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 : 2513697 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 : ['2535904'] with mtr_portfolio_ids : ['20128154'] and first list_photo_ids : [] new path : /proc/2513697/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 23 ; length of list_pids : 23 ; length of list_args : 23 time to download the photos : 4.831831216812134 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 Sat Feb 1 01:30:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10998 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-01 01:30:35.566371: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-01 01:30:35.599497: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493020000 Hz 2025-02-01 01:30:35.601959: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f67c0000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-01 01:30:35.602019: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-01 01:30:35.607071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-01 01:30:35.900152: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x20e75e70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-01 01:30:35.900195: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-01 01:30:35.901812: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-01 01:30:35.902493: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-01 01:30:35.906013: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-01 01:30:35.924350: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-01 01:30:35.924761: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-01 01:30:35.958360: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-01 01:30:35.963192: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-01 01:30:36.017237: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-01 01:30:36.019020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-01 01:30:36.019440: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-01 01:30:36.020398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-01 01:30:36.020418: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-01 01:30:36.020429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-01 01:30:36.022516: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-02-01 01:30:36.302229: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-01 01:30:36.302324: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-01 01:30:36.302345: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-01 01:30:36.302363: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-01 01:30:36.302381: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-01 01:30:36.302398: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-01 01:30:36.302415: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-01 01:30:36.302433: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-01 01:30:36.304051: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-01 01:30:36.305342: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-01 01:30:36.305373: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-01 01:30:36.305388: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-01 01:30:36.305402: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-01 01:30:36.305416: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-01 01:30:36.305430: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-01 01:30:36.305443: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-01 01:30:36.305457: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-01 01:30:36.306748: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-01 01:30:36.306779: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-01 01:30:36.306787: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-01 01:30:36.306794: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-01 01:30:36.308152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10193 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-02-01 01:30:50.906145: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-01 01:30:51.079029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 23 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 : 21 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 : 47 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 47 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 : 39 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 : 69 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 : 33 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 : 61 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 : 81 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 : 94 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 85 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 50 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 89 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 : 55 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 60 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 52 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 89 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 : 55 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 60 Detection mask done ! Trying to reset tf kernel 2514348 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5706 tf kernel not reseted sub process len(results) : 23 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 23 len(list_Values) 0 process is alive 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 : 10998 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 : 0.13093113899230957 nb_pixel_total : 113359 time to create 1 rle with old method : 0.11942648887634277 length of segment : 525 time for calcul the mask position with numpy : 0.15273451805114746 nb_pixel_total : 181194 time to create 1 rle with new method : 0.010549306869506836 length of segment : 594 time for calcul the mask position with numpy : 0.01170492172241211 nb_pixel_total : 6106 time to create 1 rle with old method : 0.010223627090454102 length of segment : 83 time for calcul the mask position with numpy : 0.10119295120239258 nb_pixel_total : 244718 time to create 1 rle with new method : 0.025844573974609375 length of segment : 373 time for calcul the mask position with numpy : 0.013255596160888672 nb_pixel_total : 8358 time to create 1 rle with old method : 0.015645980834960938 length of segment : 86 time for calcul the mask position with numpy : 0.007520914077758789 nb_pixel_total : 7721 time to create 1 rle with old method : 0.008644580841064453 length of segment : 97 time for calcul the mask position with numpy : 0.0043294429779052734 nb_pixel_total : 5329 time to create 1 rle with old method : 0.0058329105377197266 length of segment : 77 time for calcul the mask position with numpy : 0.025328397750854492 nb_pixel_total : 15175 time to create 1 rle with old method : 0.016318798065185547 length of segment : 181 time for calcul the mask position with numpy : 0.024738788604736328 nb_pixel_total : 12367 time to create 1 rle with old method : 0.013590335845947266 length of segment : 142 time for calcul the mask position with numpy : 0.0054509639739990234 nb_pixel_total : 17311 time to create 1 rle with old method : 0.018555879592895508 length of segment : 161 time for calcul the mask position with numpy : 0.00020599365234375 nb_pixel_total : 9792 time to create 1 rle with old method : 0.010048866271972656 length of segment : 105 time for calcul the mask position with numpy : 0.038103342056274414 nb_pixel_total : 46127 time to create 1 rle with old method : 0.054331064224243164 length of segment : 437 time for calcul the mask position with numpy : 0.04503941535949707 nb_pixel_total : 55843 time to create 1 rle with old method : 0.06096911430358887 length of segment : 272 time for calcul the mask position with numpy : 0.0011785030364990234 nb_pixel_total : 7708 time to create 1 rle with old method : 0.00854802131652832 length of segment : 71 time for calcul the mask position with numpy : 0.047148942947387695 nb_pixel_total : 16791 time to create 1 rle with old method : 0.017788410186767578 length of segment : 180 time for calcul the mask position with numpy : 0.04558444023132324 nb_pixel_total : 14096 time to create 1 rle with old method : 0.014543533325195312 length of segment : 205 time for calcul the mask position with numpy : 0.0619814395904541 nb_pixel_total : 48236 time to create 1 rle with old method : 0.05456042289733887 length of segment : 246 time for calcul the mask position with numpy : 0.06766963005065918 nb_pixel_total : 18894 time to create 1 rle with old method : 0.027054548263549805 length of segment : 173 time for calcul the mask position with numpy : 0.06349921226501465 nb_pixel_total : 12505 time to create 1 rle with old method : 0.021402359008789062 length of segment : 185 time for calcul the mask position with numpy : 0.012129068374633789 nb_pixel_total : 7497 time to create 1 rle with old method : 0.015558004379272461 length of segment : 153 time for calcul the mask position with numpy : 0.06751537322998047 nb_pixel_total : 29810 time to create 1 rle with old method : 0.0430300235748291 length of segment : 194 time for calcul the mask position with numpy : 0.07203412055969238 nb_pixel_total : 31646 time to create 1 rle with old method : 0.052908897399902344 length of segment : 226 time for calcul the mask position with numpy : 0.10606980323791504 nb_pixel_total : 61268 time to create 1 rle with old method : 0.08050656318664551 length of segment : 272 time for calcul the mask position with numpy : 0.05665278434753418 nb_pixel_total : 15916 time to create 1 rle with old method : 0.031221389770507812 length of segment : 200 time for calcul the mask position with numpy : 0.05547213554382324 nb_pixel_total : 11612 time to create 1 rle with old method : 0.020157814025878906 length of segment : 187 time for calcul the mask position with numpy : 0.0176239013671875 nb_pixel_total : 6966 time to create 1 rle with old method : 0.010032892227172852 length of segment : 75 time for calcul the mask position with numpy : 0.010421514511108398 nb_pixel_total : 2860 time to create 1 rle with old method : 0.0076329708099365234 length of segment : 49 time for calcul the mask position with numpy : 0.012908458709716797 nb_pixel_total : 3635 time to create 1 rle with old method : 0.009461402893066406 length of segment : 60 time for calcul the mask position with numpy : 0.04732513427734375 nb_pixel_total : 18811 time to create 1 rle with old method : 0.02883458137512207 length of segment : 180 time for calcul the mask position with numpy : 0.028728246688842773 nb_pixel_total : 12140 time to create 1 rle with old method : 0.02157115936279297 length of segment : 140 time for calcul the mask position with numpy : 0.04468703269958496 nb_pixel_total : 15399 time to create 1 rle with old method : 0.026455163955688477 length of segment : 246 time for calcul the mask position with numpy : 0.00663447380065918 nb_pixel_total : 4938 time to create 1 rle with old method : 0.007980823516845703 length of segment : 65 time for calcul the mask position with numpy : 0.0703282356262207 nb_pixel_total : 46513 time to create 1 rle with old method : 0.06228351593017578 length of segment : 466 time for calcul the mask position with numpy : 0.019149065017700195 nb_pixel_total : 13377 time to create 1 rle with old method : 0.019741535186767578 length of segment : 148 time for calcul the mask position with numpy : 0.03968358039855957 nb_pixel_total : 12865 time to create 1 rle with old method : 0.015639543533325195 length of segment : 149 time for calcul the mask position with numpy : 0.13637590408325195 nb_pixel_total : 31280 time to create 1 rle with old method : 0.04402279853820801 length of segment : 263 time for calcul the mask position with numpy : 0.16880440711975098 nb_pixel_total : 29853 time to create 1 rle with old method : 0.04487895965576172 length of segment : 380 time for calcul the mask position with numpy : 0.0909121036529541 nb_pixel_total : 21597 time to create 1 rle with old method : 0.0339512825012207 length of segment : 161 time for calcul the mask position with numpy : 0.3123204708099365 nb_pixel_total : 68865 time to create 1 rle with old method : 0.09364771842956543 length of segment : 483 time for calcul the mask position with numpy : 0.10293078422546387 nb_pixel_total : 21931 time to create 1 rle with old method : 0.0346987247467041 length of segment : 265 time for calcul the mask position with numpy : 0.12717056274414062 nb_pixel_total : 32697 time to create 1 rle with old method : 0.04184246063232422 length of segment : 270 time for calcul the mask position with numpy : 0.07732534408569336 nb_pixel_total : 14239 time to create 1 rle with old method : 0.02024054527282715 length of segment : 185 time for calcul the mask position with numpy : 0.29056501388549805 nb_pixel_total : 110281 time to create 1 rle with old method : 0.1294236183166504 length of segment : 471 time for calcul the mask position with numpy : 0.07762622833251953 nb_pixel_total : 23908 time to create 1 rle with old method : 0.02989506721496582 length of segment : 259 time for calcul the mask position with numpy : 0.11209678649902344 nb_pixel_total : 26775 time to create 1 rle with old method : 0.033011436462402344 length of segment : 244 time for calcul the mask position with numpy : 0.02577066421508789 nb_pixel_total : 4503 time to create 1 rle with old method : 0.005057811737060547 length of segment : 87 time for calcul the mask position with numpy : 0.09943747520446777 nb_pixel_total : 25312 time to create 1 rle with old method : 0.03190183639526367 length of segment : 172 time for calcul the mask position with numpy : 0.09992051124572754 nb_pixel_total : 30492 time to create 1 rle with old method : 0.03722739219665527 length of segment : 248 time for calcul the mask position with numpy : 0.8589465618133545 nb_pixel_total : 598805 time to create 1 rle with new method : 0.5190541744232178 length of segment : 869 time for calcul the mask position with numpy : 0.09173321723937988 nb_pixel_total : 33361 time to create 1 rle with old method : 0.03922724723815918 length of segment : 350 time for calcul the mask position with numpy : 0.0003724098205566406 nb_pixel_total : 15553 time to create 1 rle with old method : 0.01703476905822754 length of segment : 138 time for calcul the mask position with numpy : 0.06452751159667969 nb_pixel_total : 17425 time to create 1 rle with old method : 0.02469611167907715 length of segment : 157 time for calcul the mask position with numpy : 0.08657121658325195 nb_pixel_total : 35104 time to create 1 rle with old method : 0.042404890060424805 length of segment : 206 time for calcul the mask position with numpy : 0.020593643188476562 nb_pixel_total : 22505 time to create 1 rle with old method : 0.03036022186279297 length of segment : 166 time for calcul the mask position with numpy : 0.0015196800231933594 nb_pixel_total : 32633 time to create 1 rle with old method : 0.03529858589172363 length of segment : 222 time for calcul the mask position with numpy : 0.04523348808288574 nb_pixel_total : 21317 time to create 1 rle with old method : 0.03575921058654785 length of segment : 190 time for calcul the mask position with numpy : 0.04400062561035156 nb_pixel_total : 11505 time to create 1 rle with old method : 0.01605057716369629 length of segment : 121 time for calcul the mask position with numpy : 0.04947662353515625 nb_pixel_total : 10996 time to create 1 rle with old method : 0.019759654998779297 length of segment : 125 time for calcul the mask position with numpy : 0.30452775955200195 nb_pixel_total : 109034 time to create 1 rle with old method : 0.12343811988830566 length of segment : 427 time for calcul the mask position with numpy : 0.022919654846191406 nb_pixel_total : 5363 time to create 1 rle with old method : 0.010776042938232422 length of segment : 88 time for calcul the mask position with numpy : 0.06496739387512207 nb_pixel_total : 37573 time to create 1 rle with old method : 0.04541754722595215 length of segment : 336 time for calcul the mask position with numpy : 0.02153754234313965 nb_pixel_total : 15477 time to create 1 rle with old method : 0.022784948348999023 length of segment : 193 time for calcul the mask position with numpy : 0.059682607650756836 nb_pixel_total : 16049 time to create 1 rle with old method : 0.02063465118408203 length of segment : 173 time for calcul the mask position with numpy : 0.07047605514526367 nb_pixel_total : 24786 time to create 1 rle with old method : 0.03171801567077637 length of segment : 176 time for calcul the mask position with numpy : 0.007181644439697266 nb_pixel_total : 7389 time to create 1 rle with old method : 0.013234376907348633 length of segment : 104 time for calcul the mask position with numpy : 0.053362369537353516 nb_pixel_total : 30183 time to create 1 rle with old method : 0.03576540946960449 length of segment : 232 time for calcul the mask position with numpy : 0.057164907455444336 nb_pixel_total : 15762 time to create 1 rle with old method : 0.020372867584228516 length of segment : 340 time for calcul the mask position with numpy : 0.14911222457885742 nb_pixel_total : 63593 time to create 1 rle with old method : 0.0746915340423584 length of segment : 356 time for calcul the mask position with numpy : 0.003110170364379883 nb_pixel_total : 17167 time to create 1 rle with old method : 0.022855043411254883 length of segment : 228 time for calcul the mask position with numpy : 0.06569838523864746 nb_pixel_total : 19246 time to create 1 rle with old method : 0.028240680694580078 length of segment : 120 time for calcul the mask position with numpy : 0.015626192092895508 nb_pixel_total : 24483 time to create 1 rle with old method : 0.032414913177490234 length of segment : 196 time for calcul the mask position with numpy : 0.04069399833679199 nb_pixel_total : 18937 time to create 1 rle with old method : 0.02504253387451172 length of segment : 206 time for calcul the mask position with numpy : 0.03953671455383301 nb_pixel_total : 15502 time to create 1 rle with old method : 0.0223696231842041 length of segment : 165 time for calcul the mask position with numpy : 0.03898286819458008 nb_pixel_total : 15470 time to create 1 rle with old method : 0.021404504776000977 length of segment : 179 time for calcul the mask position with numpy : 0.04589676856994629 nb_pixel_total : 5964 time to create 1 rle with old method : 0.010023355484008789 length of segment : 96 time for calcul the mask position with numpy : 0.04000520706176758 nb_pixel_total : 14500 time to create 1 rle with old method : 0.02229142189025879 length of segment : 174 time for calcul the mask position with numpy : 0.034485578536987305 nb_pixel_total : 30465 time to create 1 rle with old method : 0.0389254093170166 length of segment : 425 time for calcul the mask position with numpy : 0.05705833435058594 nb_pixel_total : 15386 time to create 1 rle with old method : 0.022131681442260742 length of segment : 184 time for calcul the mask position with numpy : 0.026061296463012695 nb_pixel_total : 21702 time to create 1 rle with old method : 0.028583765029907227 length of segment : 262 time for calcul the mask position with numpy : 0.024233341217041016 nb_pixel_total : 28460 time to create 1 rle with old method : 0.03402876853942871 length of segment : 239 time for calcul the mask position with numpy : 0.010648012161254883 nb_pixel_total : 53360 time to create 1 rle with old method : 0.058557748794555664 length of segment : 363 time for calcul the mask position with numpy : 0.08392500877380371 nb_pixel_total : 22032 time to create 1 rle with old method : 0.029484033584594727 length of segment : 199 time for calcul the mask position with numpy : 0.0014755725860595703 nb_pixel_total : 7456 time to create 1 rle with old method : 0.008563756942749023 length of segment : 124 time for calcul the mask position with numpy : 0.07168173789978027 nb_pixel_total : 20302 time to create 1 rle with old method : 0.027402639389038086 length of segment : 224 time for calcul the mask position with numpy : 0.0234987735748291 nb_pixel_total : 17892 time to create 1 rle with old method : 0.02433609962463379 length of segment : 173 time for calcul the mask position with numpy : 0.15410184860229492 nb_pixel_total : 34327 time to create 1 rle with old method : 0.04045510292053223 length of segment : 396 time for calcul the mask position with numpy : 0.3011782169342041 nb_pixel_total : 213882 time to create 1 rle with new method : 0.021207809448242188 length of segment : 408 time for calcul the mask position with numpy : 0.06128334999084473 nb_pixel_total : 18649 time to create 1 rle with old method : 0.024146318435668945 length of segment : 258 time for calcul the mask position with numpy : 0.004209756851196289 nb_pixel_total : 4311 time to create 1 rle with old method : 0.004999399185180664 length of segment : 92 time for calcul the mask position with numpy : 0.00025343894958496094 nb_pixel_total : 9698 time to create 1 rle with old method : 0.011075258255004883 length of segment : 85 time for calcul the mask position with numpy : 0.056494712829589844 nb_pixel_total : 10376 time to create 1 rle with old method : 0.016935348510742188 length of segment : 145 time for calcul the mask position with numpy : 0.09554219245910645 nb_pixel_total : 15433 time to create 1 rle with old method : 0.02450704574584961 length of segment : 212 time for calcul the mask position with numpy : 0.10468149185180664 nb_pixel_total : 31666 time to create 1 rle with old method : 0.045020103454589844 length of segment : 232 time for calcul the mask position with numpy : 0.02696084976196289 nb_pixel_total : 6644 time to create 1 rle with old method : 0.007827043533325195 length of segment : 72 time for calcul the mask position with numpy : 0.0779118537902832 nb_pixel_total : 27587 time to create 1 rle with old method : 0.0358271598815918 length of segment : 148 time for calcul the mask position with numpy : 0.1650388240814209 nb_pixel_total : 40707 time to create 1 rle with old method : 0.04644656181335449 length of segment : 457 time for calcul the mask position with numpy : 0.12980031967163086 nb_pixel_total : 32611 time to create 1 rle with old method : 0.042649269104003906 length of segment : 210 time for calcul the mask position with numpy : 0.03627753257751465 nb_pixel_total : 13788 time to create 1 rle with old method : 0.019937992095947266 length of segment : 157 time for calcul the mask position with numpy : 0.09805774688720703 nb_pixel_total : 30454 time to create 1 rle with old method : 0.03705906867980957 length of segment : 221 time for calcul the mask position with numpy : 0.2187786102294922 nb_pixel_total : 51065 time to create 1 rle with old method : 0.06623148918151855 length of segment : 688 time for calcul the mask position with numpy : 0.03592419624328613 nb_pixel_total : 6027 time to create 1 rle with old method : 0.011668205261230469 length of segment : 105 time for calcul the mask position with numpy : 0.15868782997131348 nb_pixel_total : 26536 time to create 1 rle with old method : 0.035181522369384766 length of segment : 260 time for calcul the mask position with numpy : 0.10266876220703125 nb_pixel_total : 35646 time to create 1 rle with old method : 0.0520017147064209 length of segment : 199 time for calcul the mask position with numpy : 0.0331728458404541 nb_pixel_total : 6754 time to create 1 rle with old method : 0.012439966201782227 length of segment : 111 time for calcul the mask position with numpy : 0.14499878883361816 nb_pixel_total : 34369 time to create 1 rle with old method : 0.043292999267578125 length of segment : 402 time for calcul the mask position with numpy : 0.07241964340209961 nb_pixel_total : 26302 time to create 1 rle with old method : 0.038300275802612305 length of segment : 145 time for calcul the mask position with numpy : 0.0824742317199707 nb_pixel_total : 23655 time to create 1 rle with old method : 0.031188011169433594 length of segment : 179 time for calcul the mask position with numpy : 0.1990203857421875 nb_pixel_total : 57438 time to create 1 rle with old method : 0.07946348190307617 length of segment : 325 time for calcul the mask position with numpy : 0.07393980026245117 nb_pixel_total : 15981 time to create 1 rle with old method : 0.023249387741088867 length of segment : 165 time for calcul the mask position with numpy : 0.07369017601013184 nb_pixel_total : 30885 time to create 1 rle with old method : 0.03963804244995117 length of segment : 188 time for calcul the mask position with numpy : 0.03503775596618652 nb_pixel_total : 14171 time to create 1 rle with old method : 0.01949167251586914 length of segment : 120 time for calcul the mask position with numpy : 0.08086466789245605 nb_pixel_total : 37282 time to create 1 rle with old method : 0.04544544219970703 length of segment : 234 time for calcul the mask position with numpy : 0.029813528060913086 nb_pixel_total : 4462 time to create 1 rle with old method : 0.00654149055480957 length of segment : 84 time for calcul the mask position with numpy : 0.015791654586791992 nb_pixel_total : 3117 time to create 1 rle with old method : 0.007470130920410156 length of segment : 100 time for calcul the mask position with numpy : 0.028523683547973633 nb_pixel_total : 20384 time to create 1 rle with old method : 0.02800464630126953 length of segment : 157 time for calcul the mask position with numpy : 0.11279988288879395 nb_pixel_total : 41914 time to create 1 rle with old method : 0.061326026916503906 length of segment : 241 time for calcul the mask position with numpy : 0.07547307014465332 nb_pixel_total : 14940 time to create 1 rle with old method : 0.021044015884399414 length of segment : 231 time for calcul the mask position with numpy : 0.007245302200317383 nb_pixel_total : 6761 time to create 1 rle with old method : 0.00951242446899414 length of segment : 71 time for calcul the mask position with numpy : 0.043541669845581055 nb_pixel_total : 15148 time to create 1 rle with old method : 0.01985955238342285 length of segment : 127 time for calcul the mask position with numpy : 0.00975346565246582 nb_pixel_total : 7131 time to create 1 rle with old method : 0.011587858200073242 length of segment : 146 time for calcul the mask position with numpy : 0.004940032958984375 nb_pixel_total : 10553 time to create 1 rle with old method : 0.014379024505615234 length of segment : 155 time for calcul the mask position with numpy : 0.001163482666015625 nb_pixel_total : 4589 time to create 1 rle with old method : 0.005391597747802734 length of segment : 100 time for calcul the mask position with numpy : 0.0755009651184082 nb_pixel_total : 19521 time to create 1 rle with old method : 0.024175167083740234 length of segment : 214 time for calcul the mask position with numpy : 0.02284073829650879 nb_pixel_total : 6296 time to create 1 rle with old method : 0.010461568832397461 length of segment : 66 time for calcul the mask position with numpy : 0.0246884822845459 nb_pixel_total : 6372 time to create 1 rle with old method : 0.0093231201171875 length of segment : 105 time for calcul the mask position with numpy : 0.07170534133911133 nb_pixel_total : 27614 time to create 1 rle with old method : 0.03298544883728027 length of segment : 193 time for calcul the mask position with numpy : 0.035241127014160156 nb_pixel_total : 6890 time to create 1 rle with old method : 0.012170553207397461 length of segment : 121 time for calcul the mask position with numpy : 0.04146122932434082 nb_pixel_total : 10074 time to create 1 rle with old method : 0.015009164810180664 length of segment : 107 time for calcul the mask position with numpy : 0.07841706275939941 nb_pixel_total : 25165 time to create 1 rle with old method : 0.03327322006225586 length of segment : 193 time for calcul the mask position with numpy : 0.07219052314758301 nb_pixel_total : 16089 time to create 1 rle with old method : 0.02229785919189453 length of segment : 198 time for calcul the mask position with numpy : 0.020679473876953125 nb_pixel_total : 8661 time to create 1 rle with old method : 0.012768745422363281 length of segment : 111 time for calcul the mask position with numpy : 0.04356741905212402 nb_pixel_total : 13398 time to create 1 rle with old method : 0.019882678985595703 length of segment : 278 time for calcul the mask position with numpy : 0.020325899124145508 nb_pixel_total : 4932 time to create 1 rle with old method : 0.009155511856079102 length of segment : 87 time for calcul the mask position with numpy : 0.09075546264648438 nb_pixel_total : 39422 time to create 1 rle with old method : 0.0468597412109375 length of segment : 301 time for calcul the mask position with numpy : 0.10123968124389648 nb_pixel_total : 40722 time to create 1 rle with old method : 0.04749250411987305 length of segment : 237 time for calcul the mask position with numpy : 0.029437541961669922 nb_pixel_total : 18824 time to create 1 rle with old method : 0.026241779327392578 length of segment : 174 time for calcul the mask position with numpy : 0.03972434997558594 nb_pixel_total : 31026 time to create 1 rle with old method : 0.05198359489440918 length of segment : 266 time for calcul the mask position with numpy : 0.07242655754089355 nb_pixel_total : 33829 time to create 1 rle with old method : 0.037578582763671875 length of segment : 292 time for calcul the mask position with numpy : 0.025547266006469727 nb_pixel_total : 23009 time to create 1 rle with old method : 0.031151294708251953 length of segment : 181 time for calcul the mask position with numpy : 0.02488112449645996 nb_pixel_total : 7264 time to create 1 rle with old method : 0.012649774551391602 length of segment : 97 time for calcul the mask position with numpy : 0.009326696395874023 nb_pixel_total : 6630 time to create 1 rle with old method : 0.011256694793701172 length of segment : 103 time for calcul the mask position with numpy : 0.017399311065673828 nb_pixel_total : 3068 time to create 1 rle with old method : 0.00645756721496582 length of segment : 51 time for calcul the mask position with numpy : 0.018918752670288086 nb_pixel_total : 4057 time to create 1 rle with old method : 0.008276939392089844 length of segment : 60 time for calcul the mask position with numpy : 0.00019860267639160156 nb_pixel_total : 6597 time to create 1 rle with old method : 0.007487297058105469 length of segment : 158 time for calcul the mask position with numpy : 0.04559898376464844 nb_pixel_total : 16647 time to create 1 rle with old method : 0.024924516677856445 length of segment : 137 time for calcul the mask position with numpy : 0.0133056640625 nb_pixel_total : 10192 time to create 1 rle with old method : 0.016681671142578125 length of segment : 81 time for calcul the mask position with numpy : 0.04911208152770996 nb_pixel_total : 8551 time to create 1 rle with old method : 0.013981103897094727 length of segment : 119 time for calcul the mask position with numpy : 0.015038728713989258 nb_pixel_total : 20399 time to create 1 rle with old method : 0.027137041091918945 length of segment : 247 time for calcul the mask position with numpy : 0.028911828994750977 nb_pixel_total : 11430 time to create 1 rle with old method : 0.01745748519897461 length of segment : 123 time for calcul the mask position with numpy : 0.2335493564605713 nb_pixel_total : 179628 time to create 1 rle with new method : 0.016908645629882812 length of segment : 284 time for calcul the mask position with numpy : 0.03153514862060547 nb_pixel_total : 9609 time to create 1 rle with old method : 0.01645803451538086 length of segment : 138 time for calcul the mask position with numpy : 0.02636408805847168 nb_pixel_total : 12471 time to create 1 rle with old method : 0.024620532989501953 length of segment : 109 time for calcul the mask position with numpy : 0.020673751831054688 nb_pixel_total : 6211 time to create 1 rle with old method : 0.011324882507324219 length of segment : 103 time for calcul the mask position with numpy : 0.017357826232910156 nb_pixel_total : 7918 time to create 1 rle with old method : 0.013951539993286133 length of segment : 71 time for calcul the mask position with numpy : 0.04555821418762207 nb_pixel_total : 14385 time to create 1 rle with old method : 0.024364471435546875 length of segment : 137 time for calcul the mask position with numpy : 0.08461165428161621 nb_pixel_total : 34620 time to create 1 rle with old method : 0.04314422607421875 length of segment : 272 time for calcul the mask position with numpy : 0.013151884078979492 nb_pixel_total : 5140 time to create 1 rle with old method : 0.00767827033996582 length of segment : 80 time for calcul the mask position with numpy : 0.0314946174621582 nb_pixel_total : 8028 time to create 1 rle with old method : 0.013595104217529297 length of segment : 153 time for calcul the mask position with numpy : 0.008548974990844727 nb_pixel_total : 9662 time to create 1 rle with old method : 0.015753746032714844 length of segment : 95 time for calcul the mask position with numpy : 0.04765796661376953 nb_pixel_total : 17334 time to create 1 rle with old method : 0.0258481502532959 length of segment : 171 time for calcul the mask position with numpy : 0.08221721649169922 nb_pixel_total : 47448 time to create 1 rle with old method : 0.060428619384765625 length of segment : 339 time for calcul the mask position with numpy : 0.03686714172363281 nb_pixel_total : 25746 time to create 1 rle with old method : 0.03305244445800781 length of segment : 342 time for calcul the mask position with numpy : 0.024801015853881836 nb_pixel_total : 24840 time to create 1 rle with old method : 0.04022383689880371 length of segment : 205 time for calcul the mask position with numpy : 0.04679393768310547 nb_pixel_total : 30635 time to create 1 rle with old method : 0.036596059799194336 length of segment : 275 time for calcul the mask position with numpy : 0.05949902534484863 nb_pixel_total : 22110 time to create 1 rle with old method : 0.0268862247467041 length of segment : 314 time for calcul the mask position with numpy : 0.03187203407287598 nb_pixel_total : 8724 time to create 1 rle with old method : 0.014676094055175781 length of segment : 134 time for calcul the mask position with numpy : 0.03464245796203613 nb_pixel_total : 22070 time to create 1 rle with old method : 0.026416301727294922 length of segment : 181 time for calcul the mask position with numpy : 0.05627846717834473 nb_pixel_total : 25906 time to create 1 rle with old method : 0.032578468322753906 length of segment : 296 time for calcul the mask position with numpy : 0.0026383399963378906 nb_pixel_total : 4409 time to create 1 rle with old method : 0.005156278610229492 length of segment : 81 time for calcul the mask position with numpy : 0.0022361278533935547 nb_pixel_total : 4481 time to create 1 rle with old method : 0.005975961685180664 length of segment : 98 time for calcul the mask position with numpy : 0.006785869598388672 nb_pixel_total : 4396 time to create 1 rle with old method : 0.006598472595214844 length of segment : 65 time for calcul the mask position with numpy : 0.016452789306640625 nb_pixel_total : 5618 time to create 1 rle with old method : 0.009885072708129883 length of segment : 96 time for calcul the mask position with numpy : 0.0353093147277832 nb_pixel_total : 18720 time to create 1 rle with old method : 0.024451017379760742 length of segment : 157 time for calcul the mask position with numpy : 0.026540517807006836 nb_pixel_total : 16837 time to create 1 rle with old method : 0.022827863693237305 length of segment : 145 time for calcul the mask position with numpy : 0.1064767837524414 nb_pixel_total : 56162 time to create 1 rle with old method : 0.06898903846740723 length of segment : 420 time for calcul the mask position with numpy : 0.00909423828125 nb_pixel_total : 16672 time to create 1 rle with old method : 0.022148609161376953 length of segment : 171 time for calcul the mask position with numpy : 0.014230489730834961 nb_pixel_total : 13587 time to create 1 rle with old method : 0.01858997344970703 length of segment : 117 time for calcul the mask position with numpy : 0.02756190299987793 nb_pixel_total : 13722 time to create 1 rle with old method : 0.020672082901000977 length of segment : 192 time for calcul the mask position with numpy : 0.09338021278381348 nb_pixel_total : 20825 time to create 1 rle with old method : 0.027056455612182617 length of segment : 208 time for calcul the mask position with numpy : 0.07004523277282715 nb_pixel_total : 12565 time to create 1 rle with old method : 0.018903017044067383 length of segment : 186 time for calcul the mask position with numpy : 0.15516161918640137 nb_pixel_total : 36859 time to create 1 rle with old method : 0.044687509536743164 length of segment : 235 time for calcul the mask position with numpy : 0.04706978797912598 nb_pixel_total : 9559 time to create 1 rle with old method : 0.015064477920532227 length of segment : 105 time for calcul the mask position with numpy : 0.030358314514160156 nb_pixel_total : 5958 time to create 1 rle with old method : 0.00998687744140625 length of segment : 115 time for calcul the mask position with numpy : 0.08523344993591309 nb_pixel_total : 22287 time to create 1 rle with old method : 0.029548168182373047 length of segment : 279 time for calcul the mask position with numpy : 0.08353590965270996 nb_pixel_total : 17247 time to create 1 rle with old method : 0.025213003158569336 length of segment : 158 time for calcul the mask position with numpy : 0.05610513687133789 nb_pixel_total : 13265 time to create 1 rle with old method : 0.02925848960876465 length of segment : 175 time for calcul the mask position with numpy : 0.15364289283752441 nb_pixel_total : 33424 time to create 1 rle with old method : 0.04018807411193848 length of segment : 264 time for calcul the mask position with numpy : 0.09019708633422852 nb_pixel_total : 28415 time to create 1 rle with old method : 0.03548550605773926 length of segment : 219 time for calcul the mask position with numpy : 0.10989785194396973 nb_pixel_total : 30979 time to create 1 rle with old method : 0.039488792419433594 length of segment : 241 time for calcul the mask position with numpy : 0.020180463790893555 nb_pixel_total : 8629 time to create 1 rle with old method : 0.014770984649658203 length of segment : 121 time for calcul the mask position with numpy : 0.06946372985839844 nb_pixel_total : 21060 time to create 1 rle with old method : 0.02846050262451172 length of segment : 167 time for calcul the mask position with numpy : 0.028673887252807617 nb_pixel_total : 7489 time to create 1 rle with old method : 0.013679742813110352 length of segment : 128 time for calcul the mask position with numpy : 0.0657663345336914 nb_pixel_total : 12628 time to create 1 rle with old method : 0.019525527954101562 length of segment : 141 time for calcul the mask position with numpy : 0.06176424026489258 nb_pixel_total : 21697 time to create 1 rle with old method : 0.029170751571655273 length of segment : 186 time for calcul the mask position with numpy : 0.09073162078857422 nb_pixel_total : 18097 time to create 1 rle with old method : 0.03023505210876465 length of segment : 181 time for calcul the mask position with numpy : 0.03598904609680176 nb_pixel_total : 11299 time to create 1 rle with old method : 0.021134138107299805 length of segment : 122 time for calcul the mask position with numpy : 0.12395334243774414 nb_pixel_total : 31474 time to create 1 rle with old method : 0.03905630111694336 length of segment : 355 time for calcul the mask position with numpy : 0.05972599983215332 nb_pixel_total : 14509 time to create 1 rle with old method : 0.022275447845458984 length of segment : 165 time for calcul the mask position with numpy : 0.05487680435180664 nb_pixel_total : 13949 time to create 1 rle with old method : 0.02014899253845215 length of segment : 145 time for calcul the mask position with numpy : 0.03448653221130371 nb_pixel_total : 20007 time to create 1 rle with old method : 0.02700495719909668 length of segment : 205 time for calcul the mask position with numpy : 0.02850198745727539 nb_pixel_total : 12807 time to create 1 rle with old method : 0.021713972091674805 length of segment : 131 time for calcul the mask position with numpy : 0.057856082916259766 nb_pixel_total : 10832 time to create 1 rle with old method : 0.01249384880065918 length of segment : 173 time for calcul the mask position with numpy : 0.042021989822387695 nb_pixel_total : 24122 time to create 1 rle with old method : 0.02672600746154785 length of segment : 229 time for calcul the mask position with numpy : 0.006947040557861328 nb_pixel_total : 12057 time to create 1 rle with old method : 0.0163571834564209 length of segment : 139 time for calcul the mask position with numpy : 0.06897234916687012 nb_pixel_total : 14314 time to create 1 rle with old method : 0.018913745880126953 length of segment : 112 time for calcul the mask position with numpy : 0.05084943771362305 nb_pixel_total : 16558 time to create 1 rle with old method : 0.022028684616088867 length of segment : 155 time for calcul the mask position with numpy : 0.19885540008544922 nb_pixel_total : 56526 time to create 1 rle with old method : 0.06441712379455566 length of segment : 402 time for calcul the mask position with numpy : 0.005179882049560547 nb_pixel_total : 7209 time to create 1 rle with old method : 0.008266687393188477 length of segment : 107 time for calcul the mask position with numpy : 0.03137469291687012 nb_pixel_total : 6771 time to create 1 rle with old method : 0.013097047805786133 length of segment : 125 time for calcul the mask position with numpy : 0.013170242309570312 nb_pixel_total : 6548 time to create 1 rle with old method : 0.01109933853149414 length of segment : 139 time for calcul the mask position with numpy : 0.07896161079406738 nb_pixel_total : 17868 time to create 1 rle with old method : 0.02547430992126465 length of segment : 183 time for calcul the mask position with numpy : 0.010833024978637695 nb_pixel_total : 8126 time to create 1 rle with old method : 0.012855291366577148 length of segment : 87 time for calcul the mask position with numpy : 0.13512253761291504 nb_pixel_total : 44426 time to create 1 rle with old method : 0.05289053916931152 length of segment : 262 time for calcul the mask position with numpy : 0.038575172424316406 nb_pixel_total : 26037 time to create 1 rle with old method : 0.03406476974487305 length of segment : 388 time for calcul the mask position with numpy : 0.04182863235473633 nb_pixel_total : 7190 time to create 1 rle with old method : 0.012850761413574219 length of segment : 120 time for calcul the mask position with numpy : 0.03159642219543457 nb_pixel_total : 12365 time to create 1 rle with old method : 0.01883983612060547 length of segment : 110 time for calcul the mask position with numpy : 0.009197235107421875 nb_pixel_total : 17828 time to create 1 rle with old method : 0.02475905418395996 length of segment : 165 time for calcul the mask position with numpy : 0.06785130500793457 nb_pixel_total : 19297 time to create 1 rle with old method : 0.024013042449951172 length of segment : 195 time for calcul the mask position with numpy : 0.1158301830291748 nb_pixel_total : 44784 time to create 1 rle with old method : 0.05619978904724121 length of segment : 234 time for calcul the mask position with numpy : 0.022702455520629883 nb_pixel_total : 17581 time to create 1 rle with old method : 0.02400946617126465 length of segment : 164 time for calcul the mask position with numpy : 0.03391575813293457 nb_pixel_total : 6317 time to create 1 rle with old method : 0.010955333709716797 length of segment : 83 time for calcul the mask position with numpy : 0.03682851791381836 nb_pixel_total : 18664 time to create 1 rle with old method : 0.02525472640991211 length of segment : 207 time for calcul the mask position with numpy : 0.08040094375610352 nb_pixel_total : 39533 time to create 1 rle with old method : 0.048101186752319336 length of segment : 255 time for calcul the mask position with numpy : 0.03574013710021973 nb_pixel_total : 16915 time to create 1 rle with old method : 0.023637771606445312 length of segment : 193 time for calcul the mask position with numpy : 0.002454519271850586 nb_pixel_total : 18082 time to create 1 rle with old method : 0.02136707305908203 length of segment : 256 time for calcul the mask position with numpy : 0.03487658500671387 nb_pixel_total : 11198 time to create 1 rle with old method : 0.013065814971923828 length of segment : 147 time for calcul the mask position with numpy : 0.04395294189453125 nb_pixel_total : 26693 time to create 1 rle with old method : 0.031215190887451172 length of segment : 153 time for calcul the mask position with numpy : 0.052263736724853516 nb_pixel_total : 31394 time to create 1 rle with old method : 0.037532806396484375 length of segment : 128 time for calcul the mask position with numpy : 0.038845062255859375 nb_pixel_total : 36726 time to create 1 rle with old method : 0.04703831672668457 length of segment : 145 time for calcul the mask position with numpy : 0.02875685691833496 nb_pixel_total : 32845 time to create 1 rle with old method : 0.04343414306640625 length of segment : 185 time for calcul the mask position with numpy : 0.06819415092468262 nb_pixel_total : 22463 time to create 1 rle with old method : 0.02987217903137207 length of segment : 284 time for calcul the mask position with numpy : 0.013611316680908203 nb_pixel_total : 4527 time to create 1 rle with old method : 0.0064046382904052734 length of segment : 136 time for calcul the mask position with numpy : 0.013819217681884766 nb_pixel_total : 12380 time to create 1 rle with old method : 0.016735076904296875 length of segment : 129 time for calcul the mask position with numpy : 0.006876945495605469 nb_pixel_total : 12939 time to create 1 rle with old method : 0.01746082305908203 length of segment : 123 time for calcul the mask position with numpy : 0.11518025398254395 nb_pixel_total : 42262 time to create 1 rle with old method : 0.04923892021179199 length of segment : 246 time for calcul the mask position with numpy : 0.049317121505737305 nb_pixel_total : 13362 time to create 1 rle with old method : 0.022182941436767578 length of segment : 125 time for calcul the mask position with numpy : 0.10271167755126953 nb_pixel_total : 46315 time to create 1 rle with old method : 0.054071664810180664 length of segment : 240 time for calcul the mask position with numpy : 0.12485170364379883 nb_pixel_total : 68683 time to create 1 rle with old method : 0.10355877876281738 length of segment : 297 time for calcul the mask position with numpy : 0.046660423278808594 nb_pixel_total : 15353 time to create 1 rle with old method : 0.023268699645996094 length of segment : 194 time for calcul the mask position with numpy : 0.05424141883850098 nb_pixel_total : 25950 time to create 1 rle with old method : 0.03450179100036621 length of segment : 286 time for calcul the mask position with numpy : 0.040032386779785156 nb_pixel_total : 13020 time to create 1 rle with old method : 0.02158832550048828 length of segment : 195 time for calcul the mask position with numpy : 0.03973889350891113 nb_pixel_total : 18481 time to create 1 rle with old method : 0.021727323532104492 length of segment : 166 time for calcul the mask position with numpy : 0.04948091506958008 nb_pixel_total : 25120 time to create 1 rle with old method : 0.032438039779663086 length of segment : 238 time for calcul the mask position with numpy : 0.040204524993896484 nb_pixel_total : 12909 time to create 1 rle with old method : 0.02040266990661621 length of segment : 152 time for calcul the mask position with numpy : 0.03644871711730957 nb_pixel_total : 18895 time to create 1 rle with old method : 0.027089357376098633 length of segment : 244 time for calcul the mask position with numpy : 0.027946949005126953 nb_pixel_total : 11896 time to create 1 rle with old method : 0.018683671951293945 length of segment : 153 time for calcul the mask position with numpy : 0.009494781494140625 nb_pixel_total : 7217 time to create 1 rle with old method : 0.012279987335205078 length of segment : 134 time for calcul the mask position with numpy : 0.050020456314086914 nb_pixel_total : 25585 time to create 1 rle with old method : 0.03568243980407715 length of segment : 210 time for calcul