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 : 2844229 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 : ['2714586'] with mtr_portfolio_ids : ['21958982'] and first list_photo_ids : [] new path : /proc/2844229/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 22 ; length of list_pids : 22 ; length of list_args : 22 time to download the photos : 3.4673593044281006 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Tue Apr 1 22:00:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 10661 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-01 22:00:36.804439: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-01 22:00:36.831100: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-01 22:00:36.832733: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fde70000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-01 22:00:36.832760: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-01 22:00:36.835718: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-01 22:00:36.970852: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3641f550 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-01 22:00:36.970909: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-01 22:00:36.971702: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 22:00:36.972047: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 22:00:36.974114: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 22:00:36.976244: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 22:00:36.976576: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 22:00:36.978708: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 22:00:36.979785: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 22:00:36.984263: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 22:00:36.985784: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 22:00:36.985866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 22:00:36.986670: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 22:00:36.986687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 22:00:36.986697: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 22:00:36.988111: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9879 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-01 22:00:37.243977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 22:00:37.244095: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 22:00:37.244113: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 22:00:37.244128: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 22:00:37.244144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 22:00:37.244159: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 22:00:37.244173: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 22:00:37.244189: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 22:00:37.245392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 22:00:37.246559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-01 22:00:37.246598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 22:00:37.246615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 22:00:37.246631: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 22:00:37.246647: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 22:00:37.246663: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 22:00:37.246678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 22:00:37.246693: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 22:00:37.247963: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 22:00:37.247999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 22:00:37.248008: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 22:00:37.248016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 22:00:37.249259: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9879 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-01 22:00:49.215983: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 22:00:49.397778: 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 : 22 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 : 98 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 : 85 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 31 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 44 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 78 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 75 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 : 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 : 88 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 : 15 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 82 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 77 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 : 75 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 : 31 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 76 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 49 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 37 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 : 100 Detection mask done ! Trying to reset tf kernel 2848865 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 tf kernel not reseted sub process len(results) : 22 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 22 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 : 10593 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.13844633102416992 nb_pixel_total : 37231 time to create 1 rle with old method : 0.04403805732727051 length of segment : 211 time for calcul the mask position with numpy : 0.03965139389038086 nb_pixel_total : 9118 time to create 1 rle with old method : 0.014062881469726562 length of segment : 110 time for calcul the mask position with numpy : 0.17740392684936523 nb_pixel_total : 45912 time to create 1 rle with old method : 0.05265617370605469 length of segment : 268 time for calcul the mask position with numpy : 0.10609102249145508 nb_pixel_total : 26343 time to create 1 rle with old method : 0.03383016586303711 length of segment : 208 time for calcul the mask position with numpy : 0.1453559398651123 nb_pixel_total : 38714 time to create 1 rle with old method : 0.046503305435180664 length of segment : 294 time for calcul the mask position with numpy : 0.0665898323059082 nb_pixel_total : 11335 time to create 1 rle with old method : 0.017967700958251953 length of segment : 146 time for calcul the mask position with numpy : 0.04742550849914551 nb_pixel_total : 9889 time to create 1 rle with old method : 0.017187833786010742 length of segment : 90 time for calcul the mask position with numpy : 0.05955910682678223 nb_pixel_total : 14874 time to create 1 rle with old method : 0.023237228393554688 length of segment : 149 time for calcul the mask position with numpy : 0.09159564971923828 nb_pixel_total : 23145 time to create 1 rle with old method : 0.028276920318603516 length of segment : 192 time for calcul the mask position with numpy : 0.20795416831970215 nb_pixel_total : 46172 time to create 1 rle with old method : 0.07584238052368164 length of segment : 281 time for calcul the mask position with numpy : 0.13175654411315918 nb_pixel_total : 35109 time to create 1 rle with old method : 0.04400014877319336 length of segment : 226 time for calcul the mask position with numpy : 0.12165999412536621 nb_pixel_total : 32465 time to create 1 rle with old method : 0.049286842346191406 length of segment : 179 time for calcul the mask position with numpy : 0.019047260284423828 nb_pixel_total : 3237 time to create 1 rle with old method : 0.0048618316650390625 length of segment : 65 time for calcul the mask position with numpy : 0.08607864379882812 nb_pixel_total : 27604 time to create 1 rle with old method : 0.03550839424133301 length of segment : 246 time for calcul the mask position with numpy : 0.0374758243560791 nb_pixel_total : 12181 time to create 1 rle with old method : 0.01943349838256836 length of segment : 98 time for calcul the mask position with numpy : 0.5793137550354004 nb_pixel_total : 257696 time to create 1 rle with new method : 0.024247407913208008 length of segment : 693 time for calcul the mask position with numpy : 0.23522186279296875 nb_pixel_total : 43335 time to create 1 rle with old method : 0.07972431182861328 length of segment : 227 time for calcul the mask position with numpy : 0.2854609489440918 nb_pixel_total : 95978 time to create 1 rle with old method : 0.1400158405303955 length of segment : 490 time for calcul the mask position with numpy : 0.060561180114746094 nb_pixel_total : 19153 time to create 1 rle with old method : 0.026813745498657227 length of segment : 177 time for calcul the mask position with numpy : 0.005719184875488281 nb_pixel_total : 16763 time to create 1 rle with old method : 0.0209658145904541 length of segment : 143 time for calcul the mask position with numpy : 0.07499480247497559 nb_pixel_total : 17654 time to create 1 rle with old method : 0.019602298736572266 length of segment : 307 time for calcul the mask position with numpy : 0.12914443016052246 nb_pixel_total : 116353 time to create 1 rle with old method : 0.12897109985351562 length of segment : 513 time for calcul the mask position with numpy : 0.10354065895080566 nb_pixel_total : 25910 time to create 1 rle with old method : 0.029761314392089844 length of segment : 223 time for calcul the mask position with numpy : 0.020802974700927734 nb_pixel_total : 13000 time to create 1 rle with old method : 0.01842021942138672 length of segment : 144 time for calcul the mask position with numpy : 0.01748824119567871 nb_pixel_total : 9669 time to create 1 rle with old method : 0.017560720443725586 length of segment : 126 time for calcul the mask position with numpy : 0.04686617851257324 nb_pixel_total : 13393 time to create 1 rle with old method : 0.01981830596923828 length of segment : 155 time for calcul the mask position with numpy : 0.06773805618286133 nb_pixel_total : 22814 time to create 1 rle with old method : 0.028455495834350586 length of segment : 201 time for calcul the mask position with numpy : 0.22878265380859375 nb_pixel_total : 74179 time to create 1 rle with old method : 0.08251833915710449 length of segment : 344 time for calcul the mask position with numpy : 0.04335308074951172 nb_pixel_total : 24852 time to create 1 rle with old method : 0.04388165473937988 length of segment : 247 time for calcul the mask position with numpy : 0.19894766807556152 nb_pixel_total : 46540 time to create 1 rle with old method : 0.05400538444519043 length of segment : 295 time for calcul the mask position with numpy : 0.14192962646484375 nb_pixel_total : 73978 time to create 1 rle with old method : 0.08384990692138672 length of segment : 378 time for calcul the mask position with numpy : 0.05766415596008301 nb_pixel_total : 8584 time to create 1 rle with old method : 0.013751983642578125 length of segment : 143 time for calcul the mask position with numpy : 0.09734177589416504 nb_pixel_total : 33019 time to create 1 rle with old method : 0.038733482360839844 length of segment : 215 time for calcul the mask position with numpy : 0.06424832344055176 nb_pixel_total : 53127 time to create 1 rle with old method : 0.06441855430603027 length of segment : 241 time for calcul the mask position with numpy : 0.036028146743774414 nb_pixel_total : 20481 time to create 1 rle with old method : 0.024707317352294922 length of segment : 261 time for calcul the mask position with numpy : 0.0754387378692627 nb_pixel_total : 19685 time to create 1 rle with old method : 0.024824142456054688 length of segment : 182 time for calcul the mask position with numpy : 0.008722066879272461 nb_pixel_total : 4727 time to create 1 rle with old method : 0.008041620254516602 length of segment : 85 time for calcul the mask position with numpy : 0.08436417579650879 nb_pixel_total : 71038 time to create 1 rle with old method : 0.0876777172088623 length of segment : 376 time for calcul the mask position with numpy : 0.008454322814941406 nb_pixel_total : 21409 time to create 1 rle with old method : 0.025679588317871094 length of segment : 274 time for calcul the mask position with numpy : 0.021036148071289062 nb_pixel_total : 8484 time to create 1 rle with old method : 0.013686180114746094 length of segment : 100 time for calcul the mask position with numpy : 0.12006711959838867 nb_pixel_total : 20787 time to create 1 rle with old method : 0.029569625854492188 length of segment : 181 time for calcul the mask position with numpy : 0.05370903015136719 nb_pixel_total : 22258 time to create 1 rle with old method : 0.02732706069946289 length of segment : 182 time for calcul the mask position with numpy : 0.07175302505493164 nb_pixel_total : 16468 time to create 1 rle with old method : 0.024750471115112305 length of segment : 199 time for calcul the mask position with numpy : 0.10710906982421875 nb_pixel_total : 31986 time to create 1 rle with old method : 0.0416715145111084 length of segment : 260 time for calcul the mask position with numpy : 0.07379913330078125 nb_pixel_total : 11437 time to create 1 rle with old method : 0.01827096939086914 length of segment : 160 time for calcul the mask position with numpy : 0.01289677619934082 nb_pixel_total : 9050 time to create 1 rle with old method : 0.013913393020629883 length of segment : 163 time for calcul the mask position with numpy : 0.03684520721435547 nb_pixel_total : 9914 time to create 1 rle with old method : 0.014214038848876953 length of segment : 148 time for calcul the mask position with numpy : 0.015333890914916992 nb_pixel_total : 2646 time to create 1 rle with old method : 0.006149768829345703 length of segment : 57 time for calcul the mask position with numpy : 0.41826510429382324 nb_pixel_total : 136160 time to create 1 rle with old method : 0.1619727611541748 length of segment : 855 time for calcul the mask position with numpy : 0.18544530868530273 nb_pixel_total : 100363 time to create 1 rle with old method : 0.10917186737060547 length of segment : 568 time for calcul the mask position with numpy : 0.03286004066467285 nb_pixel_total : 75759 time to create 1 rle with old method : 0.08591794967651367 length of segment : 798 time for calcul the mask position with numpy : 0.004265308380126953 nb_pixel_total : 12573 time to create 1 rle with old method : 0.014922142028808594 length of segment : 154 time for calcul the mask position with numpy : 0.036284446716308594 nb_pixel_total : 24624 time to create 1 rle with old method : 0.03075718879699707 length of segment : 202 time for calcul the mask position with numpy : 0.057878732681274414 nb_pixel_total : 30471 time to create 1 rle with old method : 0.038967132568359375 length of segment : 239 time for calcul the mask position with numpy : 0.13672494888305664 nb_pixel_total : 54173 time to create 1 rle with old method : 0.05950522422790527 length of segment : 517 time for calcul the mask position with numpy : 0.10353231430053711 nb_pixel_total : 18410 time to create 1 rle with old method : 0.030219316482543945 length of segment : 164 time for calcul the mask position with numpy : 0.09979510307312012 nb_pixel_total : 33214 time to create 1 rle with old method : 0.04011797904968262 length of segment : 237 time for calcul the mask position with numpy : 0.034662485122680664 nb_pixel_total : 10081 time to create 1 rle with old method : 0.015004158020019531 length of segment : 97 time for calcul the mask position with numpy : 0.2535824775695801 nb_pixel_total : 111415 time to create 1 rle with old method : 0.12921714782714844 length of segment : 453 time for calcul the mask position with numpy : 0.17232990264892578 nb_pixel_total : 52999 time to create 1 rle with old method : 0.05922055244445801 length of segment : 318 time for calcul the mask position with numpy : 0.07371687889099121 nb_pixel_total : 16931 time to create 1 rle with old method : 0.02407217025756836 length of segment : 117 time for calcul the mask position with numpy : 0.4466402530670166 nb_pixel_total : 223431 time to create 1 rle with new method : 0.0199434757232666 length of segment : 790 time for calcul the mask position with numpy : 0.0680694580078125 nb_pixel_total : 17031 time to create 1 rle with old method : 0.02199530601501465 length of segment : 187 time for calcul the mask position with numpy : 0.03462052345275879 nb_pixel_total : 14369 time to create 1 rle with old method : 0.02025914192199707 length of segment : 190 time for calcul the mask position with numpy : 0.1563117504119873 nb_pixel_total : 32501 time to create 1 rle with old method : 0.0422976016998291 length of segment : 335 time for calcul the mask position with numpy : 0.04715561866760254 nb_pixel_total : 7723 time to create 1 rle with old method : 0.012477636337280273 length of segment : 117 time for calcul the mask position with numpy : 0.039758920669555664 nb_pixel_total : 13595 time to create 1 rle with old method : 0.019472122192382812 length of segment : 137 time for calcul the mask position with numpy : 0.04288315773010254 nb_pixel_total : 16663 time to create 1 rle with old method : 0.02330327033996582 length of segment : 166 time for calcul the mask position with numpy : 0.19620275497436523 nb_pixel_total : 31067 time to create 1 rle with old method : 0.038048744201660156 length of segment : 218 time for calcul the mask position with numpy : 0.05476236343383789 nb_pixel_total : 11086 time to create 1 rle with old method : 0.017786264419555664 length of segment : 100 time for calcul the mask position with numpy : 0.04454541206359863 nb_pixel_total : 9339 time to create 1 rle with old method : 0.016144275665283203 length of segment : 88 time for calcul the mask position with numpy : 0.02893376350402832 nb_pixel_total : 10202 time to create 1 rle with old method : 0.016249895095825195 length of segment : 67 time for calcul the mask position with numpy : 0.11594653129577637 nb_pixel_total : 113583 time to create 1 rle with old method : 0.13316798210144043 length of segment : 498 time for calcul the mask position with numpy : 0.08435630798339844 nb_pixel_total : 14286 time to create 1 rle with old method : 0.020188093185424805 length of segment : 212 time for calcul the mask position with numpy : 0.1160123348236084 nb_pixel_total : 58227 time to create 1 rle with old method : 0.0655984878540039 length of segment : 250 time for calcul the mask position with numpy : 0.2775249481201172 nb_pixel_total : 118513 time to create 1 rle with old method : 0.13414382934570312 length of segment : 405 time for calcul the mask position with numpy : 0.07674694061279297 nb_pixel_total : 13980 time to create 1 rle with old method : 0.017951250076293945 length of segment : 180 time for calcul the mask position with numpy : 0.09031486511230469 nb_pixel_total : 24267 time to create 1 rle with old method : 0.03128814697265625 length of segment : 181 time for calcul the mask position with numpy : 0.030830860137939453 nb_pixel_total : 8346 time to create 1 rle with old method : 0.014457225799560547 length of segment : 126 time for calcul the mask position with numpy : 0.03811931610107422 nb_pixel_total : 24528 time to create 1 rle with old method : 0.032291412353515625 length of segment : 202 time for calcul the mask position with numpy : 0.0758829116821289 nb_pixel_total : 28846 time to create 1 rle with old method : 0.038043975830078125 length of segment : 162 time for calcul the mask position with numpy : 0.016156911849975586 nb_pixel_total : 29256 time to create 1 rle with old method : 0.03891158103942871 length of segment : 488 time for calcul the mask position with numpy : 0.06896495819091797 nb_pixel_total : 16109 time to create 1 rle with old method : 0.023889780044555664 length of segment : 187 time for calcul the mask position with numpy : 0.22268152236938477 nb_pixel_total : 109156 time to create 1 rle with old method : 0.12064480781555176 length of segment : 475 time for calcul the mask position with numpy : 0.016564369201660156 nb_pixel_total : 5731 time to create 1 rle with old method : 0.010094642639160156 length of segment : 92 time for calcul the mask position with numpy : 0.03333282470703125 nb_pixel_total : 7367 time to create 1 rle with old method : 0.013289690017700195 length of segment : 68 time for calcul the mask position with numpy : 0.0016698837280273438 nb_pixel_total : 7434 time to create 1 rle with old method : 0.008450746536254883 length of segment : 109 time for calcul the mask position with numpy : 0.0004894733428955078 nb_pixel_total : 18250 time to create 1 rle with old method : 0.02050328254699707 length of segment : 176 time for calcul the mask position with numpy : 0.00035452842712402344 nb_pixel_total : 13723 time to create 1 rle with old method : 0.015627384185791016 length of segment : 147 time for calcul the mask position with numpy : 0.13848567008972168 nb_pixel_total : 48414 time to create 1 rle with old method : 0.05687975883483887 length of segment : 236 time for calcul the mask position with numpy : 0.02993607521057129 nb_pixel_total : 5414 time to create 1 rle with old method : 0.009324073791503906 length of segment : 74 time for calcul the mask position with numpy : 0.08801388740539551 nb_pixel_total : 27411 time to create 1 rle with old method : 0.034629106521606445 length of segment : 150 time for calcul the mask position with numpy : 0.04562020301818848 nb_pixel_total : 7065 time to create 1 rle with old method : 0.01314401626586914 length of segment : 99 time for calcul the mask position with numpy : 0.006042957305908203 nb_pixel_total : 2381 time to create 1 rle with old method : 0.0037298202514648438 length of segment : 61 time for calcul the mask position with numpy : 0.004854440689086914 nb_pixel_total : 11459 time to create 1 rle with old method : 0.015208005905151367 length of segment : 191 time for calcul the mask position with numpy : 0.025231599807739258 nb_pixel_total : 24659 time to create 1 rle with old method : 0.03065204620361328 length of segment : 286 time for calcul the mask position with numpy : 0.05125021934509277 nb_pixel_total : 22189 time to create 1 rle with old method : 0.024358749389648438 length of segment : 138 time for calcul the mask position with numpy : 0.04799699783325195 nb_pixel_total : 10440 time to create 1 rle with old method : 0.01695728302001953 length of segment : 124 time for calcul the mask position with numpy : 0.03434419631958008 nb_pixel_total : 6997 time to create 1 rle with old method : 0.012060403823852539 length of segment : 96 time for calcul the mask position with numpy : 0.16851592063903809 nb_pixel_total : 88582 time to create 1 rle with old method : 0.10037612915039062 length of segment : 350 time for calcul the mask position with numpy : 0.0442049503326416 nb_pixel_total : 13361 time to create 1 rle with old method : 0.01927661895751953 length of segment : 105 time for calcul the mask position with numpy : 0.04861021041870117 nb_pixel_total : 32029 time to create 1 rle with old method : 0.0558621883392334 length of segment : 296 time for calcul the mask position with numpy : 0.0003962516784667969 nb_pixel_total : 19208 time to create 1 rle with old method : 0.020602941513061523 length of segment : 141 time for calcul the mask position with numpy : 0.15352773666381836 nb_pixel_total : 45764 time to create 1 rle with old method : 0.05258321762084961 length of segment : 414 time for calcul the mask position with numpy : 0.025403499603271484 nb_pixel_total : 6814 time to create 1 rle with old method : 0.010146856307983398 length of segment : 108 time for calcul the mask position with numpy : 0.0005323886871337891 nb_pixel_total : 16593 time to create 1 rle with old method : 0.018105506896972656 length of segment : 294 time for calcul the mask position with numpy : 0.12056398391723633 nb_pixel_total : 88428 time to create 1 rle with old method : 0.09484052658081055 length of segment : 294 time for calcul the mask position with numpy : 0.00643157958984375 nb_pixel_total : 7164 time to create 1 rle with old method : 0.009322166442871094 length of segment : 112 time for calcul the mask position with numpy : 0.002646207809448242 nb_pixel_total : 22651 time to create 1 rle with old method : 0.024196863174438477 length of segment : 165 time for calcul the mask position with numpy : 0.009184598922729492 nb_pixel_total : 18617 time to create 1 rle with old method : 0.023393630981445312 length of segment : 123 time for calcul the mask position with numpy : 0.08365249633789062 nb_pixel_total : 25323 time to create 1 rle with old method : 0.030408143997192383 length of segment : 222 time for calcul the mask position with numpy : 0.010082244873046875 nb_pixel_total : 11041 time to create 1 rle with old method : 0.017500877380371094 length of segment : 104 time for calcul the mask position with numpy : 0.09322047233581543 nb_pixel_total : 66718 time to create 1 rle with old method : 0.07259774208068848 length of segment : 350 time for calcul the mask position with numpy : 0.0019369125366210938 nb_pixel_total : 74930 time to create 1 rle with old method : 0.08121109008789062 length of segment : 321 time for calcul the mask position with numpy : 0.04276394844055176 nb_pixel_total : 17816 time to create 1 rle with old method : 0.019214868545532227 length of segment : 128 time for calcul the mask position with numpy : 0.16736888885498047 nb_pixel_total : 11826 time to create 1 rle with old method : 0.017276525497436523 length of segment : 193 time for calcul the mask position with numpy : 0.02462005615234375 nb_pixel_total : 7450 time to create 1 rle with old method : 0.012321233749389648 length of segment : 167 time for calcul the mask position with numpy : 0.1574869155883789 nb_pixel_total : 6365 time to create 1 rle with old method : 0.011545896530151367 length of segment : 243 time for calcul the mask position with numpy : 0.4340341091156006 nb_pixel_total : 30935 time to create 1 rle with old method : 0.0380399227142334 length of segment : 234 time for calcul the mask position with numpy : 0.5116004943847656 nb_pixel_total : 41917 time to create 1 rle with old method : 0.05032944679260254 length of segment : 284 time for calcul the mask position with numpy : 0.16319632530212402 nb_pixel_total : 10490 time to create 1 rle with old method : 0.016964197158813477 length of segment : 97 time for calcul the mask position with numpy : 0.4820902347564697 nb_pixel_total : 17366 time to create 1 rle with old method : 0.02382946014404297 length of segment : 326 time for calcul the mask position with numpy : 0.4867103099822998 nb_pixel_total : 26385 time to create 1 rle with old method : 0.03241991996765137 length of segment : 272 time for calcul the mask position with numpy : 0.20798873901367188 nb_pixel_total : 11821 time to create 1 rle with old method : 0.017850875854492188 length of segment : 141 time for calcul the mask position with numpy : 0.45339393615722656 nb_pixel_total : 31438 time to create 1 rle with old method : 0.0429227352142334 length of segment : 346 time for calcul the mask position with numpy : 0.5876421928405762 nb_pixel_total : 90241 time to create 1 rle with old method : 0.10646820068359375 length of segment : 406 time for calcul the mask position with numpy : 0.15430235862731934 nb_pixel_total : 17587 time to create 1 rle with old method : 0.025508880615234375 length of segment : 249 time for calcul the mask position with numpy : 0.3377680778503418 nb_pixel_total : 35877 time to create 1 rle with old method : 0.043889760971069336 length of segment : 268 time for calcul the mask position with numpy : 0.7004501819610596 nb_pixel_total : 24813 time to create 1 rle with old method : 0.032095909118652344 length of segment : 320 time for calcul the mask position with numpy : 0.37774062156677246 nb_pixel_total : 30467 time to create 1 rle with old method : 0.039981842041015625 length of segment : 166 time for calcul the mask position with numpy : 0.34198856353759766 nb_pixel_total : 33181 time to create 1 rle with old method : 0.0395047664642334 length of segment : 232 time for calcul the mask position with numpy : 0.47714900970458984 nb_pixel_total : 23698 time to create 1 rle with old method : 0.03271627426147461 length of segment : 324 time for calcul the mask position with numpy : 0.3325521945953369 nb_pixel_total : 32039 time to create 1 rle with old method : 0.04248857498168945 length of segment : 157 time for calcul the mask position with numpy : 0.22170734405517578 nb_pixel_total : 18062 time to create 1 rle with old method : 0.025185823440551758 length of segment : 199 time for calcul the mask position with numpy : 0.22379779815673828 nb_pixel_total : 14903 time to create 1 rle with old method : 0.0232541561126709 length of segment : 155 time for calcul the mask position with numpy : 0.19015836715698242 nb_pixel_total : 15547 time to create 1 rle with old method : 0.023990392684936523 length of segment : 187 time for calcul the mask position with numpy : 0.24455785751342773 nb_pixel_total : 24646 time to create 1 rle with old method : 0.032863616943359375 length of segment : 182 time for calcul the mask position with numpy : 0.15402841567993164 nb_pixel_total : 26794 time to create 1 rle with old method : 0.030897855758666992 length of segment : 217 time for calcul the mask position with numpy : 0.10597467422485352 nb_pixel_total : 6373 time to create 1 rle with old method : 0.010529279708862305 length of segment : 121 time for calcul the mask position with numpy : 0.34737563133239746 nb_pixel_total : 27664 time to create 1 rle with old method : 0.03568434715270996 length of segment : 276 time for calcul the mask position with numpy : 0.5757858753204346 nb_pixel_total : 74153 time to create 1 rle with old method : 0.10395097732543945 length of segment : 303 time for calcul the mask position with numpy : 0.26020336151123047 nb_pixel_total : 20644 time to create 1 rle with old method : 0.025057315826416016 length of segment : 249 time for calcul the mask position with numpy : 0.09779143333435059 nb_pixel_total : 31309 time to create 1 rle with old method : 0.03745913505554199 length of segment : 279 time for calcul the mask position with numpy : 0.17108798027038574 nb_pixel_total : 87867 time to create 1 rle with old method : 0.10033011436462402 length of segment : 339 time for calcul the mask position with numpy : 0.037755489349365234 nb_pixel_total : 83248 time to create 1 rle with old method : 0.09418463706970215 length of segment : 411 time for calcul the mask position with numpy : 0.024382829666137695 nb_pixel_total : 6803 time to create 1 rle with old method : 0.01205134391784668 length of segment : 85 time for calcul the mask position with numpy : 0.04296565055847168 nb_pixel_total : 9658 time to create 1 rle with old method : 0.015321969985961914 length of segment : 184 time for calcul the mask position with numpy : 0.03882861137390137 nb_pixel_total : 19807 time to create 1 rle with old method : 0.026844501495361328 length of segment : 142 time for calcul the mask position with numpy : 0.061693429946899414 nb_pixel_total : 32470 time to create 1 rle with old method : 0.04446291923522949 length of segment : 387 time for calcul the mask position with numpy : 0.02422165870666504 nb_pixel_total : 10402 time to create 1 rle with old method : 0.011488199234008789 length of segment : 129 time for calcul the mask position with numpy : 0.03532719612121582 nb_pixel_total : 16489 time to create 1 rle with old method : 0.01880669593811035 length of segment : 153 time for calcul the mask position with numpy : 0.05865025520324707 nb_pixel_total : 21347 time to create 1 rle with old method : 0.02870774269104004 length of segment : 225 time for calcul the mask position with numpy : 0.015041112899780273 nb_pixel_total : 24156 time to create 1 rle with old method : 0.03354525566101074 length of segment : 158 time for calcul the mask position with numpy : 0.006708860397338867 nb_pixel_total : 7581 time to create 1 rle with old method : 0.01164388656616211 length of segment : 101 time for calcul the mask position with numpy : 0.10156917572021484 nb_pixel_total : 30574 time to create 1 rle with old method : 0.03602766990661621 length of segment : 258 time for calcul the mask position with numpy : 0.00028896331787109375 nb_pixel_total : 7162 time to create 1 rle with old method : 0.00837564468383789 length of segment : 117 time for calcul the mask position with numpy : 0.1170663833618164 nb_pixel_total : 63849 time to create 1 rle with old method : 0.07950425148010254 length of segment : 366 time for calcul the mask position with numpy : 0.007005929946899414 nb_pixel_total : 26675 time to create 1 rle with old method : 0.03917527198791504 length of segment : 241 time for calcul the mask position with numpy : 0.06704878807067871 nb_pixel_total : 19653 time to create 1 rle with old method : 0.023631811141967773 length of segment : 230 time for calcul the mask position with numpy : 0.0026624202728271484 nb_pixel_total : 8755 time to create 1 rle with old method : 0.010717630386352539 length of segment : 152 time for calcul the mask position with numpy : 0.04488110542297363 nb_pixel_total : 7263 time to create 1 rle with old method : 0.012660980224609375 length of segment : 169 time for calcul the mask position with numpy : 0.10484766960144043 nb_pixel_total : 68020 time to create 1 rle with old method : 0.08331656455993652 length of segment : 237 time for calcul the mask position with numpy : 0.09498143196105957 nb_pixel_total : 24578 time to create 1 rle with old method : 0.03107142448425293 length of segment : 303 time for calcul the mask position with numpy : 0.0029189586639404297 nb_pixel_total : 4689 time to create 1 rle with old method : 0.005414724349975586 length of segment : 83 time for calcul the mask position with numpy : 0.0716085433959961 nb_pixel_total : 13623 time to create 1 rle with old method : 0.019946575164794922 length of segment : 186 time for calcul the mask position with numpy : 0.1357862949371338 nb_pixel_total : 28215 time to create 1 rle with old method : 0.04180645942687988 length of segment : 305 time for calcul the mask position with numpy : 0.05077624320983887 nb_pixel_total : 7887 time to create 1 rle with old method : 0.01318979263305664 length of segment : 143 time for calcul the mask position with numpy : 0.032464027404785156 nb_pixel_total : 6307 time to create 1 rle with old method : 0.01183772087097168 length of segment : 77 time for calcul the mask position with numpy : 0.11214971542358398 nb_pixel_total : 18907 time to create 1 rle with old method : 0.025602340698242188 length of segment : 217 time for calcul the mask position with numpy : 0.19832563400268555 nb_pixel_total : 25866 time to create 1 rle with old method : 0.03618645668029785 length of segment : 217 time for calcul the mask position with numpy : 0.21283841133117676 nb_pixel_total : 19620 time to create 1 rle with old method : 0.026345252990722656 length of segment : 152 time for calcul the mask position with numpy : 0.055062055587768555 nb_pixel_total : 21465 time to create 1 rle with old method : 0.028808116912841797 length of segment : 221 time for calcul the mask position with numpy : 0.02919149398803711 nb_pixel_total : 6166 time to create 1 rle with old method : 0.01113581657409668 length of segment : 86 time for calcul the mask position with numpy : 0.12001609802246094 nb_pixel_total : 25265 time to create 1 rle with old method : 0.045265913009643555 length of segment : 301 time for calcul the mask position with numpy : 0.03319382667541504 nb_pixel_total : 8251 time to create 1 rle with old method : 0.014506340026855469 length of segment : 80 time for calcul the mask position with numpy : 0.17724609375 nb_pixel_total : 61144 time to create 1 rle with old method : 0.1072852611541748 length of segment : 449 time for calcul the mask position with numpy : 0.13582229614257812 nb_pixel_total : 33160 time to create 1 rle with old method : 0.04020833969116211 length of segment : 314 time for calcul the mask position with numpy : 0.3162682056427002 nb_pixel_total : 107673 time to create 1 rle with old method : 0.11892247200012207 length of segment : 441 time for calcul the mask position with numpy : 0.1385817527770996 nb_pixel_total : 34920 time to create 1 rle with old method : 0.04390120506286621 length of segment : 336 time for calcul the mask position with numpy : 0.050776004791259766 nb_pixel_total : 12189 time to create 1 rle with old method : 0.01912212371826172 length of segment : 186 time for calcul the mask position with numpy : 0.07739377021789551 nb_pixel_total : 7142 time to create 1 rle with old method : 0.012909173965454102 length of segment : 244 time for calcul the mask position with numpy : 0.2559032440185547 nb_pixel_total : 65486 time to create 1 rle with old method : 0.07549333572387695 length of segment : 290 time for calcul the mask position with numpy : 0.09111952781677246 nb_pixel_total : 17694 time to create 1 rle with old method : 0.025929927825927734 length of segment : 189 time for calcul the mask position with numpy : 0.02587723731994629 nb_pixel_total : 11495 time to create 1 rle with old method : 0.04302716255187988 length of segment : 171 time for calcul the mask position with numpy : 0.014990091323852539 nb_pixel_total : 1706 time to create 1 rle with old method : 0.0029926300048828125 length of segment : 59 time for calcul the mask position with numpy : 0.10523724555969238 nb_pixel_total : 22983 time to create 1 rle with old method : 0.03237605094909668 length of segment : 189 time for calcul the mask position with numpy : 0.7166683673858643 nb_pixel_total : 307735 time to create 1 rle with new method : 0.05427885055541992 length of segment : 816 time for calcul the mask position with numpy : 0.06473946571350098 nb_pixel_total : 15229 time to create 1 rle with old method : 0.026973962783813477 length of segment : 182 time for calcul the mask position with numpy : 0.0727391242980957 nb_pixel_total : 10320 time to create 1 rle with old method : 0.019170522689819336 length of segment : 191 time for calcul the mask position with numpy : 0.023749113082885742 nb_pixel_total : 2839 time to create 1 rle with old method : 0.005217552185058594 length of segment : 62 time for calcul the mask position with numpy : 0.04536032676696777 nb_pixel_total : 12118 time to create 1 rle with old method : 0.022019624710083008 length of segment : 107 time for calcul the mask position with numpy : 0.025931358337402344 nb_pixel_total : 4856 time to create 1 rle with old method : 0.008765697479248047 length of segment : 93 time for calcul the mask position with numpy : 0.20927977561950684 nb_pixel_total : 44395 time to create 1 rle with old method : 0.0537722110748291 length of segment : 350 time for calcul the mask position with numpy : 0.02219557762145996 nb_pixel_total : 11248 time to create 1 rle with old method : 0.016954660415649414 length of segment : 112 time for calcul the mask position with numpy : 0.06460928916931152 nb_pixel_total : 10388 time to create 1 rle with old method : 0.016562461853027344 length of segment : 179 time for calcul the mask position with numpy : 0.002881288528442383 nb_pixel_total : 14361 time to create 1 rle with old method : 0.01848888397216797 length of segment : 166 time for calcul the mask position with numpy : 0.03974175453186035 nb_pixel_total : 7748 time to create 1 rle with old method : 0.013153791427612305 length of segment : 135 time for calcul the mask position with numpy : 0.11499691009521484 nb_pixel_total : 31857 time to create 1 rle with old method : 0.03911399841308594 length of segment : 528 time for calcul the mask position with numpy : 0.0038001537322998047 nb_pixel_total : 10057 time to create 1 rle with old method : 0.01235342025756836 length of segment : 114 time for calcul the mask position with numpy : 0.03414654731750488 nb_pixel_total : 7689 time to create 1 rle with old method : 0.01310873031616211 length of segment : 119 time for calcul the mask position with numpy : 0.08261775970458984 nb_pixel_total : 26110 time to create 1 rle with old method : 0.04388260841369629 length of segment : 244 time for calcul the mask position with numpy : 0.013525724411010742 nb_pixel_total : 3752 time to create 1 rle with old method : 0.004321575164794922 length of segment : 85 time for calcul the mask position with numpy : 0.0470428466796875 nb_pixel_total : 9771 time to create 1 rle with old method : 0.01100778579711914 length of segment : 108 time for calcul the mask position with numpy : 0.11209344863891602 nb_pixel_total : 72524 time to create 1 rle with old method : 0.08187341690063477 length of segment : 334 time for calcul the mask position with numpy : 0.09802627563476562 nb_pixel_total : 24763 time to create 1 rle with old method : 0.030532360076904297 length of segment : 296 time for calcul the mask position with numpy : 0.08371114730834961 nb_pixel_total : 19727 time to create 1 rle with old method : 0.026411056518554688 length of segment : 197 time for calcul the mask position with numpy : 0.0041828155517578125 nb_pixel_total : 19168 time to create 1 rle with old method : 0.02454400062561035 length of segment : 297 time for calcul the mask position with numpy : 0.0022993087768554688 nb_pixel_total : 6921 time to create 1 rle with old method : 0.007836103439331055 length of segment : 107 time for calcul the mask position with numpy : 0.04318952560424805 nb_pixel_total : 17558 time to create 1 rle with old method : 0.021831512451171875 length of segment : 222 time for calcul the mask position with numpy : 0.20261883735656738 nb_pixel_total : 46121 time to create 1 rle with old method : 0.05542492866516113 length of segment : 324 time for calcul the mask position with numpy : 0.07585430145263672 nb_pixel_total : 13742 time to create 1 rle with old method : 0.025597095489501953 length of segment : 170 time for calcul the mask position with numpy : 0.1324167251586914 nb_pixel_total : 40678 time to create 1 rle with old method : 0.049479007720947266 length of segment : 241 time for calcul the mask position with numpy : 0.2129206657409668 nb_pixel_total : 61173 time to create 1 rle with old method : 0.07940673828125 length of segment : 335 time for calcul the mask position with numpy : 0.00048470497131347656 nb_pixel_total : 16288 time to create 1 rle with old method : 0.022128820419311523 length of segment : 205 time for calcul the mask position with numpy : 0.06648111343383789 nb_pixel_total : 30746 time to create 1 rle with old method : 0.03999924659729004 length of segment : 213 time for calcul the mask position with numpy : 0.0724785327911377 nb_pixel_total : 28059 time to create 1 rle with old method : 0.03981900215148926 length of segment : 286 time for calcul the mask position with numpy : 0.01256108283996582 nb_pixel_total : 7375 time to create 1 rle with old method : 0.012298107147216797 length of segment : 82 time for calcul the mask position with numpy : 0.0036873817443847656 nb_pixel_total : 3582 time to create 1 rle with old method : 0.0046694278717041016 length of segment : 77 time for calcul the mask position with numpy : 0.06866979598999023 nb_pixel_total : 24109 time to create 1 rle with old method : 0.03348207473754883 length of segment : 111 time for calcul the mask position with numpy : 0.03399181365966797 nb_pixel_total : 10871 time to create 1 rle with old method : 0.017877817153930664 length of segment : 137 time for calcul the mask position with numpy : 0.0060231685638427734 nb_pixel_total : 31606 time to create 1 rle with old method : 0.03776121139526367 length of segment : 200 time for calcul the mask position with numpy : 0.04951930046081543 nb_pixel_total : 60460 time to create 1 rle with old method : 0.07222604751586914 length of segment : 300 time for calcul the mask position with numpy : 0.04394960403442383 nb_pixel_total : 14632 time to create 1 rle with old method : 0.02209305763244629 length of segment : 124 time for calcul the mask position with numpy : 0.020359516143798828 nb_pixel_total : 19120 time to create 1 rle with old method : 0.0273282527923584 length of segment : 182 time for calcul the mask position with numpy : 0.007345438003540039 nb_pixel_total : 13652 time to create 1 rle with old method : 0.015840530395507812 length of segment : 115 time for calcul the mask position with numpy : 0.017555713653564453 nb_pixel_total : 8130 time to create 1 rle with old method : 0.011336326599121094 length of segment : 115 time for calcul the mask position with numpy : 0.10108637809753418 nb_pixel_total : 59499 time to create 1 rle with old method : 0.06972885131835938 length of segment : 504 time for calcul the mask position with numpy : 0.06291937828063965 nb_pixel_total : 24635 time to create 1 rle with old method : 0.029744386672973633 length of segment : 183 time for calcul the mask position with numpy : 0.030640363693237305 nb_pixel_total : 8220 time to create 1 rle with old method : 0.012573719024658203 length of segment : 117 time for calcul the mask position with numpy : 0.13038086891174316 nb_pixel_total : 63362 time to create 1 rle with old method : 0.07593441009521484 length of segment : 311 time for calcul the mask position with numpy : 0.09175395965576172 nb_pixel_total : 28681 time to create 1 rle with old method : 0.03550434112548828 length of segment : 363 time for calcul the mask position with numpy : 0.01698780059814453 nb_pixel_total : 6657 time to create 1 rle with old method : 0.012170791625976562 length of segment : 64 time for calcul the mask position with numpy : 0.051888227462768555 nb_pixel_total : 20981 time to create 1 rle with old method : 0.030437707901000977 length of segment : 162 time for calcul the mask position with numpy : 0.042558908462524414 nb_pixel_total : 9107 time to create 1 rle with old method : 0.013171195983886719 length of segment : 122 time for calcul the mask position with numpy : 0.3800351619720459 nb_pixel_total : 189255 time to create 1 rle with new method : 0.018135786056518555 length of segment : 611 time for calcul the mask position with numpy : 0.053057193756103516 nb_pixel_total : 25362 time to create 1 rle with old method : 0.033005475997924805 length of segment : 155 time for calcul the mask position with numpy : 0.010532617568969727 nb_pixel_total : 11458 time to create 1 rle with old method : 0.018922090530395508 length of segment : 109 time for calcul the mask position with numpy : 0.11774325370788574 nb_pixel_total : 85637 time to create 1 rle with old method : 0.10705399513244629 length of segment : 268 time for calcul the mask position with numpy : 0.2921440601348877 nb_pixel_total : 263107 time to create 1 rle with new method : 0.017566204071044922 length of segment : 683 time for calcul the mask position with numpy : 0.003865480422973633 nb_pixel_total : 17888 time to create 1 rle with old method : 0.02585601806640625 length of segment : 196 time for calcul the mask position with numpy : 0.17703628540039062 nb_pixel_total : 121507 time to create 1 rle with old method : 0.1629199981689453 length of segment : 534 time for calcul the mask position with numpy : 0.03102564811706543 nb_pixel_total : 271868 time to create 1 rle with new method : 0.018039703369140625 length of segment : 663 time for calcul the mask position with numpy : 0.14240026473999023 nb_pixel_total : 33148 time to create 1 rle with old method : 0.03995871543884277 length of segment : 290 time for calcul the mask position with numpy : 0.23278546333312988 nb_pixel_total : 206965 time to create 1 rle with new method : 0.016245603561401367 length of segment : 431 time for calcul the mask position with numpy : 0.0743560791015625 nb_pixel_total : 27228 time to create 1 rle with old method : 0.030461549758911133 length of segment : 185 time for calcul the mask position with numpy : 0.004834413528442383 nb_pixel_total : 8530 time to create 1 rle with old method : 0.009767293930053711 length of segment : 171 time for calcul the mask position with numpy : 0.14501261711120605 nb_pixel_total : 67872 time to create 1 rle with old method : 0.08428502082824707 length of segment : 343 time for calcul the mask position with numpy : 0.08093404769897461 nb_pixel_total : 15837 time to create 1 rle with old method : 0.02218317985534668 length of segment : 131 time for calcul the mask position with numpy : 0.6782357692718506 nb_pixel_total : 683179 time to create 1 rle with new method : 0.04703068733215332 length of segment : 1044 time for calcul the mask position with numpy : 0.04125070571899414 nb_pixel_total : 138934 time to create 1 rle with old method : 0.15570282936096191 length of segment : 374 time for calcul the mask position with numpy : 0.03035116195678711 nb_pixel_total : 40010 time to create 1 rle with old method : 0.04985356330871582 length of segment : 347 time for calcul the mask position with numpy : 0.050390005111694336 nb_pixel_total : 19521 time to create 1 rle with old method : 0.026608705520629883 length of segment : 189 time for calcul the mask position with numpy : 0.04169750213623047 nb_pixel_total : 12267 time to create 1 rle with old method : 0.017843961715698242 length of segment : 182 time for calcul the mask position with numpy : 0.03925633430480957 nb_pixel_total : 14810 time to create 1 rle with old method : 0.020635128021240234 length of segment : 134 time for calcul the mask position with numpy : 0.03193235397338867 nb_pixel_total : 19790 time to create 1 rle with old method : 0.026186466217041016 length of segment : 166 time for calcul the mask position with numpy : 0.18354558944702148 nb_pixel_total : 176236 time to create 1 rle with new method : 0.013788700103759766 length of segment : 474 time for calcul the mask position with numpy : 0.10587334632873535 nb_pixel_total : 57236 time to create 1 rle with old method : 0.0691070556640625 length of segment : 261 time for calcul the mask position with numpy : 0.05708932876586914 nb_pixel_total : 30569 time to create 1 rle with old method : 0.04102182388305664 length of segment : 194 time for calcul the mask position with numpy : 0.052080392837524414 nb_pixel_total : 15661 time to create 1 rle with old method : 0.023315906524658203 length of segment : 221 time for calcul the mask position with numpy : 0.26746082305908203 nb_pixel_total : 223656 time to create 1 rle with new method : 0.014907360076904297 length of segment : 812 time for calcul the mask position with numpy : 0.09730982780456543 nb_pixel_total : 14517 time to create 1 rle with old method : 0.021198034286499023 length of segment : 175 time for calcul the mask position with numpy : 0.36002206802368164 nb_pixel_total : 157984 time to create 1 rle with new method : 0.008616924285888672 length of segment : 471 time for calcul the mask position with numpy : 0.3294978141784668 nb_pixel_total : 61521 time to create 1 rle with old method : 0.07163715362548828 length of segment : 252 time for calcul the mask position with numpy : 0.06887102127075195 nb_pixel_total : 16433 time to create 1 rle with old method : 0.024006128311157227 length of segment : 118 time for calcul the mask position with numpy : 0.06344795227050781 nb_pixel_total : 10388 time to create 1 rle with old method : 0.01647210121154785 length of segment : 185 time for calcul the mask position with numpy : 0.16005706787109375 nb_pixel_total : 56021 time to create 1 rle with old method : 0.06740546226501465 length of segment : 312 time for calcul the mask position with numpy : 0.11010408401489258 nb_pixel_total : 22547 time to create 1 rle with old method : 0.026600122451782227 length of segment : 228 time for calcul the mask position with numpy : 0.24068999290466309 nb_pixel_total : 72095 time to create 1 rle with old method : 0.08892273902893066 length of segment : 367 time for calcul the mask position with numpy : 0.1397092342376709 nb_pixel_total : 13671 time to create 1 rle with old method : 0.02020716667175293 length of segment : 305 time for calcul the mask position with numpy : 0.0482020378112793 nb_pixel_total : 11328 time to create 1 rle with old method : 0.016286134719848633 length of segment : 104 time for calcul the mask position with numpy : 0.11772632598876953 nb_pixel_total : 80521 time to create 1 rle with old method : 0.10222840309143066 length of segment : 431 time for calcul the mask position with numpy : 0.0669090747833252 nb_pixel_total : 12658 time to create 1 rle with old method : 0.01882338523864746 length of segment : 116 time for calcul the mask position with numpy : 0.07478141784667969 nb_pixel_total : 11503 time to create 1 rle with old method : 0.018000364303588867 length of segment : 183 time for calcul the mask position with numpy : 0.0691537857055664 nb_pixel_total : 14667 time to create 1 rle with old method : 0.020830869674682617 length of segment : 115 time for calcul the mask position with numpy : 0.017428874969482422 nb_pixel_total : 15429 time to create 1 rle with old method : 0.023522138595581055 length of segment : 130 time for calcul the mask position with numpy : 0.02670884132385254 nb_pixel_total : 6623 time to create 1 rle with old method : 0.010570287704467773 length of segment : 85 time for calcul the mask position with numpy : 0.01668834686279297 nb_pixel_total : 11592 time to create 1 rle with old method : 0.017300844192504883 length of segment : 101 time for calcul the mask position with numpy : 0.16405916213989258 nb_pixel_total : 60180 time to create 1 rle with old method : 0.06949734687805176 length of segment : 362 time for calcul the mask position with numpy : 0.09479331970214844 nb_pixel_total : 60988 time to create 1 rle with old method : 0.06573796272277832 length of segment : 579 time for calcul the mask position with numpy : 0.04285478591918945 nb_pixel_total : 11050 time to create 1 rle with old method : 0.013686656951904297 length of segment : 84 time for calcul the mask position with numpy : 0.06337571144104004 nb_pixel_total : 45132 time to create 1 rle with old method : 0.05666160583496094 length of segment : 261 time for calcul the mask