask position with numpy : 0.0006709098815917969 nb_pixel_total : 15508 time to create 1 rle with old method : 0.017671823501586914 length of segment : 221 time for calcul the mask position with numpy : 0.0010807514190673828 nb_pixel_total : 17127 time to create 1 rle with old method : 0.019634246826171875 length of segment : 277 time for calcul the mask position with numpy : 0.000644683837890625 nb_pixel_total : 12042 time to create 1 rle with old method : 0.01393270492553711 length of segment : 162 time for calcul the mask position with numpy : 0.0003459453582763672 nb_pixel_total : 10808 time to create 1 rle with old method : 0.013221502304077148 length of segment : 114 time for calcul the mask position with numpy : 0.0013890266418457031 nb_pixel_total : 37965 time to create 1 rle with old method : 0.0430293083190918 length of segment : 221 time for calcul the mask position with numpy : 0.001982450485229492 nb_pixel_total : 37146 time to create 1 rle with old method : 0.04190540313720703 length of segment : 279 time for calcul the mask position with numpy : 0.00019669532775878906 nb_pixel_total : 7158 time to create 1 rle with old method : 0.00836038589477539 length of segment : 131 time for calcul the mask position with numpy : 0.0013484954833984375 nb_pixel_total : 34684 time to create 1 rle with old method : 0.03934836387634277 length of segment : 214 time for calcul the mask position with numpy : 0.0006544589996337891 nb_pixel_total : 17849 time to create 1 rle with old method : 0.021772384643554688 length of segment : 104 time for calcul the mask position with numpy : 0.0005323886871337891 nb_pixel_total : 8536 time to create 1 rle with old method : 0.012970209121704102 length of segment : 130 time for calcul the mask position with numpy : 0.0007193088531494141 nb_pixel_total : 12182 time to create 1 rle with old method : 0.01581287384033203 length of segment : 90 time for calcul the mask position with numpy : 0.00031280517578125 nb_pixel_total : 6769 time to create 1 rle with old method : 0.008365392684936523 length of segment : 86 time for calcul the mask position with numpy : 0.026664018630981445 nb_pixel_total : 598876 time to create 1 rle with new method : 0.05916547775268555 length of segment : 1123 time for calcul the mask position with numpy : 0.0006895065307617188 nb_pixel_total : 16155 time to create 1 rle with old method : 0.01834702491760254 length of segment : 208 time for calcul the mask position with numpy : 0.015499114990234375 nb_pixel_total : 255318 time to create 1 rle with new method : 0.033023834228515625 length of segment : 779 time for calcul the mask position with numpy : 0.0009555816650390625 nb_pixel_total : 52999 time to create 1 rle with old method : 0.0586698055267334 length of segment : 227 time for calcul the mask position with numpy : 0.0020360946655273438 nb_pixel_total : 29803 time to create 1 rle with old method : 0.033830881118774414 length of segment : 350 time for calcul the mask position with numpy : 0.009938955307006836 nb_pixel_total : 228253 time to create 1 rle with new method : 0.024384260177612305 length of segment : 574 time for calcul the mask position with numpy : 0.002981901168823242 nb_pixel_total : 71549 time to create 1 rle with old method : 0.09487724304199219 length of segment : 358 time for calcul the mask position with numpy : 0.0007004737854003906 nb_pixel_total : 9286 time to create 1 rle with old method : 0.010649919509887695 length of segment : 151 time for calcul the mask position with numpy : 0.0015082359313964844 nb_pixel_total : 20516 time to create 1 rle with old method : 0.023094654083251953 length of segment : 146 time for calcul the mask position with numpy : 0.0030548572540283203 nb_pixel_total : 62367 time to create 1 rle with old method : 0.06864166259765625 length of segment : 333 time for calcul the mask position with numpy : 0.004473209381103516 nb_pixel_total : 99405 time to create 1 rle with old method : 0.11037015914916992 length of segment : 409 time for calcul the mask position with numpy : 0.0035445690155029297 nb_pixel_total : 46424 time to create 1 rle with old method : 0.0525355339050293 length of segment : 630 time for calcul the mask position with numpy : 0.002713441848754883 nb_pixel_total : 70016 time to create 1 rle with old method : 0.08195209503173828 length of segment : 267 time for calcul the mask position with numpy : 0.003097057342529297 nb_pixel_total : 136150 time to create 1 rle with old method : 0.18950891494750977 length of segment : 271 time for calcul the mask position with numpy : 0.0020825862884521484 nb_pixel_total : 69184 time to create 1 rle with old method : 0.07849693298339844 length of segment : 178 time for calcul the mask position with numpy : 0.0012750625610351562 nb_pixel_total : 21745 time to create 1 rle with old method : 0.02550053596496582 length of segment : 235 time for calcul the mask position with numpy : 0.0023958683013916016 nb_pixel_total : 72221 time to create 1 rle with old method : 0.08166956901550293 length of segment : 381 time for calcul the mask position with numpy : 0.012318134307861328 nb_pixel_total : 269709 time to create 1 rle with new method : 0.020184040069580078 length of segment : 687 time for calcul the mask position with numpy : 0.00045418739318847656 nb_pixel_total : 5243 time to create 1 rle with old method : 0.00619816780090332 length of segment : 106 time for calcul the mask position with numpy : 0.012017488479614258 nb_pixel_total : 288479 time to create 1 rle with new method : 0.03282308578491211 length of segment : 1057 time for calcul the mask position with numpy : 0.007768154144287109 nb_pixel_total : 121979 time to create 1 rle with old method : 0.13576245307922363 length of segment : 627 time for calcul the mask position with numpy : 0.001260519027709961 nb_pixel_total : 69250 time to create 1 rle with old method : 0.08264350891113281 length of segment : 178 time for calcul the mask position with numpy : 0.0007796287536621094 nb_pixel_total : 31922 time to create 1 rle with old method : 0.051770687103271484 length of segment : 294 time for calcul the mask position with numpy : 0.03150010108947754 nb_pixel_total : 260959 time to create 1 rle with new method : 0.5206093788146973 length of segment : 1833 time for calcul the mask position with numpy : 0.005463838577270508 nb_pixel_total : 155499 time to create 1 rle with new method : 0.005931377410888672 length of segment : 321 time for calcul the mask position with numpy : 0.008048772811889648 nb_pixel_total : 222374 time to create 1 rle with new method : 0.010194063186645508 length of segment : 469 time for calcul the mask position with numpy : 0.0018253326416015625 nb_pixel_total : 52080 time to create 1 rle with old method : 0.05953192710876465 length of segment : 343 time for calcul the mask position with numpy : 0.0016512870788574219 nb_pixel_total : 49438 time to create 1 rle with old method : 0.05764412879943848 length of segment : 220 time for calcul the mask position with numpy : 0.007715940475463867 nb_pixel_total : 179837 time to create 1 rle with new method : 0.008381366729736328 length of segment : 405 time for calcul the mask position with numpy : 0.0007326602935791016 nb_pixel_total : 16703 time to create 1 rle with old method : 0.019000530242919922 length of segment : 137 time for calcul the mask position with numpy : 0.0008184909820556641 nb_pixel_total : 27399 time to create 1 rle with old method : 0.03182673454284668 length of segment : 137 time for calcul the mask position with numpy : 0.0021066665649414062 nb_pixel_total : 42691 time to create 1 rle with old method : 0.04831838607788086 length of segment : 390 time for calcul the mask position with numpy : 0.0012781620025634766 nb_pixel_total : 44557 time to create 1 rle with old method : 0.052396297454833984 length of segment : 220 time for calcul the mask position with numpy : 0.011844158172607422 nb_pixel_total : 289593 time to create 1 rle with new method : 0.03244638442993164 length of segment : 583 time for calcul the mask position with numpy : 0.0004901885986328125 nb_pixel_total : 14254 time to create 1 rle with old method : 0.01673746109008789 length of segment : 106 time for calcul the mask position with numpy : 0.00026345252990722656 nb_pixel_total : 12889 time to create 1 rle with old method : 0.016525745391845703 length of segment : 162 time for calcul the mask position with numpy : 0.00363922119140625 nb_pixel_total : 92888 time to create 1 rle with old method : 0.10493922233581543 length of segment : 446 time for calcul the mask position with numpy : 0.0009026527404785156 nb_pixel_total : 27070 time to create 1 rle with old method : 0.030754566192626953 length of segment : 237 time for calcul the mask position with numpy : 0.00041556358337402344 nb_pixel_total : 7979 time to create 1 rle with old method : 0.010331153869628906 length of segment : 70 time for calcul the mask position with numpy : 0.0008559226989746094 nb_pixel_total : 17871 time to create 1 rle with old method : 0.02036595344543457 length of segment : 230 time for calcul the mask position with numpy : 0.0019910335540771484 nb_pixel_total : 47669 time to create 1 rle with old method : 0.053560733795166016 length of segment : 344 time for calcul the mask position with numpy : 0.012609004974365234 nb_pixel_total : 287452 time to create 1 rle with new method : 0.014095783233642578 length of segment : 545 time for calcul the mask position with numpy : 0.00213623046875 nb_pixel_total : 40258 time to create 1 rle with old method : 0.04507756233215332 length of segment : 458 time for calcul the mask position with numpy : 0.00470280647277832 nb_pixel_total : 95692 time to create 1 rle with old method : 0.10731172561645508 length of segment : 375 time for calcul the mask position with numpy : 0.0013632774353027344 nb_pixel_total : 32362 time to create 1 rle with old method : 0.03683161735534668 length of segment : 268 time for calcul the mask position with numpy : 0.0005371570587158203 nb_pixel_total : 16965 time to create 1 rle with old method : 0.01971292495727539 length of segment : 338 time for calcul the mask position with numpy : 0.0015735626220703125 nb_pixel_total : 40286 time to create 1 rle with old method : 0.04666280746459961 length of segment : 203 time for calcul the mask position with numpy : 0.0029571056365966797 nb_pixel_total : 61462 time to create 1 rle with old method : 0.07052111625671387 length of segment : 482 time for calcul the mask position with numpy : 0.006104707717895508 nb_pixel_total : 123796 time to create 1 rle with old method : 0.13694024085998535 length of segment : 470 time for calcul the mask position with numpy : 0.002376556396484375 nb_pixel_total : 61989 time to create 1 rle with old method : 0.06927084922790527 length of segment : 314 time for calcul the mask position with numpy : 0.005455493927001953 nb_pixel_total : 118479 time to create 1 rle with old method : 0.13533401489257812 length of segment : 785 time for calcul the mask position with numpy : 0.019409656524658203 nb_pixel_total : 382313 time to create 1 rle with new method : 0.05130267143249512 length of segment : 1421 time for calcul the mask position with numpy : 0.001352548599243164 nb_pixel_total : 52409 time to create 1 rle with old method : 0.06291627883911133 length of segment : 306 time for calcul the mask position with numpy : 0.006418943405151367 nb_pixel_total : 117654 time to create 1 rle with old method : 0.13128280639648438 length of segment : 572 time for calcul the mask position with numpy : 0.005413055419921875 nb_pixel_total : 113142 time to create 1 rle with old method : 0.1239783763885498 length of segment : 395 time for calcul the mask position with numpy : 0.005178928375244141 nb_pixel_total : 74446 time to create 1 rle with old method : 0.08199191093444824 length of segment : 480 time for calcul the mask position with numpy : 0.0024280548095703125 nb_pixel_total : 76602 time to create 1 rle with old method : 0.08466792106628418 length of segment : 278 time for calcul the mask position with numpy : 0.0005316734313964844 nb_pixel_total : 10670 time to create 1 rle with old method : 0.012419700622558594 length of segment : 91 time for calcul the mask position with numpy : 0.001865386962890625 nb_pixel_total : 44576 time to create 1 rle with old method : 0.04895520210266113 length of segment : 225 time for calcul the mask position with numpy : 0.001961231231689453 nb_pixel_total : 60043 time to create 1 rle with old method : 0.06469559669494629 length of segment : 364 time for calcul the mask position with numpy : 0.024558305740356445 nb_pixel_total : 434026 time to create 1 rle with new method : 0.1096196174621582 length of segment : 1043 time for calcul the mask position with numpy : 0.0015797615051269531 nb_pixel_total : 32355 time to create 1 rle with old method : 0.03365683555603027 length of segment : 577 time for calcul the mask position with numpy : 0.0013644695281982422 nb_pixel_total : 39240 time to create 1 rle with old method : 0.04214763641357422 length of segment : 237 time for calcul the mask position with numpy : 0.0006856918334960938 nb_pixel_total : 17858 time to create 1 rle with old method : 0.0200197696685791 length of segment : 130 time for calcul the mask position with numpy : 0.008925199508666992 nb_pixel_total : 220767 time to create 1 rle with new method : 0.014741182327270508 length of segment : 584 time for calcul the mask position with numpy : 0.003281831741333008 nb_pixel_total : 105837 time to create 1 rle with old method : 0.12032079696655273 length of segment : 365 time for calcul the mask position with numpy : 0.0018734931945800781 nb_pixel_total : 34135 time to create 1 rle with old method : 0.039125680923461914 length of segment : 165 time for calcul the mask position with numpy : 0.004131317138671875 nb_pixel_total : 67083 time to create 1 rle with old method : 0.0760045051574707 length of segment : 383 time for calcul the mask position with numpy : 0.0004134178161621094 nb_pixel_total : 5555 time to create 1 rle with old method : 0.0065348148345947266 length of segment : 115 time for calcul the mask position with numpy : 0.011090755462646484 nb_pixel_total : 319354 time to create 1 rle with new method : 0.01594686508178711 length of segment : 608 time for calcul the mask position with numpy : 0.0006895065307617188 nb_pixel_total : 14869 time to create 1 rle with old method : 0.01630377769470215 length of segment : 185 time for calcul the mask position with numpy : 0.0021703243255615234 nb_pixel_total : 47065 time to create 1 rle with old method : 0.052864789962768555 length of segment : 292 time for calcul the mask position with numpy : 0.0018601417541503906 nb_pixel_total : 41426 time to create 1 rle with old method : 0.04662179946899414 length of segment : 345 time for calcul the mask position with numpy : 0.008566617965698242 nb_pixel_total : 175556 time to create 1 rle with new method : 0.010475397109985352 length of segment : 1088 time for calcul the mask position with numpy : 0.004073143005371094 nb_pixel_total : 92305 time to create 1 rle with old method : 0.1051490306854248 length of segment : 246 time for calcul the mask position with numpy : 0.0015697479248046875 nb_pixel_total : 38050 time to create 1 rle with old method : 0.04459095001220703 length of segment : 273 time for calcul the mask position with numpy : 0.0012781620025634766 nb_pixel_total : 41841 time to create 1 rle with old method : 0.047411203384399414 length of segment : 250 time for calcul the mask position with numpy : 0.00407719612121582 nb_pixel_total : 130713 time to create 1 rle with old method : 0.14629745483398438 length of segment : 426 time for calcul the mask position with numpy : 0.0020859241485595703 nb_pixel_total : 46086 time to create 1 rle with old method : 0.05399274826049805 length of segment : 247 time for calcul the mask position with numpy : 0.04303741455078125 nb_pixel_total : 979478 time to create 1 rle with new method : 0.30219054222106934 length of segment : 1584 time for calcul the mask position with numpy : 0.0010559558868408203 nb_pixel_total : 24975 time to create 1 rle with old method : 0.02897357940673828 length of segment : 183 time for calcul the mask position with numpy : 0.001817464828491211 nb_pixel_total : 124815 time to create 1 rle with old method : 0.14086627960205078 length of segment : 345 time for calcul the mask position with numpy : 0.0014851093292236328 nb_pixel_total : 30599 time to create 1 rle with old method : 0.03526139259338379 length of segment : 193 time for calcul the mask position with numpy : 0.0019414424896240234 nb_pixel_total : 46393 time to create 1 rle with old method : 0.05383038520812988 length of segment : 221 time for calcul the mask position with numpy : 0.0004475116729736328 nb_pixel_total : 9782 time to create 1 rle with old method : 0.011390447616577148 length of segment : 110 time for calcul the mask position with numpy : 0.004068613052368164 nb_pixel_total : 98994 time to create 1 rle with old method : 0.11336827278137207 length of segment : 423 time for calcul the mask position with numpy : 0.0012001991271972656 nb_pixel_total : 20495 time to create 1 rle with old method : 0.024042129516601562 length of segment : 204 time for calcul the mask position with numpy : 0.00041747093200683594 nb_pixel_total : 5630 time to create 1 rle with old method : 0.00744175910949707 length of segment : 76 time for calcul the mask position with numpy : 0.002191305160522461 nb_pixel_total : 30653 time to create 1 rle with old method : 0.036040544509887695 length of segment : 559 time for calcul the mask position with numpy : 0.0009489059448242188 nb_pixel_total : 16783 time to create 1 rle with old method : 0.019544124603271484 length of segment : 177 time for calcul the mask position with numpy : 0.005483388900756836 nb_pixel_total : 339011 time to create 1 rle with new method : 0.015659332275390625 length of segment : 495 time for calcul the mask position with numpy : 0.0007097721099853516 nb_pixel_total : 15219 time to create 1 rle with old method : 0.019135236740112305 length of segment : 171 time for calcul the mask position with numpy : 0.0015251636505126953 nb_pixel_total : 20036 time to create 1 rle with old method : 0.023688793182373047 length of segment : 210 time for calcul the mask position with numpy : 0.011548995971679688 nb_pixel_total : 230253 time to create 1 rle with new method : 0.011084556579589844 length of segment : 643 time for calcul the mask position with numpy : 0.0009868144989013672 nb_pixel_total : 16341 time to create 1 rle with old method : 0.019610166549682617 length of segment : 80 time for calcul the mask position with numpy : 0.00828695297241211 nb_pixel_total : 136199 time to create 1 rle with old method : 0.15736818313598633 length of segment : 1008 time for calcul the mask position with numpy : 0.002019643783569336 nb_pixel_total : 32962 time to create 1 rle with old method : 0.03788566589355469 length of segment : 189 time for calcul the mask position with numpy : 0.0007331371307373047 nb_pixel_total : 10170 time to create 1 rle with old method : 0.016399145126342773 length of segment : 85 time for calcul the mask position with numpy : 0.004941463470458984 nb_pixel_total : 61500 time to create 1 rle with old method : 0.07038259506225586 length of segment : 477 time for calcul the mask position with numpy : 0.001190185546875 nb_pixel_total : 17341 time to create 1 rle with old method : 0.020849943161010742 length of segment : 120 time for calcul the mask position with numpy : 0.0036618709564208984 nb_pixel_total : 55980 time to create 1 rle with old method : 0.064178466796875 length of segment : 270 time for calcul the mask position with numpy : 0.0015308856964111328 nb_pixel_total : 20440 time to create 1 rle with old method : 0.0233767032623291 length of segment : 271 time for calcul the mask position with numpy : 0.00467991828918457 nb_pixel_total : 105479 time to create 1 rle with old method : 0.12071681022644043 length of segment : 320 time for calcul the mask position with numpy : 0.002460479736328125 nb_pixel_total : 24676 time to create 1 rle with old method : 0.028465747833251953 length of segment : 302 time for calcul the mask position with numpy : 0.024871826171875 nb_pixel_total : 430862 time to create 1 rle with new method : 0.03296709060668945 length of segment : 855 time for calcul the mask position with numpy : 0.003039121627807617 nb_pixel_total : 30557 time to create 1 rle with old method : 0.03566861152648926 length of segment : 429 time for calcul the mask position with numpy : 0.007779121398925781 nb_pixel_total : 134623 time to create 1 rle with old method : 0.1515789031982422 length of segment : 475 time for calcul the mask position with numpy : 0.005048274993896484 nb_pixel_total : 63296 time to create 1 rle with old method : 0.07236599922180176 length of segment : 477 time for calcul the mask position with numpy : 0.0031516551971435547 nb_pixel_total : 46371 time to create 1 rle with old method : 0.05392289161682129 length of segment : 363 time for calcul the mask position with numpy : 0.0016660690307617188 nb_pixel_total : 24670 time to create 1 rle with old method : 0.02857685089111328 length of segment : 254 time for calcul the mask position with numpy : 0.0017006397247314453 nb_pixel_total : 17457 time to create 1 rle with old method : 0.019998550415039062 length of segment : 198 time for calcul the mask position with numpy : 0.0015513896942138672 nb_pixel_total : 27066 time to create 1 rle with old method : 0.03165888786315918 length of segment : 272 time for calcul the mask position with numpy : 0.0010247230529785156 nb_pixel_total : 15580 time to create 1 rle with old method : 0.018028736114501953 length of segment : 170 time for calcul the mask position with numpy : 0.0003714561462402344 nb_pixel_total : 7440 time to create 1 rle with old method : 0.009065866470336914 length of segment : 127 time for calcul the mask position with numpy : 0.0004069805145263672 nb_pixel_total : 7787 time to create 1 rle with old method : 0.009589433670043945 length of segment : 68 time for calcul the mask position with numpy : 0.00029015541076660156 nb_pixel_total : 8733 time to create 1 rle with old method : 0.010447025299072266 length of segment : 66 time for calcul the mask position with numpy : 0.0027451515197753906 nb_pixel_total : 50859 time to create 1 rle with old method : 0.0583643913269043 length of segment : 264 time for calcul the mask position with numpy : 0.0014064311981201172 nb_pixel_total : 22391 time to create 1 rle with old method : 0.034261465072631836 length of segment : 123 time for calcul the mask position with numpy : 0.0003936290740966797 nb_pixel_total : 10226 time to create 1 rle with old method : 0.016688108444213867 length of segment : 230 time for calcul the mask position with numpy : 0.0036873817443847656 nb_pixel_total : 40784 time to create 1 rle with old method : 0.049944400787353516 length of segment : 289 time for calcul the mask position with numpy : 0.0161895751953125 nb_pixel_total : 287594 time to create 1 rle with new method : 0.01940321922302246 length of segment : 561 time for calcul the mask position with numpy : 0.0019259452819824219 nb_pixel_total : 25930 time to create 1 rle with old method : 0.029705524444580078 length of segment : 212 time for calcul the mask position with numpy : 0.0015974044799804688 nb_pixel_total : 24395 time to create 1 rle with old method : 0.028441190719604492 length of segment : 241 time for calcul the mask position with numpy : 0.00080108642578125 nb_pixel_total : 14749 time to create 1 rle with old method : 0.017176389694213867 length of segment : 218 time for calcul the mask position with numpy : 0.0005159378051757812 nb_pixel_total : 18743 time to create 1 rle with old method : 0.022303104400634766 length of segment : 171 time for calcul the mask position with numpy : 0.0012631416320800781 nb_pixel_total : 17074 time to create 1 rle with old method : 0.0201263427734375 length of segment : 190 time for calcul the mask position with numpy : 0.0025937557220458984 nb_pixel_total : 37436 time to create 1 rle with old method : 0.04386496543884277 length of segment : 281 time for calcul the mask position with numpy : 0.0010371208190917969 nb_pixel_total : 24829 time to create 1 rle with old method : 0.028848648071289062 length of segment : 223 time for calcul the mask position with numpy : 0.0031061172485351562 nb_pixel_total : 66473 time to create 1 rle with old method : 0.07703828811645508 length of segment : 263 time for calcul the mask position with numpy : 0.002352476119995117 nb_pixel_total : 52381 time to create 1 rle with old method : 0.05917215347290039 length of segment : 345 time for calcul the mask position with numpy : 0.0002257823944091797 nb_pixel_total : 8222 time to create 1 rle with old method : 0.009729623794555664 length of segment : 177 time for calcul the mask position with numpy : 0.0019598007202148438 nb_pixel_total : 31160 time to create 1 rle with old method : 0.036773681640625 length of segment : 218 time for calcul the mask position with numpy : 0.0007505416870117188 nb_pixel_total : 12909 time to create 1 rle with old method : 0.015270471572875977 length of segment : 152 time for calcul the mask position with numpy : 0.0009679794311523438 nb_pixel_total : 12773 time to create 1 rle with old method : 0.015121698379516602 length of segment : 140 time for calcul the mask position with numpy : 0.000293731689453125 nb_pixel_total : 10331 time to create 1 rle with old method : 0.012409687042236328 length of segment : 114 time for calcul the mask position with numpy : 0.004236936569213867 nb_pixel_total : 90386 time to create 1 rle with old method : 0.10348367691040039 length of segment : 379 time for calcul the mask position with numpy : 0.00160980224609375 nb_pixel_total : 19989 time to create 1 rle with old method : 0.023965835571289062 length of segment : 217 time for calcul the mask position with numpy : 0.0007081031799316406 nb_pixel_total : 17539 time to create 1 rle with old method : 0.020063161849975586 length of segment : 220 time for calcul the mask position with numpy : 0.0006172657012939453 nb_pixel_total : 10390 time to create 1 rle with old method : 0.012350797653198242 length of segment : 149 time for calcul the mask position with numpy : 0.0005757808685302734 nb_pixel_total : 9031 time to create 1 rle with old method : 0.010854005813598633 length of segment : 129 time for calcul the mask position with numpy : 0.0007717609405517578 nb_pixel_total : 13180 time to create 1 rle with old method : 0.015313863754272461 length of segment : 171 time for calcul the mask position with numpy : 0.0014791488647460938 nb_pixel_total : 30938 time to create 1 rle with old method : 0.03537249565124512 length of segment : 172 time for calcul the mask position with numpy : 0.000423431396484375 nb_pixel_total : 8544 time to create 1 rle with old method : 0.010644197463989258 length of segment : 78 time for calcul the mask position with numpy : 0.0008759498596191406 nb_pixel_total : 22679 time to create 1 rle with old method : 0.028164386749267578 length of segment : 132 time for calcul the mask position with numpy : 0.0011363029479980469 nb_pixel_total : 25817 time to create 1 rle with old method : 0.03302764892578125 length of segment : 196 time for calcul the mask position with numpy : 0.0010600090026855469 nb_pixel_total : 26194 time to create 1 rle with old method : 0.030771493911743164 length of segment : 194 time for calcul the mask position with numpy : 0.001501321792602539 nb_pixel_total : 31948 time to create 1 rle with old method : 0.03700828552246094 length of segment : 196 time for calcul the mask position with numpy : 0.0009436607360839844 nb_pixel_total : 18463 time to create 1 rle with old method : 0.021941423416137695 length of segment : 192 time for calcul the mask position with numpy : 0.0007758140563964844 nb_pixel_total : 16937 time to create 1 rle with old method : 0.019999980926513672 length of segment : 207 time for calcul the mask position with numpy : 0.001035928726196289 nb_pixel_total : 17698 time to create 1 rle with old method : 0.020363330841064453 length of segment : 230 time for calcul the mask position with numpy : 0.0008945465087890625 nb_pixel_total : 21795 time to create 1 rle with old method : 0.0253751277923584 length of segment : 169 time for calcul the mask position with numpy : 0.0037229061126708984 nb_pixel_total : 82118 time to create 1 rle with old method : 0.09472393989562988 length of segment : 387 time for calcul the mask position with numpy : 0.0023381710052490234 nb_pixel_total : 45787 time to create 1 rle with old method : 0.052527666091918945 length of segment : 473 time for calcul the mask position with numpy : 0.0004138946533203125 nb_pixel_total : 9151 time to create 1 rle with old method : 0.010297298431396484 length of segment : 106 time for calcul the mask position with numpy : 0.001218557357788086 nb_pixel_total : 27096 time to create 1 rle with old method : 0.03384995460510254 length of segment : 231 time for calcul the mask position with numpy : 0.0008528232574462891 nb_pixel_total : 21135 time to create 1 rle with old method : 0.024302244186401367 length of segment : 228 time for calcul the mask position with numpy : 0.0006704330444335938 nb_pixel_total : 18777 time to create 1 rle with old method : 0.02172231674194336 length of segment : 177 time for calcul the mask position with numpy : 0.002336263656616211 nb_pixel_total : 46481 time to create 1 rle with old method : 0.0533442497253418 length of segment : 295 time for calcul the mask position with numpy : 0.0025663375854492188 nb_pixel_total : 60174 time to create 1 rle with old method : 0.07016372680664062 length of segment : 471 time for calcul the mask position with numpy : 0.000324249267578125 nb_pixel_total : 5069 time to create 1 rle with old method : 0.006029367446899414 length of segment : 78 time for calcul the mask position with numpy : 0.000789642333984375 nb_pixel_total : 13689 time to create 1 rle with old method : 0.022570371627807617 length of segment : 128 time for calcul the mask position with numpy : 0.0002646446228027344 nb_pixel_total : 4949 time to create 1 rle with old method : 0.00818777084350586 length of segment : 139 time for calcul the mask position with numpy : 0.0002219676971435547 nb_pixel_total : 5839 time to create 1 rle with old method : 0.009644269943237305 length of segment : 140 time for calcul the mask position with numpy : 0.0009698867797851562 nb_pixel_total : 20307 time to create 1 rle with old method : 0.023974895477294922 length of segment : 137 time for calcul the mask position with numpy : 0.0003311634063720703 nb_pixel_total : 7333 time to create 1 rle with old method : 0.008817434310913086 length of segment : 102 time for calcul the mask position with numpy : 0.028571128845214844 nb_pixel_total : 705768 time to create 1 rle with new method : 0.1331634521484375 length of segment : 1105 time for calcul the mask position with numpy : 0.0008060932159423828 nb_pixel_total : 32743 time to create 1 rle with old method : 0.03744053840637207 length of segment : 316 time for calcul the mask position with numpy : 0.0023431777954101562 nb_pixel_total : 41902 time to create 1 rle with old method : 0.04775190353393555 length of segment : 209 time for calcul the mask position with numpy : 0.006300449371337891 nb_pixel_total : 188986 time to create 1 rle with new method : 0.008615970611572266 length of segment : 400 time for calcul the mask position with numpy : 0.0009634494781494141 nb_pixel_total : 13213 time to create 1 rle with old method : 0.015434741973876953 length of segment : 133 time for calcul the mask position with numpy : 0.0023183822631835938 nb_pixel_total : 34040 time to create 1 rle with old method : 0.03921961784362793 length of segment : 209 time for calcul the mask position with numpy : 0.008706092834472656 nb_pixel_total : 113146 time to create 1 rle with old method : 0.1303119659423828 length of segment : 475 time for calcul the mask position with numpy : 0.008041143417358398 nb_pixel_total : 101307 time to create 1 rle with old method : 0.12141895294189453 length of segment : 518 time for calcul the mask position with numpy : 0.001886606216430664 nb_pixel_total : 16112 time to create 1 rle with old method : 0.020049095153808594 length of segment : 164 time for calcul the mask position with numpy : 0.0028138160705566406 nb_pixel_total : 25365 time to create 1 rle with old method : 0.029465436935424805 length of segment : 217 time for calcul the mask position with numpy : 0.0024907588958740234 nb_pixel_total : 31308 time to create 1 rle with old method : 0.03619742393493652 length of segment : 288 time for calcul the mask position with numpy : 0.0030782222747802734 nb_pixel_total : 40574 time to create 1 rle with old method : 0.04693317413330078 length of segment : 224 time for calcul the mask position with numpy : 0.002597332000732422 nb_pixel_total : 30337 time to create 1 rle with old method : 0.0356292724609375 length of segment : 218 time for calcul the mask position with numpy : 0.0020918846130371094 nb_pixel_total : 23708 time to create 1 rle with old method : 0.027291536331176758 length of segment : 229 time for calcul the mask position with numpy : 0.0034339427947998047 nb_pixel_total : 50764 time to create 1 rle with old method : 0.05929923057556152 length of segment : 369 time for calcul the mask position with numpy : 0.0036287307739257812 nb_pixel_total : 45976 time to create 1 rle with old method : 0.053034305572509766 length of segment : 219 time for calcul the mask position with numpy : 0.009434938430786133 nb_pixel_total : 104672 time to create 1 rle with old method : 0.11753511428833008 length of segment : 626 time for calcul the mask position with numpy : 0.0008540153503417969 nb_pixel_total : 23298 time to create 1 rle with old method : 0.02622365951538086 length of segment : 307 time for calcul the mask position with numpy : 0.002124786376953125 nb_pixel_total : 23733 time to create 1 rle with old method : 0.027330398559570312 length of segment : 210 time for calcul the mask position with numpy : 0.0039997100830078125 nb_pixel_total : 51371 time to create 1 rle with old method : 0.06059408187866211 length of segment : 487 time for calcul the mask position with numpy : 0.0024399757385253906 nb_pixel_total : 34549 time to create 1 rle with old method : 0.038240909576416016 length of segment : 258 time for calcul the mask position with numpy : 0.01811957359313965 nb_pixel_total : 192065 time to create 1 rle with new method : 0.020504236221313477 length of segment : 663 time for calcul the mask position with numpy : 0.0008676052093505859 nb_pixel_total : 9707 time to create 1 rle with old method : 0.01078343391418457 length of segment : 128 time for calcul the mask position with numpy : 0.0062122344970703125 nb_pixel_total : 87511 time to create 1 rle with old method : 0.09370207786560059 length of segment : 544 time for calcul the mask position with numpy : 0.0005407333374023438 nb_pixel_total : 15348 time to create 1 rle with old method : 0.017396926879882812 length of segment : 217 time for calcul the mask position with numpy : 0.0029883384704589844 nb_pixel_total : 46018 time to create 1 rle with old method : 0.05079054832458496 length of segment : 269 time for calcul the mask position with numpy : 0.001611471176147461 nb_pixel_total : 26138 time to create 1 rle with old method : 0.02929234504699707 length of segment : 231 time for calcul the mask position with numpy : 0.0014753341674804688 nb_pixel_total : 17304 time to create 1 rle with old method : 0.019474029541015625 length of segment : 177 time for calcul the mask position with numpy : 0.0005545616149902344 nb_pixel_total : 10567 time to create 1 rle with old method : 0.01234579086303711 length of segment : 175 time for calcul the mask position with numpy : 0.0010063648223876953 nb_pixel_total : 25080 time to create 1 rle with old method : 0.0283811092376709 length of segment : 239 time for calcul the mask position with numpy : 0.005343914031982422 nb_pixel_total : 75277 time to create 1 rle with old method : 0.08496522903442383 length of segment : 251 time for calcul the mask position with numpy : 0.0009090900421142578 nb_pixel_total : 10283 time to create 1 rle with old method : 0.011337518692016602 length of segment : 136 time for calcul the mask position with numpy : 0.0007221698760986328 nb_pixel_total : 10459 time to create 1 rle with old method : 0.011591672897338867 length of segment : 157 time for calcul the mask position with numpy : 0.00042939186096191406 nb_pixel_total : 5974 time to create 1 rle with old method : 0.0070934295654296875 length of segment : 96 time for calcul the mask position with numpy : 0.0011327266693115234 nb_pixel_total : 11594 time to create 1 rle with old method : 0.014196395874023438 length of segment : 114 time for calcul the mask position with numpy : 0.0010654926300048828 nb_pixel_total : 31335 time to create 1 rle with old method : 0.036244869232177734 length of segment : 239 time for calcul the mask position with numpy : 0.0015189647674560547 nb_pixel_total : 27241 time to create 1 rle with old method : 0.03112483024597168 length of segment : 225 time for calcul the mask position with numpy : 0.0011899471282958984 nb_pixel_total : 17009 time to create 1 rle with old method : 0.020164966583251953 length of segment : 157 time for calcul the mask position with numpy : 0.0010218620300292969 nb_pixel_total : 16027 time to create 1 rle with old method : 0.018525123596191406 length of segment : 126 time for calcul the mask position with numpy : 0.0012362003326416016 nb_pixel_total : 14143 time to create 1 rle with old method : 0.016700267791748047 length of segment : 147 time for calcul the mask position with numpy : 0.0010123252868652344 nb_pixel_total : 14536 time to create 1 rle with old method : 0.016811609268188477 length of segment : 122 time for calcul the mask position with numpy : 0.0019059181213378906 nb_pixel_total : 26543 time to create 1 rle with old method : 0.030289173126220703 length of segment : 280 time for calcul the mask position with numpy : 0.0010340213775634766 nb_pixel_total : 12978 time to create 1 rle with old method : 0.015042304992675781 length of segment : 103 time for calcul the mask position with numpy : 0.001055002212524414 nb_pixel_total : 11650 time to create 1 rle with old method : 0.013838052749633789 length of segment : 195 time for calcul the mask position with numpy : 0.0019447803497314453 nb_pixel_total : 31254 time to create 1 rle with old method : 0.03576803207397461 length of segment : 190 time for calcul the mask position with numpy : 0.001004934310913086 nb_pixel_total : 15193 time to create 1 rle with old method : 0.0181577205657959 length of segment : 130 time for calcul the mask position with numpy : 0.007598876953125 nb_pixel_total : 92111 time to create 1 rle with old method : 0.10631680488586426 length of segment : 434 time for calcul the mask position with numpy : 0.0037369728088378906 nb_pixel_total : 40430 time to create 1 rle with old method : 0.04620671272277832 length of segment : 292 time for calcul the mask position with numpy : 0.0005605220794677734 nb_pixel_total : 6971 time to create 1 rle with old method : 0.008214235305786133 length of segment : 125 time for calcul the mask position with numpy : 0.0013453960418701172 nb_pixel_total : 35800 time to create 1 rle with old method : 0.04252815246582031 length of segment : 197 time for calcul the mask position with numpy : 0.0013113021850585938 nb_pixel_total : 16535 time to create 1 rle with old method : 0.02199411392211914 length of segment : 191 time for calcul the mask position with numpy : 0.0014927387237548828 nb_pixel_total : 20051 time to create 1 rle with old method : 0.023441553115844727 length of segment : 168 time for calcul the mask position with numpy : 0.0019698143005371094 nb_pixel_total : 31410 time to create 1 rle with old method : 0.03761792182922363 length of segment : 149 time for calcul the mask position with numpy : 0.003907918930053711 nb_pixel_total : 43703 time to create 1 rle with old method : 0.05034065246582031 length of segment : 264 time for calcul the mask position with numpy : 0.0015866756439208984 nb_pixel_total : 22000 time to create 1 rle with old method : 0.02535533905029297 length of segment : 191 time for calcul the mask position with numpy : 0.001163482666015625 nb_pixel_total : 16570 time to create 1 rle with old method : 0.019426584243774414 length of segment : 264 time for calcul the mask position with numpy : 0.0013158321380615234 nb_pixel_total : 15014 time to create 1 rle with old method : 0.017126798629760742 length of segment : 301 time for calcul the mask position with numpy : 0.0015094280242919922 nb_pixel_total : 23303 time to create 1 rle with old method : 0.026682376861572266 length of segment : 164 time for calcul the mask position with numpy : 0.006273508071899414 nb_pixel_total : 115334 time to create 1 rle with old method : 0.13136744499206543 length of segment : 380 time for calcul the mask position with numpy : 0.001210927963256836 nb_pixel_total : 21220 time to create 1 rle with old method : 0.02504420280456543 length of segment : 153 time for calcul the mask position with numpy : 0.0006518363952636719 nb_pixel_total : 9072 time to create 1 rle with old method : 0.010991811752319336 length of segment : 68 time for calcul the mask position with numpy : 0.0002579689025878906 nb_pixel_total : 3068 time to create 1 rle with old method : 0.003757953643798828 length of segment : 62 time for calcul the mask position with numpy : 0.0004203319549560547 nb_pixel_total : 3550 time to create 1 rle with old method : 0.0043714046478271484 length of segment : 100 time for calcul the mask position with numpy : 0.0005288124084472656 nb_pixel_total : 11105 time to create 1 rle with old method : 0.013162612915039062 length of segment : 166 time spent for convertir_results : 41.96103596687317 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 503 chid ids of type : 3594 Number RLEs to save : 128761 save missing photos in datou_result : time spend for datou_step_exec : 223.39236044883728 time spend to save output : 7.36750864982605 total time spend for step 1 : 230.75986909866333 step2:crop_condition Tue Apr 1 10:24:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 17 ! batch 1 Loaded 503 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 ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 375 About to insert : list_path_to_insert length 375 new photo from crops ! About to upload 375 photos upload in portfolio : 3736932 init cache_photo without model_param we have 375 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743495944_93323 we have uploaded 375 photos in the portfolio 3736932 time of upload the photos Elapsed time : 102.72327041625977 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 ! map_result returned by crop_photo_return_map_crop : length : 68 About to insert : list_path_to_insert length 68 new photo from crops ! About to upload 68 photos upload in portfolio : 3736932 init cache_photo without model_param we have 68 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743496070_93323 we have uploaded 68 photos in the portfolio 3736932 time of upload the photos Elapsed time : 17.396362781524658 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743496090_93323 we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.8691728115081787 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 32 About to insert : list_path_to_insert length 32 new photo from crops ! About to upload 32 photos upload in portfolio : 3736932 init cache_photo without model_param we have 32 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743496106_93323 we have uploaded 32 photos in the portfolio 3736932 time of upload the photos Elapsed time : 13.32639479637146 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 12 About to insert : list_path_to_insert length 12 new photo from crops ! About to upload 12 photos upload in portfolio : 3736932 init cache_photo without model_param we have 12 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743496125_93323 we have uploaded 12 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.92450213432312 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743496132_93323 we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.1624326705932617 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743496139_93323 we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.184175729751587 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles 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 [1349219989, 1349219988, 1349219985, 1349219785, 1349219783, 1349219531, 1349219527, 1349219004, 1349218973, 1349218933, 1349218929, 1349218924, 1349218915, 1349218886, 1349218820, 1349218739, 1349218732] Looping around the photos to save general results len do output : 503 /1349245910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245913Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245917Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245930Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245931Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245932Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245933Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245941Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245946Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245950Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245952Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245963Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245965Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245967Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245969Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245973Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349245993Didn't retrieve data .Didn't retrieve data .Didn't 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('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219989', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219988', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219985', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219785', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219783', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219531', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219527', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219004', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218973', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218933', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218929', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218924', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218915', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218886', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218820', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218739', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218732', None, None, None, None, None, '2712269') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1526 time used for this insertion : 0.19417166709899902 save_final save missing photos in datou_result : time spend for datou_step_exec : 277.34396719932556 time spend to save output : 0.20807671546936035 total time spend for step 2 : 277.5520439147949 step3:rle_unique_nms_with_priority Tue Apr 1 10:29:01 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 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array 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 503 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 57 nb_hashtags : 7 time to prepare the origin masks : 4.3981852531433105 time for calcul the mask position with numpy : 0.49910497665405273 nb_pixel_total : 5616962 time to create 1 rle with new method : 0.5743470191955566 time for calcul the mask position with numpy : 0.029214859008789062 nb_pixel_total : 10612 time to create 1 rle with old method : 0.012070655822753906 time for calcul the mask position with numpy : 0.029221057891845703 nb_pixel_total : 10799 time to create 1 rle with old method : 0.01226043701171875 time for calcul the mask position with numpy : 0.029326438903808594 nb_pixel_total : 17209 time to create 1 rle with old method : 0.01929330825805664 time for calcul the mask position with numpy : 0.0303957462310791 nb_pixel_total : 29657 time to create 1 rle with old method : 0.03417611122131348 time for calcul the mask position with numpy : 0.029053211212158203 nb_pixel_total : 5711 time to create 1 rle with old method : 0.006453752517700195 time for calcul the mask position with numpy : 0.02968311309814453 nb_pixel_total : 45660 time to create 1 rle with old method : 0.05211067199707031 time for calcul the mask position with numpy : 0.029309511184692383 nb_pixel_total : 19676 time to create 1 rle with old method : 0.022157669067382812 time for calcul the mask position with numpy : 0.029200077056884766 nb_pixel_total : 13599 time to create 1 rle with old method : 0.015359163284301758 time for calcul the mask position with numpy : 0.029303789138793945 nb_pixel_total : 14392 time to create 1 rle with old method : 0.016242265701293945 time for calcul the mask position with numpy : 0.029236316680908203 nb_pixel_total : 15802 time to create 1 rle with old method : 0.01783895492553711 time for calcul the mask position with numpy : 0.0294344425201416 nb_pixel_total : 27049 time to create 1 rle with old method : 0.030795574188232422 time for calcul the mask position with numpy : 0.02931666374206543 nb_pixel_total : 5559 time to create 1 rle with old method : 0.006531476974487305 time for calcul the mask position with numpy : 0.029261350631713867 nb_pixel_total : 8226 time to create 1 rle with old method : 0.009548425674438477 time for calcul the mask position with numpy : 0.02944493293762207 nb_pixel_total : 31657 time to create 1 rle with old method : 0.03665518760681152 time for calcul the mask position with numpy : 0.029532194137573242 nb_pixel_total : 13432 time to create 1 rle with old method : 0.01584792137145996 time for calcul the mask position with numpy : 0.02989649772644043 nb_pixel_total : 74973 time to create 1 rle with old method : 0.08768248558044434 time for calcul the mask position with numpy : 0.029985427856445312 nb_pixel_total : 2410 time to create 1 rle with old method : 0.0030083656311035156 time for calcul the mask position with numpy : 0.030470609664916992 nb_pixel_total : 58411 time to create 1 rle with old method : 0.06783604621887207 time for calcul the mask position with numpy : 0.03204202651977539 nb_pixel_total : 20112 time to create 1 rle with old method : 0.023497581481933594 time for calcul the mask position with numpy : 0.029593944549560547 nb_pixel_total : 23787 time to create 1 rle with old method : 0.027538299560546875 time for calcul the mask position with numpy : 0.031102895736694336 nb_pixel_total : 33652 time to create 1 rle with old method : 0.058109283447265625 time for calcul