the mask position with numpy : 0.059358835220336914 nb_pixel_total : 30171 time to create 1 rle with old method : 0.03895282745361328 length of segment : 176 time for calcul the mask position with numpy : 0.00569605827331543 nb_pixel_total : 9379 time to create 1 rle with old method : 0.011269569396972656 length of segment : 154 time for calcul the mask position with numpy : 0.01809239387512207 nb_pixel_total : 11114 time to create 1 rle with old method : 0.015712261199951172 length of segment : 131 time for calcul the mask position with numpy : 0.1389145851135254 nb_pixel_total : 58702 time to create 1 rle with old method : 0.07192158699035645 length of segment : 575 time for calcul the mask position with numpy : 0.03230690956115723 nb_pixel_total : 18241 time to create 1 rle with old method : 0.026948213577270508 length of segment : 122 time for calcul the mask position with numpy : 0.03019571304321289 nb_pixel_total : 15509 time to create 1 rle with old method : 0.023750782012939453 length of segment : 110 time for calcul the mask position with numpy : 0.05150198936462402 nb_pixel_total : 21930 time to create 1 rle with old method : 0.031705617904663086 length of segment : 196 time for calcul the mask position with numpy : 0.018874168395996094 nb_pixel_total : 6112 time to create 1 rle with old method : 0.010171890258789062 length of segment : 93 time for calcul the mask position with numpy : 0.01344442367553711 nb_pixel_total : 6953 time to create 1 rle with old method : 0.013310432434082031 length of segment : 93 time for calcul the mask position with numpy : 0.06391763687133789 nb_pixel_total : 15516 time to create 1 rle with old method : 0.020710229873657227 length of segment : 183 time for calcul the mask position with numpy : 0.2353987693786621 nb_pixel_total : 102784 time to create 1 rle with old method : 0.11707067489624023 length of segment : 397 time for calcul the mask position with numpy : 0.18243646621704102 nb_pixel_total : 64961 time to create 1 rle with old method : 0.07497000694274902 length of segment : 332 time for calcul the mask position with numpy : 0.047010183334350586 nb_pixel_total : 11648 time to create 1 rle with old method : 0.017356157302856445 length of segment : 112 time for calcul the mask position with numpy : 0.05759930610656738 nb_pixel_total : 22455 time to create 1 rle with old method : 0.028174161911010742 length of segment : 153 time for calcul the mask position with numpy : 0.034871816635131836 nb_pixel_total : 8285 time to create 1 rle with old method : 0.013531208038330078 length of segment : 106 time for calcul the mask position with numpy : 0.03973126411437988 nb_pixel_total : 10458 time to create 1 rle with old method : 0.016326189041137695 length of segment : 130 time for calcul the mask position with numpy : 0.21181440353393555 nb_pixel_total : 92460 time to create 1 rle with old method : 0.11038661003112793 length of segment : 252 time for calcul the mask position with numpy : 0.05175280570983887 nb_pixel_total : 11425 time to create 1 rle with old method : 0.01891303062438965 length of segment : 151 time for calcul the mask position with numpy : 0.052782535552978516 nb_pixel_total : 13003 time to create 1 rle with old method : 0.020701169967651367 length of segment : 138 time for calcul the mask position with numpy : 0.07560133934020996 nb_pixel_total : 22531 time to create 1 rle with old method : 0.030467987060546875 length of segment : 165 time for calcul the mask position with numpy : 0.10613036155700684 nb_pixel_total : 28909 time to create 1 rle with old method : 0.03749203681945801 length of segment : 199 time for calcul the mask position with numpy : 0.03556942939758301 nb_pixel_total : 9910 time to create 1 rle with old method : 0.016879558563232422 length of segment : 131 time for calcul the mask position with numpy : 0.03586530685424805 nb_pixel_total : 7505 time to create 1 rle with old method : 0.011542081832885742 length of segment : 98 time for calcul the mask position with numpy : 0.030144929885864258 nb_pixel_total : 7173 time to create 1 rle with old method : 0.013199567794799805 length of segment : 97 time for calcul the mask position with numpy : 0.07232880592346191 nb_pixel_total : 12799 time to create 1 rle with old method : 0.01986527442932129 length of segment : 145 time for calcul the mask position with numpy : 0.03410506248474121 nb_pixel_total : 7803 time to create 1 rle with old method : 0.013710260391235352 length of segment : 148 time for calcul the mask position with numpy : 0.12456750869750977 nb_pixel_total : 38134 time to create 1 rle with old method : 0.049146175384521484 length of segment : 263 time for calcul the mask position with numpy : 0.021692514419555664 nb_pixel_total : 6619 time to create 1 rle with old method : 0.012363195419311523 length of segment : 144 time for calcul the mask position with numpy : 0.01882457733154297 nb_pixel_total : 42927 time to create 1 rle with old method : 0.05180931091308594 length of segment : 346 time for calcul the mask position with numpy : 0.09442138671875 nb_pixel_total : 30848 time to create 1 rle with old method : 0.05092597007751465 length of segment : 207 time for calcul the mask position with numpy : 0.03733062744140625 nb_pixel_total : 14761 time to create 1 rle with old method : 0.017023324966430664 length of segment : 201 time for calcul the mask position with numpy : 0.38950157165527344 nb_pixel_total : 92808 time to create 1 rle with old method : 0.10366463661193848 length of segment : 410 time for calcul the mask position with numpy : 0.060450077056884766 nb_pixel_total : 16231 time to create 1 rle with old method : 0.01859283447265625 length of segment : 147 time for calcul the mask position with numpy : 0.015511035919189453 nb_pixel_total : 4720 time to create 1 rle with old method : 0.008820295333862305 length of segment : 80 time for calcul the mask position with numpy : 0.03607535362243652 nb_pixel_total : 11842 time to create 1 rle with old method : 0.018725156784057617 length of segment : 185 time for calcul the mask position with numpy : 0.04400467872619629 nb_pixel_total : 8388 time to create 1 rle with old method : 0.014510869979858398 length of segment : 182 time for calcul the mask position with numpy : 0.04225873947143555 nb_pixel_total : 8127 time to create 1 rle with old method : 0.013663530349731445 length of segment : 152 time for calcul the mask position with numpy : 0.03471636772155762 nb_pixel_total : 37270 time to create 1 rle with old method : 0.04294085502624512 length of segment : 287 time for calcul the mask position with numpy : 0.01475667953491211 nb_pixel_total : 26048 time to create 1 rle with old method : 0.03582048416137695 length of segment : 255 time for calcul the mask position with numpy : 0.09456944465637207 nb_pixel_total : 138239 time to create 1 rle with old method : 0.1594386100769043 length of segment : 499 time for calcul the mask position with numpy : 0.01861429214477539 nb_pixel_total : 7049 time to create 1 rle with old method : 0.012048482894897461 length of segment : 111 time for calcul the mask position with numpy : 0.048882246017456055 nb_pixel_total : 13422 time to create 1 rle with old method : 0.018867969512939453 length of segment : 142 time for calcul the mask position with numpy : 0.06691408157348633 nb_pixel_total : 16264 time to create 1 rle with old method : 0.029042720794677734 length of segment : 399 time for calcul the mask position with numpy : 0.09103727340698242 nb_pixel_total : 32515 time to create 1 rle with old method : 0.0371701717376709 length of segment : 241 time for calcul the mask position with numpy : 0.025279998779296875 nb_pixel_total : 6068 time to create 1 rle with old method : 0.01209259033203125 length of segment : 77 time for calcul the mask position with numpy : 0.11807394027709961 nb_pixel_total : 42137 time to create 1 rle with old method : 0.05216693878173828 length of segment : 289 time for calcul the mask position with numpy : 0.11923027038574219 nb_pixel_total : 69105 time to create 1 rle with old method : 0.08241081237792969 length of segment : 404 time for calcul the mask position with numpy : 0.001355886459350586 nb_pixel_total : 3834 time to create 1 rle with old method : 0.004614591598510742 length of segment : 60 time for calcul the mask position with numpy : 0.041115522384643555 nb_pixel_total : 23634 time to create 1 rle with old method : 0.030662059783935547 length of segment : 179 time for calcul the mask position with numpy : 0.05844879150390625 nb_pixel_total : 19665 time to create 1 rle with old method : 0.02934432029724121 length of segment : 188 time for calcul the mask position with numpy : 0.0014696121215820312 nb_pixel_total : 11063 time to create 1 rle with old method : 0.01265716552734375 length of segment : 190 time for calcul the mask position with numpy : 0.03287005424499512 nb_pixel_total : 13317 time to create 1 rle with old method : 0.02003002166748047 length of segment : 91 time for calcul the mask position with numpy : 0.019010066986083984 nb_pixel_total : 5318 time to create 1 rle with old method : 0.011136054992675781 length of segment : 50 time for calcul the mask position with numpy : 0.06043553352355957 nb_pixel_total : 22452 time to create 1 rle with old method : 0.028582096099853516 length of segment : 186 time for calcul the mask position with numpy : 0.01411747932434082 nb_pixel_total : 4678 time to create 1 rle with old method : 0.007069826126098633 length of segment : 84 time for calcul the mask position with numpy : 0.2183244228363037 nb_pixel_total : 90687 time to create 1 rle with old method : 0.10715794563293457 length of segment : 370 time for calcul the mask position with numpy : 0.04485273361206055 nb_pixel_total : 17846 time to create 1 rle with old method : 0.025542020797729492 length of segment : 235 time for calcul the mask position with numpy : 0.05169963836669922 nb_pixel_total : 21766 time to create 1 rle with old method : 0.028260469436645508 length of segment : 177 time for calcul the mask position with numpy : 0.046987056732177734 nb_pixel_total : 12270 time to create 1 rle with old method : 0.017551660537719727 length of segment : 119 time for calcul the mask position with numpy : 0.14559340476989746 nb_pixel_total : 122061 time to create 1 rle with old method : 0.15723943710327148 length of segment : 376 time for calcul the mask position with numpy : 0.09781622886657715 nb_pixel_total : 52892 time to create 1 rle with old method : 0.062346458435058594 length of segment : 223 time for calcul the mask position with numpy : 0.1872100830078125 nb_pixel_total : 167742 time to create 1 rle with new method : 0.010570526123046875 length of segment : 325 time for calcul the mask position with numpy : 0.21535634994506836 nb_pixel_total : 60566 time to create 1 rle with old method : 0.07323551177978516 length of segment : 463 time for calcul the mask position with numpy : 0.09983229637145996 nb_pixel_total : 49478 time to create 1 rle with old method : 0.059255361557006836 length of segment : 192 time for calcul the mask position with numpy : 0.17415928840637207 nb_pixel_total : 72815 time to create 1 rle with old method : 0.08936738967895508 length of segment : 439 time for calcul the mask position with numpy : 0.03747200965881348 nb_pixel_total : 20845 time to create 1 rle with old method : 0.027273178100585938 length of segment : 78 time for calcul the mask position with numpy : 0.07938671112060547 nb_pixel_total : 54577 time to create 1 rle with old method : 0.06558799743652344 length of segment : 243 time for calcul the mask position with numpy : 0.06159782409667969 nb_pixel_total : 19689 time to create 1 rle with old method : 0.0268857479095459 length of segment : 188 time for calcul the mask position with numpy : 0.22844171524047852 nb_pixel_total : 156784 time to create 1 rle with new method : 0.012043476104736328 length of segment : 698 time for calcul the mask position with numpy : 0.02711629867553711 nb_pixel_total : 10961 time to create 1 rle with old method : 0.014207124710083008 length of segment : 185 time for calcul the mask position with numpy : 0.13051509857177734 nb_pixel_total : 87298 time to create 1 rle with old method : 0.09883522987365723 length of segment : 513 time for calcul the mask position with numpy : 0.08046412467956543 nb_pixel_total : 49221 time to create 1 rle with old method : 0.05710792541503906 length of segment : 609 time for calcul the mask position with numpy : 0.10596132278442383 nb_pixel_total : 106561 time to create 1 rle with old method : 0.12431168556213379 length of segment : 316 time for calcul the mask position with numpy : 0.11101770401000977 nb_pixel_total : 104230 time to create 1 rle with old method : 0.1179056167602539 length of segment : 371 time for calcul the mask position with numpy : 0.03510928153991699 nb_pixel_total : 11716 time to create 1 rle with old method : 0.015697002410888672 length of segment : 211 time for calcul the mask position with numpy : 0.015621423721313477 nb_pixel_total : 16692 time to create 1 rle with old method : 0.0228731632232666 length of segment : 150 time for calcul the mask position with numpy : 0.03834033012390137 nb_pixel_total : 6684 time to create 1 rle with old method : 0.01253366470336914 length of segment : 157 time for calcul the mask position with numpy : 0.08896017074584961 nb_pixel_total : 76724 time to create 1 rle with old method : 0.0913856029510498 length of segment : 341 time for calcul the mask position with numpy : 0.4970831871032715 nb_pixel_total : 250068 time to create 1 rle with new method : 0.051563262939453125 length of segment : 653 time for calcul the mask position with numpy : 0.04547595977783203 nb_pixel_total : 39347 time to create 1 rle with old method : 0.07487106323242188 length of segment : 236 time for calcul the mask position with numpy : 0.10240888595581055 nb_pixel_total : 104794 time to create 1 rle with old method : 0.1240990161895752 length of segment : 363 time for calcul the mask position with numpy : 0.021838903427124023 nb_pixel_total : 20555 time to create 1 rle with old method : 0.03163552284240723 length of segment : 139 time for calcul the mask position with numpy : 0.07054495811462402 nb_pixel_total : 16740 time to create 1 rle with old method : 0.02734827995300293 length of segment : 151 time for calcul the mask position with numpy : 0.03690075874328613 nb_pixel_total : 10360 time to create 1 rle with old method : 0.017414093017578125 length of segment : 88 time for calcul the mask position with numpy : 0.08238029479980469 nb_pixel_total : 16841 time to create 1 rle with old method : 0.024181365966796875 length of segment : 226 time for calcul the mask position with numpy : 0.09567904472351074 nb_pixel_total : 22932 time to create 1 rle with old method : 0.031264543533325195 length of segment : 197 time for calcul the mask position with numpy : 0.05790138244628906 nb_pixel_total : 20427 time to create 1 rle with old method : 0.027616262435913086 length of segment : 158 time for calcul the mask position with numpy : 0.06769204139709473 nb_pixel_total : 12899 time to create 1 rle with old method : 0.02278614044189453 length of segment : 154 time for calcul the mask position with numpy : 0.07616448402404785 nb_pixel_total : 49909 time to create 1 rle with old method : 0.05777478218078613 length of segment : 227 time for calcul the mask position with numpy : 0.08006954193115234 nb_pixel_total : 21158 time to create 1 rle with old method : 0.02659010887145996 length of segment : 225 time for calcul the mask position with numpy : 0.1643075942993164 nb_pixel_total : 33139 time to create 1 rle with old method : 0.0408778190612793 length of segment : 400 time for calcul the mask position with numpy : 0.05670666694641113 nb_pixel_total : 19029 time to create 1 rle with old method : 0.024748563766479492 length of segment : 170 time for calcul the mask position with numpy : 0.08255314826965332 nb_pixel_total : 21451 time to create 1 rle with old method : 0.027014970779418945 length of segment : 175 time for calcul the mask position with numpy : 0.05398869514465332 nb_pixel_total : 28651 time to create 1 rle with old method : 0.03684210777282715 length of segment : 234 time for calcul the mask position with numpy : 0.16172146797180176 nb_pixel_total : 93721 time to create 1 rle with old method : 0.10509061813354492 length of segment : 523 time for calcul the mask position with numpy : 0.09738326072692871 nb_pixel_total : 11184 time to create 1 rle with old method : 0.018393516540527344 length of segment : 189 time for calcul the mask position with numpy : 0.0699617862701416 nb_pixel_total : 30538 time to create 1 rle with old method : 0.04010438919067383 length of segment : 213 time for calcul the mask position with numpy : 0.1173546314239502 nb_pixel_total : 30484 time to create 1 rle with old method : 0.03656196594238281 length of segment : 317 time for calcul the mask position with numpy : 0.04048633575439453 nb_pixel_total : 4935 time to create 1 rle with old method : 0.00796198844909668 length of segment : 87 time for calcul the mask position with numpy : 0.0895390510559082 nb_pixel_total : 32433 time to create 1 rle with old method : 0.039823055267333984 length of segment : 251 time for calcul the mask position with numpy : 0.020560503005981445 nb_pixel_total : 16140 time to create 1 rle with old method : 0.02140021324157715 length of segment : 186 time for calcul the mask position with numpy : 0.11091494560241699 nb_pixel_total : 27243 time to create 1 rle with old method : 0.03488612174987793 length of segment : 211 time for calcul the mask position with numpy : 0.10718297958374023 nb_pixel_total : 24227 time to create 1 rle with old method : 0.03217339515686035 length of segment : 201 time for calcul the mask position with numpy : 0.07936978340148926 nb_pixel_total : 17572 time to create 1 rle with old method : 0.022794723510742188 length of segment : 211 time for calcul the mask position with numpy : 0.11374902725219727 nb_pixel_total : 18830 time to create 1 rle with old method : 0.04200243949890137 length of segment : 212 time for calcul the mask position with numpy : 0.029738426208496094 nb_pixel_total : 17101 time to create 1 rle with old method : 0.027651071548461914 length of segment : 164 time for calcul the mask position with numpy : 0.03129386901855469 nb_pixel_total : 4152 time to create 1 rle with old method : 0.008326292037963867 length of segment : 80 time for calcul the mask position with numpy : 0.03950381278991699 nb_pixel_total : 8537 time to create 1 rle with old method : 0.014505863189697266 length of segment : 112 time for calcul the mask position with numpy : 0.13939237594604492 nb_pixel_total : 46908 time to create 1 rle with old method : 0.05791878700256348 length of segment : 231 time for calcul the mask position with numpy : 0.07685375213623047 nb_pixel_total : 19579 time to create 1 rle with old method : 0.02860283851623535 length of segment : 329 time for calcul the mask position with numpy : 0.011148691177368164 nb_pixel_total : 5230 time to create 1 rle with old method : 0.010183095932006836 length of segment : 73 time for calcul the mask position with numpy : 0.06520271301269531 nb_pixel_total : 35848 time to create 1 rle with old method : 0.04434990882873535 length of segment : 198 time for calcul the mask position with numpy : 0.06950092315673828 nb_pixel_total : 31649 time to create 1 rle with old method : 0.0407106876373291 length of segment : 208 time for calcul the mask position with numpy : 0.18644332885742188 nb_pixel_total : 89517 time to create 1 rle with old method : 0.10583162307739258 length of segment : 361 time for calcul the mask position with numpy : 0.06008553504943848 nb_pixel_total : 14715 time to create 1 rle with old method : 0.022772789001464844 length of segment : 176 time for calcul the mask position with numpy : 0.02677631378173828 nb_pixel_total : 19524 time to create 1 rle with old method : 0.02772665023803711 length of segment : 172 time for calcul the mask position with numpy : 0.020848512649536133 nb_pixel_total : 8887 time to create 1 rle with old method : 0.016031980514526367 length of segment : 104 time for calcul the mask position with numpy : 0.07981991767883301 nb_pixel_total : 23361 time to create 1 rle with old method : 0.03064250946044922 length of segment : 133 time for calcul the mask position with numpy : 0.03169107437133789 nb_pixel_total : 12885 time to create 1 rle with old method : 0.017861604690551758 length of segment : 104 time for calcul the mask position with numpy : 0.2259657382965088 nb_pixel_total : 53294 time to create 1 rle with old method : 0.06144213676452637 length of segment : 337 time for calcul the mask position with numpy : 0.008933305740356445 nb_pixel_total : 6496 time to create 1 rle with old method : 0.009607553482055664 length of segment : 101 time for calcul the mask position with numpy : 0.05302715301513672 nb_pixel_total : 17780 time to create 1 rle with old method : 0.023652076721191406 length of segment : 155 time for calcul the mask position with numpy : 0.11265349388122559 nb_pixel_total : 38266 time to create 1 rle with old method : 0.05092906951904297 length of segment : 354 time for calcul the mask position with numpy : 0.06581854820251465 nb_pixel_total : 12894 time to create 1 rle with old method : 0.019191503524780273 length of segment : 322 time for calcul the mask position with numpy : 0.04319596290588379 nb_pixel_total : 19585 time to create 1 rle with old method : 0.02581310272216797 length of segment : 194 time for calcul the mask position with numpy : 0.07751774787902832 nb_pixel_total : 15528 time to create 1 rle with old method : 0.019753694534301758 length of segment : 200 time for calcul the mask position with numpy : 0.0363008975982666 nb_pixel_total : 12664 time to create 1 rle with old method : 0.020026206970214844 length of segment : 140 time for calcul the mask position with numpy : 0.20558524131774902 nb_pixel_total : 108582 time to create 1 rle with old method : 0.1414811611175537 length of segment : 290 time for calcul the mask position with numpy : 0.09688282012939453 nb_pixel_total : 31905 time to create 1 rle with old method : 0.03975629806518555 length of segment : 440 time for calcul the mask position with numpy : 0.05654025077819824 nb_pixel_total : 17338 time to create 1 rle with old method : 0.02439093589782715 length of segment : 191 time for calcul the mask position with numpy : 0.42267656326293945 nb_pixel_total : 917134 time to create 1 rle with new method : 0.19904398918151855 length of segment : 1261 time for calcul the mask position with numpy : 0.33428430557250977 nb_pixel_total : 385306 time to create 1 rle with new method : 0.2646303176879883 length of segment : 1452 time for calcul the mask position with numpy : 0.012022256851196289 nb_pixel_total : 38588 time to create 1 rle with old method : 0.04610419273376465 length of segment : 451 time for calcul the mask position with numpy : 0.001010894775390625 nb_pixel_total : 15655 time to create 1 rle with old method : 0.01770782470703125 length of segment : 222 time for calcul the mask position with numpy : 0.018396615982055664 nb_pixel_total : 15742 time to create 1 rle with old method : 0.02247786521911621 length of segment : 155 time for calcul the mask position with numpy : 0.03006601333618164 nb_pixel_total : 195815 time to create 1 rle with new method : 0.007135152816772461 length of segment : 465 time for calcul the mask position with numpy : 0.00013947486877441406 nb_pixel_total : 5983 time to create 1 rle with old method : 0.006671428680419922 length of segment : 89 time for calcul the mask position with numpy : 0.005438089370727539 nb_pixel_total : 45383 time to create 1 rle with old method : 0.051108360290527344 length of segment : 390 time for calcul the mask position with numpy : 0.0011522769927978516 nb_pixel_total : 67144 time to create 1 rle with old method : 0.07294321060180664 length of segment : 212 time for calcul the mask position with numpy : 0.027438640594482422 nb_pixel_total : 75044 time to create 1 rle with old method : 0.08584070205688477 length of segment : 497 time for calcul the mask position with numpy : 0.0008873939514160156 nb_pixel_total : 22976 time to create 1 rle with old method : 0.02612137794494629 length of segment : 203 time for calcul the mask position with numpy : 0.008764505386352539 nb_pixel_total : 82639 time to create 1 rle with old method : 0.09579110145568848 length of segment : 480 time for calcul the mask position with numpy : 0.012545347213745117 nb_pixel_total : 40137 time to create 1 rle with old method : 0.0511782169342041 length of segment : 200 time for calcul the mask position with numpy : 0.0018150806427001953 nb_pixel_total : 55886 time to create 1 rle with old method : 0.0623323917388916 length of segment : 128 time for calcul the mask position with numpy : 0.019343852996826172 nb_pixel_total : 64000 time to create 1 rle with old method : 0.08800363540649414 length of segment : 204 time for calcul the mask position with numpy : 0.020249605178833008 nb_pixel_total : 97002 time to create 1 rle with old method : 0.13332676887512207 length of segment : 359 time for calcul the mask position with numpy : 0.0003228187561035156 nb_pixel_total : 10608 time to create 1 rle with old method : 0.011973381042480469 length of segment : 125 time for calcul the mask position with numpy : 0.018797636032104492 nb_pixel_total : 33016 time to create 1 rle with old method : 0.0418243408203125 length of segment : 291 time for calcul the mask position with numpy : 0.0054285526275634766 nb_pixel_total : 7630 time to create 1 rle with old method : 0.011647701263427734 length of segment : 109 time for calcul the mask position with numpy : 0.01838231086730957 nb_pixel_total : 35724 time to create 1 rle with old method : 0.043142080307006836 length of segment : 181 time for calcul the mask position with numpy : 0.09889936447143555 nb_pixel_total : 82809 time to create 1 rle with old method : 0.0924217700958252 length of segment : 322 time for calcul the mask position with numpy : 0.08840274810791016 nb_pixel_total : 110236 time to create 1 rle with old method : 0.11672282218933105 length of segment : 690 time for calcul the mask position with numpy : 0.007374286651611328 nb_pixel_total : 52816 time to create 1 rle with old method : 0.05681252479553223 length of segment : 315 time for calcul the mask position with numpy : 0.14861178398132324 nb_pixel_total : 99861 time to create 1 rle with old method : 0.1331787109375 length of segment : 361 time for calcul the mask position with numpy : 0.07729554176330566 nb_pixel_total : 372271 time to create 1 rle with new method : 0.021718263626098633 length of segment : 702 time for calcul the mask position with numpy : 0.05880308151245117 nb_pixel_total : 104602 time to create 1 rle with old method : 0.11164975166320801 length of segment : 518 time for calcul the mask position with numpy : 0.007870197296142578 nb_pixel_total : 15294 time to create 1 rle with old method : 0.020354509353637695 length of segment : 121 time for calcul the mask position with numpy : 0.003077983856201172 nb_pixel_total : 7371 time to create 1 rle with old method : 0.008899927139282227 length of segment : 118 time for calcul the mask position with numpy : 0.00017142295837402344 nb_pixel_total : 4566 time to create 1 rle with old method : 0.0048940181732177734 length of segment : 97 time for calcul the mask position with numpy : 0.004878044128417969 nb_pixel_total : 23453 time to create 1 rle with old method : 0.029180288314819336 length of segment : 311 time for calcul the mask position with numpy : 0.006650447845458984 nb_pixel_total : 9673 time to create 1 rle with old method : 0.014042377471923828 length of segment : 58 time for calcul the mask position with numpy : 0.0008101463317871094 nb_pixel_total : 5827 time to create 1 rle with old method : 0.006305694580078125 length of segment : 76 time for calcul the mask position with numpy : 0.17746806144714355 nb_pixel_total : 112246 time to create 1 rle with old method : 0.12464570999145508 length of segment : 536 time for calcul the mask position with numpy : 0.018586397171020508 nb_pixel_total : 36135 time to create 1 rle with old method : 0.041173458099365234 length of segment : 254 time for calcul the mask position with numpy : 0.011069297790527344 nb_pixel_total : 28953 time to create 1 rle with old method : 0.034691572189331055 length of segment : 190 time for calcul the mask position with numpy : 0.0021953582763671875 nb_pixel_total : 6888 time to create 1 rle with old method : 0.008225679397583008 length of segment : 69 time for calcul the mask position with numpy : 0.024806737899780273 nb_pixel_total : 152161 time to create 1 rle with new method : 0.012738704681396484 length of segment : 1205 time for calcul the mask position with numpy : 0.007776021957397461 nb_pixel_total : 12935 time to create 1 rle with old method : 0.01605510711669922 length of segment : 137 time for calcul the mask position with numpy : 0.0020036697387695312 nb_pixel_total : 18965 time to create 1 rle with old method : 0.020817041397094727 length of segment : 171 time for calcul the mask position with numpy : 0.0044956207275390625 nb_pixel_total : 37804 time to create 1 rle with old method : 0.04368281364440918 length of segment : 288 time for calcul the mask position with numpy : 0.022507667541503906 nb_pixel_total : 22772 time to create 1 rle with old method : 0.03133726119995117 length of segment : 176 time for calcul the mask position with numpy : 0.037593841552734375 nb_pixel_total : 33156 time to create 1 rle with old method : 0.04183840751647949 length of segment : 285 time for calcul the mask position with numpy : 0.003998994827270508 nb_pixel_total : 11288 time to create 1 rle with old method : 0.015478134155273438 length of segment : 169 time for calcul the mask position with numpy : 0.012223005294799805 nb_pixel_total : 10969 time to create 1 rle with old method : 0.015964508056640625 length of segment : 139 time for calcul the mask position with numpy : 0.002951383590698242 nb_pixel_total : 8891 time to create 1 rle with old method : 0.010501623153686523 length of segment : 124 time for calcul the mask position with numpy : 0.011428117752075195 nb_pixel_total : 4252 time to create 1 rle with old method : 0.008575916290283203 length of segment : 185 time for calcul the mask position with numpy : 0.0008780956268310547 nb_pixel_total : 38935 time to create 1 rle with old method : 0.04156064987182617 length of segment : 254 time for calcul the mask position with numpy : 0.003843069076538086 nb_pixel_total : 10910 time to create 1 rle with old method : 0.012757301330566406 length of segment : 216 time for calcul the mask position with numpy : 0.0009756088256835938 nb_pixel_total : 10572 time to create 1 rle with old method : 0.012156486511230469 length of segment : 116 time for calcul the mask position with numpy : 0.0008785724639892578 nb_pixel_total : 14427 time to create 1 rle with old method : 0.016837596893310547 length of segment : 113 time for calcul the mask position with numpy : 0.002669811248779297 nb_pixel_total : 39275 time to create 1 rle with old method : 0.04224276542663574 length of segment : 277 time for calcul the mask position with numpy : 0.003161907196044922 nb_pixel_total : 37704 time to create 1 rle with old method : 0.04210162162780762 length of segment : 288 time for calcul the mask position with numpy : 0.002349376678466797 nb_pixel_total : 19176 time to create 1 rle with old method : 0.021898269653320312 length of segment : 239 time for calcul the mask position with numpy : 0.08393406867980957 nb_pixel_total : 340122 time to create 1 rle with new method : 0.027959823608398438 length of segment : 474 time for calcul the mask position with numpy : 0.0024271011352539062 nb_pixel_total : 34651 time to create 1 rle with old method : 0.037915706634521484 length of segment : 256 time for calcul the mask position with numpy : 0.010272026062011719 nb_pixel_total : 125619 time to create 1 rle with old method : 0.16806268692016602 length of segment : 593 time for calcul the mask position with numpy : 0.004822969436645508 nb_pixel_total : 54261 time to create 1 rle with old method : 0.0561070442199707 length of segment : 394 time for calcul the mask position with numpy : 0.002850055694580078 nb_pixel_total : 42045 time to create 1 rle with old method : 0.0479276180267334 length of segment : 219 time for calcul the mask position with numpy : 0.014530420303344727 nb_pixel_total : 45233 time to create 1 rle with old method : 0.05622529983520508 length of segment : 345 time for calcul the mask position with numpy : 0.0010390281677246094 nb_pixel_total : 27079 time to create 1 rle with old method : 0.030446767807006836 length of segment : 189 time for calcul the mask position with numpy : 0.0008907318115234375 nb_pixel_total : 16711 time to create 1 rle with old method : 0.019459009170532227 length of segment : 123 time for calcul the mask position with numpy : 0.0014042854309082031 nb_pixel_total : 21405 time to create 1 rle with old method : 0.023135900497436523 length of segment : 192 time for calcul the mask position with numpy : 0.002298116683959961 nb_pixel_total : 28371 time to create 1 rle with old method : 0.030776262283325195 length of segment : 398 time for calcul the mask position with numpy : 0.0025284290313720703 nb_pixel_total : 16476 time to create 1 rle with old method : 0.017995595932006836 length of segment : 138 time for calcul the mask position with numpy : 0.004736185073852539 nb_pixel_total : 57939 time to create 1 rle with old method : 0.06902742385864258 length of segment : 322 time for calcul the mask position with numpy : 0.002109527587890625 nb_pixel_total : 20894 time to create 1 rle with old method : 0.023459911346435547 length of segment : 176 time for calcul the mask position with numpy : 0.03171372413635254 nb_pixel_total : 87019 time to create 1 rle with old method : 0.09554457664489746 length of segment : 306 time for calcul the mask position with numpy : 0.0019421577453613281 nb_pixel_total : 32598 time to create 1 rle with old method : 0.03618907928466797 length of segment : 208 time for calcul the mask position with numpy : 0.00015163421630859375 nb_pixel_total : 2204 time to create 1 rle with old method : 0.0026731491088867188 length of segment : 57 time for calcul the mask position with numpy : 0.0019464492797851562 nb_pixel_total : 22110 time to create 1 rle with old method : 0.025128602981567383 length of segment : 165 time for calcul the mask position with numpy : 0.011388778686523438 nb_pixel_total : 131096 time to create 1 rle with old method : 0.15081095695495605 length of segment : 708 time for calcul the mask position with numpy : 0.002409219741821289 nb_pixel_total : 47162 time to create 1 rle with old method : 0.05163431167602539 length of segment : 519 time for calcul the mask position with numpy : 0.0006079673767089844 nb_pixel_total : 7029 time to create 1 rle with old method : 0.008043527603149414 length of segment : 102 time for calcul the mask position with numpy : 0.0004374980926513672 nb_pixel_total : 11033 time to create 1 rle with old method : 0.011858940124511719 length of segment : 116 time for calcul the mask position with numpy : 0.0005514621734619141 nb_pixel_total : 26834 time to create 1 rle with old method : 0.028087139129638672 length of segment : 214 time for calcul the mask position with numpy : 0.0050868988037109375 nb_pixel_total : 54895 time to create 1 rle with old method : 0.061975955963134766 length of segment : 452 time for calcul the mask position with numpy : 0.003241300582885742 nb_pixel_total : 56070 time to create 1 rle with old method : 0.06338715553283691 length of segment : 301 time for calcul the mask position with numpy : 0.00013899803161621094 nb_pixel_total : 4144 time to create 1 rle with old method : 0.004757881164550781 length of segment : 38 time for calcul the mask position with numpy : 0.001016855239868164 nb_pixel_total : 19175 time to create 1 rle with old method : 0.024179458618164062 length of segment : 197 time for calcul the mask position with numpy : 0.00040793418884277344 nb_pixel_total : 7861 time to create 1 rle with old method : 0.00901651382446289 length of segment : 123 time for calcul the mask position with numpy : 0.0030813217163085938 nb_pixel_total : 40359 time to create 1 rle with old method : 0.04505634307861328 length of segment : 261 time for calcul the mask position with numpy : 0.0005695819854736328 nb_pixel_total : 20868 time to create 1 rle with old method : 0.02321338653564453 length of segment : 330 time for calcul the mask position with numpy : 0.0026972293853759766 nb_pixel_total : 37666 time to create 1 rle with old method : 0.04337453842163086 length of segment : 226 time for calcul the mask position with numpy : 0.00041484832763671875 nb_pixel_total : 17000 time to create 1 rle with old method : 0.018383502960205078 length of segment : 163 time for calcul the mask position with numpy : 0.0014705657958984375 nb_pixel_total : 19700 time to create 1 rle with old method : 0.02230525016784668 length of segment : 176 time for calcul the mask position with numpy : 0.01851058006286621 nb_pixel_total : 116074 time to create 1 rle with old method : 0.13165974617004395 length of segment : 576 time for calcul the mask position with numpy : 0.0012369155883789062 nb_pixel_total : 9830 time to create 1 rle with old method : 0.011483430862426758 length of segment : 116 time for calcul the mask position with numpy : 0.030533552169799805 nb_pixel_total : 325274 time to create 1 rle with new method : 0.020768404006958008 length of segment : 430 time for calcul the mask position with numpy : 0.0024623870849609375 nb_pixel_total : 12557 time to create 1 rle with old method : 0.014336347579956055 length of segment : 171 time for calcul the mask position with numpy : 0.000461578369140625 nb_pixel_total : 6239 time to create 1 rle with old method : 0.007489681243896484 length of segment : 148 time for calcul the mask position with numpy : 0.0024683475494384766 nb_pixel_total : 30767 time to create 1 rle with old method : 0.03588247299194336 length of segment : 201 time for calcul the mask position with numpy : 0.0016093254089355469 nb_pixel_total : 7018 time to create 1 rle with old method : 0.008218050003051758 length of segment : 125 time for calcul the mask position with numpy : 0.00189971923828125 nb_pixel_total : 17078 time to create 1 rle with old method : 0.019057750701904297 length of segment : 179 time for calcul the mask position with numpy : 0.0009789466857910156 nb_pixel_total : 14453 time to create 1 rle with old method : 0.016661405563354492 length of segment : 115 time for calcul the mask position with numpy : 0.0003368854522705078 nb_pixel_total : 5186 time to create 1 rle with old method : 0.006075620651245117 length of segment : 44 time for calcul the mask position with numpy : 0.0003762245178222656 nb_pixel_total : 4502 time to create 1 rle with old method : 0.005308389663696289 length of segment : 71 time for calcul the mask position with numpy : 0.00026702880859375 nb_pixel_total : 2544 time to create 1 rle with old method : 0.0031518936157226562 length of segment : 61 time for calcul the mask position with numpy : 0.0065937042236328125 nb_pixel_total : 28973 time to create 1 rle with old method : 0.03508591651916504 length of segment : 413 time for calcul the mask position with numpy : 0.0051577091217041016 nb_pixel_total : 66044 time to create 1 rle with old method : 0.07158041000366211 length of segment : 487 time for calcul the mask position with numpy : 0.0011196136474609375 nb_pixel_total : 16979 time to create 1 rle with old method : 0.01912856101989746 length of segment : 140 time for calcul the mask position with numpy : 0.0007829666137695312 nb_pixel_total : 7454 time to create 1 rle with old method : 0.008459329605102539 length of segment : 82 time for calcul the mask position with numpy : 0.0002887248992919922 nb_pixel_total : 13267 time to create 1 rle with old method : 0.014234304428100586 length of segment : 124 time for calcul the mask position with numpy : 0.001142263412475586 nb_pixel_total : 19780 time to create 1 rle with old method : 0.021131515502929688 length of segment : 171 time for calcul the mask position with numpy : 0.0015316009521484375 nb_pixel_total : 22134 time to create 1 rle with old method : 0.024162769317626953 length of segment : 170 time for calcul the mask position with numpy : 0.0028052330017089844 nb_pixel_total : 21510 time to create 1 rle with old method : 0.023319721221923828 length of segment : 250 time for calcul the mask position with numpy : 0.0009377002716064453 nb_pixel_total : 12697 time to create 1 rle with old method : 0.014586210250854492 length of segment : 111 time for calcul the mask position with numpy : 0.0004215240478515625 nb_pixel_total : 3013 time to create 1 rle with old method : 0.0034437179565429688 length of segment : 90 time for calcul the mask position with numpy : 0.0026433467864990234 nb_pixel_total : 33546 time to create 1 rle with old method : 0.03774404525756836 length of segment : 209 time for calcul the mask position with numpy : 0.0077359676361083984 nb_pixel_total : 98080 time to create 1 rle with old method : 0.10431456565856934 length of segment : 300 time for calcul the mask position with numpy : 0.0020322799682617188 nb_pixel_total : 26078 time to create 1 rle with old method : 0.030416011810302734 length of segment : 152 time for calcul the mask position with numpy : 0.0015811920166015625 nb_pixel_total : 14323 time to create 1 rle with old method : 0.01888108253479004 length of segment : 158 time for calcul the mask position with numpy : 0.0004353523254394531 nb_pixel_total : 4510 time to create 1 rle with old method : 0.005358457565307617 length of segment : 137 time for calcul the mask position with numpy : 0.0010263919830322266 nb_pixel_total : 12315 time to create 1 rle with old method : 0.01492452621459961 length of segment : 128 time for calcul the mask position with numpy : 0.002074718475341797 nb_pixel_total : 67215 time to create 1 rle with old method : 0.07381772994995117 length of segment : 354 time for calcul the mask position with numpy : 0.0008788108825683594 nb_pixel_total : 6021 time to create 1 rle with old method : 0.0072174072265625 length of segment : 69 time for calcul the mask position with numpy : 0.005933523178100586 nb_pixel_total : 46102 time to create 1 rle with old method : 0.05252718925476074 length of segment : 322 time for calcul the mask position with numpy : 0.0012149810791015625 nb_pixel_total : 9890 time to create 1 rle with old method : 0.011907577514648438 length of segment : 131 time for calcul the mask position with numpy : 0.0031080245971679688 nb_pixel_total : 37972 time to create 1 rle with old method : 0.04245281219482422 length of segment : 319 time for calcul the mask position with numpy : 0.0003209114074707031 nb_pixel_total : 6994 time to create 1 rle with old method : 0.00769495964050293 length of segment : 60 time for calcul the mask position with numpy : 0.0027930736541748047 nb_pixel_total : 44125 time to create 1 rle with old method : 0.06155872344970703 length of segment : 397 time for calcul the mask position with numpy : 0.003756999969482422 nb_pixel_total : 25048 time to create 1 rle with old method : 0.029102325439453125 length of segment : 178 time for calcul the mask position with numpy : 0.004120588302612305 nb_pixel_total : 40585 time to create 1 rle with old method : 0.049303531646728516 length of segment : 349 time for calcul the mask position with numpy : 0.0019192695617675781 nb_pixel_total : 18709 time to create 1 rle with old method : 0.022510766983032227 length of segment : 168 time for calcul the mask position with numpy : 0.005836963653564453 nb_pixel_total : 43782 time to create 1 rle with old method : 0.05266833305358887 length of segment : 321 time for calcul the mask position with numpy : 0.004120349884033203 nb_pixel_total : 60804 time to create 1 rle with old method : 0.06672906875610352 length of segment : 296 time for calcul the mask position with numpy : 0.0019164085388183594 nb_pixel_total : 27248 time to create 1 rle with old method : 0.029998302459716797 length of segment : 190 time for calcul the mask position with numpy : 0.001306295394897461 nb_pixel_total : 15780 time to create 1 rle with old method : 0.017708301544189453 length of segment : 207 time for calcul the mask position with numpy : 0.0010385513305664062 nb_pixel_total : 14383 time to create 1 rle with old method : 0.015265941619873047 length of segment : 160 time for calcul the mask position with numpy : 0.001932382583618164 nb_pixel_total : 28960 time to create 1 rle with old method : 0.031013965606689453 length of segment : 236 time for calcul the mask position with numpy : 0.0007596015930175781 nb_pixel_total : 9064 time to create 1 rle with old method : 0.010905027389526367 length of segment : 120 time for calcul the mask position with numpy : 0.001802206039428711 nb_pixel_total : 28473 time to create 1 rle with old method : 0.04945802688598633 length of segment : 202 time for calcul the mask position with numpy : 0.0017237663269042969 nb_pixel_total : 16161 time to create 1 rle with old method : 0.019658803939819336 length of segment : 178 time for calcul the mask position with numpy : 0.005348920822143555 nb_pixel_total : 53845 time to create 1 rle with old method : 0.06190991401672363 length of segment : 361 time for calcul the mask position with numpy : 0.014078140258789062 nb_pixel_total : 187986 time to create 1 rle with new method : 0.011440515518188477 length of segment : 536 time for calcul the mask position with numpy : 0.008295536041259766 nb_pixel_total : 153274 time to create 1 rle with new method : 0.008336305618286133 length of segment : 484 time for calcul the mask position with numpy : 0.003492116928100586 nb_pixel_total : 42978 time to create 1 rle with old method : 0.049454450607299805 length of segment : 248 time for calcul the mask position with numpy : 0.0068628787994384766 nb_pixel_total : 152424 time to create 1 rle with new method : 0.00818181037902832 length of segment : 475 time for calcul the mask position with numpy : 0.0019233226776123047 nb_pixel_total : 32859 time to create 1 rle with old method : 0.037360429763793945 length of segment : 143 time for calcul the mask position with numpy : 0.0008611679077148438 nb_pixel_total : 8966 time to create 1 rle with old method : 0.010086774826049805 length of segment : 198 time for calcul the mask position with numpy : 0.018256664276123047 nb_pixel_total : 220044 time to create 1 rle with new method : 0.017375946044921875 length of segment : 711 time for calcul the mask position with numpy : 0.0022649765014648438 nb_pixel_total : 21688 time to create 1 rle with old method : 0.024077415466308594 length of segment : 423 time for calcul the mask position with numpy : 0.0074920654296875 nb_pixel_total : 42817 time to create 1 rle with old method : 0.07454204559326172 length of segment : 249 time for calcul the mask position with numpy : 0.0006949901580810547 nb_pixel_total : 15478 time to create 1 rle with old method : 0.01728987693786621 length of segment : 143 time for calcul the mask position with numpy : 0.0020639896392822266 nb_pixel_total : 28526 time to create 1 rle with old method : 0.03303384780883789 length of segment : 188 time for calcul the mask position with numpy : 0.002416849136352539 nb_pixel_total : 40665 time to create 1 rle with old method : 0.04542088508605957 length of segment : 229 time for calcul the mask position with numpy : 0.0022368431091308594 nb_pixel_total : 14216 time to create 1 rle with old method : 0.016814470291137695 length of segment : 193 time for calcul the mask position with numpy : 0.0009360313415527344 nb_pixel_total : 8981 time to create 1 rle with old method : 0.010572671890258789 length of segment : 168 time for calcul the mask position with numpy : 0.0020058155059814453 nb_pixel_total : 15541 time to create 1 rle with old method : 0.01844167709350586 length of segment : 204 time for calcul the mask position with numpy : 0.006048440933227539 nb_pixel_total : 77616 time