position with numpy : 0.03991055488586426 nb_pixel_total : 10871 time to create 1 rle with old method : 0.018004179000854492 length of segment : 115 time for calcul the mask position with numpy : 0.03322649002075195 nb_pixel_total : 9050 time to create 1 rle with old method : 0.014047384262084961 length of segment : 82 time for calcul the mask position with numpy : 0.06427550315856934 nb_pixel_total : 20047 time to create 1 rle with old method : 0.026270151138305664 length of segment : 138 time for calcul the mask position with numpy : 0.04758596420288086 nb_pixel_total : 20013 time to create 1 rle with old method : 0.027175188064575195 length of segment : 134 time for calcul the mask position with numpy : 0.024044513702392578 nb_pixel_total : 16496 time to create 1 rle with old method : 0.01833057403564453 length of segment : 179 time for calcul the mask position with numpy : 0.0619959831237793 nb_pixel_total : 20991 time to create 1 rle with old method : 0.026681184768676758 length of segment : 177 time for calcul the mask position with numpy : 0.14623427391052246 nb_pixel_total : 82413 time to create 1 rle with old method : 0.09698319435119629 length of segment : 305 time for calcul the mask position with numpy : 0.03166651725769043 nb_pixel_total : 24073 time to create 1 rle with old method : 0.03181314468383789 length of segment : 197 time for calcul the mask position with numpy : 0.10329914093017578 nb_pixel_total : 25187 time to create 1 rle with old method : 0.04772210121154785 length of segment : 153 time for calcul the mask position with numpy : 0.0962376594543457 nb_pixel_total : 43059 time to create 1 rle with old method : 0.06615400314331055 length of segment : 270 time for calcul the mask position with numpy : 0.13175702095031738 nb_pixel_total : 42951 time to create 1 rle with old method : 0.05157756805419922 length of segment : 384 time for calcul the mask position with numpy : 0.12601876258850098 nb_pixel_total : 94936 time to create 1 rle with old method : 0.11535406112670898 length of segment : 723 time for calcul the mask position with numpy : 0.42000889778137207 nb_pixel_total : 137693 time to create 1 rle with old method : 0.18502426147460938 length of segment : 416 time for calcul the mask position with numpy : 0.11381220817565918 nb_pixel_total : 24833 time to create 1 rle with old method : 0.03679919242858887 length of segment : 244 time for calcul the mask position with numpy : 0.013833045959472656 nb_pixel_total : 4777 time to create 1 rle with old method : 0.010513782501220703 length of segment : 34 time for calcul the mask position with numpy : 0.025462627410888672 nb_pixel_total : 10126 time to create 1 rle with old method : 0.017570972442626953 length of segment : 110 time for calcul the mask position with numpy : 0.10720705986022949 nb_pixel_total : 26339 time to create 1 rle with old method : 0.03660082817077637 length of segment : 264 time for calcul the mask position with numpy : 0.7613401412963867 nb_pixel_total : 603847 time to create 1 rle with new method : 0.31223344802856445 length of segment : 974 time for calcul the mask position with numpy : 0.02062058448791504 nb_pixel_total : 9533 time to create 1 rle with old method : 0.015454530715942383 length of segment : 94 time for calcul the mask position with numpy : 0.038823604583740234 nb_pixel_total : 6379 time to create 1 rle with old method : 0.01183319091796875 length of segment : 88 time for calcul the mask position with numpy : 0.015908002853393555 nb_pixel_total : 8751 time to create 1 rle with old method : 0.014759540557861328 length of segment : 113 time for calcul the mask position with numpy : 0.011831045150756836 nb_pixel_total : 28058 time to create 1 rle with old method : 0.03694629669189453 length of segment : 382 time for calcul the mask position with numpy : 0.08745622634887695 nb_pixel_total : 35978 time to create 1 rle with old method : 0.06483078002929688 length of segment : 252 time for calcul the mask position with numpy : 0.03352069854736328 nb_pixel_total : 9049 time to create 1 rle with old method : 0.014084339141845703 length of segment : 229 time for calcul the mask position with numpy : 0.15593552589416504 nb_pixel_total : 78179 time to create 1 rle with old method : 0.09248614311218262 length of segment : 510 time for calcul the mask position with numpy : 0.04577231407165527 nb_pixel_total : 597018 time to create 1 rle with new method : 0.057852983474731445 length of segment : 977 time for calcul the mask position with numpy : 0.07675361633300781 nb_pixel_total : 24758 time to create 1 rle with old method : 0.032164573669433594 length of segment : 169 time for calcul the mask position with numpy : 0.07899236679077148 nb_pixel_total : 22390 time to create 1 rle with old method : 0.03027796745300293 length of segment : 247 time for calcul the mask position with numpy : 0.028390169143676758 nb_pixel_total : 59521 time to create 1 rle with old method : 0.06954503059387207 length of segment : 567 time for calcul the mask position with numpy : 0.33289003372192383 nb_pixel_total : 126689 time to create 1 rle with old method : 0.14442014694213867 length of segment : 561 time for calcul the mask position with numpy : 0.1173396110534668 nb_pixel_total : 34806 time to create 1 rle with old method : 0.04209446907043457 length of segment : 164 time for calcul the mask position with numpy : 0.11117887496948242 nb_pixel_total : 24208 time to create 1 rle with old method : 0.030754566192626953 length of segment : 228 time for calcul the mask position with numpy : 0.03232979774475098 nb_pixel_total : 5538 time to create 1 rle with old method : 0.009319067001342773 length of segment : 75 time for calcul the mask position with numpy : 0.18246889114379883 nb_pixel_total : 42884 time to create 1 rle with old method : 0.05183577537536621 length of segment : 447 time for calcul the mask position with numpy : 0.1606600284576416 nb_pixel_total : 27712 time to create 1 rle with old method : 0.03777050971984863 length of segment : 246 time for calcul the mask position with numpy : 0.09956574440002441 nb_pixel_total : 17434 time to create 1 rle with old method : 0.028872251510620117 length of segment : 193 time for calcul the mask position with numpy : 0.06390571594238281 nb_pixel_total : 15194 time to create 1 rle with old method : 0.022287845611572266 length of segment : 151 time for calcul the mask position with numpy : 0.1305246353149414 nb_pixel_total : 33980 time to create 1 rle with old method : 0.042639970779418945 length of segment : 355 time for calcul the mask position with numpy : 0.11381006240844727 nb_pixel_total : 51175 time to create 1 rle with old method : 0.06370973587036133 length of segment : 497 time for calcul the mask position with numpy : 0.03731274604797363 nb_pixel_total : 14734 time to create 1 rle with old method : 0.023195981979370117 length of segment : 172 time for calcul the mask position with numpy : 0.04700517654418945 nb_pixel_total : 22719 time to create 1 rle with old method : 0.039549827575683594 length of segment : 157 time for calcul the mask position with numpy : 0.2637138366699219 nb_pixel_total : 45381 time to create 1 rle with old method : 0.05751848220825195 length of segment : 330 time for calcul the mask position with numpy : 0.008357048034667969 nb_pixel_total : 9792 time to create 1 rle with old method : 0.013813018798828125 length of segment : 111 time for calcul the mask position with numpy : 0.1278989315032959 nb_pixel_total : 19696 time to create 1 rle with old method : 0.026568889617919922 length of segment : 299 time for calcul the mask position with numpy : 0.1291062831878662 nb_pixel_total : 31578 time to create 1 rle with old method : 0.03855443000793457 length of segment : 244 time for calcul the mask position with numpy : 0.1275501251220703 nb_pixel_total : 37101 time to create 1 rle with old method : 0.04504966735839844 length of segment : 159 time for calcul the mask position with numpy : 0.11438226699829102 nb_pixel_total : 22422 time to create 1 rle with old method : 0.034668684005737305 length of segment : 282 time for calcul the mask position with numpy : 0.13197040557861328 nb_pixel_total : 46931 time to create 1 rle with old method : 0.055205345153808594 length of segment : 182 time for calcul the mask position with numpy : 0.15513324737548828 nb_pixel_total : 46475 time to create 1 rle with old method : 0.06089520454406738 length of segment : 234 time for calcul the mask position with numpy : 0.0724802017211914 nb_pixel_total : 14356 time to create 1 rle with old method : 0.02228569984436035 length of segment : 177 time for calcul the mask position with numpy : 0.07538008689880371 nb_pixel_total : 14059 time to create 1 rle with old method : 0.025539636611938477 length of segment : 168 time for calcul the mask position with numpy : 0.2348461151123047 nb_pixel_total : 46865 time to create 1 rle with old method : 0.05652618408203125 length of segment : 335 time for calcul the mask position with numpy : 0.03865551948547363 nb_pixel_total : 5517 time to create 1 rle with old method : 0.007928133010864258 length of segment : 94 time for calcul the mask position with numpy : 0.2000415325164795 nb_pixel_total : 51424 time to create 1 rle with old method : 0.0691518783569336 length of segment : 349 time for calcul the mask position with numpy : 0.18198728561401367 nb_pixel_total : 59912 time to create 1 rle with old method : 0.07053542137145996 length of segment : 280 time for calcul the mask position with numpy : 0.054406166076660156 nb_pixel_total : 27398 time to create 1 rle with old method : 0.04573941230773926 length of segment : 147 time for calcul the mask position with numpy : 0.033214569091796875 nb_pixel_total : 8151 time to create 1 rle with old method : 0.012028694152832031 length of segment : 68 time for calcul the mask position with numpy : 0.06654644012451172 nb_pixel_total : 20691 time to create 1 rle with old method : 0.027901172637939453 length of segment : 158 time for calcul the mask position with numpy : 0.0231473445892334 nb_pixel_total : 10188 time to create 1 rle with old method : 0.016677141189575195 length of segment : 101 time for calcul the mask position with numpy : 0.033658742904663086 nb_pixel_total : 15636 time to create 1 rle with old method : 0.017937421798706055 length of segment : 133 time for calcul the mask position with numpy : 0.04437446594238281 nb_pixel_total : 17272 time to create 1 rle with old method : 0.024697303771972656 length of segment : 150 time for calcul the mask position with numpy : 0.04301571846008301 nb_pixel_total : 14472 time to create 1 rle with old method : 0.020696401596069336 length of segment : 134 time for calcul the mask position with numpy : 0.11023592948913574 nb_pixel_total : 25599 time to create 1 rle with old method : 0.033136606216430664 length of segment : 274 time for calcul the mask position with numpy : 0.014985799789428711 nb_pixel_total : 6173 time to create 1 rle with old method : 0.01174616813659668 length of segment : 129 time for calcul the mask position with numpy : 0.031907081604003906 nb_pixel_total : 11440 time to create 1 rle with old method : 0.02689957618713379 length of segment : 121 time for calcul the mask position with numpy : 0.018365144729614258 nb_pixel_total : 8417 time to create 1 rle with old method : 0.014464139938354492 length of segment : 124 time for calcul the mask position with numpy : 0.01711869239807129 nb_pixel_total : 13366 time to create 1 rle with old method : 0.021624088287353516 length of segment : 131 time for calcul the mask position with numpy : 0.1475381851196289 nb_pixel_total : 62338 time to create 1 rle with old method : 0.07247424125671387 length of segment : 374 time for calcul the mask position with numpy : 0.018090248107910156 nb_pixel_total : 16554 time to create 1 rle with old method : 0.023569345474243164 length of segment : 216 time for calcul the mask position with numpy : 0.16203641891479492 nb_pixel_total : 86972 time to create 1 rle with old method : 0.0967106819152832 length of segment : 472 time for calcul the mask position with numpy : 0.21717548370361328 nb_pixel_total : 135561 time to create 1 rle with old method : 0.14686989784240723 length of segment : 519 time for calcul the mask position with numpy : 0.030867815017700195 nb_pixel_total : 49890 time to create 1 rle with old method : 0.054982662200927734 length of segment : 445 time for calcul the mask position with numpy : 0.029450654983520508 nb_pixel_total : 31629 time to create 1 rle with old method : 0.03795647621154785 length of segment : 200 time for calcul the mask position with numpy : 0.06507611274719238 nb_pixel_total : 58815 time to create 1 rle with old method : 0.06761288642883301 length of segment : 254 time for calcul the mask position with numpy : 0.034751176834106445 nb_pixel_total : 21558 time to create 1 rle with old method : 0.02959275245666504 length of segment : 152 time for calcul the mask position with numpy : 0.0367128849029541 nb_pixel_total : 13017 time to create 1 rle with old method : 0.0198209285736084 length of segment : 164 time for calcul the mask position with numpy : 0.033364295959472656 nb_pixel_total : 13897 time to create 1 rle with old method : 0.020343780517578125 length of segment : 127 time for calcul the mask position with numpy : 0.03154587745666504 nb_pixel_total : 21160 time to create 1 rle with old method : 0.028255939483642578 length of segment : 143 time for calcul the mask position with numpy : 0.05455803871154785 nb_pixel_total : 19184 time to create 1 rle with old method : 0.0292816162109375 length of segment : 150 time for calcul the mask position with numpy : 0.05499386787414551 nb_pixel_total : 24720 time to create 1 rle with old method : 0.037020206451416016 length of segment : 240 time for calcul the mask position with numpy : 0.03148794174194336 nb_pixel_total : 25931 time to create 1 rle with old method : 0.03471541404724121 length of segment : 123 time for calcul the mask position with numpy : 0.02935314178466797 nb_pixel_total : 11597 time to create 1 rle with old method : 0.013335704803466797 length of segment : 145 time for calcul the mask position with numpy : 0.11028623580932617 nb_pixel_total : 56932 time to create 1 rle with old method : 0.06237673759460449 length of segment : 410 time for calcul the mask position with numpy : 0.06659173965454102 nb_pixel_total : 18359 time to create 1 rle with old method : 0.024908065795898438 length of segment : 197 time for calcul the mask position with numpy : 0.04253745079040527 nb_pixel_total : 13036 time to create 1 rle with old method : 0.02122974395751953 length of segment : 167 time for calcul the mask position with numpy : 0.08092403411865234 nb_pixel_total : 15098 time to create 1 rle with old method : 0.028032302856445312 length of segment : 182 time for calcul the mask position with numpy : 0.4101090431213379 nb_pixel_total : 100060 time to create 1 rle with old method : 0.1210489273071289 length of segment : 420 time for calcul the mask position with numpy : 0.04448246955871582 nb_pixel_total : 7827 time to create 1 rle with old method : 0.012816429138183594 length of segment : 115 time for calcul the mask position with numpy : 0.1903972625732422 nb_pixel_total : 58743 time to create 1 rle with old method : 0.0715029239654541 length of segment : 328 time for calcul the mask position with numpy : 0.0813603401184082 nb_pixel_total : 12304 time to create 1 rle with old method : 0.018502235412597656 length of segment : 115 time for calcul the mask position with numpy : 0.06112360954284668 nb_pixel_total : 14067 time to create 1 rle with old method : 0.017412185668945312 length of segment : 115 time for calcul the mask position with numpy : 0.1519460678100586 nb_pixel_total : 31827 time to create 1 rle with old method : 0.038579463958740234 length of segment : 246 time for calcul the mask position with numpy : 0.1057741641998291 nb_pixel_total : 25010 time to create 1 rle with old method : 0.030766010284423828 length of segment : 267 time for calcul the mask position with numpy : 0.06841421127319336 nb_pixel_total : 9933 time to create 1 rle with old method : 0.014107704162597656 length of segment : 135 time for calcul the mask position with numpy : 0.15897727012634277 nb_pixel_total : 30529 time to create 1 rle with old method : 0.03644824028015137 length of segment : 226 time for calcul the mask position with numpy : 0.055047035217285156 nb_pixel_total : 8334 time to create 1 rle with old method : 0.01308131217956543 length of segment : 143 time for calcul the mask position with numpy : 0.04800844192504883 nb_pixel_total : 9844 time to create 1 rle with old method : 0.01603865623474121 length of segment : 126 time for calcul the mask position with numpy : 0.0888373851776123 nb_pixel_total : 25270 time to create 1 rle with old method : 0.03125476837158203 length of segment : 241 time for calcul the mask position with numpy : 0.10726189613342285 nb_pixel_total : 26800 time to create 1 rle with old method : 0.032425642013549805 length of segment : 222 time for calcul the mask position with numpy : 0.20029640197753906 nb_pixel_total : 44194 time to create 1 rle with old method : 0.051972150802612305 length of segment : 313 time for calcul the mask position with numpy : 0.36222338676452637 nb_pixel_total : 97393 time to create 1 rle with old method : 0.1040346622467041 length of segment : 363 time for calcul the mask position with numpy : 0.08458209037780762 nb_pixel_total : 16132 time to create 1 rle with old method : 0.021622657775878906 length of segment : 188 time for calcul the mask position with numpy : 0.06420660018920898 nb_pixel_total : 8536 time to create 1 rle with old method : 0.013362646102905273 length of segment : 105 time for calcul the mask position with numpy : 0.04307150840759277 nb_pixel_total : 8289 time to create 1 rle with old method : 0.011768817901611328 length of segment : 100 time for calcul the mask position with numpy : 0.09354448318481445 nb_pixel_total : 15424 time to create 1 rle with old method : 0.022162199020385742 length of segment : 176 time for calcul the mask position with numpy : 0.012417316436767578 nb_pixel_total : 24102 time to create 1 rle with old method : 0.03143310546875 length of segment : 383 time for calcul the mask position with numpy : 0.022525310516357422 nb_pixel_total : 6458 time to create 1 rle with old method : 0.009846925735473633 length of segment : 90 time for calcul the mask position with numpy : 0.020533084869384766 nb_pixel_total : 3033 time to create 1 rle with old method : 0.006486177444458008 length of segment : 69 time for calcul the mask position with numpy : 0.03780198097229004 nb_pixel_total : 6574 time to create 1 rle with old method : 0.009599924087524414 length of segment : 105 time for calcul the mask position with numpy : 0.02045464515686035 nb_pixel_total : 3601 time to create 1 rle with old method : 0.007655620574951172 length of segment : 90 time for calcul the mask position with numpy : 0.1231839656829834 nb_pixel_total : 26356 time to create 1 rle with old method : 0.033011674880981445 length of segment : 224 time for calcul the mask position with numpy : 0.03156256675720215 nb_pixel_total : 17620 time to create 1 rle with old method : 0.024415969848632812 length of segment : 239 time for calcul the mask position with numpy : 0.15528512001037598 nb_pixel_total : 45515 time to create 1 rle with old method : 0.05028843879699707 length of segment : 315 time for calcul the mask position with numpy : 0.051894426345825195 nb_pixel_total : 14427 time to create 1 rle with old method : 0.019057512283325195 length of segment : 129 time for calcul the mask position with numpy : 0.11117839813232422 nb_pixel_total : 52052 time to create 1 rle with old method : 0.05947685241699219 length of segment : 195 time for calcul the mask position with numpy : 0.0872187614440918 nb_pixel_total : 27852 time to create 1 rle with old method : 0.03354620933532715 length of segment : 151 time for calcul the mask position with numpy : 0.1547698974609375 nb_pixel_total : 47744 time to create 1 rle with old method : 0.0548243522644043 length of segment : 277 time for calcul the mask position with numpy : 0.08172178268432617 nb_pixel_total : 13565 time to create 1 rle with old method : 0.019246339797973633 length of segment : 148 time for calcul the mask position with numpy : 0.06647467613220215 nb_pixel_total : 11281 time to create 1 rle with old method : 0.01715087890625 length of segment : 161 time for calcul the mask position with numpy : 0.0949094295501709 nb_pixel_total : 40339 time to create 1 rle with old method : 0.05097651481628418 length of segment : 224 time for calcul the mask position with numpy : 0.01708817481994629 nb_pixel_total : 4650 time to create 1 rle with old method : 0.005059719085693359 length of segment : 87 time for calcul the mask position with numpy : 0.05632305145263672 nb_pixel_total : 11934 time to create 1 rle with old method : 0.02375936508178711 length of segment : 177 time for calcul the mask position with numpy : 0.06113791465759277 nb_pixel_total : 7514 time to create 1 rle with old method : 0.012984037399291992 length of segment : 200 time for calcul the mask position with numpy : 0.07791996002197266 nb_pixel_total : 17471 time to create 1 rle with old method : 0.028762340545654297 length of segment : 200 time for calcul the mask position with numpy : 0.18732547760009766 nb_pixel_total : 50488 time to create 1 rle with old method : 0.05606651306152344 length of segment : 261 time for calcul the mask position with numpy : 0.018257617950439453 nb_pixel_total : 10927 time to create 1 rle with old method : 0.01758742332458496 length of segment : 170 time for calcul the mask position with numpy : 0.0388796329498291 nb_pixel_total : 9497 time to create 1 rle with old method : 0.01780414581298828 length of segment : 130 time for calcul the mask position with numpy : 0.035172462463378906 nb_pixel_total : 13221 time to create 1 rle with old method : 0.019717693328857422 length of segment : 117 time for calcul the mask position with numpy : 0.08012199401855469 nb_pixel_total : 46789 time to create 1 rle with old method : 0.05737900733947754 length of segment : 304 time for calcul the mask position with numpy : 0.61932373046875 nb_pixel_total : 168178 time to create 1 rle with new method : 0.014518022537231445 length of segment : 577 time for calcul the mask position with numpy : 0.03699612617492676 nb_pixel_total : 10445 time to create 1 rle with old method : 0.016573667526245117 length of segment : 111 time for calcul the mask position with numpy : 0.17108654975891113 nb_pixel_total : 45338 time to create 1 rle with old method : 0.07133316993713379 length of segment : 264 time for calcul the mask position with numpy : 0.028249025344848633 nb_pixel_total : 4886 time to create 1 rle with old method : 0.005703449249267578 length of segment : 136 time for calcul the mask position with numpy : 0.035385847091674805 nb_pixel_total : 6608 time to create 1 rle with old method : 0.01263737678527832 length of segment : 92 time for calcul the mask position with numpy : 0.12285518646240234 nb_pixel_total : 36533 time to create 1 rle with old method : 0.05615234375 length of segment : 342 time for calcul the mask position with numpy : 0.08482193946838379 nb_pixel_total : 31973 time to create 1 rle with old method : 0.03853034973144531 length of segment : 291 time for calcul the mask position with numpy : 0.009429216384887695 nb_pixel_total : 10716 time to create 1 rle with old method : 0.012994050979614258 length of segment : 123 time for calcul the mask position with numpy : 0.003366708755493164 nb_pixel_total : 6497 time to create 1 rle with old method : 0.008087635040283203 length of segment : 134 time for calcul the mask position with numpy : 0.06812620162963867 nb_pixel_total : 16610 time to create 1 rle with old method : 0.022669076919555664 length of segment : 220 time for calcul the mask position with numpy : 0.036898136138916016 nb_pixel_total : 19690 time to create 1 rle with old method : 0.041448116302490234 length of segment : 178 time for calcul the mask position with numpy : 0.008394241333007812 nb_pixel_total : 1732 time to create 1 rle with old method : 0.002235889434814453 length of segment : 54 time for calcul the mask position with numpy : 0.03258514404296875 nb_pixel_total : 6610 time to create 1 rle with old method : 0.011076688766479492 length of segment : 95 time for calcul the mask position with numpy : 0.022172927856445312 nb_pixel_total : 4431 time to create 1 rle with old method : 0.008368253707885742 length of segment : 98 time for calcul the mask position with numpy : 0.18425488471984863 nb_pixel_total : 65726 time to create 1 rle with old method : 0.13375353813171387 length of segment : 490 time for calcul the mask position with numpy : 0.13850641250610352 nb_pixel_total : 25955 time to create 1 rle with old method : 0.047455549240112305 length of segment : 197 time for calcul the mask position with numpy : 0.06764721870422363 nb_pixel_total : 13528 time to create 1 rle with old method : 0.022544384002685547 length of segment : 125 time for calcul the mask position with numpy : 0.06407833099365234 nb_pixel_total : 9015 time to create 1 rle with old method : 0.013842105865478516 length of segment : 135 time for calcul the mask position with numpy : 0.04171323776245117 nb_pixel_total : 9341 time to create 1 rle with old method : 0.01630878448486328 length of segment : 106 time for calcul the mask position with numpy : 0.03628253936767578 nb_pixel_total : 5202 time to create 1 rle with old method : 0.006167173385620117 length of segment : 141 time for calcul the mask position with numpy : 0.020703792572021484 nb_pixel_total : 6246 time to create 1 rle with old method : 0.012773513793945312 length of segment : 97 time for calcul the mask position with numpy : 0.08779072761535645 nb_pixel_total : 43597 time to create 1 rle with old method : 0.05497288703918457 length of segment : 240 time for calcul the mask position with numpy : 0.26551294326782227 nb_pixel_total : 109092 time to create 1 rle with old method : 0.15079379081726074 length of segment : 452 time for calcul the mask position with numpy : 0.04712939262390137 nb_pixel_total : 31324 time to create 1 rle with old method : 0.0514986515045166 length of segment : 269 time for calcul the mask position with numpy : 0.0005991458892822266 nb_pixel_total : 10429 time to create 1 rle with old method : 0.015845298767089844 length of segment : 144 time for calcul the mask position with numpy : 0.035103559494018555 nb_pixel_total : 5738 time to create 1 rle with old method : 0.00833582878112793 length of segment : 98 time for calcul the mask position with numpy : 0.056894779205322266 nb_pixel_total : 14844 time to create 1 rle with old method : 0.02213430404663086 length of segment : 133 time for calcul the mask position with numpy : 0.00019168853759765625 nb_pixel_total : 5952 time to create 1 rle with old method : 0.007039546966552734 length of segment : 81 time for calcul the mask position with numpy : 0.17096996307373047 nb_pixel_total : 77521 time to create 1 rle with old method : 0.09477925300598145 length of segment : 209 time for calcul the mask position with numpy : 0.07156491279602051 nb_pixel_total : 28011 time to create 1 rle with old method : 0.043699026107788086 length of segment : 186 time for calcul the mask position with numpy : 0.013727903366088867 nb_pixel_total : 5288 time to create 1 rle with old method : 0.0076045989990234375 length of segment : 67 time for calcul the mask position with numpy : 0.11391806602478027 nb_pixel_total : 53194 time to create 1 rle with old method : 0.06503772735595703 length of segment : 289 time for calcul the mask position with numpy : 0.07013249397277832 nb_pixel_total : 31522 time to create 1 rle with old method : 0.03740429878234863 length of segment : 236 time for calcul the mask position with numpy : 0.022005319595336914 nb_pixel_total : 2692 time to create 1 rle with old method : 0.005998134613037109 length of segment : 73 time for calcul the mask position with numpy : 0.039353370666503906 nb_pixel_total : 8272 time to create 1 rle with old method : 0.015412092208862305 length of segment : 102 time for calcul the mask position with numpy : 0.06880331039428711 nb_pixel_total : 20606 time to create 1 rle with old method : 0.02964186668395996 length of segment : 148 time for calcul the mask position with numpy : 0.11964821815490723 nb_pixel_total : 37060 time to create 1 rle with old method : 0.06173872947692871 length of segment : 236 time for calcul the mask position with numpy : 0.05913996696472168 nb_pixel_total : 10346 time to create 1 rle with old method : 0.019038915634155273 length of segment : 105 time for calcul the mask position with numpy : 0.020052433013916016 nb_pixel_total : 7141 time to create 1 rle with old method : 0.009377241134643555 length of segment : 141 time for calcul the mask position with numpy : 0.015739917755126953 nb_pixel_total : 7399 time to create 1 rle with old method : 0.013901948928833008 length of segment : 100 time for calcul the mask position with numpy : 0.05570530891418457 nb_pixel_total : 20576 time to create 1 rle with old method : 0.03788614273071289 length of segment : 181 time for calcul the mask position with numpy : 0.06904006004333496 nb_pixel_total : 32428 time to create 1 rle with old method : 0.03832888603210449 length of segment : 339 time for calcul the mask position with numpy : 0.7804996967315674 nb_pixel_total : 442288 time to create 1 rle with new method : 0.07552552223205566 length of segment : 801 time for calcul the mask position with numpy : 0.16511821746826172 nb_pixel_total : 34561 time to create 1 rle with old method : 0.04326820373535156 length of segment : 432 time for calcul the mask position with numpy : 0.21348237991333008 nb_pixel_total : 64516 time to create 1 rle with old method : 0.08817744255065918 length of segment : 341 time for calcul the mask position with numpy : 0.038403987884521484 nb_pixel_total : 6745 time to create 1 rle with old method : 0.011184453964233398 length of segment : 152 time for calcul the mask position with numpy : 0.0817410945892334 nb_pixel_total : 40703 time to create 1 rle with old method : 0.06237220764160156 length of segment : 382 time for calcul the mask position with numpy : 0.08428096771240234 nb_pixel_total : 52597 time to create 1 rle with old method : 0.07253479957580566 length of segment : 278 time for calcul the mask position with numpy : 0.054070234298706055 nb_pixel_total : 9447 time to create 1 rle with old method : 0.017082929611206055 length of segment : 175 time for calcul the mask position with numpy : 0.13003134727478027 nb_pixel_total : 23274 time to create 1 rle with old method : 0.03037571907043457 length of segment : 230 time for calcul the mask position with numpy : 0.057265281677246094 nb_pixel_total : 8235 time to create 1 rle with old method : 0.018279314041137695 length of segment : 139 time for calcul the mask position with numpy : 0.09733295440673828 nb_pixel_total : 76911 time to create 1 rle with old method : 0.09412646293640137 length of segment : 352 time for calcul the mask position with numpy : 0.0015244483947753906 nb_pixel_total : 2115 time to create 1 rle with old method : 0.0024788379669189453 length of segment : 89 time for calcul the mask position with numpy : 0.040488481521606445 nb_pixel_total : 9949 time to create 1 rle with old method : 0.013552188873291016 length of segment : 209 time for calcul the mask position with numpy : 0.30080342292785645 nb_pixel_total : 132234 time to create 1 rle with old method : 0.16584300994873047 length of segment : 559 time for calcul the mask position with numpy : 0.029088735580444336 nb_pixel_total : 14092 time to create 1 rle with old method : 0.023160696029663086 length of segment : 169 time for calcul the mask position with numpy : 0.04204583168029785 nb_pixel_total : 54388 time to create 1 rle with old method : 0.07748031616210938 length of segment : 311 time for calcul the mask position with numpy : 0.07706546783447266 nb_pixel_total : 18981 time to create 1 rle with old method : 0.038190603256225586 length of segment : 124 time for calcul the mask position with numpy : 0.03976750373840332 nb_pixel_total : 76543 time to create 1 rle with old method : 0.08737015724182129 length of segment : 456 time for calcul the mask position with numpy : 0.0051174163818359375 nb_pixel_total : 16498 time to create 1 rle with old method : 0.022209644317626953 length of segment : 270 time for calcul the mask position with numpy : 0.1147010326385498 nb_pixel_total : 43455 time to create 1 rle with old method : 0.05914878845214844 length of segment : 157 time for calcul the mask position with numpy : 0.015247821807861328 nb_pixel_total : 11536 time to create 1 rle with old method : 0.018002748489379883 length of segment : 115 time for calcul the mask position with numpy : 0.001041412353515625 nb_pixel_total : 8835 time to create 1 rle with old method : 0.010606765747070312 length of segment : 80 time for calcul the mask position with numpy : 0.0477299690246582 nb_pixel_total : 15367 time to create 1 rle with old method : 0.021881103515625 length of segment : 121 time for calcul the mask position with numpy : 0.05861711502075195 nb_pixel_total : 15905 time to create 1 rle with old method : 0.02100062370300293 length of segment : 149 time for calcul the mask position with numpy : 0.26648569107055664 nb_pixel_total : 126695 time to create 1 rle with old method : 0.14513015747070312 length of segment : 445 time for calcul the mask position with numpy : 0.06156802177429199 nb_pixel_total : 46942 time to create 1 rle with old method : 0.06507205963134766 length of segment : 222 time for calcul the mask position with numpy : 0.005468845367431641 nb_pixel_total : 5476 time to create 1 rle with old method : 0.007461071014404297 length of segment : 55 time for calcul the mask position with numpy : 0.03729581832885742 nb_pixel_total : 50467 time to create 1 rle with old method : 0.07278299331665039 length of segment : 232 time for calcul the mask position with numpy : 0.24953866004943848 nb_pixel_total : 201458 time to create 1 rle with new method : 0.03246259689331055 length of segment : 801 time for calcul the mask position with numpy : 0.012050867080688477 nb_pixel_total : 5781 time to create 1 rle with old method : 0.011109590530395508 length of segment : 81 time for calcul the mask position with numpy : 0.0002818107604980469 nb_pixel_total : 5791 time to create 1 rle with old method : 0.0070209503173828125 length of segment : 99 time for calcul the mask position with numpy : 0.12290692329406738 nb_pixel_total : 61662 time to create 1 rle with old method : 0.07543778419494629 length of segment : 383 time for calcul the mask position with numpy : 0.004067897796630859 nb_pixel_total : 3819 time to create 1 rle with old method : 0.00824594497680664 length of segment : 78 time for calcul the mask position with numpy : 0.067169189453125 nb_pixel_total : 152158 time to create 1 rle with new method : 0.01697063446044922 length of segment : 821 time for calcul the mask position with numpy : 0.11217761039733887 nb_pixel_total : 58265 time to create 1 rle with old method : 0.07254934310913086 length of segment : 248 time for calcul the mask position with numpy : 0.010952234268188477 nb_pixel_total : 87678 time to create 1 rle with old method : 0.1021418571472168 length of segment : 692 time for calcul the mask position with numpy : 0.10594463348388672 nb_pixel_total : 53144 time to create 1 rle with old method : 0.06993222236633301 length of segment : 388 time for calcul the mask position with numpy : 0.0007596015930175781 nb_pixel_total : 4100 time to create 1 rle with old method : 0.004707813262939453 length of segment : 51 time for calcul the mask position with numpy : 0.10683298110961914 nb_pixel_total : 61745 time to create 1 rle with old method : 0.07423639297485352 length of segment : 307 time for calcul the mask position with numpy : 0.016540050506591797 nb_pixel_total : 4604 time to create 1 rle with old method : 0.010730266571044922 length of segment : 78 time for calcul the mask position with numpy : 0.01699686050415039 nb_pixel_total : 13893 time to create 1 rle with old method : 0.020479440689086914 length of segment : 103 time for calcul the mask position with numpy : 0.020090579986572266 nb_pixel_total : 14473 time to create 1 rle with old method : 0.021436691284179688 length of segment : 82 time for calcul the mask position with numpy : 0.14835858345031738 nb_pixel_total : 249025 time to create 1 rle with new method : 0.007885932922363281 length of segment : 481 time for calcul the mask position with numpy : 0.29050755500793457 nb_pixel_total : 674695 time to create 1 rle with new method : 0.07259464263916016 length of segment : 999 time for calcul the mask position with numpy : 0.08753299713134766 nb_pixel_total : 101934 time to create 1 rle with old method : 0.11884379386901855 length of segment : 327 time for calcul the mask position with numpy : 0.02385425567626953 nb_pixel_total : 120331 time to create 1 rle with old method : 0.13889384269714355 length of segment : 250 time for calcul the mask position with numpy : 0.015395879745483398 nb_pixel_total : 14386 time to create 1 rle with old method : 0.020820140838623047 length of segment : 168 time for calcul the mask position with numpy : 0.17084670066833496 nb_pixel_total : 32601 time to create 1 rle with old method : 0.038562774658203125 length of segment : 227 time for calcul the mask position with numpy : 0.11336565017700195 nb_pixel_total : 25432 time to create 1 rle with old method : 0.04325675964355469 length of segment : 208 time for calcul the mask position with numpy : 0.03530168533325195 nb_pixel_total : 5154 time to create 1 rle with old method : 0.00588226318359375 length of segment : 182 time for calcul the mask position with numpy : 0.037596940994262695 nb_pixel_total : 18315 time to create 1 rle with old method : 0.02499532699584961 length of segment : 132 time for calcul the mask position with numpy : 0.11708331108093262 nb_pixel_total : 35097 time to create 1 rle with old method : 0.04606890678405762 length of segment : 374 time for calcul the mask position with numpy : 0.4466867446899414 nb_pixel_total : 186054 time to create 1 rle with new method : 0.011495590209960938 length of segment : 563 time for calcul the mask position with numpy : 0.19557690620422363 nb_pixel_total : 79543 time to create 1 rle with old method : 0.09798908233642578 length of segment : 405 time for calcul the mask position with numpy : 0.19248318672180176 nb_pixel_total : 71111 time to create 1 rle with old method : 0.08823823928833008 length of segment : 441 time for calcul the mask position with numpy : 0.13494014739990234 nb_pixel_total : 52564 time to create 1 rle with old method : 0.07481765747070312 length of segment : 203 time for calcul the mask position with numpy : 0.07767438888549805 nb_pixel_total : 27677 time to create 1 rle with old method : 0.0416867733001709 length of segment : 383 time for calcul the mask position with numpy : 0.23006963729858398 nb_pixel_total : 105553 time to create 1 rle with old method : 0.1237022876739502 length of segment : 712 time for calcul the mask position with numpy : 0.05194973945617676 nb_pixel_total : 24725 time to create 1 rle with old method : 0.031170129776000977 length of segment : 220 time for calcul the mask position with numpy : 0.1223289966583252 nb_pixel_total : 45628 time to create 1 rle with old method : 0.0532376766204834 length of segment : 304 time for calcul the mask position with numpy : 0.00045680999755859375 nb_pixel_total : 12345 time to create 1 rle with old method : 0.014510154724121094 length of segment : 249 time for calcul the mask position with numpy : 0.04191994667053223 nb_pixel_total : 10325 time to create 1 rle with old method : 0.016684293746948242 length of segment : 139 time for calcul the mask position with numpy : 0.02842402458190918 nb_pixel_total : 36542 time to create 1 rle with old method : 0.04794025421142578 length of segment : 321 time for calcul the mask position with numpy : 0.04983663558959961 nb_pixel_total : 12604 time to create 1 rle with old method : 0.019594907760620117 length of segment : 172 time for calcul the mask position with numpy : 0.01316213607788086 nb_pixel_total : 18557 time to create 1 rle with old method : 0.030353784561157227 length of segment : 255 time for calcul the mask position with numpy : 0.1318981647491455 nb_pixel_total : 45143 time to create 1 rle with old method : 0.05466198921203613 length of segment : 434 time for calcul the mask position with numpy : 0.0757913589477539 nb_pixel_total : 28506 time to create 1 rle with old method : 0.03620624542236328 length of segment : 171 time for calcul the mask position with numpy : 0.028542518615722656 nb_pixel_total : 19338 time to create 1 rle with old method : 0.02810955047607422 length of segment : 145 time for calcul the mask position with numpy : 0.03380441665649414 nb_pixel_total : 9604 time to create 1 rle with old method : 0.016321182250976562 length of segment : 112 time for calcul the mask position with numpy : 0.038461923599243164 nb_pixel_total : 35458 time to create 1 rle with old method : 0.050124406814575195 length of segment : 309 time for calcul the mask position with numpy : 0.03330707550048828 nb_pixel_total : 13719 time to create 1 rle with old method : 0.019792556762695312 length of segment : 165 time for calcul the mask position with numpy : 0.023419857025146484 nb_pixel_total : 12341 time to create 1 rle with old method : 0.01808905601501465 length of segment : 223 time for calcul the mask position with numpy : 0.17193174362182617 nb_pixel_total : 94602 time to create 1 rle with old method : 0.11971330642700195 length of segment : 460 time for calcul the mask position with numpy : 0.022721052169799805 nb_pixel_total : 18924 time to create 1 rle with old method : 0.025092124938964844 length of segment : 258 time for calcul the mask position with numpy : 0.1329941749572754 nb_pixel_total : 76527 time to create 1 rle with old method : 0.09360575675964355 length of segment : 281 time for calcul the mask position with numpy : 0.06472277641296387 nb_pixel_total : 20058 time to create 1 rle with old method : 0.02387523651123047 length of segment : 230 time for calcul the mask position with numpy : 0.008880138397216797 nb_pixel_total : 21149 time to create 1 rle with old method : 0.029091835021972656 length of segment : 257 time for calcul the mask position with numpy : 0.04563331604003906 nb_pixel_total : 33606 time to create 1 rle with old method : 0.04407668113708496 length of segment : 240 time for calcul the mask position with numpy : 0.0003039836883544922 nb_pixel_total : 5916 time to create 1 rle with old method : 0.006694793701171875 length of segment : 99 time for calcul the mask position with numpy : 0.005265712738037109 nb_pixel_total : 2744 time to create 1 rle with old method : 0.005075693130493164 length of segment : 43 time for calcul the mask position with numpy : 0.4832131862640381 nb_pixel_total : 285626 time to create 1 rle with new method : 0.030141592025756836 length of segment : 941 time for calcul the mask position with numpy : 0.007708311080932617 nb_pixel_total : 19487 time to create 1 rle with old method : 0.02303171157836914 length of segment : 186 time for calcul the mask position with numpy : 0.11754012107849121 nb_pixel_total : 49242 time to create 1 rle with old method : 0.06455349922180176 length of segment : 372 time for calcul the mask position with numpy : 0.03034210205078125 nb_pixel_total : 21204 time to create 1 rle with old method : 0.027397632598876953 length of segment : 194 time for calcul the mask position with numpy : 0.03604936599731445 nb_pixel_total : 20802 time to create 1 rle with old method : 0.025270938873291016 length of segment : 233 time for calcul the mask position with numpy : 0.05637931823730469 nb_pixel_total : 18668 time to create 1 rle with old method : 0.023865222930908203 length of segment : 317 time for calcul the mask position with numpy : 0.016452789306640625 nb_pixel_total : 22258 time to create 1 rle with old method : 0.0287017822265625 length of segment : 281 time for calcul the mask position with numpy : 0.004782199859619141 nb_pixel_total : 29890 time to create 1 rle with old method : 0.035808563232421875 length of segment : 198 time for calcul the mask position with numpy : 0.05207943916320801 nb_pixel_total : 32401 time to create 1 rle with old method : 0.0398404598236084 length of segment : 294 time for calcul the mask position with numpy : 0.025554656982421875 nb_pixel_total : 29947 time to create 1 rle with old method : 0.037154436111450195 length of segment : 221 time for calcul the mask position with numpy : 0.43727946281433105 nb_pixel_total : 318273 time to create 1 rle with new method : 0.048407793045043945 length of segment : 1066 time for calcul the mask position with numpy : 0.035758018493652344 nb_pixel_total : 22021 time to create 1 rle with old method : 0.028838396072387695 length of segment : 202 time for calcul the mask position with numpy : 0.09070920944213867 nb_pixel_total : 80063 time to create 1 rle with old method : 0.09077596664428711 length of segment : 346 time for calcul the mask position with numpy : 0.043399810791015625 nb_pixel_total : 30352 time to create 1 rle with old method : 0.03933310508728027 length of segment : 243 time for calcul the mask position with numpy : 0.20540380477905273 nb_pixel_total : 40724 time to create 1 rle with old method : 0.04751873016357422 length of segment : 482 time for calcul the mask position with numpy : 0.05236625671386719 nb_pixel_total : 25961 time to create 1 rle with old method : 0.036121368408203125 length of segment : 199 time for calcul the mask position with numpy : 0.0007085800170898438 nb_pixel_total : 31601 time to create 1 rle with old method : 0.035310983657836914 length of segment : 287 time for calcul the mask position with numpy : 0.1508018970489502 nb_pixel_total : 127987 time to create 1 rle with old method : 0.14446616172790527 length of segment : 379 time for calcul the mask position with numpy : 0.11665582656860352 nb_pixel_total : 113604 time to create 1 rle with old method : 0.1518723964691162 length of segment : 420 time for calcul the mask position with numpy : 0.13221025466918945 nb_pixel_total : 188836 time to create 1 rle with new method : 0.023948192596435547 length of segment : 601 time for calcul the mask position with numpy : 0.14059662818908691 nb_pixel_total : 79452 time to create 1 rle with old method : 0.11910605430603027 length of segment : 376 time for calcul the mask position with numpy : 0.0016870498657226562 nb_pixel_total : 16743 time to create 1 rle with old method : 0.01857447624206543 length of segment : 225 time for calcul the mask position with numpy : 0.07680726051330566 nb_pixel_total : 44064 time to create 1 rle with old method : 0.05416607856750488 length of segment : 286 time for calcul the mask position with numpy : 0.07662463188171387 nb_pixel_total : 41731 time to create 1 rle with old method : 0.16017460823059082 length of segment : 295 time for calcul the mask position with numpy : 0.0494532585144043 nb_pixel_total : 20439 time to create 1 rle with old method : 0.04022097587585449 length of segment : 216 time for calcul the mask position with numpy : 0.00604701042175293 nb_pixel_total : 22188 time to create 1 rle with old method : 0.04120063781738281 length of segment : 173 time for calcul the mask position with numpy : 0.11394405364990234 nb_pixel_total : 225957 time to create 1 rle with new method : 0.02054286003112793 length of segment : 658 time for calcul the mask position with numpy : 0.058792829513549805 nb_pixel_total : 46109 time to create 1 rle with old method : 0.08406400680541992 length of segment : 301 time for calcul the mask position with numpy : 0.2641432285308838 nb_pixel_total : 183381 time to create 1 rle with new method : 0.009282827377319336 length of segment : 459 time for calcul the mask position with numpy : 0.16306853294372559 nb_pixel_total : 136715 time to create 1 rle with old method : 0.15413665771484375 length of segment : 956 time for calcul the mask position with numpy : 0.24516510963439941 nb_pixel_total : 80237 time to create 1 rle with old method : 0.09444332122802734 length of segment : 288 time for calcul the mask position with numpy : 0.023371458053588867 nb_pixel_total : 42519 time to create 1 rle with old method : 0.05000877380371094 length of segment : 335 time for calcul the mask position with numpy : 0.0025424957275390625 nb_pixel_total : 12594 time to create 1 rle with old method : 0.014566421508789062 length of segment : 255 time for calcul the mask position with numpy : 0.04497027397155762 nb_pixel_total : 85311 time to create 1 rle with old method : 0.0953056812286377 length of segment : 385 time for calcul the mask position with numpy : 0.01678919792175293 nb_pixel_total : 78887 time to create 1 rle with old method : 0.09281039237976074 length of segment : 269 time for calcul the mask position with numpy : 0.03652501106262207 nb_pixel_total : 11678 time to create 1 rle with old method : 0.016646385192871094 length of segment : 137 time for calcul the mask position with numpy : 0.0004127025604248047 nb_pixel_total : 16605 time to create 1 rle with old method : 0.019191265106201172 length of segment : 134 time for calcul the mask position with numpy : 0.032364845275878906 nb_pixel_total : 27043 time to create 1 rle with old method : 0.0350193977355957 length of segment : 175 time for calcul the mask position with numpy : 0.02229166030883789 nb_pixel_total : 3446 time to create 1 rle with old method : 0.00738215446472168 length of segment : 117 time for calcul the mask position with numpy : 0.02807140350341797 nb_pixel_total : 15540 time to create 1 rle with old method : 0.030974864959716797 length of segment : 182 time for calcul the mask position with numpy : 0.01892995834350586 nb_pixel_total : 10306 time to create 1 rle with old method : 0.018788576126098633 length of segment : 193 time for calcul the mask position with numpy : 0.11795258522033691 nb_pixel_total : 122565 time to create 1 rle with old method : 0.14225292205810547 length of segment : 607 time for calcul the mask position with numpy : 0.06796503067016602 nb_pixel_total : 51241 time to create 1 rle with old method : 0.06460785865783691 length of segment : 268 time for calcul the mask position with numpy : 0.004117250442504883 nb_pixel_total : 12386 time to create 1 rle with old method : 0.0191800594329834 length of segment : 176 time for calcul the mask position with numpy : 0.06491899490356445 nb_pixel_total : 52645 time to create 1 rle with old method : 0.06433701515197754 length of segment : 260 time for calcul the mask position with numpy : 0.1959671974182129 nb_pixel_total : 200620 time to create 1 rle with new method : 0.017989635467529297 length of segment : 600 time for calcul the mask position with numpy : 0.027825593948364258 nb_pixel_total : 17001 time to create 1 rle with old method : 0.02479243278503418 length of segment : 123 time for calcul the mask position with numpy : 0.021783828735351562 nb_pixel_total : 8161 time to create 1 rle with old method : 0.009411334991455078 length of segment : 104 time for calcul the mask position with numpy : 0.02053356170654297 nb_pixel_total : 14833 time to create 1 rle with old method : 0.02100229263305664 length of segment : 102 time for calcul the mask position with numpy : 0.043848276138305664 nb_pixel_total : 34632 time to create 1 rle with old method : 0.03898000717163086 length of segment : 335 time for calcul the mask position with numpy : 0.021656274795532227 nb_pixel_total : 108906 time to create 1 rle with old method : 0.12040448188781738 length of segment : 508 time for calcul the mask position with numpy : 0.041348934173583984 nb_pixel_total : 47040 time to create 1 rle with old method : 0.0672152042388916 length of segment : 471 time for calcul the mask position with numpy : 0.21258091926574707 nb_pixel_total : 209138 time to create 1 rle with new method : 0.01572585105895996 length of segment : 592 time for calcul the mask position with numpy : 0.0217282772064209 nb_pixel_total : 17298 time to create 1 rle with old method : 0.025168895721435547 length of segment : 258 time for calcul the mask position with numpy : 0.015031814575195312 nb_pixel_total : 105054 time to create 1 rle with old method : 0.14448904991149902 length of segment : 546 time for calcul the mask position with numpy : 0.02916097640991211 nb_pixel_total : 43344 time to create 1 rle with old method : 0.05244874954223633 length of segment : 293 time for calcul the mask position with numpy : 0.0067291259765625 nb_pixel_total : 15493 time to create 1 rle with old method : 0.019972801208496094 length of segment : 293 time for calcul the mask position with numpy : 0.025406360626220703 nb_pixel_total : 133889 time to create 1 rle with old method : 0.19429802894592285 length of segment : 383 time for calcul the mask position with numpy : 0.004739046096801758 nb_pixel_total : 13683 time to create 1 rle with old method : 0.01746511459350586 length of segment : 148 time for calcul the mask position with numpy : 0.04633045196533203 nb_pixel_total : 35840 time to create 1 rle with old method : 0.045003652572631836 length of segment : 187 time for calcul the mask position with numpy : 0.007628679275512695 nb_pixel_total : 3680 time to create 1 rle with old method : 0.005541324615478516 length of segment : 107 time for calcul the mask position with numpy : 0.04982566833496094 nb_pixel_total : 42319 time to