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 : 2362753 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 : ['2558005'] with mtr_portfolio_ids : ['20286245'] and first list_photo_ids : [] new path : /proc/2362753/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 12 ; length of list_pids : 12 ; length of list_args : 12 time to download the photos : 2.508291482925415 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 Thu Feb 6 06:30:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 4359 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-06 06:30:32.971612: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-06 06:30:32.999114: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-06 06:30:33.001073: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5790000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-06 06:30:33.001120: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-06 06:30:33.004689: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-06 06:30:33.229116: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3bc743d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-06 06:30:33.229159: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-06 06:30:33.230187: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-06 06:30:33.230547: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-06 06:30:33.233570: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-06 06:30:33.236293: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-06 06:30:33.236698: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-06 06:30:33.239303: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-06 06:30:33.240529: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-06 06:30:33.245558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-06 06:30:33.246981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-06 06:30:33.247062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-06 06:30:33.247759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-06 06:30:33.247777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-06 06:30:33.247788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-06 06:30:33.248922: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3907 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-02-06 06:30:33.515359: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-06 06:30:33.515441: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-06 06:30:33.515461: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-06 06:30:33.515479: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-06 06:30:33.515497: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-06 06:30:33.515514: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-06 06:30:33.515531: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-06 06:30:33.515548: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-06 06:30:33.516871: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-06 06:30:33.518337: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-02-06 06:30:33.518383: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-06 06:30:33.518408: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-06 06:30:33.518432: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-06 06:30:33.518455: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-06 06:30:33.518479: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-06 06:30:33.518502: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-06 06:30:33.518526: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-06 06:30:33.522217: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-06 06:30:33.522261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-06 06:30:33.522275: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-06 06:30:33.522288: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-06 06:30:33.525452: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3907 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-02-06 06:30:42.159100: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-06 06:30:42.387308: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-06 06:30:43.904969: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.905616: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.99G (3205693440 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.906196: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.69G (2885124096 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.906809: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.42G (2596611584 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.907408: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.18G (2336950272 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.907444: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.908063: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.908079: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.914231: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.914253: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.914866: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.914881: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.920898: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.920921: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.921568: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.921585: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.947479: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.947509: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.948127: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.948144: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.953597: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.953620: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.954234: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.954251: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-02-06 06:30:43.982033: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.982641: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.984287: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:43.984864: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.020663: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.021280: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.023318: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.023949: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.049042: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.049669: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.051328: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.051936: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.057513: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.058178: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.059966: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.060586: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.066409: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.067047: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.068660: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.069356: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.095623: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.096268: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.096875: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.097472: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.101004: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.101614: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.116860: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.117480: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.118058: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.118632: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.130762: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.131370: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.131989: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.132607: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.136822: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.137398: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.141992: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.142572: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.154244: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.154828: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.158853: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.159451: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.160033: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.160607: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.161384: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.161965: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.172535: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.173161: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.173775: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.174346: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.174939: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.175515: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.176093: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.176668: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.186038: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.186619: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.192896: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.193478: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.242506: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.242570: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-02-06 