to create 1 rle with old method : 0.08861303329467773 length of segment : 345 time for calcul the mask position with numpy : 0.002272367477416992 nb_pixel_total : 14819 time to create 1 rle with old method : 0.016208648681640625 length of segment : 179 time for calcul the mask position with numpy : 0.0009143352508544922 nb_pixel_total : 15528 time to create 1 rle with old method : 0.01763439178466797 length of segment : 146 time for calcul the mask position with numpy : 0.0032968521118164062 nb_pixel_total : 50760 time to create 1 rle with old method : 0.057041168212890625 length of segment : 391 time for calcul the mask position with numpy : 0.0004837512969970703 nb_pixel_total : 5851 time to create 1 rle with old method : 0.006821632385253906 length of segment : 92 time for calcul the mask position with numpy : 0.00609898567199707 nb_pixel_total : 116654 time to create 1 rle with old method : 0.131697416305542 length of segment : 437 time for calcul the mask position with numpy : 0.000377655029296875 nb_pixel_total : 14474 time to create 1 rle with old method : 0.016373395919799805 length of segment : 153 time for calcul the mask position with numpy : 0.0013129711151123047 nb_pixel_total : 19368 time to create 1 rle with old method : 0.021448850631713867 length of segment : 363 time for calcul the mask position with numpy : 0.0028748512268066406 nb_pixel_total : 60385 time to create 1 rle with old method : 0.07095789909362793 length of segment : 322 time for calcul the mask position with numpy : 0.0002644062042236328 nb_pixel_total : 5376 time to create 1 rle with old method : 0.0062847137451171875 length of segment : 148 time for calcul the mask position with numpy : 0.02298879623413086 nb_pixel_total : 269409 time to create 1 rle with new method : 0.02461862564086914 length of segment : 977 time for calcul the mask position with numpy : 0.0024878978729248047 nb_pixel_total : 28934 time to create 1 rle with old method : 0.033421993255615234 length of segment : 217 time for calcul the mask position with numpy : 0.0005233287811279297 nb_pixel_total : 5602 time to create 1 rle with old method : 0.006811618804931641 length of segment : 90 time for calcul the mask position with numpy : 0.0017642974853515625 nb_pixel_total : 26171 time to create 1 rle with old method : 0.030382394790649414 length of segment : 175 time for calcul the mask position with numpy : 0.0005629062652587891 nb_pixel_total : 22352 time to create 1 rle with old method : 0.02555251121520996 length of segment : 139 time for calcul the mask position with numpy : 0.0003991127014160156 nb_pixel_total : 8922 time to create 1 rle with old method : 0.012079477310180664 length of segment : 152 time for calcul the mask position with numpy : 0.030982017517089844 nb_pixel_total : 532999 time to create 1 rle with new method : 0.04720473289489746 length of segment : 850 time for calcul the mask position with numpy : 0.0027654170989990234 nb_pixel_total : 71800 time to create 1 rle with old method : 0.07859373092651367 length of segment : 338 time for calcul the mask position with numpy : 0.0011744499206542969 nb_pixel_total : 14845 time to create 1 rle with old method : 0.016499757766723633 length of segment : 168 time for calcul the mask position with numpy : 0.0007774829864501953 nb_pixel_total : 13974 time to create 1 rle with old method : 0.015851736068725586 length of segment : 172 time for calcul the mask position with numpy : 0.0010986328125 nb_pixel_total : 17525 time to create 1 rle with old method : 0.01955866813659668 length of segment : 207 time for calcul the mask position with numpy : 0.012853384017944336 nb_pixel_total : 193915 time to create 1 rle with new method : 0.01970529556274414 length of segment : 616 time for calcul the mask position with numpy : 0.0015864372253417969 nb_pixel_total : 26632 time to create 1 rle with old method : 0.033820152282714844 length of segment : 163 time for calcul the mask position with numpy : 0.0024826526641845703 nb_pixel_total : 36496 time to create 1 rle with old method : 0.041152238845825195 length of segment : 262 time for calcul the mask position with numpy : 0.0021457672119140625 nb_pixel_total : 38212 time to create 1 rle with old method : 0.04384922981262207 length of segment : 262 time for calcul the mask position with numpy : 0.0007104873657226562 nb_pixel_total : 12177 time to create 1 rle with old method : 0.01684880256652832 length of segment : 215 time for calcul the mask position with numpy : 0.0007634162902832031 nb_pixel_total : 18490 time to create 1 rle with old method : 0.025224924087524414 length of segment : 152 time for calcul the mask position with numpy : 0.006338834762573242 nb_pixel_total : 26318 time to create 1 rle with old method : 0.03150463104248047 length of segment : 189 time for calcul the mask position with numpy : 0.0038404464721679688 nb_pixel_total : 44651 time to create 1 rle with old method : 0.05483365058898926 length of segment : 364 time for calcul the mask position with numpy : 0.013850212097167969 nb_pixel_total : 177559 time to create 1 rle with new method : 0.009223222732543945 length of segment : 649 time for calcul the mask position with numpy : 0.0010251998901367188 nb_pixel_total : 19548 time to create 1 rle with old method : 0.024217844009399414 length of segment : 134 time for calcul the mask position with numpy : 0.001552581787109375 nb_pixel_total : 32423 time to create 1 rle with old method : 0.04082202911376953 length of segment : 255 time for calcul the mask position with numpy : 0.0010402202606201172 nb_pixel_total : 23545 time to create 1 rle with old method : 0.030523300170898438 length of segment : 172 time for calcul the mask position with numpy : 0.0025832653045654297 nb_pixel_total : 15384 time to create 1 rle with old method : 0.021936655044555664 length of segment : 144 time for calcul the mask position with numpy : 0.0004074573516845703 nb_pixel_total : 6904 time to create 1 rle with old method : 0.008371114730834961 length of segment : 75 time for calcul the mask position with numpy : 0.0008003711700439453 nb_pixel_total : 13338 time to create 1 rle with old method : 0.017398595809936523 length of segment : 174 time for calcul the mask position with numpy : 0.012605428695678711 nb_pixel_total : 17353 time to create 1 rle with old method : 0.0264737606048584 length of segment : 306 time for calcul the mask position with numpy : 0.0038764476776123047 nb_pixel_total : 27959 time to create 1 rle with old method : 0.032863616943359375 length of segment : 281 time for calcul the mask position with numpy : 0.021735191345214844 nb_pixel_total : 20776 time to create 1 rle with old method : 0.029246807098388672 length of segment : 254 time for calcul the mask position with numpy : 0.0008776187896728516 nb_pixel_total : 18776 time to create 1 rle with old method : 0.02228069305419922 length of segment : 132 time for calcul the mask position with numpy : 0.0005421638488769531 nb_pixel_total : 13844 time to create 1 rle with old method : 0.0158231258392334 length of segment : 127 time for calcul the mask position with numpy : 0.0007555484771728516 nb_pixel_total : 12010 time to create 1 rle with old method : 0.01546931266784668 length of segment : 141 time for calcul the mask position with numpy : 0.0006194114685058594 nb_pixel_total : 19653 time to create 1 rle with old method : 0.02329397201538086 length of segment : 201 time for calcul the mask position with numpy : 0.0008654594421386719 nb_pixel_total : 13895 time to create 1 rle with old method : 0.015608787536621094 length of segment : 154 time for calcul the mask position with numpy : 0.006222248077392578 nb_pixel_total : 20575 time to create 1 rle with old method : 0.023499727249145508 length of segment : 169 time for calcul the mask position with numpy : 0.0007939338684082031 nb_pixel_total : 23777 time to create 1 rle with old method : 0.025719642639160156 length of segment : 335 time for calcul the mask position with numpy : 0.03223586082458496 nb_pixel_total : 187507 time to create 1 rle with new method : 0.027483701705932617 length of segment : 531 time for calcul the mask position with numpy : 0.00042057037353515625 nb_pixel_total : 4846 time to create 1 rle with old method : 0.006274700164794922 length of segment : 96 time for calcul the mask position with numpy : 0.03581523895263672 nb_pixel_total : 74284 time to create 1 rle with old method : 0.09512758255004883 length of segment : 505 time for calcul the mask position with numpy : 0.0009450912475585938 nb_pixel_total : 19848 time to create 1 rle with old method : 0.022797584533691406 length of segment : 142 time for calcul the mask position with numpy : 0.00037670135498046875 nb_pixel_total : 6301 time to create 1 rle with old method : 0.007244586944580078 length of segment : 89 time for calcul the mask position with numpy : 0.007305622100830078 nb_pixel_total : 125942 time to create 1 rle with old method : 0.13967299461364746 length of segment : 621 time for calcul the mask position with numpy : 0.0034537315368652344 nb_pixel_total : 36762 time to create 1 rle with old method : 0.04883384704589844 length of segment : 314 time for calcul the mask position with numpy : 0.0019903182983398438 nb_pixel_total : 22047 time to create 1 rle with old method : 0.025966644287109375 length of segment : 313 time for calcul the mask position with numpy : 0.0005686283111572266 nb_pixel_total : 9702 time to create 1 rle with old method : 0.015990495681762695 length of segment : 101 time for calcul the mask position with numpy : 0.0012218952178955078 nb_pixel_total : 17298 time to create 1 rle with old method : 0.03081226348876953 length of segment : 121 time for calcul the mask position with numpy : 0.0027709007263183594 nb_pixel_total : 22760 time to create 1 rle with old method : 0.036513328552246094 length of segment : 244 time for calcul the mask position with numpy : 0.03696870803833008 nb_pixel_total : 132812 time to create 1 rle with old method : 0.336536169052124 length of segment : 423 time for calcul the mask position with numpy : 0.004140138626098633 nb_pixel_total : 34587 time to create 1 rle with old method : 0.04471182823181152 length of segment : 274 time for calcul the mask position with numpy : 0.008359909057617188 nb_pixel_total : 8515 time to create 1 rle with old method : 0.016198396682739258 length of segment : 135 time for calcul the mask position with numpy : 0.003850698471069336 nb_pixel_total : 30999 time to create 1 rle with old method : 0.04056429862976074 length of segment : 323 time for calcul the mask position with numpy : 0.001711130142211914 nb_pixel_total : 11782 time to create 1 rle with old method : 0.013855457305908203 length of segment : 135 time for calcul the mask position with numpy : 0.0012998580932617188 nb_pixel_total : 13025 time to create 1 rle with old method : 0.022701501846313477 length of segment : 114 time for calcul the mask position with numpy : 0.01465749740600586 nb_pixel_total : 71650 time to create 1 rle with old method : 0.08355474472045898 length of segment : 310 time for calcul the mask position with numpy : 0.01192474365234375 nb_pixel_total : 101273 time to create 1 rle with old method : 0.14960885047912598 length of segment : 572 time for calcul the mask position with numpy : 0.006562471389770508 nb_pixel_total : 90735 time to create 1 rle with old method : 0.09855151176452637 length of segment : 370 time for calcul the mask position with numpy : 0.008548974990844727 nb_pixel_total : 61444 time to create 1 rle with old method : 0.06932497024536133 length of segment : 226 time for calcul the mask position with numpy : 0.00030994415283203125 nb_pixel_total : 6213 time to create 1 rle with old method : 0.006874799728393555 length of segment : 86 time for calcul the mask position with numpy : 0.0005407333374023438 nb_pixel_total : 8202 time to create 1 rle with old method : 0.009090662002563477 length of segment : 83 time for calcul the mask position with numpy : 0.0011875629425048828 nb_pixel_total : 12090 time to create 1 rle with old method : 0.013979911804199219 length of segment : 136 time for calcul the mask position with numpy : 0.0014503002166748047 nb_pixel_total : 12901 time to create 1 rle with old method : 0.01448202133178711 length of segment : 133 time for calcul the mask position with numpy : 0.0003581047058105469 nb_pixel_total : 11696 time to create 1 rle with old method : 0.013056039810180664 length of segment : 140 time for calcul the mask position with numpy : 0.0010447502136230469 nb_pixel_total : 15185 time to create 1 rle with old method : 0.0162503719329834 length of segment : 111 time for calcul the mask position with numpy : 0.001489400863647461 nb_pixel_total : 19082 time to create 1 rle with old method : 0.020534992218017578 length of segment : 179 time for calcul the mask position with numpy : 0.0007424354553222656 nb_pixel_total : 10779 time to create 1 rle with old method : 0.01214742660522461 length of segment : 95 time for calcul the mask position with numpy : 0.000823974609375 nb_pixel_total : 9918 time to create 1 rle with old method : 0.01082158088684082 length of segment : 139 time for calcul the mask position with numpy : 0.0007503032684326172 nb_pixel_total : 9114 time to create 1 rle with old method : 0.010029077529907227 length of segment : 142 time for calcul the mask position with numpy : 0.0009188652038574219 nb_pixel_total : 5382 time to create 1 rle with old method : 0.010225057601928711 length of segment : 96 time for calcul the mask position with numpy : 0.0006971359252929688 nb_pixel_total : 13474 time to create 1 rle with old method : 0.015510797500610352 length of segment : 152 time for calcul the mask position with numpy : 0.0017170906066894531 nb_pixel_total : 15734 time to create 1 rle with old method : 0.019069671630859375 length of segment : 190 time for calcul the mask position with numpy : 0.005368471145629883 nb_pixel_total : 24493 time to create 1 rle with old method : 0.03430461883544922 length of segment : 305 time for calcul the mask position with numpy : 0.002010822296142578 nb_pixel_total : 25704 time to create 1 rle with old method : 0.029942750930786133 length of segment : 182 time for calcul the mask position with numpy : 0.0030341148376464844 nb_pixel_total : 46198 time to create 1 rle with old method : 0.05457758903503418 length of segment : 249 time for calcul the mask position with numpy : 0.0005700588226318359 nb_pixel_total : 6952 time to create 1 rle with old method : 0.007862329483032227 length of segment : 77 time for calcul the mask position with numpy : 0.001514434814453125 nb_pixel_total : 20735 time to create 1 rle with old method : 0.023098468780517578 length of segment : 226 time for calcul the mask position with numpy : 0.0009160041809082031 nb_pixel_total : 9130 time to create 1 rle with old method : 0.010700464248657227 length of segment : 107 time for calcul the mask position with numpy : 0.04486346244812012 nb_pixel_total : 265659 time to create 1 rle with new method : 0.0430750846862793 length of segment : 432 time for calcul the mask position with numpy : 0.0010852813720703125 nb_pixel_total : 15974 time to create 1 rle with old method : 0.02433156967163086 length of segment : 185 time for calcul the mask position with numpy : 0.007208347320556641 nb_pixel_total : 89447 time to create 1 rle with old method : 0.0997931957244873 length of segment : 340 time for calcul the mask position with numpy : 0.00038552284240722656 nb_pixel_total : 5064 time to create 1 rle with old method : 0.005602598190307617 length of segment : 91 time for calcul the mask position with numpy : 0.010868310928344727 nb_pixel_total : 81029 time to create 1 rle with old method : 0.0903327465057373 length of segment : 391 time for calcul the mask position with numpy : 0.0002892017364501953 nb_pixel_total : 5419 time to create 1 rle with old method : 0.0061397552490234375 length of segment : 92 time for calcul the mask position with numpy : 0.003717184066772461 nb_pixel_total : 94756 time to create 1 rle with old method : 0.10622191429138184 length of segment : 330 time for calcul the mask position with numpy : 0.1157383918762207 nb_pixel_total : 62636 time to create 1 rle with old method : 0.07579445838928223 length of segment : 227 time for calcul the mask position with numpy : 0.017117738723754883 nb_pixel_total : 86916 time to create 1 rle with old method : 0.09831500053405762 length of segment : 410 time for calcul the mask position with numpy : 0.00754094123840332 nb_pixel_total : 58566 time to create 1 rle with old method : 0.0629267692565918 length of segment : 335 time for calcul the mask position with numpy : 0.01400446891784668 nb_pixel_total : 25431 time to create 1 rle with old method : 0.029126405715942383 length of segment : 234 time for calcul the mask position with numpy : 0.000423431396484375 nb_pixel_total : 6535 time to create 1 rle with old method : 0.007868528366088867 length of segment : 96 time for calcul the mask position with numpy : 0.04270362854003906 nb_pixel_total : 240017 time to create 1 rle with new method : 0.017631053924560547 length of segment : 1039 time for calcul the mask position with numpy : 0.0018405914306640625 nb_pixel_total : 38462 time to create 1 rle with old method : 0.0433659553527832 length of segment : 192 time for calcul the mask position with numpy : 0.0003464221954345703 nb_pixel_total : 8425 time to create 1 rle with old method : 0.009466171264648438 length of segment : 94 time for calcul the mask position with numpy : 0.0054891109466552734 nb_pixel_total : 31318 time to create 1 rle with old method : 0.03843808174133301 length of segment : 188 time for calcul the mask position with numpy : 0.00039267539978027344 nb_pixel_total : 16521 time to create 1 rle with old method : 0.01895284652709961 length of segment : 95 time for calcul the mask position with numpy : 0.0006136894226074219 nb_pixel_total : 30409 time to create 1 rle with old method : 0.03253579139709473 length of segment : 280 time for calcul the mask position with numpy : 0.012656688690185547 nb_pixel_total : 53494 time to create 1 rle with old method : 0.06004762649536133 length of segment : 266 time for calcul the mask position with numpy : 0.013644933700561523 nb_pixel_total : 19903 time to create 1 rle with old method : 0.026004552841186523 length of segment : 207 time for calcul the mask position with numpy : 0.022829055786132812 nb_pixel_total : 27136 time to create 1 rle with old method : 0.03670382499694824 length of segment : 197 time for calcul the mask position with numpy : 0.028134822845458984 nb_pixel_total : 47748 time to create 1 rle with old method : 0.05949974060058594 length of segment : 292 time for calcul the mask position with numpy : 0.004891633987426758 nb_pixel_total : 42223 time to create 1 rle with old method : 0.05030322074890137 length of segment : 242 time for calcul the mask position with numpy : 0.004010438919067383 nb_pixel_total : 48286 time to create 1 rle with old method : 0.055974483489990234 length of segment : 180 time for calcul the mask position with numpy : 0.0004909038543701172 nb_pixel_total : 6560 time to create 1 rle with old method : 0.0074007511138916016 length of segment : 126 time for calcul the mask position with numpy : 0.00027441978454589844 nb_pixel_total : 3280 time to create 1 rle with old method : 0.004492282867431641 length of segment : 60 time for calcul the mask position with numpy : 0.057637691497802734 nb_pixel_total : 190951 time to create 1 rle with new method : 0.010998249053955078 length of segment : 587 time for calcul the mask position with numpy : 0.007706642150878906 nb_pixel_total : 115175 time to create 1 rle with old method : 0.1444087028503418 length of segment : 432 time for calcul the mask position with numpy : 0.002275228500366211 nb_pixel_total : 28097 time to create 1 rle with old method : 0.033194541931152344 length of segment : 144 time for calcul the mask position with numpy : 0.013002157211303711 nb_pixel_total : 44152 time to create 1 rle with old method : 0.05387544631958008 length of segment : 303 time for calcul the mask position with numpy : 0.000606536865234375 nb_pixel_total : 12043 time to create 1 rle with old method : 0.013947010040283203 length of segment : 107 time for calcul the mask position with numpy : 0.0013616085052490234 nb_pixel_total : 25921 time to create 1 rle with old method : 0.03000354766845703 length of segment : 224 time for calcul the mask position with numpy : 0.002006053924560547 nb_pixel_total : 28600 time to create 1 rle with old method : 0.03336024284362793 length of segment : 228 time for calcul the mask position with numpy : 0.002675771713256836 nb_pixel_total : 55363 time to create 1 rle with old method : 0.0701286792755127 length of segment : 179 time for calcul the mask position with numpy : 0.0004391670227050781 nb_pixel_total : 4215 time to create 1 rle with old method : 0.005436420440673828 length of segment : 80 time for calcul the mask position with numpy : 0.006754159927368164 nb_pixel_total : 60007 time to create 1 rle with old method : 0.07575726509094238 length of segment : 309 time for calcul the mask position with numpy : 0.028293848037719727 nb_pixel_total : 212597 time to create 1 rle with new method : 0.012907981872558594 length of segment : 600 time for calcul the mask position with numpy : 0.008972406387329102 nb_pixel_total : 74399 time to create 1 rle with old method : 0.08656549453735352 length of segment : 330 time for calcul the mask position with numpy : 0.04299616813659668 nb_pixel_total : 247122 time to create 1 rle with new method : 0.01237630844116211 length of segment : 518 time for calcul the mask position with numpy : 0.006544589996337891 nb_pixel_total : 8717 time to create 1 rle with old method : 0.011151790618896484 length of segment : 73 time for calcul the mask position with numpy : 0.004436492919921875 nb_pixel_total : 13814 time to create 1 rle with old method : 0.016904115676879883 length of segment : 176 time for calcul the mask position with numpy : 0.0065310001373291016 nb_pixel_total : 66235 time to create 1 rle with old method : 0.07758378982543945 length of segment : 238 time for calcul the mask position with numpy : 0.0011832714080810547 nb_pixel_total : 30917 time to create 1 rle with old method : 0.034882545471191406 length of segment : 272 time for calcul the mask position with numpy : 0.008680343627929688 nb_pixel_total : 118718 time to create 1 rle with old method : 0.12666893005371094 length of segment : 665 time for calcul the mask position with numpy : 0.0067136287689208984 nb_pixel_total : 37557 time to create 1 rle with old method : 0.04432940483093262 length of segment : 217 time for calcul the mask position with numpy : 0.001277923583984375 nb_pixel_total : 7114 time to create 1 rle with old method : 0.008542060852050781 length of segment : 188 time for calcul the mask position with numpy : 0.005518198013305664 nb_pixel_total : 154806 time to create 1 rle with new method : 0.006818294525146484 length of segment : 423 time for calcul the mask position with numpy : 0.0005757808685302734 nb_pixel_total : 15599 time to create 1 rle with old method : 0.017966747283935547 length of segment : 232 time for calcul the mask position with numpy : 0.013215780258178711 nb_pixel_total : 367847 time to create 1 rle with new method : 0.014444112777709961 length of segment : 716 time for calcul the mask position with numpy : 0.0027925968170166016 nb_pixel_total : 44653 time to create 1 rle with old method : 0.05021929740905762 length of segment : 364 time for calcul the mask position with numpy : 0.009821176528930664 nb_pixel_total : 177557 time to create 1 rle with new method : 0.009632110595703125 length of segment : 649 time for calcul the mask position with numpy : 0.0013301372528076172 nb_pixel_total : 19546 time to create 1 rle with old method : 0.022624731063842773 length of segment : 134 time for calcul the mask position with numpy : 0.0013415813446044922 nb_pixel_total : 32421 time to create 1 rle with old method : 0.0374453067779541 length of segment : 255 time for calcul the mask position with numpy : 0.0005857944488525391 nb_pixel_total : 23546 time to create 1 rle with old method : 0.027904987335205078 length of segment : 172 time for calcul the mask position with numpy : 0.0017480850219726562 nb_pixel_total : 15383 time to create 1 rle with old method : 0.0176694393157959 length of segment : 144 time for calcul the mask position with numpy : 0.0003330707550048828 nb_pixel_total : 6904 time to create 1 rle with old method : 0.007758140563964844 length of segment : 75 time for calcul the mask position with numpy : 0.0009374618530273438 nb_pixel_total : 13338 time to create 1 rle with old method : 0.01556253433227539 length of segment : 174 time for calcul the mask position with numpy : 0.001753091812133789 nb_pixel_total : 17350 time to create 1 rle with old method : 0.019594669342041016 length of segment : 307 time for calcul the mask position with numpy : 0.0027658939361572266 nb_pixel_total : 27957 time to create 1 rle with old method : 0.03130936622619629 length of segment : 281 time for calcul the mask position with numpy : 0.0013628005981445312 nb_pixel_total : 20773 time to create 1 rle with old method : 0.023747682571411133 length of segment : 254 time for calcul the mask position with numpy : 0.0008533000946044922 nb_pixel_total : 18779 time to create 1 rle with old method : 0.021656036376953125 length of segment : 132 time for calcul the mask position with numpy : 0.00045752525329589844 nb_pixel_total : 13845 time to create 1 rle with old method : 0.0154266357421875 length of segment : 127 time for calcul the mask position with numpy : 0.0018055438995361328 nb_pixel_total : 12008 time to create 1 rle with old method : 0.01349186897277832 length of segment : 141 time for calcul the mask position with numpy : 0.0005257129669189453 nb_pixel_total : 19648 time to create 1 rle with old method : 0.021387815475463867 length of segment : 201 time for calcul the mask position with numpy : 0.0011501312255859375 nb_pixel_total : 13892 time to create 1 rle with old method : 0.015396356582641602 length of segment : 154 time for calcul the mask position with numpy : 0.0011153221130371094 nb_pixel_total : 20575 time to create 1 rle with old method : 0.021974563598632812 length of segment : 169 time for calcul the mask position with numpy : 0.000804901123046875 nb_pixel_total : 23773 time to create 1 rle with old method : 0.0269467830657959 length of segment : 335 time for calcul the mask position with numpy : 0.01705765724182129 nb_pixel_total : 187532 time to create 1 rle with new method : 0.014462709426879883 length of segment : 531 time for calcul the mask position with numpy : 0.00034546852111816406 nb_pixel_total : 4845 time to create 1 rle with old method : 0.005524158477783203 length of segment : 96 time for calcul the mask position with numpy : 0.002275228500366211 nb_pixel_total : 74270 time to create 1 rle with old method : 0.08188223838806152 length of segment : 505 time for calcul the mask position with numpy : 0.0008664131164550781 nb_pixel_total : 19847 time to create 1 rle with old method : 0.023061513900756836 length of segment : 142 time for calcul the mask position with numpy : 0.00037479400634765625 nb_pixel_total : 6301 time to create 1 rle with old method : 0.007230997085571289 length of segment : 89 time for calcul the mask position with numpy : 0.008454322814941406 nb_pixel_total : 125980 time to create 1 rle with old method : 0.14222311973571777 length of segment : 621 time for calcul the mask position with numpy : 0.0025343894958496094 nb_pixel_total : 36769 time to create 1 rle with old method : 0.04169344902038574 length of segment : 314 time for calcul the mask position with numpy : 0.0014848709106445312 nb_pixel_total : 22049 time to create 1 rle with old method : 0.024946212768554688 length of segment : 313 time for calcul the mask position with numpy : 0.0004715919494628906 nb_pixel_total : 9703 time to create 1 rle with old method : 0.010877609252929688 length of segment : 101 time for calcul the mask position with numpy : 0.0009942054748535156 nb_pixel_total : 17300 time to create 1 rle with old method : 0.019132614135742188 length of segment : 121 time for calcul the mask position with numpy : 0.002122163772583008 nb_pixel_total : 22759 time to create 1 rle with old method : 0.032448530197143555 length of segment : 244 time for calcul the mask position with numpy : 0.00745081901550293 nb_pixel_total : 132865 time to create 1 rle with old method : 0.159013032913208 length of segment : 423 time for calcul the mask position with numpy : 0.002732515335083008 nb_pixel_total : 34584 time to create 1 rle with old method : 0.039106130599975586 length of segment : 274 time for calcul the mask position with numpy : 0.0006995201110839844 nb_pixel_total : 8513 time to create 1 rle with old method : 0.009325265884399414 length of segment : 135 time for calcul the mask position with numpy : 0.0021338462829589844 nb_pixel_total : 30998 time to create 1 rle with old method : 0.0345759391784668 length of segment : 323 time for calcul the mask position with numpy : 0.0010285377502441406 nb_pixel_total : 11782 time to create 1 rle with old method : 0.013292551040649414 length of segment : 135 time for calcul the mask position with numpy : 0.0009477138519287109 nb_pixel_total : 13025 time to create 1 rle with old method : 0.015229940414428711 length of segment : 114 time for calcul the mask position with numpy : 0.004114866256713867 nb_pixel_total : 71639 time to create 1 rle with old method : 0.08062863349914551 length of segment : 310 time for calcul the mask position with numpy : 0.004880428314208984 nb_pixel_total : 101359 time to create 1 rle with old method : 0.13233590126037598 length of segment : 573 time for calcul the mask position with numpy : 0.005120754241943359 nb_pixel_total : 90739 time to create 1 rle with old method : 0.09675264358520508 length of segment : 370 time for calcul the mask position with numpy : 0.004065752029418945 nb_pixel_total : 61445 time to create 1 rle with old method : 0.06838321685791016 length of segment : 226 time for calcul the mask position with numpy : 0.0003154277801513672 nb_pixel_total : 6213 time to create 1 rle with old method : 0.007138729095458984 length of segment : 86 time for calcul the mask position with numpy : 0.0004870891571044922 nb_pixel_total : 8201 time to create 1 rle with old method : 0.009499549865722656 length of segment : 83 time for calcul the mask position with numpy : 0.001132965087890625 nb_pixel_total : 12082 time to create 1 rle with old method : 0.014124870300292969 length of segment : 136 time for calcul the mask position with numpy : 0.0007603168487548828 nb_pixel_total : 12873 time to create 1 rle with old method : 0.014657735824584961 length of segment : 133 time for calcul the mask position with numpy : 0.0003643035888671875 nb_pixel_total : 11691 time to create 1 rle with old method : 0.013165950775146484 length of segment : 140 time for calcul the mask position with numpy : 0.0009462833404541016 nb_pixel_total : 15185 time to create 1 rle with old method : 0.018088579177856445 length of segment : 111 time for calcul the mask position with numpy : 0.0014102458953857422 nb_pixel_total : 19083 time to create 1 rle with old method : 0.022065162658691406 length of segment : 179 time for calcul the mask position with numpy : 0.0007464885711669922 nb_pixel_total : 10782 time to create 1 rle with old method : 0.012560606002807617 length of segment : 95 time for calcul the mask position with numpy : 0.0008721351623535156 nb_pixel_total : 9919 time to create 1 rle with old method : 0.010806083679199219 length of segment : 139 time for calcul the mask position with numpy : 0.0007991790771484375 nb_pixel_total : 9117 time to create 1 rle with old method : 0.010518312454223633 length of segment : 142 time for calcul the mask position with numpy : 0.0005125999450683594 nb_pixel_total : 5383 time to create 1 rle with old method : 0.0062863826751708984 length of segment : 96 time for calcul the mask position with numpy : 0.0008263587951660156 nb_pixel_total : 13474 time to create 1 rle with old method : 0.015777111053466797 length of segment : 152 time for calcul the mask position with numpy : 0.0019252300262451172 nb_pixel_total : 24493 time to create 1 rle with old method : 0.02760004997253418 length of segment : 305 time for calcul the mask position with numpy : 0.0015513896942138672 nb_pixel_total : 15733 time to create 1 rle with old method : 0.01815032958984375 length of segment : 190 time for calcul the mask position with numpy : 0.0032775402069091797 nb_pixel_total : 45996 time to create 1 rle with old method : 0.052398681640625 length of segment : 249 time for calcul the mask position with numpy : 0.0019040107727050781 nb_pixel_total : 25697 time to create 1 rle with old method : 0.030141115188598633 length of segment : 182 time for calcul the mask position with numpy : 0.0017750263214111328 nb_pixel_total : 20716 time to create 1 rle with old method : 0.025715112686157227 length of segment : 226 time for calcul the mask position with numpy : 0.0005910396575927734 nb_pixel_total : 6952 time to create 1 rle with old method : 0.007593631744384766 length of segment : 77 time for calcul the mask position with numpy : 0.0008072853088378906 nb_pixel_total : 9137 time to create 1 rle with old method : 0.010540485382080078 length of segment : 107 time for calcul the mask position with numpy : 0.023661375045776367 nb_pixel_total : 265708 time to create 1 rle with new method : 0.023044824600219727 length of segment : 432 time for calcul the mask position with numpy : 0.0009903907775878906 nb_pixel_total : 15976 time to create 1 rle with old method : 0.01789379119873047 length of segment : 185 time for calcul the mask position with numpy : 0.00497746467590332 nb_pixel_total : 89458 time to create 1 rle with old method : 0.10098433494567871 length of segment : 340 time for calcul the mask position with numpy : 0.0004048347473144531 nb_pixel_total : 5064 time to create 1 rle with old method : 0.005931377410888672 length of segment : 91 time for calcul the mask position with numpy : 0.01066732406616211 nb_pixel_total : 81049 time to create 1 rle with old method : 0.09165310859680176 length of segment : 391 time for calcul the mask position with numpy : 0.0003294944763183594 nb_pixel_total : 5419 time to create 1 rle with old method : 0.0061492919921875 length of segment : 92 time for calcul the mask position with numpy : 0.003900289535522461 nb_pixel_total : 94745 time to create 1 rle with old method : 0.09996485710144043 length of segment : 330 time for calcul the mask position with numpy : 0.003988027572631836 nb_pixel_total : 62624 time to create 1 rle with old method : 0.06667733192443848 length of segment : 227 time for calcul the mask position with numpy : 0.004271268844604492 nb_pixel_total : 86942 time to create 1 rle with old method : 0.10253691673278809 length of segment : 410 time for calcul the mask position with numpy : 0.0033986568450927734 nb_pixel_total : 58561 time to create 1 rle with old method : 0.0666189193725586 length of segment : 335 time for calcul the mask position with numpy : 0.001226663589477539 nb_pixel_total : 25437 time to create 1 rle with old method : 0.029439449310302734 length of segment : 234 time for calcul the mask position with numpy : 0.0004775524139404297 nb_pixel_total : 6539 time to create 1 rle with old method : 0.008166313171386719 length of segment : 96 time for calcul the mask position with numpy : 0.01364588737487793 nb_pixel_total : 240309 time to create 1 rle with new method : 0.016244173049926758 length of segment : 1041 time for calcul the mask position with numpy : 0.0020058155059814453 nb_pixel_total : 38391 time to create 1 rle with old method : 0.06347250938415527 length of segment : 193 time for calcul the mask position with numpy : 0.0003752708435058594 nb_pixel_total : 8422 time to create 1 rle with old method : 0.009717226028442383 length of segment : 94 time for calcul the mask position with numpy : 0.001798391342163086 nb_pixel_total : 31322 time to create 1 rle with old method : 0.03420758247375488 length of segment : 188 time for calcul the mask position with numpy : 0.00032711029052734375 nb_pixel_total : 16521 time to create 1 rle with old method : 0.01860666275024414 length of segment : 95 time for calcul the mask position with numpy : 0.0006525516510009766 nb_pixel_total : 30408 time to create 1 rle with old method : 0.0339808464050293 length of segment : 280 time for calcul the mask position with numpy : 0.002365589141845703 nb_pixel_total : 53495 time to create 1 rle with old method : 0.06147408485412598 length of segment : 266 time for calcul the mask position with numpy : 0.001129150390625 nb_pixel_total : 19827 time to create 1 rle with old method : 0.022269725799560547 length of segment : 207 time for calcul the mask position with numpy : 0.0013501644134521484 nb_pixel_total : 27140 time to create 1 rle with old method : 0.03156137466430664 length of segment : 197 time for calcul the mask position with numpy : 0.0020935535430908203 nb_pixel_total : 47748 time to create 1 rle with old method : 0.05214357376098633 length of segment : 292 time for calcul the mask position with numpy : 0.002026081085205078 nb_pixel_total : 42233 time to create 1 rle with old method : 0.04762554168701172 length of segment : 242 time for calcul the mask position with numpy : 0.0023093223571777344 nb_pixel_total : 48284 time to create 1 rle with old method : 0.05578041076660156 length of segment : 180 time for calcul the mask position with numpy : 0.0004591941833496094 nb_pixel_total : 6558 time to create 1 rle with old method : 0.007385969161987305 length of segment : 126 time for calcul the mask position with numpy : 0.0002532005310058594 nb_pixel_total : 3280 time to create 1 rle with old method : 0.0038056373596191406 length of segment : 60 time for calcul the mask position with numpy : 0.007678031921386719 nb_pixel_total : 190968 time to create 1 rle with new method : 0.008571863174438477 length of segment : 588 time for calcul the mask position with numpy : 0.006409168243408203 nb_pixel_total : 115203 time to create 1 rle with old method : 0.14273524284362793 length of segment : 432 time for calcul the mask position with numpy : 0.0013518333435058594 nb_pixel_total : 28090 time to create 1 rle with old method : 0.03132367134094238 length of segment : 144 time for calcul the mask position with numpy : 0.0033719539642333984 nb_pixel_total : 44134 time to create 1 rle with old method : 0.050069332122802734 length of segment : 303 time for calcul the mask position with numpy : 0.0005729198455810547 nb_pixel_total : 12040 time to create 1 rle with old method : 0.013395071029663086 length of segment : 107 time for calcul the mask position with numpy : 0.0012962818145751953 nb_pixel_total : 25926 time to create 1 rle with old method : 0.028396129608154297 length of segment : 224 time for calcul the mask position with numpy : 0.0019350051879882812 nb_pixel_total : 28597 time to create 1 rle with old method : 0.03171563148498535 length of segment : 228 time for calcul the mask position with numpy : 0.0022079944610595703 nb_pixel_total : 55363 time to create 1 rle with old method : 0.06189370155334473 length of segment : 179 time for calcul the mask position with numpy : 0.00033974647521972656 nb_pixel_total : 4214 time to create 1 rle with old method : 0.004832267761230469 length of segment : 80 time for calcul the mask position with numpy : 0.0026197433471679688 nb_pixel_total : 58629 time to create 1 rle with old method : 0.06384611129760742 length of segment : 300 time for calcul the mask position with numpy : 0.007379055023193359 nb_pixel_total : 212847 time to create 1 rle with new method : 0.01045370101928711 length of segment : 619 time for calcul the mask position with numpy : 0.0004851818084716797 nb_pixel_total : 8572 time to create 1 rle with old method : 0.009577035903930664 length of segment : 73 time for calcul the mask position with numpy : 0.0058155059814453125 nb_pixel_total : 74408 time to create 1 rle with old method : 0.08247113227844238 length of segment : 331 time for calcul the mask position with numpy : 0.009343624114990234 nb_pixel_total : 247136 time to create 1 rle with new method : 0.010616779327392578 length of segment : 518 time for calcul the mask position with numpy : 0.0009953975677490234 nb_pixel_total : 13803 time to create 1 rle with old method : 0.015537738800048828 length of segment : 176 time for calcul the mask position with numpy : 0.002736330032348633 nb_pixel_total : 66233 time to create 1 rle with old method : 0.07855558395385742 length of segment : 238 time for calcul the mask position with numpy : 0.001489877700805664 nb_pixel_total : 30923 time to create 1 rle with old method : 0.0357818603515625 length of segment : 271 time for calcul the mask position with numpy : 0.00921487808227539 nb_pixel_total : 118717 time to create 1 rle with old method : 0.1545100212097168 length of segment : 665 time for calcul the mask position with numpy : 0.002294778823852539 nb_pixel_total : 37559 time to create 1 rle with old method : 0.04324173927307129 length of segment : 217 time for calcul the mask position with numpy : 0.005136251449584961 nb_pixel_total : 153910 time to create 1 rle with new method : 0.006494045257568359 length of segment : 418 time for calcul the mask position with numpy : 0.0012683868408203125 nb_pixel_total : 7115 time to create 1 rle with old method : 0.008095026016235352 length of segment : 188 time for calcul the mask position with numpy : 0.00051116943359375 nb_pixel_total : 15602 time to create 1 rle with old method : 0.017217636108398438 length of segment : 232 time for calcul the mask position with numpy : 0.0007181167602539062 nb_pixel_total : 20325 time to create 1 rle with old method : 0.023494482040405273 length of segment : 124 time for calcul the mask position with numpy : 0.013150930404663086 nb_pixel_total : 367812 time to create 1 rle with new method : 0.014099359512329102 length of segment : 717 time spent for convertir_results : 82.72335767745972 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 1099 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 122240 save missing photos in datou_result : time spend for datou_step_exec : 370.79070806503296 time spend to save output : 22.680864810943604 total time spend for step 1 : 393.47157287597656 step2:crop_condition Sat Feb 1 01:37:05 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 : 23 ! batch 1 Loaded 1099 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! 