create 1 rle with old method : 0.05153489112854004 length of segment : 289 time for calcul the mask position with numpy : 0.12389945983886719 nb_pixel_total : 88274 time to create 1 rle with old method : 0.10112261772155762 length of segment : 389 time for calcul the mask position with numpy : 0.0006728172302246094 nb_pixel_total : 9749 time to create 1 rle with old method : 0.010945796966552734 length of segment : 97 time for calcul the mask position with numpy : 0.005111217498779297 nb_pixel_total : 39580 time to create 1 rle with old method : 0.04427075386047363 length of segment : 309 time for calcul the mask position with numpy : 0.015448570251464844 nb_pixel_total : 11826 time to create 1 rle with old method : 0.01760125160217285 length of segment : 230 time for calcul the mask position with numpy : 0.0012888908386230469 nb_pixel_total : 26845 time to create 1 rle with old method : 0.0387721061706543 length of segment : 200 time for calcul the mask position with numpy : 0.1263871192932129 nb_pixel_total : 125627 time to create 1 rle with old method : 0.1444258689880371 length of segment : 424 time for calcul the mask position with numpy : 0.0028541088104248047 nb_pixel_total : 16933 time to create 1 rle with old method : 0.019658327102661133 length of segment : 118 time for calcul the mask position with numpy : 0.01828312873840332 nb_pixel_total : 14639 time to create 1 rle with old method : 0.02080559730529785 length of segment : 171 time for calcul the mask position with numpy : 0.04234671592712402 nb_pixel_total : 22882 time to create 1 rle with old method : 0.031014442443847656 length of segment : 147 time for calcul the mask position with numpy : 0.01906752586364746 nb_pixel_total : 43963 time to create 1 rle with old method : 0.07341480255126953 length of segment : 245 time for calcul the mask position with numpy : 0.0028679370880126953 nb_pixel_total : 63734 time to create 1 rle with old method : 0.07139968872070312 length of segment : 267 time for calcul the mask position with numpy : 0.044066667556762695 nb_pixel_total : 14875 time to create 1 rle with old method : 0.025503158569335938 length of segment : 141 time for calcul the mask position with numpy : 0.0003783702850341797 nb_pixel_total : 7840 time to create 1 rle with old method : 0.009748697280883789 length of segment : 108 time for calcul the mask position with numpy : 0.058129072189331055 nb_pixel_total : 200598 time to create 1 rle with new method : 0.015421152114868164 length of segment : 634 time for calcul the mask position with numpy : 0.006781578063964844 nb_pixel_total : 44285 time to create 1 rle with old method : 0.051317453384399414 length of segment : 178 time for calcul the mask position with numpy : 0.03812074661254883 nb_pixel_total : 8254 time to create 1 rle with old method : 0.014150142669677734 length of segment : 302 time for calcul the mask position with numpy : 0.003937482833862305 nb_pixel_total : 25845 time to create 1 rle with old method : 0.03323721885681152 length of segment : 246 time for calcul the mask position with numpy : 0.0005171298980712891 nb_pixel_total : 9134 time to create 1 rle with old method : 0.010444164276123047 length of segment : 109 time for calcul the mask position with numpy : 0.0009424686431884766 nb_pixel_total : 35620 time to create 1 rle with old method : 0.04243803024291992 length of segment : 289 time for calcul the mask position with numpy : 0.02251577377319336 nb_pixel_total : 62271 time to create 1 rle with old method : 0.07094144821166992 length of segment : 371 time for calcul the mask position with numpy : 0.005265951156616211 nb_pixel_total : 58748 time to create 1 rle with old method : 0.06833934783935547 length of segment : 500 time for calcul the mask position with numpy : 0.0002200603485107422 nb_pixel_total : 4530 time to create 1 rle with old method : 0.0052411556243896484 length of segment : 76 time for calcul the mask position with numpy : 0.0002422332763671875 nb_pixel_total : 2143 time to create 1 rle with old method : 0.0026540756225585938 length of segment : 62 time for calcul the mask position with numpy : 0.000705718994140625 nb_pixel_total : 29368 time to create 1 rle with old method : 0.035309791564941406 length of segment : 218 time for calcul the mask position with numpy : 0.0004010200500488281 nb_pixel_total : 16496 time to create 1 rle with old method : 0.020832538604736328 length of segment : 128 time for calcul the mask position with numpy : 0.003573179244995117 nb_pixel_total : 72199 time to create 1 rle with old method : 0.11496257781982422 length of segment : 301 time for calcul the mask position with numpy : 0.04611349105834961 nb_pixel_total : 129353 time to create 1 rle with old method : 0.15154075622558594 length of segment : 904 time for calcul the mask position with numpy : 0.06716752052307129 nb_pixel_total : 25120 time to create 1 rle with old method : 0.03307223320007324 length of segment : 361 time for calcul the mask position with numpy : 0.00820159912109375 nb_pixel_total : 19492 time to create 1 rle with old method : 0.02528548240661621 length of segment : 103 time for calcul the mask position with numpy : 0.0003635883331298828 nb_pixel_total : 9230 time to create 1 rle with old method : 0.01096963882446289 length of segment : 103 time for calcul the mask position with numpy : 0.0018253326416015625 nb_pixel_total : 26213 time to create 1 rle with old method : 0.029575586318969727 length of segment : 263 time for calcul the mask position with numpy : 0.06128811836242676 nb_pixel_total : 163730 time to create 1 rle with new method : 0.011081933975219727 length of segment : 460 time for calcul the mask position with numpy : 0.07420182228088379 nb_pixel_total : 195525 time to create 1 rle with new method : 0.021802186965942383 length of segment : 889 time for calcul the mask position with numpy : 0.0011334419250488281 nb_pixel_total : 21863 time to create 1 rle with old method : 0.02446603775024414 length of segment : 179 time for calcul the mask position with numpy : 0.0021855831146240234 nb_pixel_total : 48047 time to create 1 rle with old method : 0.053708791732788086 length of segment : 274 time for calcul the mask position with numpy : 0.008195161819458008 nb_pixel_total : 31835 time to create 1 rle with old method : 0.03735518455505371 length of segment : 234 time for calcul the mask position with numpy : 0.03654193878173828 nb_pixel_total : 66286 time to create 1 rle with old method : 0.07682180404663086 length of segment : 198 time for calcul the mask position with numpy : 0.1353318691253662 nb_pixel_total : 507601 time to create 1 rle with new method : 0.27021193504333496 length of segment : 827 time for calcul the mask position with numpy : 0.024320602416992188 nb_pixel_total : 20177 time to create 1 rle with old method : 0.031035423278808594 length of segment : 240 time for calcul the mask position with numpy : 0.0064928531646728516 nb_pixel_total : 6809 time to create 1 rle with old method : 0.008351325988769531 length of segment : 70 time for calcul the mask position with numpy : 0.040897369384765625 nb_pixel_total : 168033 time to create 1 rle with new method : 0.007845640182495117 length of segment : 700 time for calcul the mask position with numpy : 0.021726131439208984 nb_pixel_total : 16632 time to create 1 rle with old method : 0.02315044403076172 length of segment : 192 time for calcul the mask position with numpy : 0.03217434883117676 nb_pixel_total : 19963 time to create 1 rle with old method : 0.0270843505859375 length of segment : 143 time for calcul the mask position with numpy : 0.1596839427947998 nb_pixel_total : 142959 time to create 1 rle with old method : 0.15701913833618164 length of segment : 497 time for calcul the mask position with numpy : 0.007085084915161133 nb_pixel_total : 35664 time to create 1 rle with old method : 0.05222034454345703 length of segment : 156 time for calcul the mask position with numpy : 0.0025708675384521484 nb_pixel_total : 4219 time to create 1 rle with old method : 0.005457401275634766 length of segment : 177 time for calcul the mask position with numpy : 0.02520585060119629 nb_pixel_total : 16488 time to create 1 rle with old method : 0.022980213165283203 length of segment : 142 time for calcul the mask position with numpy : 0.006355762481689453 nb_pixel_total : 12565 time to create 1 rle with old method : 0.016384601593017578 length of segment : 119 time for calcul the mask position with numpy : 0.020062685012817383 nb_pixel_total : 24733 time to create 1 rle with old method : 0.03244376182556152 length of segment : 335 time for calcul the mask position with numpy : 0.029173851013183594 nb_pixel_total : 58236 time to create 1 rle with old method : 0.06942176818847656 length of segment : 193 time for calcul the mask position with numpy : 0.04206204414367676 nb_pixel_total : 66775 time to create 1 rle with old method : 0.07592415809631348 length of segment : 412 time for calcul the mask position with numpy : 0.0010848045349121094 nb_pixel_total : 9058 time to create 1 rle with old method : 0.010468721389770508 length of segment : 139 time for calcul the mask position with numpy : 0.0032553672790527344 nb_pixel_total : 12983 time to create 1 rle with old method : 0.01621222496032715 length of segment : 222 time for calcul the mask position with numpy : 0.002299785614013672 nb_pixel_total : 8389 time to create 1 rle with old method : 0.011320829391479492 length of segment : 125 time for calcul the mask position with numpy : 0.0774223804473877 nb_pixel_total : 94145 time to create 1 rle with old method : 0.10632038116455078 length of segment : 383 time for calcul the mask position with numpy : 0.020971298217773438 nb_pixel_total : 75479 time to create 1 rle with old method : 0.08783984184265137 length of segment : 351 time for calcul the mask position with numpy : 0.026247262954711914 nb_pixel_total : 15118 time to create 1 rle with old method : 0.02427959442138672 length of segment : 204 time for calcul the mask position with numpy : 0.0054399967193603516 nb_pixel_total : 65136 time to create 1 rle with old method : 0.09125757217407227 length of segment : 366 time for calcul the mask position with numpy : 0.0064280033111572266 nb_pixel_total : 34228 time to create 1 rle with old method : 0.04150390625 length of segment : 194 time for calcul the mask position with numpy : 0.006694793701171875 nb_pixel_total : 16088 time to create 1 rle with old method : 0.02110147476196289 length of segment : 152 time for calcul the mask position with numpy : 0.018042325973510742 nb_pixel_total : 51661 time to create 1 rle with old method : 0.08114790916442871 length of segment : 325 time for calcul the mask position with numpy : 0.029963016510009766 nb_pixel_total : 109461 time to create 1 rle with old method : 0.13043618202209473 length of segment : 451 time for calcul the mask position with numpy : 0.019534587860107422 nb_pixel_total : 58392 time to create 1 rle with old method : 0.07045674324035645 length of segment : 571 time for calcul the mask position with numpy : 0.00432896614074707 nb_pixel_total : 12548 time to create 1 rle with old method : 0.017299652099609375 length of segment : 201 time for calcul the mask position with numpy : 0.0210721492767334 nb_pixel_total : 19531 time to create 1 rle with old method : 0.02816915512084961 length of segment : 138 time for calcul the mask position with numpy : 0.028606176376342773 nb_pixel_total : 85743 time to create 1 rle with old method : 0.102203369140625 length of segment : 554 time for calcul the mask position with numpy : 0.01943492889404297 nb_pixel_total : 58389 time to create 1 rle with old method : 0.06348514556884766 length of segment : 421 time for calcul the mask position with numpy : 0.00799250602722168 nb_pixel_total : 25339 time to create 1 rle with old method : 0.030889272689819336 length of segment : 404 time for calcul the mask position with numpy : 0.0025360584259033203 nb_pixel_total : 8550 time to create 1 rle with old method : 0.00977635383605957 length of segment : 69 time for calcul the mask position with numpy : 0.10173702239990234 nb_pixel_total : 93519 time to create 1 rle with old method : 0.10642290115356445 length of segment : 501 time for calcul the mask position with numpy : 0.01663684844970703 nb_pixel_total : 87237 time to create 1 rle with old method : 0.1179499626159668 length of segment : 600 time for calcul the mask position with numpy : 0.0034906864166259766 nb_pixel_total : 7999 time to create 1 rle with old method : 0.011095285415649414 length of segment : 138 time for calcul the mask position with numpy : 0.006022930145263672 nb_pixel_total : 23803 time to create 1 rle with old method : 0.02880096435546875 length of segment : 236 time for calcul the mask position with numpy : 0.001988649368286133 nb_pixel_total : 17072 time to create 1 rle with old method : 0.02739095687866211 length of segment : 129 time for calcul the mask position with numpy : 0.0034286975860595703 nb_pixel_total : 15065 time to create 1 rle with old method : 0.020022869110107422 length of segment : 197 time for calcul the mask position with numpy : 0.006259918212890625 nb_pixel_total : 11327 time to create 1 rle with old method : 0.01455068588256836 length of segment : 232 time for calcul the mask position with numpy : 0.0005085468292236328 nb_pixel_total : 6481 time to create 1 rle with old method : 0.007208824157714844 length of segment : 80 time for calcul the mask position with numpy : 0.0005185604095458984 nb_pixel_total : 18423 time to create 1 rle with old method : 0.021802186965942383 length of segment : 188 time for calcul the mask position with numpy : 0.001714468002319336 nb_pixel_total : 6213 time to create 1 rle with old method : 0.0069925785064697266 length of segment : 108 time for calcul the mask position with numpy : 0.0026340484619140625 nb_pixel_total : 13114 time to create 1 rle with old method : 0.014612913131713867 length of segment : 152 time for calcul the mask position with numpy : 0.0015959739685058594 nb_pixel_total : 5128 time to create 1 rle with old method : 0.006972074508666992 length of segment : 80 time for calcul the mask position with numpy : 0.004502296447753906 nb_pixel_total : 18262 time to create 1 rle with old method : 0.02237224578857422 length of segment : 188 time for calcul the mask position with numpy : 0.0028591156005859375 nb_pixel_total : 15045 time to create 1 rle with old method : 0.019436120986938477 length of segment : 93 time for calcul the mask position with numpy : 0.0034084320068359375 nb_pixel_total : 6049 time to create 1 rle with old method : 0.0076711177825927734 length of segment : 90 time for calcul the mask position with numpy : 0.00725865364074707 nb_pixel_total : 165570 time to create 1 rle with new method : 0.01004171371459961 length of segment : 731 time for calcul the mask position with numpy : 0.07201957702636719 nb_pixel_total : 228730 time to create 1 rle with new method : 0.017268657684326172 length of segment : 555 time for calcul the mask position with numpy : 0.0332179069519043 nb_pixel_total : 37413 time to create 1 rle with old method : 0.05204939842224121 length of segment : 411 time for calcul the mask position with numpy : 0.0033082962036132812 nb_pixel_total : 3901 time to create 1 rle with old method : 0.008053779602050781 length of segment : 193 time for calcul the mask position with numpy : 0.024877309799194336 nb_pixel_total : 75618 time to create 1 rle with old method : 0.08533120155334473 length of segment : 429 time for calcul the mask position with numpy : 0.0002765655517578125 nb_pixel_total : 6856 time to create 1 rle with old method : 0.007918596267700195 length of segment : 190 time for calcul the mask position with numpy : 0.0007810592651367188 nb_pixel_total : 13876 time to create 1 rle with old method : 0.016772031784057617 length of segment : 226 time for calcul the mask position with numpy : 0.0106048583984375 nb_pixel_total : 130834 time to create 1 rle with old method : 0.1640784740447998 length of segment : 437 time for calcul the mask position with numpy : 0.10590386390686035 nb_pixel_total : 418203 time to create 1 rle with new method : 0.03336906433105469 length of segment : 715 time for calcul the mask position with numpy : 0.015522956848144531 nb_pixel_total : 53085 time to create 1 rle with old method : 0.062221527099609375 length of segment : 283 time for calcul the mask position with numpy : 0.000982046127319336 nb_pixel_total : 6750 time to create 1 rle with old method : 0.007726430892944336 length of segment : 141 time for calcul the mask position with numpy : 0.007034778594970703 nb_pixel_total : 21071 time to create 1 rle with old method : 0.026038169860839844 length of segment : 171 time for calcul the mask position with numpy : 0.02415919303894043 nb_pixel_total : 113772 time to create 1 rle with old method : 0.1390068531036377 length of segment : 493 time for calcul the mask position with numpy : 0.00864553451538086 nb_pixel_total : 48918 time to create 1 rle with old method : 0.05599522590637207 length of segment : 329 time for calcul the mask position with numpy : 0.002340555191040039 nb_pixel_total : 15990 time to create 1 rle with old method : 0.01935100555419922 length of segment : 129 time for calcul the mask position with numpy : 0.0057566165924072266 nb_pixel_total : 238309 time to create 1 rle with new method : 0.016042709350585938 length of segment : 612 time for calcul the mask position with numpy : 0.042186737060546875 nb_pixel_total : 46258 time to create 1 rle with old method : 0.057358503341674805 length of segment : 270 time for calcul the mask position with numpy : 0.002359628677368164 nb_pixel_total : 6353 time to create 1 rle with old method : 0.007227897644042969 length of segment : 95 time for calcul the mask position with numpy : 0.001901388168334961 nb_pixel_total : 7753 time to create 1 rle with old method : 0.008745431900024414 length of segment : 119 time for calcul the mask position with numpy : 0.008433818817138672 nb_pixel_total : 21638 time to create 1 rle with old method : 0.025067567825317383 length of segment : 348 time for calcul the mask position with numpy : 0.02987384796142578 nb_pixel_total : 39379 time to create 1 rle with old method : 0.056969642639160156 length of segment : 442 time for calcul the mask position with numpy : 0.0005702972412109375 nb_pixel_total : 15197 time to create 1 rle with old method : 0.020028352737426758 length of segment : 196 time for calcul the mask position with numpy : 0.008448362350463867 nb_pixel_total : 21390 time to create 1 rle with old method : 0.02798295021057129 length of segment : 149 time for calcul the mask position with numpy : 0.0030672550201416016 nb_pixel_total : 17728 time to create 1 rle with old method : 0.020421743392944336 length of segment : 80 time for calcul the mask position with numpy : 0.03901386260986328 nb_pixel_total : 59566 time to create 1 rle with old method : 0.06709527969360352 length of segment : 382 time for calcul the mask position with numpy : 0.12900424003601074 nb_pixel_total : 156365 time to create 1 rle with new method : 0.008890867233276367 length of segment : 885 time for calcul the mask position with numpy : 0.1148223876953125 nb_pixel_total : 129101 time to create 1 rle with old method : 0.1384575366973877 length of segment : 525 time for calcul the mask position with numpy : 0.020569801330566406 nb_pixel_total : 22034 time to create 1 rle with old method : 0.0279538631439209 length of segment : 207 time for calcul the mask position with numpy : 0.12145662307739258 nb_pixel_total : 119052 time to create 1 rle with old method : 0.13349199295043945 length of segment : 448 time for calcul the mask position with numpy : 0.007112026214599609 nb_pixel_total : 37867 time to create 1 rle with old method : 0.04288983345031738 length of segment : 197 time for calcul the mask position with numpy : 0.008713006973266602 nb_pixel_total : 32373 time to create 1 rle with old method : 0.05485248565673828 length of segment : 221 time for calcul the mask position with numpy : 0.07425761222839355 nb_pixel_total : 143008 time to create 1 rle with old method : 0.15329456329345703 length of segment : 655 time for calcul the mask position with numpy : 0.00014781951904296875 nb_pixel_total : 6273 time to create 1 rle with old method : 0.009589910507202148 length of segment : 105 time for calcul the mask position with numpy : 0.01693892478942871 nb_pixel_total : 178006 time to create 1 rle with new method : 0.009650230407714844 length of segment : 624 time for calcul the mask position with numpy : 0.02263808250427246 nb_pixel_total : 93408 time to create 1 rle with old method : 0.1044924259185791 length of segment : 396 time for calcul the mask position with numpy : 0.012002706527709961 nb_pixel_total : 25771 time to create 1 rle with old method : 0.03206324577331543 length of segment : 207 time for calcul the mask position with numpy : 0.0017397403717041016 nb_pixel_total : 13218 time to create 1 rle with old method : 0.014011621475219727 length of segment : 139 time for calcul the mask position with numpy : 0.0041811466217041016 nb_pixel_total : 26570 time to create 1 rle with old method : 0.030438899993896484 length of segment : 272 time for calcul the mask position with numpy : 0.015064239501953125 nb_pixel_total : 81427 time to create 1 rle with old method : 0.09121990203857422 length of segment : 384 time for calcul the mask position with numpy : 0.025554180145263672 nb_pixel_total : 8618 time to create 1 rle with old method : 0.014099359512329102 length of segment : 110 time for calcul the mask position with numpy : 0.03488516807556152 nb_pixel_total : 12876 time to create 1 rle with old method : 0.01850128173828125 length of segment : 136 time for calcul the mask position with numpy : 0.09763813018798828 nb_pixel_total : 108319 time to create 1 rle with old method : 0.11764335632324219 length of segment : 436 time for calcul the mask position with numpy : 0.010037422180175781 nb_pixel_total : 12441 time to create 1 rle with old method : 0.018439531326293945 length of segment : 117 time for calcul the mask position with numpy : 0.01916050910949707 nb_pixel_total : 66946 time to create 1 rle with old method : 0.07614827156066895 length of segment : 218 time for calcul the mask position with numpy : 0.06255340576171875 nb_pixel_total : 63093 time to create 1 rle with old method : 0.07321667671203613 length of segment : 263 time for calcul the mask position with numpy : 0.07128190994262695 nb_pixel_total : 89364 time to create 1 rle with old method : 0.10300040245056152 length of segment : 553 time for calcul the mask position with numpy : 0.02959132194519043 nb_pixel_total : 54671 time to create 1 rle with old method : 0.06634163856506348 length of segment : 194 time for calcul the mask position with numpy : 0.05329442024230957 nb_pixel_total : 28841 time to create 1 rle with old method : 0.03708958625793457 length of segment : 231 time for calcul the mask position with numpy : 0.08596920967102051 nb_pixel_total : 82481 time to create 1 rle with old method : 0.12383508682250977 length of segment : 672 time for calcul the mask position with numpy : 0.019727230072021484 nb_pixel_total : 74738 time to create 1 rle with old method : 0.08399438858032227 length of segment : 450 time for calcul the mask position with numpy : 0.010195255279541016 nb_pixel_total : 77656 time to create 1 rle with old method : 0.08793115615844727 length of segment : 212 time for calcul the mask position with numpy : 0.18172693252563477 nb_pixel_total : 377787 time to create 1 rle with new method : 0.07309985160827637 length of segment : 508 time for calcul the mask position with numpy : 0.06358957290649414 nb_pixel_total : 89621 time to create 1 rle with old method : 0.10245203971862793 length of segment : 416 time for calcul the mask position with numpy : 0.025951623916625977 nb_pixel_total : 57845 time to create 1 rle with old method : 0.06681299209594727 length of segment : 438 time for calcul the mask position with numpy : 0.0006814002990722656 nb_pixel_total : 16758 time to create 1 rle with old method : 0.018691301345825195 length of segment : 140 time for calcul the mask position with numpy : 0.026249408721923828 nb_pixel_total : 118780 time to create 1 rle with old method : 0.13322973251342773 length of segment : 1089 time for calcul the mask position with numpy : 0.0004069805145263672 nb_pixel_total : 21405 time to create 1 rle with old method : 0.02375030517578125 length of segment : 167 time for calcul the mask position with numpy : 0.011176347732543945 nb_pixel_total : 74756 time to create 1 rle with old method : 0.08530402183532715 length of segment : 497 time for calcul the mask position with numpy : 0.006818294525146484 nb_pixel_total : 59667 time to create 1 rle with old method : 0.06767535209655762 length of segment : 251 time for calcul the mask position with numpy : 0.09659171104431152 nb_pixel_total : 66316 time to create 1 rle with old method : 0.0826103687286377 length of segment : 252 time for calcul the mask position with numpy : 0.025263547897338867 nb_pixel_total : 27405 time to create 1 rle with old method : 0.032402992248535156 length of segment : 240 time for calcul the mask position with numpy : 0.15533947944641113 nb_pixel_total : 94544 time to create 1 rle with old method : 0.11458277702331543 length of segment : 321 time for calcul the mask position with numpy : 0.037712812423706055 nb_pixel_total : 25359 time to create 1 rle with old method : 0.03225374221801758 length of segment : 356 time for calcul the mask position with numpy : 0.05246114730834961 nb_pixel_total : 19369 time to create 1 rle with old method : 0.02718639373779297 length of segment : 190 time for calcul the mask position with numpy : 0.0249936580657959 nb_pixel_total : 11123 time to create 1 rle with old method : 0.017805814743041992 length of segment : 156 time for calcul the mask position with numpy : 0.27085018157958984 nb_pixel_total : 103482 time to create 1 rle with old method : 0.11682796478271484 length of segment : 519 time for calcul the mask position with numpy : 0.10879635810852051 nb_pixel_total : 57245 time to create 1 rle with old method : 0.06504297256469727 length of segment : 348 time for calcul the mask position with numpy : 0.04017782211303711 nb_pixel_total : 26718 time to create 1 rle with old method : 0.03602910041809082 length of segment : 149 time for calcul the mask position with numpy : 0.05236077308654785 nb_pixel_total : 18632 time to create 1 rle with old method : 0.02504277229309082 length of segment : 331 time for calcul the mask position with numpy : 0.09733343124389648 nb_pixel_total : 50875 time to create 1 rle with old method : 0.06333374977111816 length of segment : 334 time for calcul the mask position with numpy : 0.021663665771484375 nb_pixel_total : 27973 time to create 1 rle with old method : 0.03610491752624512 length of segment : 245 time for calcul the mask position with numpy : 0.019020557403564453 nb_pixel_total : 15474 time to create 1 rle with old method : 0.021460533142089844 length of segment : 139 time for calcul the mask position with numpy : 0.010931968688964844 nb_pixel_total : 18296 time to create 1 rle with old method : 0.023645401000976562 length of segment : 159 time for calcul the mask position with numpy : 0.03203296661376953 nb_pixel_total : 32736 time to create 1 rle with old method : 0.041535139083862305 length of segment : 260 time for calcul the mask position with numpy : 0.0008041858673095703 nb_pixel_total : 10816 time to create 1 rle with old method : 0.012087583541870117 length of segment : 146 time for calcul the mask position with numpy : 0.021590471267700195 nb_pixel_total : 20727 time to create 1 rle with old method : 0.02704453468322754 length of segment : 181 time for calcul the mask position with numpy : 0.012030601501464844 nb_pixel_total : 11373 time to create 1 rle with old method : 0.019250154495239258 length of segment : 109 time for calcul the mask position with numpy : 0.03245186805725098 nb_pixel_total : 29702 time to create 1 rle with old method : 0.038350582122802734 length of segment : 214 time for calcul the mask position with numpy : 0.0364990234375 nb_pixel_total : 34662 time to create 1 rle with old method : 0.03795933723449707 length of segment : 207 time for calcul the mask position with numpy : 0.028450489044189453 nb_pixel_total : 20315 time to create 1 rle with old method : 0.02224421501159668 length of segment : 173 time for calcul the mask position with numpy : 0.01426553726196289 nb_pixel_total : 11839 time to create 1 rle with old method : 0.019028902053833008 length of segment : 159 time for calcul the mask position with numpy : 0.06069350242614746 nb_pixel_total : 80603 time to create 1 rle with old method : 0.09192395210266113 length of segment : 499 time for calcul the mask position with numpy : 0.010226726531982422 nb_pixel_total : 43021 time to create 1 rle with old method : 0.050292253494262695 length of segment : 235 time for calcul the mask position with numpy : 0.07690024375915527 nb_pixel_total : 59548 time to create 1 rle with old method : 0.0656731128692627 length of segment : 437 time for calcul the mask position with numpy : 0.0019693374633789062 nb_pixel_total : 24985 time to create 1 rle with old method : 0.02624678611755371 length of segment : 222 time for calcul the mask position with numpy : 0.008788824081420898 nb_pixel_total : 19087 time to create 1 rle with old method : 0.023450613021850586 length of segment : 161 time for calcul the mask position with numpy : 0.014323711395263672 nb_pixel_total : 14859 time to create 1 rle with old method : 0.021162986755371094 length of segment : 218 time for calcul the mask position with numpy : 0.036586761474609375 nb_pixel_total : 12977 time to create 1 rle with old method : 0.023546695709228516 length of segment : 124 time for calcul the mask position with numpy : 0.057342529296875 nb_pixel_total : 76273 time to create 1 rle with old method : 0.08748936653137207 length of segment : 307 time for calcul the mask position with numpy : 0.00023031234741210938 nb_pixel_total : 5352 time to create 1 rle with old method : 0.0062601566314697266 length of segment : 140 time for calcul the mask position with numpy : 0.01939225196838379 nb_pixel_total : 22152 time to create 1 rle with old method : 0.029921531677246094 length of segment : 217 time for calcul the mask position with numpy : 0.04129815101623535 nb_pixel_total : 39773 time to create 1 rle with old method : 0.050881147384643555 length of segment : 398 time for calcul the mask position with numpy : 0.019202470779418945 nb_pixel_total : 17312 time to create 1 rle with old method : 0.024721860885620117 length of segment : 139 time for calcul the mask position with numpy : 0.026254892349243164 nb_pixel_total : 37147 time to create 1 rle with old method : 0.04775857925415039 length of segment : 209 time for calcul the mask position with numpy : 0.05368781089782715 nb_pixel_total : 72892 time to create 1 rle with old method : 0.08342671394348145 length of segment : 272 time for calcul the mask position with numpy : 0.08228516578674316 nb_pixel_total : 77062 time to create 1 rle with old method : 0.10536408424377441 length of segment : 399 time for calcul the mask position with numpy : 0.0006239414215087891 nb_pixel_total : 17880 time to create 1 rle with old method : 0.02260136604309082 length of segment : 358 time for calcul the mask position with numpy : 0.04955339431762695 nb_pixel_total : 40893 time to create 1 rle with old method : 0.05491828918457031 length of segment : 268 time for calcul the mask position with numpy : 0.06413459777832031 nb_pixel_total : 17153 time to create 1 rle with old method : 0.02287459373474121 length of segment : 153 time for calcul the mask position with numpy : 0.07979655265808105 nb_pixel_total : 17218 time to create 1 rle with old method : 0.035875797271728516 length of segment : 172 time for calcul the mask position with numpy : 0.10808849334716797 nb_pixel_total : 142451 time to create 1 rle with old method : 0.17319941520690918 length of segment : 823 time for calcul the mask position with numpy : 0.054509639739990234 nb_pixel_total : 21850 time to create 1 rle with old method : 0.034209489822387695 length of segment : 205 time for calcul the mask position with numpy : 0.07248806953430176 nb_pixel_total : 31254 time to create 1 rle with old method : 0.04164838790893555 length of segment : 488 time for calcul the mask position with numpy : 0.04802584648132324 nb_pixel_total : 11015 time to create 1 rle with old method : 0.01915287971496582 length of segment : 178 time for calcul the mask position with numpy : 0.022600412368774414 nb_pixel_total : 26216 time to create 1 rle with old method : 0.03799629211425781 length of segment : 168 time for calcul the mask position with numpy : 0.044589996337890625 nb_pixel_total : 38526 time to create 1 rle with old method : 0.052970170974731445 length of segment : 198 time for calcul the mask position with numpy : 0.01282358169555664 nb_pixel_total : 22962 time to create 1 rle with old method : 0.03310728073120117 length of segment : 151 time for calcul the mask position with numpy : 0.07503986358642578 nb_pixel_total : 41299 time to create 1 rle with old method : 0.04791069030761719 length of segment : 310 time for calcul the mask position with numpy : 0.00804591178894043 nb_pixel_total : 49571 time to create 1 rle with old method : 0.057550907135009766 length of segment : 327 time for calcul the mask position with numpy : 0.010744810104370117 nb_pixel_total : 77217 time to create 1 rle with old method : 0.08800864219665527 length of segment : 402 time for calcul the mask position with numpy : 0.09546709060668945 nb_pixel_total : 74485 time to create 1 rle with old method : 0.08417081832885742 length of segment : 369 time for calcul the mask position with numpy : 0.03238964080810547 nb_pixel_total : 52512 time to create 1 rle with old method : 0.06499266624450684 length of segment : 233 time for calcul the mask position with numpy : 0.03421783447265625 nb_pixel_total : 10064 time to create 1 rle with old method : 0.016360759735107422 length of segment : 155 time for calcul the mask position with numpy : 0.0038123130798339844 nb_pixel_total : 27607 time to create 1 rle with old method : 0.028804302215576172 length of segment : 342 time for calcul the mask position with numpy : 0.10008072853088379 nb_pixel_total : 83476 time to create 1 rle with old method : 0.08951020240783691 length of segment : 281 time for calcul the mask position with numpy : 0.006643772125244141 nb_pixel_total : 12902 time to create 1 rle with old method : 0.01682901382446289 length of segment : 192 time for calcul the mask position with numpy : 0.10681462287902832 nb_pixel_total : 140405 time to create 1 rle with old method : 0.17009925842285156 length of segment : 529 time for calcul the mask position with numpy : 0.04321622848510742 nb_pixel_total : 251330 time to create 1 rle with new method : 0.02385401725769043 length of segment : 802 time for calcul the mask position with numpy : 0.00444793701171875 nb_pixel_total : 26759 time to create 1 rle with old method : 0.03154778480529785 length of segment : 243 time for calcul the mask position with numpy : 0.03647422790527344 nb_pixel_total : 16056 time to create 1 rle with old method : 0.029243946075439453 length of segment : 156 time for calcul the mask position with numpy : 0.007691860198974609 nb_pixel_total : 16417 time to create 1 rle with old method : 0.02657294273376465 length of segment : 198 time for calcul the mask position with numpy : 0.003957033157348633 nb_pixel_total : 26286 time to create 1 rle with old method : 0.030435562133789062 length of segment : 281 time for calcul the mask position with numpy : 0.006447792053222656 nb_pixel_total : 19804 time to create 1 rle with old method : 0.02380824089050293 length of segment : 200 time for calcul the mask position with numpy : 0.00585174560546875 nb_pixel_total : 17647 time to create 1 rle with old method : 0.02535271644592285 length of segment : 137 time for calcul the mask position with numpy : 0.0028018951416015625 nb_pixel_total : 35343 time to create 1 rle with old method : 0.03922867774963379 length of segment : 191 time for calcul the mask position with numpy : 0.008297443389892578 nb_pixel_total : 19254 time to create 1 rle with old method : 0.024774551391601562 length of segment : 172 time for calcul the mask position with numpy : 0.00243377685546875 nb_pixel_total : 8018 time to create 1 rle with old method : 0.009112834930419922 length of segment : 121 time for calcul the mask position with numpy : 0.008529186248779297 nb_pixel_total : 24321 time to create 1 rle with old method : 0.02957296371459961 length of segment : 278 time for calcul the mask position with numpy : 0.009327888488769531 nb_pixel_total : 24791 time to create 1 rle with old method : 0.0334477424621582 length of segment : 207 time for calcul the mask position with numpy : 0.0046427249908447266 nb_pixel_total : 34113 time to create 1 rle with old method : 0.03979992866516113 length of segment : 176 time for calcul the mask position with numpy : 0.012093544006347656 nb_pixel_total : 66814 time to create 1 rle with old method : 0.08110713958740234 length of segment : 289 time for calcul the mask position with numpy : 0.020586252212524414 nb_pixel_total : 19069 time to create 1 rle with old method : 0.027554035186767578 length of segment : 116 time for calcul the mask position with numpy : 0.0001938343048095703 nb_pixel_total : 7465 time to create 1 rle with old method : 0.008315324783325195 length of segment : 102 time for calcul the mask position with numpy : 0.007539272308349609 nb_pixel_total : 14355 time to create 1 rle with old method : 0.02111053466796875 length of segment : 234 time for calcul the mask position with numpy : 0.005804300308227539 nb_pixel_total : 6894 time to create 1 rle with old method : 0.010080337524414062 length of segment : 126 time for calcul the mask position with numpy : 0.004232883453369141 nb_pixel_total : 12318 time to create 1 rle with old method : 0.013669490814208984 length of segment : 155 time for calcul the mask position with numpy : 0.0014295578002929688 nb_pixel_total : 15136 time to create 1 rle with old method : 0.017557144165039062 length of segment : 122 time for calcul the mask position with numpy : 0.0056192874908447266 nb_pixel_total : 53759 time to create 1 rle with old method : 0.058917999267578125 length of segment : 438 time for calcul the mask position with numpy : 0.007574796676635742 nb_pixel_total : 14743 time to create 1 rle with old method : 0.021654605865478516 length of segment : 187 time for calcul the mask position with numpy : 0.005151987075805664 nb_pixel_total : 23832 time to create 1 rle with old method : 0.026973724365234375 length of segment : 174 time for calcul the mask position with numpy : 0.004270076751708984 nb_pixel_total : 26074 time to create 1 rle with old method : 0.0321955680847168 length of segment : 149 time for calcul the mask position with numpy : 0.005888223648071289 nb_pixel_total : 19082 time to create 1 rle with old method : 0.024770259857177734 length of segment : 367 time for calcul the mask position with numpy : 0.0014796257019042969 nb_pixel_total : 16764 time to create 1 rle with old method : 0.01866626739501953 length of segment : 186 time for calcul the mask position with numpy : 0.0008776187896728516 nb_pixel_total : 9757 time to create 1 rle with old method : 0.011196136474609375 length of segment : 115 time for calcul the mask position with numpy : 0.001275777816772461 nb_pixel_total : 7040 time to create 1 rle with old method : 0.007912158966064453 length of segment : 130 time for calcul the mask position with numpy : 0.004929304122924805 nb_pixel_total : 23241 time to create 1 rle with old method : 0.026026487350463867 length of segment : 207 time for calcul the mask position with numpy : 0.00496363639831543 nb_pixel_total : 22130 time to create 1 rle with old method : 0.02576756477355957 length of segment : 276 time for calcul the mask position with numpy : 0.002283811569213867 nb_pixel_total : 20822 time to create 1 rle with old method : 0.02255535125732422 length of segment : 278 time for calcul the mask position with numpy : 0.0014369487762451172 nb_pixel_total : 12641 time to create 1 rle with old method : 0.014045238494873047 length of segment : 108 time for calcul the mask position with numpy : 0.01891183853149414 nb_pixel_total : 25323 time to create 1 rle with old method : 0.03214907646179199 length of segment : 234 time for calcul the mask position with numpy : 0.009402275085449219 nb_pixel_total : 99104 time to create 1 rle with old method : 0.10278129577636719 length of segment : 421 time for calcul the mask position with numpy : 0.0035409927368164062 nb_pixel_total : 14945 time to create 1 rle with old method : 0.019278287887573242 length of segment : 93 time for calcul the mask position with numpy : 0.0012199878692626953 nb_pixel_total : 7264 time to create 1 rle with old method : 0.007759571075439453 length of segment : 98 time for calcul the mask position with numpy : 0.026918888092041016 nb_pixel_total : 12644 time to create 1 rle with old method : 0.018943309783935547 length of segment : 103 time for calcul the mask position with numpy : 0.007941961288452148 nb_pixel_total : 31720 time to create 1 rle with old method : 0.036615848541259766 length of segment : 203 time for calcul the mask position with numpy : 0.006392478942871094 nb_pixel_total : 21702 time to create 1 rle with old method : 0.025216341018676758 length of segment : 285 time for calcul the mask position with numpy : 0.0013005733489990234 nb_pixel_total : 35268 time to create 1 rle with old method : 0.036310434341430664 length of segment : 186 time for calcul the mask position with numpy : 0.0037360191345214844 nb_pixel_total : 14679 time to create 1 rle with old method : 0.0184628963470459 length of segment : 153 time for calcul the mask position with numpy : 0.014364242553710938 nb_pixel_total : 79740 time to create 1 rle with old method : 0.08432936668395996 length of segment : 462 time for calcul the mask position with numpy : 0.008313417434692383 nb_pixel_total : 26246 time to create 1 rle with old method : 0.029692888259887695 length of segment : 243 time for calcul the mask position with numpy : 0.0011570453643798828 nb_pixel_total : 15174 time to create 1 rle with old method : 0.016022443771362305 length of segment : 142 time for calcul the mask position with numpy : 0.009305000305175781 nb_pixel_total : 14723 time to create 1 rle with old method : 0.019729137420654297 length of segment : 200 time for calcul the mask position with numpy : 0.06893491744995117 nb_pixel_total : 71403 time to create 1 rle with old method : 0.0829164981842041 length of segment : 582 time for calcul the mask position with numpy : 0.016370296478271484 nb_pixel_total : 21998 time to create 1 rle with old method : 0.02717423439025879 length of segment : 231 time for calcul the mask position with numpy : 0.014272451400756836 nb_pixel_total : 34214 time to create 1 rle with old method : 0.040190935134887695 length of segment : 249 time for calcul the mask position with numpy : 0.01143789291381836 nb_pixel_total : 27036 time to create 1 rle with old method : 0.03284335136413574 length of segment : 191 time for calcul the mask position with numpy : 0.0067179203033447266 nb_pixel_total : 11674 time to create 1 rle with old method : 0.016096830368041992 length of segment : 116 time for calcul the mask position with numpy : 0.0255277156829834 nb_pixel_total : 77242 time to create 1 rle with old method : 0.09340596199035645 length of segment : 515 time for calcul the mask position with numpy : 0.02010369300842285 nb_pixel_total : 26629 time to create 1 rle with old method : 0.03463459014892578 length of segment : 162 time for calcul the mask position with numpy : 0.0002541542053222656 nb_pixel_total : 7858 time to create 1 rle with old method : 0.009243488311767578 length of segment : 120 time for calcul the mask position with numpy : 0.006719350814819336 nb_pixel_total : 22755 time to create 1 rle with old method : 0.02818131446838379 length of segment : 195 time for calcul the mask position with numpy : 0.021340131759643555 nb_pixel_total : 28227 time to create 1 rle with old method : 0.03671622276306152 length of segment : 313 time for calcul the mask position with numpy : 0.0005483627319335938 nb_pixel_total : 8191 time to create 1 rle with old method : 0.009487628936767578 length of segment : 88 time for calcul the mask position with numpy : 0.007696390151977539 nb_pixel_total : 18286 time to create 1 rle with old method : 0.023156404495239258 length of segment : 261 time for calcul the mask position with numpy : 0.0035181045532226562 nb_pixel_total : 15263 time to create 1 rle with old method : 0.01699352264404297 length of segment : 165 time for calcul the mask position with numpy : 0.0166018009185791 nb_pixel_total : 34244 time to create 1 rle with old method : 0.04139900207519531 length of segment : 281 time for calcul the mask position with numpy : 0.009933233261108398 nb_pixel_total : 19516 time to create 1 rle with old method : 0.02603745460510254 length of segment : 193 time for calcul the mask position with numpy : 0.0156404972076416 nb_pixel_total : 33313 time to create 1 rle with old method : 0.04179549217224121 length of segment : 263 time for calcul the mask position with numpy : 0.0014379024505615234 nb_pixel_total : 22687 time to create 1 rle with old method : 0.025323867797851562 length of segment : 134 time for calcul the mask position with numpy : 0.018373727798461914 nb_pixel_total : 41597 time to create 1 rle with old method : 0.04904484748840332 length of segment : 192 time for calcul the mask position with numpy : 0.007506132125854492 nb_pixel_total : 21212 time to create 1 rle with old method : 0.03494620323181152 length of segment : 209 time for calcul the mask position with numpy : 0.009980916976928711 nb_pixel_total : 19346 time to create 1 rle with old method : 0.02138543128967285 length of segment : 287 time for calcul the mask position with numpy : 0.027483463287353516 nb_pixel_total : 67805 time to create 1 rle with old method : 0.07632327079772949 length of segment : 368 time for calcul the mask position with numpy : 0.012485742568969727 nb_pixel_total : 19921 time to create 1 rle with old method : 0.02912116050720215 length of segment : 190 time for calcul the mask position with numpy : 0.00577855110168457 nb_pixel_total : 18399 time to create 1 rle with old method : 0.023393869400024414 length of segment : 258 time for calcul the mask position with numpy : 0.007958173751831055 nb_pixel_total : 13019 time to create 1 rle with old method : 0.018318891525268555 length of segment : 149 time for calcul the mask position with numpy : 0.026572465896606445 nb_pixel_total : 28853 time to create 1 rle with old method : 0.0353701114654541 length of segment : 141 time for calcul the mask position with numpy : 0.003952741622924805 nb_pixel_total : 5805 time to create 1 rle with old method : 0.006876230239868164 length of segment : 89 time for calcul the mask position with numpy : 0.0860133171081543 nb_pixel_total : 165488 time to create 1 rle with new method : 0.011100053787231445 length of segment : 551 time for calcul the mask position with numpy : 0.005481719970703125 nb_pixel_total : 14738 time to create 1 rle with old method : 0.01792168617248535 length of segment : 162 time for calcul the mask position with numpy : 0.0027952194213867188 nb_pixel_total : 16701 time to create 1 rle with old method : 0.019661426544189453 length of segment : 127 time for calcul the mask position with numpy : 0.06392979621887207 nb_pixel_total : 79872 time to create 1 rle with old method : 0.0902564525604248 length of segment : 319 time for calcul the mask position with numpy : 0.0038373470306396484 nb_pixel_total : 7407 time to create 1 rle with old method : 0.009577751159667969 length of segment : 119 time for calcul the mask position with numpy : 0.005612373352050781 nb_pixel_total : 18023 time to create 1 rle with old method : 0.021331310272216797 length of segment : 268 time for calcul the mask position with numpy : 0.019583702087402344 nb_pixel_total : 20002 time to create 1 rle with old method : 0.02687525749206543 length of segment : 284 time for calcul the mask position with numpy : 0.0014529228210449219 nb_pixel_total : 5639 time to create 1 rle with old method : 0.006244659423828125 length of segment : 77 time for calcul the mask position with numpy : 0.010748624801635742 nb_pixel_total : 16079 time to create 1 rle with old method : 0.020755529403686523 length of segment : 318 time for calcul the mask position with numpy : 0.013095855712890625 nb_pixel_total : 26080 time to create 1 rle with old method : 0.04122734069824219 length of segment : 184 time for calcul the mask position with numpy : 0.003504514694213867 nb_pixel_total : 14976 time to create 1 rle with old method : 0.01825261116027832 length of segment : 236 time for calcul the mask position with numpy : 0.009318113327026367 nb_pixel_total : 17110 time to create 1 rle with old method : 0.023759126663208008 length of segment : 184 time for calcul the mask position with numpy : 0.008228063583374023 nb_pixel_total : 30835 time to create 1 rle with old method : 0.04409146308898926 length of segment : 190 time for calcul the mask position with numpy : 0.0239105224609375 nb_pixel_total : 43309 time to create 1 rle with old method : 0.05725431442260742 length of segment : 416 time for calcul the mask position with numpy : 0.013914108276367188 nb_pixel_total : 45626 time to create 1 rle with old method : 0.05690288543701172 length of segment : 180 time for calcul the mask position with numpy : 0.0018570423126220703 nb_pixel_total : 5307 time to create 1 rle with old method : 0.006655216217041016 length of segment : 135 time for calcul the mask position with numpy : 0.051668405532836914 nb_pixel_total : 63049 time to create 1 rle with old method : 0.07953882217407227 length of segment : 348 time for calcul the mask position with numpy : 0.001550436019897461 nb_pixel_total : 8105 time to create 1 rle with old method : 0.009289026260375977 length of segment : 209 time for calcul the mask position with numpy : 0.02265477180480957 nb_pixel_total : 53804 time to create 1 rle with old method : 0.06080365180969238 length of segment : 576 time for calcul the mask position with numpy : 0.01625967025756836 nb_pixel_total : 37404 time to create 1 rle with old method : 0.0442502498626709 length of segment : 475 time for calcul the mask position with numpy : 0.01877737045288086 nb_pixel_total : 34134 time to create 1 rle with old method : 0.04140806198120117 length of segment : 249 time for calcul the mask position with numpy : 0.008466482162475586 nb_pixel_total : 24365 time to create 1 rle with old method : 0.029466867446899414 length of segment : 180 time for calcul the mask position with numpy : 0.019794702529907227 nb_pixel_total : 29789 time to create 1 rle with old method : 0.03611159324645996 length of segment : 224 time for calcul the mask position with numpy : 0.009526252746582031 nb_pixel_total : 31473 time to create 1 rle with old method : 0.03745889663696289 length of segment : 192 time for calcul the mask position with numpy : 0.008551597595214844 nb_pixel_total : 52609 time to create 1 rle with old method : 0.05872988700866699 length of segment : 267 time for calcul the mask position with numpy : 0.005454540252685547 nb_pixel_total : 11621 time to create 1 rle with old method : 0.014672994613647461 length of segment : 198 time for calcul the mask position with numpy : 0.0002429485321044922 nb_pixel_total : 8411 time to create 1 rle with old method : 0.009174585342407227 length of segment : 161 time for calcul the mask position with numpy : 0.020354509353637695 nb_pixel_total : 49004 time to create 1 rle with old method : 0.053414344787597656 length of segment : 225 time for calcul the mask position with numpy : 0.025230884552001953 nb_pixel_total : 41808 time to create 1 rle with old method : 0.04799628257751465 length of segment : 479 time for calcul the mask position with numpy : 0.010340452194213867 nb_pixel_total : 21634 time to create 1 rle with old method : 0.028172969818115234 length of segment : 240 time for calcul the mask position with numpy : 0.002687215805053711 nb_pixel_total : 11882 time to create 1 rle with old method : 0.01527857780456543 length of segment : 126 time for calcul the mask position with numpy : 0.012864351272583008 nb_pixel_total : 34535 time to create 1 rle with old method : 0.04402947425842285 length of segment : 203 time for calcul the mask position with numpy : 0.0034198760986328125 nb_pixel_total : 16246 time to create 1 rle with old method : 0.017815351486206055 length of segment : 153 time for calcul the mask position with numpy : 0.002637624740600586 nb_pixel_total : 13411 time to create 1 rle with old method : 0.01536417007446289 length of segment : 185 time for calcul the mask position with numpy : 0.0024051666259765625 nb_pixel_total : 13462 time to create 1 rle with old method : 0.015180826187133789 length of segment : 161 time for calcul the mask position with numpy : 0.004601955413818359 nb_pixel_total : 20451 time to create 1 rle with old method : 0.024239778518676758 length of segment : 136 time spent for convertir_results : 136.22406148910522 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 901 chid ids of type : 3594 Number RLEs to save : 230452 save missing photos in datou_result : time spend for datou_step_exec : 512.4855942726135 time spend to save output : 18.49213218688965 