06:30:44.243639: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.244687: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.251858: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.252621: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.253394: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.254150: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.262223: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.262784: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.293479: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.294291: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.295105: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.295844: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.311121: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.311756: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.312378: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.312994: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.314072: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.324234: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.324871: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.333826: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.334405: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.335011: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.335589: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.336170: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-02-06 06:30:44.336743: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.32G (3561881600 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 12 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 95 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 92 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 : 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 : 51 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 46 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 50 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 39 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 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 2363385 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1752 tf kernel not reseted sub process len(results) : 12 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 12 len(list_Values) 0 process is alive process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4359 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 : 0.17978477478027344 nb_pixel_total : 29310 time to create 1 rle with old method : 0.037010908126831055 length of segment : 211 time for calcul the mask position with numpy : 0.1840965747833252 nb_pixel_total : 29864 time to create 1 rle with old method : 0.03870224952697754 length of segment : 382 time for calcul the mask position with numpy : 0.007244110107421875 nb_pixel_total : 3608 time to create 1 rle with old method : 0.005018949508666992 length of segment : 83 time for calcul the mask position with numpy : 0.10999107360839844 nb_pixel_total : 43404 time to create 1 rle with old method : 0.055413007736206055 length of segment : 186 time for calcul the mask position with numpy : 0.06746506690979004 nb_pixel_total : 21492 time to create 1 rle with old method : 0.03482198715209961 length of segment : 169 time for calcul the mask position with numpy : 0.11187028884887695 nb_pixel_total : 27459 time to create 1 rle with old method : 0.03534293174743652 length of segment : 229 time for calcul the mask position with numpy : 0.17968416213989258 nb_pixel_total : 56667 time to create 1 rle with old method : 0.08443880081176758 length of segment : 305 time for calcul the mask position with numpy : 0.5757300853729248 nb_pixel_total : 238669 time to create 1 rle with new method : 0.018230676651000977 length of segment : 772 time for calcul the mask position with numpy : 0.0432133674621582 nb_pixel_total : 21330 time to create 1 rle with old method : 0.02664971351623535 length of segment : 139 time for calcul the mask position with numpy : 0.027446508407592773 nb_pixel_total : 26768 time to create 1 rle with old method : 0.03238320350646973 length of segment : 148 time for calcul the mask position with numpy : 0.05405449867248535 nb_pixel_total : 10367 time to create 1 rle with old method : 0.014510154724121094 length of segment : 276 time for calcul the mask position with numpy : 0.18119478225708008 nb_pixel_total : 51615 time to create 1 rle with old method : 0.06028580665588379 length of segment : 295 time for calcul the mask position with numpy : 0.14239788055419922 nb_pixel_total : 41896 time to create 1 rle with old method : 0.04953503608703613 length of segment : 303 time for calcul the mask position with numpy : 0.18696880340576172 nb_pixel_total : 74063 time to create 1 rle with old method : 0.09978246688842773 length of segment : 334 time for calcul the mask position with numpy : 0.35201048851013184 nb_pixel_total : 14893 time to create 1 rle with old method : 0.02651071548461914 length of segment : 137 time for calcul the mask position with numpy : 0.23369574546813965 nb_pixel_total : 14259 time to create 1 rle with old method : 0.020587921142578125 length of segment : 154 time for calcul the mask position with numpy : 0.3654592037200928 nb_pixel_total : 18513 time to create 1 rle with old method : 0.024550914764404297 length of segment : 188 time for calcul the mask position with numpy : 0.0052263736724853516 nb_pixel_total : 20729 time to create 1 rle with old method : 0.02749776840209961 length of segment : 211 time for calcul the mask position with numpy : 0.32695889472961426 nb_pixel_total : 39119 time to create 1 rle with old method : 0.051790475845336914 length of segment : 232 time for calcul the mask position with numpy : 0.20439887046813965 nb_pixel_total : 12237 time to create 1 rle with old method : 0.01825428009033203 length of segment : 147 time for calcul the mask position with numpy : 0.5047047138214111 nb_pixel_total : 26953 time to create 1 rle with old method : 0.03549337387084961 length of segment : 268 time for calcul the mask position with numpy : 0.07068777084350586 nb_pixel_total : 9356 time to create 1 rle with old method : 0.017125606536865234 length of segment : 205 time for calcul the mask position with numpy : 0.17360377311706543 nb_pixel_total : 9277 time to create 1 rle with old method : 0.015511751174926758 length of segment : 122 time for calcul the mask position with numpy : 0.09805631637573242 nb_pixel_total : 13854 time to create 1 rle with old method : 0.021000385284423828 length of segment : 151 time for calcul the mask position with numpy : 0.029758453369140625 nb_pixel_total : 1491 time to create 1 rle with old method : 0.0035169124603271484 length of segment : 121 time for calcul the mask position with numpy : 0.3647115230560303 nb_pixel_total : 23509 time to create 1 rle with old method : 0.032061100006103516 length of segment : 201 time for calcul the mask position with numpy : 0.3318476676940918 nb_pixel_total : 41406 time to create 1 rle with old method : 0.051136016845703125 length of segment : 229 time for calcul the mask position with numpy : 0.012195348739624023 nb_pixel_total : 11481 time to create 1 rle with old method : 0.022539377212524414 length of segment : 106 time for calcul the mask position with numpy : 0.29514408111572266 nb_pixel_total : 23678 time to create 1 rle with old method : 0.031168699264526367 length of segment : 144 time for calcul the mask position with numpy : 0.28832292556762695 nb_pixel_total : 45484 time to create 1 rle with old method : 0.05554819107055664 length of segment : 261 time for calcul the mask position with numpy : 0.010902166366577148 nb_pixel_total : 5216 time to create 1 rle with old method : 0.010477781295776367 length of segment : 106 time for calcul the mask position with numpy : 0.06213092803955078 nb_pixel_total : 8482 time to create 1 rle with old method : 0.01619124412536621 length of segment : 129 time for calcul the mask position with numpy : 0.012569189071655273 nb_pixel_total : 3702 time to create 1 rle with old method : 0.007344245910644531 length of segment : 60 time for calcul the mask position with numpy : 0.0503382682800293 nb_pixel_total : 18015 time to create 1 rle with old method : 0.02923297882080078 length of segment : 219 time for calcul the mask position with numpy : 0.2689337730407715 nb_pixel_total : 17908 time to create 1 rle with old method : 0.024816274642944336 length of segment : 180 time for calcul the mask position with numpy : 0.06043100357055664 nb_pixel_total : 10892 time to create 1 rle with old method : 0.017874479293823242 length of segment : 138 time for calcul the mask position with numpy : 0.7653300762176514 nb_pixel_total : 79804 time to create 1 rle with old method : 0.08832383155822754 length of segment : 384 time for calcul the mask position with numpy : 0.04891157150268555 nb_pixel_total : 20110 time to create 1 rle with old method : 0.0208132266998291 length of segment : 155 time for calcul the mask position with numpy : 0.5783631801605225 nb_pixel_total : 62552 time to create 1 rle with old method : 0.07638859748840332 length of segment : 255 time for calcul the mask position with numpy : 0.1370832920074463 nb_pixel_total : 14731 time to create 1 rle with old method : 0.017287015914916992 length of segment : 125 time for calcul the mask position with numpy : 0.02704000473022461 nb_pixel_total : 10482 time to create 1 rle with old method : 0.017044544219970703 length of segment : 129 time for calcul the mask position with numpy : 0.05707859992980957 nb_pixel_total : 9152 time to create 1 rle with old method : 0.015775680541992188 length of segment : 152 time for calcul the mask position with numpy : 0.044801950454711914 nb_pixel_total : 11068 time to create 1 rle with old method : 0.02736067771911621 length of segment : 106 time for calcul the mask position with numpy : 0.10229611396789551 nb_pixel_total : 19371 time to create 1 rle with old method : 0.03280782699584961 length of segment : 228 time for calcul the mask position with numpy : 0.10724806785583496 nb_pixel_total : 3059 time to create 1 rle with old method : 0.007198333740234375 length of segment : 91 time for calcul the mask position with numpy : 0.1584925651550293 nb_pixel_total : 16210 time to create 1 rle with old method : 0.025025606155395508 length of segment : 95 time for calcul the mask position with numpy : 0.38520383834838867 nb_pixel_total : 82847 time to create 1 rle with old method : 0.09798169136047363 length of segment : 382 time for calcul the mask position with numpy : 0.0973958969116211 nb_pixel_total : 22128 time to create 1 rle with old method : 0.02884674072265625 length of segment : 165 time for calcul the mask position with numpy : 0.006494283676147461 nb_pixel_total : 25124 time to create 1 rle with old method : 0.02841043472290039 length of segment : 321 time for calcul the mask position with numpy : 