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we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 790 About to insert : list_path_to_insert length 790 new photo from crops ! About to upload 790 photos upload in portfolio : 3736932 init cache_photo without model_param we have 790 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738370340_2513697 we have uploaded 790 photos in the portfolio 3736932 time of upload the photos Elapsed time : 186.49079656600952 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 186 About to insert : list_path_to_insert length 186 new photo from crops ! About to upload 186 photos upload in portfolio : 3736932 init cache_photo without model_param we have 186 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738370575_2513697 we have uploaded 186 photos in the portfolio 3736932 time of upload the photos Elapsed time : 45.7327618598938 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 11 About to insert : list_path_to_insert length 11 new photo from crops ! About to upload 11 photos upload in portfolio : 3736932 init cache_photo without model_param we have 11 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738370626_2513697 we have uploaded 11 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.6239047050476074 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 55 About to insert : list_path_to_insert length 55 new photo from crops ! About to upload 55 photos upload in portfolio : 3736932 init cache_photo without model_param we have 55 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738370659_2513697 we have uploaded 55 photos in the portfolio 3736932 time of upload the photos Elapsed time : 14.269675731658936 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 22 About to insert : list_path_to_insert length 22 new photo from crops ! About to upload 22 photos upload in portfolio : 3736932 init cache_photo without model_param we have 22 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738370680_2513697 we have uploaded 22 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.240267515182495 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 ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 8 About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos upload in portfolio : 3736932 init cache_photo without model_param we have 8 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738370691_2513697 we have uploaded 8 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.9267237186431885 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 19 About to insert : list_path_to_insert length 19 new photo from crops ! About to upload 19 photos upload in portfolio : 3736932 init cache_photo without model_param we have 19 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738370701_2513697 we have uploaded 19 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.20217490196228 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 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 [1333267465, 1333267458, 1333267278, 1333267265, 1333267257, 1333267252, 1333267248, 1333267244, 1333266673, 1333266660, 1333266652, 1333266647, 1333266642, 1333266632, 1333266479, 1333266472, 1333266404, 1333266394, 1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 1091 /1333538655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538789Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538894Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538906Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538930Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538932Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538946Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538950Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538952Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538963Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538965Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538967Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538969Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538970Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538973Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333538999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539005Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539007Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539014Didn't retrieve data .Didn't retrieve 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539048Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1333539067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539070Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539072Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539074Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539076Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539078Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539079Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539080Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539081Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539082Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539083Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539085Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539087Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539088Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539094Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539096Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539098Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539154Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539166Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539175Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539184Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539191Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1333539238Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539241Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539249Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539325Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539328Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539331Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539342Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539385Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539409Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539412Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539416Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539419Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539423Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1333539469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539508Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539544Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1333539608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1333539708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1333539781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539789Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333539795Didn't retrieve data .Didn't retrieve 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retrieve data . /1333540333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540334Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540337Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540339Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540341Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540356Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540371Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1333540403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540408Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540409Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540412Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540413Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1333540736Didn't retrieve data 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before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267465', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267458', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267278', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267265', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267257', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267252', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267248', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267244', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266673', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266660', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266652', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266647', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266642', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266632', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266479', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266472', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266404', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266394', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266340', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266339', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266334', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266329', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266314', None, None, None, None, None, '2535904') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3296 time used for this insertion : 1.4094834327697754 save_final save missing photos in datou_result : time spend for datou_step_exec : 479.9813344478607 time spend to save output : 1.438502550125122 total time spend for step 2 : 481.41983699798584 step3:rle_unique_nms_with_priority Sat Feb 1 01:45:06 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 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 1099 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 15 nb_hashtags : 6 time to prepare the origin masks : 6.670413494110107 time for calcul the mask position with numpy : 0.22343206405639648 nb_pixel_total : 6319000 time to create 1 rle with new method : 0.9573218822479248 time for calcul the mask position with numpy : 0.027253150939941406 nb_pixel_total : 8358 time to create 1 rle with old method : 0.009492635726928711 time for calcul the mask position with numpy : 0.028078079223632812 nb_pixel_total : 6107 time to create 1 rle with old method : 0.007021188735961914 time for calcul the mask position with numpy : 0.028080463409423828 nb_pixel_total : 15175 time to create 1 rle with old method : 0.016979217529296875 time for calcul the mask position with numpy : 0.028834104537963867 nb_pixel_total : 113381 time to create 1 rle with old method : 0.12402772903442383 time for calcul the mask position with numpy : 0.030178070068359375 nb_pixel_total : 9792 time to create 1 rle with old method : 0.01071023941040039 time for calcul the mask position with numpy : 0.028806447982788086 nb_pixel_total : 181196 time to create 1 rle with new method : 0.5437166690826416 time for calcul the mask position with numpy : 0.029010295867919922 nb_pixel_total : 55855 time to create 1 rle with old method : 0.0615842342376709 time for calcul the mask position with numpy : 0.02837538719177246 nb_pixel_total : 46142 time to create 1 rle with old method : 0.050769805908203125 time for calcul the mask position with numpy : 0.029343605041503906 nb_pixel_total : 12369 time to create 1 rle with old method : 0.014653205871582031 time for calcul the mask position with numpy : 0.030425548553466797 nb_pixel_total : 244807 time to create 1 rle with new method : 0.6628372669219971 time for calcul the mask position with numpy : 0.02790665626525879 nb_pixel_total : 17300 time to create 1 rle with old method : 0.017901897430419922 time for calcul the mask position with numpy : 0.02675151824951172 nb_pixel_total : 7721 time to create 1 rle with old method : 0.0076978206634521484 time for calcul the mask position with numpy : 0.0265810489654541 nb_pixel_total : 5329 time to create 1 rle with old method : 0.005391836166381836 time for calcul the mask position with numpy : 0.027948617935180664 nb_pixel_total : 7708 time to create 1 rle with old method : 0.008245229721069336 create new chi : 3.2270209789276123 time to delete rle : 0.01515817642211914 batch 1 Loaded 29 chid ids of type : 3594 +++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.24799132347106934 nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 6.869379997253418 time for calcul the mask position with numpy : 1.1777420043945312 nb_pixel_total : 6624782 time to create 1 rle with new method : 1.2279701232910156 time for calcul the mask position with numpy : 0.0272216796875 nb_pixel_total : 3635 time to create 1 rle with old method : 0.003892183303833008 time for calcul the mask position with numpy : 0.03763246536254883 nb_pixel_total : 2860 time to create 1 rle with old method : 0.0033385753631591797 time for calcul the mask position with numpy : 0.03322935104370117 nb_pixel_total : 61266 time to create 1 rle with old method : 0.06807422637939453 time for calcul the mask position with numpy : 0.0286712646484375 nb_pixel_total : 18895 time to create 1 rle with old method : 0.021062374114990234 time for calcul the mask position with numpy : 0.028077363967895508 nb_pixel_total : 12499 time to create 1 rle with old method : 0.013508081436157227 time for calcul the mask position with numpy : 0.02798748016357422 nb_pixel_total : 14097 time to create 1 rle with old method : 0.015839576721191406 time for calcul the mask position with numpy : 0.02819538116455078 nb_pixel_total : 12868 time to create 1 rle with old method : 0.014249324798583984 time for calcul the mask position with numpy : 0.028216123580932617 nb_pixel_total : 6965 time to create 1 rle with old method : 0.00794076919555664 time for calcul the mask position with numpy : 0.027729272842407227 nb_pixel_total : 7498 time to create 1 rle with old method : 0.008276224136352539 time for calcul the mask position with numpy : 0.027333736419677734 nb_pixel_total : 29499 time to create 1 rle with old method : 0.0320582389831543 time for calcul the mask position with numpy : 0.028172731399536133 nb_pixel_total : 27836 time to create 1 rle with old method : 0.03057861328125 time for calcul the mask position with numpy : 0.028107643127441406 nb_pixel_total : 38698 time to create 1 rle with old method : 0.04318094253540039 time for calcul the mask position with numpy : 0.028193235397338867 nb_pixel_total : 16826 time to create 1 rle with old method : 0.018819093704223633 time for calcul the mask position with numpy : 0.02763533592224121 nb_pixel_total : 31647 time to create 1 rle with old method : 0.03405356407165527 time for calcul the mask position with numpy : 0.027490854263305664 nb_pixel_total : 13372 time to create 1 rle with old method : 0.014627218246459961 time for calcul the mask position with numpy : 0.028921842575073242 nb_pixel_total : 15391 time to create 1 rle with old method : 0.017367839813232422 time for calcul the mask position with numpy : 0.027922391891479492 nb_pixel_total : 11613 time to create 1 rle with old method : 0.013167858123779297 time for calcul the mask position with numpy : 0.031101703643798828 nb_pixel_total : 15916 time to create 1 rle with old method : 0.0184633731842041 time for calcul the mask position with numpy : 0.02973198890686035 nb_pixel_total : 18811 time to create 1 rle with old method : 0.021754741668701172 time for calcul the mask position with numpy : 0.029654502868652344 nb_pixel_total : 4936 time to create 1 rle with old method : 0.005990505218505859 time for calcul the mask position with numpy : 0.028991222381591797 nb_pixel_total : 12115 time to create 1 rle with old method : 0.01392984390258789 time for calcul the mask position with numpy : 0.030032634735107422 nb_pixel_total : 48215 time to create 1 rle with old method : 0.05544304847717285 create new chi : 3.5855791568756104 time to delete rle : 0.002248048782348633 batch 1 Loaded 47 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.10633158683776855 nb_obj : 58 nb_hashtags : 6 time to prepare the origin masks : 7.312647819519043 time for calcul the mask position with numpy : 0.22427988052368164 nb_pixel_total : 4870263 time to create 1 rle with new method : 0.8715322017669678 time for calcul the mask position with numpy : 0.027252912521362305 nb_pixel_total : 17406 time to create 1 rle with old method : 0.018965959548950195 time for calcul the mask position with numpy : 0.028112173080444336 nb_pixel_total : 5363 time to create 1 rle with old method : 0.0057430267333984375 time for calcul the mask position with numpy : 0.028516292572021484 nb_pixel_total : 29858 time to create 1 rle with old method : 0.034239768981933594 time for calcul the mask position with numpy : 0.028769731521606445 nb_pixel_total : 33399 time to create 1 rle with old method : 0.036626577377319336 time for calcul the mask position with numpy : 0.028510332107543945 nb_pixel_total : 14239 time to create 1 rle with old method : 0.015254020690917969 time for calcul the mask position with numpy : 0.02910637855529785 nb_pixel_total : 24785 time to create 1 rle with old method : 0.02811264991760254 time for calcul the mask position with numpy : 0.029317140579223633 nb_pixel_total : 21946 time to create 1 rle with old method : 0.024709463119506836 time for calcul the mask position with numpy : 0.0291140079498291 nb_pixel_total : 10996 time to create 1 rle with old method : 0.012064456939697266 time for calcul the mask position with numpy : 0.027945756912231445 nb_pixel_total : 16692 time to create 1 rle with old method : 0.017865657806396484 time for calcul the mask position with numpy : 0.028033733367919922 nb_pixel_total : 63607 time to create 1 rle with old method : 0.06884384155273438 time for calcul the mask position with numpy : 0.032636165618896484 nb_pixel_total : 30493 time to create 1 rle with old method : 0.03272724151611328 time for calcul the mask position with numpy : 0.028713226318359375 nb_pixel_total : 21597 time to create 1 rle with old method : 0.02517247200012207 time for calcul the mask position with numpy : 0.02940988540649414 nb_pixel_total : 30168 time to create 1 rle with old method : 0.03257608413696289 time for calcul the mask position with numpy : 0.02933502197265625 nb_pixel_total : 15471 time to create 1 rle with old method : 0.01726508140563965 time for calcul the mask position with numpy : 0.029331445693969727 nb_pixel_total : 31256 time to create 1 rle with old method : 0.034439802169799805 time for calcul the mask position with numpy : 0.029297351837158203 nb_pixel_total : 15735 time to create 1 rle with old method : 0.017107248306274414 time for calcul the mask position with numpy : 0.030802488327026367 nb_pixel_total : 30170 time to create 1 rle with old method : 0.03381228446960449 time for calcul the mask position with numpy : 0.030482053756713867 nb_pixel_total : 26758 time to create 1 rle with old method : 0.030138492584228516 time for calcul the mask position with numpy : 0.028258562088012695 nb_pixel_total : 16052 time to create 1 rle with old method : 0.017522096633911133 time for calcul the mask position with numpy : 0.030007600784301758 nb_pixel_total : 20601 time to create 1 rle with old method : 0.023232460021972656 time for calcul the mask position with numpy : 0.028356075286865234 nb_pixel_total : 15387 time to create 1 rle with old method : 0.016381263732910156 time for calcul the mask position with numpy : 0.027966022491455078 nb_pixel_total : 28462 time to create 1 rle with old method : 0.030312299728393555 time for calcul the mask position with numpy : 0.02832961082458496 nb_pixel_total : 53345 time to create 1 rle with old method : 0.0626823902130127 time for calcul the mask position with numpy : 0.03302288055419922 nb_pixel_total : 15506 time to create 1 rle with old method : 0.024669170379638672 time for calcul the mask position with numpy : 0.029001951217651367 nb_pixel_total : 32702 time to create 1 rle with old method : 0.036383628845214844 time for calcul the mask position with numpy : 0.027567386627197266 nb_pixel_total : 35105 time to create 1 rle with old method : 0.03666806221008301 time for calcul the mask position with numpy : 0.02936387062072754 nb_pixel_total : 109600 time to create 1 rle with old method : 0.11800837516784668 time for calcul the mask position with numpy : 0.03305530548095703 nb_pixel_total : 169 time to create 1 rle with old method : 0.0008466243743896484 time for calcul the mask position with numpy : 0.032999277114868164 nb_pixel_total : 15474 time to create 1 rle with old method : 0.01715540885925293 time for calcul the mask position with numpy : 0.03235483169555664 nb_pixel_total : 597084 time to create 1 rle with new method : 0.6264791488647461 time for calcul the mask position with numpy : 0.027621984481811523 nb_pixel_total : 21319 time to create 1 rle with old method : 0.023282766342163086 time for calcul the mask position with numpy : 0.0291745662689209 nb_pixel_total : 37570 time to create 1 rle with old method : 0.03987550735473633 time for calcul the mask position with numpy : 0.027704477310180664 nb_pixel_total : 7452 time to create 1 rle with old method : 0.0077970027923583984 time for calcul the mask position with numpy : 0.033022403717041016 nb_pixel_total : 25312 time to create 1 rle with old method : 0.028219938278198242 time for calcul the mask position with numpy : 0.02889227867126465 nb_pixel_total : 10 time to create 1 rle with old method : 7.867813110351562e-05 time for calcul the mask position with numpy : 0.028528928756713867 nb_pixel_total : 7190 time to create 1 rle with old method : 0.008063793182373047 time for calcul the mask position with numpy : 0.028885602951049805 nb_pixel_total : 68856 time to create 1 rle with old method : 0.07499575614929199 time for calcul the mask position with numpy : 0.029036521911621094 nb_pixel_total : 11512 time to create 1 rle with old method : 0.012464284896850586 time for calcul the mask position with numpy : 0.029312849044799805 nb_pixel_total : 23910 time to create 1 rle with old method : 0.02522420883178711 time for calcul the mask position with numpy : 0.0290224552154541 nb_pixel_total : 18641 time to create 1 rle with old method : 0.020287752151489258 time for calcul the mask position with numpy : 0.033875465393066406 nb_pixel_total : 32629 time to create 1 rle with old method : 0.03661084175109863 time for calcul the mask position with numpy : 0.028920650482177734 nb_pixel_total : 2751 time to create 1 rle with old method : 0.0031862258911132812 time for calcul the mask position with numpy : 0.030734777450561523 nb_pixel_total : 22034 time to create 1 rle with old method : 0.024147510528564453 time for calcul the mask position with numpy : 0.029272079467773438 nb_pixel_total : 34322 time to create 1 rle with old method : 0.03729534149169922 time for calcul the mask position with numpy : 0.028737545013427734 nb_pixel_total : 15556 time to create 1 rle with old method : 0.017400741577148438 time for calcul the mask position with numpy : 0.03135085105895996 nb_pixel_total : 22507 time to create 1 rle with old method : 0.025849103927612305 time for calcul the mask position with numpy : 0.029951810836791992 nb_pixel_total : 18936 time to create 1 rle with old method : 0.02106165885925293 time for calcul the mask position with numpy : 0.02780437469482422 nb_pixel_total : 20302 time to create 1 rle with old method : 0.022517919540405273 time for calcul the mask position with numpy : 0.028920650482177734 nb_pixel_total : 19243 time to create 1 rle with old method : 0.021875381469726562 time for calcul the mask position with numpy : 0.029473543167114258 nb_pixel_total : 20484 time to create 1 rle with old method : 0.02361321449279785 time for calcul the mask position with numpy : 0.033089399337768555 nb_pixel_total : 109038 time to create 1 rle with old method : 0.12353301048278809 time for calcul the mask position with numpy : 0.02843308448791504 nb_pixel_total : 17804 time to create 1 rle with old method : 0.019415616989135742 time for calcul the mask position with numpy : 0.02763652801513672 nb_pixel_total : 4503 time to create 1 rle with old method : 0.004537343978881836 time for calcul the mask position with numpy : 0.028475046157836914 nb_pixel_total : 213901 time to create 1 rle with new method : 0.30483174324035645 time for calcul the mask position with numpy : 0.028243064880371094 nb_pixel_total : 5966 time to create 1 rle with old method : 0.006430387496948242 time for calcul the mask position with numpy : 0.02843642234802246 nb_pixel_total : 14496 time to create 1 rle with old method : 0.015608787536621094 time for calcul the mask position with numpy : 0.027901411056518555 nb_pixel_total : 4307 time to create 1 rle with old method : 0.0045812129974365234 create new chi : 5.329857349395752 time to delete rle : 0.00420689582824707 batch 1 Loaded 117 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 716 TO DO : save crop sub photo not yet done ! save time : 0.3645949363708496 nb_obj : 61 nb_hashtags : 4 time to prepare the origin masks : 7.300637483596802 time for calcul the mask position with numpy : 0.5428457260131836 nb_pixel_total : 5940251 time to create 1 rle with new method : 0.5452854633331299 time for calcul the mask position with numpy : 0.03314542770385742 nb_pixel_total : 16656 time to create 1 rle with old method : 0.02665424346923828 time for calcul the mask position with numpy : 0.03013134002685547 nb_pixel_total : 10071 time to create 1 rle with old method : 0.011520147323608398 time for calcul the mask position with numpy : 0.028954267501831055 nb_pixel_total : 10381 time to create 1 rle with old method : 0.01442265510559082 time for calcul the mask position with numpy : 0.02950739860534668 nb_pixel_total : 6372 time to create 1 rle with old method : 0.009828805923461914 time for calcul the mask position with numpy : 0.0329432487487793 nb_pixel_total : 14187 time to create 1 rle with old method : 0.023549795150756836 time for calcul the mask position with numpy : 0.03254842758178711 nb_pixel_total : 8548 time to create 1 rle with old method : 0.009634971618652344 time for calcul the mask position with numpy : 0.028914928436279297 nb_pixel_total : 15137 time to create 1 rle with old method : 0.017270565032958984 time for calcul the mask position with numpy : 0.029167890548706055 nb_pixel_total : 15982 time to create 1 rle with old method : 0.016982555389404297 time for calcul the mask position with numpy : 0.028482913970947266 nb_pixel_total : 16093 time to create 1 rle with old method : 0.018090009689331055 time for calcul the mask position with numpy : 0.030429840087890625 nb_pixel_total : 6296 time to create 1 rle with old method : 0.0068013668060302734 time for calcul the mask position with numpy : 0.02862071990966797 nb_pixel_total : 3069 time to create 1 rle with old method : 0.00353240966796875 time for calcul the mask position with numpy : 0.027886629104614258 nb_pixel_total : 40718 time to create 1 rle with old method : 0.04459881782531738 time for calcul the mask position with numpy : 0.02940225601196289 nb_pixel_total : 35652 time to create 1 rle with old method : 0.04029250144958496 time for calcul the mask position with numpy : 0.028742074966430664 nb_pixel_total : 8660 time to create 1 rle with old method : 0.0095977783203125 time for calcul the mask position with numpy : 0.0281674861907959 nb_pixel_total : 5697 time to create 1 rle with old method : 0.006180524826049805 time for calcul the mask position with numpy : 0.02926015853881836 nb_pixel_total : 31674 time to create 1 rle with old method : 0.0361328125 time for calcul the mask position with numpy : 0.029704809188842773 nb_pixel_total : 37284 time to create 1 rle with old method : 0.04100394248962402 time for calcul the mask position with numpy : 0.029722213745117188 nb_pixel_total : 6642 time to create 1 rle with old method : 0.00762486457824707 time for calcul the mask position with numpy : 0.02976512908935547 nb_pixel_total : 34270 time to create 1 rle with old method : 0.03997230529785156 time for calcul the mask position with numpy : 0.034850120544433594 nb_pixel_total : 20393 time to create 1 rle with old method : 0.027919530868530273 time for calcul the mask position with numpy : 0.030364274978637695 nb_pixel_total : 25159 time to create 1 rle with old method : 0.02825927734375 time for calcul the mask position with numpy : 0.029672622680664062 nb_pixel_total : 3117 time to create 1 rle with old method : 0.0034546852111816406 time for calcul the mask position with numpy : 0.030701875686645508 nb_pixel_total : 7264 time to create 1 rle with old method : 0.009573936462402344 time for calcul the mask position with numpy : 0.03038620948791504 nb_pixel_total : 40721 time to create 1 rle with old method : 0.04722023010253906 time for calcul the mask position with numpy : 0.029120445251464844 nb_pixel_total : 39409 time to create 1 rle with old method : 0.04370880126953125 time for calcul the mask position with numpy : 0.029366493225097656 nb_pixel_total : 13956 time to create 1 rle with old method : 0.01623249053955078 time for calcul the mask position with numpy : 0.03337693214416504 nb_pixel_total : 4930 time to create 1 rle with old method : 0.0058438777923583984 time for calcul the mask position with numpy : 0.029680967330932617 nb_pixel_total : 30894 time to create 1 rle with old method : 0.033890724182128906 time for calcul the mask position with numpy : 0.029727458953857422 nb_pixel_total : 351 time to create 1 rle with old method : 0.0004837512969970703 time for calcul the mask position with numpy : 0.028975486755371094 nb_pixel_total : 13310 time to create 1 rle with old method : 0.015058517456054688 time for calcul the mask position with numpy : 0.02879047393798828 nb_pixel_total : 51222 time to create 1 rle with old method : 0.05661416053771973 time for calcul the mask position with numpy : 0.02805352210998535 nb_pixel_total : 27341 time to create 1 rle with old method : 0.03140401840209961 time for calcul the mask position with numpy : 0.02871870994567871 nb_pixel_total : 15438 time to create 1 rle with old method : 0.0160214900970459 time for calcul the mask position with numpy : 0.02773451805114746 nb_pixel_total : 23636 time to create 1 rle with old method : 0.025476455688476562 time for calcul the mask position with numpy : 0.028308868408203125 nb_pixel_total : 4462 time to create 1 rle with old method : 0.005132198333740234 time for calcul the mask position with numpy : 0.028232812881469727 nb_pixel_total : 13788 time to create 1 rle with old method : 0.014317989349365234 time for calcul the mask position with numpy : 0.028388023376464844 nb_pixel_total : 19514 time to create 1 rle with old method : 0.021318435668945312 time for calcul the mask position with numpy : 0.029347658157348633 nb_pixel_total : 14943 time to create 1 rle with old method : 0.017135143280029297 time for calcul the mask position with numpy : 0.02898883819580078 nb_pixel_total : 30469 time to create 1 rle with old method : 0.03377270698547363 time for calcul the mask position with numpy : 0.028974294662475586 nb_pixel_total : 6026 time to create 1 rle with old method : 0.006736278533935547 time for calcul the mask position with numpy : 0.029194355010986328 nb_pixel_total : 6640 time to create 1 rle with old method : 0.007299900054931641 time for calcul the mask position with numpy : 0.029131412506103516 nb_pixel_total : 4538 time to create 1 rle with old method : 0.004807710647583008 time for calcul the mask position with numpy : 0.027840375900268555 nb_pixel_total : 6715 time to create 1 rle with old method : 0.007422685623168945 time for calcul the mask position with numpy : 0.030473709106445312 nb_pixel_total : 3 time to create 1 rle with old method : 3.457069396972656e-05 time for calcul the mask position with numpy : 0.02840423583984375 nb_pixel_total : 41924 time to create 1 rle with old method : 0.04403400421142578 time for calcul the mask position with numpy : 0.029766082763671875 nb_pixel_total : 33830 time to create 1 rle with old method : 0.03508949279785156 time for calcul the mask position with numpy : 0.027196407318115234 nb_pixel_total : 23006 time to create 1 rle with old method : 0.024092435836791992 time for calcul the mask position with numpy : 0.02798008918762207 nb_pixel_total : 26538 time to create 1 rle with old method : 0.029520034790039062 time for calcul the mask position with numpy : 0.02834296226501465 nb_pixel_total : 57423 time to create 1 rle with old method : 0.06088066101074219 time for calcul the mask position with numpy : 0.028958797454833984 nb_pixel_total : 10557 time to create 1 rle with old method : 0.011252880096435547 time for calcul the mask position with numpy : 0.027773380279541016 nb_pixel_total : 6178 time to create 1 rle with old method : 0.006529331207275391 time for calcul the mask position with numpy : 0.02858567237854004 nb_pixel_total : 30885 time to create 1 rle with old method : 0.032775163650512695 time for calcul the mask position with numpy : 0.027710437774658203 nb_pixel_total : 6890 time to create 1 rle with old method : 0.007111549377441406 time for calcul the mask position with numpy : 0.02785205841064453 nb_pixel_total : 27606 time to create 1 rle with old method : 0.03035593032836914 time for calcul the mask position with numpy : 0.030293703079223633 nb_pixel_total : 18825 time to create 1 rle with old method : 0.03008127212524414 time for calcul the mask position with numpy : 0.03356027603149414 nb_pixel_total : 32603 time to create 1 rle with old method : 0.03677725791931152 time for calcul the mask position with numpy : 0.029469013214111328 nb_pixel_total : 26302 time to create 1 rle with old method : 0.029284000396728516 time for calcul the mask position with numpy : 0.02998661994934082 nb_pixel_total : 6754 time to create 1 rle with old method : 0.007491350173950195 time for calcul the mask position with numpy : 0.03006291389465332 nb_pixel_total : 8985 time to create 1 rle with old method : 0.010010004043579102 time for calcul the mask position with numpy : 0.029981136322021484 nb_pixel_total : 4055 time to create 1 rle with old method : 0.004485130310058594 create new chi : 4.192425012588501 time to delete rle : 0.004576444625854492 batch 1 Loaded 124 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 421 TO DO : save crop sub photo not yet done ! save time : 0.20923280715942383 nb_obj : 31 nb_hashtags : 4 time to prepare the origin masks : 7.344481468200684 time for calcul the mask position with numpy : 2.1198925971984863 nb_pixel_total : 6374313 time to create 1 rle with new method : 1.1114511489868164 time for calcul the mask position with numpy : 0.0290677547454834 nb_pixel_total : 12472 time to create 1 rle with old method : 0.014205694198608398 time for calcul the mask position with numpy : 0.02913045883178711 nb_pixel_total : 18858 time to create 1 rle with old method : 0.021311283111572266 time for calcul the mask position with numpy : 0.028778553009033203 nb_pixel_total : 6211 time to create 1 rle with old method : 0.007074117660522461 time for calcul the mask position with numpy : 0.028035879135131836 nb_pixel_total : 5619 time to create 1 rle with old method : 0.0061321258544921875 time for calcul the mask position with numpy : 0.0308840274810791 nb_pixel_total : 9661 time to create 1 rle with old method : 0.010602951049804688 time for calcul the mask position with numpy : 0.030826330184936523 nb_pixel_total : 14380 time to create 1 rle with old method : 0.016657114028930664 time for calcul the mask position with numpy : 0.03000807762145996 nb_pixel_total : 13586 time to create 1 rle with old method : 0.016175031661987305 time for calcul the mask position with numpy : 0.02905416488647461 nb_pixel_total : 47445 time to create 1 rle with old method : 0.05304145812988281 time for calcul the mask position with numpy : 0.0278933048248291 nb_pixel_total : 11430 time to create 1 rle with old method : 0.012537956237792969 time for calcul the mask position with numpy : 0.02849102020263672 nb_pixel_total : 7920 time to create 1 rle with old method : 0.009241342544555664 time for calcul the mask position with numpy : 0.028872966766357422 nb_pixel_total : 4401 time to create 1 rle with old method : 0.0048236846923828125 time for calcul the mask position with numpy : 0.028006792068481445 nb_pixel_total : 9611 time to create 1 rle with old method : 0.01014566421508789 time for calcul the mask position with numpy : 0.027539968490600586 nb_pixel_total : 5144 time to create 1 rle with old method : 0.00565028190612793 time for calcul the mask position with numpy : 0.028354406356811523 nb_pixel_total : 23136 time to create 1 rle with old method : 0.026343345642089844 time for calcul the mask position with numpy : 0.028925180435180664 nb_pixel_total : 30632 time to create 1 rle with old method : 0.03420066833496094 time for calcul the mask position with numpy : 0.028307437896728516 nb_pixel_total : 4461 time to create 1 rle with old method : 0.004725456237792969 time for calcul the mask position with numpy : 0.027695894241333008 nb_pixel_total : 4410 time to create 1 rle with old method : 0.004656076431274414 time for calcul the mask position with numpy : 0.029422521591186523 nb_pixel_total : 25905 time to create 1 rle with old method : 0.029379844665527344 time for calcul the mask position with numpy : 0.029616117477416992 nb_pixel_total : 16671 time to create 1 rle with old method : 0.019214868545532227 time for calcul the mask position with numpy : 0.029051780700683594 nb_pixel_total : 22109 time to create 1 rle with old method : 0.02470993995666504 time for calcul the mask position with numpy : 0.028830289840698242 nb_pixel_total : 24843 time to create 1 rle with old method : 0.028275728225708008 time for calcul the mask position with numpy : 0.028378725051879883 nb_pixel_total : 34617 time to create 1 rle with old method : 0.0374445915222168 time for calcul the mask position with numpy : 0.029288530349731445 nb_pixel_total : 8028 time to create 1 rle with old method : 0.009203433990478516 time for calcul the mask position with numpy : 0.02880549430847168 nb_pixel_total : 17325 time to create 1 rle with old method : 0.019349336624145508 time for calcul the mask position with numpy : 0.028628826141357422 nb_pixel_total : 56088 time to create 1 rle with old method : 0.05902814865112305 time for calcul the mask position with numpy : 0.029185056686401367 nb_pixel_total : 16984 time to create 1 rle with old method : 0.018600940704345703 time for calcul the mask position with numpy : 0.029349327087402344 nb_pixel_total : 21976 time to create 1 rle with old method : 0.024092912673950195 time for calcul the mask position with numpy : 0.028461933135986328 nb_pixel_total : 8782 time to create 1 rle with old method : 0.009924650192260742 time for calcul the mask position with numpy : 0.02854299545288086 nb_pixel_total : 13722 time to create 1 rle with old method : 0.014810800552368164 time for calcul the mask position with numpy : 0.029429197311401367 nb_pixel_total : 179500 time to create 1 rle with new method : 1.0389294624328613 create new chi : 5.777422189712524 time to delete rle : 0.0019259452819824219 batch 1 Loaded 65 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.11346006393432617 nb_obj : 60 nb_hashtags : 5 time to prepare the origin masks : 7.208075284957886 time for calcul the mask position with numpy : 0.5687820911407471 nb_pixel_total : 5892004 time to create 1 rle with new method : 0.7586743831634521 time for calcul the mask position with numpy : 0.029187440872192383 nb_pixel_total : 31472 time to create 1 rle with old method : 0.035685062408447266 time for calcul the mask position with numpy : 0.03292250633239746 nb_pixel_total : 6449 time to create 1 rle with old method : 0.006888628005981445 time for calcul the mask position with numpy : 0.028700828552246094 nb_pixel_total : 22288 time to create 1 rle with old method : 0.024340152740478516 time for calcul the mask position with numpy : 0.0291292667388916 nb_pixel_total : 20009 time to create 1 rle with old method : 0.02187371253967285 time for calcul the mask position with numpy : 0.029639244079589844 nb_pixel_total : 44788 time to create 1 rle with old method : 0.046894073486328125 time for calcul the mask position with numpy : 0.02805042266845703 nb_pixel_total : 12563 time to create 1 rle with old method : 0.013686895370483398 time for calcul the mask position with numpy : 0.02914118766784668 nb_pixel_total : 18663 time to create 1 rle with old method : 0.021186351776123047 time for calcul the mask position with numpy : 0.029299259185791016 nb_pixel_total : 11299 time to create 1 rle with old method : 0.012499094009399414 time for calcul the mask position with numpy : 0.029048681259155273 nb_pixel_total : 14314 time to create 1 rle with old method : 0.01618790626525879 time for calcul the mask position with numpy : 0.027320384979248047 nb_pixel_total : 26687 time to create 1 rle with old method : 0.028934717178344727 time for calcul the mask position with numpy : 0.029113054275512695 nb_pixel_total : 16554 time to create 1 rle with old method : 0.01806330680847168 time for calcul the mask position with numpy : 0.028735876083374023 nb_pixel_total : 17246 time to create 1 rle with old method : 0.018261194229125977 time for calcul the mask position with numpy : 0.029263973236083984 nb_pixel_total : 17831 time to create 1 rle with old method : 0.01982569694519043 time for calcul the mask position with numpy : 0.02892589569091797 nb_pixel_total : 18093 time to create 1 rle with old method : 0.0193936824798584 time for calcul the mask position with numpy : 0.02855658531188965 nb_pixel_total : 21059 time to create 1 rle with old method : 0.024107933044433594 time for calcul the mask position with numpy : 0.029234647750854492 nb_pixel_total : 31382 time to create 1 rle with old method : 0.03448677062988281 time for calcul the mask position with numpy : 0.029081106185913086 nb_pixel_total : 19295 time to create 1 rle with old method : 0.02055215835571289 time for calcul the mask position with numpy : 0.027274131774902344 nb_pixel_total : 36859 time to create 1 rle with old method : 0.040013790130615234 time for calcul the mask position with numpy : 0.02906036376953125 nb_pixel_total : 36728 time to create 1 rle with old method : 0.038787841796875 time for calcul the mask position with numpy : 0.03446674346923828 nb_pixel_total : 33289 time to create 1 rle with old method : 0.03786873817443848 time for calcul the mask position with numpy : 0.029370784759521484 nb_pixel_total : 12931 time to create 1 rle with old method : 0.014750242233276367 time for calcul the mask position with numpy : 0.028195858001708984 nb_pixel_total : 12362 time to create 1 rle with old method : 0.013341665267944336 time for calcul the mask position with numpy : 0.029158353805541992 nb_pixel_total : 23944 time to create 1 rle with old method : 0.026259660720825195 time for calcul the mask position with numpy : 0.029158830642700195 nb_pixel_total : 32840 time to create 1 rle with old method : 0.03651857376098633 time for calcul the mask position with numpy : 0.02901911735534668 nb_pixel_total : 30980 time to create 1 rle with old method : 0.03336477279663086 time for calcul the mask position with numpy : 0.028514623641967773 nb_pixel_total : 8629 time to create 1 rle with old method : 0.009458303451538086 time for calcul the mask position with numpy : 0.029242992401123047 nb_pixel_total : 21698 time to create 1 rle with old method : 0.024850130081176758 time for calcul the mask position with numpy : 0.028871774673461914 nb_pixel_total : 11198 time to create 1 rle with old method : 0.011635065078735352 time for calcul the mask position with numpy : 0.02772831916809082 nb_pixel_total : 10830 time to create 1 rle with old method : 0.011502742767333984 time for calcul the mask position with numpy : 0.028001785278320312 nb_pixel_total : 12628 time to create 1 rle with old method : 0.013302087783813477 time for calcul the mask position with numpy : 0.027866363525390625 nb_pixel_total : 14510 time to create 1 rle with old method : 0.015880346298217773 time for calcul the mask position with numpy : 0.03096771240234375 nb_pixel_total : 22449 time to create 1 rle with old method : 0.024598121643066406 time for calcul the mask position with numpy : 0.02776813507080078 nb_pixel_total : 16795 time to create 1 rle with old method : 0.017925262451171875 time for calcul the mask position with numpy : 0.029126644134521484 nb_pixel_total : 12378 time to create 1 rle with old method : 0.014168262481689453 time for calcul the mask position with numpy : 0.029054880142211914 nb_pixel_total : 7263 time to create 1 rle with old method : 0.008446693420410156 time for calcul the mask position with numpy : 0.029396772384643555 nb_pixel_total : 16915 time to create 1 rle with old method : 0.019071578979492188 time for calcul the mask position with numpy : 0.028944730758666992 nb_pixel_total : 20831 time to create 1 rle with old method : 0.021909236907958984 time for calcul the mask position with numpy : 0.027932405471801758 nb_pixel_total : 7190 time to create 1 rle with old method : 0.007887601852416992 time for calcul the mask position with numpy : 0.03191113471984863 nb_pixel_total : 44452 time to create 1 rle with old method : 0.05089592933654785 time for calcul the mask position with numpy : 0.028814315795898438 nb_pixel_total : 4528 time to create 1 rle with old method : 0.004892826080322266 time for calcul the mask position with numpy : 0.027979612350463867 nb_pixel_total : 56529 time to create 1 rle with old method : 0.06439018249511719 time for calcul the mask position with numpy : 0.02953171730041504 nb_pixel_total : 28404 time to create 1 rle with old method : 0.033516883850097656 time for calcul the mask position with numpy : 0.02801036834716797 nb_pixel_total : 17870 time to create 1 rle with old method : 0.018770456314086914 time for calcul the mask position with numpy : 0.028949975967407227 nb_pixel_total : 17582 time to create 1 rle with old method : 0.01906418800354004 time for calcul the mask position with numpy : 0.029018163681030273 nb_pixel_total : 13949 time to create 1 rle with old method : 0.015500307083129883 time for calcul the mask position with numpy : 0.029911041259765625 nb_pixel_total : 39540 time to create 1 rle with old method : 0.045108795166015625 time for calcul the mask position with numpy : 0.02835559844970703 nb_pixel_total : 6546 time to create 1 rle with old method : 0.00720977783203125 time for calcul the mask position with numpy : 0.028992176055908203 nb_pixel_total : 13360 time to create 1 rle with old method : 0.014581680297851562 time for calcul the mask position with numpy : 0.029789209365844727 nb_pixel_total : 6771 time to create 1 rle with old method : 0.007372140884399414 time for calcul the mask position with numpy : 0.02889728546142578 nb_pixel_total : 3572 time to create 1 rle with old method : 0.004174470901489258 time for calcul the mask position with numpy : 0.027659893035888672 nb_pixel_total : 7489 time to create 1 rle with old method : 0.008026838302612305 time for calcul the mask position with numpy : 0.029152870178222656 nb_pixel_total : 42260 time to create 1 rle with old method : 0.04578423500061035 time for calcul the mask position with numpy : 0.029325008392333984 nb_pixel_total : 9559 time to create 1 rle with old method : 0.010499000549316406 time for calcul the mask position with numpy : 0.029175519943237305 nb_pixel_total : 46314 time to create 1 rle with old method : 0.048809051513671875 time for calcul the mask position with numpy : 0.02903604507446289 nb_pixel_total : 6317 time to create 1 rle with old method : 0.0069963932037353516 time for calcul the mask position with numpy : 0.02891397476196289 nb_pixel_total : 12808 time to create 1 rle with old method : 0.014574766159057617 time for calcul the mask position with numpy : 0.028316497802734375 nb_pixel_total : 7922 time to create 1 rle with old method : 0.008775472640991211 time for calcul the mask position with numpy : 0.029414892196655273 nb_pixel_total : 5960 time to create 1 rle with old method : 0.006608247756958008 time for calcul the mask position with numpy : 0.02897357940673828 nb_pixel_total : 13265 time to create 1 rle with old method : 0.013693094253540039 create new chi : 4.380509376525879 time to delete rle : 0.0033080577850341797 batch 1 Loaded 120 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 1.1425409317016602 nb_obj : 22 nb_hashtags : 7 time to prepare the origin masks : 7.403083801269531 time for calcul the mask position with numpy : 1.1218364238739014 nb_pixel_total : 6629070 time to create 1 rle with new method : 0.4801034927368164 time for calcul the mask position with numpy : 0.02929544448852539 nb_pixel_total : 25106 time to create 1 rle with old method : 0.02873706817626953 time for calcul the mask position with numpy : 0.02965855598449707 nb_pixel_total : 15353 time to create 1 rle with old method : 0.01676177978515625 time for calcul the mask position with numpy : 0.03001880645751953 nb_pixel_total : 25946 time to create 1 rle with old method : 0.029468059539794922 time for calcul the mask position with numpy : 0.028670310974121094 nb_pixel_total : 6951 time to create 1 rle with old method : 0.007905006408691406 time for calcul the mask position with numpy : 0.029196977615356445 nb_pixel_total : 9379 time to create 1 rle with old method : 0.012789249420166016 time for calcul the mask position with numpy : 0.03662538528442383 nb_pixel_total : 13020 time to create 1 rle with old method : 0.015137195587158203 time for calcul the mask position with numpy : 0.029396772384643555 nb_pixel_total : 18893 time to create 1 rle with old method : 0.02106952667236328 time for calcul the mask position with numpy : 0.02850627899169922 nb_pixel_total : 11901 time to create 1 rle with old method : 0.012536048889160156 time for calcul the mask position with numpy : 0.029132366180419922 nb_pixel_total : 18481 time to create 1 rle with old method : 0.020932674407958984 time for calcul the mask position with numpy : 0.029270172119140625 nb_pixel_total : 58700 time to create 1 rle with old method : 0.06430268287658691 time for calcul the mask position with numpy : 0.028683900833129883 nb_pixel_total : 11116 time to create 1 rle with old method : 0.011801719665527344 time for calcul the mask position with numpy : 0.02871990203857422 nb_pixel_total : 12909 time to create 1 rle with old method : 0.014152288436889648 time for calcul the mask position with numpy : 0.029069185256958008 nb_pixel_total : 25584 time to create 1 rle with old method : 0.028178930282592773 time for calcul the mask position with numpy : 0.02924346923828125 nb_pixel_total : 30169 time to create 1 rle with old method : 0.03182625770568848 time for calcul the mask position with numpy : 0.029362916946411133 nb_pixel_total : 6110 time to create 1 rle with old method : 0.006974458694458008 time for calcul the mask position with numpy : 0.029228687286376953 nb_pixel_total : 18241 time to create 1 rle with old method : 0.019835233688354492 time for calcul the mask position with numpy : 0.029033899307250977 nb_pixel_total : 68682 time to create 1 rle with old method : 0.07449078559875488 time for calcul the mask position with numpy : 0.029338598251342773 nb_pixel_total : 15455 time to create 1 rle with old method : 0.01692056655883789 time for calcul the mask position with numpy : 0.029059410095214844 nb_pixel_total : 70 time to create 1 rle with old method : 0.0002732276916503906 time for calcul the mask position with numpy : 0.029401302337646484 nb_pixel_total : 7175 time to create 1 rle with old method : 0.00795888900756836 time for calcul the mask position with numpy : 0.029254674911499023 nb_pixel_total : 21929 time to create 1 rle with old method : 0.02370142936706543 create new chi : 2.7549586296081543 time to delete rle : 0.0014903545379638672 batch 1 Loaded 44 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 133 TO DO : save crop sub photo not yet done ! save time : 0.12631440162658691 nb_obj : 49 nb_hashtags : 4 time to prepare the origin masks : 7.256035566329956 time for calcul the mask position with numpy : 0.4279510974884033 nb_pixel_total : 5761684 time to create 1 rle with new method : 1.2372283935546875 time for calcul the mask position with numpy : 0.02913498878479004 nb_pixel_total : 13315 time to create 1 rle with old method : 0.014526128768920898 time for calcul the mask position with numpy : 0.027804851531982422 nb_pixel_total : 9910 time to create 1 rle with old method : 0.01133418083190918 time for calcul the mask position with numpy : 0.029462814331054688 nb_pixel_total : 28907 time to create 1 rle with old method : 0.03305482864379883 time for calcul the mask position with numpy : 0.029211044311523438 nb_pixel_total : 4678 time to create 1 rle with old method : 0.005177021026611328 time for calcul the mask position with numpy : 0.028970956802368164 nb_pixel_total : 14443 time to create 1 rle with old method : 0.016011953353881836 time for calcul the mask position with numpy : 0.029709339141845703 nb_pixel_total : 11424 time to create 1 rle with old method : 0.012519359588623047 time for calcul the mask position with numpy : 0.029234886169433594 nb_pixel_total : 15515 time to create 1 rle with old method : 0.01647162437438965 time for calcul the mask position with numpy : 0.0283052921295166 nb_pixel_total : 6619 time to create 1 rle with old method : 0.007222890853881836 time for calcul the mask position with numpy : 0.02933478355407715 nb_pixel_total : 69140 time to create 1 rle with old method : 0.0742344856262207 time for calcul the mask position with numpy : 0.030360698699951172 nb_pixel_total : 12799 time to create 1 rle with old method : 0.021163225173950195 