total time spend for step 1 : 530.9777264595032 step2:crop_condition Tue Apr 1 22:09:24 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 : 22 ! batch 1 Loaded 901 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 700 About to insert : list_path_to_insert length 700 new photo from crops ! About to upload 700 photos upload in portfolio : 3736932 init cache_photo without model_param we have 700 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743538294_2844229 we have uploaded 700 photos in the portfolio 3736932 time of upload the photos Elapsed time : 244.76744866371155 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 126 About to insert : list_path_to_insert length 126 new photo from crops ! About to upload 126 photos upload in portfolio : 3736932 init cache_photo without model_param we have 126 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743538581_2844229 we have uploaded 126 photos in the portfolio 3736932 time of upload the photos Elapsed time : 48.127161502838135 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743538632_2844229 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.2070858478546143 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 47 About to insert : list_path_to_insert length 47 new photo from crops ! About to upload 47 photos upload in portfolio : 3736932 init cache_photo without model_param we have 47 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743538655_2844229 we have uploaded 47 photos in the portfolio 3736932 time of upload the photos Elapsed time : 14.945865392684937 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743538674_2844229 we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.5323576927185059 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1743538680_2844229 we have uploaded 8 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.043093204498291 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 12 About to insert : list_path_to_insert length 12 new photo from crops ! About to upload 12 photos upload in portfolio : 3736932 init cache_photo without model_param we have 12 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743538689_2844229 we have uploaded 12 photos in the portfolio 3736932 time of upload the photos Elapsed time : 6.3533289432525635 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349346432, 1349346428, 1349346348, 1349346340, 1349346305, 1349346213, 1349346201, 1349346189, 1349335811, 1349335771, 1349335768, 1349335763, 1349335728, 1349335722, 1349335711, 1349335707, 1349335673, 1349335651, 1349335647, 1349335643, 1349335638, 1349335633] Looping around the photos to save general results len do output : 901 /1349360851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360894Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360904Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360906Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360932Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360950Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360969Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349360998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361057Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361063Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361074Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361076Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361079Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361082Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361087Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361095Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361098Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361099Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361101Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361104Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361106Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361109Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361113Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361117Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361119Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361123Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361125Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361129Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361134Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361153Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361157Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361159Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361161Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361163Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361165Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361168Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361169Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361173Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361189Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361191Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361192Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361198Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361199Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361218Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361220Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361221Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361223Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361225Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361226Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361228Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361230Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361231Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361233Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361235Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361237Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361239Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361240Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361242Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361245Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361249Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361250Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361252Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361254Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361255Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361257Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361259Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361260Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361263Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361264Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361266Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361268Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361274Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361278Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361315Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361318Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361320Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361324Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361325Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361328Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361330Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361331Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361335Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361343Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361391Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361398Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361401Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361409Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361413Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361416Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361418Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361420Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361421Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361423Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361425Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361426Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361428Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361430Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361431Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361433Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361435Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361436Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361438Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361439Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361443Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361445Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361447Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361448Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361450Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361451Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361453Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361455Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361456Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361458Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361460Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361461Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361463Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361465Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361485Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349361491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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data .Didn't retrieve data . /1349363323Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346432', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346428', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346348', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346340', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346305', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346213', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346201', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346189', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335811', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335771', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335768', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335763', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335728', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335722', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335711', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335707', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335673', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335651', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335647', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335643', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335638', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335633', None, None, None, None, None, '2714586') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 2725 time used for this insertion : 2.3391804695129395 save_final save missing photos in datou_result : time spend for datou_step_exec : 531.3501806259155 time spend to save output : 2.3674521446228027 total time spend for step 2 : 533.7176327705383 step3:rle_unique_nms_with_priority Tue Apr 1 22:18:18 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 901 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 55 nb_hashtags : 3 time to prepare the origin masks : 4.357548475265503 time for calcul the mask position with numpy : 0.487774133682251 nb_pixel_total : 5016545 time to create 1 rle with new method : 1.104726791381836 time for calcul the mask position with numpy : 0.029369115829467773 nb_pixel_total : 32465 time to create 1 rle with old method : 0.04320240020751953 time for calcul the mask position with numpy : 0.030127763748168945 nb_pixel_total : 43335 time to create 1 rle with old method : 0.0471348762512207 time for calcul the mask position with numpy : 0.028725862503051758 nb_pixel_total : 22814 time to create 1 rle with old method : 0.02454996109008789 time for calcul the mask position with numpy : 0.028113126754760742 nb_pixel_total : 33019 time to create 1 rle with old method : 0.036702632904052734 time for calcul the mask position with numpy : 0.034662723541259766 nb_pixel_total : 9669 time to create 1 rle with old method : 0.0108489990234375 time for calcul the mask position with numpy : 0.02883434295654297 nb_pixel_total : 30471 time to create 1 rle with old method : 0.03399848937988281 time for calcul the mask position with numpy : 0.028869152069091797 nb_pixel_total : 20713 time to create 1 rle with old method : 0.02316737174987793 time for calcul the mask position with numpy : 0.029314517974853516 nb_pixel_total : 11437 time to create 1 rle with old method : 0.01289510726928711 time for calcul the mask position with numpy : 0.029521465301513672 nb_pixel_total : 95978 time to create 1 rle with old method : 0.10841012001037598 time for calcul the mask position with numpy : 0.02863597869873047 nb_pixel_total : 24624 time to create 1 rle with old method : 0.027480363845825195 time for calcul the mask position with numpy : 0.028571605682373047 nb_pixel_total : 16468 time to create 1 rle with old method : 0.018613100051879883 time for calcul the mask position with numpy : 0.029271841049194336 nb_pixel_total : 16669 time to create 1 rle with old method : 0.018746614456176758 time for calcul the mask position with numpy : 0.03162193298339844 nb_pixel_total : 19685 time to create 1 rle with old method : 0.05862283706665039 time for calcul the mask position with numpy : 0.0524139404296875 nb_pixel_total : 257696 time to create 1 rle with new method : 2.240431547164917 time for calcul the mask position with numpy : 0.028685569763183594 nb_pixel_total : 116353 time to create 1 rle with old method : 0.14995956420898438 time for calcul the mask position with numpy : 0.028339385986328125 nb_pixel_total : 17654 time to create 1 rle with old method : 0.018898725509643555 time for calcul the mask position with numpy : 0.032343387603759766 nb_pixel_total : 54173 time to create 1 rle with old method : 0.07749629020690918 time for calcul the mask position with numpy : 0.02880549430847168 nb_pixel_total : 14874 time to create 1 rle with old method : 0.016393423080444336 time for calcul the mask position with numpy : 0.02947688102722168 nb_pixel_total : 46540 time to create 1 rle with old method : 0.05120062828063965 time for calcul the mask position with numpy : 0.02774953842163086 nb_pixel_total : 75143 time to create 1 rle with old method : 0.08159947395324707 time for calcul the mask position with numpy : 0.02919602394104004 nb_pixel_total : 37231 time to create 1 rle with old method : 0.04061102867126465 time for calcul the mask position with numpy : 0.029304027557373047 nb_pixel_total : 129460 time to create 1 rle with old method : 0.14285707473754883 time for calcul the mask position with numpy : 0.0285336971282959 nb_pixel_total : 23145 time to create 1 rle with old method : 0.025524377822875977 time for calcul the mask position with numpy : 0.028795957565307617 nb_pixel_total : 12573 time to create 1 rle with old method : 0.013843536376953125 time for calcul the mask position with numpy : 0.029816389083862305 nb_pixel_total : 100363 time to create 1 rle with old method : 0.11108040809631348 time for calcul the mask position with numpy : 0.029622793197631836 nb_pixel_total : 35109 time to create 1 rle with old method : 0.03864765167236328 time for calcul the mask position with numpy : 0.029034852981567383 nb_pixel_total : 22258 time to create 1 rle with old method : 0.02473735809326172 time for calcul the mask position with numpy : 0.028660058975219727 nb_pixel_total : 11335 time to create 1 rle with old method : 0.012318849563598633 time for calcul the mask position with numpy : 0.028056859970092773 nb_pixel_total : 8484 time to create 1 rle with old method : 0.010140419006347656 time for calcul the mask position with numpy : 0.028701305389404297 nb_pixel_total : 74179 time to create 1 rle with old method : 0.07958221435546875 time for calcul the mask position with numpy : 0.027734041213989258 nb_pixel_total : 45912 time to create 1 rle with old method : 0.049956321716308594 time for calcul the mask position with numpy : 0.02878093719482422 nb_pixel_total : 27604 time to create 1 rle with old method : 0.030809879302978516 time for calcul the mask position with numpy : 0.02841043472290039 nb_pixel_total : 25910 time to create 1 rle with old method : 0.03009796142578125 time for calcul the mask position with numpy : 0.030675649642944336 nb_pixel_total : 53127 time to create 1 rle with old method : 0.06671667098999023 time for calcul the mask position with numpy : 0.03124856948852539 nb_pixel_total : 26343 time to create 1 rle with old method : 0.030910491943359375 time for calcul the mask position with numpy : 0.02921152114868164 nb_pixel_total : 20787 time to create 1 rle with old method : 0.024721145629882812 time for calcul the mask position with numpy : 0.029558658599853516 nb_pixel_total : 24852 time to create 1 rle with old method : 0.028435230255126953 time for calcul the mask position with numpy : 0.030417203903198242 nb_pixel_total : 13393 time to create 1 rle with old method : 0.01764392852783203 time for calcul the mask position with numpy : 0.03175854682922363 nb_pixel_total : 71038 time to create 1 rle with old method : 0.11511039733886719 time for calcul the mask position with numpy : 0.03115367889404297 nb_pixel_total : 12181 time to create 1 rle with old method : 0.020325183868408203 time for calcul the mask position with numpy : 0.03925776481628418 nb_pixel_total : 9914 time to create 1 rle with old method : 0.013434648513793945 time for calcul the mask position with numpy : 0.030340909957885742 nb_pixel_total : 13000 time to create 1 rle with old method : 0.014769315719604492 time for calcul the mask position with numpy : 0.02941107749938965 nb_pixel_total : 19153 time to create 1 rle with old method : 0.02133488655090332 time for calcul the mask position with numpy : 0.029787063598632812 nb_pixel_total : 46172 time to create 1 rle with old method : 0.054956674575805664 time for calcul the mask position with numpy : 0.031122207641601562 nb_pixel_total : 20481 time to create 1 rle with old method : 0.05351853370666504 time for calcul the mask position with numpy : 0.05181765556335449 nb_pixel_total : 31986 time to create 1 rle with old method : 0.0653388500213623 time for calcul the mask position with numpy : 0.028968334197998047 nb_pixel_total : 73978 time to create 1 rle with old method : 0.08209514617919922 time for calcul the mask position with numpy : 0.029139041900634766 nb_pixel_total : 4727 time to create 1 rle with old method : 0.005394697189331055 time for calcul the mask position with numpy : 0.03065013885498047 nb_pixel_total : 38714 time to create 1 rle with old method : 0.04389214515686035 time for calcul the mask position with numpy : 0.029860973358154297 nb_pixel_total : 9889 time to create 1 rle with old method : 0.011466503143310547 time for calcul the mask position with numpy : 0.03146076202392578 nb_pixel_total : 8584 time to create 1 rle with old method : 0.010505914688110352 time for calcul the mask position with numpy : 0.02934551239013672 nb_pixel_total : 7032 time to create 1 rle with old method : 0.007932662963867188 time for calcul the mask position with numpy : 0.0292205810546875 nb_pixel_total : 9118 time to create 1 rle with old method : 0.010162353515625 time for calcul the mask position with numpy : 0.02917337417602539 nb_pixel_total : 2646 time to create 1 rle with old method : 0.0029931068420410156 time for calcul the mask position with numpy : 0.028811931610107422 nb_pixel_total : 3237 time to create 1 rle with old method : 0.0035941600799560547 create new chi : 7.747885227203369 time to delete rle : 0.0427095890045166 batch 1 Loaded 111 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29594 TO DO : save crop sub photo not yet done ! save time : 2.6413936614990234 nb_obj : 63 nb_hashtags : 4 time to prepare the origin masks : 4.26740574836731 time for calcul the mask position with numpy : 0.6208803653717041 nb_pixel_total : 5183835 time to create 1 rle with new method : 0.9826533794403076 time for calcul the mask position with numpy : 0.028937578201293945 nb_pixel_total : 10081 time to create 1 rle with old method : 0.01160430908203125 time for calcul the mask position with numpy : 0.02929401397705078 nb_pixel_total : 7723 time to create 1 rle with old method : 0.008843421936035156 time for calcul the mask position with numpy : 0.029625415802001953 nb_pixel_total : 33214 time to create 1 rle with old method : 0.03705453872680664 time for calcul the mask position with numpy : 0.029232501983642578 nb_pixel_total : 88582 time to create 1 rle with old method : 0.09597396850585938 time for calcul the mask position with numpy : 0.02823162078857422 nb_pixel_total : 28846 time to create 1 rle with old method : 0.031150102615356445 time for calcul the mask position with numpy : 0.0290377140045166 nb_pixel_total : 7048 time to create 1 rle with old method : 0.008293867111206055 time for calcul the mask position with numpy : 0.028258800506591797 nb_pixel_total : 45434 time to create 1 rle with old method : 0.049649715423583984 time for calcul the mask position with numpy : 0.028253555297851562 nb_pixel_total : 10440 time to create 1 rle with old method : 0.011125564575195312 time for calcul the mask position with numpy : 0.028669118881225586 nb_pixel_total : 32029 time to create 1 rle with old method : 0.034912824630737305 time for calcul the mask position with numpy : 0.028756141662597656 nb_pixel_total : 11041 time to create 1 rle with old method : 0.01244354248046875 time for calcul the mask position with numpy : 0.028868436813354492 nb_pixel_total : 118513 time to create 1 rle with old method : 0.12485361099243164 time for calcul the mask position with numpy : 0.027688026428222656 nb_pixel_total : 7065 time to create 1 rle with old method : 0.007408618927001953 time for calcul the mask position with numpy : 0.028322935104370117 nb_pixel_total : 24267 time to create 1 rle with old method : 0.02608013153076172 time for calcul the mask position with numpy : 0.029172182083129883 nb_pixel_total : 7367 time to create 1 rle with old method : 0.008235454559326172 time for calcul the mask position with numpy : 0.029170513153076172 nb_pixel_total : 22047 time to create 1 rle with old method : 0.024591445922851562 time for calcul the mask position with numpy : 0.028334379196166992 nb_pixel_total : 88428 time to create 1 rle with old method : 0.09818029403686523 time for calcul the mask position with numpy : 0.0298154354095459 nb_pixel_total : 11826 time to create 1 rle with old method : 0.013558149337768555 time for calcul the mask position with numpy : 0.02903008460998535 nb_pixel_total : 16109 time to create 1 rle with old method : 0.017708539962768555 time for calcul the mask position with numpy : 0.029056549072265625 nb_pixel_total : 48414 time to create 1 rle with old method : 0.051901817321777344 time for calcul the mask position with numpy : 0.02944493293762207 nb_pixel_total : 22189 time to create 1 rle with old method : 0.02463531494140625 time for calcul the mask position with numpy : 0.029169082641601562 nb_pixel_total : 31067 time to create 1 rle with old method : 0.03483843803405762 time for calcul the mask position with numpy : 0.029265165328979492 nb_pixel_total : 13361 time to create 1 rle with old method : 0.015060901641845703 time for calcul the mask position with numpy : 0.02964162826538086 nb_pixel_total : 27411 time to create 1 rle with old method : 0.030673742294311523 time for calcul the mask position with numpy : 0.028098106384277344 nb_pixel_total : 25323 time to create 1 rle with old method : 0.027463436126708984 time for calcul the mask position with numpy : 0.030418872833251953 nb_pixel_total : 223431 time to create 1 rle with new method : 0.8637149333953857 time for calcul the mask position with numpy : 0.02882242202758789 nb_pixel_total : 7045 time to create 1 rle with old method : 0.007896900177001953 time for calcul the mask position with numpy : 0.02971792221069336 nb_pixel_total : 111415 time to create 1 rle with old method : 0.12306666374206543 time for calcul the mask position with numpy : 0.02927708625793457 nb_pixel_total : 66718 time to create 1 rle with old method : 0.07399225234985352 time for calcul the mask position with numpy : 0.02924060821533203 nb_pixel_total : 13723 time to create 1 rle with old method : 0.015366554260253906 time for calcul the mask position with numpy : 0.02938365936279297 nb_pixel_total : 21970 time to create 1 rle with old method : 0.024362802505493164 time for calcul the mask position with numpy : 0.02862238883972168 nb_pixel_total : 99627 time to create 1 rle with old method : 0.10710430145263672 time for calcul the mask position with numpy : 0.02856755256652832 nb_pixel_total : 11086 time to create 1 rle with old method : 0.011941194534301758 time for calcul the mask position with numpy : 0.028682947158813477 nb_pixel_total : 97080 time to create 1 rle with old method : 0.10664725303649902 time for calcul the mask position with numpy : 0.029819726943969727 nb_pixel_total : 52999 time to create 1 rle with old method : 0.05856823921203613 time for calcul the mask position with numpy : 0.028585433959960938 nb_pixel_total : 13269 time to create 1 rle with old method : 0.014731645584106445 time for calcul the mask position with numpy : 0.02842855453491211 nb_pixel_total : 14286 time to create 1 rle with old method : 0.015411138534545898 time for calcul the mask position with numpy : 0.02897024154663086 nb_pixel_total : 18190 time to create 1 rle with old method : 0.01959848403930664 time for calcul the mask position with numpy : 0.028774023056030273 nb_pixel_total : 7434 time to create 1 rle with old method : 0.008318424224853516 time for calcul the mask position with numpy : 0.029120922088623047 nb_pixel_total : 13408 time to create 1 rle with old method : 0.015302181243896484 time for calcul the mask position with numpy : 0.028537273406982422 nb_pixel_total : 16931 time to create 1 rle with old method : 0.018094301223754883 time for calcul the mask position with numpy : 0.027465343475341797 nb_pixel_total : 15113 time to create 1 rle with old method : 0.01621699333190918 time for calcul the mask position with numpy : 0.027639389038085938 nb_pixel_total : 16663 time to create 1 rle with old method : 0.018233299255371094 time for calcul the mask position with numpy : 0.028543949127197266 nb_pixel_total : 18410 time to create 1 rle with old method : 0.02010321617126465 time for calcul the mask position with numpy : 0.028686046600341797 nb_pixel_total : 24528 time to create 1 rle with old method : 0.03374838829040527 time for calcul the mask position with numpy : 0.03299736976623535 nb_pixel_total : 32501 time to create 1 rle with old method : 0.0453188419342041 time for calcul the mask position with numpy : 0.0288088321685791 nb_pixel_total : 14369 time to create 1 rle with old method : 0.01622772216796875 time for calcul the mask position with numpy : 0.028870820999145508 nb_pixel_total : 13980 time to create 1 rle with old method : 0.015716552734375 time for calcul the mask position with numpy : 0.028916120529174805 nb_pixel_total : 11459 time to create 1 rle with old method : 0.012782812118530273 time for calcul the mask position with numpy : 0.02869868278503418 nb_pixel_total : 8346 time to create 1 rle with old method : 0.009349822998046875 time for calcul the mask position with numpy : 0.028504371643066406 nb_pixel_total : 6386 time to create 1 rle with old method : 0.007091999053955078 time for calcul the mask position with numpy : 0.027476787567138672 nb_pixel_total : 6997 time to create 1 rle with old method : 0.0075147151947021484 time for calcul the mask position with numpy : 0.02781057357788086 nb_pixel_total : 5731 time to create 1 rle with old method : 0.00618290901184082 time for calcul the mask position with numpy : 0.028023242950439453 nb_pixel_total : 5414 time to create 1 rle with old method : 0.005973100662231445 time for calcul the mask position with numpy : 0.028133630752563477 nb_pixel_total : 6365 time to create 1 rle with old method : 0.007147073745727539 time for calcul the mask position with numpy : 0.028855562210083008 nb_pixel_total : 9339 time to create 1 rle with old method : 0.010371685028076172 time for calcul the mask position with numpy : 0.028383493423461914 nb_pixel_total : 6814 time to create 1 rle with old method : 0.0075762271881103516 time for calcul the mask position with numpy : 0.02875661849975586 nb_pixel_total : 57911 time to create 1 rle with old method : 0.0687706470489502 time for calcul the mask position with numpy : 0.029064178466796875 nb_pixel_total : 13595 time to create 1 rle with old method : 0.014826297760009766 time for calcul the mask position with numpy : 0.028936386108398438 nb_pixel_total : 17816 time to create 1 rle with old method : 0.01919078826904297 time for calcul the mask position with numpy : 0.02755141258239746 nb_pixel_total : 17031 time to create 1 rle with old method : 0.01816725730895996 time for calcul the mask position with numpy : 0.02806258201599121 nb_pixel_total : 2381 time to create 1 rle with old method : 0.0026438236236572266 time for calcul the mask position with numpy : 0.02761054039001465 nb_pixel_total : 18617 time to create 1 rle with old method : 0.019400596618652344 time for calcul the mask position with numpy : 0.028163433074951172 nb_pixel_total : 10202 time to create 1 rle with old method : 0.011374235153198242 create new chi : 6.162400007247925 time to delete rle : 0.004773616790771484 batch 1 Loaded 127 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27137 TO DO : save crop sub photo not yet done ! save time : 1.8189246654510498 nb_obj : 46 nb_hashtags : 5 time to prepare the origin masks : 3.96642804145813 time for calcul the mask position with numpy : 0.8218839168548584 nb_pixel_total : 5851570 time to create 1 rle with new method : 0.7957336902618408 time for calcul the mask position with numpy : 0.03254222869873047 nb_pixel_total : 17366 time to create 1 rle with old method : 0.019605636596679688 time for calcul the mask position with numpy : 0.028959035873413086 nb_pixel_total : 6373 time to create 1 rle with old method : 0.007224082946777344 time for calcul the mask position with numpy : 0.029005765914916992 nb_pixel_total : 87867 time to create 1 rle with old method : 0.09602093696594238 time for calcul the mask position with numpy : 0.0272676944732666 nb_pixel_total : 3176 time to create 1 rle with old method : 0.003533601760864258 time for calcul the mask position with numpy : 0.028331756591796875 nb_pixel_total : 90241 time to create 1 rle with old method : 0.09562420845031738 time for calcul the mask position with numpy : 0.028772592544555664 nb_pixel_total : 6803 time to create 1 rle with old method : 0.007674694061279297 time for calcul the mask position with numpy : 0.029030323028564453 nb_pixel_total : 32470 time to create 1 rle with old method : 0.03629803657531738 time for calcul the mask position with numpy : 0.0292356014251709 nb_pixel_total : 24156 time to create 1 rle with old method : 0.030310869216918945 time for calcul the mask position with numpy : 0.029057741165161133 nb_pixel_total : 8755 time to create 1 rle with old method : 0.009848356246948242 time for calcul the mask position with numpy : 0.029169559478759766 nb_pixel_total : 24813 time to create 1 rle with old method : 0.027587175369262695 time for calcul the mask position with numpy : 0.02901315689086914 nb_pixel_total : 20644 time to create 1 rle with old method : 0.02302098274230957 time for calcul the mask position with numpy : 0.028824806213378906 nb_pixel_total : 63849 time to create 1 rle with old method : 0.07058167457580566 time for calcul the mask position with numpy : 0.02909255027770996 nb_pixel_total : 12834 time to create 1 rle with old method : 0.014676094055175781 time for calcul the mask position with numpy : 0.029164791107177734 nb_pixel_total : 10402 time to create 1 rle with old method : 0.011891365051269531 time for calcul the mask position with numpy : 0.029050350189208984 nb_pixel_total : 68020 time to create 1 rle with old method : 0.07617568969726562 time for calcul the mask position with numpy : 0.02875971794128418 nb_pixel_total : 74016 time to create 1 rle with old method : 0.08201360702514648 time for calcul the mask position with numpy : 0.03276228904724121 nb_pixel_total : 11821 time to create 1 rle with old method : 0.012987613677978516 time for calcul the mask position with numpy : 0.029100656509399414 nb_pixel_total : 19807 time to create 1 rle with old method : 0.022086143493652344 time for calcul the mask position with numpy : 0.029111623764038086 nb_pixel_total : 9658 time to create 1 rle with old method : 0.010913372039794922 time for calcul the mask position with numpy : 0.02937602996826172 nb_pixel_total : 27664 time to create 1 rle with old method : 0.030973434448242188 time for calcul the mask position with numpy : 0.029061555862426758 nb_pixel_total : 524 time to create 1 rle with old method : 0.0006756782531738281 time for calcul the mask position with numpy : 0.02926802635192871 nb_pixel_total : 7581 time to create 1 rle with old method : 0.008484601974487305 time for calcul the mask position with numpy : 0.029462814331054688 nb_pixel_total : 31309 time to create 1 rle with old method : 0.03464698791503906 time for calcul the mask position with numpy : 0.02935004234313965 nb_pixel_total : 15547 time to create 1 rle with old method : 0.0175018310546875 time for calcul the mask position with numpy : 0.029163360595703125 nb_pixel_total : 26385 time to create 1 rle with old method : 0.028905630111694336 time for calcul the mask position with numpy : 0.02853107452392578 nb_pixel_total : 17587 time to create 1 rle with old method : 0.01974010467529297 time for calcul the mask position with numpy : 0.029428720474243164 nb_pixel_total : 23698 time to create 1 rle with old method : 0.026484012603759766 time for calcul the mask position with numpy : 0.0314946174621582 nb_pixel_total : 14903 time to create 1 rle with old method : 0.01776266098022461 time for calcul the mask position with numpy : 0.029809951782226562 nb_pixel_total : 30467 time to create 1 rle with old method : 0.03394055366516113 time for calcul the mask position with numpy : 0.029665708541870117 nb_pixel_total : 31438 time to create 1 rle with old method : 0.03443479537963867 time for calcul the mask position with numpy : 0.027551651000976562 nb_pixel_total : 26756 time to create 1 rle with old method : 0.031009435653686523 time for calcul the mask position with numpy : 0.02750396728515625 nb_pixel_total : 35877 time to create 1 rle with old method : 0.03790616989135742 time for calcul the mask position with numpy : 0.028753995895385742 nb_pixel_total : 24646 time to create 1 rle with old method : 0.02730584144592285 time for calcul the mask position with numpy : 0.02876758575439453 nb_pixel_total : 16489 time to create 1 rle with old method : 0.018292665481567383 time for calcul the mask position with numpy : 0.028679847717285156 nb_pixel_total : 10490 time to create 1 rle with old method : 0.011638879776000977 time for calcul the mask position with numpy : 0.028606653213500977 nb_pixel_total : 4689 time to create 1 rle with old method : 0.005263566970825195 time for calcul the mask position with numpy : 0.02878284454345703 nb_pixel_total : 30574 time to create 1 rle with old method : 0.034018754959106445 time for calcul the mask position with numpy : 0.028154611587524414 nb_pixel_total : 32039 time to create 1 rle with old method : 0.03335404396057129 time for calcul the mask position with numpy : 0.03150582313537598 nb_pixel_total : 18062 time to create 1 rle with old method : 0.029427766799926758 time for calcul the mask position with numpy : 0.032057762145996094 nb_pixel_total : 7263 time to create 1 rle with old method : 0.008030414581298828 time for calcul the mask position with numpy : 0.027826309204101562 nb_pixel_total : 41917 time to create 1 rle with old method : 0.04374289512634277 time for calcul the mask position with numpy : 0.028574705123901367 nb_pixel_total : 24578 time to create 1 rle with old method : 0.026450395584106445 time for calcul the mask position with numpy : 0.027778148651123047 nb_pixel_total : 33181 time to create 1 rle with old method : 0.03533029556274414 time for calcul the mask position with numpy : 0.02843308448791504 nb_pixel_total : 21347 time to create 1 rle with old method : 0.02276611328125 time for calcul the mask position with numpy : 0.027925968170166016 nb_pixel_total : 19653 time to create 1 rle with old method : 0.021095991134643555 time for calcul the mask position with numpy : 0.028577089309692383 nb_pixel_total : 30935 time to create 1 rle with old method : 0.03347611427307129 create new chi : 4.329604864120483 time to delete rle : 0.004823923110961914 batch 1 Loaded 93 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22255 TO DO : save crop sub photo not yet done ! save time : 2.6818926334381104 nb_obj : 62 nb_hashtags : 6 time to prepare the origin masks : 4.259780645370483 time for calcul the mask position with numpy : 0.5197391510009766 nb_pixel_total : 5428195 time to create 1 rle with new method : 0.8262243270874023 time for calcul the mask position with numpy : 0.029072046279907227 nb_pixel_total : 24763 time to create 1 rle with old method : 0.027926206588745117 time for calcul the mask position with numpy : 0.030597686767578125 nb_pixel_total : 2839 time to create 1 rle with old method : 0.003346681594848633 time for calcul the mask position with numpy : 0.02961564064025879 nb_pixel_total : 24109 time to create 1 rle with old method : 0.02688908576965332 time for calcul the mask position with numpy : 0.028433799743652344 nb_pixel_total : 7748 time to create 1 rle with old method : 0.00842905044555664 time for calcul the mask position with numpy : 0.03142595291137695 nb_pixel_total : 13742 time to create 1 rle with old method : 0.018586397171020508 time for calcul the mask position with numpy : 0.029796123504638672 nb_pixel_total : 107673 time to create 1 rle with old method : 0.11522102355957031 time for calcul the mask position with numpy : 0.028386592864990234 nb_pixel_total : 60460 time to create 1 rle with old method : 0.0648488998413086 time for calcul the mask position with numpy : 0.028497695922851562 nb_pixel_total : 22983 time to create 1 rle with old method : 0.025454044342041016 time for calcul the mask position with numpy : 0.03092193603515625 nb_pixel_total : 307735 time to create 1 rle with new method : 0.8087968826293945 time for calcul the mask position with numpy : 0.028989553451538086 nb_pixel_total : 31857 time to create 1 rle with old method : 0.035169363021850586 time for calcul the mask position with numpy : 0.029508113861083984 nb_pixel_total : 16159 time to create 1 rle with old method : 0.02633953094482422 time for calcul the mask position with numpy : 0.03314542770385742 nb_pixel_total : 18742 time to create 1 rle with old method : 0.028323650360107422 time for calcul the mask position with numpy : 0.028980731964111328 nb_pixel_total : 10871 time to create 1 rle with old method : 0.012138128280639648 time for calcul the mask position with numpy : 0.028919219970703125 nb_pixel_total : 11495 time to create 1 rle with old method : 0.012930154800415039 time for calcul the mask position with numpy : 0.028552532196044922 nb_pixel_total : 4157 time to create 1 rle with old method : 0.005185127258300781 time for calcul the mask position with numpy : 0.029096364974975586 nb_pixel_total : 17694 time to create 1 rle with old method : 0.019692182540893555 time for calcul the mask position with numpy : 0.029043197631835938 nb_pixel_total : 28059 time to create 1 rle with old method : 0.031063318252563477 time for calcul the mask position with numpy : 0.036307573318481445 nb_pixel_total : 16672 time to create 1 rle with old method : 0.018740415573120117 time for calcul the mask position with numpy : 0.03458094596862793 nb_pixel_total : 61173 time to create 1 rle with old method : 0.0687253475189209 time for calcul the mask position with numpy : 0.028446674346923828 nb_pixel_total : 19727 time to create 1 rle with old method : 0.02129650115966797 time for calcul the mask position with numpy : 0.02884960174560547 nb_pixel_total : 12189 time to create 1 rle with old method : 0.01386880874633789 time for calcul the mask position with numpy : 0.029688596725463867 nb_pixel_total : 10320 time to create 1 rle with old method : 0.013715982437133789 time for calcul the mask position with numpy : 0.03127574920654297 nb_pixel_total : 72524 time to create 1 rle with old method : 0.08069777488708496 time for calcul the mask position with numpy : 0.029584646224975586 nb_pixel_total : 46121 time to create 1 rle with old method : 0.051259756088256836 time for calcul the mask position with numpy : 0.029802799224853516 nb_pixel_total : 30746 time to create 1 rle with old method : 0.03441333770751953 time for calcul the mask position with numpy : 0.030177593231201172 nb_pixel_total : 44395 time to create 1 rle with old method : 0.04997611045837402 time for calcul the mask position with numpy : 0.032058000564575195 nb_pixel_total : 65486 time to create 1 rle with old method : 0.07366704940795898 time for calcul the mask position with numpy : 0.0290679931640625 nb_pixel_total : 2309 time to create 1 rle with old method : 0.0027666091918945312 time for calcul the mask position with numpy : 0.029212236404418945 nb_pixel_total : 18907 time to create 1 rle with old method : 0.021124839782714844 time for calcul the mask position with numpy : 0.029185056686401367 nb_pixel_total : 19620 time to create 1 rle with old method : 0.021871089935302734 time for calcul the mask position with numpy : 0.028564453125 nb_pixel_total : 7363 time to create 1 rle with old method : 0.008306264877319336 time for calcul the mask position with numpy : 0.0310208797454834 nb_pixel_total : 14632 time to create 1 rle with old method : 0.016550779342651367 time for calcul the mask position with numpy : 0.028834819793701172 nb_pixel_total : 7142 time to create 1 rle with old method : 0.00815725326538086 time for calcul the mask position with numpy : 0.028941869735717773 nb_pixel_total : 4856 time to create 1 rle with old method : 0.005499601364135742 time for calcul the mask position with numpy : 0.029128313064575195 nb_pixel_total : 307 time to create 1 rle with old method : 0.0005123615264892578 time for calcul the mask position with numpy : 0.02922654151916504 nb_pixel_total : 15229 time to create 1 rle with old method : 0.017038345336914062 time for calcul the mask position with numpy : 0.029305219650268555 nb_pixel_total : 34920 time to create 1 rle with old method : 0.03879237174987793 time for calcul the mask position with numpy : 0.029134511947631836 nb_pixel_total : 40678 time to create 1 rle with old method : 0.04561448097229004 time for calcul the mask position with numpy : 0.029192447662353516 nb_pixel_total : 9771 time to create 1 rle with old method : 0.01087188720703125 time for calcul the mask position with numpy : 0.029181241989135742 nb_pixel_total : 10388 time to create 1 rle with old method : 0.011601448059082031 time for calcul the mask position with numpy : 0.028508901596069336 nb_pixel_total : 33160 time to create 1 rle with old method : 0.03582000732421875 time for calcul the mask position with numpy : 0.028198957443237305 nb_pixel_total : 1706 time to create 1 rle with old method : 0.0018055438995361328 time for calcul the mask position with numpy : 0.02872180938720703 nb_pixel_total : 8130 time to create 1 rle with old method : 0.00861668586730957 time for calcul the mask position with numpy : 0.028970718383789062 nb_pixel_total : 7689 time to create 1 rle with old method : 0.008431673049926758 time for calcul the mask position with numpy : 0.02962803840637207 nb_pixel_total : 13623 time to create 1 rle with old method : 0.014754056930541992 time for calcul the mask position with numpy : 0.02922987937927246 nb_pixel_total : 12118 time to create 1 rle with old method : 0.013284921646118164 time for calcul the mask position with numpy : 0.02939605712890625 nb_pixel_total : 3582 time to create 1 rle with old method : 0.004216432571411133 time for calcul the mask position with numpy : 0.029796838760375977 nb_pixel_total : 6166 time to create 1 rle with old method : 0.007097959518432617 time for calcul the mask position with numpy : 0.02971029281616211 nb_pixel_total : 6307 time to create 1 rle with old method : 0.007151603698730469 time for calcul the mask position with numpy : 0.02942061424255371 nb_pixel_total : 58785 time to create 1 rle with old method : 0.06501889228820801 time for calcul the mask position with numpy : 0.02855706214904785 nb_pixel_total : 16339 time to create 1 rle with old method : 0.017887592315673828 time for calcul the mask position with numpy : 0.02818465232849121 nb_pixel_total : 14361 time to create 1 rle with old method : 0.015765666961669922 time for calcul the mask position with numpy : 0.027951478958129883 nb_pixel_total : 25265 time to create 1 rle with old method : 0.027588844299316406 time for calcul the mask position with numpy : 0.02823185920715332 nb_pixel_total : 3752 time to create 1 rle with old method : 0.00547480583190918 time for calcul the mask position with numpy : 0.03061223030090332 nb_pixel_total : 21465 time to create 1 rle with old method : 0.022771835327148438 time for calcul the mask position with numpy : 0.029028654098510742 nb_pixel_total : 26110 time to create 1 rle with old method : 0.02919316291809082 time for calcul the mask position with numpy : 0.0290834903717041 nb_pixel_total : 28215 time to create 1 rle with old method : 0.03137350082397461 time for calcul the mask position with numpy : 0.028876543045043945 nb_pixel_total : 7887 time to create 1 rle with old method : 0.008806467056274414 time for calcul the mask position with numpy : 0.028859853744506836 nb_pixel_total : 5459 time to create 1 rle with old method : 0.006136178970336914 time for calcul the mask position with numpy : 0.028878211975097656 nb_pixel_total : 25866 time to create 1 rle with old method : 0.028516054153442383 time for calcul the mask position with numpy : 0.027758121490478516 nb_pixel_total : 11248 time to create 1 rle with old method : 0.011889219284057617 time for calcul the mask position with numpy : 0.027173280715942383 nb_pixel_total : 8251 time to create 1 rle with old method : 0.008615493774414062 create new chi : 5.526947736740112 time to delete rle : 0.003944873809814453 batch 1 Loaded 125 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27225 TO DO : save crop sub photo not yet done ! save time : 1.876887559890747 nb_obj : 16 nb_hashtags : 3 time to prepare the origin masks : 4.797126054763794 time for calcul the mask position with numpy : 0.8529567718505859 nb_pixel_total : 6116507 time to create 1 rle with new method : 0.5935254096984863 time for calcul the mask position with numpy : 0.035948753356933594 nb_pixel_total : 14044 time to create 1 rle with old method : 0.01656055450439453 time for calcul the mask position with numpy : 0.028009653091430664 nb_pixel_total : 121507 time to create 1 rle with old method : 0.13351988792419434 time for calcul the mask position with numpy : 0.022273778915405273 nb_pixel_total : 2221 time to create 1 rle with old method : 0.00262451171875 time for calcul the mask position with numpy : 0.024474382400512695 nb_pixel_total : 263107 time to create 1 rle with new method : 2.2078773975372314 time for calcul the mask position with numpy : 0.02559685707092285 nb_pixel_total : 85637 time to create 1 rle with old method : 0.09366559982299805 time for calcul the mask position with numpy : 0.02423405647277832 nb_pixel_total : 11458 time to create 1 rle with old method : 0.012777090072631836 time for calcul the mask position with numpy : 0.024224042892456055 nb_pixel_total : 25362 time to create 1 rle with old method : 0.02805161476135254 time for calcul the mask position with numpy : 0.023879528045654297 nb_pixel_total : 189255 time to create 1 rle with new method : 1.4723358154296875 time for calcul the mask position with numpy : 0.021494150161743164 nb_pixel_total : 9107 time to create 1 rle with old method : 0.009938478469848633 time for calcul the mask position with numpy : 0.02369379997253418 nb_pixel_total : 20981 time to create 1 rle with old method : 0.02349066734313965 time for calcul the mask position with numpy : 0.023366689682006836 nb_pixel_total : 6657 time to create 1 rle with old method : 0.007608175277709961 time for calcul the mask position with numpy : 0.024945735931396484 nb_pixel_total : 28681 time to create 1 rle with old method : 0.0320589542388916 time for calcul the mask position with numpy : 0.022027254104614258 nb_pixel_total : 63362 time to create 1 rle with old method : 0.0655820369720459 time for calcul the mask position with numpy : 0.030623912811279297 nb_pixel_total : 8220 time to create 1 rle with old method : 0.008826732635498047 time for calcul the mask position with numpy : 0.032654762268066406 nb_pixel_total : 24635 time to create 1 rle with old method : 0.026014328002929688 time for calcul the mask position with numpy : 0.0329747200012207 nb_pixel_total : 59499 time to create 1 rle with old method : 0.06197023391723633 create new chi : 6.1515936851501465 time to delete rle : 0.002514362335205078 batch 1 Loaded 33 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 11749 TO DO : save crop sub photo not yet done ! save time : 2.340505599975586 nb_obj : 18 nb_hashtags : 4 time to prepare the origin masks : 6.860216856002808 time for calcul the mask position with numpy : 0.42536211013793945 nb_pixel_total : 5281835 time to create 1 rle with new method : 0.8155438899993896 time for calcul the mask position with numpy : 0.036084890365600586 nb_pixel_total : 223656 time to create 1 rle with new method : 0.5145840644836426 time for calcul the mask position with numpy : 0.03517770767211914 nb_pixel_total : 15661 time to create 1 rle with old method : 0.0176239013671875 time for calcul the mask position with numpy : 0.03510713577270508 nb_pixel_total : 30569 time to create 1 rle with old method : 0.03394961357116699 time for calcul the mask position with numpy : 0.034731149673461914 nb_pixel_total : 57236 time to create 1 rle with old method : 0.06368136405944824 time for calcul the mask position with numpy : 0.037349700927734375 nb_pixel_total : 176236 time to create 1 rle with new method : 0.6119816303253174 time for calcul the mask position with numpy : 0.034830570220947266 nb_pixel_total : 19790 time to create 1 rle with old method : 0.022641420364379883 time for calcul the mask position with numpy : 0.033365726470947266 nb_pixel_total : 14810 time to create 1 rle with old method : 0.016784191131591797 time for calcul the mask position with numpy : 0.03456521034240723 nb_pixel_total : 12267 time to create 1 rle with old method : 0.013915777206420898 time for calcul the mask position with numpy : 0.03229856491088867 nb_pixel_total : 19521 time to create 1 rle with old method : 0.02183985710144043 time for calcul the mask position with numpy : 0.030389785766601562 nb_pixel_total : 40010 time to create 1 rle with old method : 0.04496955871582031 time for calcul the mask position with numpy : 0.03285789489746094 nb_pixel_total : 135738 time to create 1 rle with old method : 0.19701743125915527 time for calcul the mask position with numpy : 0.028502941131591797 nb_pixel_total : 667342 time to create 1 rle with new method : 0.45888495445251465 time for calcul the mask position with numpy : 0.022702932357788086 nb_pixel_total : 15837 time to create 1 rle with old method : 0.017461061477661133 time for calcul the mask position with numpy : 0.023866653442382812 nb_pixel_total : 67872 time to create 1 rle with old method : 0.09305524826049805 time for calcul the mask position with numpy : 0.021216392517089844 nb_pixel_total : 4519 time to create 1 rle with old method : 0.0049097537994384766 time for calcul the mask position with numpy : 0.021247148513793945 nb_pixel_total : 27228 time to create 1 rle with old method : 0.03387641906738281 time for calcul the mask position with numpy : 0.03620004653930664 nb_pixel_total : 206965 time to create 1 rle with new method : 0.5261256694793701 time for calcul the mask position with numpy : 0.03498244285583496 nb_pixel_total : 33148 time to create 1 rle with old method : 0.04130673408508301 create new chi : 4.680108547210693 time to delete rle : 0.0024580955505371094 batch 1 Loaded 37 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 13859 TO DO : save crop sub photo not yet done ! save time : 0.8576416969299316 nb_obj : 50 nb_hashtags : 4 time to prepare the origin masks : 4.7725958824157715 time for calcul the mask position with numpy : 0.49205636978149414 nb_pixel_total : 4838199 time to create 1 rle with new method : 0.5416402816772461 time for calcul the mask position with numpy : 0.028937101364135742 nb_pixel_total : 9050 time to create 1 rle with old method : 0.010184526443481445 time for calcul the mask position with numpy : 0.02949666976928711 nb_pixel_total : 61521 time to create 1 rle with old method : 0.06836462020874023 time for calcul the mask position with numpy : 0.028971433639526367 nb_pixel_total : 16496 time to create 1 