the mask position with numpy : 0.030555248260498047 nb_pixel_total : 26443 time to create 1 rle with old method : 0.03005051612854004 time for calcul the mask position with numpy : 0.029218673706054688 nb_pixel_total : 47966 time to create 1 rle with old method : 0.05457639694213867 time for calcul the mask position with numpy : 0.029422283172607422 nb_pixel_total : 11306 time to create 1 rle with old method : 0.01292729377746582 time for calcul the mask position with numpy : 0.029821395874023438 nb_pixel_total : 34041 time to create 1 rle with old method : 0.03885674476623535 time for calcul the mask position with numpy : 0.029300689697265625 nb_pixel_total : 11271 time to create 1 rle with old method : 0.01361989974975586 time for calcul the mask position with numpy : 0.03357648849487305 nb_pixel_total : 34784 time to create 1 rle with old method : 0.041358232498168945 time for calcul the mask position with numpy : 0.028951406478881836 nb_pixel_total : 7112 time to create 1 rle with old method : 0.009497404098510742 time for calcul the mask position with numpy : 0.029144287109375 nb_pixel_total : 41728 time to create 1 rle with old method : 0.05135011672973633 time for calcul the mask position with numpy : 0.02912592887878418 nb_pixel_total : 19602 time to create 1 rle with old method : 0.02219986915588379 time for calcul the mask position with numpy : 0.029012441635131836 nb_pixel_total : 10924 time to create 1 rle with old method : 0.012388467788696289 time for calcul the mask position with numpy : 0.02890300750732422 nb_pixel_total : 9832 time to create 1 rle with old method : 0.011275768280029297 time for calcul the mask position with numpy : 0.0413663387298584 nb_pixel_total : 29270 time to create 1 rle with old method : 0.03824567794799805 time for calcul the mask position with numpy : 0.02908611297607422 nb_pixel_total : 63825 time to create 1 rle with old method : 0.07203149795532227 time for calcul the mask position with numpy : 0.02899003028869629 nb_pixel_total : 23546 time to create 1 rle with old method : 0.02597522735595703 time for calcul the mask position with numpy : 0.028611421585083008 nb_pixel_total : 22546 time to create 1 rle with old method : 0.025820493698120117 time for calcul the mask position with numpy : 0.029083251953125 nb_pixel_total : 82517 time to create 1 rle with old method : 0.09284806251525879 time for calcul the mask position with numpy : 0.028719186782836914 nb_pixel_total : 49446 time to create 1 rle with old method : 0.055792808532714844 time for calcul the mask position with numpy : 0.028856277465820312 nb_pixel_total : 1402 time to create 1 rle with old method : 0.0016908645629882812 time for calcul the mask position with numpy : 0.028554439544677734 nb_pixel_total : 26013 time to create 1 rle with old method : 0.030726909637451172 time for calcul the mask position with numpy : 0.030742645263671875 nb_pixel_total : 15684 time to create 1 rle with old method : 0.01812291145324707 time for calcul the mask position with numpy : 0.029157400131225586 nb_pixel_total : 18513 time to create 1 rle with old method : 0.023457050323486328 time for calcul the mask position with numpy : 0.02971029281616211 nb_pixel_total : 25669 time to create 1 rle with old method : 0.02946639060974121 time for calcul the mask position with numpy : 0.02907872200012207 nb_pixel_total : 44570 time to create 1 rle with old method : 0.05045795440673828 time for calcul the mask position with numpy : 0.02969813346862793 nb_pixel_total : 123247 time to create 1 rle with old method : 0.14174914360046387 time for calcul the mask position with numpy : 0.028733015060424805 nb_pixel_total : 34655 time to create 1 rle with old method : 0.03955221176147461 time for calcul the mask position with numpy : 0.029006004333496094 nb_pixel_total : 12489 time to create 1 rle with old method : 0.014470338821411133 time for calcul the mask position with numpy : 0.02900385856628418 nb_pixel_total : 53929 time to create 1 rle with old method : 0.061452627182006836 time for calcul the mask position with numpy : 0.028952836990356445 nb_pixel_total : 7809 time to create 1 rle with old method : 0.008936882019042969 time for calcul the mask position with numpy : 0.028954744338989258 nb_pixel_total : 10046 time to create 1 rle with old method : 0.011605262756347656 time for calcul the mask position with numpy : 0.02903008460998535 nb_pixel_total : 5806 time to create 1 rle with old method : 0.006788015365600586 time for calcul the mask position with numpy : 0.028877973556518555 nb_pixel_total : 10085 time to create 1 rle with old method : 0.011754274368286133 time for calcul the mask position with numpy : 0.028966665267944336 nb_pixel_total : 9318 time to create 1 rle with old method : 0.010847330093383789 time for calcul the mask position with numpy : 0.028977632522583008 nb_pixel_total : 7145 time to create 1 rle with old method : 0.008332014083862305 time for calcul the mask position with numpy : 0.029007911682128906 nb_pixel_total : 10121 time to create 1 rle with old method : 0.012083053588867188 time for calcul the mask position with numpy : 0.030828237533569336 nb_pixel_total : 4435 time to create 1 rle with old method : 0.005095481872558594 time for calcul the mask position with numpy : 0.030649423599243164 nb_pixel_total : 3798 time to create 1 rle with old method : 0.004411220550537109 create new chi : 4.481051445007324 time to delete rle : 0.02398538589477539 batch 1 Loaded 115 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24861 TO DO : save crop sub photo not yet done ! save time : 1.4296882152557373 nb_obj : 60 nb_hashtags : 4 time to prepare the origin masks : 3.950831890106201 time for calcul the mask position with numpy : 0.2671520709991455 nb_pixel_total : 5765688 time to create 1 rle with new method : 1.5343809127807617 time for calcul the mask position with numpy : 0.029647350311279297 nb_pixel_total : 35899 time to create 1 rle with old method : 0.04148125648498535 time for calcul the mask position with numpy : 0.029071331024169922 nb_pixel_total : 34849 time to create 1 rle with old method : 0.04051685333251953 time for calcul the mask position with numpy : 0.028658151626586914 nb_pixel_total : 33862 time to create 1 rle with old method : 0.04001188278198242 time for calcul the mask position with numpy : 0.02821826934814453 nb_pixel_total : 7625 time to create 1 rle with old method : 0.008680582046508789 time for calcul the mask position with numpy : 0.02920365333557129 nb_pixel_total : 31190 time to create 1 rle with old method : 0.035013675689697266 time for calcul the mask position with numpy : 0.02853989601135254 nb_pixel_total : 16310 time to create 1 rle with old method : 0.018300771713256836 time for calcul the mask position with numpy : 0.028598546981811523 nb_pixel_total : 32684 time to create 1 rle with old method : 0.03542971611022949 time for calcul the mask position with numpy : 0.02813243865966797 nb_pixel_total : 5111 time to create 1 rle with old method : 0.005484104156494141 time for calcul the mask position with numpy : 0.028844118118286133 nb_pixel_total : 11395 time to create 1 rle with old method : 0.013419151306152344 time for calcul the mask position with numpy : 0.029664993286132812 nb_pixel_total : 33127 time to create 1 rle with old method : 0.03607511520385742 time for calcul the mask position with numpy : 0.028765439987182617 nb_pixel_total : 20455 time to create 1 rle with old method : 0.02207636833190918 time for calcul the mask position with numpy : 0.027519702911376953 nb_pixel_total : 35109 time to create 1 rle with old method : 0.037943363189697266 time for calcul the mask position with numpy : 0.028209447860717773 nb_pixel_total : 33645 time to create 1 rle with old method : 0.03752326965332031 time for calcul the mask position with numpy : 0.027894258499145508 nb_pixel_total : 10528 time to create 1 rle with old method : 0.01162099838256836 time for calcul the mask position with numpy : 0.02791142463684082 nb_pixel_total : 20443 time to create 1 rle with old method : 0.022277355194091797 time for calcul the mask position with numpy : 0.027655839920043945 nb_pixel_total : 11841 time to create 1 rle with old method : 0.013160228729248047 time for calcul the mask position with numpy : 0.02796196937561035 nb_pixel_total : 44605 time to create 1 rle with old method : 0.05009317398071289 time for calcul the mask position with numpy : 0.028981685638427734 nb_pixel_total : 5028 time to create 1 rle with old method : 0.005753993988037109 time for calcul the mask position with numpy : 0.029057741165161133 nb_pixel_total : 15032 time to create 1 rle with old method : 0.017086505889892578 time for calcul the mask position with numpy : 0.028150081634521484 nb_pixel_total : 41631 time to create 1 rle with old method : 0.044936180114746094 time for calcul the mask position with numpy : 0.027873754501342773 nb_pixel_total : 6501 time to create 1 rle with old method : 0.00722050666809082 time for calcul the mask position with numpy : 0.028093814849853516 nb_pixel_total : 59136 time to create 1 rle with old method : 0.0638892650604248 time for calcul the mask position with numpy : 0.028456926345825195 nb_pixel_total : 29961 time to create 1 rle with old method : 0.033371925354003906 time for calcul the mask position with numpy : 0.028211355209350586 nb_pixel_total : 11777 time to create 1 rle with old method : 0.013507843017578125 time for calcul the mask position with numpy : 0.028745174407958984 nb_pixel_total : 10043 time to create 1 rle with old method : 0.011345624923706055 time for calcul the mask position with numpy : 0.028824567794799805 nb_pixel_total : 12773 time to create 1 rle with old method : 0.014172554016113281 time for calcul the mask position with numpy : 0.0277707576751709 nb_pixel_total : 58120 time to create 1 rle with old method : 0.0620877742767334 time for calcul the mask position with numpy : 0.027582883834838867 nb_pixel_total : 10234 time to create 1 rle with old method : 0.011418342590332031 time for calcul the mask position with numpy : 0.027524232864379883 nb_pixel_total : 8596 time to create 1 rle with old method : 0.009371042251586914 time for calcul the mask position with numpy : 0.02734208106994629 nb_pixel_total : 38418 time to create 1 rle with old method : 0.04160046577453613 time for calcul the mask position with numpy : 0.027472257614135742 nb_pixel_total : 30329 time to create 1 rle with old method : 0.0329132080078125 time for calcul the mask position with numpy : 0.027418851852416992 nb_pixel_total : 28456 time to create 1 rle with old method : 0.030923128128051758 time for calcul the mask position with numpy : 0.02785944938659668 nb_pixel_total : 91380 time to create 1 rle with old method : 0.12326169013977051 time for calcul the mask position with numpy : 0.03063654899597168 nb_pixel_total : 19575 time to create 1 rle with old method : 0.02087569236755371 time for calcul the mask position with numpy : 0.027858972549438477 nb_pixel_total : 11616 time to create 1 rle with old method : 0.012753963470458984 time for calcul the mask position with numpy : 0.02749013900756836 nb_pixel_total : 5820 time to create 1 rle with old method : 0.0062634944915771484 time for calcul the mask position with numpy : 0.027242422103881836 nb_pixel_total : 50832 time to create 1 rle with old method : 0.05534482002258301 time for calcul the mask position with numpy : 0.027892112731933594 nb_pixel_total : 30116 time to create 1 rle with old method : 0.03271603584289551 time for calcul the mask position with numpy : 0.02777409553527832 nb_pixel_total : 16110 time to create 1 rle with old method : 0.018009424209594727 time for calcul the mask position with numpy : 0.028229475021362305 nb_pixel_total : 12354 time to create 1 rle with old method : 0.01360011100769043 time for calcul the mask position with numpy : 0.028309106826782227 nb_pixel_total : 7024 time to create 1 rle with old method : 0.008016824722290039 time for calcul the mask position with numpy : 0.028439760208129883 nb_pixel_total : 16891 time to create 1 rle with old method : 0.01866912841796875 time for calcul the mask position with numpy : 0.027911663055419922 nb_pixel_total : 4401 time to create 1 rle with old method : 0.004808187484741211 time for calcul the mask position with numpy : 0.027130842208862305 nb_pixel_total : 6007 time to create 1 rle with old method : 0.006630659103393555 time for calcul the mask position with numpy : 0.027225732803344727 nb_pixel_total : 5999 time to create 1 rle with old method : 0.0069124698638916016 time for calcul the mask position with numpy : 0.028603076934814453 nb_pixel_total : 33002 time to create 1 rle with old method : 0.036411285400390625 time for calcul the mask position with numpy : 0.027367591857910156 nb_pixel_total : 12779 time to create 1 rle with old method : 0.014183282852172852 time for calcul the mask position with numpy : 0.027086257934570312 nb_pixel_total : 9456 time to create 1 rle with old method : 0.010308027267456055 time for calcul the mask position with numpy : 0.027576684951782227 nb_pixel_total : 2651 time to create 1 rle with old method : 0.002923727035522461 time for calcul the mask position with numpy : 0.027953624725341797 nb_pixel_total : 12928 time to create 1 rle with old method : 0.014423131942749023 time for calcul the mask position with numpy : 0.028623104095458984 nb_pixel_total : 5748 time to create 1 rle with old method : 0.006526947021484375 time for calcul the mask position with numpy : 0.02923274040222168 nb_pixel_total : 11410 time to create 1 rle with old method : 0.013341426849365234 time for calcul the mask position with numpy : 0.029320478439331055 nb_pixel_total : 24201 time to create 1 rle with old method : 0.027864456176757812 time for calcul the mask position with numpy : 0.02904033660888672 nb_pixel_total : 20002 time to create 1 rle with old method : 0.022595882415771484 time for calcul the mask position with numpy : 0.028482913970947266 nb_pixel_total : 47257 time to create 1 rle with old method : 0.053032636642456055 time for calcul the mask position with numpy : 0.02707815170288086 nb_pixel_total : 5631 time to create 1 rle with old method : 0.006295442581176758 time for calcul the mask position with numpy : 0.02806997299194336 nb_pixel_total : 10350 time to create 1 rle with old method : 0.011193513870239258 time for calcul the mask position with numpy : 0.027622699737548828 nb_pixel_total : 11565 time to create 1 rle with old method : 0.012723922729492188 time for calcul the mask position with numpy : 0.02778911590576172 nb_pixel_total : 10638 time to create 1 rle with old method : 0.012157917022705078 time for calcul the mask position with numpy : 0.027632713317871094 nb_pixel_total : 4096 time to create 1 rle with old method : 0.004518985748291016 create new chi : 4.976049184799194 time to delete rle : 0.0029494762420654297 batch 1 Loaded 121 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22549 TO DO : save crop sub photo not yet done ! save time : 2.4460999965667725 nb_obj : 25 nb_hashtags : 4 time to prepare the origin masks : 15.184151649475098 time for calcul the mask position with numpy : 0.4615035057067871 nb_pixel_total : 5908422 time to create 1 rle with new method : 1.1164188385009766 time for calcul the mask position with numpy : 0.03682112693786621 nb_pixel_total : 11957 time to create 1 rle with old method : 0.020251989364624023 time for calcul the mask position with numpy : 0.0424649715423584 nb_pixel_total : 6626 time to create 1 rle with old method : 0.011636495590209961 time for calcul the mask position with numpy : 0.0405886173248291 nb_pixel_total : 16698 time to create 1 rle with old method : 0.02497386932373047 time for calcul the mask position with numpy : 0.03834199905395508 nb_pixel_total : 21679 time to create 1 rle with old method : 0.028682947158813477 time for calcul the mask position with numpy : 0.03915667533874512 nb_pixel_total : 75210 time to create 1 rle with old method : 0.1056058406829834 time for calcul the mask position with numpy : 0.03859376907348633 nb_pixel_total : 9701 time to create 1 rle with old method : 0.012621164321899414 time for calcul the mask position with numpy : 0.03495311737060547 nb_pixel_total : 92851 time to create 1 rle with old method : 0.1056816577911377 time for calcul the mask position with numpy : 0.03399324417114258 nb_pixel_total : 15348 time to create 1 rle with old method : 0.017444849014282227 time for calcul the mask position with numpy : 0.0386202335357666 nb_pixel_total : 5509 time to create 1 rle with old method : 0.007940053939819336 time for calcul the mask position with numpy : 0.04032111167907715 nb_pixel_total : 11543 time to create 1 rle with old method : 0.019026756286621094 time for calcul the mask position with numpy : 0.022614240646362305 nb_pixel_total : 104339 time to create 1 rle with old method : 0.12420105934143066 time for calcul the mask position with numpy : 0.022427797317504883 nb_pixel_total : 59161 time to create 1 rle with old method : 0.0703730583190918 time for calcul the mask position with numpy : 0.03760480880737305 nb_pixel_total : 191983 time to create 1 rle with new method : 0.5824558734893799 time for calcul the mask position with numpy : 0.0405731201171875 nb_pixel_total : 7407 time to create 1 rle with old method : 0.01062774658203125 time for calcul the mask position with numpy : 0.03726959228515625 nb_pixel_total : 19115 time to create 1 rle with old method : 0.02717733383178711 time for calcul the mask position with numpy : 0.042725563049316406 nb_pixel_total : 58399 time to create 1 rle with old method : 0.07623934745788574 time for calcul the mask position with numpy : 0.03815197944641113 nb_pixel_total : 89556 time to create 1 rle with old method : 0.1114952564239502 time for calcul the mask position with numpy : 0.03535914421081543 nb_pixel_total : 107429 time to create 1 rle with old method : 0.12247610092163086 time for calcul the mask position with numpy : 0.0358891487121582 nb_pixel_total : 47715 time to create 1 rle with old method : 0.06207084655761719 time for calcul the mask position with numpy : 0.03294706344604492 nb_pixel_total : 23377 time to create 1 rle with old method : 0.026613473892211914 time for calcul the mask position with numpy : 0.02197742462158203 nb_pixel_total : 20664 time to create 1 rle with old method : 0.02344059944152832 time for calcul the mask position with numpy : 0.027333974838256836 nb_pixel_total : 100204 time to create 1 rle with old method : 0.1129603385925293 time for calcul the mask position with numpy : 0.022387027740478516 nb_pixel_total : 12906 time to create 1 rle with old method : 0.016472578048706055 time for calcul the mask position with numpy : 0.02218461036682129 nb_pixel_total : 13625 time to create 1 rle with old method : 0.015500783920288086 time for calcul the mask position with numpy : 0.02285480499267578 nb_pixel_total : 18816 time to create 1 rle with old method : 0.035877227783203125 create new chi : 4.2546226978302 time to delete rle : 0.0026628971099853516 batch 1 Loaded 51 chid ids of type : 3594 +++++++++++++++++++++++++++++Number RLEs to save : 14445 TO DO : save crop sub photo not yet done ! save time : 1.3491425514221191 nb_obj : 14 nb_hashtags : 3 time to prepare the origin masks : 8.274469137191772 time for calcul the mask position with numpy : 0.7123169898986816 nb_pixel_total : 6800935 time to create 1 rle with new method : 1.161407470703125 time for calcul the mask position with numpy : 0.02309703826904297 nb_pixel_total : 18753 time to create 1 rle with old method : 0.020620346069335938 time for calcul the mask position with numpy : 0.02092432975769043 nb_pixel_total : 4761 time to create 1 rle with old method : 0.0053997039794921875 time for calcul the mask position with numpy : 0.020882129669189453 nb_pixel_total : 28602 time to create 1 rle with old method : 0.03176093101501465 time for calcul the mask position with numpy : 0.02015519142150879 nb_pixel_total : 106 time to create 1 rle with old method : 0.0001857280731201172 time for calcul the mask position with numpy : 0.020902395248413086 nb_pixel_total : 19054 time to create 1 rle with old method : 0.021388530731201172 time for calcul the mask position with numpy : 0.02137017250061035 nb_pixel_total : 3655 time to create 1 rle with old method : 0.004261970520019531 time for calcul the mask position with numpy : 0.020763635635375977 nb_pixel_total : 27285 time to create 1 rle with old method : 0.03013300895690918 time for calcul the mask position with numpy : 0.020539283752441406 nb_pixel_total : 30146 time to create 1 rle with old method : 0.04373931884765625 time for calcul the mask position with numpy : 0.022153615951538086 nb_pixel_total : 16620 time to create 1 rle with old method : 0.01914072036743164 time for calcul the mask position with numpy : 0.020726442337036133 nb_pixel_total : 13234 time to create 1 rle with old method : 0.015089273452758789 time for calcul the mask position with numpy : 0.022090911865234375 nb_pixel_total : 13898 time to create 1 rle with old method : 0.02240300178527832 time for calcul the mask position with numpy : 0.02404642105102539 nb_pixel_total : 15984 time to create 1 rle with old method : 0.02595043182373047 time for calcul the mask position with numpy : 0.02110004425048828 nb_pixel_total : 13685 time to create 1 rle with old method : 0.015520572662353516 time for calcul the mask position with numpy : 0.02242445945739746 nb_pixel_total : 43522 time to create 1 rle with old method : 0.04892396926879883 create new chi : 2.5196993350982666 time to delete rle : 0.0009589195251464844 batch 1 Loaded 29 chid ids of type : 3594 ++++++++++++++++++Number RLEs to save : 5902 TO DO : save crop sub photo not yet done ! save time : 0.7043435573577881 nb_obj : 10 nb_hashtags : 4 time to prepare the origin masks : 3.384697675704956 time for calcul the mask position with numpy : 2.045693874359131 nb_pixel_total : 6641113 time to create 1 rle with new method : 1.046090841293335 time for calcul the mask position with numpy : 0.02965855598449707 nb_pixel_total : 13773 time to create 1 rle with old method : 0.01574540138244629 time for calcul the mask position with numpy : 0.021409034729003906 nb_pixel_total : 104666 time to create 1 rle with old method : 0.12328672409057617 time for calcul the mask position with numpy : 0.021920204162597656 nb_pixel_total : 30681 time to create 1 rle with old method : 0.03466320037841797 time for calcul the mask position with numpy : 0.023562908172607422 nb_pixel_total : 31153 time to create 1 rle with old method : 0.03898453712463379 time for calcul the mask position with numpy : 0.02307891845703125 nb_pixel_total : 62405 time to create 1 rle with old method : 0.07424211502075195 time for calcul the mask position with numpy : 0.022524118423461914 nb_pixel_total : 23608 time to create 1 rle with old method : 0.026217222213745117 time for calcul the mask position with numpy : 0.021090030670166016 nb_pixel_total : 56896 time to create 1 rle with old method : 0.06432700157165527 time for calcul the mask position with numpy : 0.023752212524414062 nb_pixel_total : 17134 time to create 1 rle with old method : 0.02235126495361328 time for calcul the mask position with numpy : 0.02092576026916504 nb_pixel_total : 19267 time to create 1 rle with old method : 0.026174068450927734 time for calcul the mask position with numpy : 0.025866985321044922 nb_pixel_total : 49544 time to create 1 rle with old method : 0.056990861892700195 create new chi : 3.8481714725494385 time to delete rle : 0.0009438991546630859 batch 1 Loaded 21 chid ids of type : 3594 +++++++++++++Number RLEs to save : 6778 TO DO : save crop sub photo not yet done ! save time : 0.849003791809082 nb_obj : 12 nb_hashtags : 3 time to prepare the origin masks : 4.46655011177063 time for calcul the mask position with numpy : 1.358144998550415 nb_pixel_total : 6435451 time to create 1 rle with new method : 0.8334956169128418 time for calcul the mask position with numpy : 0.020908355712890625 nb_pixel_total : 17575 time to create 1 rle with old method : 0.020004749298095703 time for calcul the mask position with numpy : 0.02264881134033203 nb_pixel_total : 174211 time to create 1 rle with new method : 0.6465930938720703 time for calcul the mask position with numpy : 0.03319215774536133 nb_pixel_total : 14176 time to create 1 rle with old method : 0.01623368263244629 time for calcul the mask position with numpy : 0.03631329536437988 nb_pixel_total : 9624 time to create 1 rle with old method : 0.010886430740356445 time for calcul the mask position with numpy : 0.03574228286743164 nb_pixel_total : 113371 time to create 1 rle with old method : 0.12789273262023926 time for calcul the mask position with numpy : 0.03661012649536133 nb_pixel_total : 19590 time to create 1 rle with old method : 0.027404308319091797 time for calcul the mask position with numpy : 0.03472137451171875 nb_pixel_total : 22903 time to create 1 rle with old method : 0.02608203887939453 time for calcul the mask position with numpy : 0.03489971160888672 nb_pixel_total : 122307 time to create 1 rle with old method : 0.1459815502166748 time for calcul the mask position with numpy : 0.03700423240661621 nb_pixel_total : 14564 time to create 1 rle with old method : 0.01670098304748535 time for calcul the mask position with numpy : 0.03711676597595215 nb_pixel_total : 17723 time to create 1 rle with old method : 0.019976377487182617 time for calcul the mask position with numpy : 0.039614200592041016 nb_pixel_total : 60908 time to create 1 