0.29575586318969727 nb_pixel_total : 51766 time to create 1 rle with old method : 0.059610605239868164 length of segment : 288 time for calcul the mask position with numpy : 0.3747725486755371 nb_pixel_total : 86499 time to create 1 rle with old method : 0.09528183937072754 length of segment : 340 time for calcul the mask position with numpy : 0.09528398513793945 nb_pixel_total : 26159 time to create 1 rle with old method : 0.0334467887878418 length of segment : 213 time for calcul the mask position with numpy : 0.052098751068115234 nb_pixel_total : 14190 time to create 1 rle with old method : 0.024086475372314453 length of segment : 169 time for calcul the mask position with numpy : 0.10299420356750488 nb_pixel_total : 85064 time to create 1 rle with old method : 0.10001349449157715 length of segment : 302 time for calcul the mask position with numpy : 0.026685237884521484 nb_pixel_total : 26801 time to create 1 rle with old method : 0.03856825828552246 length of segment : 288 time for calcul the mask position with numpy : 0.0040340423583984375 nb_pixel_total : 5155 time to create 1 rle with old method : 0.006765842437744141 length of segment : 130 time for calcul the mask position with numpy : 0.11165094375610352 nb_pixel_total : 23799 time to create 1 rle with old method : 0.0321805477142334 length of segment : 366 time for calcul the mask position with numpy : 0.020968198776245117 nb_pixel_total : 29547 time to create 1 rle with old method : 0.03963184356689453 length of segment : 180 time for calcul the mask position with numpy : 0.001336812973022461 nb_pixel_total : 5860 time to create 1 rle with old method : 0.007160186767578125 length of segment : 95 time for calcul the mask position with numpy : 0.0039441585540771484 nb_pixel_total : 8709 time to create 1 rle with old method : 0.011371850967407227 length of segment : 117 time for calcul the mask position with numpy : 0.011570215225219727 nb_pixel_total : 25796 time to create 1 rle with old method : 0.030101299285888672 length of segment : 196 time for calcul the mask position with numpy : 0.008686304092407227 nb_pixel_total : 21055 time to create 1 rle with old method : 0.028219223022460938 length of segment : 228 time for calcul the mask position with numpy : 0.006837606430053711 nb_pixel_total : 26565 time to create 1 rle with old method : 0.03197979927062988 length of segment : 363 time for calcul the mask position with numpy : 0.005807638168334961 nb_pixel_total : 18981 time to create 1 rle with old method : 0.022966384887695312 length of segment : 307 time for calcul the mask position with numpy : 0.0016825199127197266 nb_pixel_total : 6425 time to create 1 rle with old method : 0.008054494857788086 length of segment : 143 time for calcul the mask position with numpy : 0.006109476089477539 nb_pixel_total : 28862 time to create 1 rle with old method : 0.03670334815979004 length of segment : 176 time for calcul the mask position with numpy : 0.004223346710205078 nb_pixel_total : 11052 time to create 1 rle with old method : 0.014282703399658203 length of segment : 124 time for calcul the mask position with numpy : 0.006437778472900391 nb_pixel_total : 29782 time to create 1 rle with old method : 0.036434173583984375 length of segment : 223 time for calcul the mask position with numpy : 0.006493091583251953 nb_pixel_total : 23704 time to create 1 rle with old method : 0.028060436248779297 length of segment : 246 time for calcul the mask position with numpy : 0.0033082962036132812 nb_pixel_total : 10616 time to create 1 rle with old method : 0.013741493225097656 length of segment : 160 time for calcul the mask position with numpy : 0.0005035400390625 nb_pixel_total : 9714 time to create 1 rle with old method : 0.011331796646118164 length of segment : 128 time for calcul the mask position with numpy : 0.015897750854492188 nb_pixel_total : 25226 time to create 1 rle with old method : 0.03241562843322754 length of segment : 270 time for calcul the mask position with numpy : 0.008290529251098633 nb_pixel_total : 28690 time to create 1 rle with old method : 0.036348819732666016 length of segment : 161 time for calcul the mask position with numpy : 0.016756057739257812 nb_pixel_total : 34138 time to create 1 rle with old method : 0.043891191482543945 length of segment : 252 time for calcul the mask position with numpy : 0.005432844161987305 nb_pixel_total : 17167 time to create 1 rle with old method : 0.02193593978881836 length of segment : 230 time for calcul the mask position with numpy : 0.07390975952148438 nb_pixel_total : 186606 time to create 1 rle with new method : 0.02060699462890625 length of segment : 565 time for calcul the mask position with numpy : 0.0039179325103759766 nb_pixel_total : 9700 time to create 1 rle with old method : 0.011484861373901367 length of segment : 152 time for calcul the mask position with numpy : 0.0028972625732421875 nb_pixel_total : 13576 time to create 1 rle with old method : 0.016150474548339844 length of segment : 117 time for calcul the mask position with numpy : 0.009653091430664062 nb_pixel_total : 59350 time to create 1 rle with old method : 0.06939172744750977 length of segment : 354 time for calcul the mask position with numpy : 0.0016529560089111328 nb_pixel_total : 5265 time to create 1 rle with old method : 0.0061872005462646484 length of segment : 107 time for calcul the mask position with numpy : 0.005646467208862305 nb_pixel_total : 30755 time to create 1 rle with old method : 0.03952765464782715 length of segment : 177 time for calcul the mask position with numpy : 0.004849433898925781 nb_pixel_total : 13873 time to create 1 rle with old method : 0.02944040298461914 length of segment : 139 time for calcul the mask position with numpy : 0.005693674087524414 nb_pixel_total : 47611 time to create 1 rle with old method : 0.05402231216430664 length of segment : 259 time for calcul the mask position with numpy : 0.0024034976959228516 nb_pixel_total : 17992 time to create 1 rle with old method : 0.01967024803161621 length of segment : 124 time for calcul the mask position with numpy : 0.004178524017333984 nb_pixel_total : 27196 time to create 1 rle with old method : 0.03341388702392578 length of segment : 217 time for calcul the mask position with numpy : 0.0044100284576416016 nb_pixel_total : 18651 time to create 1 rle with old method : 0.02083730697631836 length of segment : 201 time for calcul the mask position with numpy : 0.007226705551147461 nb_pixel_total : 24231 time to create 1 rle with old method : 0.029633045196533203 length of segment : 218 time for calcul the mask position with numpy : 0.0015933513641357422 nb_pixel_total : 11148 time to create 1 rle with old method : 0.013094186782836914 length of segment : 152 time for calcul the mask position with numpy : 0.006869792938232422 nb_pixel_total : 21737 time to create 1 rle with old method : 0.03945732116699219 length of segment : 183 time for calcul the mask position with numpy : 0.00015473365783691406 nb_pixel_total : 3395 time to create 1 rle with old method : 0.004266500473022461 length of segment : 63 time for calcul the mask position with numpy : 0.0022563934326171875 nb_pixel_total : 12896 time to create 1 rle with old method : 0.015320301055908203 length of segment : 122 time for calcul the mask position with numpy : 0.0028390884399414062 nb_pixel_total : 17621 time to create 1 rle with old method : 0.01972508430480957 length of segment : 161 time for calcul the mask position with numpy : 0.005852699279785156 nb_pixel_total : 19432 time to create 1 rle with old method : 0.02348494529724121 length of segment : 263 time for calcul the mask position with numpy : 0.003720521926879883 nb_pixel_total : 19981 time to create 1 rle with old method : 0.02520751953125 length of segment : 194 time for calcul the mask position with numpy : 0.008358001708984375 nb_pixel_total : 34940 time to create 1 rle with old method : 0.04309654235839844 length of segment : 271 time for calcul the mask position with numpy : 0.0016322135925292969 nb_pixel_total : 8202 time to create 1 rle with old method : 0.009790182113647461 length of segment : 118 time for calcul the mask position with numpy : 0.0016314983367919922 nb_pixel_total : 17262 time to create 1 rle with old method : 0.02034926414489746 length of segment : 155 time for calcul the mask position with numpy : 0.0015652179718017578 nb_pixel_total : 5660 time to create 1 rle with old method : 0.007020235061645508 length of segment : 77 time for calcul the mask position with numpy : 0.001434326171875 nb_pixel_total : 7690 time to create 1 rle with old method : 0.009241580963134766 length of segment : 211 time for calcul the mask position with numpy : 0.004897594451904297 nb_pixel_total : 23568 time to create 1 rle with old method : 0.03904151916503906 length of segment : 334 time for calcul the mask position with numpy : 0.004567146301269531 nb_pixel_total : 24308 time to create 1 rle with old method : 0.029139995574951172 length of segment : 171 time for calcul the mask position with numpy : 0.0015399456024169922 nb_pixel_total : 20699 time to create 1 rle with old method : 0.0244905948638916 length of segment : 170 time for calcul the mask position with numpy : 0.004437446594238281 nb_pixel_total : 15735 time to create 1 rle with old method : 0.020773887634277344 length of segment : 231 time for calcul the mask position with numpy : 0.0007531642913818359 nb_pixel_total : 9332 time to create 1 rle with old method : 0.010419368743896484 length of segment : 130 time for calcul the mask position with numpy : 0.0019011497497558594 nb_pixel_total : 8367 time to create 1 rle with old method : 0.009980916976928711 length of segment : 110 time for calcul the mask position with numpy : 0.002623319625854492 nb_pixel_total : 10230 time to create 1 rle with old method : 0.013280391693115234 length of segment : 111 time for calcul the mask position with numpy : 0.0002815723419189453 nb_pixel_total : 10755 time to create 1 rle with old method : 0.012324094772338867 length of segment : 112 time for calcul the mask position with numpy : 0.0035691261291503906 nb_pixel_total : 9437 time to create 1 rle with old method : 0.012366771697998047 length of segment : 