rle with old method : 0.018390417098999023 time for calcul the mask position with numpy : 0.028873682022094727 nb_pixel_total : 11503 time to create 1 rle with old method : 0.012880086898803711 time for calcul the mask position with numpy : 0.028966426849365234 nb_pixel_total : 20991 time to create 1 rle with old method : 0.0235137939453125 time for calcul the mask position with numpy : 0.028930187225341797 nb_pixel_total : 10388 time to create 1 rle with old method : 0.011623859405517578 time for calcul the mask position with numpy : 0.02924203872680664 nb_pixel_total : 26339 time to create 1 rle with old method : 0.045960187911987305 time for calcul the mask position with numpy : 0.033260345458984375 nb_pixel_total : 8452 time to create 1 rle with old method : 0.01120138168334961 time for calcul the mask position with numpy : 0.03611135482788086 nb_pixel_total : 602802 time to create 1 rle with new method : 0.7646915912628174 time for calcul the mask position with numpy : 0.029743432998657227 nb_pixel_total : 14517 time to create 1 rle with old method : 0.017024517059326172 time for calcul the mask position with numpy : 0.030004024505615234 nb_pixel_total : 60180 time to create 1 rle with old method : 0.06846380233764648 time for calcul the mask position with numpy : 0.029460668563842773 nb_pixel_total : 12658 time to create 1 rle with old method : 0.014446496963500977 time for calcul the mask position with numpy : 0.02957606315612793 nb_pixel_total : 18112 time to create 1 rle with old method : 0.02149200439453125 time for calcul the mask position with numpy : 0.030701637268066406 nb_pixel_total : 137693 time to create 1 rle with old method : 0.1557919979095459 time for calcul the mask position with numpy : 0.029211997985839844 nb_pixel_total : 11050 time to create 1 rle with old method : 0.015810728073120117 time for calcul the mask position with numpy : 0.0332493782043457 nb_pixel_total : 11328 time to create 1 rle with old method : 0.018325090408325195 time for calcul the mask position with numpy : 0.03342556953430176 nb_pixel_total : 82413 time to create 1 rle with old method : 0.09141063690185547 time for calcul the mask position with numpy : 0.030182600021362305 nb_pixel_total : 78179 time to create 1 rle with old method : 0.08784270286560059 time for calcul the mask position with numpy : 0.029953479766845703 nb_pixel_total : 80521 time to create 1 rle with old method : 0.08996033668518066 time for calcul the mask position with numpy : 0.029386520385742188 nb_pixel_total : 13671 time to create 1 rle with old method : 0.015305280685424805 time for calcul the mask position with numpy : 0.030371427536010742 nb_pixel_total : 157984 time to create 1 rle with new method : 1.413419485092163 time for calcul the mask position with numpy : 0.02931833267211914 nb_pixel_total : 16433 time to create 1 rle with old method : 0.01886606216430664 time for calcul the mask position with numpy : 0.029657363891601562 nb_pixel_total : 24073 time to create 1 rle with old method : 0.02664923667907715 time for calcul the mask position with numpy : 0.02911686897277832 nb_pixel_total : 15429 time to create 1 rle with old method : 0.017164230346679688 time for calcul the mask position with numpy : 0.03186821937561035 nb_pixel_total : 28058 time to create 1 rle with old method : 0.03319883346557617 time for calcul the mask position with numpy : 0.029156208038330078 nb_pixel_total : 10871 time to create 1 rle with old method : 0.012281417846679688 time for calcul the mask position with numpy : 0.02920842170715332 nb_pixel_total : 40936 time to create 1 rle with old method : 0.04662322998046875 time for calcul the mask position with numpy : 0.029685497283935547 nb_pixel_total : 22390 time to create 1 rle with old method : 0.02489638328552246 time for calcul the mask position with numpy : 0.029784202575683594 nb_pixel_total : 56021 time to create 1 rle with old method : 0.06285429000854492 time for calcul the mask position with numpy : 0.028937101364135742 nb_pixel_total : 45132 time to create 1 rle with old method : 0.050191402435302734 time for calcul the mask position with numpy : 0.028942108154296875 nb_pixel_total : 24758 time to create 1 rle with old method : 0.027659177780151367 time for calcul the mask position with numpy : 0.028917551040649414 nb_pixel_total : 42951 time to create 1 rle with old method : 0.047711849212646484 time for calcul the mask position with numpy : 0.0289762020111084 nb_pixel_total : 59232 time to create 1 rle with old method : 0.06567239761352539 time for calcul the mask position with numpy : 0.028968334197998047 nb_pixel_total : 43059 time to create 1 rle with old method : 0.06599783897399902 time for calcul the mask position with numpy : 0.032538414001464844 nb_pixel_total : 25187 time to create 1 rle with old method : 0.028209686279296875 time for calcul the mask position with numpy : 0.028977155685424805 nb_pixel_total : 14667 time to create 1 rle with old method : 0.016603946685791016 time for calcul the mask position with numpy : 0.029002666473388672 nb_pixel_total : 60988 time to create 1 rle with old method : 0.06827092170715332 time for calcul the mask position with numpy : 0.02936530113220215 nb_pixel_total : 72095 time to create 1 rle with old method : 0.08013463020324707 time for calcul the mask position with numpy : 0.029090404510498047 nb_pixel_total : 9533 time to create 1 rle with old method : 0.010648250579833984 time for calcul the mask position with numpy : 0.029017210006713867 nb_pixel_total : 35978 time to create 1 rle with old method : 0.0401005744934082 time for calcul the mask position with numpy : 0.02882075309753418 nb_pixel_total : 24833 time to create 1 rle with old method : 0.027560710906982422 time for calcul the mask position with numpy : 0.02909708023071289 nb_pixel_total : 10126 time to create 1 rle with old method : 0.011301755905151367 time for calcul the mask position with numpy : 0.028805971145629883 nb_pixel_total : 22547 time to create 1 rle with old method : 0.025107622146606445 time for calcul the mask position with numpy : 0.028760910034179688 nb_pixel_total : 11592 time to create 1 rle with old method : 0.01293492317199707 time for calcul the mask position with numpy : 0.02888178825378418 nb_pixel_total : 6623 time to create 1 rle with old method : 0.007492542266845703 time for calcul the mask position with numpy : 0.028834819793701172 nb_pixel_total : 9049 time to create 1 rle with old method : 0.01018667221069336 time for calcul the mask position with numpy : 0.028899192810058594 nb_pixel_total : 20047 time to create 1 rle with old method : 0.022525787353515625 time for calcul the mask position with numpy : 0.02891254425048828 nb_pixel_total : 2429 time to create 1 rle with old method : 0.002853870391845703 time for calcul the mask position with numpy : 0.028665542602539062 nb_pixel_total : 6379 time to create 1 rle with old method : 0.0071222782135009766 time for calcul the mask position with numpy : 0.028617143630981445 nb_pixel_total : 4777 time to create 1 rle with old method : 0.005354404449462891 create new chi : 6.454323768615723 time to delete rle : 0.0063304901123046875 batch 1 Loaded 101 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26948 TO DO : save crop sub photo not yet done ! save time : 2.067044496536255 nb_obj : 45 nb_hashtags : 4 time to prepare the origin masks : 3.9760124683380127 time for calcul the mask position with numpy : 0.8561394214630127 nb_pixel_total : 5566881 time to create 1 rle with new method : 1.210005283355713 time for calcul the mask position with numpy : 0.027817726135253906 nb_pixel_total : 31578 time to create 1 rle with old method : 0.034107208251953125 time for calcul the mask position with numpy : 0.02783203125 nb_pixel_total : 17434 time to create 1 rle with old method : 0.019123315811157227 time for calcul the mask position with numpy : 0.028252124786376953 nb_pixel_total : 19696 time to create 1 rle with old method : 0.021900415420532227 time for calcul the mask position with numpy : 0.029050111770629883 nb_pixel_total : 14734 time to create 1 rle with old method : 0.01653909683227539 time for calcul the mask position with numpy : 0.029297590255737305 nb_pixel_total : 10188 time to create 1 rle with old method : 0.011551380157470703 time for calcul the mask position with numpy : 0.03386378288269043 nb_pixel_total : 51424 time to create 1 rle with old method : 0.06400275230407715 time for calcul the mask position with numpy : 0.029824495315551758 nb_pixel_total : 22422 time to create 1 rle with old method : 0.025366783142089844 time for calcul the mask position with numpy : 0.029009580612182617 nb_pixel_total : 20691 time to create 1 rle with old method : 0.023194074630737305 time for calcul the mask position with numpy : 0.029474973678588867 nb_pixel_total : 126689 time to create 1 rle with old method : 0.13742303848266602 time for calcul the mask position with numpy : 0.029010295867919922 nb_pixel_total : 14356 time to create 1 rle with old method : 0.015695571899414062 time for calcul the mask position with numpy : 0.028969526290893555 nb_pixel_total : 58815 time to create 1 rle with old method : 0.06370735168457031 time for calcul the mask position with numpy : 0.028357982635498047 nb_pixel_total : 24208 time to create 1 rle with old method : 0.027080059051513672 time for calcul the mask position with numpy : 0.03176522254943848 nb_pixel_total : 8417 time to create 1 rle with old method : 0.013358116149902344 time for calcul the mask position with numpy : 0.03294634819030762 nb_pixel_total : 6173 time to create 1 rle with old method : 0.010078907012939453 time for calcul the mask position with numpy : 0.02935481071472168 nb_pixel_total : 16554 time to create 1 rle with old method : 0.018692970275878906 time for calcul the mask position with numpy : 0.029256582260131836 nb_pixel_total : 51175 time to create 1 rle with old method : 0.05745983123779297 time for calcul the mask position with numpy : 0.029024839401245117 nb_pixel_total : 33980 time to create 1 rle with old method : 0.03792381286621094 time for calcul the mask position with numpy : 0.029758930206298828 nb_pixel_total : 135561 time to create 1 rle with old method : 0.15024471282958984 time for calcul the mask position with numpy : 0.029055356979370117 nb_pixel_total : 86972 time to create 1 rle with old method : 0.09517121315002441 time for calcul the mask position with numpy : 0.028369903564453125 nb_pixel_total : 15194 time to create 1 rle with old method : 0.016221284866333008 time for calcul the mask position with numpy : 0.027789592742919922 nb_pixel_total : 9792 time to create 1 rle with old method : 0.010916709899902344 time for calcul the mask position with numpy : 0.028305768966674805 nb_pixel_total : 17272 time to create 1 rle with old method : 0.01900768280029297 time for calcul the mask position with numpy : 0.028505802154541016 nb_pixel_total : 42884 time to create 1 rle with old method : 0.04736685752868652 time for calcul the mask position with numpy : 0.028219938278198242 nb_pixel_total : 13366 time to create 1 rle with old method : 0.01447439193725586 time for calcul the mask position with numpy : 0.027534961700439453 nb_pixel_total : 5517 time to create 1 rle with old method : 0.006059408187866211 time for calcul the mask position with numpy : 0.02810382843017578 nb_pixel_total : 11440 time to create 1 rle with old method : 0.012673139572143555 time for calcul the mask position with numpy : 0.028655052185058594 nb_pixel_total : 21763 time to create 1 rle with old method : 0.023509979248046875 time for calcul the mask position with numpy : 0.029329538345336914 nb_pixel_total : 45381 time to create 1 rle with old method : 0.04928898811340332 time for calcul the mask position with numpy : 0.027404069900512695 nb_pixel_total : 46475 time to create 1 rle with old method : 0.051306962966918945 time for calcul the mask position with numpy : 0.028928279876708984 nb_pixel_total : 47941 time to create 1 rle with old method : 0.05359196662902832 time for calcul the mask position with numpy : 0.028629064559936523 nb_pixel_total : 14059 time to create 1 rle with old method : 0.015459299087524414 time for calcul the mask position with numpy : 0.028558969497680664 nb_pixel_total : 46865 time to create 1 rle with old method : 0.05147600173950195 time for calcul the mask position with numpy : 0.029598712921142578 nb_pixel_total : 27712 time to create 1 rle with old method : 0.032419443130493164 time for calcul the mask position with numpy : 0.02914905548095703 nb_pixel_total : 14472 time to create 1 rle with old method : 0.01608443260192871 time for calcul the mask position with numpy : 0.029441356658935547 nb_pixel_total : 62338 time to create 1 rle with old method : 0.06881499290466309 time for calcul the mask position with numpy : 0.029036521911621094 nb_pixel_total : 34806 time to create 1 rle with old method : 0.03858780860900879 time for calcul the mask position with numpy : 0.028952836990356445 nb_pixel_total : 22719 time to create 1 rle with old method : 0.025171518325805664 time for calcul the mask position with numpy : 0.02905750274658203 nb_pixel_total : 8151 time to create 1 rle with old method : 0.009143352508544922 time for calcul the mask position with numpy : 0.029048681259155273 nb_pixel_total : 59912 time to create 1 rle with old method : 0.06654882431030273 time for calcul the mask position with numpy : 0.028905630111694336 nb_pixel_total : 15636 time to create 1 rle with old method : 0.01746535301208496 time for calcul the mask position with numpy : 0.028931617736816406 nb_pixel_total : 31629 time to create 1 rle with old method : 0.0438079833984375 time for calcul the mask position with numpy : 0.0291445255279541 nb_pixel_total : 5538 time to create 1 rle with old method : 0.006329536437988281 time for calcul the mask position with numpy : 0.02943110466003418 nb_pixel_total : 46931 time to create 1 rle with old method : 0.05173683166503906 time for calcul the mask position with numpy : 0.029140472412109375 nb_pixel_total : 37101 time to create 1 rle with old method : 0.04105782508850098 time for calcul the mask position with numpy : 0.02976846694946289 nb_pixel_total : 27398 time to create 1 rle with old method : 0.03058910369873047 create new chi : 5.070809364318848 time to delete rle : 0.005648136138916016 batch 1 Loaded 91 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23226 TO DO : save crop sub photo not yet done ! save time : 2.3320493698120117 nb_obj : 11 nb_hashtags : 3 time to prepare the origin masks : 4.108435153961182 time for calcul the mask position with numpy : 0.35750532150268555 nb_pixel_total : 6811082 time to create 1 rle with new method : 0.8543059825897217 time for calcul the mask position with numpy : 0.0222930908203125 nb_pixel_total : 13036 time to create 1 rle with old method : 0.014526128768920898 time for calcul the mask position with numpy : 0.02196216583251953 nb_pixel_total : 18359 time to create 1 rle with old method : 0.02032613754272461 time for calcul the mask position with numpy : 0.02225351333618164 nb_pixel_total : 56932 time to create 1 rle with old method : 0.07325983047485352 time for calcul the mask position with numpy : 0.02455759048461914 nb_pixel_total : 11597 time to create 1 rle with old method : 0.01881885528564453 time for calcul the mask position with numpy : 0.023019075393676758 nb_pixel_total : 25931 time to create 1 rle with old method : 0.028980493545532227 time for calcul the mask position with numpy : 0.02181720733642578 nb_pixel_total : 24720 time to create 1 rle with old method : 0.02778482437133789 time for calcul the mask position with numpy : 0.022480249404907227 nb_pixel_total : 19184 time to create 1 rle with old method : 0.02170705795288086 time for calcul the mask position with numpy : 0.02131485939025879 nb_pixel_total : 20927 time to create 1 rle with old method : 0.0234835147857666 time for calcul the mask position with numpy : 0.021155595779418945 nb_pixel_total : 13897 time to create 1 rle with old method : 0.01567983627319336 time for calcul the mask position with numpy : 0.029471397399902344 nb_pixel_total : 13017 time to create 1 rle with old method : 0.01453852653503418 time for calcul the mask position with numpy : 0.037286996841430664 nb_pixel_total : 21558 time to create 1 rle with old method : 0.02417135238647461 create new chi : 1.7967000007629395 time to delete rle : 0.0010073184967041016 batch 1 Loaded 23 chid ids of type : 3594 ++++++++++++Number RLEs to save : 6146 TO DO : save crop sub photo not yet done ! save time : 0.5210556983947754 nb_obj : 72 nb_hashtags : 4 time to prepare the origin masks : 4.626421689987183 time for calcul the mask position with numpy : 0.6042337417602539 nb_pixel_total : 5296714 time to create 1 rle with new method : 0.8165180683135986 time for calcul the mask position with numpy : 0.029690265655517578 nb_pixel_total : 58743 time to create 1 rle with old method : 0.06638431549072266 time for calcul the mask position with numpy : 0.029043197631835938 nb_pixel_total : 4431 time to create 1 rle with old method : 0.0049059391021728516 time for calcul the mask position with numpy : 0.030488967895507812 nb_pixel_total : 43597 time to create 1 rle with old method : 0.04633474349975586 time for calcul the mask position with numpy : 0.027875661849975586 nb_pixel_total : 5679 time to create 1 rle with old method : 0.006394386291503906 time for calcul the mask position with numpy : 0.02823638916015625 nb_pixel_total : 3601 time to create 1 rle with old method : 0.004098415374755859 time for calcul the mask position with numpy : 0.029854774475097656 nb_pixel_total : 168178 time to create 1 rle with new method : 0.5279755592346191 time for calcul the mask position with numpy : 0.02810978889465332 nb_pixel_total : 16438 time to create 1 rle with old method : 0.01798558235168457 time for calcul the mask position with numpy : 0.02817225456237793 nb_pixel_total : 16132 time to create 1 rle with old method : 0.017586469650268555 time for calcul the mask position with numpy : 0.02931666374206543 nb_pixel_total : 5202 time to create 1 rle with old method : 0.006302356719970703 time for calcul the mask position with numpy : 0.028586864471435547 nb_pixel_total : 13528 time to create 1 rle with old method : 0.015052318572998047 time for calcul the mask position with numpy : 0.028719186782836914 nb_pixel_total : 6246 time to create 1 rle with old method : 0.006908416748046875 time for calcul the mask position with numpy : 0.02814316749572754 nb_pixel_total : 52052 time to create 1 rle with old method : 0.057312965393066406 time for calcul the mask position with numpy : 0.028503894805908203 nb_pixel_total : 31324 time to create 1 rle with old method : 0.03476524353027344 time for calcul the mask position with numpy : 0.02880239486694336 nb_pixel_total : 14844 time to create 1 rle with old method : 0.016084671020507812 time for calcul the mask position with numpy : 0.028663158416748047 nb_pixel_total : 14067 time to create 1 rle with old method : 0.015625715255737305 time for calcul the mask position with numpy : 0.02814030647277832 nb_pixel_total : 15098 time to create 1 rle with old method : 0.016270160675048828 time for calcul the mask position with numpy : 0.02866506576538086 nb_pixel_total : 45338 time to create 1 rle with old method : 0.04875540733337402 time for calcul the mask position with numpy : 0.027910232543945312 nb_pixel_total : 4886 time to create 1 rle with old method : 0.005483865737915039 time for calcul the mask position with numpy : 0.02878880500793457 nb_pixel_total : 97393 time to create 1 rle with old method : 0.10539054870605469 time for calcul the mask position with numpy : 0.027405977249145508 nb_pixel_total : 31827 time to create 1 rle with old method : 0.03460192680358887 time for calcul the mask position with numpy : 0.028784751892089844 nb_pixel_total : 4650 time to create 1 rle with old method : 0.005120515823364258 time for calcul the mask position with numpy : 0.028598785400390625 nb_pixel_total : 19690 time to create 1 rle with old method : 0.025371551513671875 time for calcul the mask position with numpy : 0.028838634490966797 nb_pixel_total : 100060 time to create 1 rle with old method : 0.10848832130432129 time for calcul the mask position with numpy : 0.027423381805419922 nb_pixel_total : 48882 time to create 1 rle with old method : 0.051923274993896484 time for calcul the mask position with numpy : 0.027878522872924805 nb_pixel_total : 13221 time to create 1 rle with old method : 0.014055490493774414 time for calcul the mask position with numpy : 0.028222084045410156 nb_pixel_total : 26356 time to create 1 rle with old method : 0.029171466827392578 time for calcul the mask position with numpy : 0.030011653900146484 nb_pixel_total : 11281 time to create 1 rle with old method : 0.013187170028686523 time for calcul the mask position with numpy : 0.029738664627075195 nb_pixel_total : 12415 time to create 1 rle with old method : 0.014549493789672852 time for calcul the mask position with numpy : 0.030687808990478516 nb_pixel_total : 46789 time to create 1 rle with old method : 0.0645284652709961 time for calcul the mask position with numpy : 0.029138803482055664 nb_pixel_total : 36533 time to create 1 rle with old method : 0.04095959663391113 time for calcul the mask position with numpy : 0.029159069061279297 nb_pixel_total : 25955 time to create 1 rle with old method : 0.029764175415039062 time for calcul the mask position with numpy : 0.02923297882080078 nb_pixel_total : 9015 time to create 1 rle with old method : 0.010126352310180664 time for calcul the mask position with numpy : 0.0296328067779541 nb_pixel_total : 17471 time to create 1 rle with old method : 0.021368980407714844 time for calcul the mask position with numpy : 0.029813051223754883 nb_pixel_total : 45515 time to create 1 rle with old method : 0.052989959716796875 time for calcul the mask position with numpy : 0.030276060104370117 nb_pixel_total : 12304 time to create 1 rle with old method : 0.01445627212524414 time for calcul the mask position with numpy : 0.028735876083374023 nb_pixel_total : 98752 time to create 1 rle with old method : 0.11029338836669922 time for calcul the mask position with numpy : 0.02945733070373535 nb_pixel_total : 8536 time to create 1 rle with old method : 0.010312080383300781 time for calcul the mask position with numpy : 0.033331871032714844 nb_pixel_total : 40339 time to create 1 rle with old method : 0.060175418853759766 time for calcul the mask position with numpy : 0.028345346450805664 nb_pixel_total : 9989 time to create 1 rle with old method : 0.010930776596069336 time for calcul the mask position with numpy : 0.029059886932373047 nb_pixel_total : 9497 time to create 1 rle with old method : 0.010642290115356445 time for calcul the mask position with numpy : 0.028874635696411133 nb_pixel_total : 8334 time to create 1 rle with old method : 0.011118173599243164 time for calcul the mask position with numpy : 0.03335833549499512 nb_pixel_total : 130 time to create 1 rle with old method : 0.0002892017364501953 time for calcul the mask position with numpy : 0.03334188461303711 nb_pixel_total : 6458 time to create 1 rle with old method : 0.01054239273071289 time for calcul the mask position with numpy : 0.02979111671447754 nb_pixel_total : 30529 time to create 1 rle with old method : 0.03403043746948242 time for calcul the mask position with numpy : 0.029439210891723633 nb_pixel_total : 10927 time to create 1 rle with old method : 0.012274026870727539 time for calcul the mask position with numpy : 0.02877640724182129 nb_pixel_total : 8289 time to create 1 rle with old method : 0.009077787399291992 time for calcul the mask position with numpy : 0.028894424438476562 nb_pixel_total : 3033 time to create 1 rle with old method : 0.003416776657104492 time for calcul the mask position with numpy : 0.029418468475341797 nb_pixel_total : 1637 time to create 1 rle with old method : 0.0029180049896240234 time for calcul the mask position with numpy : 0.03295469284057617 nb_pixel_total : 6610 time to create 1 rle with old method : 0.008152961730957031 time for calcul the mask position with numpy : 0.029056549072265625 nb_pixel_total : 10445 time to create 1 rle with old method : 0.011538028717041016 time for calcul the mask position with numpy : 0.02941107749938965 nb_pixel_total : 47744 time to create 1 rle with old method : 0.05379629135131836 time for calcul the mask position with numpy : 0.02900075912475586 nb_pixel_total : 31973 time to create 1 rle with old method : 0.03693747520446777 time for calcul the mask position with numpy : 0.029737472534179688 nb_pixel_total : 17620 time to create 1 rle with old method : 0.020586013793945312 time for calcul the mask position with numpy : 0.02994227409362793 nb_pixel_total : 26800 time to create 1 rle with old method : 0.029642105102539062 time for calcul the mask position with numpy : 0.029786348342895508 nb_pixel_total : 44194 time to create 1 rle with old method : 0.050385475158691406 time for calcul the mask position with numpy : 0.028975486755371094 nb_pixel_total : 10716 time to create 1 rle with old method : 0.01190328598022461 time for calcul the mask position with numpy : 0.029080629348754883 nb_pixel_total : 15424 time to create 1 rle with old method : 0.018354177474975586 time for calcul the mask position with numpy : 0.02859330177307129 nb_pixel_total : 6608 time to create 1 rle with old method : 0.007264614105224609 time for calcul the mask position with numpy : 0.028774261474609375 nb_pixel_total : 9933 time to create 1 rle with old method : 0.011507987976074219 time for calcul the mask position with numpy : 0.029502153396606445 nb_pixel_total : 50488 time to create 1 rle with old method : 0.05834221839904785 time for calcul the mask position with numpy : 0.029458999633789062 nb_pixel_total : 7827 time to create 1 rle with old method : 0.009815692901611328 time for calcul the mask position with numpy : 0.030579328536987305 nb_pixel_total : 20154 time to create 1 rle with old method : 0.022423505783081055 time for calcul the mask position with numpy : 0.0287930965423584 nb_pixel_total : 25010 time to create 1 rle with old method : 0.027463197708129883 time for calcul the mask position with numpy : 0.028116703033447266 nb_pixel_total : 5738 time to create 1 rle with old method : 0.006440877914428711 time for calcul the mask position with numpy : 0.029094934463500977 nb_pixel_total : 7514 time to create 1 rle with old method : 0.008382081985473633 time for calcul the mask position with numpy : 0.029196739196777344 nb_pixel_total : 6574 time to create 1 rle with old method : 0.0075092315673828125 time for calcul the mask position with numpy : 0.029154062271118164 nb_pixel_total : 9341 time to create 1 rle with old method : 0.01047205924987793 time for calcul the mask position with numpy : 0.029019832611083984 nb_pixel_total : 13565 time to create 1 rle with old method : 0.015327930450439453 time for calcul the mask position with numpy : 0.029064178466796875 nb_pixel_total : 11934 time to create 1 rle with old method : 0.013424873352050781 time for calcul the mask position with numpy : 0.029598474502563477 nb_pixel_total : 14427 time to create 1 rle with old method : 0.01623368263244629 time for calcul the mask position with numpy : 0.029265880584716797 nb_pixel_total : 27852 time to create 1 rle with old method : 0.031259775161743164 time for calcul the mask position with numpy : 0.029228925704956055 nb_pixel_total : 9844 time to create 1 rle with old method : 0.010956525802612305 create new chi : 5.9195640087127686 time to delete rle : 0.0044519901275634766 batch 1 Loaded 145 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28897 TO DO : save crop sub photo not yet done ! save time : 5.144479036331177 nb_obj : 51 nb_hashtags : 7 time to prepare the origin masks : 5.429636001586914 time for calcul the mask position with numpy : 0.2897982597351074 nb_pixel_total : 4713968 time to create 1 rle with new method : 0.882760763168335 time for calcul the mask position with numpy : 0.03121209144592285 nb_pixel_total : 31522 time to create 1 rle with old method : 0.0517725944519043 time for calcul the mask position with numpy : 0.03194856643676758 nb_pixel_total : 15905 time to create 1 rle with old method : 0.017709732055664062 time for calcul the mask position with numpy : 0.02902388572692871 nb_pixel_total : 2692 time to create 1 rle with old method : 0.0030317306518554688 time for calcul the mask position with numpy : 0.029534578323364258 nb_pixel_total : 3819 time to create 1 rle with old method : 0.00434112548828125 time for calcul the mask position with numpy : 0.029810190200805664 nb_pixel_total : 18981 time to create 1 rle with old method : 0.02136063575744629 time for calcul the mask position with numpy : 0.029517412185668945 nb_pixel_total : 54302 time to create 1 rle with old method : 0.06085062026977539 time for calcul the mask position with numpy : 0.030768632888793945 nb_pixel_total : 34561 time to create 1 rle with old method : 0.03840994834899902 time for calcul the mask position with numpy : 0.029229402542114258 nb_pixel_total : 11536 time to create 1 rle with old method : 0.012838363647460938 time for calcul the mask position with numpy : 0.02998805046081543 nb_pixel_total : 61662 time to create 1 rle with old method : 0.06843948364257812 time for calcul the mask position with numpy : 0.028979778289794922 nb_pixel_total : 20606 time to create 1 rle with old method : 0.02321481704711914 time for calcul the mask position with numpy : 0.03054046630859375 nb_pixel_total : 132234 time to create 1 rle with old method : 0.1471247673034668 time for calcul the mask position with numpy : 0.029274940490722656 nb_pixel_total : 10346 time to create 1 rle with old method : 0.011613607406616211 time for calcul the mask position with numpy : 0.029169082641601562 nb_pixel_total : 43455 time to create 1 rle with old method : 0.04782557487487793 time for calcul the mask position with numpy : 0.030226945877075195 nb_pixel_total : 126695 time to create 1 rle with old method : 0.1487712860107422 time for calcul the mask position with numpy : 0.028797388076782227 nb_pixel_total : 14092 time to create 1 rle with old method : 0.015865564346313477 time for calcul the mask position with numpy : 0.02958226203918457 nb_pixel_total : 23274 time to create 1 rle with old method : 0.036298513412475586 time for calcul the mask position with numpy : 0.03229975700378418 nb_pixel_total : 5781 time to create 1 rle with old method : 0.00890350341796875 time for calcul the mask position with numpy : 0.03313732147216797 nb_pixel_total : 64516 time to create 1 rle with old method : 0.07178282737731934 time for calcul the mask position with numpy : 0.029100894927978516 nb_pixel_total : 8235 time to create 1 rle with old method : 0.010628938674926758 time for calcul the mask position with numpy : 0.02927231788635254 nb_pixel_total : 9949 time to create 1 rle with old method : 0.01122283935546875 time for calcul the mask position with numpy : 0.03027176856994629 nb_pixel_total : 152158 time to create 1 rle with new method : 0.6271111965179443 time for calcul the mask position with numpy : 0.029056310653686523 nb_pixel_total : 28011 time to create 1 rle with old method : 0.03080892562866211 time for calcul the mask position with numpy : 0.028992414474487305 nb_pixel_total : 8272 time to create 1 rle with old method : 0.00921177864074707 time for calcul the mask position with numpy : 0.03259992599487305 nb_pixel_total : 37060 time to create 1 rle with old method : 0.04558229446411133 time for calcul the mask position with numpy : 0.029140233993530273 nb_pixel_total : 52597 time to create 1 rle with old method : 0.0579228401184082 time for calcul the mask position with numpy : 0.02870941162109375 nb_pixel_total : 6745 time to create 1 rle with old method : 0.007405996322631836 time for calcul the mask position with numpy : 0.030269384384155273 nb_pixel_total : 45966 time to create 1 rle with old method : 0.05115985870361328 time for calcul the mask position with numpy : 0.029154062271118164 nb_pixel_total : 14296 time to create 1 rle with old method : 0.016086816787719727 time for calcul the mask position with numpy : 0.030292272567749023 nb_pixel_total : 201458 time to create 1 rle with new method : 0.36469125747680664 time for calcul the mask position with numpy : 0.028550386428833008 nb_pixel_total : 5687 time to create 1 rle with old method : 0.00667881965637207 time for calcul the mask position with numpy : 0.027994394302368164 nb_pixel_total : 65405 time to create 1 rle with old method : 0.07175469398498535 time for calcul the mask position with numpy : 0.029012203216552734 nb_pixel_total : 40703 time to create 1 rle with old method : 0.04537487030029297 time for calcul the mask position with numpy : 0.03337574005126953 nb_pixel_total : 442288 time to create 1 rle with new method : 0.5575578212738037 time for calcul the mask position with numpy : 0.029816865921020508 nb_pixel_total : 74009 time to create 1 rle with old method : 0.08391284942626953 time for calcul the mask position with numpy : 0.028904199600219727 nb_pixel_total : 8835 time to create 1 rle with old method : 0.010001182556152344 time for calcul the mask position with numpy : 0.02840423583984375 nb_pixel_total : 7141 time to create 1 rle with old method : 0.007999181747436523 time for calcul the mask position with numpy : 0.029008865356445312 nb_pixel_total : 32428 time to create 1 rle with old method : 0.0369725227355957 time for calcul the mask position with numpy : 0.03276681900024414 nb_pixel_total : 46942 time to create 1 rle with old method : 0.05287003517150879 time for calcul the mask position with numpy : 0.029293537139892578 nb_pixel_total : 61745 time to create 1 rle with old method : 0.06974458694458008 time for calcul the mask position with numpy : 0.030142545700073242 nb_pixel_total : 53194 time to create 1 rle with old method : 0.05936241149902344 time for calcul the mask position with numpy : 0.02947688102722168 nb_pixel_total : 53144 time to create 1 rle with old method : 0.059226036071777344 time for calcul the mask position with numpy : 0.029268503189086914 nb_pixel_total : 9447 time to create 1 rle with old method : 0.010696172714233398 time for calcul the mask position with numpy : 0.02944636344909668 nb_pixel_total : 77521 time to create 1 rle with old method : 0.08669924736022949 time for calcul the mask position with numpy : 0.03315877914428711 nb_pixel_total : 15367 time to create 1 rle with old method : 0.024765729904174805 time for calcul the mask position with numpy : 0.03266024589538574 nb_pixel_total : 58142 time to create 1 rle with old method : 0.06627917289733887 time for calcul the mask position with numpy : 0.02912616729736328 nb_pixel_total : 5288 time to create 1 rle with old method : 0.005929231643676758 time for calcul the mask position with numpy : 0.02897191047668457 nb_pixel_total : 4100 time to create 1 rle with old method : 0.004675865173339844 time for calcul the mask position with numpy : 0.02924180030822754 nb_pixel_total : 20576 time to create 1 rle with old method : 0.022890806198120117 time for calcul the mask position with numpy : 0.029083967208862305 nb_pixel_total : 7399 time to create 1 rle with old method : 0.008389711380004883 time for calcul the mask position with numpy : 0.029031753540039062 nb_pixel_total : 709 time to create 1 rle with old method : 0.0009763240814208984 time for calcul the mask position with numpy : 0.029041051864624023 nb_pixel_total : 5476 time to create 1 rle with old method : 0.006220102310180664 create new chi : 6.1349778175354 time to delete rle : 0.006666660308837891 batch 1 Loaded 104 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27017 TO DO : save crop sub photo not yet done ! save time : 1.9913051128387451 nb_obj : 8 nb_hashtags : 3 time to prepare the origin masks : 4.14001727104187 time for calcul the mask position with numpy : 0.23353123664855957 nb_pixel_total : 5856960 time to create 1 rle with new method : 1.014068603515625 time for calcul the mask position with numpy : 0.02379155158996582 nb_pixel_total : 14386 time to create 1 rle with old method : 0.01892375946044922 time for calcul the mask position with numpy : 0.02607560157775879 nb_pixel_total : 120331 time to create 1 rle with old method : 0.13565587997436523 time for calcul the mask position with numpy : 0.026636123657226562 nb_pixel_total : 101873 time to create 1 rle with old method : 0.12047290802001953 time for calcul the mask position with numpy : 0.031810760498046875 nb_pixel_total : 674695 time to create 1 rle with new method : 1.094174861907959 time for calcul the mask position with numpy : 0.025559425354003906 nb_pixel_total : 249025 time to create 1 rle with new method : 0.5358884334564209 time for calcul the mask position with numpy : 0.02561473846435547 nb_pixel_total : 14473 time to create 1 rle with old method : 0.022699356079101562 time for calcul the mask position with numpy : 0.023630857467651367 nb_pixel_total : 13893 time to create 1 rle with old method : 0.01642465591430664 time for calcul the mask position with numpy : 0.03415679931640625 nb_pixel_total : 4604 time to create 1 rle with old method : 0.005326986312866211 create new chi : 3.511643648147583 time to delete rle : 0.0011506080627441406 batch 1 Loaded 17 chid ids of type : 3594 ++++++++++Number RLEs to save : 7124 TO DO : save crop sub photo not yet done ! save time : 1.119415283203125 nb_obj : 50 nb_hashtags : 3 time to prepare the origin masks : 4.8977296352386475 time for calcul the mask position with numpy : 0.7257812023162842 nb_pixel_total : 4838253 time to create 1 rle with new method : 0.8218402862548828 time for calcul the mask position with numpy : 0.033240318298339844 nb_pixel_total : 33606 time to create 1 rle with old method : 0.04887890815734863 time for calcul the mask position with numpy : 0.03409457206726074 nb_pixel_total : 2744 time to create 1 rle with old method : 0.004060983657836914 time for calcul the mask position with numpy : 0.032929182052612305 nb_pixel_total : 12341 time to create 1 rle with old method : 0.015816211700439453 time for calcul the mask position with numpy : 0.03130984306335449 nb_pixel_total : 22021 time to create 1 rle with old method : 0.02757430076599121 time for calcul the mask position with numpy : 0.03156447410583496 nb_pixel_total : 5154 time to create 1 rle with old method : 0.0059549808502197266 time for calcul the mask position with numpy : 0.03233027458190918 nb_pixel_total : 12604 time to create 1 rle with old method : 0.014392852783203125 time for calcul the mask position with numpy : 0.03134632110595703 nb_pixel_total : 94602 time to create 1 rle with old method : 0.12285852432250977 time for calcul the mask position with numpy : 0.031461238861083984 nb_pixel_total : 45143 time to create 1 rle with old method : 0.05154705047607422 time for calcul the mask position with numpy : 0.032624006271362305 nb_pixel_total : 52564 time to create 1 rle with old method : 0.11348915100097656 time for calcul the mask position with numpy : 0.03675127029418945 nb_pixel_total : 25961 time to create 1 rle with old method : 0.04081535339355469 time for calcul the mask position with numpy : 0.0365138053894043 nb_pixel_total : 49242 time to create 1 rle with old method : 0.07680249214172363 time for calcul the mask position with numpy : 0.037834882736206055 nb_pixel_total : 24725 time to create 1 rle with old method : 0.03087639808654785 time for calcul the mask position with numpy : 0.03089165687561035 nb_pixel_total : 21204 time to create 1 rle with old method : 0.02440476417541504 time for calcul the mask position with numpy : 0.03007984161376953 nb_pixel_total : 45628 time to create 1 rle with old method : 0.052564382553100586 time for calcul the mask position with numpy : 0.029989242553710938 nb_pixel_total : 19338 time to create 1 rle with old method : 0.021765470504760742 time for calcul the mask position with numpy : 0.03650856018066406 nb_pixel_total : 301147 time to create 1 rle with new method : 0.36733078956604004 time for calcul the mask position with numpy : 0.030498504638671875 nb_pixel_total : 28506 time to create 1 rle with old method : 0.03251481056213379 time for calcul the mask position with numpy : 0.030608177185058594 nb_pixel_total : 9604 time to create 1 rle with old method : 0.01096487045288086 time for calcul the mask position with numpy : 0.030704021453857422 nb_pixel_total : 10325 time to create 1 rle with old method : 0.011685609817504883 time for calcul the mask position with numpy : 0.030170917510986328 nb_pixel_total : 40724 time to create 1 rle with old method : 0.047951698303222656 time for calcul the mask position with numpy : 0.03221750259399414 nb_pixel_total : 76527 time to create 1 rle with old method : 0.09491658210754395 time for calcul the mask position with numpy : 0.030498743057250977 nb_pixel_total : 4183 time to create 1 rle with old method : 0.00501561164855957 time for calcul the mask position with numpy : 0.031648874282836914 nb_pixel_total : 105553 time to create 1 rle with old method : 0.12508249282836914 time for calcul the mask position with numpy : 0.033286333084106445 nb_pixel_total : 285461 time to create 1 rle with new method : 0.5458593368530273 time for calcul the mask position with numpy : 0.030642986297607422 nb_pixel_total : 186054 time to create 1 rle with new method : 0.38976287841796875 time for calcul the mask position with numpy : 0.030331134796142578 nb_pixel_total : 27677 time to create 1 rle with old method : 0.03722047805786133 time for calcul the mask position with numpy : 0.037534475326538086 nb_pixel_total : 30352 time to create 1 rle with old method : 0.05225992202758789 time for calcul the mask position with numpy : 0.030478477478027344 nb_pixel_total : 5024 time to create 1 rle with old method : 0.005900382995605469 time for calcul the mask position with numpy : 0.030353307723999023 nb_pixel_total : 32601 time to create 1 rle with old method : 0.04704904556274414 time for calcul the mask position with numpy : 0.04243206977844238 nb_pixel_total : 80063 time to create 1 rle with old method : 0.10408377647399902 time for calcul the mask position with numpy : 0.029851675033569336 nb_pixel_total : 35097 time to create 1 rle with old method : 0.046617746353149414 time for calcul the mask position with numpy : 0.032396554946899414 nb_pixel_total : 13719 time to create 1 rle with old method : 0.016892433166503906 time for calcul the mask position with numpy : 0.030362367630004883 nb_pixel_total : 71111 time to create 1 rle with old method : 0.08620619773864746 time for calcul the mask position with numpy : 0.034651756286621094 nb_pixel_total : 36542 time to create 1 rle with old method : 0.045049428939819336 time for calcul the mask position with numpy : 0.0298311710357666 nb_pixel_total : 18315 time to create 1 rle with old method : 0.02366495132446289 time for calcul the mask position with numpy : 0.035622358322143555 nb_pixel_total : 35458 time to create 1 rle with old method : 0.041773319244384766 time for calcul the mask position with numpy : 0.029369354248046875 nb_pixel_total : 3604 time to create 1 rle with old method : 0.0041046142578125 time for calcul the mask position with numpy : 0.029714345932006836 nb_pixel_total : 21149 time to create 1 rle with old method : 0.023548364639282227 time for calcul the mask position with numpy : 0.029373645782470703 nb_pixel_total : 17642 time to create 1 rle with old method : 0.019634246826171875 time for calcul the mask position with numpy : 0.029713153839111328 nb_pixel_total : 29947 time to create 1 rle with old method : 0.03635549545288086 time for calcul the mask position with numpy : 0.031094789505004883 nb_pixel_total : 5587 time to create 1 rle with old method : 0.007485389709472656 time for calcul the mask position with numpy : 0.03150224685668945 nb_pixel_total : 20058 time to create 1 rle with old method : 0.024046659469604492 time for calcul the mask position with numpy : 0.03376126289367676 nb_pixel_total : 79543 time to create 1 rle with old method : 0.09389543533325195 time for calcul the mask position with numpy : 0.031188249588012695 nb_pixel_total : 18924 time to create 1 rle with old method : 0.02477097511291504 time for calcul the mask position with numpy : 0.03147745132446289 nb_pixel_total : 32401 time to create 1 rle with old method : 0.03642129898071289 time for calcul the mask position with numpy : 0.02953934669494629 nb_pixel_total : 18668 time to create 1 rle with old method : 0.02122020721435547 time for calcul the mask position with numpy : 0.030318498611450195 nb_pixel_total : 219 time to create 1 rle with old method : 0.00040149688720703125 time for calcul the mask position with numpy : 0.029680252075195312 nb_pixel_total : 25432 time to create 1 rle with old method : 0.028905391693115234 time for calcul the mask position with numpy : 0.03010106086730957 nb_pixel_total : 17636 time to create 1 rle with old method : 0.01978445053100586 time for calcul the mask position with numpy : 0.029465675354003906 nb_pixel_total : 20257 time to create 1 rle with old method : 0.023702144622802734 create new chi : 6.426865339279175 time to delete rle : 0.0061740875244140625 batch 1 Loaded 101 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 30233 TO DO : save crop sub photo not yet done ! save time : 2.3023617267608643 nb_obj : 33 nb_hashtags : 4 time to prepare the origin masks : 5.036189079284668 time for calcul the mask position with numpy : 0.4134945869445801 nb_pixel_total : 4916413 time to create 1 rle with new method : 0.7534012794494629 time for calcul the mask position with numpy : 0.02927684783935547 nb_pixel_total : 44064 time to create 1 rle with old method : 0.04949545860290527 time for calcul the mask position with numpy : 0.02944159507751465 nb_pixel_total : 3446 time to create 1 rle with old method : 0.003952980041503906 time for calcul the mask position with numpy : 0.029330015182495117 nb_pixel_total : 27043 time to create 1 rle with old method : 0.02988910675048828 time for calcul the mask position with numpy : 0.028908491134643555 nb_pixel_total : 34632 time to create 1 rle with old method : 0.038971662521362305 time for calcul the mask position with numpy : 0.029308319091796875 nb_pixel_total : 46109 time to create 1 rle with old method : 0.050632476806640625 time for calcul the mask position with numpy : 0.029923677444458008 nb_pixel_total : 79452 time to create 1 rle with old method : 0.0941159725189209 time for calcul the mask position with numpy : 0.034417152404785156 nb_pixel_total : 10306 time to create 1 rle with old method : 0.011645317077636719 time for calcul the mask position with numpy : 0.029251813888549805 nb_pixel_total : 17001 time to create 1 rle with old method : 0.01902604103088379 time for calcul the mask position with numpy : 0.02983880043029785 nb_pixel_total : 51241 time to create 1 rle with old method : 0.057399749755859375 time for calcul the mask position with numpy : 0.02954387664794922 nb_pixel_total : 8161 time to create 1 rle with old method : 0.009257078170776367 time for calcul the mask position with numpy : 0.029330968856811523 nb_pixel_total : 20439 time to create 1 rle with old method : 0.02306842803955078 time for calcul the mask position with numpy : 0.031247377395629883 nb_pixel_total : 80237 time to create 1 rle with old method : 0.08973217010498047 time for calcul the mask position with numpy : 0.030551433563232422 nb_pixel_total : 127987 time to create 1 rle with old method : 0.14241480827331543 time for calcul the mask position with numpy : 0.02991652488708496 nb_pixel_total : 11678 time to create 1 rle with old method : 0.013162374496459961 time for calcul the mask position with numpy : 0.03287220001220703 nb_pixel_total : 136715 time to create 1 rle with old method : 0.1530439853668213 time for calcul the mask position with numpy : 0.029529094696044922 nb_pixel_total : 12386 time to create 1 rle with old method : 0.013972282409667969 time for calcul the mask position with numpy : 0.030163288116455078 nb_pixel_total : 78887 time to create 1 rle with old method : 0.08774495124816895 time for calcul the mask position with numpy : 0.03311610221862793 nb_pixel_total : 12594 time to create 1 rle with old method : 0.014303445816040039 time for calcul the mask position with numpy : 0.02968287467956543 nb_pixel_total : 22188 time to create 1 rle with old method : 0.02497577667236328 time for calcul the mask position with numpy : 0.031172752380371094 nb_pixel_total : 111426 time to create 1 rle with old method : 0.12428593635559082 time for calcul the mask position with numpy : 0.02970099449157715 nb_pixel_total : 16155 time to create 1 rle with old method : 0.018238067626953125 time for calcul the mask position with numpy : 0.03208804130554199 nb_pixel_total : 200590 time to create 1 rle with new method : 0.9810700416564941 time for calcul the mask position with numpy : 0.03477883338928223 nb_pixel_total : 183381 time to create 1 rle with new method : 0.8621642589569092 time for calcul the mask position with numpy : 0.038585662841796875 nb_pixel_total : 52645 time to create 1 rle with old method : 0.07453179359436035 time for calcul the mask position with numpy : 0.034897804260253906 nb_pixel_total : 14833 time to create 1 rle with old method : 0.024434328079223633 time for calcul the mask position with numpy : 0.03602313995361328 nb_pixel_total : 113604 time to create 1 rle with old method : 0.1365959644317627 time for calcul the mask position with numpy : 0.02953791618347168 nb_pixel_total : 85301 time to create 1 rle with old method : 0.10052037239074707 time for calcul the mask position with numpy : 0.03895258903503418 nb_pixel_total : 225957 time to create 1 rle with new method : 0.5982732772827148 time for calcul the mask position with numpy : 0.03485608100891113 nb_pixel_total : 42519 time to create 1 rle with old method : 0.047809600830078125 time for calcul the mask position with numpy : 0.03176307678222656 nb_pixel_total : 41731 time to create 1 rle with old method : 0.05302596092224121 time for calcul the mask position with numpy : 0.031545400619506836 nb_pixel_total : 15540 time to create 1 rle with old method : 0.032178640365600586 time for calcul the mask position with numpy : 0.03361821174621582 nb_pixel_total : 188836 time to create 1 rle with new method : 0.3769214153289795 time for calcul the mask position with numpy : 0.02977728843688965 nb_pixel_total : 16743 time to create 1 rle with old method : 0.018621444702148438 create new chi : 6.738653182983398 time to delete rle : 0.0058171749114990234 batch 1 Loaded 67 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22628 TO DO : save crop sub photo not yet done ! save time : 2.074652671813965 nb_obj : 49 nb_hashtags : 4 time to prepare the origin masks : 5.307914733886719 time for calcul the mask position with numpy : 0.6122360229492188 nb_pixel_total : 4657567 time to create 1 rle with new method : 0.9293732643127441 time for calcul the mask position with numpy : 0.03326582908630371 nb_pixel_total : 16196 time to create 1 rle with old method : 0.01847100257873535 time for calcul the mask position with numpy : 0.030199766159057617 nb_pixel_total : 22419 time to create 1 rle with old method : 0.02576160430908203 time for calcul the mask position with numpy : 0.030077695846557617 nb_pixel_total : 14875 time to create 1 rle with old method : 0.01681041717529297 time for calcul the mask position with numpy : 0.0299837589263916 nb_pixel_total : 7942 time to create 1 rle with old method : 0.009457588195800781 time for calcul the mask position with numpy : 0.03106999397277832 nb_pixel_total : 35840 time to create 1 rle with old method : 0.07407832145690918 time for calcul the mask position with numpy : 0.057938337326049805 nb_pixel_total : 3680 time to create 1 rle with old method : 0.016115665435791016 time for calcul the mask position with numpy : 0.050484657287597656 nb_pixel_total : 43344 time to create 1 rle with old method : 0.050481319427490234 time for calcul the mask position with numpy : 0.030627012252807617 nb_pixel_total : 47040 time to create 1 rle with old method : 0.05475568771362305 time for calcul the mask position with numpy : 0.030138015747070312 nb_pixel_total : 17298 time to create 1 rle with old method : 0.019814729690551758 time for calcul the mask position with numpy : 0.030582189559936523 nb_pixel_total : 25845 time to create 1 rle with old method : 0.02983689308166504 time for calcul the mask position with numpy : 0.030101537704467773 nb_pixel_total : 26213 time to create 1 rle with old method : 0.030227184295654297 time for calcul the mask position with numpy : 0.030289411544799805 nb_pixel_total : 16496 time to create 1 rle with old method : 0.01849222183227539 time for calcul the mask position with numpy : 0.030991315841674805 nb_pixel_total : 133889 time to create 1 rle with old method : 0.17010807991027832 time for calcul the mask position with numpy : 0.02995467185974121 nb_pixel_total : 9749 time to create 1 rle with old method : 0.011124372482299805 time for calcul the mask position with numpy : 0.030892610549926758 nb_pixel_total : 9230 time to create 1 rle with old method : 0.01063227653503418 time for calcul the mask position with numpy : 0.030428409576416016 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0019741058349609375 time for calcul the mask position with numpy : 0.03214073181152344 nb_pixel_total : 43963 time to create 1 rle with old method : 0.06749582290649414 time for calcul the mask position with numpy : 0.03609442710876465 nb_pixel_total : 9134 time to create 1 rle with old method : 0.014317512512207031 time for calcul the mask position with numpy : 0.03648090362548828 