rle with old method : 0.0681159496307373 time for calcul the mask position with numpy : 0.03282284736633301 nb_pixel_total : 27837 time to create 1 rle with old method : 0.03145909309387207 create new chi : 3.816714286804199 time to delete rle : 0.0014469623565673828 batch 1 Loaded 25 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 8035 TO DO : save crop sub photo not yet done ! save time : 0.9840087890625 nb_obj : 25 nb_hashtags : 5 time to prepare the origin masks : 11.115270376205444 time for calcul the mask position with numpy : 1.5650434494018555 nb_pixel_total : 5786843 time to create 1 rle with new method : 1.083972692489624 time for calcul the mask position with numpy : 0.03637099266052246 nb_pixel_total : 15421 time to create 1 rle with old method : 0.017539024353027344 time for calcul the mask position with numpy : 0.03925967216491699 nb_pixel_total : 38759 time to create 1 rle with old method : 0.043562889099121094 time for calcul the mask position with numpy : 0.03629040718078613 nb_pixel_total : 1291 time to create 1 rle with old method : 0.0016045570373535156 time for calcul the mask position with numpy : 0.03485369682312012 nb_pixel_total : 6144 time to create 1 rle with old method : 0.007112264633178711 time for calcul the mask position with numpy : 0.038031578063964844 nb_pixel_total : 9388 time to create 1 rle with old method : 0.010815858840942383 time for calcul the mask position with numpy : 0.04091477394104004 nb_pixel_total : 20874 time to create 1 rle with old method : 0.028092384338378906 time for calcul the mask position with numpy : 0.03604912757873535 nb_pixel_total : 25562 time to create 1 rle with old method : 0.028665781021118164 time for calcul the mask position with numpy : 0.0352025032043457 nb_pixel_total : 6717 time to create 1 rle with old method : 0.007762432098388672 time for calcul the mask position with numpy : 0.03566575050354004 nb_pixel_total : 5084 time to create 1 rle with old method : 0.005949497222900391 time for calcul the mask position with numpy : 0.0342411994934082 nb_pixel_total : 14927 time to create 1 rle with old method : 0.016959667205810547 time for calcul the mask position with numpy : 0.03779482841491699 nb_pixel_total : 31191 time to create 1 rle with old method : 0.03492856025695801 time for calcul the mask position with numpy : 0.022549867630004883 nb_pixel_total : 2997 time to create 1 rle with old method : 0.003536224365234375 time for calcul the mask position with numpy : 0.022577524185180664 nb_pixel_total : 17349 time to create 1 rle with old method : 0.0196835994720459 time for calcul the mask position with numpy : 0.023757219314575195 nb_pixel_total : 5612 time to create 1 rle with old method : 0.0064198970794677734 time for calcul the mask position with numpy : 0.023926734924316406 nb_pixel_total : 11922 time to create 1 rle with old method : 0.013669252395629883 time for calcul the mask position with numpy : 0.03125452995300293 nb_pixel_total : 378278 time to create 1 rle with new method : 0.4387493133544922 time for calcul the mask position with numpy : 0.03902888298034668 nb_pixel_total : 6898 time to create 1 rle with old method : 0.008095264434814453 time for calcul the mask position with numpy : 0.04093647003173828 nb_pixel_total : 5582 time to create 1 rle with old method : 0.006522655487060547 time for calcul the mask position with numpy : 0.040186405181884766 nb_pixel_total : 378978 time to create 1 rle with new method : 0.4331626892089844 time for calcul the mask position with numpy : 0.041887521743774414 nb_pixel_total : 29304 time to create 1 rle with old method : 0.03422188758850098 time for calcul the mask position with numpy : 0.03916215896606445 nb_pixel_total : 81694 time to create 1 rle with old method : 0.09846305847167969 time for calcul the mask position with numpy : 0.03964734077453613 nb_pixel_total : 99228 time to create 1 rle with old method : 0.11774659156799316 time for calcul the mask position with numpy : 0.039250850677490234 nb_pixel_total : 19647 time to create 1 rle with old method : 0.022429943084716797 time for calcul the mask position with numpy : 0.04474353790283203 nb_pixel_total : 29570 time to create 1 rle with old method : 0.0344700813293457 time for calcul the mask position with numpy : 0.042418479919433594 nb_pixel_total : 20980 time to create 1 rle with old method : 0.02484869956970215 create new chi : 5.099400043487549 time to delete rle : 0.004431009292602539 batch 1 Loaded 51 chid ids of type : 3594 +++++++++++++++++++++++++++++++++Number RLEs to save : 13933 TO DO : save crop sub photo not yet done ! save time : 1.4213571548461914 nb_obj : 21 nb_hashtags : 3 time to prepare the origin masks : 7.293433904647827 time for calcul the mask position with numpy : 0.5120365619659424 nb_pixel_total : 5896585 time to create 1 rle with new method : 0.749962329864502 time for calcul the mask position with numpy : 0.034926414489746094 nb_pixel_total : 27803 time to create 1 rle with old method : 0.03185892105102539 time for calcul the mask position with numpy : 0.043998003005981445 nb_pixel_total : 7327 time to create 1 rle with old method : 0.01242685317993164 time for calcul the mask position with numpy : 0.044673919677734375 nb_pixel_total : 172788 time to create 1 rle with new method : 0.4985036849975586 time for calcul the mask position with numpy : 0.03719830513000488 nb_pixel_total : 185532 time to create 1 rle with new method : 0.5057773590087891 time for calcul the mask position with numpy : 0.03711247444152832 nb_pixel_total : 5955 time to create 1 rle with old method : 0.008464336395263672 time for calcul the mask position with numpy : 0.038269996643066406 nb_pixel_total : 21600 time to create 1 rle with old method : 0.025516748428344727 time for calcul the mask position with numpy : 0.03481578826904297 nb_pixel_total : 70175 time to create 1 rle with old method : 0.07747888565063477 time for calcul the mask position with numpy : 0.034487009048461914 nb_pixel_total : 16839 time to create 1 rle with old method : 0.01920342445373535 time for calcul the mask position with numpy : 0.03374648094177246 nb_pixel_total : 13465 time to create 1 rle with old method : 0.015386819839477539 time for calcul the mask position with numpy : 0.03313589096069336 nb_pixel_total : 23721 time to create 1 rle with old method : 0.026662588119506836 time for calcul the mask position with numpy : 0.030299663543701172 nb_pixel_total : 31483 time to create 1 rle with old method : 0.035935163497924805 time for calcul the mask position with numpy : 0.02300858497619629 nb_pixel_total : 37165 time to create 1 rle with old method : 0.05548572540283203 time for calcul the mask position with numpy : 0.02383255958557129 nb_pixel_total : 22145 time to create 1 rle with old method : 0.02787470817565918 time for calcul the mask position with numpy : 0.023257017135620117 nb_pixel_total : 202444 time to create 1 rle with new method : 0.5404205322265625 time for calcul the mask position with numpy : 0.021953344345092773 nb_pixel_total : 14337 time to create 1 rle with old method : 0.01852250099182129 time for calcul the mask position with numpy : 0.02234792709350586 nb_pixel_total : 14981 time to create 1 rle with old method : 0.017138957977294922 time for calcul the mask position with numpy : 0.022204160690307617 nb_pixel_total : 19810 time to create 1 rle with old method : 0.02260565757751465 time for calcul the mask position with numpy : 0.02410745620727539 nb_pixel_total : 11905 time to create 1 rle with old method : 0.013710498809814453 time for calcul the mask position with numpy : 0.022615432739257812 nb_pixel_total : 13877 time to create 1 rle with old method : 0.015677452087402344 time for calcul the mask position with numpy : 0.021547555923461914 nb_pixel_total : 59766 time to create 1 rle with old method : 0.0675663948059082 time for calcul the mask position with numpy : 0.030024290084838867 nb_pixel_total : 180537 time to create 1 rle with new method : 0.6384930610656738 create new chi : 4.722141981124878 time to delete rle : 0.002875089645385742 batch 1 Loaded 43 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 12435 TO DO : save crop sub photo not yet done ! save time : 0.7845795154571533 nb_obj : 27 nb_hashtags : 6 time to prepare the origin masks : 3.8695480823516846 time for calcul the mask position with numpy : 0.38045263290405273 nb_pixel_total : 6297792 time to create 1 rle with new method : 0.7821846008300781 time for calcul the mask position with numpy : 0.02955341339111328 nb_pixel_total : 24939 time to create 1 rle with old method : 0.028029203414916992 time for calcul the mask position with numpy : 0.029158353805541992 nb_pixel_total : 6406 time to create 1 rle with old method : 0.007272005081176758 time for calcul the mask position with numpy : 0.03014063835144043 nb_pixel_total : 105363 time to create 1 rle with old method : 0.1199789047241211 time for calcul the mask position with numpy : 0.029231786727905273 nb_pixel_total : 6765 time to create 1 rle with old method : 0.007707118988037109 time for calcul the mask position with numpy : 0.029610157012939453 nb_pixel_total : 92541 time to create 1 rle with old method : 0.10425996780395508 time for calcul the mask position with numpy : 0.02922987937927246 nb_pixel_total : 28550 time to create 1 rle with old method : 0.032390594482421875 time for calcul the mask position with numpy : 0.02899456024169922 nb_pixel_total : 5249 time to create 1 rle with old method : 0.006041049957275391 time for calcul the mask position with numpy : 0.029164791107177734 nb_pixel_total : 31301 time to create 1 rle with old method : 0.0353093147277832 time for calcul the mask position with numpy : 0.029148101806640625 nb_pixel_total : 16368 time to create 1 rle with old method : 0.01875901222229004 time for calcul the mask position with numpy : 0.028986454010009766 nb_pixel_total : 30447 time to create 1 rle with old method : 0.03439736366271973 time for calcul the mask position with numpy : 0.028957366943359375 nb_pixel_total : 2659 time to create 1 rle with old method : 0.003053426742553711 time for calcul the mask position with numpy : 0.02880072593688965 nb_pixel_total : 13489 time to create 1 rle with old method : 0.020752906799316406 time for calcul the mask position with numpy : 0.03367972373962402 nb_pixel_total : 70636 time to create 1 rle with old method : 0.09118771553039551 time for calcul the mask position with numpy : 0.028805255889892578 nb_pixel_total : 19330 time to create 1 rle with old method : 0.021619796752929688 time for calcul the mask position with numpy : 0.029523134231567383 nb_pixel_total : 65656 time to create 1 rle with old method : 0.08720636367797852 time for calcul the mask position with numpy : 0.035271644592285156 nb_pixel_total : 35208 time to create 1 rle with old method : 0.040006160736083984 time for calcul the mask position with numpy : 0.03188490867614746 nb_pixel_total : 38925 time to create 1 rle with old method : 0.044657230377197266 time for calcul the mask position with numpy : 0.028949975967407227 nb_pixel_total : 10139 time to create 1 rle with old method : 0.011542081832885742 time for calcul the mask position with numpy : 0.028925418853759766 nb_pixel_total : 13873 time to create 1 rle with old method : 0.015949249267578125 time for calcul the mask position with numpy : 0.029330968856811523 nb_pixel_total : 23666 time to create 1 rle with old method : 0.026831626892089844 time for calcul the mask position with numpy : 0.028977394104003906 nb_pixel_total : 38493 time to create 1 rle with old method : 0.04351019859313965 time for calcul the mask position with numpy : 0.030463218688964844 nb_pixel_total : 10620 time to create 1 rle with old method : 0.020264625549316406 time for calcul the mask position with numpy : 0.039696693420410156 nb_pixel_total : 14826 time to create 1 rle with old method : 0.017075538635253906 time for calcul the mask position with numpy : 0.029057025909423828 nb_pixel_total : 15245 time to create 1 rle with old method : 0.017435312271118164 time for calcul the mask position with numpy : 0.029635190963745117 nb_pixel_total : 10300 time to create 1 rle with old method : 0.011700153350830078 time for calcul the mask position with numpy : 0.030206680297851562 nb_pixel_total : 11510 time to create 1 rle with old method : 0.01557302474975586 time for calcul the mask position with numpy : 0.02932143211364746 nb_pixel_total : 9944 time to create 1 rle with old method : 0.011221647262573242 create new chi : 2.9067394733428955 time to delete rle : 0.0022776126861572266 batch 1 Loaded 55 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++Number RLEs to save : 13181 TO DO : save crop sub photo not yet done ! save time : 0.7947633266448975 nb_obj : 25 nb_hashtags : 4 time to prepare the origin masks : 13.479667663574219 time for calcul the mask position with numpy : 0.7902920246124268 nb_pixel_total : 5920225 time to create 1 rle with new method : 0.580451250076294 time for calcul the mask position with numpy : 0.04472088813781738 nb_pixel_total : 16155 time to create 1 rle with old method : 0.01953125 time for calcul the mask position with numpy : 0.05404400825500488 nb_pixel_total : 562552 time to create 1 rle with new method : 0.7861347198486328 time for calcul the mask position with numpy : 0.05357241630554199 nb_pixel_total : 6769 time to create 1 rle with old method : 0.008004903793334961 time for calcul the mask position with numpy : 0.05205559730529785 nb_pixel_total : 12182 time to create 1 rle with old method : 0.01445150375366211 time for calcul the mask position with numpy : 0.04703044891357422 nb_pixel_total : 8536 time to create 1 rle with old method : 0.009713888168334961 time for calcul the mask position with numpy : 0.04637598991394043 nb_pixel_total : 17849 time to create 1 rle with old method : 0.021445274353027344 time for calcul the mask position with numpy : 0.0573430061340332 nb_pixel_total : 34532 time to create 1 rle with old method : 0.05487775802612305 time for calcul the mask position with numpy : 0.054802656173706055 nb_pixel_total : 6396 time to create 1 rle with old method : 0.010451793670654297 time for calcul the mask position with numpy : 0.053343772888183594 nb_pixel_total : 37146 time to create 1 rle with old method : 0.06543278694152832 time for calcul the mask position with numpy : 0.04229927062988281 nb_pixel_total : 37965 time to create 1 rle with old method : 0.05873703956604004 time for calcul the mask position with numpy : 0.038732290267944336 nb_pixel_total : 10507 time to create 1 rle with old method : 0.014858245849609375 time for calcul the mask position with numpy : 0.03670549392700195 nb_pixel_total : 12042 time to create 1 rle with old method : 0.02396225929260254 time for calcul the mask position with numpy : 0.038684844970703125 nb_pixel_total : 17127 time to create 1 rle with old method : 0.02619457244873047 time for calcul the mask position with numpy : 0.04400062561035156 nb_pixel_total : 15508 time to create 1 rle with old method : 0.017709016799926758 time for calcul the mask position with numpy : 0.046370506286621094 nb_pixel_total : 14264 time to create 1 rle with old method : 0.018674850463867188 time for calcul the mask position with numpy : 0.04937124252319336 nb_pixel_total : 27138 time to create 1 rle with old method : 0.031529903411865234 time for calcul the mask position with numpy : 0.04308462142944336 nb_pixel_total : 26292 time to create 1 rle with old method : 0.0297849178314209 time for calcul the mask position with numpy : 0.04593467712402344 nb_pixel_total : 11343 time to create 1 rle with old method : 0.012993574142456055 time for calcul the mask position with numpy : 0.04235219955444336 nb_pixel_total : 17041 time to create 1 rle with old method : 0.019742965698242188 time for calcul the mask position with numpy : 0.04414868354797363 nb_pixel_total : 27779 time to create 1 rle with old method : 0.03731131553649902 time for calcul the mask position with numpy : 0.041578054428100586 nb_pixel_total : 20835 time to create 1 rle with old method : 0.023804187774658203 time for calcul the mask position with numpy : 0.0409088134765625 nb_pixel_total : 16728 time to create 1 rle with old method : 0.019602298736572266 time for calcul the mask position with numpy : 0.03672528266906738 nb_pixel_total : 106306 time to create 1 rle with old method : 0.13980412483215332 time for calcul the mask position with numpy : 0.028314828872680664 nb_pixel_total : 51839 time to create 1 rle with old method : 0.05966591835021973 time for calcul the mask position with numpy : 0.028958559036254883 nb_pixel_total : 15184 time to create 1 rle with old method : 0.020421266555786133 create new chi : 4.104340076446533 time to delete rle : 0.0025925636291503906 batch 1 Loaded 51 chid ids of type : 3594 +++++++++++++++++++++++++++++Number RLEs to save : 13421 TO DO : save crop sub photo not yet done ! save time : 1.0427680015563965 nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 9.163116216659546 time for calcul the mask position with numpy : 0.2672536373138428 nb_pixel_total : 4705590 time to create 1 rle with new method : 0.38008975982666016 time for calcul the mask position with numpy : 0.06618332862854004 nb_pixel_total : 155499 time to create 1 rle with new method : 0.4957704544067383 time for calcul the mask position with numpy : 0.05022168159484863 nb_pixel_total : 260436 time to create 1 rle with new method : 0.3942108154296875 time for calcul the mask position with numpy : 0.04992341995239258 nb_pixel_total : 35 time to create 1 rle with old method : 0.00010609626770019531 time for calcul the mask position with numpy : 0.04448103904724121 nb_pixel_total : 1358 time to create 1 rle with old method : 0.0019214153289794922 time for calcul the mask position with numpy : 0.042200565338134766 nb_pixel_total : 121979 time to create 1 rle with old method : 0.14089417457580566 time for calcul the mask position with numpy : 0.0483088493347168 nb_pixel_total : 288479 time to create 1 rle with new method : 0.37265515327453613 time for calcul the mask position with numpy : 0.052103519439697266 nb_pixel_total : 5243 time to create 1 rle with old method : 0.006143808364868164 time for calcul the mask position with numpy : 0.05035591125488281 nb_pixel_total : 269709 time to create 1 rle with new method : 0.3857383728027344 time for calcul the mask position with numpy : 0.045569658279418945 nb_pixel_total : 72221 time to create 1 rle with old method : 0.08861637115478516 time for calcul the mask position with numpy : 0.0439457893371582 nb_pixel_total : 21745 time to create 1 rle with old method : 0.026267051696777344 time for calcul the mask position with numpy : 0.041329145431518555 nb_pixel_total : 69184 time to create 1 rle with old method : 0.09048652648925781 time for calcul the mask position with numpy : 0.055295467376708984 nb_pixel_total : 135557 time to create 1 rle with old method : 0.1548755168914795 time for calcul the mask position with numpy : 0.04425621032714844 nb_pixel_total : 70016 time to create 1 rle with old method : 0.08167004585266113 time for calcul the mask position with numpy : 0.039575815200805664 nb_pixel_total : 46424 time to create 1 rle with old method : 0.05336403846740723 time for calcul the mask position with numpy : 0.0380399227142334 nb_pixel_total : 99405 time to create 1 rle with old method : 0.1414504051208496 time for calcul the mask position with numpy : 0.04338479042053223 nb_pixel_total : 62367 time to create 1 rle with old method : 0.0800931453704834 time for calcul the mask position with numpy : 0.049698591232299805 nb_pixel_total : 20516 time to create 1 rle with old method : 0.024968862533569336 time for calcul the mask position with numpy : 0.05484485626220703 nb_pixel_total : 9286 time to create 1 rle with old method : 0.011917829513549805 time for calcul the mask position with numpy : 0.04373621940612793 nb_pixel_total : 71549 time to create 1 rle with old method : 0.09505963325500488 time for calcul the mask position with numpy : 0.0480802059173584 nb_pixel_total : 228253 time to create 1 rle with new method : 0.3407762050628662 time for calcul the mask position with numpy : 0.04546546936035156 nb_pixel_total : 29803 time to create 1 rle with old method : 0.05158209800720215 time for calcul the mask position with numpy : 0.04350161552429199 nb_pixel_total : 50268 time to create 1 rle with old method : 0.06481671333312988 time for calcul the mask position with numpy : 0.055481672286987305 nb_pixel_total : 255318 time to create 1 rle with new method : 0.42610812187194824 create new chi : 5.4769346714019775 time to delete rle : 0.008428335189819336 batch 1 Loaded 47 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22083 TO DO : save crop sub photo not yet done ! save time : 1.4326367378234863 nb_obj : 15 nb_hashtags : 3 time to prepare the origin masks : 5.846215724945068 time for calcul the mask position with numpy : 0.26372623443603516 nb_pixel_total : 5952617 time to create 1 rle with new method : 0.3046565055847168 time for calcul the mask position with numpy : 0.049495697021484375 nb_pixel_total : 17871 time to create 1 rle with old method : 0.028984546661376953 time for calcul the mask position with numpy : 0.046205997467041016 nb_pixel_total : 7979 time to create 1 rle with old method : 0.009730339050292969 time for calcul the mask position with numpy : 0.05290961265563965 nb_pixel_total : 27070 time to create 1 rle with old method : 0.03146076202392578 time for calcul the mask position with numpy : 0.04593372344970703 nb_pixel_total : 92888 time to create 1 rle with old method : 0.10693192481994629 time for calcul the mask position with numpy : 0.02791428565979004 nb_pixel_total : 12889 time to create 1 rle with old method : 0.01475071907043457 time for calcul the mask position with numpy : 0.029397964477539062 nb_pixel_total : 14254 time to create 1 rle with old method : 0.01659393310546875 time for calcul the mask position with numpy : 0.033882856369018555 nb_pixel_total : 289593 time to create 1 rle with new method : 0.33336448669433594 time for calcul the mask position with numpy : 0.023548126220703125 nb_pixel_total : 44557 time to create 1 rle with old method : 0.05353140830993652 time for calcul the mask position with numpy : 0.0232541561126709 nb_pixel_total : 42691 time to create 1 rle with old method : 0.05144929885864258 time for calcul the mask position with numpy : 0.022644519805908203 nb_pixel_total : 27399 time to create 1 rle with old method : 0.03383636474609375 time for calcul the mask position with numpy : 0.023357629776000977 nb_pixel_total : 16703 time to create 1 rle with old method : 0.020765066146850586 time for calcul the mask position with numpy : 0.023199081420898438 nb_pixel_total : 179837 time to create 1 rle with new method : 0.39664173126220703 time for calcul the mask position with numpy : 0.04665517807006836 nb_pixel_total : 49438 time to create 1 rle with old method : 0.07663846015930176 time for calcul the mask position with numpy : 0.043109893798828125 nb_pixel_total : 52080 time to create 1 rle with old method : 0.05904102325439453 time for calcul the mask position with numpy : 0.049651145935058594 nb_pixel_total : 222374 time to create 1 rle with new method : 0.3991062641143799 create new chi : 2.852111339569092 time to delete rle : 0.0029785633087158203 batch 1 Loaded 31 chid ids of type : 3594 ++++++++++++++++Number RLEs to save : 10470 TO DO : save crop sub photo not yet done ! save time : 0.6929762363433838 nb_obj : 29 nb_hashtags : 4 time to prepare the origin masks : 4.92140007019043 time for calcul the mask position with numpy : 0.26301145553588867 nb_pixel_total : 4581490 time to create 1 rle with new method : 0.6394670009613037 time for calcul the mask position with numpy : 0.029985904693603516 nb_pixel_total : 95692 time to create 1 rle with old method : 0.11014938354492188 time for calcul the mask position with numpy : 0.02913808822631836 nb_pixel_total : 17858 time to create 1 rle with old method : 0.020447969436645508 time for calcul the mask position with numpy : 0.031137943267822266 nb_pixel_total : 374527 time to create 1 rle with new method : 0.45482444763183594 time for calcul the mask position with numpy : 0.029733657836914062 nb_pixel_total : 40258 time to create 1 rle with old method : 0.04601740837097168 time for calcul the mask position with numpy : 0.030666351318359375 nb_pixel_total : 118479 time to create 1 rle with old method : 0.1376359462738037 time for calcul the mask position with numpy : 0.03355598449707031 nb_pixel_total : 52409 time to create 1 rle with old method : 0.07097291946411133 time for calcul the mask position with numpy : 0.029819011688232422 nb_pixel_total : 15736 time to create 1 rle with old method : 0.0181734561920166 time for calcul the mask position with numpy : 0.029187440872192383 nb_pixel_total : 117654 time to create 1 rle with old method : 0.13329291343688965 time for calcul the mask position with numpy : 0.029395103454589844 nb_pixel_total : 108829 time to create 1 rle with old method : 0.1234292984008789 time for calcul the mask position with numpy : 0.03614926338195801 nb_pixel_total : 54961 time to create 1 rle with old method : 0.0887765884399414 time for calcul the mask position with numpy : 0.04562878608703613 nb_pixel_total : 76602 time to create 1 rle with old method : 0.0904850959777832 time for calcul the mask position with numpy : 0.032243967056274414 nb_pixel_total : 287452 time to create 1 rle with new method : 0.8172643184661865 time for calcul