time for calcul the mask position with numpy : 0.03314399719238281 nb_pixel_total : 19660 time to create 1 rle with old method : 0.025500774383544922 time for calcul the mask position with numpy : 0.029279470443725586 nb_pixel_total : 37260 time to create 1 rle with old method : 0.04262828826904297 time for calcul the mask position with numpy : 0.03014373779296875 nb_pixel_total : 3835 time to create 1 rle with old method : 0.004549264907836914 time for calcul the mask position with numpy : 0.02991461753845215 nb_pixel_total : 4720 time to create 1 rle with old method : 0.005343437194824219 time for calcul the mask position with numpy : 0.030775070190429688 nb_pixel_total : 22453 time to create 1 rle with old method : 0.026845455169677734 time for calcul the mask position with numpy : 0.02937150001525879 nb_pixel_total : 8286 time to create 1 rle with old method : 0.009488105773925781 time for calcul the mask position with numpy : 0.029361486434936523 nb_pixel_total : 92796 time to create 1 rle with old method : 0.1017446517944336 time for calcul the mask position with numpy : 0.029902219772338867 nb_pixel_total : 102767 time to create 1 rle with old method : 0.11113810539245605 time for calcul the mask position with numpy : 0.029639482498168945 nb_pixel_total : 42130 time to create 1 rle with old method : 0.048645734786987305 time for calcul the mask position with numpy : 0.028469324111938477 nb_pixel_total : 26051 time to create 1 rle with old method : 0.03356480598449707 time for calcul the mask position with numpy : 0.030364036560058594 nb_pixel_total : 138247 time to create 1 rle with old method : 0.1627798080444336 time for calcul the mask position with numpy : 0.02948307991027832 nb_pixel_total : 7175 time to create 1 rle with old method : 0.007767915725708008 time for calcul the mask position with numpy : 0.030040264129638672 nb_pixel_total : 23633 time to create 1 rle with old method : 0.03386187553405762 time for calcul the mask position with numpy : 0.030052661895751953 nb_pixel_total : 64962 time to create 1 rle with old method : 0.07603573799133301 time for calcul the mask position with numpy : 0.03047943115234375 nb_pixel_total : 11841 time to create 1 rle with old method : 0.01352548599243164 time for calcul the mask position with numpy : 0.03037714958190918 nb_pixel_total : 7505 time to create 1 rle with old method : 0.008663177490234375 time for calcul the mask position with numpy : 0.030695676803588867 nb_pixel_total : 3089 time to create 1 rle with old method : 0.005006074905395508 time for calcul the mask position with numpy : 0.03170442581176758 nb_pixel_total : 32516 time to create 1 rle with old method : 0.03667426109313965 time for calcul the mask position with numpy : 0.02930593490600586 nb_pixel_total : 30843 time to create 1 rle with old method : 0.03393292427062988 time for calcul the mask position with numpy : 0.02940678596496582 nb_pixel_total : 6068 time to create 1 rle with old method : 0.007016897201538086 time for calcul the mask position with numpy : 0.028476238250732422 nb_pixel_total : 7803 time to create 1 rle with old method : 0.008792400360107422 time for calcul the mask position with numpy : 0.028937339782714844 nb_pixel_total : 17845 time to create 1 rle with old method : 0.020322561264038086 time for calcul the mask position with numpy : 0.02934122085571289 nb_pixel_total : 10458 time to create 1 rle with old method : 0.011999845504760742 time for calcul the mask position with numpy : 0.029371023178100586 nb_pixel_total : 6514 time to create 1 rle with old method : 0.0075244903564453125 time for calcul the mask position with numpy : 0.029610633850097656 nb_pixel_total : 13003 time to create 1 rle with old method : 0.014806509017944336 time for calcul the mask position with numpy : 0.028625011444091797 nb_pixel_total : 13420 time to create 1 rle with old method : 0.014964818954467773 time for calcul the mask position with numpy : 0.028361797332763672 nb_pixel_total : 16232 time to create 1 rle with old method : 0.017695903778076172 time for calcul the mask position with numpy : 0.02925848960876465 nb_pixel_total : 8391 time to create 1 rle with old method : 0.009668588638305664 time for calcul the mask position with numpy : 0.02928471565246582 nb_pixel_total : 22526 time to create 1 rle with old method : 0.02577042579650879 time for calcul the mask position with numpy : 0.02894902229309082 nb_pixel_total : 16272 time to create 1 rle with old method : 0.019021987915039062 time for calcul the mask position with numpy : 0.030074357986450195 nb_pixel_total : 22449 time to create 1 rle with old method : 0.02534031867980957 time for calcul the mask position with numpy : 0.02904510498046875 nb_pixel_total : 11647 time to create 1 rle with old method : 0.013190507888793945 time for calcul the mask position with numpy : 0.029082775115966797 nb_pixel_total : 14757 time to create 1 rle with old method : 0.017155885696411133 time for calcul the mask position with numpy : 0.029614686965942383 nb_pixel_total : 5319 time to create 1 rle with old method : 0.006184816360473633 time for calcul the mask position with numpy : 0.028647899627685547 nb_pixel_total : 8131 time to create 1 rle with old method : 0.00939035415649414 time for calcul the mask position with numpy : 0.029485225677490234 nb_pixel_total : 90691 time to create 1 rle with old method : 0.09972310066223145 time for calcul the mask position with numpy : 0.03739118576049805 nb_pixel_total : 38132 time to create 1 rle with old method : 0.06298446655273438 time for calcul the mask position with numpy : 0.030312538146972656 nb_pixel_total : 92379 time to create 1 rle with old method : 0.10549211502075195 create new chi : 4.657633543014526 time to delete rle : 0.0032913684844970703 batch 1 Loaded 97 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.7513728141784668 nb_obj : 25 nb_hashtags : 6 time to prepare the origin masks : 11.719120979309082 time for calcul the mask position with numpy : 0.5156490802764893 nb_pixel_total : 5367618 time to create 1 rle with new method : 0.9574325084686279 time for calcul the mask position with numpy : 0.019755840301513672 nb_pixel_total : 20364 time to create 1 rle with old method : 0.022120952606201172 time for calcul the mask position with numpy : 0.02210235595703125 nb_pixel_total : 104794 time to create 1 rle with old method : 0.1139669418334961 time for calcul the mask position with numpy : 0.021475791931152344 nb_pixel_total : 39347 time to create 1 rle with old method : 0.04262733459472656 time for calcul the mask position with numpy : 0.023441076278686523 nb_pixel_total : 250068 time to create 1 rle with new method : 0.9898741245269775 time for calcul the mask position with numpy : 0.02143383026123047 nb_pixel_total : 76724 time to create 1 rle with old method : 0.08248686790466309 time for calcul the mask position with numpy : 0.0218961238861084 nb_pixel_total : 6684 time to create 1 rle with old method : 0.007472038269042969 time for calcul the mask position with numpy : 0.02276325225830078 nb_pixel_total : 16692 time to create 1 rle with old method : 0.0182950496673584 time for calcul the mask position with numpy : 0.02102804183959961 nb_pixel_total : 11716 time to create 1 rle with old method : 0.013035774230957031 time for calcul the mask position with numpy : 0.022019147872924805 nb_pixel_total : 104230 time to create 1 rle with old method : 0.11321043968200684 time for calcul the mask position with numpy : 0.02220892906188965 nb_pixel_total : 106561 time to create 1 rle with old method : 0.14232349395751953 time for calcul the mask position with numpy : 0.02203083038330078 nb_pixel_total : 49221 time to create 1 rle with old method : 0.0569000244140625 time for calcul the mask position with numpy : 0.02451300621032715 nb_pixel_total : 73775 time to create 1 rle with old method : 0.0805506706237793 time for calcul the mask position with numpy : 0.022161245346069336 nb_pixel_total : 10961 time to create 1 rle with old method : 0.012131452560424805 time for calcul the mask position with numpy : 0.022227764129638672 nb_pixel_total : 156784 time to create 1 rle with new method : 0.546440839767456 time for calcul the mask position with numpy : 0.020737171173095703 nb_pixel_total : 19689 time to create 1 rle with old method : 0.02144002914428711 time for calcul the mask position with numpy : 0.02093648910522461 nb_pixel_total : 54577 time to create 1 rle with old method : 0.060265541076660156 time for calcul the mask position with numpy : 0.022310495376586914 nb_pixel_total : 20845 time to create 1 rle with old method : 0.02408766746520996 time for calcul the mask position with numpy : 0.023331403732299805 nb_pixel_total : 72815 time to create 1 rle with old method : 0.0819249153137207 time for calcul the mask position with numpy : 0.023538827896118164 nb_pixel_total : 49478 time to create 1 rle with old method : 0.05210065841674805 time for calcul the mask position with numpy : 0.021441221237182617 nb_pixel_total : 60566 time to create 1 rle with old method : 0.06591391563415527 time for calcul the mask position with numpy : 0.023422956466674805 nb_pixel_total : 167742 time to create 1 rle with new method : 0.6459381580352783 time for calcul the mask position with numpy : 0.021712541580200195 nb_pixel_total : 52892 time to create 1 rle with old method : 0.059999942779541016 time for calcul the mask position with numpy : 0.02144336700439453 nb_pixel_total : 122061 time to create 1 rle with old method : 0.13164162635803223 time for calcul the mask position with numpy : 0.020995378494262695 nb_pixel_total : 12270 time to create 1 rle with old method : 0.013401269912719727 time for calcul the mask position with numpy : 0.02152538299560547 nb_pixel_total : 21766 time to create 1 rle with old method : 0.024249792098999023 create new chi : 5.546050548553467 time to delete rle : 0.0026323795318603516 batch 1 Loaded 51 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17473 TO DO : save crop sub photo not yet done ! save time : 2.046895980834961 nb_obj : 48 nb_hashtags : 5 time to prepare the origin masks : 3.6109561920166016 time for calcul the mask position with numpy : 0.616091251373291 nb_pixel_total : 5797352 time to create 1 rle with new method : 0.5293452739715576 time for calcul the mask position with numpy : 0.026593923568725586 nb_pixel_total : 15528 time to create 1 rle with old method : 0.016829967498779297 time for calcul the mask position with numpy : 0.02648162841796875 nb_pixel_total : 16841 time to create 1 rle with old method : 0.017032146453857422 time for calcul the mask position with numpy : 0.02752232551574707 nb_pixel_total : 21158 time to create 1 rle with old method : 0.022473812103271484 time for calcul the mask position with numpy : 0.02647876739501953 nb_pixel_total : 20427 time to create 1 rle with old method : 0.020377397537231445 time for calcul the mask position with numpy : 0.026226520538330078 nb_pixel_total : 17338 time to create 1 rle with old method : 0.017514944076538086 time for calcul the mask position with numpy : 0.026694774627685547 nb_pixel_total : 46908 time to create 1 rle with old method : 0.04661059379577637 time for calcul the mask position with numpy : 0.026623010635375977 nb_pixel_total : 23361 time to create 1 rle with old method : 0.024356842041015625 time for calcul the mask position with numpy : 0.026707172393798828 nb_pixel_total : 33139 time to create 1 rle with old method : 0.03499197959899902 time for calcul the mask position with numpy : 0.026718854904174805 nb_pixel_total : 16740 time to create 1 rle with old method : 0.01667618751525879 time for calcul the mask position with numpy : 0.02637791633605957 nb_pixel_total : 19524 time to create 1 rle with old method : 0.019510269165039062 time for calcul the mask position with numpy : 0.026422739028930664 nb_pixel_total : 28651 time to create 1 rle with old method : 0.0292818546295166 time for calcul the mask position with numpy : 0.02648615837097168 nb_pixel_total : 17780 time to create 1 rle with old method : 0.01857471466064453 time for calcul the mask position with numpy : 0.027534008026123047 nb_pixel_total : 6496 time to create 1 rle with old method : 0.006977081298828125 time for calcul the mask position with numpy : 0.02719879150390625 nb_pixel_total : 16140 time to create 1 rle with old method : 0.016223669052124023 time for calcul the mask position with numpy : 0.027430057525634766 nb_pixel_total : 17572 time to create 1 rle with old method : 0.018018484115600586 time for calcul the mask position with numpy : 0.027219057083129883 nb_pixel_total : 89517 time to create 1 rle with old method : 0.08980321884155273 time for calcul the mask position with numpy : 0.027222156524658203 nb_pixel_total : 30538 time to create 1 rle with old method : 0.03064417839050293 time for calcul the mask position with numpy : 0.02642226219177246 nb_pixel_total : 19029 time to create 1 rle with old method : 0.018983125686645508 time for calcul the mask position with numpy : 0.026548385620117188 nb_pixel_total : 35848 time to create 1 rle with old method : 0.035669565200805664 time for calcul the mask position with numpy : 0.027339696884155273 nb_pixel_total : 19579 time to create 1 rle with old method : 0.02085590362548828 time for calcul the mask position with numpy : 0.02709650993347168 nb_pixel_total : 32433 time to create 1 rle with old method : 0.03308749198913574 time for calcul the mask position with numpy : 0.026663541793823242 nb_pixel_total : 30484 time to create 1 rle with old method : 0.03041553497314453 time for calcul the mask position with numpy : 0.026805400848388672 nb_pixel_total : 31905 time to create 1 rle with old method : 0.03218650817871094 time for calcul the mask position with numpy : 0.02696847915649414 nb_pixel_total : 10360 time to create 1 rle with old method : 0.0104217529296875 time for calcul the mask position with numpy : 0.02697157859802246 nb_pixel_total : 93721 time to create 1 rle with old method : 0.09856295585632324 time for calcul the mask position with numpy : 0.027278900146484375 nb_pixel_total : 49858 time to create 1 rle with old method : 0.050421714782714844 time for calcul the mask position with numpy : 0.027896404266357422 nb_pixel_total : 17101 time to create 1 rle with old method : 0.01795339584350586 time for calcul the mask position with numpy : 0.028061389923095703 nb_pixel_total : 12894 time to create 1 rle with old method : 0.01461935043334961 time for calcul the mask position with numpy : 0.028168439865112305 nb_pixel_total : 18830 time to create 1 rle with old method : 0.020080089569091797 time for calcul the mask position with numpy : 0.027791738510131836 nb_pixel_total : 38266 time to create 1 rle with old method : 0.039403676986694336 time for calcul the mask position with numpy : 0.0276944637298584 nb_pixel_total : 4935 time to create 1 rle with old method : 0.005289316177368164 time for calcul the mask position with numpy : 0.027080059051513672 nb_pixel_total : 27243 time to create 1 rle with old method : 0.027538299560546875 time for calcul the mask position with numpy : 0.02705097198486328 nb_pixel_total : 12885 time to create 1 rle with old method : 0.013238668441772461 time for calcul the mask position with numpy : 0.02678394317626953 nb_pixel_total : 12664 time to create 1 rle with old method : 0.012772083282470703 time for calcul the mask position with numpy : 0.027008771896362305 nb_pixel_total : 12899 time to create 1 rle with old method : 0.013778448104858398 time for calcul the mask position with numpy : 0.02649688720703125 nb_pixel_total : 53294 time to create 1 rle with old method : 0.05276775360107422 time for calcul the mask position with numpy : 0.02666473388671875 nb_pixel_total : 24227 time to create 1 rle with old method : 0.025354385375976562 time for calcul the mask position with numpy : 0.02649688720703125 nb_pixel_total : 19585 time to create 1 rle with old method : 0.01955699920654297 time for calcul the mask position with numpy : 0.02673482894897461 nb_pixel_total : 8537 time to create 1 rle with old method : 0.008989810943603516 time for calcul the mask position with numpy : 0.02724432945251465 nb_pixel_total : 108453 time to create 1 rle with old method : 0.10836935043334961 time for calcul the mask position with numpy : 0.02696394920349121 nb_pixel_total : 14715 time to create 1 rle with old method : 0.014792680740356445 time for calcul the mask position with numpy : 0.026481151580810547 nb_pixel_total : 8887 time to create 1 rle with old method : 0.009246110916137695 time for calcul the mask position with numpy : 0.026916980743408203 nb_pixel_total : 22932 time to create 1 rle with old method : 0.023849010467529297 time for calcul the mask position with numpy : 0.02651190757751465 nb_pixel_total : 11184 time to create 1 rle with old method : 0.011768817901611328 time for calcul the mask position with numpy : 0.026324748992919922 nb_pixel_total : 5230 time to create 1 rle with old method : 0.0052454471588134766 time for calcul the mask position with numpy : 0.02657461166381836 nb_pixel_total : 31649 time to create 1 rle with old method : 0.03343677520751953 time for calcul the mask position with numpy : 0.02667069435119629 nb_pixel_total : 21451 time to create 1 rle with old method : 0.021765708923339844 time for calcul the mask position with numpy : 0.02651357650756836 nb_pixel_total : 4152 time to create 1 rle with old method : 0.004259347915649414 create new chi : 3.7498579025268555 time to delete rle : 0.003153562545776367 batch 1 Loaded 97 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22496 TO DO : save crop sub photo not yet done ! save time : 2.8858816623687744 nb_obj : 17 nb_hashtags : 6 time to prepare the origin masks : 5.074171304702759 time for calcul the mask position with numpy : 0.3665330410003662 nb_pixel_total : 4994333 time to create 1 rle with new method : 0.5441794395446777 time for calcul the mask position with numpy : 0.019588708877563477 nb_pixel_total : 10608 time to create 1 rle with old method : 0.011586189270019531 time for calcul the mask position with numpy : 0.019901275634765625 nb_pixel_total : 97002 time to create 1 rle with old method : 0.09963846206665039 time for calcul the mask position with numpy : 0.020160913467407227 nb_pixel_total : 64000 time to create 1 rle with old method : 0.06520557403564453 time for calcul the mask position with numpy : 0.01995086669921875 nb_pixel_total : 9360 time to create 1 rle with old method : 0.009818315505981445 time for calcul the mask position with numpy : 0.019442319869995117 nb_pixel_total : 40082 time to create 1 rle with old method : 0.040801286697387695 time for calcul the mask position with numpy : 0.02053666114807129 nb_pixel_total : 82639 time to create 1 rle with old method : 0.0824880599975586 time for calcul the mask position with numpy : 0.019535303115844727 nb_pixel_total : 22976 time to create 1 rle with old method : 0.022829055786132812 time for calcul the mask position with numpy : 0.019396543502807617 nb_pixel_total : 75044 time to create 1 rle with old method : 0.07739782333374023 time for calcul the mask position with numpy : 0.02016282081604004 nb_pixel_total : 43718 time to create 1 rle with old method : 0.04378008842468262 time for calcul the mask position with numpy : 0.020297765731811523 nb_pixel_total : 45383 time to create 1 rle with old method : 0.04579615592956543 time for calcul the mask position with numpy : 0.01903057098388672 nb_pixel_total : 5983 time to create 1 rle with old method : 0.006070613861083984 time for calcul the mask position with numpy : 0.020377635955810547 nb_pixel_total : 195815 time to create 1 rle with new method : 0.5424642562866211 time for calcul the mask position with numpy : 0.021079540252685547 nb_pixel_total : 15742 time to create 1 rle with old method : 0.016259193420410156 time for calcul the mask position with numpy : 0.02035808563232422 nb_pixel_total : 6527 time to create 1 rle with old method : 0.007044315338134766 time for calcul the mask position with numpy : 0.020156383514404297 nb_pixel_total : 38588 time to create 1 rle with old method : 0.041074514389038086 time for calcul the mask position with numpy : 0.023229360580444336 nb_pixel_total : 385306 time to create 1 rle with new method : 0.5904476642608643 time for calcul the mask position with numpy : 0.04024934768676758 nb_pixel_total : 917134 time to create 1 rle with new method : 0.4855468273162842 create new chi : 3.5517776012420654 time to delete rle : 0.0020644664764404297 batch 1 Loaded 35 chid ids of type : 3594 +++++++++++++++++++++++++++++Number RLEs to save : 15208 TO DO : save crop sub photo not yet done ! save time : 1.661829948425293 nb_obj : 31 nb_hashtags : 3 time to prepare the origin masks : 3.8904519081115723 time for calcul the mask position with numpy : 0.44817686080932617 nb_pixel_total : 5592587 time to create 1 rle with new method : 0.3553462028503418 time for calcul the mask position with numpy : 0.02830815315246582 nb_pixel_total : 6888 time to create 1 rle with old method : 0.007614612579345703 time for calcul the mask position with numpy : 0.0279386043548584 nb_pixel_total : 5827 time to create 1 rle with old method : 0.0061647891998291016 time for calcul the mask position with numpy : 0.02811408042907715 nb_pixel_total : 36135 time to create 1 rle with old method : 0.03873324394226074 time for calcul the mask position with numpy : 0.02821636199951172 nb_pixel_total : 7630 time to create 1 rle with old method : 0.008376836776733398 time for calcul the mask position with numpy : 0.03147101402282715 nb_pixel_total : 104602 time to create 1 rle with old method : 0.14008259773254395 time for calcul the mask position with numpy : 0.028614521026611328 nb_pixel_total : 12935 time to create 1 rle with old method : 0.013681411743164062 time for calcul the mask position with numpy : 0.028733491897583008 nb_pixel_total : 152161 time to create 1 rle with new method : 0.5532407760620117 time for calcul the mask position with numpy : 0.028567075729370117 nb_pixel_total : 23453 time to create 1 rle with old method : 0.02641916275024414 time for calcul the mask position with numpy : 0.03154182434082031 nb_pixel_total : 28953 time to create 1 rle with old method : 0.03197336196899414 time for calcul the mask position with numpy : 0.02926802635192871 nb_pixel_total : 35724 time to create 1 rle with old method : 0.041638851165771484 time for calcul the mask position with numpy : 0.028981685638427734 nb_pixel_total : 52816 time to create 1 rle with old method : 0.05508136749267578 time for calcul the mask position with numpy : 0.029716968536376953 nb_pixel_total : 372271 time to create 1 rle with new method : 0.4640541076660156 time for calcul the mask position with numpy : 0.029247045516967773 nb_pixel_total : 18965 time to create 1 rle with old method : 0.021721601486206055 time for calcul the mask position with numpy : 0.029436588287353516 nb_pixel_total : 23801 time to create 1 rle with old method : 0.027470827102661133 time for calcul the mask position with numpy : 0.02912282943725586 nb_pixel_total : 11288 time to create 1 rle with old method : 0.013009309768676758 time for calcul the mask position with numpy : 0.029681921005249023 nb_pixel_total : 33016 time to create 1 rle with old method : 0.04128670692443848 time for calcul the mask position with numpy : 0.02937769889831543 nb_pixel_total : 110236 time to create 1 rle with old method : 0.12231564521789551 time for calcul the mask position with numpy : 0.028672456741333008 nb_pixel_total : 82809 time to create 1 rle with old method : 0.09289216995239258 time for calcul the mask position with numpy : 0.02858114242553711 nb_pixel_total : 7625 time to create 1 rle with old method : 0.008780956268310547 time for calcul the mask position with numpy : 0.02913522720336914 nb_pixel_total : 15294 time to create 1 rle with old method : 0.017139673233032227 time for calcul the mask position with numpy : 0.02897930145263672 nb_pixel_total : 4252 time to create 1 rle with old method : 0.004720449447631836 time for calcul the mask position with numpy : 0.02808976173400879 nb_pixel_total : 8886 time to create 1 rle with old method : 0.009494304656982422 time for calcul the mask position with numpy : 0.02805805206298828 nb_pixel_total : 22772 time to create 1 rle with old method : 0.024321556091308594 time for calcul the mask position with numpy : 0.030612945556640625 nb_pixel_total : 33156 time to create 1 rle with old method : 0.03536677360534668 time for calcul the mask position with numpy : 0.02866959571838379 nb_pixel_total : 112246 time to create 1 rle with old method : 0.12366437911987305 time for calcul the mask position with numpy : 0.02873516082763672 nb_pixel_total : 99861 time to create 1 rle with old method : 0.1104428768157959 time for calcul the mask position with numpy : 0.02846813201904297 nb_pixel_total : 10969 time to create 1 rle with old method : 0.012319087982177734 time for calcul the mask position with numpy : 0.028424501419067383 nb_pixel_total : 5383 time to create 1 rle with old method : 0.006134033203125 time for calcul the mask position with numpy : 0.028837919235229492 nb_pixel_total : 655 time to create 1 rle with old method : 0.0008175373077392578 time for calcul the mask position with numpy : 0.02884817123413086 nb_pixel_total : 7371 time to create 1 rle with old method : 0.008168697357177734 time for calcul the mask position with numpy : 0.029205322265625 nb_pixel_total : 9673 time to create 1 rle with old method : 0.011099100112915039 create new chi : 3.851174831390381 time to delete rle : 0.0028274059295654297 batch 1 Loaded 63 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18641 TO DO : save crop sub photo not yet done ! save time : 3.044065237045288 nb_obj : 37 nb_hashtags : 3 time to prepare the origin masks : 3.897552728652954 time for calcul the mask position with numpy : 0.5159628391265869 nb_pixel_total : 5476181 time to create 1 rle with new method : 0.37790966033935547 time for calcul the mask position with numpy : 0.028711795806884766 nb_pixel_total : 45233 time to create 1 rle with old method : 0.04847455024719238 time for calcul the mask position with numpy : 0.0289764404296875 nb_pixel_total : 40359 time to create 1 rle with old method : 0.04613614082336426 time for calcul the mask position with numpy : 0.029460430145263672 nb_pixel_total : 20894 time to create 1 rle with old method : 0.022813081741333008 time for calcul the mask position with numpy : 0.02878713607788086 nb_pixel_total : 14427 time to create 1 rle with old method : 0.01579141616821289 time for calcul the mask position with numpy : 0.02845597267150879 nb_pixel_total : 41617 time to create 1 rle with old method : 0.04462552070617676 time for calcul the mask position with numpy : 0.028433799743652344 nb_pixel_total : 54895 time to create 1 rle with old method : 0.06180763244628906 time for calcul the mask position with numpy : 0.028384685516357422 nb_pixel_total : 37704 time to create 1 rle with old method : 0.04026389122009277 time for calcul the mask position with numpy : 0.029380083084106445 nb_pixel_total : 56070 time to create 1 rle with old method : 0.06076622009277344 time for calcul the mask position with numpy : 0.029195547103881836 nb_pixel_total : 19175 time to create 1 rle with old method : 0.021613597869873047 time for calcul the mask position with numpy : 0.029538393020629883 nb_pixel_total : 19700 time to create 1 rle with old method : 0.03242373466491699 time for calcul the mask position with numpy : 0.03294658660888672 nb_pixel_total : 21405 time to create 1 rle with old method : 0.025869369506835938 time for calcul the mask position with numpy : 0.028237104415893555 nb_pixel_total : 39275 time to create 1 rle with old method : 0.042520999908447266 time for calcul the mask position with numpy : 0.027787208557128906 nb_pixel_total : 34651 time to create 1 rle with old method : 0.03538250923156738 time for calcul the mask position with numpy : 0.027415037155151367 nb_pixel_total : 37666 time to create 1 rle with old method : 0.03921103477478027 time for calcul the mask position with numpy : 0.028564453125 nb_pixel_total : 19176 time to create 1 rle with old method : 0.020696640014648438 time for calcul the mask position with numpy : 0.028603076934814453 nb_pixel_total : 22110 time to create 1 rle with old method : 0.023974180221557617 time for calcul the mask position with numpy : 0.029246807098388672 nb_pixel_total : 125619 time to create 1 rle with old method : 0.13443613052368164 time for calcul the mask position with numpy : 0.029259681701660156 nb_pixel_total : 7029 time to create 1 rle with old method : 0.008123636245727539 time for calcul the mask position with numpy : 0.029242753982543945 nb_pixel_total : 16711 time to create 1 rle with old method : 0.017953872680664062 time for calcul the mask position with numpy : 0.028664350509643555 nb_pixel_total : 131096 time to create 1 rle with old method : 0.1416182518005371 time for calcul the mask position with numpy : 0.029254674911499023 nb_pixel_total : 26850 time to create 1 rle with old method : 0.029311180114746094 time for calcul the mask position with numpy : 0.02897953987121582 nb_pixel_total : 6089 time to create 1 rle with old method : 0.007034778594970703 time for calcul the mask position with numpy : 0.028569936752319336 nb_pixel_total : 28371 time to create 1 rle with old method : 0.03115105628967285 time for calcul the mask position with numpy : 0.02775859832763672 nb_pixel_total : 32598 time to create 1 rle with old method : 0.035588741302490234 time for calcul the mask position with numpy : 0.029114961624145508 nb_pixel_total : 26834 time to create 1 rle with old method : 0.03193068504333496 time for calcul the mask position with numpy : 0.03339242935180664 nb_pixel_total : 87019 time to create 1 rle with old method : 0.10828256607055664 time for calcul the mask position with numpy : 0.029448747634887695 nb_pixel_total : 17000 time to create 1 rle with old method : 0.01960277557373047 time for calcul the mask position with numpy : 0.030703067779541016 nb_pixel_total : 340122 time to create 1 rle with new method : 0.5651698112487793 time for calcul the mask position with numpy : 0.029104232788085938 nb_pixel_total : 54261 time to create 1 rle with old method : 0.06107139587402344 time for calcul the mask position with numpy : 0.029018402099609375 nb_pixel_total : 57939 time to create 1 rle with old method : 0.06396937370300293 time for calcul the mask position with numpy : 0.028155803680419922 nb_pixel_total : 4128 time to create 1 rle with old method : 0.004614591598510742 time for calcul the mask position with numpy : 0.029018402099609375 nb_pixel_total : 16476 time to create 1 rle with old method : 0.01898050308227539 time for calcul the mask position with numpy : 0.029062509536743164 nb_pixel_total : 8878 time to create 1 rle with old method : 0.009492158889770508 time for calcul the mask position with numpy : 0.028536081314086914 nb_pixel_total : 42045 time to create 1 rle with old method : 0.04479217529296875 time for calcul the mask position with numpy : 0.028093338012695312 nb_pixel_total : 7861 time to create 1 rle with old method : 0.008536577224731445 time for calcul the mask position with numpy : 0.028682947158813477 nb_pixel_total : 10572 time to create 1 rle with old method : 0.011585474014282227 time for calcul the mask position with numpy : 0.0313718318939209 nb_pixel_total : 2204 time to create 1 rle with old method : 0.0036885738372802734 create new chi : 3.9646589756011963 time to delete rle : 0.0040323734283447266 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20407 TO DO : save crop sub photo not yet done ! save time : 2.75418758392334 nb_obj : 48 nb_hashtags : 3 time to prepare the origin masks : 3.853776693344116 time for calcul the mask position with numpy : 0.581270694732666 nb_pixel_total : 5506137 time to create 1 rle with new method : 1.5393257141113281 time for calcul the mask position with numpy : 0.02982926368713379 nb_pixel_total : 19780 time to create 1 rle with old method : 0.024704933166503906 time for calcul the mask position with numpy : 0.030620336532592773 nb_pixel_total : 43782 time to create 1 rle with old method : 0.0498805046081543 time for calcul the mask position with numpy : 0.029981374740600586 nb_pixel_total : 9890 time to create 1 rle with old method : 0.011534690856933594 time for calcul the mask position with numpy : 0.029608488082885742 nb_pixel_total : 14453 time to create 1 rle with old method : 0.016395092010498047 time for calcul the mask position with numpy : 0.029013872146606445 nb_pixel_total : 28473 time to create 1 rle with old method : 0.031587839126586914 time for calcul the mask position with numpy : 0.028461456298828125 nb_pixel_total : 44125 time to create 1 rle with old method : 0.04830765724182129 time for calcul the mask position with numpy : 0.02900242805480957 nb_pixel_total : 98080 time to create 1 rle with old method : 0.10815119743347168 time for calcul the mask position with numpy : 0.028376102447509766 nb_pixel_total : 26078 time to create 1 rle with old method : 0.02910590171813965 time for calcul the mask position with numpy : 0.028326034545898438 nb_pixel_total : 15527 time to create 1 rle with old method : 0.01756596565246582 time for calcul the mask position with numpy : 0.028520584106445312 nb_pixel_total : 16161 time to create 1 rle with old method : 0.01950550079345703 time for calcul the mask position with numpy : 0.03349876403808594 nb_pixel_total : 37972 time to create 1 rle with old method : 0.055556297302246094 time for calcul the mask position with numpy : 0.029129743576049805 nb_pixel_total : 18709 time to create 1 rle with old method : 0.021291255950927734 time for calcul the mask position with numpy : 0.029165983200073242 nb_pixel_total : 16979 time to create 1 rle with old method : 0.019269704818725586 time for calcul the mask position with numpy : 0.029001951217651367 nb_pixel_total : 60804 time to create 1 rle with old method : 0.06705045700073242 time for calcul the mask position with numpy : 0.02896285057067871 nb_pixel_total : 30767 time to create 1 rle with old method : 0.03359079360961914 time for calcul the mask position with numpy : 0.02910590171813965 nb_pixel_total : 14383 time to create 1 rle with old method : 0.017421722412109375 time for calcul the mask position with numpy : 0.030092239379882812 nb_pixel_total : 17078 time to create 1 rle with old method : 0.019479990005493164 time for calcul the mask position with numpy : 0.02889227867126465 nb_pixel_total : 27248 time to create 1 rle with old method : 0.030719280242919922 time for calcul the mask position with numpy : 0.028977632522583008 nb_pixel_total : 21510 time to create 1 rle with old method : 0.023883342742919922 time for calcul the mask position with numpy : 0.030431270599365234 nb_pixel_total : 46102 time to create 1 rle with old method : 0.05157065391540527 time for calcul the mask position with numpy : 0.028962373733520508 nb_pixel_total : 3052 time to create 1 rle with old method : 0.003724813461303711 time for calcul the mask position with numpy : 0.02877640724182129 nb_pixel_total : 116074 time to create 1 rle with old method : 0.12349820137023926 time for calcul the mask position with numpy : 0.02848076820373535 nb_pixel_total : 7467 time to create 1 rle with old method : 0.00833439826965332 time for calcul the mask position with numpy : 0.028264284133911133 nb_pixel_total : 25037 time to create 1 rle with old method : 0.02828073501586914 time for calcul the mask position with numpy : 0.029343843460083008 nb_pixel_total : 53845 time to create 1 rle with old method : 0.059291839599609375 time for calcul the mask position with numpy : 0.02870798110961914 nb_pixel_total : 12557 time to create 1 rle with old method : 0.014284610748291016 time for calcul the mask position with numpy : 0.028738975524902344 nb_pixel_total : 40585 time to create 1 rle with old method : 0.04680895805358887 time for calcul the mask position with numpy : 0.029745101928710938 nb_pixel_total : 28973 time to create 1 rle with old method : 0.03331327438354492 time for calcul the mask position with numpy : 0.03011631965637207 nb_pixel_total : 28960 time to create 1 rle with old method : 0.03310680389404297 time for calcul the mask position with numpy : 0.029640913009643555 nb_pixel_total : 14323 time to create 1 rle with old method : 0.01612114906311035 time for calcul the mask position with numpy : 0.028876543045043945 nb_pixel_total : 7454 time to create 1 rle with old method : 0.00855875015258789 time for calcul the mask position with numpy : 0.029109954833984375 nb_pixel_total : 13267 time to create 1 rle with old method : 0.015163898468017578 time for calcul the mask position with numpy : 0.030749797821044922 nb_pixel_total : 325274 time to create 1 rle with new method : 2.018019676208496 time for calcul the mask position with numpy : 0.02892756462097168 nb_pixel_total : 64240 time to create 1 rle with old method : 0.0733633041381836 time for calcul the mask position with numpy : 0.030320405960083008 nb_pixel_total : 66044 time to create 1 rle with old method : 0.07549715042114258 time for calcul the mask position with numpy : 0.03252911567687988 nb_pixel_total : 6021 time to create 1 rle with old method : 0.01000666618347168 time for calcul the mask position with numpy : 0.03223896026611328 nb_pixel_total : 12315 time to create 1 rle with old method : 0.014183282852172852 time for calcul the mask position with numpy : 0.029720306396484375 nb_pixel_total : 22134 time to create 1 rle with old method : 0.028949737548828125 time for calcul the mask position with numpy : 0.03033590316772461 nb_pixel_total : 32942 time to create 1 rle with old method : 0.04848313331604004 time for calcul the mask position with numpy : 0.03326010704040527 nb_pixel_total : 7018 time to create 1 rle with old method : 0.011814355850219727 time for calcul the mask position with numpy : 0.030524015426635742 nb_pixel_total : 9830 time to create 1 rle with old method : 0.011750221252441406 time for calcul the mask position with numpy : 0.029128551483154297 nb_pixel_total : 4510 time to create 1 rle with old method : 0.0049898624420166016 time for calcul the mask position with numpy : 0.029237031936645508 nb_pixel_total : 3013 time to create 1 rle with old method : 0.003450632095336914 time for calcul the mask position with numpy : 0.029064178466796875 nb_pixel_total : 12697 time to create 1 rle with old method : 0.01386880874633789 time for calcul the mask position with numpy : 0.029479026794433594 nb_pixel_total : 4502 time to create 1 rle with old method : 0.0051746368408203125 time for calcul the mask position with numpy : 0.028769731521606445 nb_pixel_total : 2544 time to create 1 rle with old method : 0.0029337406158447266 time for calcul the mask position with numpy : 0.028577804565429688 nb_pixel_total : 6338 time to create 1 rle with old method : 0.00690007209777832 time for calcul the mask position with numpy : 0.028642892837524414 nb_pixel_total : 5186 time to create 1 rle with old method : 0.005960226058959961 create new chi : 7.0201568603515625 time to delete rle : 0.0032203197479248047 batch 1 Loaded 97 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21583 TO DO : save crop sub photo not yet done ! save time : 2.609797716140747 nb_obj : 43 nb_hashtags : 5 time to prepare the origin masks : 4.525462865829468 time for calcul the mask position with numpy : 0.4800987243652344 nb_pixel_total : 4351500 time to create 1 rle with new method : 0.8370785713195801 time for calcul the mask position with numpy : 0.029330968856811523 nb_pixel_total : 77616 time to create 1 rle with old method : 0.0863027572631836 time for calcul the mask position with numpy : 0.028951644897460938 nb_pixel_total : 28934 time to create 1 rle with old method : 0.03143811225891113 time for calcul the mask position with numpy : 0.02888631820678711 nb_pixel_total : 15541 time to create 1 rle with old method : 0.01735377311706543 time for calcul the mask position with numpy : 0.02887701988220215 nb_pixel_total : 8966 time to create 1 rle with old method : 0.010270118713378906 time for calcul the mask position with numpy : 0.028712749481201172 nb_pixel_total : 15528 time to create 1 rle with old method : 0.017551660537719727 time for calcul the mask position with numpy : 0.029074430465698242 nb_pixel_total : 26171 time to create 1 rle with old method : 0.02765631675720215 time for calcul the mask position with numpy : 0.027922630310058594 nb_pixel_total : 42978 time to create 1 rle with old method : 0.05381965637207031 time for calcul the mask position with numpy : 0.03281998634338379 nb_pixel_total : 14216 time to create 1 rle with old method : 0.023406505584716797 time for calcul the mask position with numpy : 0.02913522720336914 nb_pixel_total : 185497 time to create 1 rle with new method : 0.4381377696990967 time for calcul the mask position with numpy : 0.02908921241760254 nb_pixel_total : 17525 time to create 1 rle with old method : 0.01878666877746582 time for calcul the mask position with numpy : 0.028577089309692383 nb_pixel_total : 40665 time to create 1 rle with old method : 0.046415090560913086 time for calcul the mask position with numpy : 0.028832435607910156 nb_pixel_total : 14845 time to create 1 rle with old method : 0.01679849624633789 time for calcul the mask position with numpy : 0.02867722511291504 nb_pixel_total : 2971 time to create 1 rle with old method : 0.0035622119903564453 time for calcul the mask position with numpy : 0.028944969177246094 nb_pixel_total : 187986 time to create 1 rle with new method : 0.38039088249206543 time for calcul the mask position with numpy : 0.02718329429626465 nb_pixel_total : 19368 time to create 1 rle with old method : 0.020752906799316406 time for calcul the mask position with numpy : 0.027553319931030273 nb_pixel_total : 8981 time to create 1 rle with old method : 0.009117364883422852 time for calcul the mask position with numpy : 0.027508974075317383 nb_pixel_total : 36980 time to create 1 rle with old method : 0.03937792778015137 time for calcul the mask position with numpy : 0.02880549430847168 nb_pixel_total : 26632 time to create 1 rle with old method : 0.029112815856933594 time for calcul the mask position with numpy : 0.028966665267944336 nb_pixel_total : 50760 time to create 1 rle with old method : 0.05378890037536621 time for calcul the mask position with numpy : 0.02927851676940918 nb_pixel_total : 116654 time to create 1 rle with old method : 0.13033246994018555 time for calcul the mask position with numpy : 0.02785968780517578 nb_pixel_total : 28526 time to create 1 rle with old method : 0.0320889949798584 time for calcul the mask position with numpy : 0.02785181999206543 nb_pixel_total : 5602 time to create 1 rle with old method : 0.006219625473022461 time for calcul the mask position with numpy : 0.02774834632873535 nb_pixel_total : 22352 time to create 1 rle with old method : 0.023898601531982422 time for calcul the mask position with numpy : 0.028668880462646484 nb_pixel_total : 269409 time to create 1 rle with new method : 0.5265252590179443 time for calcul the mask position with numpy : 0.03011345863342285 nb_pixel_total : 13974 time to create 1 rle with old method : 0.016859054565429688 time for calcul the mask position with numpy : 0.03090810775756836 nb_pixel_total : 42817 time to create 1 rle with old method : 0.0492863655090332 time for calcul the mask position with numpy : 0.029678821563720703 nb_pixel_total : 14819 time to create 1 rle with old method : 0.01584935188293457 time for calcul the mask position with numpy : 0.0282442569732666 nb_pixel_total : 198 time to create 1 rle with old method : 0.0003223419189453125 time for calcul the mask position with numpy : 0.02834606170654297 nb_pixel_total : 26318 time to create 1 rle with old method : 0.028618812561035156 time for calcul the mask position with numpy : 0.03032517433166504 nb_pixel_total : 532399 time to create 1 rle with new method : 0.5447609424591064 time for calcul the mask position with numpy : 0.028487443923950195 nb_pixel_total : 71800 time to create 1 rle with old method : 0.0789194107055664 time for calcul the mask position with numpy : 0.02808213233947754 nb_pixel_total : 152424 time to create 1 rle with new method : 0.4950706958770752 time for calcul the mask position with numpy : 0.030242204666137695 nb_pixel_total : 220044 time to create 1 rle with new method : 0.43767690658569336 time for calcul the mask position with numpy : 0.027843952178955078 nb_pixel_total : 11534 time to create 1 rle with old method : 0.012426614761352539 time for calcul the mask position with numpy : 0.032503366470336914 nb_pixel_total : 60385 time to create 1 rle with old method : 0.08031749725341797 time for calcul the mask position with numpy : 0.028623580932617188 nb_pixel_total : 153274 time to create 1 rle with new method : 0.4700288772583008 time for calcul the mask position with numpy : 0.02938675880432129 nb_pixel_total : 18490 time to create 1 rle with old method : 0.020761489868164062 time for calcul the mask position with numpy : 0.02851080894470215 nb_pixel_total : 32859 time to create 1 rle with old method : 0.035375356674194336 time for calcul the mask position with numpy : 0.02780771255493164 nb_pixel_total : 21688 time to create 1 rle with old method : 0.022703170776367188 time for calcul the mask position with numpy : 0.027143001556396484 nb_pixel_total : 13291 time to create 1 rle with old method : 0.014126777648925781 time for calcul the mask position with numpy : 0.027890682220458984 nb_pixel_total : 5851 time to create 1 rle with old method : 0.006074666976928711 time for calcul the mask position with numpy : 0.028189420700073242 nb_pixel_total : 36496 time to create 1 rle with old method : 0.04106330871582031 time for calcul the mask position with numpy : 0.02874755859375 nb_pixel_total : 5376 time to create 1 rle with old method : 0.006119966506958008 create new chi : 7.17315411567688 time to delete rle : 0.003988981246948242 batch 1 Loaded 87 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26336 TO DO : save crop sub photo not yet done ! save time : 3.337918996810913 nb_obj : 27 nb_hashtags : 3 time to prepare the origin masks : 3.3677849769592285 time for calcul the mask position with numpy : 0.3821756839752197 nb_pixel_total : 6051338 time to create 1 rle with new method : 0.5601959228515625 time for calcul the mask position with numpy : 0.02760028839111328 nb_pixel_total : 18776 time to create 1 rle with old method : 0.02007746696472168 time for calcul the mask position with numpy : 0.0272064208984375 nb_pixel_total : 13844 time to create 1 rle with old method : 0.015173673629760742 time for calcul the mask position with numpy : 0.02830982208251953 nb_pixel_total : 9702 time to create 1 rle with old method : 0.010721683502197266 time for calcul the mask position with numpy : 0.028905153274536133 nb_pixel_total : 177559 time to create 1 rle with new method : 0.6576895713806152 time for calcul the mask position with numpy : 0.028684139251708984 nb_pixel_total : 20008 time to create 1 rle with old method : 0.022179841995239258 time for calcul the mask position with numpy : 0.028147459030151367 nb_pixel_total : 16926 time to create 1 rle with old method : 0.018017053604125977 time for calcul the mask position with numpy : 0.028534889221191406 nb_pixel_total : 20575 time to create 1 rle with old method : 0.022080421447753906 time for calcul the mask position with numpy : 0.0286715030670166 nb_pixel_total : 44651 time to create 1 rle with old method : 0.051025390625 time for calcul the mask position with numpy : 0.029832839965820312 nb_pixel_total : 32423 time to create 1 rle with old method : 0.03705191612243652 time for calcul the mask position with numpy : 0.029944896697998047 nb_pixel_total : 23704 time to create 1 rle with old method : 0.027141571044921875 time for calcul the mask position with numpy : 0.02965831756591797 nb_pixel_total : 13338 time to create 1 rle with old method : 0.014689445495605469 time for calcul the mask position with numpy : 0.029677391052246094 nb_pixel_total : 19848 time to create 1 rle with old method : 0.021897315979003906 time for calcul the mask position with numpy : 