nb_pixel_total : 39580 time to create 1 rle with old method : 0.05880427360534668 time for calcul the mask position with numpy : 0.03039240837097168 nb_pixel_total : 108906 time to create 1 rle with old method : 0.12928032875061035 time for calcul the mask position with numpy : 0.02988433837890625 nb_pixel_total : 105054 time to create 1 rle with old method : 0.11679959297180176 time for calcul the mask position with numpy : 0.03016376495361328 nb_pixel_total : 130718 time to create 1 rle with old method : 0.1471693515777588 time for calcul the mask position with numpy : 0.029525279998779297 nb_pixel_total : 16933 time to create 1 rle with old method : 0.019028425216674805 time for calcul the mask position with numpy : 0.03188180923461914 nb_pixel_total : 2143 time to create 1 rle with old method : 0.002657651901245117 time for calcul the mask position with numpy : 0.03000473976135254 nb_pixel_total : 71932 time to create 1 rle with old method : 0.10625433921813965 time for calcul the mask position with numpy : 0.031832218170166016 nb_pixel_total : 21863 time to create 1 rle with old method : 0.02577805519104004 time for calcul the mask position with numpy : 0.03170371055603027 nb_pixel_total : 63734 time to create 1 rle with old method : 0.07108497619628906 time for calcul the mask position with numpy : 0.029427766799926758 nb_pixel_total : 7840 time to create 1 rle with old method : 0.008958578109741211 time for calcul the mask position with numpy : 0.030374765396118164 nb_pixel_total : 35620 time to create 1 rle with old method : 0.04027724266052246 time for calcul the mask position with numpy : 0.03428077697753906 nb_pixel_total : 200156 time to create 1 rle with new method : 0.8161134719848633 time for calcul the mask position with numpy : 0.031205415725708008 nb_pixel_total : 48043 time to create 1 rle with old method : 0.0599515438079834 time for calcul the mask position with numpy : 0.0336458683013916 nb_pixel_total : 163730 time to create 1 rle with new method : 0.9499707221984863 time for calcul the mask position with numpy : 0.03144192695617676 nb_pixel_total : 489 time to create 1 rle with old method : 0.001314401626586914 time for calcul the mask position with numpy : 0.03375124931335449 nb_pixel_total : 26845 time to create 1 rle with old method : 0.03868508338928223 time for calcul the mask position with numpy : 0.032296180725097656 nb_pixel_total : 62271 time to create 1 rle with old method : 0.07622003555297852 time for calcul the mask position with numpy : 0.031078577041625977 nb_pixel_total : 31822 time to create 1 rle with old method : 0.03771710395812988 time for calcul the mask position with numpy : 0.030551671981811523 nb_pixel_total : 55727 time to create 1 rle with old method : 0.06722617149353027 time for calcul the mask position with numpy : 0.03139162063598633 nb_pixel_total : 125627 time to create 1 rle with old method : 0.15364980697631836 time for calcul the mask position with numpy : 0.030201435089111328 nb_pixel_total : 44285 time to create 1 rle with old method : 0.054030418395996094 time for calcul the mask position with numpy : 0.03054022789001465 nb_pixel_total : 88270 time to create 1 rle with old method : 0.11417531967163086 time for calcul the mask position with numpy : 0.03552436828613281 nb_pixel_total : 22882 time to create 1 rle with old method : 0.03283286094665527 time for calcul the mask position with numpy : 0.033423662185668945 nb_pixel_total : 60004 time to create 1 rle with old method : 0.09346532821655273 time for calcul the mask position with numpy : 0.03590846061706543 nb_pixel_total : 42319 time to create 1 rle with old method : 0.07633352279663086 time for calcul the mask position with numpy : 0.03807187080383301 nb_pixel_total : 13683 time to create 1 rle with old method : 0.025404691696166992 time for calcul the mask position with numpy : 0.03850913047790527 nb_pixel_total : 14639 time to create 1 rle with old method : 0.03244495391845703 time for calcul the mask position with numpy : 0.037871599197387695 nb_pixel_total : 15493 time to create 1 rle with old method : 0.02793717384338379 time for calcul the mask position with numpy : 0.039693355560302734 nb_pixel_total : 209138 time to create 1 rle with new method : 0.9152469635009766 time for calcul the mask position with numpy : 0.03297901153564453 nb_pixel_total : 11826 time to create 1 rle with old method : 0.013303756713867188 time for calcul the mask position with numpy : 0.030539751052856445 nb_pixel_total : 66286 time to create 1 rle with old method : 0.0902397632598877 create new chi : 8.34603238105774 time to delete rle : 0.009812593460083008 batch 1 Loaded 99 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28899 TO DO : save crop sub photo not yet done ! save time : 3.041166305541992 nb_obj : 21 nb_hashtags : 4 time to prepare the origin masks : 7.532935619354248 time for calcul the mask position with numpy : 0.6367154121398926 nb_pixel_total : 5669807 time to create 1 rle with new method : 0.476686954498291 time for calcul the mask position with numpy : 0.028130531311035156 nb_pixel_total : 65136 time to create 1 rle with old method : 0.07983994483947754 time for calcul the mask position with numpy : 0.025217533111572266 nb_pixel_total : 15118 time to create 1 rle with old method : 0.01865673065185547 time for calcul the mask position with numpy : 0.026888608932495117 nb_pixel_total : 75479 time to create 1 rle with old method : 0.12108969688415527 time for calcul the mask position with numpy : 0.025653600692749023 nb_pixel_total : 94145 time to create 1 rle with old method : 0.10613369941711426 time for calcul the mask position with numpy : 0.02355051040649414 nb_pixel_total : 8389 time to create 1 rle with old method : 0.00943446159362793 time for calcul the mask position with numpy : 0.021718502044677734 nb_pixel_total : 12983 time to create 1 rle with old method : 0.014765739440917969 time for calcul the mask position with numpy : 0.022326231002807617 nb_pixel_total : 9058 time to create 1 rle with old method : 0.010147333145141602 time for calcul the mask position with numpy : 0.02326059341430664 nb_pixel_total : 66775 time to create 1 rle with old method : 0.08102107048034668 time for calcul the mask position with numpy : 0.029205799102783203 nb_pixel_total : 57507 time to create 1 rle with old method : 0.06444215774536133 time for calcul the mask position with numpy : 0.037641286849975586 nb_pixel_total : 24733 time to create 1 rle with old method : 0.02785801887512207 time for calcul the mask position with numpy : 0.03785419464111328 nb_pixel_total : 12565 time to create 1 rle with old method : 0.021322011947631836 time for calcul the mask position with numpy : 0.042227745056152344 nb_pixel_total : 16488 time to create 1 rle with old method : 0.023287534713745117 time for calcul the mask position with numpy : 0.03519487380981445 nb_pixel_total : 4219 time to create 1 rle with old method : 0.004975557327270508 time for calcul the mask position with numpy : 0.03525996208190918 nb_pixel_total : 35664 time to create 1 rle with old method : 0.039632320404052734 time for calcul the mask position with numpy : 0.03629803657531738 nb_pixel_total : 142959 time to create 1 rle with old method : 0.16173672676086426 time for calcul the mask position with numpy : 0.03228139877319336 nb_pixel_total : 19963 time to create 1 rle with old method : 0.02250957489013672 time for calcul the mask position with numpy : 0.02296757698059082 nb_pixel_total : 16632 time to create 1 rle with old method : 0.020415544509887695 time for calcul the mask position with numpy : 0.026303529739379883 nb_pixel_total : 168033 time to create 1 rle with new method : 0.4396846294403076 time for calcul the mask position with numpy : 0.02743840217590332 nb_pixel_total : 6809 time to create 1 rle with old method : 0.009846687316894531 time for calcul the mask position with numpy : 0.029480695724487305 nb_pixel_total : 20177 time to create 1 rle with old method : 0.02626943588256836 time for calcul the mask position with numpy : 0.02830648422241211 nb_pixel_total : 507601 time to create 1 rle with new method : 0.6856484413146973 create new chi : 3.8099517822265625 time to delete rle : 0.002772092819213867 batch 1 Loaded 43 chid ids of type : 3594 +++++++++++++++++++++++++++++++Number RLEs to save : 14081 TO DO : save crop sub photo not yet done ! save time : 1.244126558303833 nb_obj : 50 nb_hashtags : 4 time to prepare the origin masks : 5.189166784286499 time for calcul the mask position with numpy : 0.21347761154174805 nb_pixel_total : 4715097 time to create 1 rle with new method : 0.7298436164855957 time for calcul the mask position with numpy : 0.031203746795654297 nb_pixel_total : 7753 time to create 1 rle with old method : 0.008913040161132812 time for calcul the mask position with numpy : 0.03095388412475586 nb_pixel_total : 6353 time to create 1 rle with old method : 0.007719755172729492 time for calcul the mask position with numpy : 0.02959465980529785 nb_pixel_total : 8550 time to create 1 rle with old method : 0.009847402572631836 time for calcul the mask position with numpy : 0.030036449432373047 nb_pixel_total : 15045 time to create 1 rle with old method : 0.018874645233154297 time for calcul the mask position with numpy : 0.034888505935668945 nb_pixel_total : 6213 time to create 1 rle with old method : 0.007302522659301758 time for calcul the mask position with numpy : 0.03041982650756836 nb_pixel_total : 58392 time to create 1 rle with old method : 0.09484601020812988 time for calcul the mask position with numpy : 0.03822779655456543 nb_pixel_total : 6856 time to create 1 rle with old method : 0.012526273727416992 time for calcul the mask position with numpy : 0.039601802825927734 nb_pixel_total : 21638 time to create 1 rle with old method : 0.03473663330078125 time for calcul the mask position with numpy : 0.03640103340148926 nb_pixel_total : 85743 time to create 1 rle with old method : 0.10919666290283203 time for calcul the mask position with numpy : 0.03042006492614746 nb_pixel_total : 53085 time to create 1 rle with old method : 0.059731483459472656 time for calcul the mask position with numpy : 0.030225515365600586 nb_pixel_total : 3237 time to create 1 rle with old method : 0.003777027130126953 time for calcul the mask position with numpy : 0.0335850715637207 nb_pixel_total : 58389 time to create 1 rle with old method : 0.0650489330291748 time for calcul the mask position with numpy : 0.02983689308166504 nb_pixel_total : 16088 time to create 1 rle with old method : 0.019097328186035156 time for calcul the mask position with numpy : 0.03005242347717285 nb_pixel_total : 21071 time to create 1 rle with old method : 0.023476362228393555 time for calcul the mask position with numpy : 0.031777381896972656 nb_pixel_total : 7999 time to create 1 rle with old method : 0.013319730758666992 time for calcul the mask position with numpy : 0.03379321098327637 nb_pixel_total : 13114 time to create 1 rle with old method : 0.016955852508544922 time for calcul the mask position with numpy : 0.030029773712158203 nb_pixel_total : 15990 time to create 1 rle with old method : 0.017893075942993164 time for calcul the mask position with numpy : 0.029906749725341797 nb_pixel_total : 6481 time to create 1 rle with old method : 0.007395744323730469 time for calcul the mask position with numpy : 0.03008270263671875 nb_pixel_total : 12053 time to create 1 rle with old method : 0.014544010162353516 time for calcul the mask position with numpy : 0.031822919845581055 nb_pixel_total : 228730 time to create 1 rle with new method : 0.748723030090332 time for calcul the mask position with numpy : 0.034201622009277344 nb_pixel_total : 12548 time to create 1 rle with old method : 0.014267444610595703 time for calcul the mask position with numpy : 0.030208110809326172 nb_pixel_total : 6750 time to create 1 rle with old method : 0.00793766975402832 time for calcul the mask position with numpy : 0.0301358699798584 nb_pixel_total : 18262 time to create 1 rle with old method : 0.02055048942565918 time for calcul the mask position with numpy : 0.03072500228881836 nb_pixel_total : 109461 time to create 1 rle with old method : 0.12225651741027832 time for calcul the mask position with numpy : 0.029999732971191406 nb_pixel_total : 23803 time to create 1 rle with old method : 0.02678966522216797 time for calcul the mask position with numpy : 0.030298709869384766 nb_pixel_total : 34228 time to create 1 rle with old method : 0.03986334800720215 time for calcul the mask position with numpy : 0.03036046028137207 nb_pixel_total : 21390 time to create 1 rle with old method : 0.024312496185302734 time for calcul the mask position with numpy : 0.03016519546508789 nb_pixel_total : 13876 time to create 1 rle with old method : 0.015719175338745117 time for calcul the mask position with numpy : 0.0304567813873291 nb_pixel_total : 75618 time to create 1 rle with old method : 0.08460164070129395 time for calcul the mask position with numpy : 0.028551340103149414 nb_pixel_total : 470 time to create 1 rle with old method : 0.0015552043914794922 time for calcul the mask position with numpy : 0.032393693923950195 nb_pixel_total : 15065 time to create 1 rle with old method : 0.01708507537841797 time for calcul the mask position with numpy : 0.030160903930664062 nb_pixel_total : 11327 time to create 1 rle with old method : 0.012839555740356445 time for calcul the mask position with numpy : 0.034268856048583984 nb_pixel_total : 130834 time to create 1 rle with old method : 0.1496598720550537 time for calcul the mask position with numpy : 0.03068828582763672 nb_pixel_total : 113772 time to create 1 rle with old method : 0.1382150650024414 time for calcul the mask position with numpy : 0.03171253204345703 nb_pixel_total : 51661 time to create 1 rle with old method : 0.06275796890258789 time for calcul the mask position with numpy : 0.03130769729614258 nb_pixel_total : 17072 time to create 1 rle with old method : 0.0214688777923584 time for calcul the mask position with numpy : 0.033943891525268555 nb_pixel_total : 6049 time to create 1 rle with old method : 0.007458209991455078 time for calcul the mask position with numpy : 0.031719207763671875 nb_pixel_total : 87237 time to create 1 rle with old method : 0.0975944995880127 time for calcul the mask position with numpy : 0.03281426429748535 nb_pixel_total : 165570 time to create 1 rle with new method : 0.8174853324890137 time for calcul the mask position with numpy : 0.03859829902648926 nb_pixel_total : 418203 time to create 1 rle with new method : 0.7808897495269775 time for calcul the mask position with numpy : 0.030492305755615234 nb_pixel_total : 25339 time to create 1 rle with old method : 0.028708934783935547 time for calcul the mask position with numpy : 0.030228614807128906 nb_pixel_total : 15954 time to create 1 rle with old method : 0.018111467361450195 time for calcul the mask position with numpy : 0.03141975402832031 nb_pixel_total : 48918 time to create 1 rle with old method : 0.05507326126098633 time for calcul the mask position with numpy : 0.031186819076538086 nb_pixel_total : 93519 time to create 1 rle with old method : 0.10407495498657227 time for calcul the mask position with numpy : 0.03181147575378418 nb_pixel_total : 37413 time to create 1 rle with old method : 0.04208707809448242 time for calcul the mask position with numpy : 0.030472993850708008 nb_pixel_total : 39379 time to create 1 rle with old method : 0.04432392120361328 time for calcul the mask position with numpy : 0.030599355697631836 nb_pixel_total : 46258 time to create 1 rle with old method : 0.053267717361450195 time for calcul the mask position with numpy : 0.034003496170043945 nb_pixel_total : 19531 time to create 1 rle with old method : 0.031546592712402344 time for calcul the mask position with numpy : 0.032929182052612305 nb_pixel_total : 5128 time to create 1 rle with old method : 0.0058519840240478516 time for calcul the mask position with numpy : 0.031224727630615234 nb_pixel_total : 17728 time to create 1 rle with old method : 0.019963502883911133 create new chi : 6.851218938827515 time to delete rle : 0.010969400405883789 batch 1 Loaded 101 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29874 TO DO : save crop sub photo not yet done ! save time : 2.393720865249634 nb_obj : 17 nb_hashtags : 3 time to prepare the origin masks : 5.515948295593262 time for calcul the mask position with numpy : 0.5927181243896484 nb_pixel_total : 5909002 time to create 1 rle with new method : 0.5675506591796875 time for calcul the mask position with numpy : 0.03662824630737305 nb_pixel_total : 12876 time to create 1 rle with old method : 0.014621496200561523 time for calcul the mask position with numpy : 0.03521990776062012 nb_pixel_total : 8618 time to create 1 rle with old method : 0.010143041610717773 time for calcul the mask position with numpy : 0.035516977310180664 nb_pixel_total : 81427 time to create 1 rle with old method : 0.09874653816223145 time for calcul the mask position with numpy : 0.03601241111755371 nb_pixel_total : 26570 time to create 1 rle with old method : 0.030385732650756836 time for calcul the mask position with numpy : 0.03657674789428711 nb_pixel_total : 13218 time to create 1 rle with old method : 0.014737129211425781 time for calcul the mask position with numpy : 0.03899812698364258 nb_pixel_total : 25771 time to create 1 rle with old method : 0.02934575080871582 time for calcul the mask position with numpy : 0.03770256042480469 nb_pixel_total : 90244 time to create 1 rle with old method : 0.10026860237121582 time for calcul the mask position with numpy : 0.04242110252380371 nb_pixel_total : 178006 time to create 1 rle with new method : 0.556908130645752 time for calcul the mask position with numpy : 0.03425335884094238 nb_pixel_total : 5142 time to create 1 rle with old method : 0.005914926528930664 time for calcul the mask position with numpy : 0.03790616989135742 nb_pixel_total : 143008 time to create 1 rle with old method : 0.21532344818115234 time for calcul the mask position with numpy : 0.05421638488769531 nb_pixel_total : 32373 time to create 1 rle with old method : 0.04168987274169922 time for calcul the mask position with numpy : 0.05440473556518555 nb_pixel_total : 37867 time to create 1 rle with old method : 0.044740915298461914 time for calcul the mask position with numpy : 0.05521821975708008 nb_pixel_total : 119052 time to create 1 rle with old method : 0.15894865989685059 time for calcul the mask position with numpy : 0.08058786392211914 nb_pixel_total : 22034 time to create 1 rle with old method : 0.026575803756713867 time for calcul the mask position with numpy : 0.047341346740722656 nb_pixel_total : 129101 time to create 1 rle with old method : 0.15111494064331055 time for calcul the mask position with numpy : 0.040231943130493164 nb_pixel_total : 156365 time to create 1 rle with new method : 0.6591863632202148 time for calcul the mask position with numpy : 0.0438385009765625 nb_pixel_total : 59566 time to create 1 rle with old method : 0.09546351432800293 create new chi : 4.256044626235962 time to delete rle : 0.0059812068939208984 batch 1 Loaded 35 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 13788 TO DO : save crop sub photo not yet done ! save time : 1.0323495864868164 nb_obj : 18 nb_hashtags : 3 time to prepare the origin masks : 5.566660642623901 time for calcul the mask position with numpy : 0.22176718711853027 nb_pixel_total : 5590718 time to create 1 rle with new method : 0.5648174285888672 time for calcul the mask position with numpy : 0.03344011306762695 nb_pixel_total : 59667 time to create 1 rle with old method : 0.07843780517578125 time for calcul the mask position with numpy : 0.03390789031982422 nb_pixel_total : 74756 time to create 1 rle with old method : 0.0935356616973877 time for calcul the mask position with numpy : 0.03958010673522949 nb_pixel_total : 7091 time to create 1 rle with old method : 0.011479377746582031 time for calcul the mask position with numpy : 0.04856371879577637 nb_pixel_total : 118780 time to create 1 rle with old method : 0.13936138153076172 time for calcul the mask position with numpy : 0.057074546813964844 nb_pixel_total : 15817 time to create 1 rle with old method : 0.01788616180419922 time for calcul the mask position with numpy : 0.04517364501953125 nb_pixel_total : 57845 time to create 1 rle with old method : 0.06569743156433105 time for calcul the mask position with numpy : 0.04437708854675293 nb_pixel_total : 89621 time to create 1 rle with old method : 0.11673903465270996 time for calcul the mask position with numpy : 0.06285667419433594 nb_pixel_total : 377787 time to create 1 rle with new method : 0.7220089435577393 time for calcul the mask position with numpy : 0.04135394096374512 nb_pixel_total : 77656 time to create 1 rle with old method : 0.0858457088470459 time for calcul the mask position with numpy : 0.043666839599609375 nb_pixel_total : 74346 time to create 1 rle with old method : 0.08329916000366211 time for calcul the mask position with numpy : 0.036978721618652344 nb_pixel_total : 82481 time to create 1 rle with old method : 0.09240198135375977 time for calcul the mask position with numpy : 0.03778982162475586 nb_pixel_total : 28841 time to create 1 rle with old method : 0.04453253746032715 time for calcul the mask position with numpy : 0.050211429595947266 nb_pixel_total : 54671 time to create 1 rle with old method : 0.061557769775390625 time for calcul the mask position with numpy : 0.04262495040893555 nb_pixel_total : 89364 time to create 1 rle with old method : 0.09971427917480469 time for calcul the mask position with numpy : 0.04244112968444824 nb_pixel_total : 63093 time to create 1 rle with old method : 0.07044076919555664 time for calcul the mask position with numpy : 0.04381871223449707 nb_pixel_total : 66946 time to create 1 rle with old method : 0.07744836807250977 time for calcul the mask position with numpy : 0.04954338073730469 nb_pixel_total : 12441 time to create 1 rle with old method : 0.014026165008544922 time for calcul the mask position with numpy : 0.0440981388092041 nb_pixel_total : 108319 time to create 1 rle with old method : 0.11937928199768066 create new chi : 3.6683030128479004 time to delete rle : 0.004926919937133789 batch 1 Loaded 37 chid ids of type : 3594 ++++++++++++++++++++++++++++++Number RLEs to save : 15599 TO DO : save crop sub photo not yet done ! save time : 1.3327345848083496 nb_obj : 49 nb_hashtags : 3 time to prepare the origin masks : 4.678124904632568 time for calcul the mask position with numpy : 0.3979916572570801 nb_pixel_total : 5341437 time to create 1 rle with new method : 0.9501867294311523 time for calcul the mask position with numpy : 0.03479743003845215 nb_pixel_total : 17312 time to create 1 rle with old method : 0.02825474739074707 time for calcul the mask position with numpy : 0.03541922569274902 nb_pixel_total : 72892 time to create 1 rle with old method : 0.09581518173217773 time for calcul the mask position with numpy : 0.029741764068603516 nb_pixel_total : 29702 time to create 1 rle with old method : 0.044641733169555664 time for calcul the mask position with numpy : 0.030436277389526367 nb_pixel_total : 14859 time to create 1 rle with old method : 0.016859054565429688 time for calcul the mask position with numpy : 0.0318295955657959 nb_pixel_total : 11373 time to create 1 rle with old method : 0.018914461135864258 time for calcul the mask position with numpy : 0.03433489799499512 nb_pixel_total : 11123 time to create 1 rle with old method : 0.019611120223999023 time for calcul the mask position with numpy : 0.05948138236999512 nb_pixel_total : 26718 time to create 1 rle with old method : 0.04724717140197754 time for calcul the mask position with numpy : 0.04705452919006348 nb_pixel_total : 11839 time to create 1 rle with old method : 0.02028179168701172 time for calcul the mask position with numpy : 0.03398585319519043 nb_pixel_total : 15474 time to create 1 rle with old method : 0.02412104606628418 time for calcul the mask position with numpy : 0.03595089912414551 nb_pixel_total : 80603 time to create 1 rle with old method : 0.11857342720031738 time for calcul the mask position with numpy : 0.0323486328125 nb_pixel_total : 10816 time to create 1 rle with old method : 0.01305532455444336 time for calcul the mask position with numpy : 0.030276775360107422 nb_pixel_total : 2614 time to create 1 rle with old method : 0.003127574920654297 time for calcul the mask position with numpy : 0.03727602958679199 nb_pixel_total : 142451 time to create 1 rle with old method : 0.24359655380249023 time for calcul the mask position with numpy : 0.03595471382141113 nb_pixel_total : 19087 time to create 1 rle with old method : 0.03311300277709961 time for calcul the mask position with numpy : 0.039987802505493164 nb_pixel_total : 76273 time to create 1 rle with old method : 0.11484575271606445 time for calcul the mask position with numpy : 0.031670570373535156 nb_pixel_total : 27405 time to create 1 rle with old method : 0.036196231842041016 time for calcul the mask position with numpy : 0.03868436813354492 nb_pixel_total : 37147 time to create 1 rle with old method : 0.04999208450317383 time for calcul the mask position with numpy : 0.042475223541259766 nb_pixel_total : 25359 time to create 1 rle with old method : 0.0499575138092041 time for calcul the mask position with numpy : 0.049930572509765625 nb_pixel_total : 40893 time to create 1 rle with old method : 0.04766106605529785 time for calcul the mask position with numpy : 0.03980064392089844 nb_pixel_total : 9834 time to create 1 rle with old method : 0.01800847053527832 time for calcul the mask position with numpy : 0.05230307579040527 nb_pixel_total : 39773 time to create 1 rle with old method : 0.05642533302307129 time for calcul the mask position with numpy : 0.032105207443237305 nb_pixel_total : 20727 time to create 1 rle with old method : 0.02579021453857422 time for calcul the mask position with numpy : 0.03368878364562988 nb_pixel_total : 59548 time to create 1 rle with old method : 0.11445999145507812 time for calcul the mask position with numpy : 0.032498836517333984 nb_pixel_total : 20315 time to create 1 rle with old method : 0.022780418395996094 time for calcul the mask position with numpy : 0.04448509216308594 nb_pixel_total : 38526 time to create 1 rle with old method : 0.07279396057128906 time for calcul the mask position with numpy : 0.05323004722595215 nb_pixel_total : 94544 time to create 1 rle with old method : 0.22711801528930664 time for calcul the mask position with numpy : 0.03287386894226074 nb_pixel_total : 22152 time to create 1 rle with old method : 0.037979841232299805 time for calcul the mask position with numpy : 0.03546881675720215 nb_pixel_total : 12977 time to create 1 rle with old method : 0.014808893203735352 time for calcul the mask position with numpy : 0.03124833106994629 nb_pixel_total : 11015 time to create 1 rle with old method : 0.013768911361694336 time for calcul the mask position with numpy : 0.031876564025878906 nb_pixel_total : 31254 time to create 1 rle with old method : 0.03879570960998535 time for calcul the mask position with numpy : 0.03126049041748047 nb_pixel_total : 17153 time to create 1 rle with old method : 0.02116537094116211 time for calcul the mask position with numpy : 0.039287567138671875 nb_pixel_total : 17218 time to create 1 rle with old method : 0.020805835723876953 time for calcul the mask position with numpy : 0.031449317932128906 nb_pixel_total : 25873 time to create 1 rle with old method : 0.0302734375 time for calcul the mask position with numpy : 0.032151222229003906 nb_pixel_total : 103482 time to create 1 rle with old method : 0.12353205680847168 time for calcul the mask position with numpy : 0.031598806381225586 nb_pixel_total : 50875 time to create 1 rle with old method : 0.07303285598754883 time for calcul the mask position with numpy : 0.0651240348815918 nb_pixel_total : 41299 time to create 1 rle with old method : 0.08097362518310547 time for calcul the mask position with numpy : 0.04992818832397461 nb_pixel_total : 57245 time to create 1 rle with old method : 0.06916308403015137 time for calcul the mask position with numpy : 0.030659198760986328 nb_pixel_total : 12571 time to create 1 rle with old method : 0.0152130126953125 time for calcul the mask position with numpy : 0.03199481964111328 nb_pixel_total : 22962 time to create 1 rle with old method : 0.03646397590637207 time for calcul the mask position with numpy : 0.03638648986816406 nb_pixel_total : 66316 time to create 1 rle with old method : 0.10663962364196777 time for calcul the mask position with numpy : 0.03393816947937012 nb_pixel_total : 77062 time to create 1 rle with old method : 0.08672475814819336 time for calcul the mask position with numpy : 0.030141353607177734 nb_pixel_total : 27973 time to create 1 rle with old method : 0.031978607177734375 time for calcul the mask position with numpy : 0.03211379051208496 nb_pixel_total : 18296 time to create 1 rle with old method : 0.020711898803710938 time for calcul the mask position with numpy : 0.02968883514404297 nb_pixel_total : 21850 time to create 1 rle with old method : 0.02497100830078125 time for calcul the mask position with numpy : 0.029927730560302734 nb_pixel_total : 19369 time to create 1 rle with old method : 0.022444486618041992 time for calcul the mask position with numpy : 0.02991652488708496 nb_pixel_total : 32736 time to create 1 rle with old method : 0.037094831466674805 time for calcul the mask position with numpy : 0.029738664627075195 nb_pixel_total : 18632 time to create 1 rle with old method : 0.022914648056030273 time for calcul the mask position with numpy : 0.0326540470123291 nb_pixel_total : 43021 time to create 1 rle with old method : 0.04874730110168457 time for calcul the mask position with numpy : 0.02983856201171875 nb_pixel_total : 265 time to create 1 rle with old method : 0.00039267539978027344 create new chi : 5.729126691818237 time to delete rle : 0.009877920150756836 batch 1 Loaded 99 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26361 TO DO : save crop sub photo not yet done ! save time : 2.0892417430877686 nb_obj : 52 nb_hashtags : 5 time to prepare the origin masks : 4.590933799743652 time for calcul the mask position with numpy : 0.23959708213806152 nb_pixel_total : 5263492 time to create 1 rle with new method : 0.8252182006835938 time for calcul the mask position with numpy : 0.029316186904907227 nb_pixel_total : 19254 time to create 1 rle with old method : 0.021526098251342773 time for calcul the mask position with numpy : 0.03262591361999512 nb_pixel_total : 14743 time to create 1 rle with old method : 0.016505002975463867 time for calcul the mask position with numpy : 0.029224634170532227 nb_pixel_total : 19804 time to create 1 rle with old method : 0.021992206573486328 time for calcul the mask position with numpy : 0.029282093048095703 nb_pixel_total : 14355 time to create 1 rle with old method : 0.016279935836791992 time for calcul the mask position with numpy : 0.029572725296020508 nb_pixel_total : 22130 time to create 1 rle with old method : 0.024887561798095703 time for calcul the mask position with numpy : 0.029288768768310547 nb_pixel_total : 7264 time to create 1 rle with old method : 0.008098840713500977 time for calcul the mask position with numpy : 0.02913069725036621 nb_pixel_total : 26246 time to create 1 rle with old method : 0.029463768005371094 time for calcul the mask position with numpy : 0.029124021530151367 nb_pixel_total : 24321 time to create 1 rle with old method : 0.027313709259033203 time for calcul the mask position with numpy : 0.028667688369750977 nb_pixel_total : 16417 time to create 1 rle with old method : 0.018270254135131836 time for calcul the mask position with numpy : 0.02872014045715332 nb_pixel_total : 8018 time to create 1 rle with old method : 0.009062767028808594 time for calcul the mask position with numpy : 0.029430866241455078 nb_pixel_total : 12157 time to create 1 rle with old method : 0.013600349426269531 time for calcul the mask position with numpy : 0.029271364212036133 nb_pixel_total : 12318 time to create 1 rle with old method : 0.013720273971557617 time for calcul the mask position with numpy : 0.028890371322631836 nb_pixel_total : 14945 time to create 1 rle with old method : 0.016989707946777344 time for calcul the mask position with numpy : 0.0315854549407959 nb_pixel_total : 21702 time to create 1 rle with old method : 0.024419784545898438 time for calcul the mask position with numpy : 0.029390573501586914 nb_pixel_total : 12902 time to create 1 rle with old method : 0.01465463638305664 time for calcul the mask position with numpy : 0.029391765594482422 nb_pixel_total : 14679 time to create 1 rle with old method : 0.016475439071655273 time for calcul the mask position with numpy : 0.02939748764038086 nb_pixel_total : 31720 time to create 1 rle with old method : 0.035280704498291016 time for calcul the mask position with numpy : 0.029011964797973633 nb_pixel_total : 7040 time to create 1 rle with old method : 0.00803685188293457 time for calcul the mask position with numpy : 0.029198408126831055 nb_pixel_total : 17647 time to create 1 rle with old method : 0.01997089385986328 time for calcul the mask position with numpy : 0.029309511184692383 nb_pixel_total : 27607 time to create 1 rle with old method : 0.03085470199584961 time for calcul the mask position with numpy : 0.029181480407714844 nb_pixel_total : 19082 time to create 1 rle with old method : 0.021555423736572266 time for calcul the mask position with numpy : 0.02993917465209961 nb_pixel_total : 79508 time to create 1 rle with old method : 0.08921146392822266 time for calcul the mask position with numpy : 0.03001546859741211 nb_pixel_total : 35266 time to create 1 rle with old method : 0.039609432220458984 time for calcul the mask position with numpy : 0.029863595962524414 nb_pixel_total : 77217 time to create 1 rle with old method : 0.08627939224243164 time for calcul the mask position with numpy : 0.030730247497558594 nb_pixel_total : 99104 time to create 1 rle with old method : 0.11080551147460938 time for calcul the mask position with numpy : 0.030689477920532227 nb_pixel_total : 49571 time to create 1 rle with old method : 0.05524301528930664 time for calcul the mask position with numpy : 0.029687881469726562 nb_pixel_total : 7465 time to create 1 rle with old method : 0.008527755737304688 time for calcul the mask position with numpy : 0.029963970184326172 nb_pixel_total : 26759 time to create 1 rle with old method : 0.029984474182128906 time for calcul the mask position with numpy : 0.029699087142944336 nb_pixel_total : 15136 time to create 1 rle with old method : 0.017063140869140625 time for calcul the mask position with numpy : 0.030495882034301758 nb_pixel_total : 53759 time to create 1 rle with old method : 0.06022953987121582 time for calcul the mask position with numpy : 0.029763221740722656 nb_pixel_total : 34113 time to create 1 rle with old method : 0.03820919990539551 time for calcul the mask position with numpy : 0.034000396728515625 nb_pixel_total : 251330 time to create 1 rle with new method : 0.6065633296966553 time for calcul the mask position with numpy : 0.030141830444335938 nb_pixel_total : 16764 time to create 1 rle with old method : 0.01878952980041504 time for calcul the mask position with numpy : 0.02953958511352539 nb_pixel_total : 9757 time to create 1 rle with old method : 0.010920286178588867 time for calcul the mask position with numpy : 0.029813766479492188 nb_pixel_total : 26286 time to create 1 rle with old method : 0.02936530113220215 time for calcul the mask position with numpy : 0.02983403205871582 nb_pixel_total : 23241 time to create 1 rle with old method : 0.025989294052124023 time for calcul the mask position with numpy : 0.029767990112304688 nb_pixel_total : 14003 time to create 1 rle with old method : 0.015795230865478516 time for calcul the mask position with numpy : 0.03049945831298828 nb_pixel_total : 66814 time to create 1 rle with old method : 0.07706546783447266 time for calcul the mask position with numpy : 0.02997112274169922 nb_pixel_total : 23832 time to create 1 rle with old method : 0.03125405311584473 time for calcul the mask position with numpy : 0.03293347358703613 nb_pixel_total : 26074 time to create 1 rle with old method : 0.02902960777282715 time for calcul the mask position with numpy : 0.029480934143066406 nb_pixel_total : 20679 time to create 1 rle with old method : 0.023250102996826172 time for calcul the mask position with numpy : 0.029666662216186523 nb_pixel_total : 24791 time to create 1 rle with old method : 0.027673006057739258 time for calcul the mask position with numpy : 0.029524564743041992 nb_pixel_total : 25323 time to create 1 rle with old method : 0.028374671936035156 time for calcul the mask position with numpy : 0.02986454963684082 nb_pixel_total : 52512 time to create 1 rle with old method : 0.05823040008544922 time for calcul the mask position with numpy : 0.02959275245666504 nb_pixel_total : 19069 time to create 1 rle with old method : 0.02145242691040039 time for calcul the mask position with numpy : 0.030924320220947266 nb_pixel_total : 140405 time to create 1 rle with old method : 0.1561424732208252 time for calcul the mask position with numpy : 0.03053903579711914 nb_pixel_total : 74485 time to create 1 rle with old method : 0.08336400985717773 time for calcul the mask position with numpy : 0.02974987030029297 nb_pixel_total : 83476 time to create 1 rle with old method : 0.09302139282226562 time for calcul the mask position with numpy : 0.0296633243560791 nb_pixel_total : 12644 time to create 1 rle with old method : 0.014271736145019531 time for calcul the mask position with numpy : 0.029581069946289062 nb_pixel_total : 6894 time to create 1 rle with old method : 0.008192777633666992 time for calcul the mask position with numpy : 0.029580354690551758 nb_pixel_total : 10064 time to create 1 rle with old method : 0.011331796646118164 time for calcul the mask position with numpy : 0.02953791618347168 nb_pixel_total : 16056 time to create 1 rle with old method : 0.01808452606201172 create new chi : 5.015552759170532 time to delete rle : 0.006579875946044922 batch 1 Loaded 105 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26132 TO DO : save crop sub photo not yet done ! save time : 1.9782884120941162 nb_obj : 64 nb_hashtags : 4 time to prepare the origin masks : 4.7113189697265625 time for calcul the mask position with numpy : 0.5096344947814941 nb_pixel_total : 5227500 time to create 1 rle with new method : 0.9340920448303223 time for calcul the mask position with numpy : 0.030208587646484375 nb_pixel_total : 8191 time to create 1 rle with old method : 0.009257316589355469 time for calcul the mask position with numpy : 0.030025959014892578 nb_pixel_total : 22687 time to create 1 rle with old method : 0.027938127517700195 time for calcul the mask position with numpy : 0.03113532066345215 nb_pixel_total : 15174 time to create 1 rle with old method : 0.017104387283325195 time for calcul the mask position with numpy : 0.029720544815063477 nb_pixel_total : 19516 time to create 1 rle with old method : 0.021805763244628906 time for calcul the mask position with numpy : 0.02995610237121582 nb_pixel_total : 37404 time to create 1 rle with old method : 0.042024850845336914 time for calcul the mask position with numpy : 0.03005814552307129 nb_pixel_total : 41597 time to create 1 rle with old method : 0.04596734046936035 time for calcul the mask position with numpy : 0.02981257438659668 nb_pixel_total : 19346 time to create 1 rle with old method : 0.021921634674072266 time for calcul the mask position with numpy : 0.02993178367614746 nb_pixel_total : 43309 time to create 1 rle with old method : 0.04828381538391113 time for calcul the mask position with numpy : 0.029986143112182617 nb_pixel_total : 33313 time to create 1 rle with old method : 0.037148237228393555 time for calcul the mask position with numpy : 0.029361248016357422 nb_pixel_total : 31473 time to create 1 rle with old method : 0.03552508354187012 time for calcul the mask position with numpy : 0.030169010162353516 nb_pixel_total : 18399 time to create 1 rle with old method : 0.029401540756225586 time for calcul the mask position with numpy : 0.03371453285217285 nb_pixel_total : 34244 time to create 1 rle with old method : 0.06218075752258301 time for calcul the mask position with numpy : 0.043155670166015625 nb_pixel_total : 34214 time to create 1 rle with old method : 0.05974006652832031 time for calcul the mask position with numpy : 0.04214191436767578 nb_pixel_total : 21212 time to create 1 rle with old method : 0.03631782531738281 time for calcul the mask position with numpy : 0.03819084167480469 nb_pixel_total : 15263 time to create 1 rle with old method : 0.027431011199951172 time for calcul the mask position with numpy : 0.03839468955993652 nb_pixel_total : 6530 time to create 1 rle with old method : 0.012181758880615234 time for calcul the mask position with numpy : 0.0383760929107666 nb_pixel_total : 22755 time to create 1 rle with old method : 0.04045724868774414 time for calcul the mask position with numpy : 0.03920745849609375 nb_pixel_total : 16701 time to create 1 rle with old method : 0.027170419692993164 time for calcul the mask position with numpy : 0.039688825607299805 nb_pixel_total : 14738 time to create 1 rle with old method : 0.02588939666748047 time for calcul the mask position with numpy : 0.03826189041137695 nb_pixel_total : 16079 time to create 1 rle with old method : 0.03080272674560547 time for calcul the mask position with numpy : 0.04261445999145508 nb_pixel_total : 13462 time to create 1 rle with old method : 0.02460622787475586 time for calcul the mask position with numpy : 0.03716874122619629 nb_pixel_total : 27515 time to create 1 rle with old method : 0.05317211151123047 time for calcul the mask position with numpy : 0.037590980529785156 nb_pixel_total : 29789 time to create 1 rle with old method : 0.03986358642578125 time for calcul the mask position with numpy : 0.028731107711791992 nb_pixel_total : 28227 time to create 1 rle with old method : 0.03363680839538574 time for calcul the mask position with numpy : 0.041979074478149414 nb_pixel_total : 63049 time to create 1 rle with old method : 0.06510376930236816 time for calcul the mask position with numpy : 0.02849435806274414 nb_pixel_total : 26629 time to create 1 rle with old method : 0.02832651138305664 time for calcul the mask position with numpy : 0.029752016067504883 nb_pixel_total : 19921 time to create 1 rle with old method : 0.029685497283935547 time for calcul the mask position with numpy : 0.0396730899810791 nb_pixel_total : 28853 time to create 1 rle with old method : 0.0314021110534668 time for calcul the mask position with numpy : 0.03033590316772461 nb_pixel_total : 26080 time to create 1 rle with old method : 0.03880906105041504 time for calcul the mask position with numpy : 0.03969526290893555 nb_pixel_total : 11621 time to create 1 rle with old method : 0.014747142791748047 time for calcul the mask position with numpy : 0.03083515167236328 nb_pixel_total : 34535 time to create 1 rle with old method : 0.05363798141479492 time for calcul the mask position with numpy : 0.033780813217163086 nb_pixel_total : 20451 time to create 1 rle with old method : 0.023777484893798828 time for calcul the mask position with numpy : 0.03129172325134277 nb_pixel_total : 21634 time to create 1 rle with old method : 0.024715662002563477 time for calcul the mask position with numpy : 0.03373241424560547 nb_pixel_total : 79872 time to create 1 rle with old method : 0.0949699878692627 time for calcul the mask position with numpy : 0.03155398368835449 nb_pixel_total : 14976 time to create 1 rle with old method : 0.016887426376342773 time for calcul the mask position with numpy : 0.0307309627532959 nb_pixel_total : 20002 time to create 1 rle with old method : 0.02243971824645996 time for calcul the mask position with numpy : 0.030321121215820312 nb_pixel_total : 11674 time to create 1 rle with old method : 0.013373374938964844 time for calcul the mask position with numpy : 0.03236222267150879 nb_pixel_total : 34134 time to create 1 rle with old method : 0.038637638092041016 time for calcul the mask position with numpy : 0.032573699951171875 nb_pixel_total : 24365 time to create 1 rle with old method : 0.03064560890197754 time for calcul the mask position with numpy : 0.03927874565124512 nb_pixel_total : 49004 time to create 1 rle with old method : 0.07987689971923828 time for calcul the mask position with numpy : 0.03951215744018555 nb_pixel_total : 21998 time to create 1 rle with old method : 0.03643655776977539 time for calcul the mask position with numpy : 0.03862738609313965 nb_pixel_total : 47535 time to create 1 rle with old method : 0.05440187454223633 time for calcul the mask position with numpy : 0.03131222724914551 nb_pixel_total : 17110 time to create 1 rle with old method : 0.035889625549316406 time for calcul the mask position with numpy : 0.031332969665527344 nb_pixel_total : 27036 time to create 1 rle with old method : 0.031606197357177734 time for calcul the mask position with numpy : 0.03030848503112793 nb_pixel_total : 18286 time to create 1 rle with old method : 0.02081298828125 time for calcul the mask position with numpy : 0.03069019317626953 nb_pixel_total : 13411 time to create 1 rle with old method : 0.015258550643920898 time for calcul the mask position with numpy : 0.030085086822509766 nb_pixel_total : 26467 time to create 1 rle with old method : 0.029808759689331055 time for calcul the mask position with numpy : 0.029821395874023438 nb_pixel_total : 18023 time to create 1 rle with old method : 0.02041912078857422 time for calcul the mask position with numpy : 0.031081438064575195 nb_pixel_total : 165296 time to create 1 rle with new method : 0.7221901416778564 time for calcul the mask position with numpy : 0.029669761657714844 nb_pixel_total : 45626 time to create 1 rle with old method : 0.05072641372680664 time for calcul the mask position with numpy : 0.029586315155029297 nb_pixel_total : 8411 time to create 1 rle with old method : 0.009557723999023438 time for calcul the mask position with numpy : 0.02969050407409668 nb_pixel_total : 5639 time to create 1 rle with old method : 0.007507801055908203 time for calcul the mask position with numpy : 0.030177831649780273 nb_pixel_total : 67805 time to create 1 rle with old method : 0.07873106002807617 time for calcul the mask position with numpy : 0.02959299087524414 nb_pixel_total : 7858 time to create 1 rle with old method : 0.010073184967041016 time for calcul the mask position with numpy : 0.03249669075012207 nb_pixel_total : 71403 time to create 1 rle with old method : 0.08262801170349121 time for calcul the mask position with numpy : 0.0296328067779541 nb_pixel_total : 76267 time to create 1 rle with old method : 0.08832836151123047 time for calcul the mask position with numpy : 0.0305478572845459 nb_pixel_total : 52242 time to create 1 rle with old method : 0.06556034088134766 time for calcul the mask position with numpy : 0.03132772445678711 nb_pixel_total : 13019 time to create 1 rle with old method : 0.014598846435546875 time for calcul the mask position with numpy : 0.029512405395507812 nb_pixel_total : 7407 time to create 1 rle with old method : 0.008453130722045898 time for calcul the mask position with numpy : 0.03242945671081543 nb_pixel_total : 5307 time to create 1 rle with old method : 0.0060138702392578125 time for calcul the mask position with numpy : 0.029418468475341797 nb_pixel_total : 14723 time to create 1 rle with old method : 0.020061016082763672 time for calcul the mask position with numpy : 0.0319211483001709 nb_pixel_total : 16246 time to create 1 rle with old method : 0.020043373107910156 time for calcul the mask position with numpy : 0.03204798698425293 nb_pixel_total : 5805 time to create 1 rle with old method : 0.008275508880615234 time for calcul the mask position with numpy : 0.029675960540771484 nb_pixel_total : 11882 time to create 1 rle with old method : 0.015921592712402344 create new chi : 6.509434700012207 time to delete rle : 0.006127357482910156 batch 1 Loaded 129 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 31397 TO DO : save crop sub photo not yet done ! save time : 3.769794225692749 map_output_result : {1349346432: (0.0, 'Should be the crop_list due to order', 0), 1349346428: (0.0, 'Should be the crop_list due to order', 0), 1349346348: (0.0, 'Should be the crop_list due to order', 0), 1349346340: (0.0, 'Should be the crop_list due to order', 0), 1349346305: (0.0, 'Should be the crop_list due to order', 0), 1349346213: (0.0, 'Should be the crop_list due to order', 0), 1349346201: (0.0, 'Should be the crop_list due to order', 0), 1349346189: (0.0, 'Should be the crop_list due to order', 0), 1349335811: (0.0, 'Should be the crop_list due to order', 0), 1349335771: (0.0, 'Should be the crop_list due to order', 0), 1349335768: (0.0, 'Should be the crop_list due to order', 0), 1349335763: (0.0, 'Should be the crop_list due to order', 0), 1349335728: (0.0, 'Should be the crop_list due to order', 0), 1349335722: (0.0, 'Should be the crop_list due to order', 0), 1349335711: (0.0, 'Should be the crop_list due to order', 0), 1349335707: (0.0, 'Should be the crop_list due to order', 0), 1349335673: (0.0, 'Should be the crop_list due to order', 0), 1349335651: (0.0, 'Should be the crop_list due to order', 0), 1349335647: (0.0, 'Should be the crop_list due to order', 0), 1349335643: (0.0, 'Should be the crop_list due to order', 0), 1349335638: (0.0, 'Should be the crop_list due to order', 0), 1349335633: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1349346432, 1349346428, 1349346348, 1349346340, 1349346305, 1349346213, 1349346201, 1349346189, 1349335811, 1349335771, 1349335768, 1349335763, 1349335728, 1349335722, 1349335711, 1349335707, 1349335673, 1349335651, 1349335647, 1349335643, 1349335638, 1349335633] Looping around the photos to save general results len do output : 22 /1349346432.Didn't retrieve data . /1349346428.Didn't retrieve data . /1349346348.Didn't retrieve data . /1349346340.Didn't retrieve data . /1349346305.Didn't retrieve data . /1349346213.Didn't retrieve data . /1349346201.Didn't retrieve data . /1349346189.Didn't retrieve data . /1349335811.Didn't retrieve data . /1349335771.Didn't retrieve data . /1349335768.Didn't retrieve data . /1349335763.Didn't retrieve data . /1349335728.Didn't retrieve data . /1349335722.Didn't retrieve data . /1349335711.Didn't retrieve data . /1349335707.Didn't retrieve data . /1349335673.Didn't retrieve data . /1349335651.Didn't retrieve data . /1349335647.Didn't retrieve data . /1349335643.Didn't retrieve data . /1349335638.Didn't retrieve data . /1349335633.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, '2714586') ('3318', '21958982', '1349346432', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346428', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346348', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346340', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346305', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346213', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346201', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346189', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335811', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335771', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335768', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335763', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335728', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335722', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335711', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335707', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335673', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335651', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335647', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335643', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335638', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335633', None, None, None, None, None, '2714586') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 66 time used for this insertion : 0.015887022018432617 save_final save missing photos in datou_result : time spend for datou_step_exec : 280.35924077033997 time spend to save output : 0.04491734504699707 total time spend for step 3 : 280.40415811538696 step4:ventilate_hashtags_in_portfolio Tue Apr 1 22:22:59 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 : 21958982 get user id for portfolio 21958982 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`=21958982 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','papier','flou','metal','autre','pehd','pet_fonce','mal_croppe','background','environnement')) 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`=21958982 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','papier','flou','metal','autre','pehd','pet_fonce','mal_croppe','background','environnement')) 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`=21958982 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','papier','flou','metal','autre','pehd','pet_fonce','mal_croppe','background','environnement')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21959937,21959938,21959939,21959940,21959941,21959942,21959943,21959944,21959945,21959946,21959947?tags=carton,pet_clair,papier,flou,metal,autre,pehd,pet_fonce,mal_croppe,background,environnement Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349346432, 1349346428, 1349346348, 1349346340, 1349346305, 1349346213, 1349346201, 1349346189, 1349335811, 1349335771, 1349335768, 1349335763, 1349335728, 1349335722, 1349335711, 1349335707, 1349335673, 1349335651, 1349335647, 1349335643, 1349335638, 1349335633] Looping around the photos to save general results len do output : 1 /21958982. 