the mask position with numpy : 0.02939438819885254 nb_pixel_total : 39240 time to create 1 rle with old method : 0.04555702209472656 time for calcul the mask position with numpy : 0.033461570739746094 nb_pixel_total : 44576 time to create 1 rle with old method : 0.06291818618774414 time for calcul the mask position with numpy : 0.029517173767089844 nb_pixel_total : 61462 time to create 1 rle with old method : 0.06958460807800293 time for calcul the mask position with numpy : 0.031087398529052734 nb_pixel_total : 47669 time to create 1 rle with old method : 0.07510709762573242 time for calcul the mask position with numpy : 0.0521550178527832 nb_pixel_total : 40286 time to create 1 rle with old method : 0.04804182052612305 time for calcul the mask position with numpy : 0.032346248626708984 nb_pixel_total : 220767 time to create 1 rle with new method : 2.4214367866516113 time for calcul the mask position with numpy : 0.029547691345214844 nb_pixel_total : 60006 time to create 1 rle with old method : 0.06885409355163574 time for calcul the mask position with numpy : 0.029416799545288086 nb_pixel_total : 32362 time to create 1 rle with old method : 0.038437843322753906 time for calcul the mask position with numpy : 0.029384136199951172 nb_pixel_total : 105837 time to create 1 rle with old method : 0.12032604217529297 time for calcul the mask position with numpy : 0.02963995933532715 nb_pixel_total : 113142 time to create 1 rle with old method : 0.1415729522705078 time for calcul the mask position with numpy : 0.029346227645874023 nb_pixel_total : 32355 time to create 1 rle with old method : 0.03934431076049805 time for calcul the mask position with numpy : 0.03108835220336914 nb_pixel_total : 123796 time to create 1 rle with old method : 0.13994407653808594 time for calcul the mask position with numpy : 0.029716968536376953 nb_pixel_total : 74446 time to create 1 rle with old method : 0.08444905281066895 time for calcul the mask position with numpy : 0.029761314392089844 nb_pixel_total : 61989 time to create 1 rle with old method : 0.07013130187988281 time for calcul the mask position with numpy : 0.028876543045043945 nb_pixel_total : 5555 time to create 1 rle with old method : 0.006428718566894531 time for calcul the mask position with numpy : 0.0288999080657959 nb_pixel_total : 34135 time to create 1 rle with old method : 0.03881025314331055 time for calcul the mask position with numpy : 0.029773235321044922 nb_pixel_total : 10670 time to create 1 rle with old method : 0.01222538948059082 create new chi : 7.5297346115112305 time to delete rle : 0.004190206527709961 batch 1 Loaded 59 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25202 TO DO : save crop sub photo not yet done ! save time : 1.4824702739715576 nb_obj : 23 nb_hashtags : 3 time to prepare the origin masks : 8.791807889938354 time for calcul the mask position with numpy : 0.5684802532196045 nb_pixel_total : 4708543 time to create 1 rle with new method : 0.8013486862182617 time for calcul the mask position with numpy : 0.037446022033691406 nb_pixel_total : 15219 time to create 1 rle with old method : 0.017505884170532227 time for calcul the mask position with numpy : 0.03565669059753418 nb_pixel_total : 26454 time to create 1 rle with old method : 0.03594565391540527 time for calcul the mask position with numpy : 0.0380854606628418 nb_pixel_total : 16783 time to create 1 rle with old method : 0.01934051513671875 time for calcul the mask position with numpy : 0.03597712516784668 nb_pixel_total : 16 time to create 1 rle with old method : 4.315376281738281e-05 time for calcul the mask position with numpy : 0.03557610511779785 nb_pixel_total : 5630 time to create 1 rle with old method : 0.006536006927490234 time for calcul the mask position with numpy : 0.03800010681152344 nb_pixel_total : 20495 time to create 1 rle with old method : 0.02344369888305664 time for calcul the mask position with numpy : 0.03427696228027344 nb_pixel_total : 98994 time to create 1 rle with old method : 0.1113290786743164 time for calcul the mask position with numpy : 0.03193020820617676 nb_pixel_total : 9782 time to create 1 rle with old method : 0.01122593879699707 time for calcul the mask position with numpy : 0.03255629539489746 nb_pixel_total : 46393 time to create 1 rle with old method : 0.056613922119140625 time for calcul the mask position with numpy : 0.0317540168762207 nb_pixel_total : 30599 time to create 1 rle with old method : 0.03479361534118652 time for calcul the mask position with numpy : 0.03141927719116211 nb_pixel_total : 124815 time to create 1 rle with old method : 0.1431581974029541 time for calcul the mask position with numpy : 0.021126270294189453 nb_pixel_total : 24975 time to create 1 rle with old method : 0.03233742713928223 time for calcul the mask position with numpy : 0.04589438438415527 nb_pixel_total : 979478 time to create 1 rle with new method : 1.8837192058563232 time for calcul the mask position with numpy : 0.03514266014099121 nb_pixel_total : 46086 time to create 1 rle with old method : 0.053128719329833984 time for calcul the mask position with numpy : 0.035494327545166016 nb_pixel_total : 130713 time to create 1 rle with old method : 0.15238142013549805 time for calcul the mask position with numpy : 0.037599802017211914 nb_pixel_total : 36640 time to create 1 rle with old method : 0.042108774185180664 time for calcul the mask position with numpy : 0.04178047180175781 nb_pixel_total : 38050 time to create 1 rle with old method : 0.0434873104095459 time for calcul the mask position with numpy : 0.03623557090759277 nb_pixel_total : 92305 time to create 1 rle with old method : 0.10534548759460449 time for calcul the mask position with numpy : 0.03755617141723633 nb_pixel_total : 175556 time to create 1 rle with new method : 0.40430474281311035 time for calcul the mask position with numpy : 0.04145455360412598 nb_pixel_total : 41426 time to create 1 rle with old method : 0.04669380187988281 time for calcul the mask position with numpy : 0.03621983528137207 nb_pixel_total : 47065 time to create 1 rle with old method : 0.05338144302368164 time for calcul the mask position with numpy : 0.03614377975463867 nb_pixel_total : 14869 time to create 1 rle with old method : 0.023023605346679688 time for calcul the mask position with numpy : 0.050264835357666016 nb_pixel_total : 319354 time to create 1 rle with new method : 0.9212095737457275 create new chi : 6.53974461555481 time to delete rle : 0.005452632904052734 batch 1 Loaded 47 chid ids of type : 3594 +++++++++++++++++++++++++++++++++Number RLEs to save : 17505 TO DO : save crop sub photo not yet done ! save time : 1.0949864387512207 nb_obj : 46 nb_hashtags : 5 time to prepare the origin masks : 5.199898719787598 time for calcul the mask position with numpy : 0.9470398426055908 nb_pixel_total : 4642188 time to create 1 rle with new method : 0.714292049407959 time for calcul the mask position with numpy : 0.028096437454223633 nb_pixel_total : 16341 time to create 1 rle with old method : 0.018192052841186523 time for calcul the mask position with numpy : 0.029114723205566406 nb_pixel_total : 17341 time to create 1 rle with old method : 0.019735097885131836 time for calcul the mask position with numpy : 0.03159761428833008 nb_pixel_total : 7787 time to create 1 rle with old method : 0.010625362396240234 time for calcul the mask position with numpy : 0.03361344337463379 nb_pixel_total : 134623 time to create 1 rle with old method : 0.17957592010498047 time for calcul the mask position with numpy : 0.03417468070983887 nb_pixel_total : 287594 time to create 1 rle with new method : 0.8802392482757568 time for calcul the mask position with numpy : 0.03490638732910156 nb_pixel_total : 430862 time to create 1 rle with new method : 1.5179944038391113 time for calcul the mask position with numpy : 0.029781579971313477 nb_pixel_total : 52381 time to create 1 rle with old method : 0.059764862060546875 time for calcul the mask position with numpy : 0.0296173095703125 nb_pixel_total : 10194 time to create 1 rle with old method : 0.01179194450378418 time for calcul the mask position with numpy : 0.030727386474609375 nb_pixel_total : 17074 time to create 1 rle with old method : 0.021216392517089844 time for calcul the mask position with numpy : 0.02989792823791504 nb_pixel_total : 7440 time to create 1 rle with old method : 0.008733510971069336 time for calcul the mask position with numpy : 0.029662132263183594 nb_pixel_total : 20036 time to create 1 rle with old method : 0.02326512336730957 time for calcul the mask position with numpy : 0.029691457748413086 nb_pixel_total : 32962 time to create 1 rle with old method : 0.037610769271850586 time for calcul the mask position with numpy : 0.030678987503051758 nb_pixel_total : 105450 time to create 1 rle with old method : 0.11990666389465332 time for calcul the mask position with numpy : 0.029494524002075195 nb_pixel_total : 24829 time to create 1 rle with old method : 0.028455495834350586 time for calcul the mask position with numpy : 0.030011892318725586 nb_pixel_total : 40784 time to create 1 rle with old method : 0.06580471992492676 time for calcul the mask position with numpy : 0.030467987060546875 nb_pixel_total : 61500 time to create 1 rle with old method : 0.07565164566040039 time for calcul the mask position with numpy : 0.03381490707397461 nb_pixel_total : 90386 time to create 1 rle with old method : 0.1370406150817871 time for calcul the mask position with numpy : 0.02924203872680664 nb_pixel_total : 14749 time to create 1 rle with old method : 0.01800084114074707 time for calcul the mask position with numpy : 0.03082442283630371 nb_pixel_total : 25930 time to create 1 rle with old method : 0.03135061264038086 time for calcul the mask position with numpy : 0.02917766571044922 nb_pixel_total : 20440 time to create 1 rle with old method : 0.023700952529907227 time for calcul the mask position with numpy : 0.029654264450073242 nb_pixel_total : 136199 time to create 1 rle with old method : 0.1763324737548828 time for calcul the mask position with numpy : 0.03182172775268555 nb_pixel_total : 24676 time to create 1 rle with old method : 0.02789759635925293 time for calcul the mask position with numpy : 0.029040098190307617 nb_pixel_total : 1086 time to create 1 rle with old method : 0.001359701156616211 time for calcul the mask position with numpy : 0.034313201904296875 nb_pixel_total : 41717 time to create 1 rle with old method : 0.0481114387512207 time for calcul the mask position with numpy : 0.029409408569335938 nb_pixel_total : 19989 time to create 1 rle with old method : 0.022652387619018555 time for calcul the mask position with numpy : 0.029332637786865234 nb_pixel_total : 17457 time to create 1 rle with old method : 0.019881725311279297 time for calcul the mask position with numpy : 0.029307842254638672 nb_pixel_total : 24670 time to create 1 rle with old method : 0.02788376808166504 time for calcul the mask position with numpy : 0.029258012771606445 nb_pixel_total : 22391 time to create 1 rle with old method : 0.025864839553833008 time for calcul the mask position with numpy : 0.030899763107299805 nb_pixel_total : 230253 time to create 1 rle with new method : 1.0187125205993652 time for calcul the mask position with numpy : 0.029052734375 nb_pixel_total : 27066 time to create 1 rle with old method : 0.03073406219482422 time for calcul the mask position with numpy : 0.029105663299560547 nb_pixel_total : 50859 time to create 1 rle with old method : 0.05716705322265625 time for calcul the mask position with numpy : 0.02901744842529297 nb_pixel_total : 30557 time to create 1 rle with old method : 0.03483295440673828 time for calcul the mask position with numpy : 0.02929973602294922 nb_pixel_total : 66473 time to create 1 rle with old method : 0.07534670829772949 time for calcul the mask position with numpy : 0.02975749969482422 nb_pixel_total : 55980 time to create 1 rle with old method : 0.06329226493835449 time for calcul the mask position with numpy : 0.029180526733398438 nb_pixel_total : 77 time to create 1 rle with old method : 0.00015544891357421875 time for calcul the mask position with numpy : 0.029826641082763672 nb_pixel_total : 63296 time to create 1 rle with old method : 0.07203245162963867 time for calcul the mask position with numpy : 0.029340028762817383 nb_pixel_total : 8733 time to create 1 rle with old method : 0.009780406951904297 time for calcul the mask position with numpy : 0.029260873794555664 nb_pixel_total : 11873 time to create 1 rle with old method : 0.013602733612060547 time for calcul the mask position with numpy : 0.029544591903686523 nb_pixel_total : 12909 time to create 1 rle with old method : 0.014839887619018555 time for calcul the mask position with numpy : 0.0298006534576416 nb_pixel_total : 24395 time to create 1 rle with old method : 0.02812933921813965 time for calcul the mask position with numpy : 0.030518293380737305 nb_pixel_total : 37436 time to create 1 rle with old method : 0.04290437698364258 time for calcul the mask position with numpy : 0.029483795166015625 nb_pixel_total : 11574 time to create 1 rle with old method : 0.013249397277832031 time for calcul the mask position with numpy : 0.02957630157470703 nb_pixel_total : 31160 time to create 1 rle with old method : 0.035544633865356445 time for calcul the mask position with numpy : 0.02942800521850586 nb_pixel_total : 12773 time to create 1 rle with old method : 0.01473379135131836 time for calcul the mask position with numpy : 0.03171825408935547 nb_pixel_total : 15580 time to create 1 rle with old method : 0.020929336547851562 time for calcul the mask position with numpy : 0.030370473861694336 nb_pixel_total : 10170 time to create 1 rle with old method : 0.01180124282836914 create new chi : 8.353366613388062 time to delete rle : 0.004947662353515625 batch 1 Loaded 93 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27275 TO DO : save crop sub photo not yet done ! save time : 1.5679130554199219 nb_obj : 31 nb_hashtags : 4 time to prepare the origin masks : 4.856637716293335 time for calcul the mask position with numpy : 0.8522801399230957 nb_pixel_total : 5459707 time to create 1 rle with new method : 0.6943790912628174 time for calcul the mask position with numpy : 0.029325008392333984 nb_pixel_total : 8544 time to create 1 rle with old method : 0.009894132614135742 time for calcul the mask position with numpy : 0.03713679313659668 nb_pixel_total : 705768 time to create 1 rle with new method : 0.6131839752197266 time for calcul the mask position with numpy : 0.03519558906555176 nb_pixel_total : 13180 time to create 1 rle with old method : 0.023928165435791016 time for calcul the mask position with numpy : 0.03625988960266113 nb_pixel_total : 25817 time to create 1 rle with old method : 0.029920578002929688 time for calcul the mask position with numpy : 0.034085988998413086 nb_pixel_total : 32497 time to create 1 rle with old method : 0.036820411682128906 time for calcul the mask position with numpy : 0.029189348220825195 nb_pixel_total : 10390 time to create 1 rle with old method : 0.012267827987670898 time for calcul the mask position with numpy : 0.029630184173583984 nb_pixel_total : 45787 time to create 1 rle with old method : 0.057973384857177734 time for calcul the mask position with numpy : 0.029496192932128906 nb_pixel_total : 18463 time to create 1 rle with old method : 0.022998809814453125 time for calcul the mask position with numpy : 0.029424428939819336 nb_pixel_total : 9031 time to create 1 rle with old method : 0.010579586029052734 time for calcul the mask position with numpy : 0.03548765182495117 nb_pixel_total : 16937 time to create 1 rle with old method : 0.019759416580200195 time for calcul the mask position with numpy : 0.030962467193603516 nb_pixel_total : 17698 time to create 1 rle with old method : 0.02009439468383789 time for calcul the mask position with numpy : 0.029059171676635742 nb_pixel_total : 18777 time to create 1 rle with old method : 0.02252340316772461 time for calcul the mask position with numpy : 0.03080439567565918 nb_pixel_total : 188986 time to create 1 rle with new method : 0.6398391723632812 time for calcul the mask position with numpy : 0.029588699340820312 nb_pixel_total : 46481 time to create 1 rle with old method : 0.054659366607666016 time for calcul the mask position with numpy : 0.030291080474853516 nb_pixel_total : 41902 time to create 1 rle with old method : 0.051283836364746094 time for calcul the mask position with numpy : 0.031090974807739258 nb_pixel_total : 5839 time to create 1 rle with old method : 0.007460355758666992 time for calcul the mask position with numpy : 0.031011581420898438 nb_pixel_total : 4810 time to create 1 rle with old method : 0.0057256221771240234 time for calcul the mask position with numpy : 0.030675411224365234 nb_pixel_total : 26194 time to create 1 rle with old method : 0.033911705017089844 time for calcul the mask position with numpy : 0.03015756607055664 nb_pixel_total : 22679 time to create 1 rle with old method : 0.02618551254272461 time for calcul the mask position with numpy : 0.029732704162597656 nb_pixel_total : 30938 time to create 1 rle with old method : 0.041040658950805664 time for calcul the mask position with numpy : 0.0312952995300293 nb_pixel_total : 82118 time to create 1 rle with old method : 0.10110831260681152 time for calcul the mask position with numpy : 0.02990102767944336 nb_pixel_total : 27096 time to create 1 rle with old method : 0.03129839897155762 time for calcul the mask position with numpy : 0.029264450073242188 nb_pixel_total : 21135 time to create 1 rle with old method : 0.028864622116088867 time for calcul the mask position with numpy : 0.03507637977600098 nb_pixel_total : 5069 time to create 1 rle with old method : 0.005902290344238281 time for calcul the mask position with numpy : 0.029162168502807617 nb_pixel_total : 13689 time to create 1 rle with old method : 0.015820026397705078 time for calcul the mask position with numpy : 0.029801607131958008 nb_pixel_total : 9151 time to create 1 rle with old method : 0.010432243347167969 time for calcul the mask position with numpy : 0.03149819374084473 nb_pixel_total : 20307 time to create 1 rle with old method : 0.031130313873291016 time for calcul the mask position with numpy : 0.038202524185180664 nb_pixel_total : 31948 time to create 1 rle with old method : 0.04015231132507324 time for calcul the mask position with numpy : 0.030586719512939453 nb_pixel_total : 60174 time to create 1 rle with old method : 0.07084226608276367 time for calcul the mask position with numpy : 0.029785871505737305 nb_pixel_total : 21795 time to create 1 rle with old method : 0.025639057159423828 time for calcul the mask position with numpy : 0.029495716094970703 nb_pixel_total : 7333 time to create 1 rle with old method : 0.008504867553710938 create new chi : 4.727184534072876 time to delete rle : 0.004199981689453125 batch 1 Loaded 63 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++Number RLEs to save : 16818 TO DO : save crop sub photo not yet done ! save time : 1.1149499416351318 nb_obj : 60 nb_hashtags : 5 time to prepare the origin masks : 4.690035581588745 time for calcul the mask position with numpy : 0.5962917804718018 nb_pixel_total : 5033279 time to create 1 rle with new method : 0.8368403911590576 time for calcul the mask position with numpy : 0.02938103675842285 nb_pixel_total : 9707 time to create 1 rle with old method : 0.01102900505065918 time for calcul the mask position with numpy : 0.02964162826538086 nb_pixel_total : 16027 time to create 1 rle with old method : 0.018184423446655273 time for calcul the mask position with numpy : 0.031075716018676758 nb_pixel_total : 3550 time to create 1 rle with old method : 0.004598379135131836 time for calcul the mask position with numpy : 0.03151726722717285 nb_pixel_total : 17009 time to create 1 rle with old method : 0.01955723762512207 time for calcul the mask position with numpy : 0.029490947723388672 nb_pixel_total : 16570 time to create 1 rle with old method : 0.018967866897583008 time for calcul the mask position with numpy : 0.029306888580322266 nb_pixel_total : 23733 time to create 1 rle with old method : 0.027464628219604492 time for calcul the mask position with numpy : 0.03187155723571777 nb_pixel_total : 34549 time to create 1 rle with old method : 0.0390934944152832 time for calcul the mask position with numpy : 0.029217243194580078 nb_pixel_total : 10089 time to create 1 rle with old method : 0.01141977310180664 time for calcul the mask position with numpy : 0.029181957244873047 nb_pixel_total : 43703 time to create 1 rle with old method : 0.04996299743652344 time for calcul the mask position with numpy : 0.029388904571533203 nb_pixel_total : 14143 time to create 1 rle with old method : 0.016382217407226562 time for calcul the mask position with numpy : 0.02904200553894043 nb_pixel_total : 23708 time to create 1 rle with old method : 0.027145862579345703 time for calcul the mask position with numpy : 0.030354022979736328 nb_pixel_total : 14536 time to create 1 rle with old method : 0.023810625076293945 time for calcul the mask position with numpy : 0.03364396095275879 nb_pixel_total : 31308 time to create 1 rle with old method : 0.0377957820892334 time for calcul the mask position with numpy : 0.029871702194213867 nb_pixel_total : 11650 time to create 1 rle with old method : 0.013240575790405273 time for calcul the mask position with numpy : 0.029510021209716797 nb_pixel_total : 115334 time to create 1 rle with old method : 0.12975406646728516 time for calcul the mask position with numpy : 0.029068470001220703 nb_pixel_total : 10530 time to create 1 rle with old method : 0.012023210525512695 time for calcul the mask position with numpy : 0.029080867767333984 nb_pixel_total : 10283 time to create 1 rle with old method : 0.011780738830566406 time for calcul the mask position with numpy : 0.02915811538696289 nb_pixel_total : 15014 time to create 1 rle with old method : 0.016982555389404297 time for calcul the mask position with numpy : 0.029363632202148438 nb_pixel_total : 22000 time to create 1 rle with old method : 0.025049686431884766 time for calcul the mask position with numpy : 0.0292360782623291 nb_pixel_total : 31254 time to create 1 rle with old method : 0.0352780818939209 time for calcul the mask position with numpy : 0.02899622917175293 nb_pixel_total : 6971 time to create 1 rle with old method : 0.007905244827270508 time for calcul the mask position with numpy : 0.02899646759033203 nb_pixel_total : 15193 time to create 1 rle with old method : 0.0173342227935791 time for calcul the mask position with numpy : 0.029106855392456055 nb_pixel_total : 10459 time to create 1 rle with old method : 0.011894941329956055 time for calcul the mask position with numpy : 0.029142379760742188 nb_pixel_total : 30337 time to create 1 rle with old method : 0.03443598747253418 time for calcul the mask position with numpy : 0.02927851676940918 nb_pixel_total : 75277 time to create 1 rle with old method : 0.08548903465270996 time for calcul the mask position with numpy : 0.02911663055419922 nb_pixel_total : 21220 time to create 1 rle with old method : 0.024567842483520508 time for calcul the mask position with numpy : 0.028940677642822266 nb_pixel_total : 20051 time to create 1 rle with old method : 0.02303290367126465 time for calcul the mask position with numpy : 0.032782554626464844 nb_pixel_total : 51371 time to create 1 rle with old method : 0.05945110321044922 time for calcul the mask position with numpy : 0.029469728469848633 nb_pixel_total : 16112 time to create 1 rle with old method : 0.018575429916381836 time for calcul the mask position with numpy : 0.029696941375732422 nb_pixel_total : 40574 time to create 1 rle with old method : 0.04584765434265137 time for calcul the mask position with numpy : 0.029028892517089844 nb_pixel_total : 16535 time to create 1 rle with old method : 0.018794536590576172 time for calcul the mask position with numpy : 0.028965473175048828 nb_pixel_total : 24072 time to create 1 rle with old method : 0.027341604232788086 time for calcul the mask position with numpy : 0.028888225555419922 nb_pixel_total : 15321 time to create 1 rle with old method : 0.017328739166259766 time for calcul the mask position with numpy : 0.029173612594604492 nb_pixel_total : 101307 time to create 1 rle with old method : 0.11634087562561035 time for calcul the mask position with numpy : 0.02915358543395996 nb_pixel_total : 25365 time to create 1 rle with old method : 0.02883148193359375 time for calcul the mask position with numpy : 0.031127214431762695 nb_pixel_total : 104672 time to create 1 rle with old method : 0.1213381290435791 time for calcul the mask position with numpy : 0.029115676879882812 nb_pixel_total : 23298 time to create 1 rle with old method : 0.026401042938232422 time for calcul the mask position with numpy : 0.02882552146911621 nb_pixel_total : 10567 time to create 1 rle with old method : 0.011970043182373047 time for calcul the mask position with numpy : 0.028760433197021484 nb_pixel_total : 13213 time to create 1 rle with old method : 0.015025854110717773 time for calcul the mask position with numpy : 0.028823137283325195 nb_pixel_total : 26543 time to create 1 rle with old method : 0.0334172248840332 time for calcul the mask position with numpy : 0.029102802276611328 nb_pixel_total : 87511 time to create 1 rle with old method : 0.10468935966491699 time for calcul the mask position with numpy : 0.028950214385986328 nb_pixel_total : 40430 time to create 