119 time for calcul the mask position with numpy : 0.020844697952270508 nb_pixel_total : 93499 time to create 1 rle with old method : 0.10469174385070801 length of segment : 474 time for calcul the mask position with numpy : 0.004534006118774414 nb_pixel_total : 17989 time to create 1 rle with old method : 0.02125096321105957 length of segment : 192 time for calcul the mask position with numpy : 0.005119800567626953 nb_pixel_total : 36530 time to create 1 rle with old method : 0.045732736587524414 length of segment : 242 time for calcul the mask position with numpy : 0.0013947486877441406 nb_pixel_total : 18158 time to create 1 rle with old method : 0.02252483367919922 length of segment : 152 time for calcul the mask position with numpy : 0.002814769744873047 nb_pixel_total : 12775 time to create 1 rle with old method : 0.01444554328918457 length of segment : 153 time for calcul the mask position with numpy : 0.03783702850341797 nb_pixel_total : 145261 time to create 1 rle with old method : 0.16073989868164062 length of segment : 437 time for calcul the mask position with numpy : 0.021184444427490234 nb_pixel_total : 21768 time to create 1 rle with old method : 0.030435562133789062 length of segment : 200 time for calcul the mask position with numpy : 0.018268823623657227 nb_pixel_total : 15786 time to create 1 rle with old method : 0.02002882957458496 length of segment : 108 time for calcul the mask position with numpy : 0.028935670852661133 nb_pixel_total : 49728 time to create 1 rle with old method : 0.05910801887512207 length of segment : 193 time for calcul the mask position with numpy : 0.009430170059204102 nb_pixel_total : 19518 time to create 1 rle with old method : 0.028233051300048828 length of segment : 189 time for calcul the mask position with numpy : 0.02136969566345215 nb_pixel_total : 24728 time to create 1 rle with old method : 0.03398537635803223 length of segment : 255 time for calcul the mask position with numpy : 0.005759716033935547 nb_pixel_total : 23319 time to create 1 rle with old method : 0.03156113624572754 length of segment : 195 time for calcul the mask position with numpy : 0.013269662857055664 nb_pixel_total : 56787 time to create 1 rle with old method : 0.07641458511352539 length of segment : 314 time for calcul the mask position with numpy : 0.004813671112060547 nb_pixel_total : 32280 time to create 1 rle with old method : 0.03509116172790527 length of segment : 213 time for calcul the mask position with numpy : 0.009443998336791992 nb_pixel_total : 12255 time to create 1 rle with old method : 0.018944978713989258 length of segment : 204 time for calcul the mask position with numpy : 0.0029680728912353516 nb_pixel_total : 24666 time to create 1 rle with old method : 0.02721238136291504 length of segment : 283 time for calcul the mask position with numpy : 0.00909280776977539 nb_pixel_total : 17487 time to create 1 rle with old method : 0.025001049041748047 length of segment : 139 time for calcul the mask position with numpy : 0.0006127357482910156 nb_pixel_total : 4114 time to create 1 rle with old method : 0.005657196044921875 length of segment : 59 time for calcul the mask position with numpy : 0.0023844242095947266 nb_pixel_total : 11703 time to create 1 rle with old method : 0.012861490249633789 length of segment : 150 time for calcul the mask position with numpy : 0.0009369850158691406 nb_pixel_total : 3471 time to create 1 rle with old method : 0.0038177967071533203 length of segment : 126 time for calcul the mask position with numpy : 0.0019359588623046875 nb_pixel_total : 23884 time to create 1 rle with old method : 0.028313398361206055 length of segment : 223 time for calcul the mask position with numpy : 0.012300968170166016 nb_pixel_total : 66822 time to create 1 rle with old method : 0.07536149024963379 length of segment : 298 time for calcul the mask position with numpy : 0.03304791450500488 nb_pixel_total : 118707 time to create 1 rle with old method : 0.13179326057434082 length of segment : 308 time for calcul the mask position with numpy : 0.0009210109710693359 nb_pixel_total : 13104 time to create 1 rle with old method : 0.015857458114624023 length of segment : 271 time for calcul the mask position with numpy : 0.007738590240478516 nb_pixel_total : 15984 time to create 1 rle with old method : 0.020734548568725586 length of segment : 254 time for calcul the mask position with numpy : 0.07911872863769531 nb_pixel_total : 104998 time to create 1 rle with old method : 0.11655235290527344 length of segment : 218 time for calcul the mask position with numpy : 0.001531839370727539 nb_pixel_total : 6480 time to create 1 rle with old method : 0.007533073425292969 length of segment : 92 time for calcul the mask position with numpy : 0.0077822208404541016 nb_pixel_total : 4454 time to create 1 rle with old method : 0.0067653656005859375 length of segment : 74 time for calcul the mask position with numpy : 0.0016660690307617188 nb_pixel_total : 10550 time to create 1 rle with old method : 0.01218724250793457 length of segment : 120 time for calcul the mask position with numpy : 0.01939988136291504 nb_pixel_total : 23609 time to create 1 rle with old method : 0.03310847282409668 length of segment : 319 time for calcul the mask position with numpy : 0.0032618045806884766 nb_pixel_total : 9357 time to create 1 rle with old method : 0.010933160781860352 length of segment : 134 time for calcul the mask position with numpy : 0.003330707550048828 nb_pixel_total : 10285 time to create 1 rle with old method : 0.012094974517822266 length of segment : 158 time for calcul the mask position with numpy : 0.0135040283203125 nb_pixel_total : 18167 time to create 1 rle with old method : 0.022658586502075195 length of segment : 157 time for calcul the mask position with numpy : 0.0017151832580566406 nb_pixel_total : 19379 time to create 1 rle with old method : 0.022287368774414062 length of segment : 140 time for calcul the mask position with numpy : 0.0024657249450683594 nb_pixel_total : 10683 time to create 1 rle with old method : 0.012549877166748047 length of segment : 137 time for calcul the mask position with numpy : 0.016576051712036133 nb_pixel_total : 30996 time to create 1 rle with old method : 0.03966808319091797 length of segment : 191 time for calcul the mask position with numpy : 0.009063482284545898 nb_pixel_total : 45390 time to create 1 rle with old method : 0.05205082893371582 length of segment : 304 time for calcul the mask position with numpy : 0.001119375228881836 nb_pixel_total : 16661 time to create 1 rle with old method : 0.01929020881652832 length of segment : 95 time for calcul the mask position with numpy : 0.00012826919555664062 nb_pixel_total : 3487 time to create 1 rle with old method : 0.0039048194885253906 length of segment : 74 time for calcul the mask position with numpy : 0.0033750534057617188 nb_pixel_total : 3513 time to create 1 rle with old method : 0.004464387893676758 length of segment : 44 time for calcul the mask position with numpy : 0.002221822738647461 nb_pixel_total : 10927 time to create 1 rle with old method : 0.013240814208984375 length of segment : 131 time for calcul the mask position with numpy : 0.002645730972290039 nb_pixel_total : 13980 time to create 1 rle with old method : 0.015662670135498047 length of segment : 132 time for calcul the mask position with numpy : 0.015349626541137695 nb_pixel_total : 64561 time to create 1 rle with old method : 0.07150101661682129 length of segment : 318 time for calcul the mask position with numpy : 0.0035598278045654297 nb_pixel_total : 19449 time to create 1 rle with old method : 0.022930383682250977 length of segment : 142 time for calcul the mask position with numpy : 0.0008690357208251953 nb_pixel_total : 21885 time to create 1 rle with old method : 0.030150890350341797 length of segment : 193 time for calcul the mask position with numpy : 0.03412008285522461 nb_pixel_total : 51076 time to create 1 rle with old method : 0.05761241912841797 length of segment : 187 time for calcul the mask position with numpy : 0.025682926177978516 nb_pixel_total : 16018 time to create 1 rle with old method : 0.024776935577392578 length of segment : 136 time for calcul the mask position with numpy : 0.044431209564208984 nb_pixel_total : 302885 time to create 1 rle with new method : 0.01712656021118164 length of segment : 524 time for calcul the mask position with numpy : 0.00552821159362793 nb_pixel_total : 5333 time to create 1 rle with old method : 0.0063822269439697266 length of segment : 88 time for calcul the mask position with numpy : 0.01414632797241211 nb_pixel_total : 73348 time to create 1 rle with old method : 0.08195257186889648 length of segment : 445 time for calcul the mask position with numpy : 0.01317453384399414 nb_pixel_total : 19465 time to create 1 rle with old method : 0.0254974365234375 length of segment : 194 time for calcul the mask position with numpy : 0.07736897468566895 nb_pixel_total : 85853 time to create 1 rle with old method : 0.09822940826416016 length of segment : 286 time for calcul the mask position with numpy : 0.00030303001403808594 nb_pixel_total : 7967 time to create 1 rle with old method : 0.010298013687133789 length of segment : 56 time for calcul the mask position with numpy : 0.00016236305236816406 nb_pixel_total : 6110 time to create 1 rle with old method : 0.007164716720581055 length of segment : 79 time for calcul the mask position with numpy : 0.2922234535217285 nb_pixel_total : 424769 time to create 1 rle with new method : 0.03173828125 length of segment : 1210 time for calcul the mask position with numpy : 0.0004532337188720703 nb_pixel_total : 20520 time to create 1 rle with old method : 0.023158550262451172 length of segment : 214 time for calcul the mask position with numpy : 0.02150726318359375 nb_pixel_total : 54965 time to create 1 rle with old method : 0.06554388999938965 length of segment : 304 time for calcul the mask position with numpy : 0.0008988380432128906 nb_pixel_total : 22254 time to create 1 rle with old method : 0.02650284767150879 length of segment : 270 time for calcul the mask position with numpy : 0.00026488304138183594 nb_pixel_total : 15101 time to create 1 rle with old