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, '2714586') ('3318', '21958982', '1349346432', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346428', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346348', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346340', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346305', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346213', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346201', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346189', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335811', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335771', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335768', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335763', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335728', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335722', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335711', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335707', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335673', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335651', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335647', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335643', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335638', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335633', None, None, None, None, None, '2714586') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 23 time used for this insertion : 0.017798423767089844 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.7628257274627686 time spend to save output : 0.018558502197265625 total time spend for step 4 : 2.781384229660034 step5:final Tue Apr 1 22:23:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : {1349346432: ('0.23863599614403855',), 1349346428: ('0.23863599614403855',), 1349346348: ('0.23863599614403855',), 1349346340: ('0.23863599614403855',), 1349346305: ('0.23863599614403855',), 1349346213: ('0.23863599614403855',), 1349346201: ('0.23863599614403855',), 1349346189: ('0.23863599614403855',), 1349335811: ('0.23863599614403855',), 1349335771: ('0.23863599614403855',), 1349335768: ('0.23863599614403855',), 1349335763: ('0.23863599614403855',), 1349335728: ('0.23863599614403855',), 1349335722: ('0.23863599614403855',), 1349335711: ('0.23863599614403855',), 1349335707: ('0.23863599614403855',), 1349335673: ('0.23863599614403855',), 1349335651: ('0.23863599614403855',), 1349335647: ('0.23863599614403855',), 1349335643: ('0.23863599614403855',), 1349335638: ('0.23863599614403855',), 1349335633: ('0.23863599614403855',)} new output for save of step final : {1349346432: ('0.23863599614403855',), 1349346428: ('0.23863599614403855',), 1349346348: ('0.23863599614403855',), 1349346340: ('0.23863599614403855',), 1349346305: ('0.23863599614403855',), 1349346213: ('0.23863599614403855',), 1349346201: ('0.23863599614403855',), 1349346189: ('0.23863599614403855',), 1349335811: ('0.23863599614403855',), 1349335771: ('0.23863599614403855',), 1349335768: ('0.23863599614403855',), 1349335763: ('0.23863599614403855',), 1349335728: ('0.23863599614403855',), 1349335722: ('0.23863599614403855',), 1349335711: ('0.23863599614403855',), 1349335707: ('0.23863599614403855',), 1349335673: ('0.23863599614403855',), 1349335651: ('0.23863599614403855',), 1349335647: ('0.23863599614403855',), 1349335643: ('0.23863599614403855',), 1349335638: ('0.23863599614403855',), 1349335633: ('0.23863599614403855',)} [1349346432, 1349346428, 1349346348, 1349346340, 1349346305, 1349346213, 1349346201, 1349346189, 1349335811, 1349335771, 1349335768, 1349335763, 1349335728, 1349335722, 1349335711, 1349335707, 1349335673, 1349335651, 1349335647, 1349335643, 1349335638, 1349335633] Looping around the photos to save general results len do output : 22 /1349346432.Didn't retrieve data . /1349346428.Didn't retrieve data . /1349346348.Didn't retrieve data . /1349346340.Didn't retrieve data . /1349346305.Didn't retrieve data . /1349346213.Didn't retrieve data . /1349346201.Didn't retrieve data . /1349346189.Didn't retrieve data . /1349335811.Didn't retrieve data . /1349335771.Didn't retrieve data . /1349335768.Didn't retrieve data . /1349335763.Didn't retrieve data . /1349335728.Didn't retrieve data . /1349335722.Didn't retrieve data . /1349335711.Didn't retrieve data . /1349335707.Didn't retrieve data . /1349335673.Didn't retrieve data . /1349335651.Didn't retrieve data . /1349335647.Didn't retrieve data . /1349335643.Didn't retrieve data . /1349335638.Didn't retrieve data . /1349335633.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, '2714586') ('3318', '21958982', '1349346432', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346428', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346348', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346340', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346305', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346213', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346201', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346189', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335811', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335771', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335768', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335763', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335728', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335722', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335711', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335707', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335673', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335651', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335647', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335643', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335638', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335633', None, None, None, None, None, '2714586') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 66 time used for this insertion : 0.02263927459716797 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.1776292324066162 time spend to save output : 0.025538921356201172 total time spend for step 5 : 0.20316815376281738 step6:blur_detection Tue Apr 1 22:23:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5.jpg resize: (2160, 3264) 1349346432 -3.4719225522844654 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980.jpg resize: (2160, 3264) 1349346428 -7.336459387859378 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b.jpg resize: (2160, 3264) 1349346348 -3.918108297415554 treat image : temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef.jpg resize: (2160, 3264) 1349346340 -4.713791093220756 treat image : temp/1743537630_2844229_1349346305_29103387b775a6e1f384ff7ee58e7ec0.jpg resize: (2160, 3264) 1349346305 -3.1291185697527415 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080.jpg resize: (2160, 3264) 1349346213 -2.8379750300381135 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e.jpg resize: (2160, 3264) 1349346201 -3.0346157122109134 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003.jpg resize: (2160, 3264) 1349346189 -3.7972292573703847 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90.jpg resize: (2160, 3264) 1349335811 2.710804203370057 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4.jpg resize: (2160, 3264) 1349335771 -6.414157042987834 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52.jpg resize: (2160, 3264) 1349335768 -5.654027035385974 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270.jpg resize: (2160, 3264) 1349335763 -2.180256128507998 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f.jpg resize: (2160, 3264) 1349335728 -3.81083284346196 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648.jpg resize: (2160, 3264) 1349335722 -6.910740939984066 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854.jpg resize: (2160, 3264) 1349335711 -6.345759460878192 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926.jpg resize: (2160, 3264) 1349335707 -2.3872358222445267 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4.jpg resize: (2160, 3264) 1349335673 -4.277494046980123 treat image : temp/1743537630_2844229_1349335651_ab82dcd69b33d78e2957ba4bf3562612.jpg resize: (2160, 3264) 1349335651 -4.547252752338885 treat image : temp/1743537630_2844229_1349335647_397067bb5c5e6da24dc3d9e6464154c3.jpg resize: (2160, 3264) 1349335647 -4.16388550522749 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3.jpg resize: (2160, 3264) 1349335643 -4.7350360656257635 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3.jpg resize: (2160, 3264) 1349335638 -5.351902817017774 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2.jpg resize: (2160, 3264) 1349335633 -6.16261890898672 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233916_0.png resize: (447, 217) 1349360851 -2.2227646795981095 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233904_0.png resize: (157, 185) 1349360853 -2.826974586947354 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233884_0.png resize: (220, 161) 1349360856 -1.9046538009526452 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233888_0.png resize: (173, 194) 1349360860 -2.0198436079703628 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233887_0.png resize: (151, 163) 1349360862 -2.2197084207464317 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233891_0.png resize: (304, 333) 1349360866 -1.428520113304401 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233899_0.png resize: (327, 349) 1349360868 -2.2800271444358406 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233889_0.png resize: (344, 405) 1349360870 -2.0599987616023867 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233873_0.png resize: (175, 291) 1349360874 -0.5919364486031365 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233900_0.png resize: (204, 170) 1349360876 -3.428912262479396 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233871_0.png resize: (272, 352) 1349360880 -1.5094593817973339 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233879_0.png resize: (403, 359) 1349360882 -2.1146898870831494 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233886_0.png resize: (124, 139) 1349360884 -1.0068479451086985 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233890_0.png resize: (238, 141) 1349360888 -2.709545268400785 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233892_0.png resize: (353, 384) 1349360890 -2.6107397858357455 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233865_0.png resize: (208, 189) 1349360894 -2.926370633534602 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233885_0.png resize: (144, 157) 1349360896 -1.8532695331397087 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233875_0.png resize: (245, 177) 1349360898 -2.0806614923126925 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233876_0.png resize: (98, 155) 1349360902 -0.984119096070858 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233896_0.png resize: (203, 206) 1349360904 -3.645213168206314 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233911_0.png resize: (451, 559) 1349360906 -1.8860983928817046 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233878_0.png resize: (224, 255) 1349360910 -1.2748602861049674 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233895_0.png resize: (230, 330) 1349360912 -0.6568695402804435 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233874_0.png resize: (64, 75) 1349360915 -2.0902063718449275 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233868_0.png resize: (83, 154) 1349360918 -1.5181332528934182 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233914_0.png resize: (202, 193) 1349360920 -0.9202557700497249 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233907_0.png resize: (153, 111) 1349360924 -3.2165508683798567 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233898_0.png resize: (80, 80) 1349360926 -0.5410418740019785 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233863_0.png resize: (110, 115) 1349360928 -1.5831979817613873 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233881_0.png resize: (143, 219) 1349360932 1.988635154812276 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233901_0.png resize: (100, 116) 1349360934 -1.596635252513491 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233883_0.png resize: (509, 300) 1349360936 -1.5073470443517758 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233909_0.png resize: (39, 93) 1349360940 -1.38600317593159 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233912_0.png resize: (398, 412) 1349360942 -2.5214009881736694 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233893_0.png resize: (142, 129) 1349360944 -2.650215799072046 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233915_0.png resize: (235, 167) 1349360948 -2.609549814205479 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233882_0.png resize: (289, 90) 1349360950 -3.1538123049152618 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233910_0.png resize: (503, 703) 1349360953 -2.5071533002086532 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233902_0.png resize: (176, 281) 1349360956 -2.8958179431193973 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233906_0.png resize: (158, 121) 1349360958 0.7373877122837412 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233897_0.png resize: (164, 189) 1349360961 -2.33820769629751 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233908_0.png resize: (148, 154) 1349360964 -3.565922599605894 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233862_0.png resize: (211, 277) 1349360966 -2.2380703872002394 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233866_0.png resize: (264, 212) 1349360969 -2.908160988513681 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233894_0.png resize: (215, 239) 1349360972 -3.594403595871467 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233880_0.png resize: (176, 150) 1349360974 -3.9510604559477 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233918_0.png resize: (229, 248) 1349360977 -2.7287934103635365 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233976_0.png resize: (123, 171) 1349360980 -0.3609178137905332 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233965_0.png resize: (414, 325) 1349360982 -2.367031750978062 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233920_0.png resize: (435, 456) 1349360985 -2.32955428680311 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233966_0.png resize: (102, 113) 1349360988 -3.1179082699175935 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233972_0.png resize: (197, 168) 1349360990 -4.4068960253663265 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233944_0.png resize: (187, 111) 1349360993 -2.0511498156138086 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233942_0.png resize: (159, 288) 1349360996 -2.3739271905371457 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233946_0.png resize: (89, 81) 1349360998 0.5017144109431951 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233956_0.png resize: (189, 84) 1349361001 -1.7514782228241377 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233962_0.png resize: (103, 152) 1349361004 -5.125147412461873 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233926_0.png resize: (305, 324) 1349361006 -3.4336501601236926 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233968_0.png resize: (260, 504) 1349361009 -4.9782360975318785 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233936_0.png resize: (269, 378) 1349361012 -3.6374744692791743 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233953_0.png resize: (146, 234) 1349361014 -3.4384876912699345 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233964_0.png resize: (138, 174) 1349361017 -2.995343466791931 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233963_0.png resize: (275, 164) 1349361020 -4.619838659159117 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233947_0.png resize: (68, 141) 1349361022 -2.422466487587614 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233934_0.png resize: (481, 337) 1349361025 -5.787980341519267 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233928_0.png resize: (125, 161) 1349361028 -2.7551718240649765 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233961_0.png resize: (284, 531) 1349361030 -5.197962768029947 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233917_0.png resize: (164, 205) 1349361033 -3.4180165630438797 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233955_0.png resize: (58, 53) 1349361036 -0.45561542989037285 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233937_0.png resize: (346, 513) 1349361038 -4.269954893766842 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233971_0.png resize: (117, 209) 1349361041 -4.363623216111993 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233923_0.png resize: (734, 485) 1349361044 -5.204905364479649 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233949_0.png resize: (176, 181) 1349361046 -5.443079992658163 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233925_0.png resize: (190, 95) 1349361049 -0.9326452042079688 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233924_0.png resize: (185, 166) 1349361052 -2.7897938437991177 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233941_0.png resize: (199, 193) 1349361055 -5.412068392826896 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233921_0.png resize: (297, 286) 1349361057 -5.7205432114000905 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233970_0.png resize: (145, 308) 1349361060 -2.724249818551584 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233929_0.png resize: (164, 151) 1349361063 -3.436518257104044 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233957_0.png resize: (222, 200) 1349361066 -5.532614421197633 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233958_0.png resize: (135, 236) 1349361068 -4.569353195127449 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233948_0.png resize: (109, 110) 1349361071 -5.407106330255811 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233939_0.png resize: (154, 226) 1349361074 -2.219152735744691 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233943_0.png resize: (213, 242) 1349361076 -4.100006472158888 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233978_0.png resize: (109, 134) 1349361079 -4.3493612905855805 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233969_0.png resize: (112, 93) 1349361082 -2.38961353178274 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233967_0.png resize: (214, 138) 1349361084 -4.13250180564056 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233933_0.png resize: (62, 192) 1349361087 -3.090879849780727 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233940_0.png resize: (115, 116) 1349361090 -3.7423209334141747 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233922_0.png resize: (114, 183) 1349361092 -4.6798298662594355 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233945_0.png resize: (409, 395) 1349361095 -6.2426464548466365 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233950_0.png resize: (147, 147) 1349361098 -4.064517913037211 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233932_0.png resize: (88, 146) 1349361099 -4.487327351147622 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233960_0.png resize: (95, 106) 1349361101 -4.218027061242935 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233979_0.png resize: (139, 127) 1349361103 -4.251173206089847 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233977_0.png resize: (138, 132) 1349361104 -4.854406827026772 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233951_0.png resize: (228, 323) 1349361106 -3.8796319769784287 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233959_0.png resize: (122, 115) 1349361108 -2.849309861256612 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233938_0.png resize: (178, 111) 1349361109 -4.152030602805965 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234019_0.png resize: (241, 158) 1349361111 -0.72105541221909 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233987_0.png resize: (406, 343) 1349361113 -0.894492979298818 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234009_0.png resize: (138, 168) 1349361116 -1.6744380449251062 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233983_0.png resize: (230, 148) 1349361117 -3.8566149116415964 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234011_0.png resize: (129, 132) 1349361119 -1.2860805400422441 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233992_0.png resize: (227, 288) 1349361122 -2.5610722533691797 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234003_0.png resize: (243, 193) 1349361123 -3.088585524331887 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234022_0.png resize: (169, 74) 1349361125 -2.4096765580106987 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234012_0.png resize: (150, 174) 1349361128 -2.0101582986406243 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234006_0.png resize: (361, 323) 1349361129 -2.9541486374204906 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234024_0.png resize: (294, 98) 1349361132 -1.6478019935422892 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233999_0.png resize: (214, 166) 1349361134 0.19913147198578998 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234014_0.png resize: (158, 322) 1349361136 -1.6183811979666034 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234005_0.png resize: (335, 339) 1349361138 -3.273380430760405 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234001_0.png resize: (269, 159) 1349361140 -1.781815963791543 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233996_0.png resize: (155, 138) 1349361142 -0.9754319778924705 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234025_0.png resize: (75, 75) 1349361145 0.699149099430269 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233982_0.png resize: (97, 129) 1349361146 -1.2555296833227698 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234013_0.png resize: (145, 205) 1349361148 -3.0039012031969294 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234010_0.png resize: (263, 224) 1349361151 -2.7985976169989146 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233986_0.png resize: (296, 221) 1349361153 -0.0799609613044804 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233994_0.png resize: (157, 273) 1349361155 -3.1603512076556384 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233989_0.png resize: (251, 228) 1349361157 -2.047098952235949 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233984_0.png resize: (267, 184) 1349361159 -2.7698154208142145 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233988_0.png resize: (210, 117) 1349361161 -2.1124608806519887 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234015_0.png resize: (100, 100) 1349361163 -1.6063681032530703 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233990_0.png resize: (290, 128) 1349361165 -2.779325057456525 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233997_0.png resize: (182, 146) 1349361168 -1.3759602067696892 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234017_0.png resize: (116, 81) 1349361169 -1.9834189226023673 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233991_0.png resize: (148, 352) 1349361171 -2.5505593887110027 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234018_0.png resize: (291, 318) 1349361173 -4.4890276035741925 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234020_0.png resize: (163, 221) 1349361189 -3.7438169760323508 treat image : 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temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234158_0.png resize: (110, 105) 1349361393 -0.19997415768334142 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234138_0.png resize: (96, 129) 1349361394 1.48600880711349 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234122_0.png resize: (175, 159) 1349361396 -1.039563610177155 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234161_0.png resize: (94, 180) 1349361398 -3.8522623766299966 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234165_0.png resize: (250, 202) 1349361399 -2.4790242000820317 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234137_0.png resize: (85, 108) 1349361401 -1.7731171510638892 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234126_0.png resize: (184, 69) 1349361403 -2.1948993506790595 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234139_0.png resize: (275, 363) 1349361404 -1.0652984032179014 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234155_0.png resize: (368, 475) 1349361406 -0.39592888523456543 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234130_0.png resize: (265, 184) 1349361409 -1.8176372897019495 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234195_0.png resize: (94, 76) 1349361410 -0.5957280871070235 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234205_0.png resize: (247, 156) 1349361413 -2.813308000454097 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234178_0.png resize: (193, 109) 1349361415 -0.5039951347751174 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234192_0.png resize: (148, 153) 1349361416 -2.380280510130845 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234182_0.png resize: (169, 139) 1349361418 -1.7904132577531984 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234204_0.png resize: (134, 164) 1349361420 -1.3806484358462152 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234179_0.png resize: (146, 135) 1349361421 -0.6217964963027937 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234175_0.png resize: (73, 101) 1349361423 -0.8374076160381795 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234198_0.png resize: (143, 235) 1349361425 -0.6918450414865159 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234203_0.png resize: (115, 225) 1349361426 -2.9625698295772644 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234207_0.png resize: (121, 144) 1349361428 -1.8917864114871412 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234202_0.png resize: (132, 154) 1349361430 -1.8260462344411614 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234201_0.png resize: (100, 125) 1349361431 0.20466041818678926 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234185_0.png resize: (109, 117) 1349361433 -0.3121032695350244 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234189_0.png resize: (204, 203) 1349361435 -2.810465452933343 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234174_0.png resize: (217, 161) 1349361436 -1.6393149727544685 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234176_0.png resize: (287, 350) 1349361438 -2.3318166023918465 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234180_0.png resize: (242, 287) 1349361439 -3.2499755669479797 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234190_0.png resize: (170, 445) 1349361441 -1.7663039772400384 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234184_0.png resize: (305, 265) 1349361443 -1.6549227618413 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234214_0.png resize: (281, 282) 1349361445 -3.295635471739111 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234181_0.png resize: (269, 341) 1349361447 -3.530920168618153 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234177_0.png resize: (246, 209) 1349361448 -2.1286524018943016 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234191_0.png resize: (217, 335) 1349361450 -2.576541982424279 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234206_0.png resize: (129, 70) 1349361451 -1.363222740663856 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234186_0.png resize: (298, 164) 1349361453 -2.022725270528449 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234187_0.png resize: (244, 191) 1349361455 -0.9975611488901723 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234211_0.png resize: (216, 112) 1349361456 -2.164512550296659 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234193_0.png resize: (168, 125) 1349361458 -2.008647556607286 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234183_0.png resize: (157, 216) 1349361460 -1.6741486199618276 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234208_0.png resize: (97, 123) 1349361461 -2.8508871224067063 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234200_0.png resize: (144, 257) 1349361463 -2.4050186084461798 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234210_0.png resize: (287, 384) 1349361465 -2.847467436469718 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234226_0.png resize: (197, 132) 1349361466 -1.2064707632970646 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234227_0.png resize: (167, 110) 1349361468 -0.13084995075740624 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234221_0.png resize: (131, 214) 1349361470 0.0016126990742674836 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234225_0.png resize: (409, 197) 1349361471 -0.421178306460754 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234217_0.png resize: (136, 213) 1349361473 -0.6773611326629301 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234218_0.png resize: (164, 116) 1349361475 0.772645428690947 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234220_0.png resize: (129, 282) 1349361476 -0.2473624435806867 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234224_0.png resize: (144, 115) 1349361478 1.5645937559710241 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234222_0.png resize: (247, 212) 1349361480 0.6952700909354582 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234291_0.png resize: (135, 53) 1349361481 -2.121280764979956 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234286_0.png resize: (419, 184) 1349361483 -3.161842105392867 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234298_0.png resize: (125, 152) 1349361485 -3.465612006799422 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234238_0.png resize: (142, 72) 1349361486 0.503653771973056 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234282_0.png resize: (178, 141) 1349361488 -2.451663452458633 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234239_0.png resize: (126, 124) 1349361490 -1.8472480620292746 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234249_0.png resize: (90, 90) 1349361491 -0.6390203042535424 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234294_0.png resize: (403, 502) 1349361493 -3.843593224643805 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234243_0.png resize: (363, 407) 1349361494 -3.901170555467554 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234283_0.png resize: (36, 82) 1349361497 -4.226854725608361 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234276_0.png resize: (92, 106) 1349361498 -1.9861650270842692 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234230_0.png resize: (113, 82) 1349361499 0.16118567535545986 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234292_0.png resize: (97, 86) 1349361502 -1.9264605089023048 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234289_0.png resize: (122, 144) 1349361504 -2.5523633345497427 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234296_0.png resize: (104, 164) 1349361505 -3.659061266333295 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234288_0.png resize: (119, 157) 1349361508 -2.2902011233862254 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234299_0.png resize: (76, 104) 1349361509 -3.2621508010311793 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234250_0.png resize: (67, 63) 1349361510 -1.5980703978063577 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234233_0.png resize: (109, 170) 1349361513 -2.8866238350220934 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234245_0.png resize: (103, 119) 1349361514 -2.6490940775223226 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234297_0.png resize: (98, 83) 1349361515 -2.6050107813247534 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234258_0.png resize: (141, 287) 1349361519 -2.7443078533154543 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234259_0.png resize: (260, 307) 1349361520 -4.630283674302744 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234268_0.png resize: (168, 82) 1349361521 -2.4680451259639224 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234281_0.png resize: (161, 167) 1349361525 -3.8530495193718655 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234285_0.png resize: (97, 69) 1349361526 -2.707889990248607 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234246_0.png resize: (100, 103) 1349361538 -2.5977737693495584 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234240_0.png resize: (186, 230) 1349361541 -3.964713138291009 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234251_0.png resize: (76, 163) 1349361542 -4.4845483968213005 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234229_0.png resize: (403, 356) 1349361543 -3.8539839075108344 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234236_0.png resize: (130, 113) 1349361546 -3.135521217182143 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234279_0.png resize: (116, 124) 1349361547 -5.038517616662984 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234237_0.png resize: (189, 258) 1349361553 -4.37403544773241 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234248_0.png resize: (289, 188) 1349361554 -2.000257025243096 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234295_0.png resize: (269, 191) 1349361555 -4.772908489839358 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234244_0.png resize: (188, 155) 1349361557 -3.483979876074934 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234290_0.png resize: (104, 122) 1349361559 -2.5220577244270532 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234241_0.png resize: (222, 177) 1349361560 -4.587407489597147 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234262_0.png resize: (220, 259) 1349361562 -4.590238058309295 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234260_0.png resize: (114, 198) 1349361564 -3.7398131859633654 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234270_0.png resize: (114, 141) 1349361565 -0.44627126654387345 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234247_0.png resize: (170, 182) 1349361567 -3.958613297144022 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234280_0.png resize: (134, 76) 1349361569 -2.3195612185992545 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234266_0.png resize: (169, 146) 1349361570 -3.549869581564985 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234273_0.png resize: (111, 134) 1349361572 -3.1775705282315916 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234253_0.png resize: (223, 192) 1349361574 -4.242664109595413 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234256_0.png resize: (128, 160) 1349361575 0.5587049135866694 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234255_0.png resize: (249, 262) 1349361577 -4.682680911891192 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234231_0.png resize: (298, 337) 1349361579 -1.3000565368603854 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234274_0.png resize: (252, 294) 1349361580 -4.335101481545247 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234264_0.png resize: (175, 81) 1349361582 -0.7677229985413141 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234277_0.png resize: (308, 133) 1349361584 -4.092115958609647 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234265_0.png resize: (192, 53) 1349361585 -2.0548091589029123 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234267_0.png resize: (228, 340) 1349361586 -4.8140205075213585 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234261_0.png resize: (125, 144) 1349361589 -4.573950524575869 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234228_0.png resize: (181, 134) 1349361590 -1.4947666153356503 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234232_0.png resize: (114, 178) 1349361591 -2.060632951689657 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234234_0.png resize: (245, 180) 1349361594 -2.8459479175655975 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234278_0.png resize: (272, 202) 1349361595 -5.525860002816081 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234287_0.png resize: (183, 245) 1349361596 -4.303018738808048 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234242_0.png resize: (307, 219) 1349361599 -4.870644715781261 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234263_0.png resize: (86, 72) 1349361600 -2.5311218021104116 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234284_0.png resize: (95, 98) 1349361601 -2.692118096883736 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234235_0.png resize: (186, 186) 1349361604 -3.086830496848006 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234254_0.png resize: (206, 112) 1349361605 -2.6402153494241682 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234275_0.png resize: (119, 52) 1349361606 -3.5928021283434664 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234252_0.png resize: (90, 56) 1349361609 -2.3241428035444147 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234313_0.png resize: (319, 144) 1349361612 -2.333920378167372 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234325_0.png resize: (192, 56) 1349361613 1.7125706910473657 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234347_0.png resize: (222, 327) 1349361616 -2.005863485083895 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234306_0.png resize: (100, 147) 1349361617 -2.921372402269895 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234317_0.png resize: (146, 90) 1349361618 -2.6978677775641957 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234315_0.png resize: (412, 187) 1349361620 -2.4134384464057446 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234334_0.png resize: (79, 169) 1349361622 -3.8361956156289323 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234319_0.png resize: (273, 280) 1349361623 -2.049922151088818 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234322_0.png resize: (138, 113) 1349361625 -1.9299924949899017 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234305_0.png resize: (70, 67) 1349361627 -3.678385568447028 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234328_0.png resize: (268, 358) 1349361629 -4.803987379671757 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234335_0.png resize: (120, 165) 1349361631 -2.466340622835398 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234345_0.png resize: (78, 59) 1349361632 3.6415562990879398 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234308_0.png resize: (236, 295) 1349361634 -3.6062906687296534 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234323_0.png resize: (296, 384) 1349361636 -4.316353981299456 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234327_0.png resize: (166, 126) 1349361637 -2.47637318385274 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234321_0.png resize: (230, 214) 1349361639 -3.2410038541025616 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234348_0.png resize: (464, 353) 1349361641 -4.0941291890478935 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234303_0.png resize: (259, 321) 1349361642 -3.980065896395365 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234302_0.png resize: (65, 97) 1349361644 0.969969012322248 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234307_0.png resize: (147, 216) 1349361647 -3.5960695530187494 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234350_0.png resize: (51, 105) 1349361648 -2.9525999461169325 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234301_0.png resize: (183, 193) 1349361650 -2.0616899041583467 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234336_0.png resize: (149, 197) 1349361652 -4.3505921350684105 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234311_0.png resize: (86, 115) 1349361653 -2.6141686014937258 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234324_0.png resize: (88, 35) 1349361655 -2.0574053685186295 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234333_0.png resize: (115, 142) 1349361657 -1.2023366896631524 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234316_0.png resize: (307, 318) 1349361658 -3.4643844960859833 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234329_0.png resize: (110, 234) 1349361660 -3.2502174664142394 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234309_0.png resize: (105, 137) 1349361661 -2.9220875617899535 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234344_0.png resize: (271, 432) 1349361663 -3.4553856809215295 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234343_0.png resize: (96, 95) 1349361665 -2.919841978997199 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234312_0.png resize: (181, 153) 1349361666 -2.786295820593082 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234310_0.png resize: (141, 77) 1349361668 -2.8589523443283804 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234320_0.png resize: (175, 77) 1349361670 -0.41503904167974565 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234339_0.png resize: (54, 123) 1349361672 7.402452998250621 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234331_0.png resize: (198, 141) 1349361674 -4.29310559371059 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234318_0.png resize: (382, 175) 1349361675 -3.7635885408579774 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270_rle_crop_3743234354_0.png resize: (54, 413) 1349361677 -2.465309054518745 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270_rle_crop_3743234357_0.png resize: (317, 431) 1349361679 -2.6573115086405856 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270_rle_crop_3743234352_0.png resize: (72, 80) 1349361681 -0.6239449569544601 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270_rle_crop_3743234353_0.png resize: (103, 190) 1349361682 -1.6258562469839932 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270_rle_crop_3743234359_0.png resize: (170, 226) 1349361684 -2.288754169244108 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234406_0.png resize: (242, 147) 1349361685 -0.6194460866956801 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234366_0.png resize: (336, 327) 1349361687 -2.1880703510877364 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234383_0.png resize: (162, 116) 1349361689 -1.6863132934328515 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234377_0.png resize: (255, 120) 1349361690 -2.3242520487970157 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234394_0.png resize: (180, 146) 1349361692 -1.8285627925948396 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234370_0.png resize: (426, 466) 1349361694 -3.305221431311653 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234385_0.png resize: (395, 352) 1349361695 -3.440910693716068 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234409_0.png resize: (287, 177) 1349361697 -2.0849595706392945 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234400_0.png resize: (195, 269) 1349361699 -3.1770259759254738 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234375_0.png resize: (265, 213) 1349361700 -1.7427395410857356 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234403_0.png resize: (847, 787) 1349361702 -3.243342631890868 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234399_0.png resize: (242, 160) 1349361704 -2.570515716114029 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234390_0.png resize: (224, 204) 1349361705 -3.116509398780744 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234371_0.png resize: (220, 160) 1349361707 -1.8734125111242028 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234391_0.png resize: (99, 79) 1349361710 -0.16991443028101338 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234369_0.png resize: (334, 223) 1349361711 -3.4292475301814322 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234386_0.png resize: (255, 108) 1349361713 -1.7216640780217178 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234367_0.png resize: (393, 339) 1349361715 -2.4357140993313204 treat image : 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temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234380_0.png resize: (144, 177) 1349361729 -1.0601707185599398 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234378_0.png resize: (300, 267) 1349361730 -4.175225456584906 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234379_0.png resize: (171, 202) 1349361731 -2.2370596667551315 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234404_0.png resize: (175, 191) 1349361733 -1.8342261388657115 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234381_0.png resize: (112, 168) 1349361735 0.4909102906212979 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234384_0.png resize: (152, 146) 1349361736 -2.4836107838796786 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234408_0.png resize: (191, 203) 1349361739 -3.2439965836801674 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234376_0.png resize: (172, 137) 1349361740 -3.1759819771674738 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234402_0.png resize: (221, 213) 1349361741 -2.072596948664553 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234360_0.png resize: (216, 178) 1349361744 -1.4882718275864 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234398_0.png resize: (278, 167) 1349361745 -2.7160069750007167 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234374_0.png resize: (139, 118) 1349361746 -3.7159667298709578 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234362_0.png resize: (176, 51) 1349361749 -2.811595868220711 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234411_0.png resize: (420, 362) 1349361750 -1.2811333579746198 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234436_0.png resize: (176, 99) 1349361751 -2.3258374176621164 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234422_0.png resize: (581, 483) 1349361753 -3.6108256968499886 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234410_0.png resize: (379, 467) 1349361754 -3.619089152232362 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234426_0.png resize: (385, 390) 1349361755 -4.302637658158964 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234416_0.png resize: (261, 273) 1349361757 -5.524478232356231 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234431_0.png resize: (80, 80) 1349361758 -3.450152402631726 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234414_0.png resize: (222, 112) 1349361759 -3.177969459189084 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234432_0.png resize: (182, 195) 1349361762 -3.686291963298536 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234418_0.png resize: (137, 232) 1349361763 -4.922694690999211 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234435_0.png resize: (239, 296) 1349361764 -4.230014425167966 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234412_0.png resize: (421, 951) 1349361765 -4.622341446038258 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234424_0.png resize: (296, 263) 1349361769 -5.18533839333629 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234437_0.png resize: (235, 370) 1349361772 -5.085542565288118 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234440_0.png resize: (104, 90) 1349361773 -4.935651339961602 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234434_0.png resize: (556, 479) 1349361775 -5.623809110943129 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234423_0.png resize: (230, 418) 1349361777 -6.307910693044369 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234430_0.png resize: (173, 258) 1349361779 -4.906121258067992 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234425_0.png resize: (199, 146) 1349361780 -3.878493600054407 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234433_0.png resize: (193, 95) 1349361782 -6.178848297732407 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234419_0.png resize: (502, 755) 1349361784 -5.347312556628352 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234427_0.png resize: (256, 407) 1349361785 -5.987773551364608 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234420_0.png resize: (292, 231) 1349361787 -4.814193130663267 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234421_0.png resize: (459, 521) 1349361789 -2.1470204008591978 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234429_0.png resize: (134, 176) 1349361790 -1.7128670277246725 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234442_0.png resize: (333, 164) 1349361792 -4.01090180457283 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234460_0.png resize: (424, 399) 1349361794 -1.2747924664050752 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234447_0.png resize: (487, 380) 1349361796 -2.8095173580367474 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234459_0.png resize: (200, 161) 1349361798 -2.3139899875659555 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234443_0.png resize: (362, 434) 1349361800 -3.849425103859476 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234475_0.png resize: (379, 343) 1349361802 -3.100231674295258 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234455_0.png resize: (389, 397) 1349361804 -4.024896763029175 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234454_0.png resize: (262, 252) 1349361806 -4.60015066643814 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234470_0.png resize: (139, 198) 1349361808 -4.251976547209789 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234444_0.png resize: (433, 129) 1349361810 -3.4694605315543035 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234453_0.png resize: (62, 87) 1349361811 -2.6022828347058136 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234476_0.png resize: (76, 81) 1349361813 -3.296812095888082 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234458_0.png resize: (192, 100) 1349361815 -3.5315968296552973 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234457_0.png resize: (286, 237) 1349361816 -4.051956668543009 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234484_0.png resize: (102, 117) 1349361819 -4.13769636375931 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234471_0.png resize: (246, 142) 1349361820 -4.798504207728646 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234462_0.png resize: (171, 185) 1349361825 -2.893935242568288 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234450_0.png resize: (378, 494) 1349361826 -5.140416848763511 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234473_0.png resize: (264, 196) 1349361827 -4.217755683581011 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234488_0.png resize: (179, 208) 1349361830 -4.305285769805465 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234489_0.png resize: (238, 333) 1349361831 -4.294851813148733 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234461_0.png resize: (117, 193) 1349361832 -4.6406269959562145 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234449_0.png resize: (261, 134) 1349361835 -4.436230626678248 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234445_0.png resize: (426, 913) 1349361836 -4.513674356804566 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234463_0.png resize: (135, 223) 1349361837 -3.850939799318246 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234469_0.png resize: (169, 348) 1349361840 -3.533762933909877 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234465_0.png resize: (239, 384) 1349361841 -4.561154631370271 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234451_0.png resize: (147, 124) 1349361842 -2.9985487999515774 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234472_0.png resize: (109, 103) 1349361845 -5.329317673261203 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234490_0.png resize: (211, 249) 1349361846 -4.9071758745154295 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234487_0.png resize: (701, 654) 1349361847 -5.047283900824092 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234477_0.png resize: (60, 57) 1349361850 -3.843553578671837 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234464_0.png resize: (249, 259) 1349361851 -5.294545755897857 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234480_0.png resize: (267, 409) 1349361852 -4.93088048830547 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234456_0.png resize: (97, 159) 1349361855 -5.699860319190708 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234467_0.png resize: (106, 100) 1349361856 -4.025315116237971 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234448_0.png resize: (293, 224) 1349361857 -4.609026965623662 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234486_0.png resize: (458, 463) 1349361860 -1.7156180842658935 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234485_0.png resize: (251, 