1 rle with old method : 0.04551339149475098 time for calcul the mask position with numpy : 0.02889537811279297 nb_pixel_total : 50764 time to create 1 rle with old method : 0.06230497360229492 time for calcul the mask position with numpy : 0.029515981674194336 nb_pixel_total : 12978 time to create 1 rle with old method : 0.016435623168945312 time for calcul the mask position with numpy : 0.03289175033569336 nb_pixel_total : 5974 time to create 1 rle with old method : 0.0075991153717041016 time for calcul the mask position with numpy : 0.02913665771484375 nb_pixel_total : 17304 time to create 1 rle with old method : 0.019652605056762695 time for calcul the mask position with numpy : 0.02960515022277832 nb_pixel_total : 45976 time to create 1 rle with old method : 0.05171370506286621 time for calcul the mask position with numpy : 0.030434131622314453 nb_pixel_total : 2100 time to create 1 rle with old method : 0.0024776458740234375 time for calcul the mask position with numpy : 0.02902698516845703 nb_pixel_total : 92111 time to create 1 rle with old method : 0.10303068161010742 time for calcul the mask position with numpy : 0.02964615821838379 nb_pixel_total : 192065 time to create 1 rle with new method : 0.5430476665496826 time for calcul the mask position with numpy : 0.02896571159362793 nb_pixel_total : 27241 time to create 1 rle with old method : 0.030818939208984375 time for calcul the mask position with numpy : 0.02949690818786621 nb_pixel_total : 113146 time to create 1 rle with old method : 0.12696242332458496 time for calcul the mask position with numpy : 0.029195547103881836 nb_pixel_total : 46018 time to create 1 rle with old method : 0.05170464515686035 time for calcul the mask position with numpy : 0.028875350952148438 nb_pixel_total : 28767 time to create 1 rle with old method : 0.03261208534240723 time for calcul the mask position with numpy : 0.02889871597290039 nb_pixel_total : 34040 time to create 1 rle with old method : 0.03808784484863281 time for calcul the mask position with numpy : 0.02774190902709961 nb_pixel_total : 9072 time to create 1 rle with old method : 0.010174751281738281 time for calcul the mask position with numpy : 0.032781362533569336 nb_pixel_total : 26138 time to create 1 rle with old method : 0.03018951416015625 time for calcul the mask position with numpy : 0.030240535736083984 nb_pixel_total : 23303 time to create 1 rle with old method : 0.02698063850402832 time for calcul the mask position with numpy : 0.029384851455688477 nb_pixel_total : 35800 time to create 1 rle with old method : 0.0404055118560791 time for calcul the mask position with numpy : 0.029124736785888672 nb_pixel_total : 3068 time to create 1 rle with old method : 0.003525972366333008 create new chi : 5.916539669036865 time to delete rle : 0.004711627960205078 batch 1 Loaded 121 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29924 TO DO : save crop sub photo not yet done ! save time : 2.2182023525238037 map_output_result : {1349219989: (0.0, 'Should be the crop_list due to order', 0), 1349219988: (0.0, 'Should be the crop_list due to order', 0), 1349219985: (0.0, 'Should be the crop_list due to order', 0), 1349219785: (0.0, 'Should be the crop_list due to order', 0), 1349219783: (0.0, 'Should be the crop_list due to order', 0), 1349219531: (0.0, 'Should be the crop_list due to order', 0), 1349219527: (0.0, 'Should be the crop_list due to order', 0), 1349219004: (0.0, 'Should be the crop_list due to order', 0), 1349218973: (0.0, 'Should be the crop_list due to order', 0), 1349218933: (0.0, 'Should be the crop_list due to order', 0), 1349218929: (0.0, 'Should be the crop_list due to order', 0), 1349218924: (0.0, 'Should be the crop_list due to order', 0), 1349218915: (0.0, 'Should be the crop_list due to order', 0), 1349218886: (0.0, 'Should be the crop_list due to order', 0), 1349218820: (0.0, 'Should be the crop_list due to order', 0), 1349218739: (0.0, 'Should be the crop_list due to order', 0), 1349218732: (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 [1349219989, 1349219988, 1349219985, 1349219785, 1349219783, 1349219531, 1349219527, 1349219004, 1349218973, 1349218933, 1349218929, 1349218924, 1349218915, 1349218886, 1349218820, 1349218739, 1349218732] Looping around the photos to save general results len do output : 17 /1349219989.Didn't retrieve data . /1349219988.Didn't retrieve data . /1349219985.Didn't retrieve data . /1349219785.Didn't retrieve data . /1349219783.Didn't retrieve data . /1349219531.Didn't retrieve data . /1349219527.Didn't retrieve data . /1349219004.Didn't retrieve data . /1349218973.Didn't retrieve data . /1349218933.Didn't retrieve data . /1349218929.Didn't retrieve data . /1349218924.Didn't retrieve data . /1349218915.Didn't retrieve data . /1349218886.Didn't retrieve data . /1349218820.Didn't retrieve data . /1349218739.Didn't retrieve data . /1349218732.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, '2712269') ('3318', '21941555', '1349219989', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219988', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219985', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219785', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219783', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219531', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219527', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219004', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218973', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218933', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218929', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218924', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218915', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218886', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218820', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218739', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218732', None, None, None, None, None, '2712269') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 51 time used for this insertion : 0.013826131820678711 save_final save missing photos in datou_result : time spend for datou_step_exec : 224.71960878372192 time spend to save output : 0.017093420028686523 total time spend for step 3 : 224.7367022037506 step4:ventilate_hashtags_in_portfolio Tue Apr 1 10:32:45 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 beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 21941555 get user id for portfolio 21941555 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag 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 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','background','autre','pet_clair','mal_croppe','flou','environnement','pet_fonce','metal','papier','pehd')) 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`=21941555 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','background','autre','pet_clair','mal_croppe','flou','environnement','pet_fonce','metal','papier','pehd')) 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`=21941555 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','background','autre','pet_clair','mal_croppe','flou','environnement','pet_fonce','metal','papier','pehd')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21942690,21942691,21942692,21942693,21942694,21942695,21942696,21942697,21942698,21942699,21942700?tags=carton,background,autre,pet_clair,mal_croppe,flou,environnement,pet_fonce,metal,papier,pehd Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349219989, 1349219988, 1349219985, 1349219785, 1349219783, 1349219531, 1349219527, 1349219004, 1349218973, 1349218933, 1349218929, 1349218924, 1349218915, 1349218886, 1349218820, 1349218739, 1349218732] Looping around the photos to save general results len do output : 1 /21941555. 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, '2712269') ('3318', '21941555', '1349219989', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219988', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219985', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219785', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219783', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219531', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219527', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219004', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218973', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218933', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218929', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218924', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218915', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218886', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218820', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218739', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218732', None, None, None, None, None, '2712269') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 18 time used for this insertion : 0.015180349349975586 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.665036678314209 time spend to save output : 0.015825748443603516 total time spend for step 4 : 1.6808624267578125 step5:final Tue Apr 1 10:32:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1349219989: ('0.19774587565145885',), 1349219988: ('0.19774587565145885',), 1349219985: ('0.19774587565145885',), 1349219785: ('0.19774587565145885',), 1349219783: ('0.19774587565145885',), 1349219531: ('0.19774587565145885',), 1349219527: ('0.19774587565145885',), 1349219004: ('0.19774587565145885',), 1349218973: ('0.19774587565145885',), 1349218933: ('0.19774587565145885',), 1349218929: ('0.19774587565145885',), 1349218924: ('0.19774587565145885',), 1349218915: ('0.19774587565145885',), 1349218886: ('0.19774587565145885',), 1349218820: ('0.19774587565145885',), 1349218739: ('0.19774587565145885',), 1349218732: ('0.19774587565145885',)} new output for save of step final : {1349219989: ('0.19774587565145885',), 1349219988: ('0.19774587565145885',), 1349219985: ('0.19774587565145885',), 1349219785: ('0.19774587565145885',), 1349219783: ('0.19774587565145885',), 1349219531: ('0.19774587565145885',), 1349219527: ('0.19774587565145885',), 1349219004: ('0.19774587565145885',), 1349218973: ('0.19774587565145885',), 1349218933: ('0.19774587565145885',), 1349218929: ('0.19774587565145885',), 1349218924: ('0.19774587565145885',), 1349218915: ('0.19774587565145885',), 1349218886: ('0.19774587565145885',), 1349218820: ('0.19774587565145885',), 1349218739: ('0.19774587565145885',), 1349218732: ('0.19774587565145885',)} [1349219989, 1349219988, 1349219985, 1349219785, 1349219783, 1349219531, 1349219527, 1349219004, 1349218973, 1349218933, 1349218929, 1349218924, 1349218915, 1349218886, 1349218820, 1349218739, 1349218732] Looping around the photos to save general results len do output : 17 /1349219989.Didn't retrieve data . /1349219988.Didn't retrieve data . /1349219985.Didn't retrieve data . /1349219785.Didn't retrieve data . /1349219783.Didn't retrieve data . /1349219531.Didn't retrieve data . /1349219527.Didn't retrieve data . /1349219004.Didn't retrieve data . /1349218973.Didn't retrieve data . /1349218933.Didn't retrieve data . /1349218929.Didn't retrieve data . /1349218924.Didn't retrieve data . /1349218915.Didn't retrieve data . /1349218886.Didn't retrieve data . /1349218820.Didn't retrieve data . /1349218739.Didn't retrieve data . /1349218732.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, '2712269') ('3318', '21941555', '1349219989', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219988', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219985', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219785', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219783', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219531', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219527', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219004', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218973', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218933', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218929', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218924', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218915', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218886', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218820', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218739', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218732', None, None, None, None, None, '2712269') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 51 time used for this insertion : 0.014129400253295898 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.390167474746704 time spend to save output : 0.014992952346801758 total time spend for step 5 : 1.4051604270935059 step6:blur_detection Tue Apr 1 10:32:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7.jpg resize: (2160, 3264) 1349219989 -5.139328427826677 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e.jpg resize: (2160, 3264) 1349219988 -5.826158409805016 treat image : temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65.jpg resize: (2160, 3264) 1349219985 -4.413498820247617 treat image : temp/1743495629_93323_1349219785_f407fa7fd4599fc30d75093f8c5b3e69.jpg resize: (2160, 3264) 1349219785 2.3806779203509256 treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414.jpg resize: (2160, 3264) 1349219783 -1.911092303342901 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874.jpg resize: (2160, 3264) 1349219531 -2.4830681719207215 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e.jpg resize: (2160, 3264) 1349219527 -4.0414988056915995 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748.jpg resize: (2160, 3264) 1349219004 0.812007579271673 treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5.jpg resize: (2160, 3264) 1349218973 -3.1200356680889927 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7.jpg resize: (2160, 3264) 1349218933 -1.5352989784954123 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735.jpg resize: (2160, 3264) 1349218929 -2.891520638798591 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df.jpg resize: (2160, 3264) 1349218924 -2.5321700621560366 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be.jpg resize: (2160, 3264) 1349218915 -2.4928050198574017 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b.jpg resize: (2160, 3264) 1349218886 -3.828055698925716 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f.jpg resize: (2160, 3264) 1349218820 -3.3362142649277313 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d.jpg resize: (2160, 3264) 1349218739 -3.2970589089355613 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282.jpg resize: (2160, 3264) 1349218732 -5.670823558719501 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541561_0.png resize: (178, 142) 1349245910 -2.156497301264845 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541555_0.png resize: (206, 329) 1349245911 -2.4909240088946896 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541547_0.png resize: (74, 120) 1349245912 3.274425221925938 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541539_0.png resize: (111, 142) 1349245913 -1.7441600489182398 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541531_0.png resize: (373, 168) 1349245914 -2.376944438690702 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541567_0.png resize: (84, 110) 1349245915 -1.0645300184177118 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541543_0.png resize: (284, 149) 1349245916 -3.25389586380753 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541574_0.png resize: (260, 199) 1349245917 -4.02335444542445 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541573_0.png resize: (175, 181) 1349245918 -1.811645441618046 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541570_0.png resize: (131, 142) 1349245919 -4.036523365566867 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541536_0.png resize: (106, 110) 1349245920 -3.357314019759537 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541522_0.png resize: (330, 487) 1349245921 -3.363297612741758 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541537_0.png resize: (185, 266) 1349245922 -3.2365177759230277 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541530_0.png resize: (100, 228) 1349245923 -3.5983337481879927 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541563_0.png resize: (238, 162) 1349245924 -4.206158491924122 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541520_0.png resize: (279, 238) 1349245925 -2.4174313627257544 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541532_0.png resize: (148, 175) 1349245926 -4.777421665221726 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541545_0.png resize: (246, 412) 1349245927 -1.9487528505595022 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541554_0.png resize: (225, 231) 1349245928 -2.536971173720089 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541527_0.png resize: (199, 199) 1349245929 -1.026603365326408 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541550_0.png resize: (128, 132) 1349245930 -2.489812709521863 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541526_0.png resize: (167, 244) 1349245931 -1.9620564961750622 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541528_0.png resize: (146, 141) 1349245932 -1.705554521124928 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541521_0.png resize: (87, 176) 1349245933 -1.716533063115675 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541569_0.png resize: (58, 97) 1349245934 -2.3748725786352054 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541556_0.png resize: (237, 270) 1349245935 -1.965340012628659 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541546_0.png resize: (671, 360) 1349245936 -2.4860366884728418 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541544_0.png resize: (246, 100) 1349245937 -2.3458510738178426 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541552_0.png resize: (86, 180) 1349245938 -2.86024347274961 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541523_0.png resize: (338, 247) 1349245939 -2.1600405518524566 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541571_0.png resize: (116, 127) 1349245940 -2.167482773594848 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541541_0.png resize: (137, 99) 1349245941 -2.187161479127424 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541525_0.png resize: (220, 96) 1349245942 -3.5081258497532004 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541518_0.png resize: (156, 120) 1349245943 -1.4325525267645651 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541524_0.png resize: (295, 163) 1349245944 -2.4588669668794423 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541533_0.png resize: (85, 81) 1349245945 -1.955207342207298 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541529_0.png resize: (234, 285) 1349245946 -2.470718316588228 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541572_0.png resize: (145, 127) 1349245947 -2.066698726697001 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541534_0.png resize: (271, 301) 1349245948 -3.6219823461380964 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541519_0.png resize: (106, 78) 1349245949 -2.5833944898627825 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541627_0.png resize: (224, 163) 1349245950 -2.458523824746907 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541626_0.png resize: (115, 154) 1349245951 -2.2349610912515576 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541582_0.png resize: (139, 156) 1349245952 -1.882396644755677 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541602_0.png resize: (123, 133) 1349245953 -1.4800723202551611 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541611_0.png resize: (137, 159) 1349245954 -2.287753148152746 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541625_0.png resize: (197, 208) 1349245955 -2.0708539102281223 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541580_0.png resize: (100, 77) 1349245956 -1.7741144370661022 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541624_0.png resize: (103, 81) 1349245957 -1.9215803375423848 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541620_0.png resize: (80, 187) 1349245958 -2.777393355213348 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541577_0.png resize: (341, 310) 1349245959 -2.922942390419356 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541596_0.png resize: (172, 273) 1349245960 -2.3674010143027506 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541603_0.png resize: (72, 129) 1349245961 -1.7018683495181495 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541595_0.png resize: (176, 175) 1349245962 -3.5598078259330928 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541606_0.png resize: (124, 112) 1349245963 -1.8492838038716473 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541575_0.png resize: (186, 200) 1349245964 -3.2518052964326527 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541592_0.png resize: (244, 210) 1349245965 -2.993475835033924 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541597_0.png resize: (73, 143) 1349245966 -2.1837142883879563 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541634_0.png resize: (155, 149) 1349245967 -2.4915904497601105 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541587_0.png resize: (106, 139) 1349245968 -2.6705163990895224 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541593_0.png resize: (120, 103) 1349245969 -2.370964709210703 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541583_0.png resize: (176, 240) 1349245970 -3.1085627433878935 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541630_0.png resize: (75, 108) 1349245971 -2.1409175544109376 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541610_0.png resize: (348, 335) 1349245972 -3.9474050278588226 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541609_0.png resize: (55, 99) 1349245973 -1.9780455491311877 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541605_0.png resize: (218, 219) 1349245974 -2.926653673749897 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541615_0.png resize: (69, 117) 1349245975 -3.9117136520237823 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541581_0.png resize: (146, 113) 1349245976 -2.490790298055538 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541614_0.png resize: (71, 113) 1349245977 -2.8196148238065755 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541612_0.png resize: (88, 81) 1349245978 -2.188042857246525 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541578_0.png resize: (118, 195) 1349245979 -3.1107892542276936 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541576_0.png resize: (185, 190) 1349245980 -3.7739482892007072 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541586_0.png resize: (195, 148) 1349245981 -2.458274131907948 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541588_0.png resize: (111, 187) 1349245982 -3.1245671825455057 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541594_0.png resize: (117, 162) 1349245983 -2.4552467504799655 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541589_0.png resize: (303, 211) 1349245984 -4.228822864062461 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541579_0.png resize: (123, 98) 1349245985 -3.3499043175405103 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541618_0.png resize: (165, 271) 1349245986 -3.956650970129014 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541591_0.png resize: (169, 120) 1349245987 -3.9669696059886546 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541623_0.png resize: (208, 260) 1349245988 -3.408383619378673 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541585_0.png resize: (264, 445) 1349245989 -5.42445147394506 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541601_0.png resize: (192, 238) 1349245990 -3.5684501915925093 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541631_0.png resize: (115, 118) 1349245991 -3.506912216084112 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541616_0.png resize: (135, 100) 1349245992 -3.854333937462642 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541622_0.png resize: (79, 102) 1349245993 -0.8442358490945301 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541600_0.png resize: (149, 227) 1349245994 -2.812622330118199 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541629_0.png resize: (95, 188) 1349245995 -2.0351134588591546 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541584_0.png resize: (162, 302) 1349245996 -3.6951884230740206 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541590_0.png resize: (258, 187) 1349245997 -3.854517877448447 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541633_0.png resize: (59, 57) 1349245998 -3.035959449744031 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541621_0.png resize: (222, 380) 1349245999 -3.192511290780902 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541604_0.png resize: (125, 92) 1349246000 -2.6828783645902625 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541599_0.png resize: (191, 191) 1349246001 -2.0378432180055133 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541628_0.png resize: (296, 196) 1349246002 -3.3444174262856143 treat image : 