method : 0.017562389373779297 length of segment : 122 time for calcul the mask position with numpy : 0.028533458709716797 nb_pixel_total : 96266 time to create 1 rle with old method : 0.1059720516204834 length of segment : 241 time for calcul the mask position with numpy : 0.002864360809326172 nb_pixel_total : 45230 time to create 1 rle with old method : 0.04997682571411133 length of segment : 298 time for calcul the mask position with numpy : 0.007898330688476562 nb_pixel_total : 39088 time to create 1 rle with old method : 0.04549717903137207 length of segment : 275 time for calcul the mask position with numpy : 0.003109455108642578 nb_pixel_total : 39170 time to create 1 rle with old method : 0.04369640350341797 length of segment : 339 time for calcul the mask position with numpy : 0.0005195140838623047 nb_pixel_total : 8217 time to create 1 rle with old method : 0.009385108947753906 length of segment : 108 time for calcul the mask position with numpy : 0.0013608932495117188 nb_pixel_total : 15340 time to create 1 rle with old method : 0.017830848693847656 length of segment : 150 time for calcul the mask position with numpy : 0.0023865699768066406 nb_pixel_total : 32409 time to create 1 rle with old method : 0.038172006607055664 length of segment : 309 time for calcul the mask position with numpy : 0.009010553359985352 nb_pixel_total : 17087 time to create 1 rle with old method : 0.02373051643371582 length of segment : 148 time for calcul the mask position with numpy : 0.0022203922271728516 nb_pixel_total : 30875 time to create 1 rle with old method : 0.036019086837768555 length of segment : 212 time for calcul the mask position with numpy : 0.00249481201171875 nb_pixel_total : 30069 time to create 1 rle with old method : 0.03452420234680176 length of segment : 185 time for calcul the mask position with numpy : 0.0005371570587158203 nb_pixel_total : 6398 time to create 1 rle with old method : 0.007564544677734375 length of segment : 95 time for calcul the mask position with numpy : 0.0026733875274658203 nb_pixel_total : 33350 time to create 1 rle with old method : 0.0380549430847168 length of segment : 310 time for calcul the mask position with numpy : 0.002685546875 nb_pixel_total : 37143 time to create 1 rle with old method : 0.04269146919250488 length of segment : 285 time for calcul the mask position with numpy : 0.0014214515686035156 nb_pixel_total : 16590 time to create 1 rle with old method : 0.01833653450012207 length of segment : 165 time for calcul the mask position with numpy : 0.0004875659942626953 nb_pixel_total : 5958 time to create 1 rle with old method : 0.011145353317260742 length of segment : 97 time for calcul the mask position with numpy : 0.0005424022674560547 nb_pixel_total : 7874 time to create 1 rle with old method : 0.00923299789428711 length of segment : 96 time for calcul the mask position with numpy : 0.0025436878204345703 nb_pixel_total : 25347 time to create 1 rle with old method : 0.029466867446899414 length of segment : 202 time for calcul the mask position with numpy : 0.0014870166778564453 nb_pixel_total : 15543 time to create 1 rle with old method : 0.01774001121520996 length of segment : 200 time for calcul the mask position with numpy : 0.0016405582427978516 nb_pixel_total : 21825 time to create 1 rle with old method : 0.025216102600097656 length of segment : 136 time for calcul the mask position with numpy : 0.009333133697509766 nb_pixel_total : 20959 time to create 1 rle with old method : 0.02827763557434082 length of segment : 174 time for calcul the mask position with numpy : 0.0004849433898925781 nb_pixel_total : 11269 time to create 1 rle with old method : 0.012227773666381836 length of segment : 154 time for calcul the mask position with numpy : 0.0015778541564941406 nb_pixel_total : 25087 time to create 1 rle with old method : 0.02775263786315918 length of segment : 164 time for calcul the mask position with numpy : 0.0006015300750732422 nb_pixel_total : 8849 time to create 1 rle with old method : 0.010617733001708984 length of segment : 162 time for calcul the mask position with numpy : 0.0010561943054199219 nb_pixel_total : 9455 time to create 1 rle with old method : 0.011901617050170898 length of segment : 147 time for calcul the mask position with numpy : 0.03433036804199219 nb_pixel_total : 44205 time to create 1 rle with old method : 0.05125236511230469 length of segment : 286 time for calcul the mask position with numpy : 0.00019669532775878906 nb_pixel_total : 4604 time to create 1 rle with old method : 0.005466461181640625 length of segment : 84 time for calcul the mask position with numpy : 0.0006127357482910156 nb_pixel_total : 8256 time to create 1 rle with old method : 0.009897708892822266 length of segment : 128 time for calcul the mask position with numpy : 0.00168609619140625 nb_pixel_total : 30561 time to create 1 rle with old method : 0.034876108169555664 length of segment : 191 time for calcul the mask position with numpy : 0.00022101402282714844 nb_pixel_total : 2535 time to create 1 rle with old method : 0.003087759017944336 length of segment : 48 time for calcul the mask position with numpy : 0.0027129650115966797 nb_pixel_total : 26119 time to create 1 rle with old method : 0.03013896942138672 length of segment : 430 time for calcul the mask position with numpy : 0.0009627342224121094 nb_pixel_total : 12344 time to create 1 rle with old method : 0.014235973358154297 length of segment : 107 time for calcul the mask position with numpy : 0.0013124942779541016 nb_pixel_total : 14569 time to create 1 rle with old method : 0.017343759536743164 length of segment : 139 time for calcul the mask position with numpy : 0.001180887222290039 nb_pixel_total : 4889 time to create 1 rle with old method : 0.0057599544525146484 length of segment : 81 time for calcul the mask position with numpy : 0.00054168701171875 nb_pixel_total : 7048 time to create 1 rle with old method : 0.008373260498046875 length of segment : 99 time for calcul the mask position with numpy : 0.0017719268798828125 nb_pixel_total : 9005 time to create 1 rle with old method : 0.010564327239990234 length of segment : 85 time for calcul the mask position with numpy : 0.00023674964904785156 nb_pixel_total : 2204 time to create 1 rle with old method : 0.00267791748046875 length of segment : 53 time for calcul the mask position with numpy : 0.0013892650604248047 nb_pixel_total : 23491 time to create 1 rle with old method : 0.02600383758544922 length of segment : 204 time for calcul the mask position with numpy : 0.0007448196411132812 nb_pixel_total : 9440 time to create 1 rle with old method : 0.010960102081298828 length of segment : 132 time for calcul the mask position with numpy : 0.002527475357055664 nb_pixel_total : 24910 time to create 1 rle with old method : 0.028522968292236328 length of segment : 373 time for calcul the mask position with numpy : 0.02141737937927246 nb_pixel_total : 28529 time to create 1 rle with old method : 0.03402256965637207 length of segment : 196 time for calcul the mask position with numpy : 0.0007328987121582031 nb_pixel_total : 14303 time to create 1 rle with old method : 0.01599264144897461 length of segment : 159 time for calcul the mask position with numpy : 0.0019421577453613281 nb_pixel_total : 25712 time to create 1 rle with old method : 0.028922319412231445 length of segment : 199 time for calcul the mask position with numpy : 0.004445552825927734 nb_pixel_total : 28082 time to create 1 rle with old method : 0.03320455551147461 length of segment : 258 time for calcul the mask position with numpy : 0.0024187564849853516 nb_pixel_total : 38134 time to create 1 rle with old method : 0.06052255630493164 length of segment : 273 time for calcul the mask position with numpy : 0.00179290771484375 nb_pixel_total : 17260 time to create 1 rle with old method : 0.01955437660217285 length of segment : 137 time for calcul the mask position with numpy : 0.009095430374145508 nb_pixel_total : 3526 time to create 1 rle with old method : 0.008431673049926758 length of segment : 88 time for calcul the mask position with numpy : 0.0006048679351806641 nb_pixel_total : 6283 time to create 1 rle with old method : 0.007405281066894531 length of segment : 106 time for calcul the mask position with numpy : 0.00131988525390625 nb_pixel_total : 19303 time to create 1 rle with old method : 0.02228713035583496 length of segment : 181 time for calcul the mask position with numpy : 0.00014400482177734375 nb_pixel_total : 2128 time to create 1 rle with old method : 0.002727508544921875 length of segment : 53 time for calcul the mask position with numpy : 0.0003769397735595703 nb_pixel_total : 10968 time to create 1 rle with old method : 0.012569904327392578 length of segment : 151 time for calcul the mask position with numpy : 0.0004963874816894531 nb_pixel_total : 7107 time to create 1 rle with old method : 0.008513212203979492 length of segment : 98 time for calcul the mask position with numpy : 0.002600431442260742 nb_pixel_total : 47445 time to create 1 rle with old method : 0.05238056182861328 length of segment : 409 time for calcul the mask position with numpy : 0.0009725093841552734 nb_pixel_total : 14225 time to create 1 rle with old method : 0.01673269271850586 length of segment : 137 time for calcul the mask position with numpy : 0.004179954528808594 nb_pixel_total : 67059 time to create 1 rle with old method : 0.0759267807006836 length of segment : 541 time for calcul the mask position with numpy : 0.0005681514739990234 nb_pixel_total : 7736 time to create 1 rle with old method : 0.008980512619018555 length of segment : 109 time for calcul the mask position with numpy : 0.013076305389404297 nb_pixel_total : 36435 time to create 1 rle with old method : 0.04381918907165527 length of segment : 241 time for calcul the mask position with numpy : 0.001260995864868164 nb_pixel_total : 15922 time to create 1 rle with old method : 0.018564939498901367 length of segment : 160 time for calcul the mask position with numpy : 0.0014045238494873047 nb_pixel_total : 15860 time to create 1 rle with old method : 0.0187375545501709 length of segment : 160 time for calcul the mask position with numpy : 0.0007123947143554688 