0.029908180236816406 nb_pixel_total : 36762 time to create 1 rle with old method : 0.04080653190612793 time for calcul the mask position with numpy : 0.03096151351928711 nb_pixel_total : 22047 time to create 1 rle with old method : 0.025063037872314453 time for calcul the mask position with numpy : 0.0304412841796875 nb_pixel_total : 6301 time to create 1 rle with old method : 0.007135629653930664 time for calcul the mask position with numpy : 0.027651548385620117 nb_pixel_total : 12010 time to create 1 rle with old method : 0.012427091598510742 time for calcul the mask position with numpy : 0.02806401252746582 nb_pixel_total : 125942 time to create 1 rle with old method : 0.13036847114562988 time for calcul the mask position with numpy : 0.027267932891845703 nb_pixel_total : 6904 time to create 1 rle with old method : 0.006875753402709961 time for calcul the mask position with numpy : 0.028174877166748047 nb_pixel_total : 19548 time to create 1 rle with old method : 0.02082991600036621 time for calcul the mask position with numpy : 0.02733016014099121 nb_pixel_total : 12695 time to create 1 rle with old method : 0.013263702392578125 time for calcul the mask position with numpy : 0.02717304229736328 nb_pixel_total : 17353 time to create 1 rle with old method : 0.017713546752929688 time for calcul the mask position with numpy : 0.027801036834716797 nb_pixel_total : 27959 time to create 1 rle with old method : 0.03079986572265625 time for calcul the mask position with numpy : 0.0270235538482666 nb_pixel_total : 20776 time to create 1 rle with old method : 0.0218045711517334 time for calcul the mask position with numpy : 0.02782607078552246 nb_pixel_total : 71514 time to create 1 rle with old method : 0.07410573959350586 time for calcul the mask position with numpy : 0.028078794479370117 nb_pixel_total : 187507 time to create 1 rle with new method : 0.318068265914917 time for calcul the mask position with numpy : 0.02823328971862793 nb_pixel_total : 4846 time to create 1 rle with old method : 0.005456447601318359 time for calcul the mask position with numpy : 0.027977466583251953 nb_pixel_total : 15384 time to create 1 rle with old method : 0.016322612762451172 create new chi : 3.447713613510132 time to delete rle : 0.0022161006927490234 batch 1 Loaded 55 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++Number RLEs to save : 15373 TO DO : save crop sub photo not yet done ! save time : 2.1332356929779053 nb_obj : 37 nb_hashtags : 4 time to prepare the origin masks : 4.0481650829315186 time for calcul the mask position with numpy : 0.42907261848449707 nb_pixel_total : 5730878 time to create 1 rle with new method : 1.313706636428833 time for calcul the mask position with numpy : 0.029430389404296875 nb_pixel_total : 9130 time to create 1 rle with old method : 0.010501861572265625 time for calcul the mask position with numpy : 0.029812335968017578 nb_pixel_total : 8202 time to create 1 rle with old method : 0.009495735168457031 time for calcul the mask position with numpy : 0.030002117156982422 nb_pixel_total : 71650 time to create 1 rle with old method : 0.08401298522949219 time for calcul the mask position with numpy : 0.030057907104492188 nb_pixel_total : 19082 time to create 1 rle with old method : 0.02230691909790039 time for calcul the mask position with numpy : 0.02922201156616211 nb_pixel_total : 12090 time to create 1 rle with old method : 0.013697624206542969 time for calcul the mask position with numpy : 0.02863931655883789 nb_pixel_total : 15185 time to create 1 rle with old method : 0.01710057258605957 time for calcul the mask position with numpy : 0.03001236915588379 nb_pixel_total : 9918 time to create 1 rle with old method : 0.011090755462646484 time for calcul the mask position with numpy : 0.030793428421020508 nb_pixel_total : 15734 time to create 1 rle with old method : 0.018149375915527344 time for calcul the mask position with numpy : 0.029423952102661133 nb_pixel_total : 22760 time to create 1 rle with old method : 0.026103734970092773 time for calcul the mask position with numpy : 0.029307126998901367 nb_pixel_total : 10779 time to create 1 rle with old method : 0.012491941452026367 time for calcul the mask position with numpy : 0.029621601104736328 nb_pixel_total : 72679 time to create 1 rle with old method : 0.07981348037719727 time for calcul the mask position with numpy : 0.029122352600097656 nb_pixel_total : 6213 time to create 1 rle with old method : 0.006918430328369141 time for calcul the mask position with numpy : 0.029525279998779297 nb_pixel_total : 13705 time to create 1 rle with old method : 0.015053033828735352 time for calcul the mask position with numpy : 0.029112815856933594 nb_pixel_total : 34587 time to create 1 rle with old method : 0.03749656677246094 time for calcul the mask position with numpy : 0.029471397399902344 nb_pixel_total : 6952 time to create 1 rle with old method : 0.00777745246887207 time for calcul the mask position with numpy : 0.02947402000427246 nb_pixel_total : 5382 time to create 1 rle with old method : 0.0062770843505859375 time for calcul the mask position with numpy : 0.028948545455932617 nb_pixel_total : 11782 time to create 1 rle with old method : 0.012836694717407227 time for calcul the mask position with numpy : 0.02870631217956543 nb_pixel_total : 132812 time to create 1 rle with old method : 0.1479649543762207 time for calcul the mask position with numpy : 0.028172731399536133 nb_pixel_total : 7712 time to create 1 rle with old method : 0.008088350296020508 time for calcul the mask position with numpy : 0.027544260025024414 nb_pixel_total : 61444 time to create 1 rle with old method : 0.06399703025817871 time for calcul the mask position with numpy : 0.028516054153442383 nb_pixel_total : 81029 time to create 1 rle with old method : 0.0884714126586914 time for calcul the mask position with numpy : 0.02865123748779297 nb_pixel_total : 8515 time to create 1 rle with old method : 0.009527921676635742 time for calcul the mask position with numpy : 0.027927875518798828 nb_pixel_total : 5369 time to create 1 rle with old method : 0.005686521530151367 time for calcul the mask position with numpy : 0.0277407169342041 nb_pixel_total : 30978 time to create 1 rle with old method : 0.03317117691040039 time for calcul the mask position with numpy : 0.029323577880859375 nb_pixel_total : 101273 time to create 1 rle with old method : 0.1107642650604248 time for calcul the mask position with numpy : 0.028515100479125977 nb_pixel_total : 25704 time to create 1 rle with old method : 0.026585817337036133 time for calcul the mask position with numpy : 0.027101516723632812 nb_pixel_total : 24493 time to create 1 rle with old method : 0.024966716766357422 time for calcul the mask position with numpy : 0.02751612663269043 nb_pixel_total : 5064 time to create 1 rle with old method : 0.005484580993652344 time for calcul the mask position with numpy : 0.02656841278076172 nb_pixel_total : 13025 time to create 1 rle with old method : 0.013159036636352539 time for calcul the mask position with numpy : 0.02745199203491211 nb_pixel_total : 9114 time to create 1 rle with old method : 0.009579658508300781 time for calcul the mask position with numpy : 0.027152299880981445 nb_pixel_total : 12901 time to create 1 rle with old method : 0.0132598876953125 time for calcul the mask position with numpy : 0.028847694396972656 nb_pixel_total : 265659 time to create 1 rle with new method : 0.502800464630127 time for calcul the mask position with numpy : 0.02955341339111328 nb_pixel_total : 46198 time to create 1 rle with old method : 0.05350494384765625 time for calcul the mask position with numpy : 0.032794952392578125 nb_pixel_total : 90735 time to create 1 rle with old method : 0.09533858299255371 time for calcul the mask position with numpy : 0.02810835838317871 nb_pixel_total : 20735 time to create 1 rle with old method : 0.02169179916381836 time for calcul the mask position with numpy : 0.0275113582611084 nb_pixel_total : 13474 time to create 1 rle with old method : 0.014600276947021484 time for calcul the mask position with numpy : 0.028089523315429688 nb_pixel_total : 17298 time to create 1 rle with old method : 0.01929950714111328 create new chi : 4.52863335609436 time to delete rle : 0.002361774444580078 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16963 TO DO : save crop sub photo not yet done ! save time : 2.085423707962036 nb_obj : 21 nb_hashtags : 6 time to prepare the origin masks : 8.598133563995361 time for calcul the mask position with numpy : 0.4410972595214844 nb_pixel_total : 5910667 time to create 1 rle with new method : 0.343433141708374 time for calcul the mask position with numpy : 0.022076129913330078 nb_pixel_total : 190951 time to create 1 rle with new method : 0.7260189056396484 time for calcul the mask position with numpy : 0.02112436294555664 nb_pixel_total : 3280 time to create 1 rle with old method : 0.003547191619873047 time for calcul the mask position with numpy : 0.02044391632080078 nb_pixel_total : 6560 time to create 1 rle with old method : 0.006832599639892578 time for calcul the mask position with numpy : 0.021433115005493164 nb_pixel_total : 48286 time to create 1 rle with old method : 0.05109715461730957 time for calcul the mask position with numpy : 0.020514965057373047 nb_pixel_total : 42223 time to create 1 rle with old method : 0.044092416763305664 time for calcul the mask position with numpy : 0.021372556686401367 nb_pixel_total : 47748 time to create 1 rle with old method : 0.050936222076416016 time for calcul the mask position with numpy : 0.02184152603149414 nb_pixel_total : 27136 time to create 1 rle with old method : 0.02809286117553711 time for calcul the mask position with numpy : 0.021069765090942383 nb_pixel_total : 19903 time to create 1 rle with old method : 0.020910024642944336 time for calcul the mask position with numpy : 0.02111363410949707 nb_pixel_total : 53494 time to create 1 rle with old method : 0.05816769599914551 time for calcul the mask position with numpy : 0.021788835525512695 nb_pixel_total : 30409 time to create 1 rle with old method : 0.032979726791381836 time for calcul the mask position with numpy : 0.020478010177612305 nb_pixel_total : 16521 time to create 1 rle with old method : 0.017611980438232422 time for calcul the mask position with numpy : 0.021268844604492188 nb_pixel_total : 31318 time to create 1 rle with old method : 0.033013343811035156 time for calcul the mask position with numpy : 0.02117609977722168 nb_pixel_total : 8425 time to create 1 rle with old method : 0.008934259414672852 time for calcul the mask position with numpy : 0.020635128021240234 nb_pixel_total : 38462 time to create 1 rle with old method : 0.04152417182922363 time for calcul the mask position with numpy : 0.02275228500366211 nb_pixel_total : 240017 time to create 1 rle with new method : 0.5199475288391113 time for calcul the mask position with numpy : 0.02333545684814453 nb_pixel_total : 6535 time to create 1 rle with old method : 0.007475852966308594 time for calcul the mask position with numpy : 0.024259090423583984 nb_pixel_total : 25431 time to create 1 rle with old method : 0.02786850929260254 time for calcul the mask position with numpy : 0.02384471893310547 nb_pixel_total : 58566 time to create 1 rle with old method : 0.07236719131469727 time for calcul the mask position with numpy : 0.02312159538269043 nb_pixel_total : 86916 time to create 1 rle with old method : 0.09664654731750488 time for calcul the mask position with numpy : 0.021995067596435547 nb_pixel_total : 62636 time to create 1 rle with old method : 0.06827998161315918 time for calcul the mask position with numpy : 0.022706270217895508 nb_pixel_total : 94756 time to create 1 rle with old method : 0.10403323173522949 create new chi : 3.342467784881592 time to delete rle : 0.002006053924560547 batch 1 Loaded 43 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 13514 TO DO : save crop sub photo not yet done ! save time : 6.709317684173584 nb_obj : 22 nb_hashtags : 4 time to prepare the origin masks : 7.660167217254639 time for calcul the mask position with numpy : 0.2774808406829834 nb_pixel_total : 5347800 time to create 1 rle with new method : 0.7776415348052979 time for calcul the mask position with numpy : 0.02264237403869629 nb_pixel_total : 367847 time to create 1 rle with new method : 0.8568432331085205 time for calcul the mask position with numpy : 0.019914627075195312 nb_pixel_total : 14366 time to create 1 rle with old method : 0.015839338302612305 time for calcul the mask position with numpy : 0.022052288055419922 nb_pixel_total : 154806 time to create 1 rle with new method : 0.4794747829437256 time for calcul the mask position with numpy : 0.02435588836669922 nb_pixel_total : 7114 time to create 1 rle with old method : 0.00796818733215332 time for calcul the mask position with numpy : 0.022173166275024414 nb_pixel_total : 37557 time to create 1 rle with old method : 0.0410616397857666 time for calcul the mask position with numpy : 0.023464679718017578 nb_pixel_total : 118718 time to create 1 rle with old method : 0.1286478042602539 time for calcul the mask position with numpy : 0.022049427032470703 nb_pixel_total : 30917 time to create 1 rle with old method : 0.033554792404174805 time for calcul the mask position with numpy : 0.02337336540222168 nb_pixel_total : 66235 time to create 1 rle with old method : 0.07208490371704102 time for calcul the mask position with numpy : 0.021935224533081055 nb_pixel_total : 13814 time to create 1 rle with old method : 0.015229940414428711 time for calcul the mask position with numpy : 0.02264547348022461 nb_pixel_total : 8717 time to create 1 rle with old method : 0.009262323379516602 time for calcul the mask position with numpy : 0.023029327392578125 nb_pixel_total : 246350 time to create 1 rle with new method : 0.5933060646057129 time for calcul the mask position with numpy : 0.020313262939453125 nb_pixel_total : 74399 time to create 1 rle with old method : 0.07434868812561035 time for calcul the mask position with numpy : 0.020984649658203125 nb_pixel_total : 188027 time to create 1 rle with new method : 0.4910283088684082 time for calcul the mask position with numpy : 0.0205838680267334 nb_pixel_total : 60007 time to create 1 rle with old method : 0.06699872016906738 time for calcul the mask position with numpy : 0.02110600471496582 nb_pixel_total : 4215 time to create 1 rle with old method : 0.004724740982055664 time for calcul the mask position with numpy : 0.02178049087524414 nb_pixel_total : 55363 time to create 1 rle with old method : 0.059062957763671875 time for calcul the mask position with numpy : 0.022343873977661133 nb_pixel_total : 28600 time to create 1 rle with old method : 0.030709266662597656 time for calcul the mask position with numpy : 0.023069381713867188 nb_pixel_total : 25921 time to create 1 rle with old method : 0.02754807472229004 time for calcul the mask position with numpy : 0.02083110809326172 nb_pixel_total : 12043 time to create 1 rle with old method : 0.012845277786254883 time for calcul the mask position with numpy : 0.02098679542541504 nb_pixel_total : 44152 time to create 1 rle with old method : 0.04594755172729492 time for calcul the mask position with numpy : 0.021297216415405273 nb_pixel_total : 28097 time to create 1 rle with old method : 0.029503822326660156 time for calcul the mask position with numpy : 0.022025108337402344 nb_pixel_total : 115175 time to create 1 rle with old method : 0.12405109405517578 create new chi : 4.882169961929321 time to delete rle : 0.0021486282348632812 batch 1 Loaded 45 chid ids of type : 3594 ++++++++++++++++++++++++++Number RLEs to save : 14869 TO DO : save crop sub photo not yet done ! save time : 2.17769193649292 nb_obj : 27 nb_hashtags : 3 time to prepare the origin masks : 3.571758508682251 time for calcul the mask position with numpy : 0.8914265632629395 nb_pixel_total : 6051298 time to create 1 rle with new method : 0.740553617477417 time for calcul the mask position with numpy : 0.027120590209960938 nb_pixel_total : 18779 time to create 1 rle with old method : 0.02052474021911621 time for calcul the mask position with numpy : 0.028842926025390625 nb_pixel_total : 13845 time to create 1 rle with old method : 0.014980316162109375 time for calcul the mask position with numpy : 0.02851247787475586 nb_pixel_total : 9703 time to create 1 rle with old method : 0.010855436325073242 time for calcul the mask position with numpy : 0.0287015438079834 nb_pixel_total : 177557 time to create 1 rle with new method : 0.5763957500457764 time for calcul the mask position with numpy : 0.028249025344848633 nb_pixel_total : 20009 time to create 1 rle with old method : 0.022678613662719727 time for calcul the mask position with numpy : 0.02935028076171875 nb_pixel_total : 16922 time to create 1 rle with old method : 0.01847100257873535 time for calcul the mask position with numpy : 0.029140710830688477 nb_pixel_total : 20575 time to create 1 rle with old method : 0.02240586280822754 time for calcul the mask position with numpy : 0.029486417770385742 nb_pixel_total : 44653 time to create 1 rle with old method : 0.04884219169616699 time for calcul the mask position with numpy : 0.029141902923583984 nb_pixel_total : 32421 time to create 1 rle with old method : 0.03529787063598633 time for calcul the mask position with numpy : 0.029532432556152344 nb_pixel_total : 23700 time to create 1 rle with old method : 0.025977373123168945 time for calcul the mask position with numpy : 0.029799699783325195 nb_pixel_total : 13338 time to create 1 rle with old method : 0.014641284942626953 time for calcul the mask position with numpy : 0.029384136199951172 nb_pixel_total : 19847 time to create 1 rle with old method : 0.021477937698364258 time for calcul the mask position with numpy : 0.028601646423339844 nb_pixel_total : 36769 time to create 1 rle with old method : 0.0384526252746582 time for calcul the mask position with numpy : 0.02757716178894043 nb_pixel_total : 22049 time to create 1 rle with old method : 0.023638248443603516 time for calcul the mask position with numpy : 0.02837991714477539 nb_pixel_total : 6301 time to create 1 rle with old method : 0.006783246994018555 time for calcul the mask position with numpy : 0.027515172958374023 nb_pixel_total : 12008 time to create 1 rle with old method : 0.012733936309814453 time for calcul the mask position with numpy : 0.02749156951904297 nb_pixel_total : 125980 time to create 1 rle with old method : 0.12909674644470215 time for calcul the mask position with numpy : 0.027981042861938477 nb_pixel_total : 6904 time to create 1 rle with old method : 0.0072956085205078125 time for calcul the mask position with numpy : 0.027209758758544922 nb_pixel_total : 19546 time to create 1 rle with old method : 0.019976377487182617 time for calcul the mask position with numpy : 0.026724576950073242 nb_pixel_total : 12695 time to create 1 rle with old method : 0.013319253921508789 time for calcul the mask position with numpy : 0.02738499641418457 nb_pixel_total : 17350 time to create 1 rle with old method : 0.018331050872802734 time for calcul the mask position with numpy : 0.026941299438476562 nb_pixel_total : 27957 time to create 1 rle with old method : 0.028228282928466797 time for calcul the mask position with numpy : 0.027546405792236328 nb_pixel_total : 20773 time to create 1 rle with old method : 0.022093772888183594 time for calcul the mask position with numpy : 0.028537988662719727 nb_pixel_total : 71501 time to create 1 rle with old method : 0.07334327697753906 time for calcul the mask position with numpy : 0.028294801712036133 nb_pixel_total : 187532 time to create 1 rle with new method : 0.37905097007751465 time for calcul the mask position with numpy : 0.027631044387817383 nb_pixel_total : 4845 time to create 1 rle with old method : 0.005171537399291992 time for calcul the mask position with numpy : 0.0282590389251709 nb_pixel_total : 15383 time to create 1 rle with old method : 0.01621532440185547 create new chi : 4.1025707721710205 time to delete rle : 0.0021359920501708984 batch 1 Loaded 55 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++Number RLEs to save : 15375 TO DO : save crop sub photo not yet done ! save time : 2.2178640365600586 nb_obj : 37 nb_hashtags : 4 time to prepare the origin masks : 3.67657732963562 time for calcul the mask position with numpy : 0.719386100769043 nb_pixel_total : 5730932 time to create 1 rle with new method : 0.39575767517089844 time for calcul the mask position with numpy : 0.029006004333496094 nb_pixel_total : 9137 time to create 1 rle with old method : 0.010480165481567383 time for calcul the mask position with numpy : 0.029217004776000977 nb_pixel_total : 8201 time to create 1 rle with old method : 0.009216547012329102 time for calcul the mask position with numpy : 0.029540300369262695 nb_pixel_total : 71639 time to create 1 rle with old method : 0.08162546157836914 time for calcul the mask position with numpy : 0.02936530113220215 nb_pixel_total : 19083 time to create 1 rle with old method : 0.02180624008178711 time for calcul the mask position with numpy : 0.02950453758239746 nb_pixel_total : 12082 time to create 1 rle with old method : 0.013834238052368164 time for calcul the mask position with numpy : 0.0295407772064209 nb_pixel_total : 15185 time to create 1 rle with old method : 0.017473936080932617 time for calcul the mask position with numpy : 0.029381752014160156 nb_pixel_total : 9919 time to create 1 rle with old method : 0.01134037971496582 time for calcul the mask position with numpy : 0.02951979637145996 nb_pixel_total : 15733 time to create 1 rle with old method : 0.018841028213500977 time for calcul the mask position with numpy : 0.029432296752929688 nb_pixel_total : 22759 time to create 1 rle with old method : 0.026462793350219727 time for calcul the mask position with numpy : 0.02877354621887207 nb_pixel_total : 10782 time to create 1 rle with old method : 0.012630701065063477 time for calcul the mask position with numpy : 0.0299530029296875 nb_pixel_total : 72680 time to create 1 rle with old method : 0.08555364608764648 time for calcul the mask position with numpy : 0.028043270111083984 nb_pixel_total : 6213 time to create 1 rle with old method : 0.006836891174316406 time for calcul the mask position with numpy : 0.02924656867980957 nb_pixel_total : 13707 time to create 1 rle with old method : 0.015314579010009766 time for calcul the mask position with numpy : 0.029023408889770508 nb_pixel_total : 34584 time to create 1 rle with old method : 0.036843299865722656 time for calcul the mask position with numpy : 0.028117656707763672 nb_pixel_total : 6952 time to create 1 rle with old method : 0.007959842681884766 time for calcul the mask position with numpy : 0.028498411178588867 nb_pixel_total : 5383 time to create 1 rle with old method : 0.0058135986328125 time for calcul the mask position with numpy : 0.028184175491333008 nb_pixel_total : 11782 time to create 1 rle with old method : 0.012586593627929688 time for calcul the mask position with numpy : 0.028655529022216797 nb_pixel_total : 132865 time to create 1 rle with old method : 0.14254093170166016 time for calcul the mask position with numpy : 0.028143644332885742 nb_pixel_total : 7708 time to create 1 rle with old method : 0.00829172134399414 time for calcul the mask position with numpy : 0.02808976173400879 nb_pixel_total : 61445 time to create 1 rle with old method : 0.06432819366455078 time for calcul the mask position with numpy : 0.02978658676147461 nb_pixel_total : 81049 time to create 1 rle with old method : 0.11021876335144043 time for calcul the mask position with numpy : 0.02840876579284668 nb_pixel_total : 8513 time to create 1 rle with old method : 0.009468555450439453 time for calcul the mask position with numpy : 0.02738213539123535 nb_pixel_total : 5369 time to create 1 rle with old method : 0.005775928497314453 time for calcul the mask position with numpy : 0.028885602951049805 nb_pixel_total : 30977 time to create 1 rle with old method : 0.03274941444396973 time for calcul the mask position with numpy : 0.028058767318725586 nb_pixel_total : 101359 time to create 1 rle with old method : 0.10731291770935059 time for calcul the mask position with numpy : 0.02855849266052246 nb_pixel_total : 25697 time to create 1 rle with old method : 0.027303218841552734 time for calcul the mask position with numpy : 0.02840900421142578 nb_pixel_total : 24493 time to create 1 rle with old method : 0.02579641342163086 time for calcul the mask position with numpy : 0.027497053146362305 nb_pixel_total : 5064 time to create 1 rle with old method : 0.005199432373046875 time for calcul the mask position with numpy : 0.027550220489501953 nb_pixel_total : 13025 time to create 1 rle with old method : 0.013683795928955078 time for calcul the mask position with numpy : 0.028097867965698242 nb_pixel_total : 9117 time to create 1 rle with old method : 0.009598255157470703 time for calcul the mask position with numpy : 0.02895379066467285 nb_pixel_total : 12873 time to create 1 rle with old method : 0.013866424560546875 time for calcul the mask position with numpy : 0.028800010681152344 nb_pixel_total : 265708 time to create 1 rle with new method : 0.5006101131439209 time for calcul the mask position with numpy : 0.029048442840576172 nb_pixel_total : 45996 time to create 1 rle with old method : 0.04996919631958008 time for calcul the mask position with numpy : 0.029695749282836914 nb_pixel_total : 90739 time to create 1 rle with old method : 0.10379314422607422 time for calcul the mask position with numpy : 0.029504776000976562 nb_pixel_total : 20716 time to create 1 rle with old method : 0.02372455596923828 time for calcul the mask position with numpy : 0.028879880905151367 nb_pixel_total : 13474 time to create 1 rle with old method : 0.014790773391723633 time for calcul the mask position with numpy : 0.02901434898376465 nb_pixel_total : 17300 time to create 1 rle with old method : 0.018857240676879883 create new chi : 3.9212610721588135 time to delete rle : 0.002825021743774414 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16966 TO DO : save crop sub photo not yet done ! save time : 2.0322377681732178 nb_obj : 21 nb_hashtags : 6 time to prepare the origin masks : 6.919635534286499 time for calcul the mask position with numpy : 0.4830317497253418 nb_pixel_total : 5910486 time to create 1 rle with new method : 0.5442616939544678 time for calcul the mask position with numpy : 0.02175593376159668 nb_pixel_total : 190968 time to create 1 rle with new method : 0.6543145179748535 time for calcul the mask position with numpy : 0.03642749786376953 nb_pixel_total : 3280 time to create 1 rle with old method : 0.0037298202514648438 time for calcul the mask position with numpy : 0.028321027755737305 nb_pixel_total : 6558 time to create 1 rle with old method : 0.007565975189208984 time for calcul the mask position with numpy : 0.020520448684692383 nb_pixel_total : 48284 time to create 1 rle with old method : 0.05188250541687012 time for calcul the mask position with numpy : 0.021169662475585938 nb_pixel_total : 42233 time to create 1 rle with old method : 0.044966697692871094 time for calcul the mask position with numpy : 0.020686626434326172 nb_pixel_total : 47748 time to create 1 rle with old method : 0.051987409591674805 time for calcul the mask position with numpy : 0.02142500877380371 nb_pixel_total : 27140 time to create 1 rle with old method : 0.029189586639404297 time for calcul the mask position with numpy : 0.021496295928955078 nb_pixel_total : 19827 time to create 1 rle with old method : 0.02243661880493164 time for calcul the mask position with numpy : 0.0211336612701416 nb_pixel_total : 53495 time to create 1 rle with old method : 0.05622291564941406 time for calcul the mask position with numpy : 0.02064228057861328 nb_pixel_total : 30408 time to create 1 rle with old method : 0.03218483924865723 time for calcul the mask position with numpy : 0.020899534225463867 nb_pixel_total : 16521 time to create 1 rle with old method : 0.01739215850830078 time for calcul the mask position with numpy : 0.020626544952392578 nb_pixel_total : 31322 time to create 1 rle with old method : 0.03278541564941406 time for calcul the mask position with numpy : 0.020740985870361328 nb_pixel_total : 8422 time to create 1 rle with old method : 0.008851766586303711 time for calcul the mask position with numpy : 0.021947860717773438 nb_pixel_total : 38391 time to create 1 rle with old method : 0.040158748626708984 time for calcul the mask position with numpy : 0.021978139877319336 nb_pixel_total : 240309 time to create 1 rle with new method : 0.4949803352355957 time for calcul the mask position with numpy : 0.022060155868530273 nb_pixel_total : 6539 time to create 1 rle with old method : 0.007225990295410156 time for calcul the mask position with numpy : 0.021090030670166016 nb_pixel_total : 25437 time to create 1 rle with old method : 0.0274350643157959 time for calcul the mask position with numpy : 0.021358728408813477 nb_pixel_total : 58561 time to create 1 rle with old method : 0.06138253211975098 time for calcul the mask position with numpy : 0.024521350860595703 nb_pixel_total : 86942 time to create 1 rle with old method : 0.09663581848144531 time for calcul the mask position with numpy : 0.021084070205688477 nb_pixel_total : 62624 time to create 1 rle with old method : 0.06510376930236816 time for calcul the mask position with numpy : 0.021205902099609375 nb_pixel_total : 94745 time to create 1 rle with old method : 0.10424089431762695 create new chi : 3.4888572692871094 time to delete rle : 0.0018723011016845703 batch 1 Loaded 43 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 13522 TO DO : save crop sub photo not yet done ! save time : 1.7521724700927734 nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 6.875625133514404 time for calcul the mask position with numpy : 0.4040257930755615 nb_pixel_total : 5329637 time to create 1 rle with new method : 0.696399450302124 time for calcul the mask position with numpy : 0.02565741539001465 nb_pixel_total : 367812 time to create 1 rle with new method : 0.5552723407745361 time for calcul the mask position with numpy : 0.02095651626586914 nb_pixel_total : 20325 time to create 1 rle with old method : 0.021938800811767578 time for calcul the mask position with numpy : 0.020364046096801758 nb_pixel_total : 14368 time to create 1 rle with old method : 0.019637346267700195 time for calcul the mask position with numpy : 0.02062225341796875 nb_pixel_total : 7115 time to create 1 rle with old method : 0.008242368698120117 time for calcul the mask position with numpy : 0.021506309509277344 nb_pixel_total : 153910 time to create 1 rle with new method : 0.5688872337341309 time for calcul the mask position with numpy : 0.03388047218322754 nb_pixel_total : 37559 time to create 1 rle with old method : 0.03976798057556152 time for calcul the mask position with numpy : 0.03427720069885254 nb_pixel_total : 118717 time to create 1 rle with old method : 0.13178277015686035 time for calcul the mask position with numpy : 0.03933238983154297 nb_pixel_total : 30923 time to create 1 rle with old method : 0.03363633155822754 time for calcul the mask position with numpy : 0.03335142135620117 nb_pixel_total : 66233 time to create 1 rle with old method : 0.07120275497436523 time for calcul the mask position with numpy : 0.03313279151916504 nb_pixel_total : 13803 time to create 1 rle with old method : 0.015299558639526367 time for calcul the mask position with numpy : 0.035097360610961914 nb_pixel_total : 246357 time to create 1 rle with new method : 0.5935328006744385 time for calcul the mask position with numpy : 0.034392356872558594 nb_pixel_total : 74408 time to create 1 rle with old method : 0.08133625984191895 time for calcul the mask position with numpy : 0.0326085090637207 nb_pixel_total : 8572 time to create 1 rle with old method : 0.009045839309692383 time for calcul the mask position with numpy : 0.03353285789489746 nb_pixel_total : 188305 time to create 1 rle with new method : 0.4330742359161377 time for calcul the mask position with numpy : 0.03310060501098633 nb_pixel_total : 58629 time to create 1 rle with old method : 0.06185650825500488 time for calcul the mask position with numpy : 0.03313851356506348 nb_pixel_total : 4214 time to create 1 rle with old method : 0.004506111145019531 time for calcul the mask position with numpy : 0.03266334533691406 nb_pixel_total : 55363 time to create 1 rle with old method : 0.05864262580871582 time for calcul the mask position with numpy : 0.03354167938232422 nb_pixel_total : 28597 time to create 1 rle with old method : 0.031523942947387695 time for calcul the mask position with numpy : 0.03377699851989746 nb_pixel_total : 25926 time to create 1 rle with old method : 0.029254674911499023 time for calcul the mask position with numpy : 0.03191876411437988 nb_pixel_total : 12040 time to create 1 rle with old method : 0.01360011100769043 time for calcul the mask position with numpy : 0.03155970573425293 nb_pixel_total : 44134 time to create 1 rle with old method : 0.04865694046020508 time for calcul the mask position with numpy : 0.03294205665588379 nb_pixel_total : 28090 time to create 1 rle with old method : 0.031479597091674805 time for calcul the mask position with numpy : 0.02588200569152832 nb_pixel_total : 115203 time to create 1 rle with old method : 0.15413665771484375 create new chi : 4.952202558517456 time to delete rle : 0.003782033920288086 batch 1 Loaded 47 chid ids of type : 3594 +++++++++++++++++++++++++++Number RLEs to save : 15136 TO DO : save crop sub photo not yet done ! save time : 2.0759682655334473 map_output_result : {1333267465: (0.0, 'Should be the crop_list due to order', 0.0), 1333267458: (0.0, 'Should be the crop_list due to order', 0.0), 1333267278: (0.0, 'Should be the crop_list due to order', 0.0), 1333267265: (0.0, 'Should be the crop_list due to order', 0.0), 1333267257: (0.0, 'Should be the crop_list due to order', 0.0), 1333267252: (0.0, 'Should be the crop_list due to order', 0.0), 1333267248: (0.0, 'Should be the crop_list due to order', 0.0), 1333267244: (0.0, 'Should be the crop_list due to order', 0.0), 1333266673: (0.0, 'Should be the crop_list due to order', 0.0), 1333266660: (0.0, 'Should be the crop_list due to order', 0.0), 1333266652: (0.0, 'Should be the crop_list due to order', 0.0), 1333266647: (0.0, 'Should be the crop_list due to order', 0.0), 1333266642: (0.0, 'Should be the crop_list due to order', 0.0), 1333266632: (0.0, 'Should be the crop_list due to order', 0.0), 1333266479: (0.0, 'Should be the crop_list due to order', 0.0), 1333266472: (0.0, 'Should be the crop_list due to order', 0.0), 1333266404: (0.0, 'Should be the crop_list due to order', 0.0), 1333266394: (0.0, 'Should be the crop_list due to order', 0.0), 1333266340: (0.0, 'Should be the crop_list due to order', 0.0), 1333266339: (0.0, 'Should be the crop_list due to order', 0.0), 1333266334: (0.0, 'Should be the crop_list due to order', 0.0), 1333266329: (0.0, 'Should be the crop_list due to order', 0.0), 1333266314: (0.0, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1333267465, 1333267458, 1333267278, 1333267265, 1333267257, 1333267252, 1333267248, 1333267244, 1333266673, 1333266660, 1333266652, 1333266647, 1333266642, 1333266632, 1333266479, 1333266472, 1333266404, 1333266394, 1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 23 /1333267465.Didn't retrieve data . /1333267458.Didn't retrieve data . /1333267278.Didn't retrieve data . /1333267265.Didn't retrieve data . /1333267257.Didn't retrieve data . /1333267252.Didn't retrieve data . /1333267248.Didn't retrieve data . /1333267244.Didn't retrieve data . /1333266673.Didn't retrieve data . /1333266660.Didn't retrieve data . /1333266652.Didn't retrieve data . /1333266647.Didn't retrieve data . /1333266642.Didn't retrieve data . /1333266632.Didn't retrieve data . /1333266479.Didn't retrieve data . /1333266472.Didn't retrieve data . /1333266404.Didn't retrieve data . /1333266394.Didn't retrieve data . /1333266340.Didn't retrieve data . /1333266339.Didn't retrieve data . /1333266334.Didn't retrieve data . /1333266329.Didn't retrieve data . /1333266314.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, '2535904') ('3318', '20128154', '1333267465', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267458', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267278', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267265', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267257', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267252', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267248', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267244', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266673', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266660', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266652', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266647', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266642', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266632', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266479', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266472', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266404', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266394', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266340', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266339', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266334', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266329', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266314', None, None, None, None, None, '2535904') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 69 time used for this insertion : 0.017086505889892578 save_final save missing photos in datou_result : time spend for datou_step_exec : 287.1345040798187 time spend to save output : 0.017789602279663086 total time spend for step 3 : 287.1522936820984 step4:ventilate_hashtags_in_portfolio Sat Feb 1 01:49:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 20128154 get user id for portfolio 20128154 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`=20128154 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','mal_croppe','pehd','autre','metal','background','pet_fonce','environnement','flou','papier','pet_clair')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20128154 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','mal_croppe','pehd','autre','metal','background','pet_fonce','environnement','flou','papier','pet_clair')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20128154 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','mal_croppe','pehd','autre','metal','background','pet_fonce','environnement','flou','papier','pet_clair')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20128661,20128662,20128663,20128664,20128665,20128666,20128667,20128668,20128669,20128670,20128671?tags=carton,mal_croppe,pehd,autre,metal,background,pet_fonce,environnement,flou,papier,pet_clair Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1333267465, 1333267458, 1333267278, 1333267265, 1333267257, 1333267252, 1333267248, 1333267244, 1333266673, 1333266660, 1333266652, 1333266647, 1333266642, 1333266632, 1333266479, 1333266472, 1333266404, 1333266394, 1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 1 /20128154. 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, '2535904') ('3318', '20128154', '1333267465', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267458', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267278', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267265', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267257', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267252', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267248', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267244', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266673', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266660', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266652', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266647', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266642', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266632', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266479', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266472', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266404', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266394', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266340', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266339', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266334', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266329', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266314', None, None, None, None, None, '2535904') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.02129983901977539 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.2441859245300293 time spend to save output : 0.02165365219116211 total time spend for step 4 : 2.2658395767211914 step5:final Sat Feb 1 01:49:56 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 : {1333267465: ('0.18867941714225955',), 1333267458: ('0.18867941714225955',), 1333267278: ('0.18867941714225955',), 1333267265: ('0.18867941714225955',), 1333267257: ('0.18867941714225955',), 1333267252: ('0.18867941714225955',), 1333267248: ('0.18867941714225955',), 1333267244: ('0.18867941714225955',), 1333266673: ('0.18867941714225955',), 1333266660: ('0.18867941714225955',), 1333266652: ('0.18867941714225955',), 1333266647: ('0.18867941714225955',), 1333266642: ('0.18867941714225955',), 1333266632: ('0.18867941714225955',), 1333266479: ('0.18867941714225955',), 1333266472: ('0.18867941714225955',), 1333266404: ('0.18867941714225955',), 1333266394: ('0.18867941714225955',), 1333266340: ('0.18867941714225955',), 1333266339: ('0.18867941714225955',), 1333266334: ('0.18867941714225955',), 1333266329: ('0.18867941714225955',), 1333266314: ('0.18867941714225955',)} new output for save of step final : {1333267465: ('0.18867941714225955',), 1333267458: ('0.18867941714225955',), 1333267278: ('0.18867941714225955',), 1333267265: ('0.18867941714225955',), 1333267257: ('0.18867941714225955',), 1333267252: ('0.18867941714225955',), 1333267248: ('0.18867941714225955',), 1333267244: ('0.18867941714225955',), 1333266673: ('0.18867941714225955',), 1333266660: ('0.18867941714225955',), 1333266652: ('0.18867941714225955',), 1333266647: ('0.18867941714225955',), 1333266642: ('0.18867941714225955',), 1333266632: ('0.18867941714225955',), 1333266479: ('0.18867941714225955',), 1333266472: ('0.18867941714225955',), 1333266404: ('0.18867941714225955',), 1333266394: ('0.18867941714225955',), 1333266340: ('0.18867941714225955',), 1333266339: ('0.18867941714225955',), 1333266334: ('0.18867941714225955',), 1333266329: ('0.18867941714225955',), 1333266314: ('0.18867941714225955',)} [1333267465, 1333267458, 1333267278, 1333267265, 1333267257, 1333267252, 1333267248, 1333267244, 1333266673, 1333266660, 1333266652, 1333266647, 1333266642, 1333266632, 1333266479, 1333266472, 1333266404, 1333266394, 1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 23 /1333267465.Didn't retrieve data . /1333267458.Didn't retrieve data . /1333267278.Didn't retrieve data . /1333267265.Didn't retrieve data . /1333267257.Didn't retrieve data . /1333267252.Didn't retrieve data . /1333267248.Didn't retrieve data . /1333267244.Didn't retrieve data . /1333266673.Didn't retrieve data . /1333266660.Didn't retrieve data . /1333266652.Didn't retrieve data . /1333266647.Didn't retrieve data . /1333266642.Didn't retrieve data . /1333266632.Didn't retrieve data . /1333266479.Didn't retrieve data . /1333266472.Didn't retrieve data . /1333266404.Didn't retrieve data . /1333266394.Didn't retrieve data . /1333266340.Didn't retrieve data . /1333266339.Didn't retrieve data . /1333266334.Didn't retrieve data . /1333266329.Didn't retrieve data . /1333266314.