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temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65_rle_crop_3742541646_0.png resize: (94, 116) 1349246021 -3.2502301371090807 treat image : temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65_rle_crop_3742541644_0.png resize: (287, 307) 1349246022 -3.9962547424536203 treat image : temp/1743495629_93323_1349219785_f407fa7fd4599fc30d75093f8c5b3e69_rle_crop_3742541664_0.png resize: (75, 220) 1349246023 -1.412723597579713 treat image : temp/1743495629_93323_1349219785_f407fa7fd4599fc30d75093f8c5b3e69_rle_crop_3742541661_0.png resize: (181, 158) 1349246024 -2.206851206719564 treat image : temp/1743495629_93323_1349219785_f407fa7fd4599fc30d75093f8c5b3e69_rle_crop_3742541660_0.png resize: (298, 280) 1349246025 -0.014898507644460959 treat image : temp/1743495629_93323_1349219785_f407fa7fd4599fc30d75093f8c5b3e69_rle_crop_3742541668_0.png resize: (82, 272) 1349246026 -0.2685936770071078 treat image : 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temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541677_0.png resize: (460, 185) 1349246033 -1.8326802585544153 treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541678_0.png resize: (99, 272) 1349246034 -0.8457405130365326 treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541674_0.png resize: (261, 306) 1349246035 -0.2986098296788703 treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541683_0.png resize: (159, 120) 1349246036 -1.4176197299596125 treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541675_0.png resize: (126, 216) 1349246037 -1.986564719544985 treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541676_0.png resize: (168, 151) 1349246038 -1.491663805645441 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541685_0.png resize: (335, 309) 1349246039 -1.5231341396184903 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541686_0.png resize: (125, 267) 1349246040 -2.0141831632424085 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541684_0.png resize: (191, 197) 1349246041 -1.2574015926795516 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541690_0.png resize: (222, 190) 1349246042 -1.9630848456469296 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541693_0.png resize: (196, 134) 1349246043 -2.003667745262174 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541691_0.png resize: (436, 366) 1349246044 -1.1704080744219187 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541694_0.png resize: (370, 574) 1349246045 -1.5830319155730384 treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541692_0.png resize: (130, 110) 1349246046 -2.461107892669882 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541697_0.png resize: (182, 214) 1349246047 -1.2987382157627723 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541696_0.png resize: (255, 145) 1349246048 -2.498181498117098 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541704_0.png resize: (87, 102) 1349246049 -0.953574320999712 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541707_0.png resize: (121, 69) 1349246050 -1.993623991727326 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541717_0.png resize: (92, 131) 1349246051 -2.7752052800801477 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541705_0.png resize: (917, 803) 1349246052 -2.912551062564817 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541715_0.png resize: (154, 202) 1349246053 -2.2318167321443934 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541709_0.png resize: (73, 70) 1349246054 -2.7764044464953352 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541718_0.png resize: (271, 295) 1349246055 -2.36053285776816 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541708_0.png resize: (232, 159) 1349246056 -1.9809989520483529 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541713_0.png resize: (122, 73) 1349246057 -0.5156724887057039 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541703_0.png resize: (81, 96) 1349246058 -2.1198263274265954 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541714_0.png resize: (155, 282) 1349246059 -2.4644006102808067 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541716_0.png resize: (189, 193) 1349246060 -3.393259393351853 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541706_0.png resize: (126, 135) 1349246061 -1.867185527736602 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541711_0.png resize: (229, 98) 1349246062 -0.8915263709228782 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541698_0.png resize: (128, 237) 1349246063 -1.4404611977691018 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541712_0.png resize: (154, 131) 1349246064 -2.342001951702785 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541710_0.png resize: (208, 212) 1349246065 -2.3469938088971096 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541737_0.png resize: (80, 99) 1349246066 -1.1563535120017834 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541731_0.png resize: (314, 122) 1349246067 -1.2639519496484548 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541722_0.png resize: (232, 368) 1349246068 -1.0615944871009746 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541723_0.png resize: (107, 177) 1349246069 -1.9317107737126182 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541728_0.png resize: (538, 652) 1349246070 -0.6594139408625914 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541729_0.png resize: (182, 190) 1349246071 -1.926585332247571 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541727_0.png resize: (135, 168) 1349246072 -1.4249894954005144 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541733_0.png resize: (128, 143) 1349246073 -0.29289176911005543 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541736_0.png resize: (183, 197) 1349246074 2.2357609924240416 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541721_0.png resize: (516, 510) 1349246075 1.039536000551581 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541726_0.png resize: (151, 154) 1349246076 0.2684132842914802 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541740_0.png resize: (69, 124) 1349246077 -0.9433870287812078 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541724_0.png resize: (127, 152) 1349246078 -1.4421248027067872 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541741_0.png resize: (189, 219) 1349246079 -2.2877960836612417 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541725_0.png resize: (141, 177) 1349246080 -0.7475231738099857 treat image : 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temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541785_0.png resize: (279, 198) 1349246105 -2.1689994231139313 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541783_0.png resize: (77, 226) 1349246106 -2.9317724360744855 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541782_0.png resize: (140, 118) 1349246107 -0.7232070785041727 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541773_0.png resize: (186, 155) 1349246108 -2.4189039370583654 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541779_0.png resize: (134, 160) 1349246109 -1.9942769321887943 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541790_0.png resize: (87, 188) 1349246110 -1.3983148651994373 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541791_0.png resize: (85, 119) 1349246111 -0.6284105490094702 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541776_0.png resize: (154, 102) 1349246112 -1.0831962584163009 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541777_0.png resize: (247, 142) 1349246113 -1.5410715173394804 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541769_0.png resize: (186, 112) 1349246114 -0.522887654308136 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541772_0.png resize: (183, 131) 1349246115 -1.1279540361966145 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541786_0.png resize: (130, 85) 1349246116 -0.9365316669813927 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541780_0.png resize: (217, 114) 1349246117 -2.6880508202324935 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541774_0.png resize: (244, 258) 1349246118 -1.013382908255123 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541781_0.png resize: (276, 117) 1349246119 0.2337549426908108 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541794_0.png resize: (701, 858) 1349246120 -1.8904065618182542 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541797_0.png resize: (531, 675) 1349246121 -1.5963500822875816 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541805_0.png resize: (261, 752) 1349246122 -1.6366123795785488 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541795_0.png resize: (225, 326) 1349246123 -3.7120612577097303 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541814_0.png resize: (259, 216) 1349246124 -2.4503548652447553 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541815_0.png resize: (1342, 1208) 1349246125 -1.5587811683504156 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541809_0.png resize: (680, 611) 1349246126 -0.44979325670576553 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541803_0.png resize: (277, 481) 1349246127 -2.497592012957808 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541812_0.png resize: (627, 347) 1349246128 -1.5819069606157066 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541799_0.png resize: (145, 103) 1349246130 -1.546411650109706 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541806_0.png resize: (173, 474) 1349246131 -0.7442142355424277 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541802_0.png resize: (409, 369) 1349246132 0.22788690182334323 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541796_0.png resize: (329, 187) 1349246133 -1.9721184342196565 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541804_0.png resize: (228, 483) 1349246134 -2.6794663381382287 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541826_0.png resize: (102, 182) 1349246135 -1.5204525055326663 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541821_0.png resize: (137, 182) 1349246136 -1.9116023804582716 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541823_0.png resize: (352, 289) 1349246137 -1.7072499841620594 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541827_0.png resize: (162, 103) 1349246138 -1.4226660079476305 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541825_0.png resize: (549, 909) 1349246139 -2.2474773342582886 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541819_0.png resize: (214, 334) 1349246140 -2.029237540729017 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541830_0.png resize: (69, 174) 1349246141 -4.272840811103556 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541829_0.png resize: (233, 196) 1349246142 -0.4627916543932648 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541828_0.png resize: (444, 336) 1349246143 -1.4559054180029265 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541837_0.png resize: (338, 72) 1349246144 -1.3380296126956963 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541853_0.png resize: (574, 85) 1349246145 -2.3656423245765796 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541835_0.png resize: (374, 405) 1349246146 -1.485509386472889 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541843_0.png resize: (933, 1123) 1349246147 -1.2960696086760277 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541839_0.png resize: (250, 412) 1349246148 -2.371488064864274 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541860_0.png resize: (115, 62) 1349246149 -0.4110532678184791 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541858_0.png resize: (164, 294) 1349246150 -2.0387732300643533 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541850_0.png resize: (215, 325) 1349246151 -1.550173891198031 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541847_0.png resize: (473, 453) 1349246152 -3.3305283107955566 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541857_0.png resize: (345, 417) 1349246153 -3.1266380744426594 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541855_0.png resize: (111, 229) 1349246154 -2.955492568129053 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541854_0.png resize: (224, 301) 1349246155 -0.06277621579333603 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541842_0.png resize: (785, 367) 1349246156 -2.3449287378024812 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541834_0.png resize: (392, 152) 1349246157 -1.476090521989185 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541840_0.png resize: (403, 547) 1349246158 -1.3814717731305957 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541833_0.png resize: (489, 837) 1349246159 -3.2177270833665164 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541841_0.png resize: (254, 365) 1349246160 -2.0470747905510307 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541862_0.png resize: (185, 102) 1349246161 -1.4201870970738393 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541872_0.png resize: (167, 217) 1349246162 -2.6519996868137428 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541866_0.png resize: (246, 551) 1349246163 -3.6133295415718334 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541867_0.png resize: (267, 203) 1349246164 -1.9014213063352927 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541874_0.png resize: (192, 239) 1349246165 -1.6727156708873765 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541879_0.png resize: (76, 106) 1349246166 -3.252621262360843 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541875_0.png resize: (208, 352) 1349246167 -3.019707694955279 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541881_0.png resize: (145, 163) 1349246168 -2.778588882925981 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541883_0.png resize: (171, 121) 1349246169 -4.256405334217223 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541863_0.png resize: (227, 272) 1349246170 -3.986114204706439 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541865_0.png resize: (597, 616) 1349246171 -3.6385690661214247 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541868_0.png resize: (238, 286) 1349246172 -2.8047141136264564 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541876_0.png resize: (110, 111) 1349246173 -1.326852039466341 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541871_0.png resize: (1500, 1144) 1349246174 -2.220449896423522 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541870_0.png resize: (198, 393) 1349246175 -2.8597455735035253 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541882_0.png resize: (365, 1111) 1349246176 -1.5949617756396164 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541873_0.png resize: (343, 425) 1349246177 -1.88242054724414 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541861_0.png resize: (528, 795) 1349246178 -2.960690228057831 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541880_0.png resize: (332, 188) 1349246179 -2.3854830249799086 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541913_0.png resize: (209, 187) 1349246180 -2.4928835982634157 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541914_0.png resize: (212, 196) 1349246181 -2.1656590918487875 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541895_0.png resize: (302, 147) 1349246182 -2.4735095987158346 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541902_0.png resize: (198, 162) 1349246183 -2.5840009646583995 treat image : 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temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541905_0.png resize: (121, 99) 1349246190 -4.1961295506688145 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541929_0.png resize: (187, 202) 1349246191 -2.8629632491029655 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541892_0.png resize: (270, 344) 1349246192 -2.4757889459607774 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541916_0.png resize: (137, 206) 1349246193 -3.630974180505571 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541925_0.png resize: (139, 138) 1349246194 -1.9036784282429677 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541889_0.png resize: (74, 181) 1349246195 -2.4335494518870577 treat image : 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temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541936_0.png resize: (196, 175) 1349246214 -1.6111319499813532 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541958_0.png resize: (285, 174) 1349246215 -2.0579420618411457 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541937_0.png resize: (194, 207) 1349246216 -1.6532755469991065 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541940_0.png resize: (203, 98) 1349246217 0.03582517644880592 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541932_0.png resize: (171, 152) 1349246218 -2.2265973105372754 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541944_0.png resize: (274, 295) 1349246219 -2.7276855831904467 treat image : 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temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541954_0.png resize: (137, 52) 1349246226 2.905361551364318 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541941_0.png resize: (212, 136) 1349246227 -2.5110510679492375 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541952_0.png resize: (127, 166) 1349246228 -2.369115495388004 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541949_0.png resize: (295, 224) 1349246229 -1.7113726537892304 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541938_0.png resize: (196, 257) 1349246230 -1.5821275364244403 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541939_0.png resize: (192, 151) 1349246231 -2.2236546630878182 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541930_0.png resize: (149, 105) 1349246232 -1.6854132281421892 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541935_0.png resize: (132, 200) 1349246233 0.422734846538956 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541950_0.png resize: (305, 352) 1349246234 -2.747257544355917 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541956_0.png resize: (101, 90) 1349246235 -0.22653036765540832 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541946_0.png resize: (231, 189) 1349246236 -1.3529211019007419 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541934_0.png resize: (71, 187) 1349246237 -2.574961457731472 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541957_0.png resize: (1105, 989) 1349246238 -2.829514793736472 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542003_0.png resize: (434, 421) 1349246239 -2.3492739080176626 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541990_0.png resize: (92, 81) 1349246240 -0.7760278839313233 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541998_0.png resize: (232, 185) 1349246241 -3.3424083508836384 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541975_0.png resize: (200, 215) 1349246242 -2.6399989916891795 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541997_0.png resize: (119, 169) 1349246243 -2.07074097102859 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542012_0.png resize: (249, 93) 1349246244 -2.504969101289747 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541995_0.png resize: (125, 165) 1349246245 -1.4675655876017841 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541994_0.png resize: (157, 162) 1349246246 -1.9859863161928069 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542004_0.png resize: (273, 225) 1349246247 -3.8124256527172773 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542011_0.png resize: (186, 152) 1349246248 -2.0531374577232144 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541982_0.png resize: (269, 233) 1349246249 -3.230123529676305 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541985_0.png resize: (173, 104) 1349246250 -2.4085478472115787 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541976_0.png resize: (445, 192) 1349246251 -3.914443696222647 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542010_0.png resize: (250, 343) 1349246253 -3.152320094414123 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541966_0.png resize: (211, 226) 1349246254 -4.304158182873052 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541984_0.png resize: (149, 184) 1349246255 -3.0386055403518646 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542013_0.png resize: (290, 89) 1349246256 -3.0810365159791995 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542020_0.png resize: (118, 138) 1349246257 -2.52697286867905 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542002_0.png resize: (130, 160) 1349246258 -3.4069917874898175 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541969_0.png resize: (169, 354) 1349246259 -3.2857757602165503 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541988_0.png resize: (128, 118) 1349246260 -2.5271741349530172 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541971_0.png resize: (361, 217) 1349246261 -2.929220095803557 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541963_0.png resize: (405, 489) 1349246262 -4.176704097937201 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541968_0.png resize: (221, 272) 1349246263 -4.289242671198611 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541967_0.png resize: (217, 215) 1349246264 -3.738766676225976 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541991_0.png resize: (111, 159) 1349246265 -3.1159675262172417 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541977_0.png resize: (173, 274) 1349246266 -2.3133481588804963 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541974_0.png resize: (307, 128) 1349246267 -3.1998642707385017 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541987_0.png resize: (222, 455) 1349246268 -4.056445581218611 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541973_0.png resize: (597, 352) 1349246269 -3.196846273163531 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542017_0.png resize: (67, 181) 1349246270 -1.2528861765591455 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541983_0.png resize: (218, 232) 1349246271 -4.016405149039842 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541986_0.png resize: (182, 260) 1349246272 -3.6592707082901788 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541992_0.png resize: (236, 231) 1349246273 -2.2022924092682636 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541978_0.png resize: (507, 798) 1349246274 -3.504793346996055 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541964_0.png resize: (518, 293) 1349246275 -4.77470606035873 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542009_0.png resize: (149, 284) 1349246276 -2.400829670773715 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542006_0.png resize: (184, 270) 1349246277 -3.1202435657916414 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541981_0.png resize: (217, 129) 1349246278 -3.7841539994095754 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541972_0.png resize: (203, 397) 1349246279 -1.9238874646369766 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542008_0.png resize: (168, 161) 1349246280 -2.976295004598651 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542018_0.png resize: (62, 58) 1349246281 4.879539105283935 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542007_0.png resize: (171, 149) 1349246282 -3.7065015371475627 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541970_0.png resize: (202, 217) 1349246283 -2.9218319790067047 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541989_0.png resize: (157, 86) 1349246284 -2.885879388190716 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542016_0.png resize: (153, 161) 1349246285 -2.0605294611498195 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541961_0.png resize: (133, 151) 1349246286 -2.992514273608884 treat image : 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temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541558_0.png resize: (98, 136) 1349246317 -1.1392389288165574 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541551_0.png resize: (153, 246) 1349246318 -2.868552794611735 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541619_0.png resize: (94, 145) 1349246319 -0.06856862037482737 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541598_0.png resize: (171, 153) 1349246320 -1.146204641324191 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541608_0.png resize: (94, 150) 1349246321 -1.9153534830787362 treat image : temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65_rle_crop_3742541647_0.png resize: (556, 564) 1349246322 -1.4697547375699178 treat image : 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temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541702_0.png resize: (836, 1134) 1349246329 -2.1405812692369524 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541701_0.png resize: (303, 260) 1349246330 -2.4334462676967594 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541730_0.png resize: (191, 312) 1349246331 -1.8163458443791953 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541732_0.png resize: (138, 235) 1349246332 -1.0036970214766325 treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5_rle_crop_3742541752_0.png resize: (210, 301) 1349246333 -1.1524086107953266 treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5_rle_crop_3742541768_0.png