nb_pixel_total : 9080 time to create 1 rle with old method : 0.011076927185058594 length of segment : 212 time for calcul the mask position with numpy : 0.004988193511962891 nb_pixel_total : 63946 time to create 1 rle with old method : 0.07212662696838379 length of segment : 383 time for calcul the mask position with numpy : 0.010760068893432617 nb_pixel_total : 12487 time to create 1 rle with old method : 0.01546931266784668 length of segment : 294 time for calcul the mask position with numpy : 0.018840312957763672 nb_pixel_total : 79600 time to create 1 rle with old method : 0.08992481231689453 length of segment : 331 time for calcul the mask position with numpy : 0.03402113914489746 nb_pixel_total : 23420 time to create 1 rle with old method : 0.03279519081115723 length of segment : 164 time for calcul the mask position with numpy : 0.004636049270629883 nb_pixel_total : 110108 time to create 1 rle with old method : 0.14829564094543457 length of segment : 331 time for calcul the mask position with numpy : 0.0009129047393798828 nb_pixel_total : 13585 time to create 1 rle with old method : 0.016724586486816406 length of segment : 186 time for calcul the mask position with numpy : 0.10800457000732422 nb_pixel_total : 152338 time to create 1 rle with new method : 0.01292729377746582 length of segment : 408 time for calcul the mask position with numpy : 0.0015363693237304688 nb_pixel_total : 28536 time to create 1 rle with old method : 0.04676461219787598 length of segment : 218 time for calcul the mask position with numpy : 0.003406524658203125 nb_pixel_total : 20791 time to create 1 rle with old method : 0.026025056838989258 length of segment : 219 time for calcul the mask position with numpy : 0.009816884994506836 nb_pixel_total : 10246 time to create 1 rle with old method : 0.015220403671264648 length of segment : 171 time for calcul the mask position with numpy : 0.0009033679962158203 nb_pixel_total : 21364 time to create 1 rle with old method : 0.024437665939331055 length of segment : 196 time for calcul the mask position with numpy : 0.0010819435119628906 nb_pixel_total : 27245 time to create 1 rle with old method : 0.031560420989990234 length of segment : 145 time for calcul the mask position with numpy : 0.00503230094909668 nb_pixel_total : 6570 time to create 1 rle with old method : 0.008792877197265625 length of segment : 95 time for calcul the mask position with numpy : 0.0042455196380615234 nb_pixel_total : 118139 time to create 1 rle with old method : 0.1269676685333252 length of segment : 333 time for calcul the mask position with numpy : 0.011772871017456055 nb_pixel_total : 32128 time to create 1 rle with old method : 0.03842759132385254 length of segment : 274 time for calcul the mask position with numpy : 0.020572900772094727 nb_pixel_total : 78738 time to create 1 rle with old method : 0.09351372718811035 length of segment : 373 time for calcul the mask position with numpy : 0.015549421310424805 nb_pixel_total : 13464 time to create 1 rle with old method : 0.018715858459472656 length of segment : 127 time for calcul the mask position with numpy : 0.0006756782531738281 nb_pixel_total : 14698 time to create 1 rle with old method : 0.01748514175415039 length of segment : 124 time for calcul the mask position with numpy : 0.0005936622619628906 nb_pixel_total : 8040 time to create 1 rle with old method : 0.009784936904907227 length of segment : 115 time for calcul the mask position with numpy : 0.011726856231689453 nb_pixel_total : 137011 time to create 1 rle with old method : 0.1561892032623291 length of segment : 358 time for calcul the mask position with numpy : 0.0035004615783691406 nb_pixel_total : 76517 time to create 1 rle with old method : 0.08437204360961914 length of segment : 416 time for calcul the mask position with numpy : 0.004664182662963867 nb_pixel_total : 17721 time to create 1 rle with old method : 0.02052450180053711 length of segment : 141 time for calcul the mask position with numpy : 0.0030663013458251953 nb_pixel_total : 28865 time to create 1 rle with old method : 0.03455924987792969 length of segment : 235 time for calcul the mask position with numpy : 0.0015077590942382812 nb_pixel_total : 12088 time to create 1 rle with old method : 0.013916254043579102 length of segment : 117 time for calcul the mask position with numpy : 0.011897087097167969 nb_pixel_total : 125732 time to create 1 rle with old method : 0.14301753044128418 length of segment : 333 time for calcul the mask position with numpy : 0.0040056705474853516 nb_pixel_total : 115141 time to create 1 rle with old method : 0.12484240531921387 length of segment : 345 time for calcul the mask position with numpy : 0.0044019222259521484 nb_pixel_total : 24061 time to create 1 rle with old method : 0.03731822967529297 length of segment : 555 time for calcul the mask position with numpy : 0.0023076534271240234 nb_pixel_total : 52302 time to create 1 rle with old method : 0.06401753425598145 length of segment : 235 time for calcul the mask position with numpy : 0.03613877296447754 nb_pixel_total : 332140 time to create 1 rle with new method : 0.022358417510986328 length of segment : 525 time for calcul the mask position with numpy : 0.004283428192138672 nb_pixel_total : 54352 time to create 1 rle with old method : 0.09855985641479492 length of segment : 239 time for calcul the mask position with numpy : 0.0032346248626708984 nb_pixel_total : 67527 time to create 1 rle with old method : 0.12714862823486328 length of segment : 241 time for calcul the mask position with numpy : 0.005594968795776367 nb_pixel_total : 44094 time to create 1 rle with old method : 0.08170032501220703 length of segment : 545 time for calcul the mask position with numpy : 0.03406262397766113 nb_pixel_total : 354708 time to create 1 rle with new method : 0.0486752986907959 length of segment : 1072 time for calcul the mask position with numpy : 0.0026340484619140625 nb_pixel_total : 33297 time to create 1 rle with old method : 0.04014992713928223 length of segment : 339 time for calcul the mask position with numpy : 0.006849050521850586 nb_pixel_total : 184234 time to create 1 rle with new method : 0.011502504348754883 length of segment : 968 time for calcul the mask position with numpy : 0.0009937286376953125 nb_pixel_total : 11504 time to create 1 rle with old method : 0.013968467712402344 length of segment : 122 time for calcul the mask position with numpy : 0.005354881286621094 nb_pixel_total : 116685 time to create 1 rle with old method : 0.1340022087097168 length of segment : 382 time for calcul the mask position with numpy : 0.02235269546508789 nb_pixel_total : 337961 time to create 1 rle with new method : 0.01968526840209961 length of segment : 701 time for calcul the mask position with numpy : 0.0005402565002441406 nb_pixel_total : 23595 time to create 1 rle with old method : 0.026880741119384766 length of segment : 183 time for calcul the mask position with numpy : 0.013045787811279297 nb_pixel_total : 293565 time to create 1 rle with new method : 0.02032303810119629 length of segment : 665 time for calcul the mask position with numpy : 0.0009686946868896484 nb_pixel_total : 16845 time to create 1 rle with old method : 0.018901348114013672 length of segment : 291 time for calcul the mask position with numpy : 0.0017619132995605469 nb_pixel_total : 52057 time to create 1 rle with old method : 0.06694197654724121 length of segment : 221 time for calcul the mask position with numpy : 0.008710861206054688 nb_pixel_total : 135463 time to create 1 rle with old method : 0.1545717716217041 length of segment : 405 time for calcul the mask position with numpy : 0.0019872188568115234 nb_pixel_total : 61347 time to create 1 rle with old method : 0.06951141357421875 length of segment : 286 time for calcul the mask position with numpy : 0.0017795562744140625 nb_pixel_total : 32485 time to create 1 rle with old method : 0.03539323806762695 length of segment : 253 time for calcul the mask position with numpy : 0.003770112991333008 nb_pixel_total : 103102 time to create 1 rle with old method : 0.12334299087524414 length of segment : 248 time for calcul the mask position with numpy : 0.010761499404907227 nb_pixel_total : 355240 time to create 1 rle with new method : 0.03465151786804199 length of segment : 593 time for calcul the mask position with numpy : 0.001071929931640625 nb_pixel_total : 23018 time to create 1 rle with old method : 0.026564359664916992 length of segment : 249 time for calcul the mask position with numpy : 0.0020737648010253906 nb_pixel_total : 36995 time to create 1 rle with old method : 0.044443368911743164 length of segment : 347 time for calcul the mask position with numpy : 0.0005404949188232422 nb_pixel_total : 11258 time to create 1 rle with old method : 0.013545989990234375 length of segment : 104 time for calcul the mask position with numpy : 0.000705718994140625 nb_pixel_total : 8051 time to create 1 rle with old method : 0.009167194366455078 length of segment : 215 time for calcul the mask position with numpy : 0.05675697326660156 nb_pixel_total : 946058 time to create 1 rle with new method : 0.0886068344116211 length of segment : 1389 time for calcul the mask position with numpy : 0.0023741722106933594 nb_pixel_total : 49946 time to create 1 rle with old method : 0.0562586784362793 length of segment : 361 time for calcul the mask position with numpy : 0.002244710922241211 nb_pixel_total : 43732 time to create 1 rle with old method : 0.05205392837524414 length of segment : 321 time for calcul the mask position with numpy : 0.001974344253540039 nb_pixel_total : 36201 time to create 1 rle with old method : 0.041394710540771484 length of segment : 311 time for calcul the mask position with numpy : 0.03150367736816406 nb_pixel_total : 794682 time to create 1 rle with new method : 0.04007220268249512 length of segment : 1167 time for calcul the mask position with numpy : 0.005181550979614258 nb_pixel_total : 101732 time to create 1 rle with old method : 0.11214876174926758 length of segment : 570 time for calcul the mask position with numpy : 0.0003044605255126953 nb_pixel_total : 6380 time to create 1 rle with old method : 0.007862567901611328 length of segment : 