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, '2535904') ('3318', '20128154', '1333267465', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267458', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267278', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267265', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267257', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267252', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267248', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267244', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266673', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266660', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266652', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266647', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266642', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266632', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266479', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266472', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266404', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266394', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266340', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266339', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266334', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266329', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266314', None, None, None, None, None, '2535904') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 69 time used for this insertion : 0.023577451705932617 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10888910293579102 time spend to save output : 0.024526357650756836 total time spend for step 5 : 0.13341546058654785 step6:blur_detection Sat Feb 1 01:49:56 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/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f.jpg resize: (2160, 3264) 1333267465 -3.551899833615219 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c.jpg resize: (2160, 3264) 1333267458 -4.45061959069737 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636.jpg resize: (2160, 3264) 1333267278 -6.210102909005981 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac.jpg resize: (2160, 3264) 1333267265 -6.376878716920928 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9.jpg resize: (2160, 3264) 1333267257 -4.3381115157056 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620.jpg resize: (2160, 3264) 1333267252 -6.990896170975222 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe.jpg resize: (2160, 3264) 1333267248 -4.927118146986952 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d.jpg resize: (2160, 3264) 1333267244 -5.5867978170183585 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7.jpg resize: (2160, 3264) 1333266673 -5.348264674215929 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370.jpg resize: (2160, 3264) 1333266660 -6.214105943050729 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250.jpg resize: (2160, 3264) 1333266652 -4.7658102550043715 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3.jpg resize: (2160, 3264) 1333266647 -5.8964463688251705 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f.jpg resize: (2160, 3264) 1333266642 -6.321482641867532 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf.jpg resize: (2160, 3264) 1333266632 -7.062255079566302 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467.jpg resize: (2160, 3264) 1333266479 -5.792363992831691 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9.jpg resize: (2160, 3264) 1333266472 -4.650026136384781 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8.jpg resize: (2160, 3264) 1333266404 -5.3013162617903795 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134.jpg resize: (2160, 3264) 1333266394 -4.38897844226345 treat image : temp/1738369827_2513697_1333266340_33011a221e7155810755e10cb8549aea.jpg resize: (2160, 3264) 1333266340 -4.956128758846408 treat image : temp/1738369827_2513697_1333266339_18223fe76cf16e9d2bd847e0fdf79cfe.jpg resize: (2160, 3264) 1333266339 -4.650026136384781 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48.jpg resize: (2160, 3264) 1333266334 -5.3013162617903795 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88.jpg resize: (2160, 3264) 1333266329 -4.38897844226345 treat image : temp/1738369827_2513697_1333266314_f849b92c05bc376d8fa628a79697da53.jpg resize: (2160, 3264) 1333266314 -4.956128758846408 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996447_0.png resize: (65, 145) 1333538655 -2.3856184324586773 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646998995_0.png resize: (65, 145) 1333538656 -2.3856184324586773 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996440_0.png resize: (74, 115) 1333538657 -2.426862728178276 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646999002_0.png resize: (74, 115) 1333538658 -2.426862728178276 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996445_0.png resize: (411, 217) 1333538659 -3.2160042556150246 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646999003_0.png resize: (97, 120) 1333538660 -2.0406166216812673 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996439_0.png resize: (97, 120) 1333538661 -2.0406166216812673 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646998997_0.png resize: (411, 217) 1333538662 -3.2160042556150246 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996436_0.png resize: (82, 92) 1333538663 -0.42422103205498823 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646999006_0.png resize: (82, 92) 1333538664 -0.42422103205498823 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996458_0.png resize: (187, 117) 1333538665 -2.9772701473935452 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999021_0.png resize: (187, 117) 1333538666 -2.9772701473935452 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999020_0.png resize: (73, 112) 1333538667 1.00279011000041 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996459_0.png resize: (73, 112) 1333538668 1.00279011000041 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996467_0.png resize: (148, 128) 1333538669 -3.381294606669094 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999012_0.png resize: (148, 128) 1333538670 -3.381294606669094 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996454_0.png resize: (174, 294) 1333538671 -3.954666646598374 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3648002820_0.png resize: (178, 294) 1333538672 -3.9072677422905855 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999025_0.png resize: (174, 294) 1333538673 -3.954666646598374 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996456_0.png resize: (272, 342) 1333538674 -3.8171717440253183 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999023_0.png resize: (272, 342) 1333538675 -3.8171717440253183 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996469_0.png resize: (241, 159) 1333538676 -3.3338416535172684 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999010_0.png resize: (241, 159) 1333538677 -3.3338416535172684 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996460_0.png resize: (49, 86) 1333538678 -2.9846782591661434 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999019_0.png resize: (49, 86) 1333538679 -2.9846782591661434 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996466_0.png resize: (429, 304) 1333538680 -2.761196577729898 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3648002821_0.png resize: (426, 301) 1333538681 -2.610022457206872 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999013_0.png resize: (429, 277) 1333538682 -2.776726036862685 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999028_0.png resize: (157, 184) 1333538683 -2.7724188107325944 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996451_0.png resize: (157, 184) 1333538684 -2.7724188107325944 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996465_0.png resize: (63, 115) 1333538685 -3.444426602269337 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999014_0.png resize: (63, 115) 1333538686 -3.444426602269337 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996464_0.png resize: (174, 170) 1333538687 -3.413522644397778 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999015_0.png resize: (174, 170) 1333538688 -3.413522644397778 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996463_0.png resize: (112, 166) 1333538689 -3.1523702483048592 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999016_0.png resize: (112, 166) 1333538690 -3.1523702483048592 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996457_0.png resize: (200, 129) 1333538691 -3.5089150505066886 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999022_0.png resize: (200, 129) 1333538692 -3.5089150505066886 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996461_0.png resize: (60, 81) 1333538693 -3.584635676322047 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999018_0.png resize: (60, 81) 1333538694 -3.584635676322047 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999024_0.png resize: (226, 239) 1333538695 -5.229361411990694 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996455_0.png resize: (226, 239) 1333538696 -5.229361411990694 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996453_0.png resize: (116, 81) 1333538697 -2.644762868245082 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999026_0.png resize: (116, 81) 1333538698 -2.644762868245082 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996449_0.png resize: (204, 103) 1333538699 -2.758318899114472 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999030_0.png resize: (204, 103) 1333538700 -2.758318899114472 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996468_0.png resize: (149, 118) 1333538701 -3.6920115542774936 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999011_0.png resize: (149, 118) 1333538702 -3.6920115542774936 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646996452_0.png resize: (182, 115) 1333538703 -2.9533187465637987 treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c_rle_crop_3646999027_0.png resize: (182, 115) 1333538704 -2.9533187465637987 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999088_0.png resize: (200, 122) 1333538705 -3.8104061272714445 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996506_0.png resize: (200, 122) 1333538706 -3.8104061272714445 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999083_0.png resize: (78, 172) 1333538707 -0.7472811290146417 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996524_0.png resize: (80, 172) 1333538708 -3.219407789790653 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996473_0.png resize: (478, 197) 1333538709 -4.13230819359797 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999078_0.png resize: (478, 197) 1333538710 -4.13230819359797 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996510_0.png resize: (141, 134) 1333538711 -3.912959567804913 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999097_0.png resize: (141, 134) 1333538712 -3.912959567804913 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996508_0.png resize: (96, 104) 1333538713 -2.7984667528578577 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996488_0.png resize: (166, 196) 1333538714 -3.3786097800227473 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999087_0.png resize: (166, 196) 1333538715 -3.3786097800227473 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999096_0.png resize: (96, 104) 1333538716 -2.7984667528578577 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996478_0.png resize: (256, 149) 1333538717 -3.415365030890175 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996489_0.png resize: (222, 227) 1333538718 -3.542472536470654 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999082_0.png resize: (222, 227) 1333538719 -3.542472536470654 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999080_0.png resize: (256, 149) 1333538721 -3.415365030890175 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996485_0.png resize: (138, 166) 1333538722 -4.245287953174677 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999086_0.png resize: (138, 166) 1333538723 -4.245287953174677 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996503_0.png resize: (147, 233) 1333538724 -3.066566401444165 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999052_0.png resize: (147, 226) 1333538725 -3.0982190671134986 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996500_0.png resize: (224, 191) 1333538726 -4.580744256837183 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999056_0.png resize: (224, 191) 1333538727 -4.580744256837183 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996492_0.png resize: (125, 117) 1333538728 0.6918052067499483 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999051_0.png resize: (125, 117) 1333538729 0.6918052067499483 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996482_0.png resize: (237, 197) 1333538730 -4.2900779989108795 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999054_0.png resize: (237, 197) 1333538731 -4.2900779989108795 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996523_0.png resize: (84, 73) 1333538732 -2.0401152884120815 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999098_0.png resize: (84, 73) 1333538733 -2.0401152884120815 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996493_0.png resize: (427, 442) 1333538734 -4.266327900335634 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996479_0.png resize: (239, 198) 1333538735 -4.1058577726368055 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999061_0.png resize: (239, 198) 1333538736 -4.1058577726368055 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999092_0.png resize: (427, 442) 1333538737 -4.266327900335634 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996471_0.png resize: (331, 138) 1333538738 -4.683368952931711 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999046_0.png resize: (331, 138) 1333538739 -4.683368952931711 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996484_0.png resize: (283, 202) 1333538740 -4.7483941554452365 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999047_0.png resize: (283, 202) 1333538741 -4.7483941554452365 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996518_0.png resize: (201, 150) 1333538742 -5.124958708008766 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996509_0.png resize: (178, 129) 1333538743 -3.644985333200364 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999089_0.png resize: (201, 150) 1333538744 -5.124958708008766 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999057_0.png resize: (178, 129) 1333538745 -3.644985333200364 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996515_0.png resize: (198, 156) 1333538746 -4.156750562965957 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999084_0.png resize: (198, 156) 1333538747 -4.156750562965957 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996472_0.png resize: (157, 213) 1333538748 -4.397791167570055 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999055_0.png resize: (157, 213) 1333538749 -4.397791167570055 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996517_0.png resize: (124, 150) 1333538750 -4.35579225952874 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999075_0.png resize: (124, 150) 1333538751 -4.35579225952874 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996512_0.png resize: (184, 148) 1333538752 -2.9252337945283284 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999064_0.png resize: (184, 148) 1333538753 -2.9252337945283284 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996480_0.png resize: (84, 66) 1333538754 0.26827403458679694 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999094_0.png resize: (84, 66) 1333538755 0.26827403458679694 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996513_0.png resize: (200, 256) 1333538756 -4.463381070569289 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996487_0.png resize: (203, 222) 1333538757 -4.648526492894326 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999069_0.png resize: (203, 222) 1333538758 -4.648526492894326 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996521_0.png resize: (306, 173) 1333538759 -3.186481397415982 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999063_0.png resize: (200, 193) 1333538760 -4.883136723901337 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999085_0.png resize: (306, 173) 1333538761 -3.186481397415982 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996522_0.png resize: (254, 95) 1333538762 -3.5832007690585157 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999081_0.png resize: (254, 95) 1333538763 -3.5832007690585157 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996490_0.png resize: (181, 190) 1333538764 -4.789524606564732 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999073_0.png resize: (181, 190) 1333538765 -4.789524606564732 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999090_0.png resize: (119, 222) 1333538766 -4.325887007252498 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996504_0.png resize: (119, 222) 1333538767 -4.325887007252498 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996470_0.png resize: (230, 243) 1333538768 -4.815914402537134 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999058_0.png resize: (230, 243) 1333538769 -4.815914402537134 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996474_0.png resize: (220, 190) 1333538770 -4.029082903207 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999050_0.png resize: (220, 190) 1333538771 -4.029082903207 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996501_0.png resize: (261, 174) 1333538772 -3.366275541394419 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999059_0.png resize: (261, 174) 1333538773 -3.3616238367824463 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996511_0.png resize: (352, 183) 1333538774 -4.444872224822041 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999060_0.png resize: (352, 182) 1333538775 -4.4615258266089475 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996548_0.png resize: (98, 48) 1333538776 -2.8685223625719867 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999121_0.png resize: (98, 48) 1333538777 -2.8685223625719867 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996532_0.png resize: (150, 109) 1333538778 -3.3433847059963226 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999134_0.png resize: (150, 109) 1333538779 -3.3433847059963226 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996526_0.png resize: (212, 108) 1333538780 -3.1533027207597435 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999131_0.png resize: (212, 108) 1333538781 -3.1533027207597435 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996561_0.png resize: (121, 91) 1333538782 -2.6554980579661844 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999150_0.png resize: (121, 91) 1333538783 -2.6554980579661844 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996572_0.png resize: (281, 167) 1333538784 -3.4363722567636854 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996527_0.png resize: (232, 237) 1333538785 -3.117621119966573 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999143_0.png resize: (281, 167) 1333538786 -3.4363722567636854 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996568_0.png resize: (191, 342) 1333538787 -4.172432153040607 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999115_0.png resize: (232, 237) 1333538788 -3.117621119966573 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999124_0.png resize: (191, 342) 1333538789 -4.172432153040607 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996533_0.png resize: (184, 224) 1333538790 -3.094501189267716 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999137_0.png resize: (184, 224) 1333538791 -3.094501189267716 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996549_0.png resize: (156, 173) 1333538792 -3.065935944787787 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999119_0.png resize: (156, 173) 1333538793 -3.065935944787787 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996575_0.png resize: (91, 124) 1333538794 -2.846281953631525 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999114_0.png resize: (91, 124) 1333538795 -2.3541489444554213 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996582_0.png resize: (170, 227) 1333538796 -4.796866485580703 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996543_0.png resize: (165, 127) 1333538797 -2.6446419975264432 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999107_0.png resize: (165, 127) 1333538798 -2.6446419975264432 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999125_0.png resize: (170, 191) 1333538799 -4.569142505443393 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996540_0.png resize: (143, 246) 1333538800 -2.231235834758757 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999154_0.png resize: (143, 246) 1333538801 -2.231235834758757 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996542_0.png resize: (285, 309) 1333538803 -3.898840561065867 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999146_0.png resize: (285, 309) 1333538805 -3.898840561065867 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996546_0.png resize: (234, 231) 1333538807 -2.9723600013572264 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999116_0.png resize: (234, 231) 1333538809 -2.9723600013572264 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996565_0.png resize: (111, 126) 1333538811 -1.935085441158808 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999113_0.png resize: (111, 126) 1333538813 -1.935085441158808 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996557_0.png resize: (193, 201) 1333538815 -3.684953237496125 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999135_0.png resize: (193, 201) 1333538818 -3.684953237496125 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996571_0.png resize: (169, 190) 1333538820 -3.539612380826226 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999152_0.png resize: (169, 190) 1333538822 -3.539612380826226 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996580_0.png resize: (73, 185) 1333538824 -3.1205097420764973 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999156_0.png resize: (73, 175) 1333538826 -3.355849671897012 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996528_0.png resize: (72, 134) 1333538828 -3.2525164838273923 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999117_0.png resize: (72, 134) 1333538830 -3.2525164838273923 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996577_0.png resize: (59, 93) 1333538832 -2.9624269453164924 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999157_0.png resize: (59, 93) 1333538834 -2.9624269453164924 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996530_0.png resize: (316, 285) 1333538836 -3.8325802772332365 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999123_0.png resize: (316, 285) 1333538838 -3.8325802772332365 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996574_0.png resize: (87, 116) 1333538840 -3.3571421068850773 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996531_0.png resize: (203, 256) 1333538842 -4.3467611805042985 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999122_0.png resize: (87, 116) 1333538844 -3.3571421068850773 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999153_0.png resize: (203, 256) 1333538846 -4.3467611805042985 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996539_0.png resize: (95, 110) 1333538848 -3.4014560508235174 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996581_0.png resize: (116, 102) 1333538850 -2.116902176348388 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999155_0.png resize: (95, 110) 1333538852 -3.4014560508235174 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999105_0.png resize: (116, 102) 1333538854 -2.116902176348388 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996537_0.png resize: (190, 219) 1333538856 -4.287563535226985 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999112_0.png resize: (190, 219) 1333538858 -4.287563535226985 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999109_0.png resize: (66, 114) 1333538860 -3.1052821306227854 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996558_0.png resize: (66, 114) 1333538862 -3.1052821306227854 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996534_0.png resize: (389, 281) 1333538864 -3.359601165444233 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999129_0.png resize: (389, 281) 1333538866 -3.359601165444233 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999151_0.png resize: (131, 316) 1333538868 -3.2988757573883274 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996529_0.png resize: (131, 316) 1333538870 -3.2988757573883274 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996562_0.png resize: (190, 203) 1333538872 -2.2000459063182265 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999120_0.png resize: (190, 203) 1333538874 -2.2000459063182265 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3648002827_0.png resize: (261, 262) 1333538876 -3.4882299384116235 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996569_0.png resize: (260, 262) 1333538878 -3.488334835700718 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999127_0.png resize: (260, 262) 1333538880 -3.488334835700718 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996538_0.png resize: (258, 225) 1333538882 -2.9634754383074307 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999118_0.png resize: (258, 225) 1333538884 -2.9634754383074307 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996550_0.png resize: (224, 241) 1333538886 -3.377133838994275 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999142_0.png resize: (224, 241) 1333538888 -3.377133838994275 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996554_0.png resize: (125, 187) 1333538890 -3.120196584906842 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996544_0.png resize: (187, 239) 1333538892 -4.665612969473907 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999149_0.png resize: (187, 239) 1333538894 -4.665612969473907 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996553_0.png resize: (146, 93) 1333538896 -4.380035041768552 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999106_0.png resize: (125, 187) 1333538898 -3.120196584906842 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999139_0.png resize: (125, 93) 1333538900 -4.483122934533579 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3648002825_0.png resize: (195, 229) 1333538902 -4.042411687803812 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996559_0.png resize: (180, 229) 1333538904 -4.0500698872474015 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999130_0.png resize: (180, 229) 1333538906 -4.0500698872474015 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996535_0.png resize: (104, 73) 1333538908 -3.0729559395263513 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999138_0.png resize: (104, 73) 1333538910 -3.0729559395263513 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996555_0.png resize: (152, 131) 1333538912 -4.29207892380373 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999147_0.png resize: (152, 131) 1333538914 -4.29207892380373 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996556_0.png resize: (100, 64) 1333538916 -2.8250427589326987 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996541_0.png resize: (173, 172) 1333538918 -3.411250039726237 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999140_0.png resize: (100, 63) 1333538920 -2.794801989322039 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999132_0.png resize: (173, 172) 1333538922 -3.411250039726237 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996573_0.png resize: (176, 175) 1333538924 -4.534501226051416 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999144_0.png resize: (176, 175) 1333538926 -4.534501226051416 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996567_0.png resize: (283, 87) 1333538928 -3.442009296625814 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3648002826_0.png resize: (278, 87) 1333538930 -3.4201234979632216 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999128_0.png resize: (283, 87) 1333538932 -3.442009296625814 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996552_0.png resize: (63, 138) 1333538934 -3.999117236167972 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3648002824_0.png resize: (61, 138) 1333538936 -3.87762442540916 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999102_0.png resize: (145, 115) 1333538938 -2.1823386102540314 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996525_0.png resize: (145, 115) 1333538940 -2.1823386102540314 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999141_0.png resize: (63, 138) 1333538942 -3.999117236167972 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996578_0.png resize: (135, 75) 1333538944 -4.536252869535495 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999148_0.png resize: (135, 73) 1333538946 -4.531491558529655 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996564_0.png resize: (198, 142) 1333538948 -3.5897978902679673 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999108_0.png resize: (198, 142) 1333538949 -3.5897978902679673 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996536_0.png resize: (258, 194) 1333538950 -3.8322678695346504 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999145_0.png resize: (258, 194) 1333538951 -3.8322678695346504 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996545_0.png resize: (119, 187) 1333538952 -3.4812750193223363 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996551_0.png resize: (200, 189) 1333538953 -4.575699347883298 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999104_0.png resize: (119, 187) 1333538954 -3.4812750193223363 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999136_0.png resize: (200, 189) 1333538955 -4.575699347883298 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996570_0.png resize: (218, 227) 1333538956 -5.195330976929747 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999111_0.png resize: (218, 227) 1333538957 -5.195330976929747 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996547_0.png resize: (84, 76) 1333538958 -2.7339617665322984 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999133_0.png resize: (84, 76) 1333538959 -2.7339617665322984 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999126_0.png resize: (87, 77) 1333538960 -3.327720608861469 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996566_0.png resize: (87, 77) 1333538961 -3.327720608861469 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996587_0.png resize: (102, 97) 1333538962 -2.742496548493411 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999172_0.png resize: (102, 97) 1333538963 -2.742496548493411 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999179_0.png resize: (70, 163) 1333538964 -2.725018420808353 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996588_0.png resize: (70, 163) 1333538965 -2.725018420808353 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996583_0.png resize: (123, 162) 1333538966 -2.9893370359869653 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999178_0.png resize: (123, 162) 1333538967 -2.9893370359869653 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996606_0.png resize: (96, 80) 1333538968 -2.2010929430861923 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999173_0.png resize: (96, 80) 1333538969 -2.2010929430861923 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996611_0.png resize: (111, 158) 1333538970 -3.694303970568852 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996598_0.png resize: (221, 261) 1333538971 -2.6572351595109884 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999176_0.png resize: (111, 158) 1333538972 -3.694303970568852 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999184_0.png resize: (221, 261) 1333538973 -2.6572351595109884 treat image : 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temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999389_0.png resize: (248, 168) 1333539416 -3.5648418050266986 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996702_0.png resize: (147, 235) 1333539419 -3.3189241611672418 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999408_0.png resize: (147, 235) 1333539423 -3.3189241611672418 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996694_0.png resize: (319, 306) 1333539426 -3.9088114502425273 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999393_0.png resize: (319, 306) 1333539430 -3.9088114502425273 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996704_0.png resize: (131, 126) 1333539434 -2.843786458125375 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999371_0.png resize: (131, 126) 1333539438 -2.843786458125375 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996709_0.png resize: (250, 226) 1333539441 -4.313416918005308 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999416_0.png resize: (250, 226) 1333539444 -4.313416918005308 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999404_0.png resize: (138, 142) 1333539447 -3.960058595391311 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996701_0.png resize: (138, 142) 1333539450 -3.960058595391311 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996733_0.png resize: (190, 122) 1333539454 -3.692144686054777 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996727_0.png resize: (77, 103) 1333539457 -3.6171268483487693 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999399_0.png resize: (77, 103) 1333539460 -3.6171268483487693 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999396_0.png resize: (189, 119) 1333539463 -2.641561699012456 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996715_0.png resize: (142, 191) 1333539466 -3.041871912372081 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999406_0.png resize: (142, 191) 1333539469 -3.041871912372081 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996723_0.png resize: (111, 133) 1333539472 -3.3868170438686387 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999403_0.png resize: (105, 133) 1333539476 -3.2726224863458646 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996731_0.png resize: (177, 205) 1333539479 -3.8052976029173537 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999392_0.png resize: (177, 205) 1333539481 -3.8052976029173537 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996700_0.png resize: (151, 126) 1333539484 -2.747456180987007 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999375_0.png resize: (151, 126) 1333539487 -2.747456180987007 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996739_0.png resize: (187, 135) 1333539490 -4.043008924341931 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999401_0.png resize: (187, 135) 1333539493 -4.043008924341931 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996718_0.png resize: (181, 79) 1333539496 -3.972153440585523 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999407_0.png resize: (181, 79) 1333539498 -3.972153440585523 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996730_0.png resize: (57, 98) 1333539502 -3.2802041927259853 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999382_0.png resize: (57, 98) 1333539505 -3.2802041927259853 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996705_0.png resize: (97, 121) 1333539508 -2.1220360665385565 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999395_0.png resize: (97, 121) 1333539511 -2.1220360665385565 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996729_0.png resize: (213, 327) 1333539513 -3.809393055084498 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999388_0.png resize: (213, 327) 1333539516 -3.809393055084498 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999410_0.png resize: (152, 228) 1333539519 -3.192257214954796 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996736_0.png resize: (152, 228) 1333539522 -3.192257214954796 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999400_0.png resize: (132, 83) 1333539525 -4.118818008442844 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996708_0.png resize: (132, 83) 1333539529 -4.118818008442844 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996697_0.png resize: (106, 97) 1333539531 -0.4334989128747233 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999385_0.png resize: (106, 97) 1333539535 -0.4334989128747233 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996698_0.png resize: (130, 108) 1333539537 -2.998015173311327 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999402_0.png resize: (130, 108) 1333539541 -2.998015173311327 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002838_0.png resize: (217, 418) 1333539544 -3.1441771356917596 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002855_0.png resize: (315, 419) 1333539548 -4.29931823945799 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002843_0.png resize: (74, 390) 1333539551 -3.3145628631537987 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002853_0.png resize: (150, 134) 1333539554 -2.093590839085069 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002840_0.png resize: (366, 238) 1333539558 -4.134364753047441 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002847_0.png resize: (175, 89) 1333539561 -1.8833016049256701 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002854_0.png resize: (143, 65) 1333539564 -3.3253975869532146 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002866_0.png resize: (195, 352) 1333539567 -4.4814316603513555 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002895_0.png resize: (133, 323) 1333539570 -4.492337118229081 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002900_0.png resize: (269, 290) 1333539573 -4.416509442247519 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002861_0.png resize: (88, 157) 1333539576 -2.818421726267215 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002897_0.png resize: (303, 295) 1333539579 -3.1952180478340795 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002872_0.png resize: (379, 345) 1333539580 -4.9495722208782 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002880_0.png resize: (172, 311) 1333539581 -3.8600070936435826 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002904_0.png resize: (138, 183) 1333539582 -2.877745401199518 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002869_0.png resize: (164, 237) 1333539583 -3.5807917983818673 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002894_0.png resize: (103, 120) 1333539584 -2.387869825564071 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002883_0.png resize: (161, 150) 1333539585 -4.64796851446905 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002892_0.png resize: (169, 106) 1333539586 -1.2803242539689264 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002893_0.png resize: (168, 196) 1333539587 -3.4923531178120215 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002879_0.png resize: (211, 184) 1333539588 -5.053417892379319 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002863_0.png resize: (179, 212) 1333539589 -3.685751041939915 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002901_0.png resize: (188, 153) 1333539590 -4.653616581612795 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002899_0.png resize: (137, 167) 1333539591 -3.363415586895372 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002888_0.png resize: (72, 104) 1333539592 -3.2714191888160897 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002877_0.png resize: (240, 193) 1333539593 -3.349969184086776 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002874_0.png resize: (200, 191) 1333539594 -4.807289346631565 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002907_0.png resize: (191, 131) 1333539596 -3.653932800525636 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002889_0.png resize: (168, 262) 1333539597 -4.7398464949725545 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002870_0.png resize: (165, 209) 1333539598 -4.415712002558435 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002898_0.png resize: (101, 97) 1333539599 -2.6475605061672582 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002865_0.png resize: (151, 114) 1333539600 -1.9469692633953306 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002867_0.png resize: (217, 157) 1333539601 -5.053638986211737 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002871_0.png resize: (202, 206) 1333539602 -4.639729664875383 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002885_0.png resize: (112, 115) 1333539603 -3.093097106035077 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002868_0.png resize: (386, 135) 1333539604 -4.2213277179454245 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002906_0.png resize: (358, 180) 1333539605 -3.931539912255614 treat image : 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temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002916_0.png resize: (211, 450) 1333539618 -4.053883725462247 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002911_0.png resize: (177, 156) 1333539619 -1.3397559671639092 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002910_0.png resize: (258, 359) 1333539620 -2.452910340118225 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002923_0.png resize: (320, 556) 1333539621 -3.024096812278617 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002912_0.png resize: (148, 174) 1333539622 -3.3876005308180264 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002924_0.png resize: (124, 117) 1333539623 -3.970298364664069 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002930_0.png resize: (312, 226) 1333539624 -3.658223813783303 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002925_0.png resize: (273, 168) 1333539625 -3.0150632583152763 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002929_0.png resize: (516, 348) 1333539626 -4.246334784225997 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002926_0.png resize: (108, 86) 1333539627 -2.1211708468127903 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002927_0.png resize: (166, 324) 1333539628 -3.119241226879775 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002938_0.png resize: (57, 211) 1333539629 -4.306582412751435 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002932_0.png resize: (561, 898) 1333539630 -3.0846980989838104 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002931_0.png resize: (326, 473) 1333539631 -4.5056603939433035 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002934_0.png resize: (110, 217) 1333539632 -3.19157353584671 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002955_0.png resize: (144, 171) 1333539633 -3.9825618826396103 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002935_0.png resize: (89, 141) 1333539634 -4.659729204879752 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002936_0.png resize: (90, 91) 1333539635 -3.634757777490476 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002948_0.png resize: (166, 276) 1333539636 -4.753361315346518 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002944_0.png resize: (659, 588) 1333539637 -3.7307576658804376 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002941_0.png resize: (254, 215) 1333539638 -3.024866126106407 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002954_0.png resize: (220, 286) 1333539640 -3.3958198000672963 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002950_0.png resize: (125, 132) 1333539642 -3.839147098114657 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002937_0.png resize: (292, 126) 1333539644 -3.062627815615825 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002940_0.png resize: (534, 326) 1333539646 -5.22892896091361 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002946_0.png resize: (144, 222) 1333539648 -4.14196500768605 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002943_0.png resize: (67, 127) 1333539650 -1.50007385148657 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002945_0.png resize: (113, 190) 1333539652 -3.8850966791203017 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002953_0.png resize: (144, 74) 1333539654 -3.8109827381445114 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002942_0.png resize: (165, 252) 1333539656 -4.31330180250309 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002970_0.png resize: (369, 113) 1333539659 -3.571014806866568 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002960_0.png resize: (237, 209) 1333539661 -2.8068241143632786 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002957_0.png resize: (113, 198) 1333539663 -3.365275249201321 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002975_0.png resize: (201, 255) 1333539665 -4.442622190697939 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002987_0.png resize: (101, 139) 1333539667 -3.273789794948035 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002976_0.png resize: (53, 66) 1333539669 -2.533382781092565 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002965_0.png resize: (191, 342) 1333539671 -3.687151778409046 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002973_0.png resize: (176, 158) 1333539673 -2.643380771362256 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002974_0.png resize: (304, 483) 1333539675 -4.988448903180012 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002985_0.png resize: (36, 138) 1333539677 -6.014791553500336 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002971_0.png resize: (122, 226) 1333539679 -4.4886249701112835 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002956_0.png resize: (115, 139) 1333539681 -2.9357562360859895 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002962_0.png resize: (256, 247) 1333539683 -2.970560321506883 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002977_0.png resize: (159, 212) 1333539685 -4.021703497504226 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002958_0.png resize: (273, 185) 1333539687 -3.8712966506535276 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002978_0.png resize: (589, 470) 1333539689 -4.770416195100612 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002969_0.png resize: (183, 165) 1333539691 -3.9078936270801266 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002966_0.png resize: (436, 252) 1333539693 -2.0376243554356055 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002964_0.png resize: (385, 298) 1333539695 -4.452143196745284 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002989_0.png resize: (173, 180) 1333539697 -4.841230085138446 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002968_0.png resize: (121, 190) 1333539699 -4.217828692646015 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002981_0.png resize: (115, 167) 1333539701 -3.4623805373854366 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002982_0.png resize: (183, 202) 1333539704 -5.4297360133644785 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002979_0.png resize: (328, 247) 1333539706 -1.2501054586201545 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002967_0.png resize: (188, 187) 1333539708 -3.037173576291609 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002991_0.png resize: (163, 158) 1333539710 -3.8412770971545758 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002963_0.png resize: (593, 432) 1333539712 -3.961466947625382 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002959_0.png resize: (286, 254) 1333539714 -2.792557817808557 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002983_0.png resize: (329, 513) 1333539716 -3.3646946018819146 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002988_0.png resize: (219, 301) 1333539717 -2.8920401901161203 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002980_0.png resize: (102, 98) 1333539718 -4.759328514630198 treat image : temp/1738369827_2513697_1333266642_f6faa53d8428cecf70fc97b09af1147f_rle_crop_3648002986_0.png resize: (190, 120) 1333539719 -4.51703516673294 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003005_0.png resize: (316, 167) 1333539720 -4.189091438325194 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003011_0.png resize: (170, 159) 1333539721 -4.162701022854657 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003000_0.png resize: (174, 177) 1333539722 -3.582966731860313 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003003_0.png resize: (71, 84) 1333539723 -4.111821474327442 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003326_0.png resize: (212, 207) 1333539724 -4.411644098727348 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003026_0.png resize: (55, 167) 1333539725 -3.4044793616286393 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003019_0.png resize: (95, 88) 1333539726 -4.801997609395541 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003001_0.png resize: (115, 195) 1333539727 -4.591956893248975 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003029_0.png resize: (292, 242) 1333539728 -5.396118645434401 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003002_0.png resize: (42, 160) 1333539729 -3.7118799469584256 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003015_0.png resize: (181, 259) 1333539730 -4.423519667936849 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003008_0.png resize: (82, 109) 1333539731 -2.8294219097406623 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003014_0.png resize: (74, 71) 1333539732 -2.811566682733757 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648002999_0.png resize: (111, 113) 1333539733 -4.180918527438956 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003325_0.png resize: (159, 141) 1333539734 -4.690667988290116 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003022_0.png resize: (67, 112) 1333539736 -0.8126913961957754 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003023_0.png resize: (291, 222) 1333539738 -4.117917199438814 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648002996_0.png resize: (171, 107) 1333539739 -2.9965362180808146 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003013_0.png resize: (109, 212) 1333539740 -3.884285328204579 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003322_0.png resize: (292, 309) 1333539741 -4.9641056342565975 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003010_0.png resize: (171, 155) 1333539742 -3.11457215568138 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003004_0.png resize: (61, 64) 1333539743 -3.5213227371691587 treat image : 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temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003028_0.png resize: (178, 184) 1333539750 -3.349734369110633 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003009_0.png resize: (124, 136) 1333539751 -4.024365926897306 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003027_0.png resize: (314, 280) 1333539752 -3.3346326111809645 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648002997_0.png resize: (141, 212) 1333539753 -2.6636937211551794 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648002993_0.png resize: (576, 406) 1333539754 -4.066893770858619 treat image : temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648003012_0.png resize: (235, 399) 1333539755 -4.0570335078151984 treat image : 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temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003338_0.png resize: (298, 215) 1333539769 -3.5237674395451832 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003365_0.png resize: (153, 148) 1333539770 -2.7634834356893974 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003359_0.png resize: (159, 231) 1333539771 -3.882598975679421 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003361_0.png