resize: (124, 194) 1349246334 0.2007944694165254 treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5_rle_crop_3742541762_0.png resize: (149, 157) 1349246335 -0.6403158263099596 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541793_0.png resize: (208, 114) 1349246336 -1.183651198645468 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541792_0.png resize: (1123, 957) 1349246337 -0.9522001245902526 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541788_0.png resize: (104, 206) 1349246338 -2.4224186977542748 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541798_0.png resize: (316, 331) 1349246339 -2.1959245096204953 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541810_0.png resize: (99, 104) 1349246340 -4.103957190091282 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541813_0.png resize: (170, 481) 1349246341 -0.7041863148085237 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541811_0.png resize: (974, 503) 1349246342 -3.5316374012262113 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541820_0.png resize: (403, 717) 1349246343 -1.2668530012586348 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541831_0.png resize: (213, 165) 1349246344 -2.3961263865614826 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541817_0.png resize: (469, 763) 1349246345 -2.693088322360233 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541818_0.png resize: (342, 297) 1349246346 -1.8695874905436325 treat image : temp/1743495629_93323_1349218924_be8bb5142c09ede4e47c1ec2f018f0df_rle_crop_3742541824_0.png resize: (212, 289) 1349246347 -2.0482454005355697 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541852_0.png resize: (850, 1448) 1349246348 -1.1658239798257204 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541849_0.png resize: (91, 191) 1349246349 -2.951487530640902 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541845_0.png resize: (564, 290) 1349246350 -1.1185462864084306 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541832_0.png resize: (326, 204) 1349246351 -2.321556380583291 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541838_0.png resize: (189, 366) 1349246352 -1.868197264758265 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541844_0.png resize: (280, 250) 1349246353 -3.503472954106349 treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541836_0.png resize: (259, 162) 1349246354 -2.3296655299298417 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541869_0.png resize: (311, 552) 1349246355 -2.932895998292697 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541878_0.png resize: (202, 136) 1349246356 -2.654633952910931 treat image : temp/1743495629_93323_1349218886_8af5703b782872def93a75b729409f2b_rle_crop_3742541864_0.png resize: (322, 181) 1349246357 -3.2852887900734316 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541921_0.png resize: (344, 182) 1349246358 -0.4110039625997957 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541901_0.png resize: (179, 220) 1349246359 -3.3019511935267056 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541896_0.png resize: (855, 787) 1349246360 -1.6458476939259234 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541915_0.png resize: (217, 114) 1349246361 -1.796643253728269 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541909_0.png resize: (119, 226) 1349246362 0.42530182199462535 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541886_0.png resize: (70, 284) 1349246363 -2.6194970173443446 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541906_0.png resize: (66, 152) 1349246364 -1.6711331906545965 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541891_0.png resize: (119, 240) 1349246365 -2.2732595175432864 treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541917_0.png resize: (176, 151) 1349246366 -2.4104262161915937 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541942_0.png resize: (157, 201) 1349246367 -1.62413032042355 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541943_0.png resize: (380, 326) 1349246368 -2.176462881402792 treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541959_0.png resize: (202, 361) 1349246369 -1.7885543753010191 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542005_0.png resize: (125, 73) 1349246370 -1.021998905620262 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541980_0.png resize: (469, 300) 1349246371 -3.153590729559391 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541996_0.png resize: (122, 190) 1349246372 -3.635720366569251 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541965_0.png resize: (164, 171) 1349246373 -3.46341428823209 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541979_0.png resize: (126, 99) 1349246374 -0.9472114446872549 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542000_0.png resize: (159, 130) 1349246375 -3.2859559559607106 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542019_0.png resize: (98, 59) 1349246376 -3.1513880626038935 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541962_0.png resize: (209, 215) 1349246377 -2.7243822876047066 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541993_0.png resize: (209, 197) 1349246378 -3.1905258353193653 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541553_0.png resize: (94, 107) 1349246381 -3.7187795717300176 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541568_0.png resize: (118, 136) 1349246382 -4.04391265017946 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541559_0.png resize: (121, 201) 1349246383 -4.001230859746959 treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5_rle_crop_3742541757_0.png resize: (223, 177) 1349246384 -2.175543026434979 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541566_0.png resize: (249, 354) 1349246400 -2.915191295591092 treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541535_0.png resize: (297, 241) 1349246401 -4.1124896885556605 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541632_0.png resize: (289, 202) 1349246402 -4.291025594597892 treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541607_0.png resize: (251, 238) 1349246403 -4.1393239359639695 treat image : temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65_rle_crop_3742541638_0.png resize: (523, 266) 1349246404 -3.784496840036045 treat image : temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65_rle_crop_3742541645_0.png resize: (240, 169) 1349246405 -2.678131115096403 treat image : temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65_rle_crop_3742541643_0.png resize: (301, 453) 1349246406 -2.942893964442655 treat image : temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65_rle_crop_3742541648_0.png resize: (428, 192) 1349246407 -3.7741546489391156 treat image : temp/1743495629_93323_1349219785_f407fa7fd4599fc30d75093f8c5b3e69_rle_crop_3742541669_0.png resize: (109, 253) 1349246408 -0.8818692507396668 treat image : temp/1743495629_93323_1349219785_f407fa7fd4599fc30d75093f8c5b3e69_rle_crop_3742541666_0.png resize: (191, 196) 1349246409 -0.9470490780494746 treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541680_0.png resize: (151, 264) 1349246410 -4.175491464012145 treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541679_0.png resize: (231, 444) 1349246411 0.03785213078126907 treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541699_0.png resize: (454, 308) 1349246412 -4.819227159477854 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541738_0.png resize: (536, 538) 1349246413 -1.3332939948600502 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541735_0.png resize: (346, 298) 1349246414 -0.6681835267662086 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541739_0.png resize: (464, 658) 1349246415 -1.449297347679907 treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748_rle_crop_3742541734_0.png resize: (215, 111) 1349246416 -2.6681483118231757 treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5_rle_crop_3742541759_0.png resize: (745, 457) 1349246417 -2.3755984260237346 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541784_0.png resize: (217, 224) 1349246418 -2.5142016772718496 treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541771_0.png resize: (304, 394) 1349246419 -3.0801093984312042 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541808_0.png resize: (347, 262) 1349246420 -2.103521475625141 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541800_0.png resize: (125, 230) 1349246421 -1.9531312948272093 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541801_0.png resize: (331, 347) 1349246422 -2.0592742165630646 treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541816_0.png resize: (282, 712) 1349246423 -2.6551212647274984 treat image : 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temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542001_0.png resize: (189, 211) 1349246473 -4.002344882530051 treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541999_0.png resize: (103, 193) 1349246474 -1.6464398693065774 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 : 520 time used for this insertion : 0.04499220848083496 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 520 time used for this insertion : 0.09195089340209961 save missing photos in datou_result : time spend for datou_step_exec : 73.545254945755 time spend to save output : 0.14499211311340332 total time spend for step 6 : 73.69024705886841 step7:brightness Tue Apr 1 10:34:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7.jpg treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e.jpg treat image : temp/1743495629_93323_1349219985_0f1e15a747fb87634b772a76c9be5d65.jpg treat image : temp/1743495629_93323_1349219785_f407fa7fd4599fc30d75093f8c5b3e69.jpg treat image : temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414.jpg treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874.jpg treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e.jpg treat image : temp/1743495629_93323_1349219004_f670ca6b569e5c86f2113d8599f40748.jpg treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5.jpg treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7.jpg treat image : 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temp/1743495629_93323_1349219783_58de6ce5d3b2f6a424d6b52b70a2c414_rle_crop_3742541682_0.png treat image : temp/1743495629_93323_1349219531_51907e51ad022cd4b1ed705d3f7c4874_rle_crop_3742541688_0.png treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541700_0.png treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541720_0.png treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5_rle_crop_3742541763_0.png treat image : temp/1743495629_93323_1349218929_c21ba0d13daabba6ba15c1458137a735_rle_crop_3742541807_0.png treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541898_0.png treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541947_0.png treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541945_0.png treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542014_0.png treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541565_0.png treat image : temp/1743495629_93323_1349219527_10e1db93b358037f9f23370dd1a27d4e_rle_crop_3742541719_0.png treat image : temp/1743495629_93323_1349218933_b2d97d026fc6dedbf3129eefa93b90a7_rle_crop_3742541789_0.png treat image : temp/1743495629_93323_1349218915_ad18ad0486ab7e07555efc563cc2b8be_rle_crop_3742541859_0.png treat image : temp/1743495629_93323_1349218820_2aca334c4d64772e5db537039c98798f_rle_crop_3742541920_0.png treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541557_0.png treat image : temp/1743495629_93323_1349219989_7e64626a6bf436b346404fe1759611c7_rle_crop_3742541560_0.png treat image : temp/1743495629_93323_1349219988_f71d04f179f58f063c7535ca8e87d13e_rle_crop_3742541613_0.png treat image : temp/1743495629_93323_1349218973_ee024e9b8a46d4fe13ed28b5e0ef41e5_rle_crop_3742541766_0.png treat image : temp/1743495629_93323_1349218739_121abc63d1d62ac68e87268449610d3d_rle_crop_3742541960_0.png treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742542001_0.png treat image : temp/1743495629_93323_1349218732_f435bfa199f8745c80df2f61b075a282_rle_crop_3742541999_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 : 520 time used for this insertion : 0.32700467109680176 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 520 time used for this insertion : 0.25958704948425293 save missing photos in datou_result : time spend for datou_step_exec : 18.084086656570435 time spend to save output : 0.5932869911193848 total time spend for step 7 : 18.67737364768982 step8:velours_tree Tue Apr 1 10:34:21 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 0.3641037940979004 time spend to save output : 3.7670135498046875e-05 total time spend for step 8 : 0.36414146423339844 step9:send_mail_cod Tue Apr 1 10:34:21 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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_P21941555_01-04-2025_10_34_21.pdf 21942690 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219426901743496461 21942691 imagette219426911743496463 21942692 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219426921743496463 21942693 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219426931743496463 21942694 imagette219426941743496465 21942695 imagette219426951743496465 21942697 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219426971743496465 21942698 change filename to text .change filename to text .change filename to text .change filename to text .imagette219426981743496465 21942699 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219426991743496466 21942700 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219427001743496467 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21941555 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21942690,21942691,21942692,21942693,21942694,21942695,21942696,21942697,21942698,21942699,21942700?tags=carton,background,autre,pet_clair,mal_croppe,flou,environnement,pet_fonce,metal,papier,pehd args[1349219989] : ((1349219989, -5.139328427826677, 492609224), (1349219989, -0.125697662438834, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349219988] : ((1349219988, -5.826158409805016, 492609224), (1349219988, -0.11754198543800415, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349219985] : ((1349219985, -4.413498820247617, 492609224), (1349219985, -0.290045348737254, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349219785] : ((1349219785, 2.3806779203509256, 492688767), (1349219785, -0.14018736045682806, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349219783] : ((1349219783, -1.911092303342901, 492688767), (1349219783, -0.14573666876445082, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349219531] : ((1349219531, -2.4830681719207215, 492609224), (1349219531, -0.062941457255489, 2107752395), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349219527] : ((1349219527, -4.0414988056915995, 492609224), (1349219527, -0.47230976453641665, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349219004] : ((1349219004, 0.812007579271673, 492688767), (1349219004, -0.28241229357392017, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218973] : ((1349218973, -3.1200356680889927, 492609224), (1349218973, -0.3449086124900077, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218933] : ((1349218933, -1.5352989784954123, 492688767), (1349218933, -0.12997951681992853, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218929] : ((1349218929, -2.891520638798591, 492609224), (1349218929, -0.24985694334198585, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218924] : ((1349218924, -2.5321700621560366, 492609224), (1349218924, -0.44294604480409683, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218915] : ((1349218915, -2.4928050198574017, 492609224), (1349218915, -0.13289542574243907, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218886] : ((1349218886, -3.828055698925716, 492609224), (1349218886, -0.2479120408025445, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218820] : ((1349218820, -3.3362142649277313, 492609224), (1349218820, -0.24611842971777234, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218739] : ((1349218739, -3.2970589089355613, 492609224), (1349218739, -0.1457371933989393, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com args[1349218732] : ((1349218732, -5.670823558719501, 492609224), (1349218732, -0.13268816942532602, 496442774), '0.19774587565145885') We are sending mail with results at report@fotonower.com refus_total : 0.19774587565145885 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=21941555 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349218886,1349218915,1349218924,1349218820,1349218732,1349218973,1349219989,1349219988,1349219985,1349219785,1349219783,1349219531,1349219527,1349219004,1349218933,1349218929,1349218739) Found this number of photos: 17 begin to download photo : 1349218886 begin to download photo : 1349218732 begin to download photo : 1349219985 begin to download photo : 1349219527 begin to download photo : 1349218739 download finish for photo 1349218739 download finish for photo 1349218886 begin to download photo : 1349218915 download finish for photo 1349219527 begin to download photo : 1349219004 download finish for photo 1349218732 begin to download photo : 1349218973 download finish for photo 1349219985 begin to download photo : 1349219785 download finish for photo 1349219785 begin to download photo : 1349219783 download finish for photo 1349219004 begin to download photo : 1349218933 download finish for photo 1349218915 begin to download photo : 1349218924 download finish for photo 1349218973 begin to download photo : 1349219989 download finish for photo 1349219783 begin to download photo : 1349219531 download finish for photo 1349218924 begin to download photo : 1349218820 download finish for photo 1349219989 begin to download photo : 1349219988 download finish for photo 1349218933 begin to download photo : 1349218929 download finish for photo 1349219531 download finish for photo 1349218820 download finish for photo 1349218929 download finish for photo 1349219988 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21941555_01-04-2025_10_34_21.pdf results_Auto_P21941555_01-04-2025_10_34_21.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21941555_01-04-2025_10_34_21.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','21941555','results_Auto_P21941555_01-04-2025_10_34_21.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21941555_01-04-2025_10_34_21.pdf','pdf','','1.18','0.19774587565145885') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21941555

https://www.fotonower.com/image?json=false&list_photos_id=1349219989
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349219988
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349219985
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349219785
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.3806779203509256)
https://www.fotonower.com/image?json=false&list_photos_id=1349219783
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349219531
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349219527
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349219004
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218973
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218933
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218929
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218924
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218915
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218886
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218820
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218739
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349218732
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 19.77%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/21942690?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21942692?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21942693?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21942697?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21942698?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/21942699?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/21942700?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21941555_01-04-2025_10_34_21.pdf.

Lien vers velours :https://www.fotonower.com/velours/21942690,21942691,21942692,21942693,21942694,21942695,21942696,21942697,21942698,21942699,21942700?tags=carton,background,autre,pet_clair,mal_croppe,flou,environnement,pet_fonce,metal,papier,pehd.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 01 Apr 2025 08:34:33 GMT Content-Length: 0 Connection: close X-Message-Id: SFWlWZZPSmmtcVqdwl36Sg 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 [1349219989, 1349219988, 1349219985, 1349219785, 1349219783, 1349219531, 1349219527, 1349219004, 1349218973, 1349218933, 1349218929, 1349218924, 1349218915, 1349218886, 1349218820, 1349218739, 1349218732] 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, '2712269') ('3318', '21941555', '1349219989', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219988', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219985', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219785', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219783', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219531', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219527', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219004', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218973', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218933', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218929', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218924', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218915', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218886', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218820', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218739', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218732', None, None, None, None, None, '2712269') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 17 time used for this insertion : 0.015190839767456055 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.437432050704956 time spend to save output : 0.01540684700012207 total time spend for step 9 : 11.452838897705078 step10:split_time_score Tue Apr 1 10:34:33 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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'}] (('07', 17),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 01042025 21941555 Nombre de photos uploadées : 17 / 23040 (0%) 01042025 21941555 Nombre de photos taguées (types de déchets): 0 / 17 (0%) 01042025 21941555 Nombre de photos taguées (volume) : 0 / 17 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 3.814697265625e-06 ????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0005002021789550781 elapsed_time : insert_dashboard_record_day_entry 0.6014134883880615 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 : 0.0654170001969457 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21942440_01-04-2025_10_14_41.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21942440 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number 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`=21942440 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'01042025': {'nb_upload': 17, '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 [1349219989, 1349219988, 1349219985, 1349219785, 1349219783, 1349219531, 1349219527, 1349219004, 1349218973, 1349218933, 1349218929, 1349218924, 1349218915, 1349218886, 1349218820, 1349218739, 1349218732] Looping around the photos to save general results len do output : 1 /21941555Didn'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, '2712269') ('3318', '21941555', '1349219989', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219988', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219985', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219785', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219783', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219531', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219527', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349219004', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218973', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218933', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218929', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218924', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218915', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218886', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218820', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218739', None, None, None, None, None, '2712269') ('3318', None, None, None, None, None, None, None, '2712269') ('3318', '21941555', '1349218732', None, None, None, None, None, '2712269') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 18 time used for this insertion : 0.014400720596313477 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.8836970329284668 time spend to save output : 0.014697074890136719 total time spend for step 10 : 0.8983941078186035 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 17 set_done_treatment 396.56user 256.78system 14:08.65elapsed 76%CPU (0avgtext+0avgdata 9991560maxresident)k 7147688inputs+292504outputs (269928major+38241333minor)pagefaults 0swaps