114 time for calcul the mask position with numpy : 0.001615285873413086 nb_pixel_total : 58263 time to create 1 rle with old method : 0.0660703182220459 length of segment : 250 time for calcul the mask position with numpy : 0.0017087459564208984 nb_pixel_total : 41700 time to create 1 rle with old method : 0.0487673282623291 length of segment : 175 time for calcul the mask position with numpy : 0.0026483535766601562 nb_pixel_total : 42653 time to create 1 rle with old method : 0.04990792274475098 length of segment : 324 time for calcul the mask position with numpy : 0.010133504867553711 nb_pixel_total : 282854 time to create 1 rle with new method : 0.010417461395263672 length of segment : 427 time for calcul the mask position with numpy : 0.0005755424499511719 nb_pixel_total : 8329 time to create 1 rle with old method : 0.01026606559753418 length of segment : 58 time for calcul the mask position with numpy : 0.000194549560546875 nb_pixel_total : 7088 time to create 1 rle with old method : 0.008185625076293945 length of segment : 96 time for calcul the mask position with numpy : 0.00032901763916015625 nb_pixel_total : 9962 time to create 1 rle with old method : 0.011754512786865234 length of segment : 116 time for calcul the mask position with numpy : 0.0021371841430664062 nb_pixel_total : 40803 time to create 1 rle with old method : 0.04591703414916992 length of segment : 243 time for calcul the mask position with numpy : 0.0007393360137939453 nb_pixel_total : 16650 time to create 1 rle with old method : 0.020173072814941406 length of segment : 133 time for calcul the mask position with numpy : 0.0005426406860351562 nb_pixel_total : 13008 time to create 1 rle with old method : 0.014732122421264648 length of segment : 101 time for calcul the mask position with numpy : 0.003660917282104492 nb_pixel_total : 136272 time to create 1 rle with old method : 0.14984822273254395 length of segment : 344 time for calcul the mask position with numpy : 0.0013964176177978516 nb_pixel_total : 27753 time to create 1 rle with old method : 0.04499006271362305 length of segment : 231 time for calcul the mask position with numpy : 0.004777193069458008 nb_pixel_total : 131556 time to create 1 rle with old method : 0.14201760292053223 length of segment : 312 time for calcul the mask position with numpy : 0.007107257843017578 nb_pixel_total : 204204 time to create 1 rle with new method : 0.008767127990722656 length of segment : 337 time for calcul the mask position with numpy : 0.00080108642578125 nb_pixel_total : 18401 time to create 1 rle with old method : 0.02137446403503418 length of segment : 128 time for calcul the mask position with numpy : 0.001669168472290039 nb_pixel_total : 36314 time to create 1 rle with old method : 0.040511369705200195 length of segment : 211 time for calcul the mask position with numpy : 0.0006213188171386719 nb_pixel_total : 13801 time to create 1 rle with old method : 0.01588892936706543 length of segment : 128 time for calcul the mask position with numpy : 0.0009613037109375 nb_pixel_total : 20222 time to create 1 rle with old method : 0.023714780807495117 length of segment : 156 time for calcul the mask position with numpy : 0.0004668235778808594 nb_pixel_total : 9474 time to create 1 rle with old method : 0.01084280014038086 length of segment : 135 time for calcul the mask position with numpy : 0.005049705505371094 nb_pixel_total : 164018 time to create 1 rle with new method : 0.0063288211822509766 length of segment : 540 time for calcul the mask position with numpy : 0.0002110004425048828 nb_pixel_total : 2811 time to create 1 rle with old method : 0.0033197402954101562 length of segment : 56 time for calcul the mask position with numpy : 0.0061228275299072266 nb_pixel_total : 201983 time to create 1 rle with new method : 0.0069599151611328125 length of segment : 399 time for calcul the mask position with numpy : 0.0005576610565185547 nb_pixel_total : 14958 time to create 1 rle with old method : 0.016699552536010742 length of segment : 107 time for calcul the mask position with numpy : 0.003748655319213867 nb_pixel_total : 120374 time to create 1 rle with old method : 0.13433361053466797 length of segment : 527 time for calcul the mask position with numpy : 0.0007917881011962891 nb_pixel_total : 21086 time to create 1 rle with old method : 0.024071931838989258 length of segment : 196 time for calcul the mask position with numpy : 0.0005712509155273438 nb_pixel_total : 10520 time to create 1 rle with old method : 0.01275181770324707 length of segment : 169 time for calcul the mask position with numpy : 0.0018801689147949219 nb_pixel_total : 39038 time to create 1 rle with old method : 0.04515790939331055 length of segment : 439 time for calcul the mask position with numpy : 0.00042319297790527344 nb_pixel_total : 11729 time to create 1 rle with old method : 0.013632059097290039 length of segment : 183 time for calcul the mask position with numpy : 0.002147674560546875 nb_pixel_total : 56011 time to create 1 rle with old method : 0.06322097778320312 length of segment : 342 time for calcul the mask position with numpy : 0.0029408931732177734 nb_pixel_total : 41349 time to create 1 rle with old method : 0.048084259033203125 length of segment : 323 time for calcul the mask position with numpy : 0.0008938312530517578 nb_pixel_total : 11437 time to create 1 rle with old method : 0.013703584671020508 length of segment : 124 time for calcul the mask position with numpy : 0.002534627914428711 nb_pixel_total : 33856 time to create 1 rle with old method : 0.03755044937133789 length of segment : 219 time for calcul the mask position with numpy : 0.0016188621520996094 nb_pixel_total : 22088 time to create 1 rle with old method : 0.025397300720214844 length of segment : 145 time for calcul the mask position with numpy : 0.001615762710571289 nb_pixel_total : 21132 time to create 1 rle with old method : 0.02371811866760254 length of segment : 404 time for calcul the mask position with numpy : 0.0036847591400146484 nb_pixel_total : 37911 time to create 1 rle with old method : 0.04365706443786621 length of segment : 351 time for calcul the mask position with numpy : 0.0007150173187255859 nb_pixel_total : 8612 time to create 1 rle with old method : 0.010413885116577148 length of segment : 130 time for calcul the mask position with numpy : 0.0018486976623535156 nb_pixel_total : 23874 time to create 1 rle with old method : 0.027077913284301758 length of segment : 268 time for calcul the mask position with numpy : 0.0008256435394287109 nb_pixel_total : 12204 time to create 1 rle with old method : 0.014498710632324219 length of segment : 109 time for calcul the mask position with numpy : 0.0017817020416259766 nb_pixel_total : 23299 time to create 1 rle with old method : 0.027008533477783203 length of segment : 242 time for calcul the mask position with numpy : 0.0013575553894042969 nb_pixel_total : 15080 time to create 1 rle with old method : 0.01752758026123047 length of segment : 159 time for calcul the mask position with numpy : 0.0016121864318847656 nb_pixel_total : 17631 time to create 1 rle with old method : 0.02884650230407715 length of segment : 185 time for calcul the mask position with numpy : 0.0010898113250732422 nb_pixel_total : 9709 time to create 1 rle with old method : 0.016132831573486328 length of segment : 128 time for calcul the mask position with numpy : 0.0008280277252197266 nb_pixel_total : 7181 time to create 1 rle with old method : 0.008876562118530273 length of segment : 117 time for calcul the mask position with numpy : 0.0021071434020996094 nb_pixel_total : 33274 time to create 1 rle with old method : 0.03795003890991211 length of segment : 405 time for calcul the mask position with numpy : 0.0006103515625 nb_pixel_total : 5706 time to create 1 rle with old method : 0.006800174713134766 length of segment : 81 time for calcul the mask position with numpy : 0.0004718303680419922 nb_pixel_total : 9167 time to create 1 rle with old method : 0.011016607284545898 length of segment : 144 time for calcul the mask position with numpy : 0.0022439956665039062 nb_pixel_total : 23544 time to create 1 rle with old method : 0.027878761291503906 length of segment : 468 time for calcul the mask position with numpy : 0.0008223056793212891 nb_pixel_total : 9890 time to create 1 rle with old method : 0.01190805435180664 length of segment : 142 time for calcul the mask position with numpy : 0.0021049976348876953 nb_pixel_total : 30793 time to create 1 rle with old method : 0.03623652458190918 length of segment : 171 time for calcul the mask position with numpy : 0.001237630844116211 nb_pixel_total : 13994 time to create 1 rle with old method : 0.016124248504638672 length of segment : 179 time for calcul the mask position with numpy : 0.001622915267944336 nb_pixel_total : 21068 time to create 1 rle with old method : 0.025347471237182617 length of segment : 220 time for calcul the mask position with numpy : 0.0003941059112548828 nb_pixel_total : 8270 time to create 1 rle with old method : 0.009962320327758789 length of segment : 132 time for calcul the mask position with numpy : 0.0009210109710693359 nb_pixel_total : 11457 time to create 1 rle with old method : 0.013035774230957031 length of segment : 159 time for calcul the mask position with numpy : 0.003796815872192383 nb_pixel_total : 34751 time to create 1 rle with old method : 0.039819955825805664 length of segment : 294 time for calcul the mask position with numpy : 0.001817464828491211 nb_pixel_total : 19132 time to create 1 rle with old method : 0.0229799747467041 length of segment : 166 time for calcul the mask position with numpy : 0.0025758743286132812 nb_pixel_total : 33665 time to create 1 rle with old method : 0.0388944149017334 length of segment : 324 time for calcul the mask position with numpy : 0.0012531280517578125 nb_pixel_total : 18041 time to create 1 rle with old method : 0.020479440689086914 length of segment : 169 time for calcul the mask position with numpy : 0.001894235610961914 nb_pixel_total : 23530 time to create 1 rle with old method : 0.026340961456298828 length of segment : 237 time for calcul the mask position with numpy : 0.0021893978118896484 nb_pixel_total : 