resize: (148, 76) 1333539772 -2.2286185833500225 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003353_0.png resize: (296, 85) 1333539773 -2.2814105528555357 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003373_0.png resize: (177, 205) 1333539774 -5.126807077362189 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003342_0.png resize: (219, 249) 1333539775 -4.370219102008493 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003349_0.png resize: (296, 240) 1333539776 -4.352036984637782 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003333_0.png resize: (233, 285) 1333539777 -3.3581165807249405 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003351_0.png resize: (437, 444) 1333539779 -3.960620036891186 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003364_0.png resize: (148, 156) 1333539781 -4.292504921836641 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003348_0.png resize: (146, 138) 1333539783 -4.05798580448445 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003369_0.png resize: (230, 288) 1333539785 -2.968409405755125 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003362_0.png resize: (832, 938) 1333539786 -2.0003965433683075 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003335_0.png resize: (136, 310) 1333539787 -3.898837423607165 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003357_0.png resize: (214, 211) 1333539788 -5.246010045273869 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003340_0.png resize: (140, 168) 1333539789 -3.2103555829248673 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003346_0.png resize: (312, 444) 1333539790 -3.7150440595781076 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003370_0.png resize: (234, 324) 1333539791 -3.8106998539301298 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003347_0.png resize: (178, 106) 1333539792 -2.681106572923798 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003375_0.png resize: (645, 444) 1333539793 -2.4079854656326307 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003393_0.png resize: (96, 70) 1333539794 -2.452062604210536 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003376_0.png resize: (133, 213) 1333539795 -4.5937040323881675 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003385_0.png resize: (100, 287) 1333539797 -3.097237191878148 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003383_0.png resize: (281, 178) 1333539798 -3.688988468709179 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003387_0.png resize: (140, 128) 1333539799 -4.250009336500394 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003386_0.png resize: (116, 178) 1333539800 -1.7424493432592905 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003389_0.png resize: (136, 194) 1333539801 -4.373164620749082 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003382_0.png resize: (219, 131) 1333539802 -4.303490063093566 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003398_0.png resize: (263, 293) 1333539803 -2.7949109619718437 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003380_0.png resize: (74, 106) 1333539804 -4.423212059553746 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003395_0.png resize: (135, 200) 1333539805 -3.8297227170139085 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003396_0.png resize: (89, 98) 1333539806 -2.8037253809975997 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003381_0.png resize: (148, 149) 1333539807 -5.063728454409 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003391_0.png resize: (220, 197) 1333539808 -4.226227953499187 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003377_0.png resize: (241, 205) 1333539809 -4.154272755397365 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003384_0.png resize: (250, 148) 1333539810 -3.308043206720648 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003403_0.png resize: (399, 433) 1333539811 -3.722937751588718 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003421_0.png resize: (139, 102) 1333539812 -3.3445858426835935 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003406_0.png resize: (323, 156) 1333539813 -2.028466874679821 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003411_0.png resize: (370, 340) 1333539814 -2.8096457446601546 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003416_0.png resize: (115, 146) 1333539815 -2.64799315469444 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003417_0.png resize: (115, 229) 1333539816 -3.4638259236509907 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003429_0.png resize: (75, 145) 1333539817 -4.085525083793649 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003414_0.png resize: (71, 174) 1333539818 -1.3879459012898296 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003427_0.png resize: (177, 236) 1333539819 -2.6121225356984397 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003404_0.png resize: (253, 241) 1333539820 -3.270081151418265 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003418_0.png resize: (110, 193) 1333539821 -1.4581229597393701 treat image : 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temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003424_0.png resize: (152, 102) 1333539828 -5.070845406046872 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003428_0.png resize: (236, 265) 1333539829 -4.394423655301766 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003401_0.png resize: (115, 189) 1333539830 -3.7389900648745806 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003415_0.png resize: (107, 162) 1333539832 -3.2089534495798433 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003435_0.png resize: (91, 74) 1333539833 -2.1701635369521064 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003422_0.png resize: (142, 107) 1333539834 -4.118206170645003 treat image : 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temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003511_0.png resize: (253, 241) 1333539923 -3.2692833658212894 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003525_0.png resize: (110, 193) 1333539924 -1.4581229597393701 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003527_0.png resize: (91, 153) 1333539925 -0.08587547494667981 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003532_0.png resize: (260, 167) 1333539926 -2.6953085992720927 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003520_0.png resize: (85, 105) 1333539927 -2.123923535242415 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003514_0.png resize: (116, 181) 1333539928 -3.4623697669479525 treat image : 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temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003542_0.png resize: (91, 74) 1333539936 -2.1701635369521064 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003529_0.png resize: (142, 107) 1333539937 -4.1193863092187 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003509_0.png resize: (244, 193) 1333539938 -0.41491783187690207 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003526_0.png resize: (175, 155) 1333539939 -1.3802182909472263 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003541_0.png resize: (312, 406) 1333539940 -3.673442556322439 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003533_0.png resize: (184, 144) 1333539941 -3.6352675998312223 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003540_0.png resize: (138, 191) 1333539942 -2.915114270276357 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003530_0.png resize: (96, 91) 1333539943 -2.2858824260842443 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003544_0.png resize: (77, 94) 1333539944 -4.4853929162118655 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003512_0.png resize: (135, 91) 1333539945 -2.5161939890805636 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003549_0.png resize: (234, 163) 1333539946 -2.886605161116944 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003564_0.png resize: (58, 80) 1333539947 -0.852042879578426 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003550_0.png resize: (95, 99) 1333539948 -0.5129906717401228 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003558_0.png resize: (201, 132) 1333539949 -3.3270317815597936 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003547_0.png resize: (383, 361) 1333539950 -3.5280440339086256 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003553_0.png resize: (92, 130) 1333539951 -2.6680666952230276 treat image : temp/1738369827_2513697_1333266314_f849b92c05bc376d8fa628a79697da53_rle_crop_3648003571_0.png resize: (226, 235) 1333539952 -4.056186138001487 treat image : temp/1738369827_2513697_1333266314_f849b92c05bc376d8fa628a79697da53_rle_crop_3648003578_0.png resize: (515, 636) 1333539953 -2.5746231270536484 treat image : 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temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996443_0.png resize: (146, 170) 1333540209 -2.355687568351337 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646998999_0.png resize: (146, 170) 1333540210 -2.355687568351337 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996434_0.png resize: (525, 386) 1333540211 -2.5849782895172284 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646999008_0.png resize: (525, 386) 1333540212 -2.5849782895172284 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996444_0.png resize: (105, 122) 1333540213 -0.8483845180999676 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646998998_0.png resize: (105, 122) 1333540214 -0.8483845180999676 treat image : 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temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999067_0.png resize: (165, 166) 1333540239 -4.7171903109451385 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996495_0.png resize: (325, 203) 1333540240 -3.933628168431104 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999074_0.png resize: (325, 203) 1333540241 -3.933628168431104 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996514_0.png resize: (239, 166) 1333540242 -3.041557360116937 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999065_0.png resize: (239, 166) 1333540243 -3.041557360116937 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996481_0.png resize: (160, 262) 1333540244 -4.189363960673535 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999076_0.png resize: (160, 262) 1333540245 -4.189363960673535 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996497_0.png resize: (156, 150) 1333540247 -3.8095549460861764 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999062_0.png resize: (156, 150) 1333540248 -3.8095549460861764 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996491_0.png resize: (115, 151) 1333540249 -3.9106388495791906 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999079_0.png resize: (115, 151) 1333540250 -3.9106388495791906 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996494_0.png resize: (83, 88) 1333540251 -3.021089128575826 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999045_0.png resize: (83, 88) 1333540252 -3.021089128575826 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996475_0.png resize: (244, 217) 1333540253 -2.933127125389938 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999068_0.png resize: (244, 217) 1333540254 -2.933127125389938 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996486_0.png resize: (148, 171) 1333540255 -4.746681572057523 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999044_0.png resize: (148, 171) 1333540256 -4.746681572057523 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996476_0.png resize: (184, 106) 1333540257 -2.8419101370722473 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999048_0.png resize: (184, 106) 1333540258 -2.8419101370722473 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996576_0.png resize: (51, 86) 1333540259 -3.951306254393577 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999110_0.png resize: (51, 86) 1333540260 -3.951306254393577 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996579_0.png resize: (124, 287) 1333540261 -4.306538794827146 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999100_0.png resize: (124, 287) 1333540262 -4.306538794827146 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996563_0.png resize: (107, 120) 1333540263 -0.842261585909641 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999101_0.png resize: (107, 120) 1333540264 -0.842261585909641 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996597_0.png resize: (203, 161) 1333540265 -4.556411606636947 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999190_0.png resize: (203, 161) 1333540266 -4.556411606636947 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996609_0.png resize: (387, 213) 1333540267 -3.4853277286266775 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999194_0.png resize: (387, 213) 1333540268 -3.4832349856129188 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996590_0.png resize: (250, 196) 1333540269 -3.05872933752192 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999191_0.png resize: (250, 196) 1333540270 -3.05872933752192 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996612_0.png resize: (153, 171) 1333540271 -3.7991150651472396 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999198_0.png resize: (153, 171) 1333540272 -3.7991150651472396 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999170_0.png resize: (93, 157) 1333540273 -1.8706552586510985 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996586_0.png resize: (93, 157) 1333540274 -1.8706552586510985 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646996601_0.png resize: (167, 185) 1333540275 -3.6832146600332596 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3648002829_0.png resize: (168, 185) 1333540276 -3.750655902414744 treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9_rle_crop_3646999196_0.png resize: (167, 185) 1333540277 -3.6832146600332596 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646996622_0.png resize: (186, 184) 1333540278 -3.7341595432906476 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646999247_0.png resize: (186, 184) 1333540279 -3.7341595432906476 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646996632_0.png resize: (165, 156) 1333540280 -3.263783837346164 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646999236_0.png resize: (165, 156) 1333540281 -3.263783837346164 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646996661_0.png resize: (150, 259) 1333540282 -5.401978444035672 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646999215_0.png resize: (150, 259) 1333540283 -5.401978444035672 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646996682_0.png resize: (195, 181) 1333540284 -4.837588520863735 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646999355_0.png resize: (195, 181) 1333540285 -4.837588520863735 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646996676_0.png resize: (156, 174) 1333540286 -2.209645042952418 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646999361_0.png resize: (156, 174) 1333540287 -2.209645042952418 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3648002834_0.png resize: (109, 180) 1333540288 -1.1550761807114667 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646996688_0.png resize: (108, 180) 1333540289 -1.1303599748250064 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646999349_0.png resize: (108, 180) 1333540290 -1.1303599748250064 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646996672_0.png resize: (272, 415) 1333540291 -2.5888590465285053 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646999365_0.png resize: (272, 415) 1333540292 -2.5888590465285053 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646996678_0.png resize: (149, 121) 1333540293 -1.3727572427158037 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646999359_0.png resize: (149, 121) 1333540294 -1.3727572427158037 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996737_0.png resize: (69, 92) 1333540295 -2.2242348028386774 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999373_0.png resize: (69, 92) 1333540296 -2.2242348028386774 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996734_0.png resize: (91, 196) 1333540297 -3.1438173260166296 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999370_0.png resize: (91, 196) 1333540298 -3.1438173260166296 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996703_0.png resize: (189, 253) 1333540299 -2.1822845217714426 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999372_0.png resize: (189, 253) 1333540300 -2.1822845217714426 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996692_0.png resize: (183, 110) 1333540301 -2.2759142989194885 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999376_0.png resize: (183, 110) 1333540302 -2.2759142989194885 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646996726_0.png resize: (221, 227) 1333540303 -3.7008007167536903 treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d_rle_crop_3646999397_0.png resize: (221, 227) 1333540304 -3.7008007167536903 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002842_0.png resize: (401, 284) 1333540305 -3.985845284024886 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002852_0.png resize: (138, 98) 1333540306 -2.375597337322291 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002851_0.png resize: (371, 402) 1333540307 -4.05227035616935 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002836_0.png resize: (119, 154) 1333540308 -2.8721157346854045 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002856_0.png resize: (606, 982) 1333540309 -3.536405103907856 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002837_0.png resize: (300, 760) 1333540310 -3.8331646244740805 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002859_0.png resize: (127, 393) 1333540311 -1.6684963482191146 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002835_0.png resize: (160, 240) 1333540312 -2.688446774481651 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002846_0.png resize: (627, 423) 1333540313 -3.710371978276676 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002857_0.png resize: (215, 273) 1333540314 -3.0933169253060844 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002849_0.png resize: (343, 288) 1333540315 -4.815846223162987 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002881_0.png resize: (174, 150) 1333540316 -2.7347111088165112 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002860_0.png resize: (151, 174) 1333540317 -2.568847470136083 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002882_0.png resize: (200, 164) 1333540318 -4.923400636671641 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002896_0.png resize: (102, 176) 1333540319 -4.632298157978933 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002918_0.png resize: (192, 236) 1333540320 -4.238861246150271 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002922_0.png resize: (192, 466) 1333540321 -1.9588359955029353 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002919_0.png resize: (337, 370) 1333540322 -2.201762618420483 treat image : temp/1738369827_2513697_1333266647_647e1dd2c7f07563634ee905e64032b3_rle_crop_3648002952_0.png resize: (121, 130) 1333540323 -3.7279157072541538 treat image : 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temp/1738369827_2513697_1333266632_9608e8dec4c3e827fe56a646298f16bf_rle_crop_3648002995_0.png resize: (426, 1220) 1333540748 -1.4102579844947254 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003354_0.png resize: (314, 263) 1333540749 -4.600952705345106 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003399_0.png resize: (313, 125) 1333540751 -3.950075996617841 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003392_0.png resize: (479, 916) 1333540752 -1.406187378215549 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003390_0.png resize: (169, 159) 1333540753 -3.202229666109825 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003388_0.png resize: (194, 149) 1333540754 -2.821937163948577 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003374_0.png resize: (268, 303) 1333540755 -3.5625184468354623 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003378_0.png resize: (168, 167) 1333540756 -3.9228092971914963 treat image : temp/1738369827_2513697_1333266472_2e643517b4546a4fd2e35b8872a75fd9_rle_crop_3648003397_0.png resize: (621, 301) 1333540757 -3.5941366799542185 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003409_0.png resize: (305, 337) 1333540758 -3.8153211773735904 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003432_0.png resize: (406, 1340) 1333540759 -1.763650050573878 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134_rle_crop_3648003450_0.png resize: (266, 261) 1333540760 -3.5135378564322366 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134_rle_crop_3648003444_0.png resize: (671, 864) 1333540761 -3.1742523830505185 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134_rle_crop_3648003441_0.png resize: (332, 281) 1333540762 -2.753445780365314 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134_rle_crop_3648003458_0.png resize: (568, 474) 1333540763 -4.0978003750432395 treat image : temp/1738369827_2513697_1333266340_33011a221e7155810755e10cb8549aea_rle_crop_3648003468_0.png resize: (393, 932) 1333540764 -2.840225350540279 treat image : temp/1738369827_2513697_1333266339_18223fe76cf16e9d2bd847e0fdf79cfe_rle_crop_3648003506_0.png resize: (313, 125) 1333540765 -3.950161159481374 treat image : temp/1738369827_2513697_1333266339_18223fe76cf16e9d2bd847e0fdf79cfe_rle_crop_3648003499_0.png resize: (479, 916) 1333540766 -1.406088424605891 treat image : temp/1738369827_2513697_1333266339_18223fe76cf16e9d2bd847e0fdf79cfe_rle_crop_3648003497_0.png resize: (169, 159) 1333540767 -3.202229666109825 treat image : temp/1738369827_2513697_1333266339_18223fe76cf16e9d2bd847e0fdf79cfe_rle_crop_3648003495_0.png resize: (194, 149) 1333540768 -2.8219913089452313 treat image : temp/1738369827_2513697_1333266339_18223fe76cf16e9d2bd847e0fdf79cfe_rle_crop_3648003481_0.png resize: (268, 303) 1333540769 -3.562473516698053 treat image : temp/1738369827_2513697_1333266339_18223fe76cf16e9d2bd847e0fdf79cfe_rle_crop_3648003485_0.png resize: (168, 167) 1333540770 -3.9228092971914963 treat image : temp/1738369827_2513697_1333266339_18223fe76cf16e9d2bd847e0fdf79cfe_rle_crop_3648003504_0.png resize: (621, 301) 1333540771 -3.5939464202531615 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003516_0.png resize: (305, 337) 1333540774 -3.8144990985119636 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003539_0.png resize: (406, 1341) 1333540777 -1.7614325870158234 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003557_0.png resize: (266, 261) 1333540780 -3.5145965383876026 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003551_0.png resize: (671, 863) 1333540781 -3.1787651972671567 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003548_0.png resize: (332, 281) 1333540782 -2.75375436625481 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003565_0.png resize: (569, 474) 1333540783 -4.0999480644146 treat image : temp/1738369827_2513697_1333266314_f849b92c05bc376d8fa628a79697da53_rle_crop_3648003575_0.png resize: (398, 932) 1333540784 -2.8454677700336406 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646996442_0.png resize: (140, 115) 1333540794 -2.340495418977993 treat image : temp/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f_rle_crop_3646999000_0.png resize: (140, 115) 1333540795 -2.340495418977993 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996498_0.png resize: (176, 197) 1333540796 -4.047637413336688 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999049_0.png resize: (176, 197) 1333540797 -4.047637413336688 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646996560_0.png resize: (105, 90) 1333540799 -4.199641007271866 treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac_rle_crop_3646999103_0.png resize: (105, 90) 1333540800 -4.199641007271866 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646996657_0.png resize: (240, 253) 1333540801 -5.096303501252932 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646999251_0.png resize: (240, 253) 1333540802 -5.096303501252932 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646996683_0.png resize: (176, 213) 1333540803 -1.1889931174511785 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646999354_0.png resize: (176, 213) 1333540804 -1.1889931174511785 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002858_0.png resize: (361, 467) 1333540805 -2.181969343148338 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002886_0.png resize: (210, 294) 1333540806 -2.297116100904567 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002862_0.png resize: (226, 117) 1333540807 -1.695244192830682 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002890_0.png resize: (208, 178) 1333540808 -3.2978328099328524 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002884_0.png resize: (80, 81) 1333540809 -2.7413298599590092 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002914_0.png resize: (89, 91) 1333540810 -1.8981036808321174 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003352_0.png resize: (153, 131) 1333540811 -2.2510608690568827 treat image : temp/1738369827_2513697_1333266479_35bc57a2fc6413987d8bfc5a52c87467_rle_crop_3648003358_0.png resize: (90, 79) 1333540812 -2.697148913415136 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134_rle_crop_3648003439_0.png resize: (221, 354) 1333540813 -2.1141857639734716 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134_rle_crop_3648003448_0.png resize: (94, 259) 1333540814 -4.150096254219558 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003546_0.png resize: (221, 354) 1333540815 -2.112705301511318 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003555_0.png resize: (94, 259) 1333540816 -4.150096254219558 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646996499_0.png resize: (101, 89) 1333540848 -3.1003994077258468 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3648002823_0.png resize: (104, 90) 1333540849 -3.1859094812929185 treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636_rle_crop_3646999077_0.png resize: (101, 89) 1333540850 -3.1003994077258468 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002850_0.png resize: (302, 491) 1333540851 -3.8377978956314367 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002839_0.png resize: (321, 704) 1333540852 -2.9240677233923575 treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7_rle_crop_3648002841_0.png resize: (192, 369) 1333540853 -4.905062284766476 treat image : temp/1738369827_2513697_1333266404_16d9051dae58378685afa575df1af1f8_rle_crop_3648003412_0.png resize: (223, 398) 1333540854 -4.147558152558078 treat image : temp/1738369827_2513697_1333266334_778e22836a84afecbe8c90b102063f48_rle_crop_3648003519_0.png resize: (223, 398) 1333540855 -4.147633690321308 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646996652_0.png resize: (160, 175) 1333540862 -4.222342191237289 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646999222_0.png resize: (160, 175) 1333540863 -4.222342191237289 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646996663_0.png resize: (141, 327) 1333540864 -3.9399847681680873 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646999224_0.png resize: (141, 327) 1333540865 -3.9399847681680873 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646996662_0.png resize: (128, 370) 1333540866 -4.44373347086882 treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620_rle_crop_3646999221_0.png resize: (128, 370) 1333540867 -4.44373347086882 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646996689_0.png resize: (196, 194) 1333540868 -2.566967645992516 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646999348_0.png resize: (196, 194) 1333540869 -2.566967645992516 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646999350_0.png resize: (120, 199) 1333540870 -2.8286952971113406 treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe_rle_crop_3646996687_0.png resize: (120, 199) 1333540871 -2.8286952971113406 treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370_rle_crop_3648002902_0.png resize: (190, 152) 1333540872 -3.5905560332399293 treat image : temp/1738369827_2513697_1333266652_afab0f2ca37e77d469196d9af629a250_rle_crop_3648002913_0.png resize: (424, 643) 1333540873 -2.9558587317800478 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134_rle_crop_3648003449_0.png resize: (280, 149) 1333540874 -3.909892768086487 treat image : temp/1738369827_2513697_1333266394_267df5fca4d093e7fb033a1c99033134_rle_crop_3648003453_0.png resize: (288, 222) 1333540875 -1.4853050549408657 treat image : temp/1738369827_2513697_1333266340_33011a221e7155810755e10cb8549aea_rle_crop_3648003478_0.png resize: (421, 487) 1333540876 -4.51741575956572 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003556_0.png resize: (280, 149) 1333540877 -3.9098929044588306 treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003560_0.png resize: (288, 222) 1333540878 -1.4853050549408657 treat image : temp/1738369827_2513697_1333266314_f849b92c05bc376d8fa628a79697da53_rle_crop_3648003587_0.png resize: (118, 259) 1333540879 -3.522429091430931 treat image : temp/1738369827_2513697_1333266314_f849b92c05bc376d8fa628a79697da53_rle_crop_3648003584_0.png resize: (416, 488) 1333540880 -4.51367756733321 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 : 1114 time used for this insertion : 0.08884358406066895 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1114 time used for this insertion : 1.1828131675720215 save missing photos in datou_result : time spend for datou_step_exec : 90.07794141769409 time spend to save output : 1.2810776233673096 total time spend for step 6 : 91.3590190410614 step7:brightness Sat Feb 1 01:51:27 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/1738369827_2513697_1333267465_afb9d0d0d5531f8a79d4eeb11f66888f.jpg treat image : temp/1738369827_2513697_1333267458_cc030c099f0511d9784dd05961c0706c.jpg treat image : temp/1738369827_2513697_1333267278_9f08b66b8cab3646058e3c492b7b1636.jpg treat image : temp/1738369827_2513697_1333267265_15b045d1e781cfd085728d3f3e41ebac.jpg treat image : temp/1738369827_2513697_1333267257_1dd4fb415e64a697c8fc3b81c5e558d9.jpg treat image : temp/1738369827_2513697_1333267252_fe85cb0e38bd541fdf3d471095188620.jpg treat image : temp/1738369827_2513697_1333267248_862bf177a0eabb85821e152ef180fbbe.jpg treat image : temp/1738369827_2513697_1333267244_79bd9adfb25aec4b265d900f60c9008d.jpg treat image : temp/1738369827_2513697_1333266673_04d0b2dde7b060b8ad5d406623d677d7.jpg treat image : temp/1738369827_2513697_1333266660_ee3e4972cb5c12b4660bba7c7ab3d370.jpg treat image : 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temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003556_0.png treat image : temp/1738369827_2513697_1333266329_a36cd4dac22e667bd99815c83e6d5f88_rle_crop_3648003560_0.png treat image : temp/1738369827_2513697_1333266314_f849b92c05bc376d8fa628a79697da53_rle_crop_3648003587_0.png treat image : temp/1738369827_2513697_1333266314_f849b92c05bc376d8fa628a79697da53_rle_crop_3648003584_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 : 1114 time used for this insertion : 0.09577798843383789 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1114 time used for this insertion : 0.4866511821746826 save missing photos in datou_result : time spend for datou_step_exec : 25.947202682495117 time spend to save output : 0.5917582511901855 total time spend for step 7 : 26.538960933685303 step8:velours_tree Sat Feb 1 01:51:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure 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 : 0.4884376525878906 time spend to save output : 5.078315734863281e-05 total time spend for step 8 : 0.48848843574523926 step9:send_mail_cod Sat Feb 1 01:51:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 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_P20128154_01-02-2025_01_51_54.pdf 20128661 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 .imagette201286611738371114 20128662 imagette201286621738371116 20128663 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 .imagette201286631738371116 20128664 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 .imagette201286641738371117 20128665 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 .imagette201286651738371118 20128666 imagette201286661738371118 20128667 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 .imagette201286671738371118 20128669 imagette201286691738371120 20128670 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 .imagette201286701738371120 20128671 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 .imagette201286711738371121 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20128154 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20128661,20128662,20128663,20128664,20128665,20128666,20128667,20128668,20128669,20128670,20128671?tags=carton,mal_croppe,pehd,autre,metal,background,pet_fonce,environnement,flou,papier,pet_clair args[1333267465] : ((1333267465, -3.551899833615219, 492609224), (1333267465, 0.14098512801072113, 2107752395), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333267458] : ((1333267458, -4.45061959069737, 492609224), (1333267458, -0.3976757948759783, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333267278] : ((1333267278, -6.210102909005981, 492609224), (1333267278, -0.1708255946988089, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333267265] : ((1333267265, -6.376878716920928, 492609224), (1333267265, -0.08894503977861769, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333267257] : ((1333267257, -4.3381115157056, 492609224), (1333267257, -0.2692913053886143, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333267252] : ((1333267252, -6.990896170975222, 492609224), (1333267252, -0.21863103173224366, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333267248] : ((1333267248, -4.927118146986952, 492609224), (1333267248, 0.03071999439412058, 2107752395), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333267244] : ((1333267244, -5.5867978170183585, 492609224), (1333267244, 0.03646452236270374, 2107752395), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266673] : ((1333266673, -5.348264674215929, 492609224), (1333266673, -0.4160047298203499, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266660] : ((1333266660, -6.214105943050729, 492609224), (1333266660, -0.33391446942964825, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266652] : ((1333266652, -4.7658102550043715, 492609224), (1333266652, 0.3129240344595746, 2107752395), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266647] : ((1333266647, -5.8964463688251705, 492609224), (1333266647, -0.12555771768232116, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266642] : ((1333266642, -6.321482641867532, 492609224), (1333266642, -0.28837277611642054, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266632] : ((1333266632, -7.062255079566302, 492609224), (1333266632, -0.15717534917841813, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266479] : ((1333266479, -5.792363992831691, 492609224), (1333266479, -0.05230476697133251, 2107752395), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266472] : ((1333266472, -4.650026136384781, 492609224), (1333266472, -0.14216069553099292, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266404] : ((1333266404, -5.3013162617903795, 492609224), (1333266404, -0.03621191838470336, 2107752395), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266394] : ((1333266394, -4.38897844226345, 492609224), (1333266394, -0.3315296051552947, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266340] : ((1333266340, -4.956128758846408, 492609224), (1333266340, -0.30826919034581396, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266339] : ((1333266339, -4.650026136384781, 492609224), (1333266339, -0.14216069553099292, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266334] : ((1333266334, -5.3013162617903795, 492609224), (1333266334, -0.03621191838470336, 2107752395), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266329] : ((1333266329, -4.38897844226345, 492609224), (1333266329, -0.3315296051552947, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com args[1333266314] : ((1333266314, -4.956128758846408, 492609224), (1333266314, -0.30826919034581396, 496442774), '0.18867941714225955') We are sending mail with results at report@fotonower.com refus_total : 0.18867941714225955 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=20128154 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1333267265,1333267252,1333266314,1333266340,1333266673,1333266647,1333266652,1333266660,1333267465,1333267458,1333267278,1333267257,1333267248,1333267244,1333266642,1333266632,1333266479,1333266472,1333266404,1333266394) Found this number of photos: 20 begin to download photo : 1333267265 begin to download photo : 1333266647 begin to download photo : 1333267278 begin to download photo : 1333266632 download finish for photo 1333266647 begin to download photo : 1333266652 download finish for photo 1333267278 begin to download photo : 1333267257 download finish for photo 1333267265 begin to download photo : 1333267252 download finish for photo 1333266632 begin to download photo : 1333266479 download finish for photo 1333267257 begin to download photo : 1333267248 download finish for photo 1333266652 begin to download photo : 1333266660 download finish for photo 1333267252 begin to download photo : 1333266314 download finish for photo 1333267248 begin to download photo : 1333267244 download finish for photo 1333266479 begin to download photo : 1333266472 download finish for photo 1333266660 begin to download photo : 1333267465 download finish for photo 1333266314 begin to download photo : 1333266340 download finish for photo 1333267244 begin to download photo : 1333266642 download finish for photo 1333267465 begin to download photo : 1333267458 download finish for photo 1333266472 begin to download photo : 1333266404 download finish for photo 1333266642 download finish for photo 1333266340 begin to download photo : 1333266673 download finish for photo 1333267458 download finish for photo 1333266673 download finish for photo 1333266404 begin to download photo : 1333266394 download finish for photo 1333266394 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128154_01-02-2025_01_51_54.pdf results_Auto_P20128154_01-02-2025_01_51_54.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128154_01-02-2025_01_51_54.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3318','20128154','results_Auto_P20128154_01-02-2025_01_51_54.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128154_01-02-2025_01_51_54.pdf','pdf','','1.35','0.18867941714225955') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/20128154

https://www.fotonower.com/image?json=false&list_photos_id=1333267465
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333267458
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333267278
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333267265
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333267257
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333267252
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333267248
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333267244
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266673
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266660
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266652
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266647
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266642
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266632
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266479
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266472
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266404
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266394
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266340
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266339
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266334
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266329
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1333266314
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 18.87%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/20128661?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/20128663?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/20128664?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/20128665?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/20128667?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/20128670?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/20128671?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128154_01-02-2025_01_51_54.pdf.

Lien vers velours :https://www.fotonower.com/velours/20128661,20128662,20128663,20128664,20128665,20128666,20128667,20128668,20128669,20128670,20128671?tags=carton,mal_croppe,pehd,autre,metal,background,pet_fonce,environnement,flou,papier,pet_clair.


L'équipe Fotonower 202 b'' Server: nginx Date: Sat, 01 Feb 2025 00:52:07 GMT Content-Length: 0 Connection: close X-Message-Id: pMZfVJhxSIae0fk_ESv-bg 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 [1333267465, 1333267458, 1333267278, 1333267265, 1333267257, 1333267252, 1333267248, 1333267244, 1333266673, 1333266660, 1333266652, 1333266647, 1333266642, 1333266632, 1333266479, 1333266472, 1333266404, 1333266394, 1333266340, 1333266339, 1333266334, 1333266329, 1333266314] 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, '2535904') ('3318', '20128154', '1333267465', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267458', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267278', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267265', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267257', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267252', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267248', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267244', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266673', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266660', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266652', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266647', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266642', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266632', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266479', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266472', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266404', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266394', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266340', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266339', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266334', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266329', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266314', None, None, None, None, None, '2535904') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 23 time used for this insertion : 0.16291189193725586 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.14561152458191 time spend to save output : 0.16327714920043945 total time spend for step 9 : 13.308888673782349 step10:split_time_score Sat Feb 1 01:52:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure begin split time score Catched exception ! Connect or reconnect ! TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('09', 23),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 31012025 20128154 Nombre de photos uploadées : 23 / 23040 (0%) 31012025 20128154 Nombre de photos taguées (types de déchets): 0 / 23 (0%) 31012025 20128154 Nombre de photos taguées (volume) : 0 / 23 (0%) elapsed_time : load_data_split_time_score 1.1920928955078125e-06 elapsed_time : order_list_meta_photo_and_scores 4.76837158203125e-06 ??????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.001010894775390625 elapsed_time : insert_dashboard_record_day_entry 0.02941298484802246 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 : 0.08565223865925542 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128153_01-02-2025_01_34_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20128153 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`=20128153 AND mptpi.`type`=3726 To do Qualite : 0.18867941714225955 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128154_01-02-2025_01_51_54.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20128154 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`=20128154 AND mptpi.`type`=3594 To do Qualite : 0.25667745159932664 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128159_01-02-2025_01_30_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20128159 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`=20128159 AND mptpi.`type`=3594 To do Qualite : 0.20874959339445653 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128161_01-02-2025_01_24_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20128161 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`=20128161 AND mptpi.`type`=3594 To do Qualite : 0.07726311033869263 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20128169_01-02-2025_01_28_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20128169 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`=20128169 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'31012025': {'nb_upload': 23, '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 [1333267465, 1333267458, 1333267278, 1333267265, 1333267257, 1333267252, 1333267248, 1333267244, 1333266673, 1333266660, 1333266652, 1333266647, 1333266642, 1333266632, 1333266479, 1333266472, 1333266404, 1333266394, 1333266340, 1333266339, 1333266334, 1333266329, 1333266314] Looping around the photos to save general results len do output : 1 /20128154Didn'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, '2535904') ('3318', '20128154', '1333267465', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267458', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267278', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267265', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267257', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267252', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267248', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333267244', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266673', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266660', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266652', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266647', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266642', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266632', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266479', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266472', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266404', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266394', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266340', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266339', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266334', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266329', None, None, None, None, None, '2535904') ('3318', None, None, None, None, None, None, None, '2535904') ('3318', '20128154', '1333266314', None, None, None, None, None, '2535904') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.024898767471313477 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.5043244361877441 time spend to save output : 0.025177001953125 total time spend for step 10 : 0.5295014381408691 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 23 set_done_treatment 620.46user 303.83system 21:44.05elapsed 70%CPU (0avgtext+0avgdata 12135280maxresident)k 24836680inputs+525048outputs (714902major+57951643minor)pagefaults 0swaps