216) 1349361861 -4.68605549817695 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234446_0.png resize: (258, 108) 1349361863 -3.1882500467859716 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234496_0.png resize: (189, 119) 1349361866 -1.0210788865485707 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234497_0.png resize: (142, 210) 1349361867 -1.9486810926865545 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234494_0.png resize: (68, 143) 1349361868 -2.283872979326141 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234493_0.png resize: (240, 136) 1349361871 -2.4754627825599758 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234498_0.png resize: (490, 377) 1349361872 -2.442716962942749 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234495_0.png resize: (487, 534) 1349361873 -3.5385518388387345 treat image : 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temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234612_0.png resize: (246, 186) 1349361970 -2.0701710723286446 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234599_0.png resize: (238, 173) 1349361973 -2.3502018817552353 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234605_0.png resize: (347, 247) 1349361974 -2.666284906327076 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234603_0.png resize: (156, 105) 1349361975 -2.752214615813017 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234633_0.png resize: (232, 427) 1349361978 -2.629239281938952 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234617_0.png resize: (153, 356) 1349361979 -3.443633094813379 treat image : 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temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234598_0.png resize: (221, 393) 1349361993 -3.139628520205964 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234610_0.png resize: (138, 181) 1349361996 -1.824720156279731 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234621_0.png resize: (216, 330) 1349361997 -2.175550355638093 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234602_0.png resize: (177, 200) 1349361998 -2.419048164577707 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234630_0.png resize: (244, 284) 1349362001 -3.603380160659068 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234645_0.png resize: (149, 253) 1349362002 -2.4864434854218738 treat image : 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temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234659_0.png resize: (193, 121) 1349362058 -3.5106671273935808 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234697_0.png resize: (422, 585) 1349362060 -3.3013058126008787 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234674_0.png resize: (154, 108) 1349362061 -3.596877357387714 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234660_0.png resize: (252, 134) 1349362064 -4.411232186788882 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234672_0.png resize: (228, 161) 1349362065 -2.285481584503942 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234680_0.png resize: (247, 251) 1349362066 -2.78734640143026 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234657_0.png resize: (243, 178) 1349362069 -1.5430649391964866 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234664_0.png resize: (171, 166) 1349362070 -1.1352525092839156 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234667_0.png resize: (199, 181) 1349362071 -4.176157311013217 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234652_0.png resize: (304, 136) 1349362074 -2.3707615332950156 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234658_0.png resize: (125, 207) 1349362075 -3.243115953343981 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234686_0.png resize: (243, 178) 1349362076 -2.6407564687852436 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234682_0.png resize: (115, 118) 1349362080 -0.435074830580925 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234648_0.png resize: (321, 378) 1349362081 -3.2676197384250036 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234650_0.png resize: (231, 285) 1349362082 -1.5848574985486916 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234671_0.png resize: (102, 101) 1349362085 -1.736324912146438 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234668_0.png resize: (167, 270) 1349362086 -3.496337397327262 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234684_0.png resize: (203, 184) 1349362087 -3.1189945823207026 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234677_0.png resize: (187, 138) 1349362091 -2.9312457477354994 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234683_0.png resize: (130, 75) 1349362093 -3.090585345407092 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234649_0.png resize: (369, 323) 1349362094 -2.437594042682744 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234690_0.png resize: (91, 207) 1349362096 -3.8542339982728606 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234662_0.png resize: (135, 197) 1349362098 -3.5360227166191036 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234679_0.png resize: (148, 233) 1349362099 -3.326586178649141 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234689_0.png resize: (392, 431) 1349362102 -4.874832212683438 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234696_0.png resize: (149, 132) 1349362104 -3.4764116549759767 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234656_0.png resize: (486, 751) 1349362106 -4.534761305715503 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234670_0.png resize: (116, 237) 1349362108 -2.254634216813199 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234647_0.png resize: (307, 369) 1349362109 -2.062524439290877 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234673_0.png resize: (110, 112) 1349362110 -2.0306203841402035 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234663_0.png resize: (176, 253) 1349362113 -3.4799200542421693 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234651_0.png resize: (154, 100) 1349362114 -2.034803285624187 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234676_0.png resize: (261, 429) 1349362115 -2.6504399668832557 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234692_0.png resize: (120, 177) 1349362118 -2.729013474583363 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234688_0.png resize: (225, 184) 1349362119 -2.642397586078635 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234694_0.png resize: (252, 110) 1349362120 -3.788068576370035 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234704_0.png resize: (191, 203) 1349362121 -2.7467515207489805 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234705_0.png resize: (114, 155) 1349362124 -2.4784924654188765 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234719_0.png resize: (202, 152) 1349362125 -3.3098316239230074 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234748_0.png resize: (171, 186) 1349362126 -2.1408063643847544 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234761_0.png resize: (134, 156) 1349362129 -4.426271190350042 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234751_0.png resize: (216, 378) 1349362130 -3.8204880180850695 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234707_0.png resize: (153, 252) 1349362131 -3.19224518640474 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234746_0.png resize: (365, 221) 1349362134 -3.581699121554962 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234747_0.png resize: (249, 220) 1349362135 -2.661153990830391 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234702_0.png resize: (231, 225) 1349362136 -2.336384731917981 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234714_0.png resize: (271, 158) 1349362139 -3.6370004390086366 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234720_0.png resize: (207, 214) 1349362140 -3.8501351557425205 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234756_0.png resize: (239, 113) 1349362141 -2.463239776825975 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234710_0.png resize: (257, 211) 1349362144 -3.730735221993249 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234723_0.png resize: (245, 113) 1349362145 -3.2431724135499347 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234743_0.png resize: (318, 307) 1349362146 -4.516562940540897 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234715_0.png resize: (193, 212) 1349362149 -0.9091503110882372 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234731_0.png resize: (118, 93) 1349362150 -0.9997125755953391 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234755_0.png resize: (313, 388) 1349362151 -3.21303832314236 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234757_0.png resize: (119, 149) 1349362153 -3.374487116246996 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234732_0.png resize: (268, 95) 1349362155 -3.382835799228227 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234700_0.png resize: (188, 139) 1349362156 -2.718260079205891 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234745_0.png resize: (400, 254) 1349362158 -3.694074906758493 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234726_0.png resize: (89, 84) 1349362160 -0.520999068531061 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234725_0.png resize: (140, 306) 1349362161 -4.188042098918537 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234703_0.png resize: (249, 217) 1349362163 -3.9285663939497155 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234738_0.png resize: (167, 171) 1349362165 -2.963338004402363 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234727_0.png resize: (531, 484) 1349362166 -3.69959071491655 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234740_0.png resize: (368, 198) 1349362168 -4.577298108614027 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234749_0.png resize: (224, 236) 1349362170 -3.860802713751938 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234729_0.png resize: (126, 169) 1349362171 -4.872283339757517 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234758_0.png resize: (178, 316) 1349362173 -3.580230186294617 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234750_0.png resize: (155, 250) 1349362175 -5.036146830887791 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234724_0.png resize: (149, 135) 1349362176 -3.8955107934601747 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234709_0.png resize: (191, 198) 1349362178 -4.628782598811084 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234739_0.png resize: (186, 268) 1349362185 -4.770597442574594 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234735_0.png resize: (192, 182) 1349362186 -4.003588311269125 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234742_0.png resize: (135, 54) 1349362190 -0.05797101259810976 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234716_0.png resize: (199, 228) 1349362191 -4.684695648295268 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234706_0.png resize: (402, 336) 1349362192 -2.9112311477241826 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234708_0.png resize: (119, 110) 1349362196 -3.371307571302754 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234734_0.png resize: (75, 95) 1349362197 -4.687026745119933 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234760_0.png resize: (143, 149) 1349362198 -3.2274864354661124 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234737_0.png resize: (235, 105) 1349362202 -3.225044007208957 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234753_0.png resize: (142, 91) 1349362203 -3.5938188030528853 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234701_0.png resize: (378, 492) 1349362204 -2.8774709348268193 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234752_0.png resize: (193, 105) 1349362208 -2.1764152556407033 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234744_0.png resize: (134, 141) 1349362209 -4.555248201377423 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234733_0.png resize: (282, 219) 1349362210 -2.8905059049692667 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234722_0.png resize: (190, 128) 1349362214 -2.493160901929289 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234754_0.png resize: (205, 355) 1349362217 -3.3429767091908196 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234759_0.png resize: (139, 158) 1349362218 -2.9349528375395852 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234712_0.png resize: (255, 97) 1349362222 -3.6381938699266474 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233913_0.png resize: (153, 104) 1349362728 0.05352508070781385 treat image : 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temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233903_0.png resize: (182, 203) 1349362738 -0.2902715187041397 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233919_0.png resize: (92, 120) 1349362739 1.7554338319444651 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233954_0.png resize: (99, 97) 1349362740 -3.8271883099507407 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233973_0.png resize: (90, 182) 1349362743 -4.8601650881265765 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233975_0.png resize: (239, 494) 1349362744 -5.477882682019382 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233927_0.png resize: (100, 145) 1349362745 -4.762315894996713 treat image : 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temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef_rle_crop_3743234051_0.png resize: (50, 105) 1349362759 -4.988380911442267 treat image : temp/1743537630_2844229_1349346305_29103387b775a6e1f384ff7ee58e7ec0_rle_crop_3743234103_0.png resize: (663, 574) 1349362760 -1.5705239139528346 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234121_0.png resize: (613, 560) 1349362761 -2.9650700578584424 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234112_0.png resize: (247, 239) 1349362762 -2.5495432611533175 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234118_0.png resize: (234, 297) 1349362763 -1.2376942091256598 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234110_0.png resize: (980, 914) 1349362764 -0.7541111411543183 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234107_0.png resize: (157, 110) 1349362765 -2.6640049615235735 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234104_0.png resize: (271, 180) 1349362766 -1.1434946118348812 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234169_0.png resize: (154, 234) 1349362767 -2.332927674029586 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234131_0.png resize: (103, 147) 1349362768 -1.7325042913019455 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234160_0.png resize: (972, 916) 1349362770 -1.5258859555815227 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234162_0.png resize: (85, 109) 1349362771 -0.9784889560867919 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234144_0.png resize: (79, 147) 1349362772 0.024817140378051127 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234147_0.png resize: (179, 146) 1349362774 -1.8719278342114207 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234125_0.png resize: (116, 241) 1349362775 -1.1243865594681313 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234134_0.png resize: (174, 199) 1349362776 -2.2246449805577346 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234194_0.png resize: (333, 179) 1349362778 -1.5873219087297745 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234199_0.png resize: (67, 185) 1349362779 -2.5270450123237063 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234172_0.png resize: (554, 399) 1349362780 -0.6412720316551761 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234173_0.png resize: (153, 342) 1349362781 -2.5155489319236635 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234197_0.png resize: (263, 287) 1349362782 -1.6755476798777453 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234188_0.png resize: (156, 346) 1349362783 -1.2683409229099358 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234219_0.png resize: (127, 140) 1349362784 -1.2191989810940798 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234269_0.png resize: (101, 164) 1349362785 -2.9600009089073733 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234304_0.png resize: (232, 159) 1349362827 -3.0762077893377056 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234337_0.png resize: (445, 396) 1349362829 -2.597955995638125 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234314_0.png resize: (783, 815) 1349362830 -4.892585252597816 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234300_0.png resize: (191, 518) 1349362831 -1.670820494183928 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234346_0.png resize: (708, 473) 1349362833 -1.8479803456694497 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234341_0.png resize: (622, 517) 1349362834 -4.954814886650771 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270_rle_crop_3743234356_0.png resize: (990, 1024) 1349362835 -1.1503629295388358 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234401_0.png resize: (294, 184) 1349362837 -2.2453286483685924 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234368_0.png resize: (181, 386) 1349362838 -1.6919893255345542 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234396_0.png resize: (135, 217) 1349362839 -3.1479840469925504 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234361_0.png resize: (199, 269) 1349362841 -2.733968377309208 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234397_0.png resize: (155, 320) 1349362842 -3.017802349110379 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234382_0.png resize: (284, 175) 1349362843 -2.217828279194206 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234392_0.png resize: (37, 124) 1349362845 -4.381646336772831 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234395_0.png resize: (367, 302) 1349362846 -2.452475532103024 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234388_0.png resize: (198, 192) 1349362847 -3.0066115696739826 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234363_0.png resize: (121, 169) 1349362849 0.2790902202500183 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234438_0.png resize: (572, 561) 1349362850 -4.141669591061168 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234439_0.png resize: (123, 179) 1349362851 -2.3294566580093132 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234415_0.png resize: (240, 473) 1349362853 -2.416309943292185 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234481_0.png resize: (531, 441) 1349362854 -4.298682320284586 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234468_0.png resize: (632, 472) 1349362856 -3.382306249251484 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234483_0.png resize: (100, 229) 1349362857 -0.9702604025183176 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234482_0.png resize: (257, 213) 1349362858 -4.453124972498579 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234479_0.png resize: (123, 178) 1349362860 -2.3037592513203764 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234478_0.png resize: (218, 180) 1349362861 -3.9254845086177887 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234466_0.png resize: (141, 171) 1349362862 -4.867210335662916 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234452_0.png resize: (183, 301) 1349362864 -3.0899038642743393 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234492_0.png resize: (688, 1119) 1349362865 -0.8228648964847539 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234504_0.png resize: (186, 482) 1349362866 -1.501170888175204 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234502_0.png resize: (118, 144) 1349362867 -1.6943492764104853 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234508_0.png resize: (125, 84) 1349362869 -1.989929569829448 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234499_0.png resize: (154, 287) 1349362870 0.006375607123164163 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234511_0.png resize: (162, 134) 1349362871 -2.8571784122713852 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234500_0.png resize: (121, 67) 1349362873 -2.8406747021167877 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234506_0.png resize: (126, 93) 1349362874 -2.5058729763139684 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234560_0.png resize: (193, 120) 1349362875 -2.5854453080434348 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234537_0.png resize: (89, 212) 1349362877 -0.4312438914576488 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234556_0.png resize: (83, 119) 1349362878 -2.1925320726431217 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234544_0.png resize: (128, 91) 1349362879 -3.073527744537812 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234533_0.png resize: (108, 84) 1349362881 -1.0844242524310592 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234535_0.png resize: (80, 122) 1349362883 -2.853289986124738 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234515_0.png resize: (316, 278) 1349362884 -2.218336952582758 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234552_0.png resize: (309, 257) 1349362885 -2.569900335967311 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234520_0.png resize: (511, 346) 1349362886 -1.3545747017622058 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234554_0.png resize: (552, 694) 1349362887 -2.1719278026077298 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234559_0.png resize: (341, 144) 1349362888 -2.074417968967868 treat image : temp/1743537630_2844229_1349335651_ab82dcd69b33d78e2957ba4bf3562612_rle_crop_3743234569_0.png resize: (200, 208) 1349362889 -4.373124438035497 treat image : 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temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234600_0.png resize: (321, 453) 1349362902 -3.665097359948945 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234625_0.png resize: (216, 112) 1349362903 -2.4545659486065134 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234629_0.png resize: (206, 223) 1349362904 -3.011647667456634 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234616_0.png resize: (151, 364) 1349362905 -1.6183691927514947 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234615_0.png resize: (98, 180) 1349362906 -1.962214597033767 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234678_0.png resize: (165, 219) 1349362907 -3.4440748595006747 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234693_0.png resize: (189, 228) 1349362908 -4.1003984229291826 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234665_0.png resize: (120, 85) 1349362909 -1.7036784336292727 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234711_0.png resize: (82, 121) 1349362910 1.7808389681297714 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234741_0.png resize: (179, 386) 1349362911 -2.9686091037475757 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234717_0.png resize: (128, 258) 1349362912 -3.894955771723092 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234762_0.png resize: (136, 199) 1349362913 -3.045935696493275 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234699_0.png resize: (131, 156) 1349362914 -2.8116119841626386 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234730_0.png resize: (291, 460) 1349362915 -4.0410521826646155 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234728_0.png resize: (154, 151) 1349362916 -3.1699729033703763 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234721_0.png resize: (358, 227) 1349362917 -1.4273760592799305 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234713_0.png resize: (165, 120) 1349362918 -1.5555390505054145 treat image : temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef_rle_crop_3743234087_0.png resize: (100, 107) 1349362922 -3.382565029346027 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234332_0.png resize: (156, 372) 1349362923 -3.3718680492210584 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234342_0.png resize: (80, 112) 1349362924 -4.053148055538235 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233974_0.png resize: (330, 296) 1349363128 -4.4146013127364485 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234023_0.png resize: (214, 449) 1349363130 -3.3899942998021912 treat image : temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef_rle_crop_3743234065_0.png resize: (327, 312) 1349363131 -3.602673390478965 treat image : temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef_rle_crop_3743234043_0.png resize: (252, 406) 1349363132 -2.9062265620719665 treat image : temp/1743537630_2844229_1349346305_29103387b775a6e1f384ff7ee58e7ec0_rle_crop_3743234099_0.png resize: (268, 450) 1349363134 -3.828401921517723 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234117_0.png resize: (388, 774) 1349363135 -2.0910884979088524 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234113_0.png resize: (176, 155) 1349363136 -1.3596007368839207 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234108_0.png resize: (323, 324) 1349363138 -3.4013640088411283 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234152_0.png resize: (257, 241) 1349363139 -2.9178524924859106 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234153_0.png resize: (287, 236) 1349363140 -3.4275920089254757 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234170_0.png resize: (247, 125) 1349363142 -2.3311637471456574 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234216_0.png resize: (252, 319) 1349363143 -1.4493929971585326 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234212_0.png resize: (418, 337) 1349363144 -2.4630798009702874 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234213_0.png resize: (459, 436) 1349363146 -3.3631888468289777 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234196_0.png resize: (330, 267) 1349363147 -3.333073580495762 treat image : temp/1743537630_2844229_1349335811_49dd88abcbaa0c0a1a77d1ddf3d31d90_rle_crop_3743234223_0.png resize: (123, 263) 1349363148 -0.09379732594389477 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234257_0.png resize: (195, 366) 1349363151 -3.6535806120801517 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234293_0.png resize: (231, 302) 1349363152 -2.1646964983021255 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234272_0.png resize: (577, 488) 1349363153 -3.0123626614011996 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234349_0.png resize: (368, 209) 1349363155 -3.810389540944262 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270_rle_crop_3743234355_0.png resize: (441, 598) 1349363156 -5.470709029993367 treat image : temp/1743537630_2844229_1349335763_d789211c01160512c231334543969270_rle_crop_3743234358_0.png resize: (244, 659) 1349363157 -1.9500903815389539 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234387_0.png resize: (233, 452) 1349363159 -3.3701410813415626 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234393_0.png resize: (770, 605) 1349363160 -2.509065857151259 treat image : temp/1743537630_2844229_1349335728_f8443fdfb612a59563ef875a976d716f_rle_crop_3743234405_0.png resize: (258, 483) 1349363161 -4.037990668735436 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234413_0.png resize: (370, 334) 1349363163 -4.331663746496687 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234474_0.png resize: (300, 330) 1349363164 -4.4035766095064055 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234509_0.png resize: (383, 380) 1349363165 -3.1444584034385543 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234510_0.png resize: (311, 291) 1349363167 -3.183220427287016 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234505_0.png resize: (339, 327) 1349363168 -3.337694030549026 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234516_0.png resize: (354, 516) 1349363169 -3.6426219998378766 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234551_0.png resize: (490, 397) 1349363170 -3.1361324301051936 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234547_0.png resize: (715, 786) 1349363172 -4.1391960812396835 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234523_0.png resize: (69, 168) 1349363173 -1.178019445900051 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234557_0.png resize: (117, 90) 1349363174 -2.2490956284398043 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234550_0.png resize: (170, 175) 1349363176 -2.809439324169531 treat image : temp/1743537630_2844229_1349335651_ab82dcd69b33d78e2957ba4bf3562612_rle_crop_3743234572_0.png resize: (506, 513) 1349363178 -3.473775287590521 treat image : temp/1743537630_2844229_1349335651_ab82dcd69b33d78e2957ba4bf3562612_rle_crop_3743234577_0.png resize: (377, 309) 1349363179 -3.375328023571625 treat image : temp/1743537630_2844229_1349335651_ab82dcd69b33d78e2957ba4bf3562612_rle_crop_3743234570_0.png resize: (528, 446) 1349363181 -3.3661419103018226 treat image : temp/1743537630_2844229_1349335647_397067bb5c5e6da24dc3d9e6464154c3_rle_crop_3743234589_0.png resize: (209, 447) 1349363183 -0.88173444640473 treat image : temp/1743537630_2844229_1349335647_397067bb5c5e6da24dc3d9e6464154c3_rle_crop_3743234597_0.png resize: (240, 284) 1349363184 -3.0942199063423708 treat image : temp/1743537630_2844229_1349335647_397067bb5c5e6da24dc3d9e6464154c3_rle_crop_3743234582_0.png resize: (209, 421) 1349363185 -1.3046327632233796 treat image : temp/1743537630_2844229_1349335647_397067bb5c5e6da24dc3d9e6464154c3_rle_crop_3743234596_0.png resize: (441, 237) 1349363187 -2.6803566000835737 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234634_0.png resize: (396, 257) 1349363188 -1.3823449808017634 treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234622_0.png resize: (437, 187) 1349363189 -2.9254447938587225 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234698_0.png resize: (239, 173) 1349363191 -4.821539008201023 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234718_0.png resize: (166, 403) 1349363192 -2.5001325121602274 treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233905_0.png resize: (260, 179) 1349363217 -2.564164688302162 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233993_0.png resize: (222, 222) 1349363219 -2.9021204267907614 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234209_0.png resize: (131, 122) 1349363220 -0.8975111471811396 treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234215_0.png resize: (191, 246) 1349363221 -2.987031158104093 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234326_0.png resize: (518, 441) 1349363223 -2.1591589534378386 treat image : temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef_rle_crop_3743234081_0.png resize: (135, 131) 1349363262 -1.8436407286308 treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234136_0.png resize: (130, 153) 1349363264 -0.7318428293945429 treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234271_0.png resize: (271, 258) 1349363265 -3.2514724909965436 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234338_0.png resize: (221, 258) 1349363266 -1.2601407394476942 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234351_0.png resize: (304, 277) 1349363268 -3.1647536256126703 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234330_0.png resize: (431, 285) 1349363269 -4.181290618001431 treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234561_0.png resize: (149, 173) 1349363270 -2.957750699288978 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234687_0.png resize: (108, 136) 1349363272 0.03962272910348853 treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233931_0.png resize: (100, 155) 1349363307 -4.0717253507022555 treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234007_0.png resize: (85, 96) 1349363308 -1.4756077532050234 treat image : temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef_rle_crop_3743234085_0.png resize: (166, 195) 1349363310 -3.755327029443978 treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234119_0.png resize: (191, 249) 1349363311 -3.666802421554252 treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234340_0.png resize: (231, 294) 1349363312 -4.589602227966192 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234417_0.png resize: (201, 131) 1349363314 -5.402763024297145 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234428_0.png resize: (135, 128) 1349363316 -5.05660590937699 treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234441_0.png resize: (116, 208) 1349363317 -4.472908997777533 treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234491_0.png resize: (198, 472) 1349363319 -1.8105175963142528 treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234501_0.png resize: (130, 210) 1349363320 -2.4152338072204813 treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234695_0.png resize: (149, 427) 1349363321 -3.1849211414671856 treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234736_0.png resize: (182, 170) 1349363323 -4.001158653291035 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 : 923 time used for this insertion : 0.0838768482208252 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 923 time used for this insertion : 0.20496892929077148 save missing photos in datou_result : time spend for datou_step_exec : 95.14318346977234 time spend to save output : 0.3421816825866699 total time spend for step 6 : 95.48536515235901 step7:brightness Tue Apr 1 22:24:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234634_0.png treat image : temp/1743537630_2844229_1349335643_ad65a1fc261530ee95a79b2cbfeab8f3_rle_crop_3743234622_0.png treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234698_0.png treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234718_0.png treat image : temp/1743537630_2844229_1349346432_c4f34f03808b31b5fcb830583930bee5_rle_crop_3743233905_0.png treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743233993_0.png treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234209_0.png treat image : temp/1743537630_2844229_1349346189_ac0df2f66b581d67d1a100079090c003_rle_crop_3743234215_0.png treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234326_0.png treat image : temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef_rle_crop_3743234081_0.png treat image : temp/1743537630_2844229_1349346201_5a34106213b44563131b4446267eb19e_rle_crop_3743234136_0.png treat image : temp/1743537630_2844229_1349335771_cacbf3bc2e88b285a0f63899d884cdd4_rle_crop_3743234271_0.png treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234338_0.png treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234351_0.png treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234330_0.png treat image : temp/1743537630_2844229_1349335673_90aa0cca12af95d82cd9c2995e10e4a4_rle_crop_3743234561_0.png treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234687_0.png treat image : temp/1743537630_2844229_1349346428_e4d4f76f6eb6435dfb1495f664ba1980_rle_crop_3743233931_0.png treat image : temp/1743537630_2844229_1349346348_9c82d62023ae91647772de4c1eed093b_rle_crop_3743234007_0.png treat image : temp/1743537630_2844229_1349346340_154382c2afd041ef56559f652e0f48ef_rle_crop_3743234085_0.png treat image : temp/1743537630_2844229_1349346213_d40f63b5a3aed38f82eb252fd7488080_rle_crop_3743234119_0.png treat image : temp/1743537630_2844229_1349335768_9c4ee3fdd58333f561bd69a081b6cc52_rle_crop_3743234340_0.png treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234417_0.png treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234428_0.png treat image : temp/1743537630_2844229_1349335722_676d34a7a721d65d3f65d23f3b746648_rle_crop_3743234441_0.png treat image : temp/1743537630_2844229_1349335711_f4ea863a03ff82275cbb1537ffab0854_rle_crop_3743234491_0.png treat image : temp/1743537630_2844229_1349335707_73adc8528e51bdfd273e60f936b00926_rle_crop_3743234501_0.png treat image : temp/1743537630_2844229_1349335638_824df864d8236bfbe886caa3117b44b3_rle_crop_3743234695_0.png treat image : temp/1743537630_2844229_1349335633_876e268d9b8495ccce2e3ad90e883de2_rle_crop_3743234736_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 : 923 time used for this insertion : 0.06984066963195801 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 923 time used for this insertion : 1.5084493160247803 save missing photos in datou_result : time spend for datou_step_exec : 26.318097352981567 time spend to save output : 1.5900859832763672 total time spend for step 7 : 27.908183336257935 step8:velours_tree Tue Apr 1 22:25:05 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 complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 2.8627047538757324 time spend to save output : 5.650520324707031e-05 total time spend for step 8 : 2.8627612590789795 step9:send_mail_cod Tue Apr 1 22:25:08 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 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_P21958982_01-04-2025_22_25_08.pdf 21959937 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 .imagette219599371743539108 21959938 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 .imagette219599381743539109 21959939 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 .imagette219599391743539111 21959940 imagette219599401743539113 21959941 change filename to text .change filename to text .change filename to text .imagette219599411743539113 21959942 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219599421743539113 21959943 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 .imagette219599431743539113 21959944 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 .imagette219599441743539114 21959945 imagette219599451743539114 21959946 imagette219599461743539114 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21958982 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21959937,21959938,21959939,21959940,21959941,21959942,21959943,21959944,21959945,21959946,21959947?tags=carton,pet_clair,papier,flou,metal,autre,pehd,pet_fonce,mal_croppe,background,environnement args[1349346432] : ((1349346432, -3.4719225522844654, 492609224), (1349346432, 0.05472177303410254, 2107752395), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349346428] : ((1349346428, -7.336459387859378, 492609224), (1349346428, -0.15551515418298248, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349346348] : ((1349346348, -3.918108297415554, 492609224), (1349346348, -0.07822080822231028, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349346340] : ((1349346340, -4.713791093220756, 492609224), (1349346340, -0.32819941540371483, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349346305] : ((1349346305, -3.1291185697527415, 492609224), (1349346305, -0.16275926521038186, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349346213] : ((1349346213, -2.8379750300381135, 492609224), (1349346213, -0.05661127828122603, 2107752395), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349346201] : ((1349346201, -3.0346157122109134, 492609224), (1349346201, -0.23578600301891445, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349346189] : ((1349346189, -3.7972292573703847, 492609224), (1349346189, -0.1819913531931122, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335811] : ((1349335811, 2.710804203370057, 492688767), (1349335811, 0.047377471184282, 2107752395), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335771] : ((1349335771, -6.414157042987834, 492609224), (1349335771, -0.15248351407820943, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335768] : ((1349335768, -5.654027035385974, 492609224), (1349335768, -0.3243686383767659, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335763] : ((1349335763, -2.180256128507998, 492609224), (1349335763, -0.589593967553922, 501862349), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335728] : ((1349335728, -3.81083284346196, 492609224), (1349335728, -0.31692622060036657, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335722] : ((1349335722, -6.910740939984066, 492609224), (1349335722, -0.19472968435400267, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335711] : ((1349335711, -6.345759460878192, 492609224), (1349335711, -0.17630973253816087, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335707] : ((1349335707, -2.3872358222445267, 492609224), (1349335707, -0.2670927574007538, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335673] : ((1349335673, -4.277494046980123, 492609224), (1349335673, -0.13049757216861838, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335651] : ((1349335651, -4.547252752338885, 492609224), (1349335651, -0.017416166370095774, 2107752395), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335647] : ((1349335647, -4.16388550522749, 492609224), (1349335647, -0.05563635882467062, 2107752395), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335643] : ((1349335643, -4.7350360656257635, 492609224), (1349335643, -0.11013911310412532, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335638] : ((1349335638, -5.351902817017774, 492609224), (1349335638, -0.16461676301833741, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com args[1349335633] : ((1349335633, -6.16261890898672, 492609224), (1349335633, -0.09035311335130429, 496442774), '0.23863599614403855') We are sending mail with results at report@fotonower.com refus_total : 0.23863599614403855 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=21958982 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349335647,1349335768,1349346432,1349346428,1349335771,1349335633,1349335728,1349335638,1349335711,1349335722,1349335707,1349335763,1349335673,1349335651,1349335811,1349346189,1349346201,1349346213,1349346305,1349346340) Found this number of photos: 20 begin to download photo : 1349335647 begin to download photo : 1349335633 begin to download photo : 1349335707 begin to download photo : 1349346189 download finish for photo 1349335647 begin to download photo : 1349335768 download finish for photo 1349335707 begin to download photo : 1349335763 download finish for photo 1349346189 begin to download photo : 1349346201 download finish for photo 1349335763 begin to download photo : 1349335673 download finish for photo 1349335768 begin to download photo : 1349346432 download finish for photo 1349335633 begin to download photo : 1349335728 download finish for photo 1349346201 begin to download photo : 1349346213 download finish for photo 1349335673 begin to download photo : 1349335651 download finish for photo 1349346432 begin to download photo : 1349346428 download finish for photo 1349346213 begin to download photo : 1349346305 download finish for photo 1349335728 begin to download photo : 1349335638 download finish for photo 1349346428 begin to download photo : 1349335771 download finish for photo 1349335638 begin to download photo : 1349335711 download finish for photo 1349335651 begin to download photo : 1349335811 download finish for photo 1349346305 begin to download photo : 1349346340 download finish for photo 1349335771 download finish for photo 1349335811 download finish for photo 1349335711 begin to download photo : 1349335722 download finish for photo 1349346340 download finish for photo 1349335722 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21958982_01-04-2025_22_25_08.pdf results_Auto_P21958982_01-04-2025_22_25_08.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21958982_01-04-2025_22_25_08.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','21958982','results_Auto_P21958982_01-04-2025_22_25_08.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21958982_01-04-2025_22_25_08.pdf','pdf','','1.2','0.23863599614403855') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21958982

https://www.fotonower.com/image?json=false&list_photos_id=1349346432
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349346428
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349346348
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349346340
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349346305
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349346213
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349346201
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349346189
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335811
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.710804203370057)
https://www.fotonower.com/image?json=false&list_photos_id=1349335771
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335768
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335763
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335728
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335722
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335711
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335707
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335673
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335651
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335647
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335643
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335638
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349335633
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 23.86%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/21959937?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21959938?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/21959939?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21959941?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21959942?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/21959943?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21959944?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21958982_01-04-2025_22_25_08.pdf.

Lien vers velours :https://www.fotonower.com/velours/21959937,21959938,21959939,21959940,21959941,21959942,21959943,21959944,21959945,21959946,21959947?tags=carton,pet_clair,papier,flou,metal,autre,pehd,pet_fonce,mal_croppe,background,environnement.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 01 Apr 2025 20:25:20 GMT Content-Length: 0 Connection: close X-Message-Id: V6SU4g01SSOzdCH3iuwepA 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 [1349346432, 1349346428, 1349346348, 1349346340, 1349346305, 1349346213, 1349346201, 1349346189, 1349335811, 1349335771, 1349335768, 1349335763, 1349335728, 1349335722, 1349335711, 1349335707, 1349335673, 1349335651, 1349335647, 1349335643, 1349335638, 1349335633] 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, '2714586') ('3318', '21958982', '1349346432', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346428', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346348', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346340', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346305', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346213', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346201', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346189', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335811', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335771', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335768', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335763', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335728', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335722', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335711', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335707', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335673', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335651', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335647', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335643', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335638', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335633', None, None, None, None, None, '2714586') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 22 time used for this insertion : 0.11890316009521484 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.916094779968262 time spend to save output : 0.11929130554199219 total time spend for step 9 : 12.035386085510254 step10:split_time_score Tue Apr 1 22:25:20 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'}] (('20', 22),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 01042025 21958982 Nombre de photos uploadées : 22 / 23040 (0%) 01042025 21958982 Nombre de photos taguées (types de déchets): 0 / 22 (0%) 01042025 21958982 Nombre de photos taguées (volume) : 0 / 22 (0%) elapsed_time : load_data_split_time_score 1.6689300537109375e-06 elapsed_time : order_list_meta_photo_and_scores 4.5299530029296875e-06 ?????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.001035451889038086 elapsed_time : insert_dashboard_record_day_entry 0.028371572494506836 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.19774587565145885 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21941555_01-04-2025_10_34_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21941555 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21941555 AND mptpi.`type`=3594 To do Qualite : 0.10205312159586055 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21944149 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21944149 AND mptpi.`type`=3594 To do Qualite : 0.06586668547792947 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21943744_01-04-2025_12_02_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21943744 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21943744 AND mptpi.`type`=3726 To do Qualite : 0.12964564531795053 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21958966_01-04-2025_22_12_27.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21958966 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`=21958966 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21958970 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21958972 order by id desc limit 1 Qualite : 0.19322973402323884 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21951755_01-04-2025_18_08_54.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21951755 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21951755 AND mptpi.`type`=3594 To do Qualite : 0.12287221650636104 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21958978_01-04-2025_22_03_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21958978 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`=21958978 AND mptpi.`type`=3726 To do Qualite : 0.0620171856580458 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21956987_01-04-2025_20_24_39.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21956987 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21956987 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21958979 order by id desc limit 1 Qualite : 0.23863599614403855 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21958982_01-04-2025_22_25_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21958982 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`=21958982 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'01042025': {'nb_upload': 22, '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 [1349346432, 1349346428, 1349346348, 1349346340, 1349346305, 1349346213, 1349346201, 1349346189, 1349335811, 1349335771, 1349335768, 1349335763, 1349335728, 1349335722, 1349335711, 1349335707, 1349335673, 1349335651, 1349335647, 1349335643, 1349335638, 1349335633] Looping around the photos to save general results len do output : 1 /21958982Didn'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, '2714586') ('3318', '21958982', '1349346432', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346428', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346348', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346340', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346305', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346213', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346201', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349346189', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335811', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335771', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335768', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335763', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335728', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335722', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335711', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335707', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335673', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335651', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335647', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335643', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335638', None, None, None, None, None, '2714586') ('3318', None, None, None, None, None, None, None, '2714586') ('3318', '21958982', '1349335633', None, None, None, None, None, '2714586') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 23 time used for this insertion : 0.02042531967163086 save_final save missing photos in datou_result : time spend for datou_step_exec : 3.9204320907592773 time spend to save output : 0.020826101303100586 total time spend for step 10 : 3.941258192062378 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 22 set_done_treatment 650.72user 338.61system 24:59.90elapsed 65%CPU (0avgtext+0avgdata 11401256maxresident)k 34155312inputs+468416outputs (1219647major+60182898minor)pagefaults 0swaps