25022 time to create 1 rle with old method : 0.02868032455444336 length of segment : 286 time for calcul the mask position with numpy : 0.0011305809020996094 nb_pixel_total : 8641 time to create 1 rle with old method : 0.010402202606201172 length of segment : 159 time for calcul the mask position with numpy : 0.001096963882446289 nb_pixel_total : 19805 time to create 1 rle with old method : 0.023200273513793945 length of segment : 137 time for calcul the mask position with numpy : 0.0013697147369384766 nb_pixel_total : 17730 time to create 1 rle with old method : 0.02066826820373535 length of segment : 282 time for calcul the mask position with numpy : 0.0050792694091796875 nb_pixel_total : 49269 time to create 1 rle with old method : 0.08032488822937012 length of segment : 368 time for calcul the mask position with numpy : 0.0017006397247314453 nb_pixel_total : 27811 time to create 1 rle with old method : 0.0315399169921875 length of segment : 165 time for calcul the mask position with numpy : 0.0004799365997314453 nb_pixel_total : 4031 time to create 1 rle with old method : 0.005060911178588867 length of segment : 118 time for calcul the mask position with numpy : 0.0018703937530517578 nb_pixel_total : 26805 time to create 1 rle with old method : 0.030358314514160156 length of segment : 188 time for calcul the mask position with numpy : 0.0014765262603759766 nb_pixel_total : 25091 time to create 1 rle with old method : 0.03112196922302246 length of segment : 247 time for calcul the mask position with numpy : 0.0004935264587402344 nb_pixel_total : 5476 time to create 1 rle with old method : 0.006615161895751953 length of segment : 81 time for calcul the mask position with numpy : 0.0024209022521972656 nb_pixel_total : 37309 time to create 1 rle with old method : 0.042758941650390625 length of segment : 303 time for calcul the mask position with numpy : 0.0020656585693359375 nb_pixel_total : 28492 time to create 1 rle with old method : 0.033765316009521484 length of segment : 226 time for calcul the mask position with numpy : 0.0037412643432617188 nb_pixel_total : 63991 time to create 1 rle with old method : 0.07513070106506348 length of segment : 454 time for calcul the mask position with numpy : 0.001064300537109375 nb_pixel_total : 15120 time to create 1 rle with old method : 0.01752638816833496 length of segment : 161 time for calcul the mask position with numpy : 0.0012369155883789062 nb_pixel_total : 13431 time to create 1 rle with old method : 0.016446352005004883 length of segment : 147 time for calcul the mask position with numpy : 0.0012960433959960938 nb_pixel_total : 21604 time to create 1 rle with old method : 0.025440454483032227 length of segment : 332 time for calcul the mask position with numpy : 0.0028629302978515625 nb_pixel_total : 43510 time to create 1 rle with old method : 0.05073857307434082 length of segment : 316 time for calcul the mask position with numpy : 0.0023491382598876953 nb_pixel_total : 24648 time to create 1 rle with old method : 0.02815556526184082 length of segment : 333 time for calcul the mask position with numpy : 0.0006313323974609375 nb_pixel_total : 7318 time to create 1 rle with old method : 0.010598421096801758 length of segment : 113 time for calcul the mask position with numpy : 0.0008399486541748047 nb_pixel_total : 12873 time to create 1 rle with old method : 0.015151262283325195 length of segment : 134 time for calcul the mask position with numpy : 0.0010027885437011719 nb_pixel_total : 13018 time to create 1 rle with old method : 0.016202688217163086 length of segment : 85 time for calcul the mask position with numpy : 0.0013384819030761719 nb_pixel_total : 20454 time to create 1 rle with old method : 0.026137351989746094 length of segment : 147 time for calcul the mask position with numpy : 0.0027151107788085938 nb_pixel_total : 30324 time to create 1 rle with old method : 0.03529095649719238 length of segment : 322 time for calcul the mask position with numpy : 0.00048232078552246094 nb_pixel_total : 6323 time to create 1 rle with old method : 0.009338855743408203 length of segment : 76 time for calcul the mask position with numpy : 0.0016865730285644531 nb_pixel_total : 17577 time to create 1 rle with old method : 0.02099609375 length of segment : 191 time for calcul the mask position with numpy : 0.0005803108215332031 nb_pixel_total : 6525 time to create 1 rle with old method : 0.008855581283569336 length of segment : 112 time for calcul the mask position with numpy : 0.000675201416015625 nb_pixel_total : 7285 time to create 1 rle with old method : 0.010601997375488281 length of segment : 141 time for calcul the mask position with numpy : 0.0009076595306396484 nb_pixel_total : 7276 time to create 1 rle with old method : 0.012312173843383789 length of segment : 96 time for calcul the mask position with numpy : 0.0009725093841552734 nb_pixel_total : 14562 time to create 1 rle with old method : 0.016683101654052734 length of segment : 189 time for calcul the mask position with numpy : 0.0005242824554443359 nb_pixel_total : 6144 time to create 1 rle with old method : 0.0071582794189453125 length of segment : 118 time for calcul the mask position with numpy : 0.0003440380096435547 nb_pixel_total : 5167 time to create 1 rle with old method : 0.006195783615112305 length of segment : 127 time for calcul the mask position with numpy : 0.0014679431915283203 nb_pixel_total : 17725 time to create 1 rle with old method : 0.0205230712890625 length of segment : 166 time for calcul the mask position with numpy : 0.00241851806640625 nb_pixel_total : 37711 time to create 1 rle with old method : 0.0410151481628418 length of segment : 380 time for calcul the mask position with numpy : 0.0016155242919921875 nb_pixel_total : 16929 time to create 1 rle with old method : 0.01918625831604004 length of segment : 237 time for calcul the mask position with numpy : 0.0047397613525390625 nb_pixel_total : 78439 time to create 1 rle with old method : 0.09053373336791992 length of segment : 330 time for calcul the mask position with numpy : 0.0019791126251220703 nb_pixel_total : 17592 time to create 1 rle with old method : 0.02017688751220703 length of segment : 220 time for calcul the mask position with numpy : 0.0007355213165283203 nb_pixel_total : 10533 time to create 1 rle with old method : 0.012332677841186523 length of segment : 192 time for calcul the mask position with numpy : 0.0020744800567626953 nb_pixel_total : 28430 time to create 1 rle with old method : 0.03276371955871582 length of segment : 194 time for calcul the mask position with numpy : 0.002372264862060547 nb_pixel_total : 34633 time to create 1 rle with old method : 0.04036545753479004 length of segment : 224 time for calcul the mask position with numpy : 0.0011584758758544922 nb_pixel_total : 15293 time to create 1 rle with old method : 0.01827096939086914 length of segment : 181 time for calcul the mask position with numpy : 0.00038814544677734375 nb_pixel_total : 4042 time to create 1 rle with old method : 0.005062580108642578 length of segment : 69 time for calcul the mask position with numpy : 0.0016551017761230469 nb_pixel_total : 29360 time to create 1 rle with old method : 0.034899234771728516 length of segment : 153 time for calcul the mask position with numpy : 0.0006558895111083984 nb_pixel_total : 7243 time to create 1 rle with old method : 0.008833646774291992 length of segment : 121 time for calcul the mask position with numpy : 0.0010726451873779297 nb_pixel_total : 9699 time to create 1 rle with old method : 0.011698007583618164 length of segment : 154 time for calcul the mask position with numpy : 0.00015211105346679688 nb_pixel_total : 5648 time to create 1 rle with old method : 0.0067768096923828125 length of segment : 94 time for calcul the mask position with numpy : 0.0007309913635253906 nb_pixel_total : 7604 time to create 1 rle with old method : 0.009215354919433594 length of segment : 81 time for calcul the mask position with numpy : 0.005705595016479492 nb_pixel_total : 85906 time to create 1 rle with old method : 0.09795451164245605 length of segment : 593 time for calcul the mask position with numpy : 0.0003108978271484375 nb_pixel_total : 3117 time to create 1 rle with old method : 0.003729581832885742 length of segment : 72 time for calcul the mask position with numpy : 0.0035827159881591797 nb_pixel_total : 45787 time to create 1 rle with old method : 0.05266165733337402 length of segment : 326 time for calcul the mask position with numpy : 0.00048279762268066406 nb_pixel_total : 5636 time to create 1 rle with old method : 0.006703615188598633 length of segment : 87 time for calcul the mask position with numpy : 0.003628969192504883 nb_pixel_total : 32290 time to create 1 rle with old method : 0.038411855697631836 length of segment : 292 time for calcul the mask position with numpy : 0.0008668899536132812 nb_pixel_total : 11697 time to create 1 rle with old method : 0.013963699340820312 length of segment : 157 time for calcul the mask position with numpy : 0.001336812973022461 nb_pixel_total : 25134 time to create 1 rle with old method : 0.02950119972229004 length of segment : 242 time for calcul the mask position with numpy : 0.00033020973205566406 nb_pixel_total : 4634 time to create 1 rle with old method : 0.0056536197662353516 length of segment : 82 time for calcul the mask position with numpy : 0.0033822059631347656 nb_pixel_total : 44791 time to create 1 rle with old method : 0.052858591079711914 length of segment : 538 time for calcul the mask position with numpy : 0.0024535655975341797 nb_pixel_total : 28187 time to create 1 rle with old method : 0.03359484672546387 length of segment : 210 time for calcul the mask position with numpy : 0.0016562938690185547 nb_pixel_total : 12259 time to create 1 rle with old method : 0.020437002182006836 length of segment : 162 time for calcul the mask position with numpy : 0.001218557357788086 nb_pixel_total : 17639 time to create 1 rle with old method : 0.020241260528564453 length of segment : 162 time for calcul the mask position with numpy : 0.0013027191162109375 nb_pixel_total : 22884 time to create 1 rle with old method : 0.027097702026367188 length of segment : 141 time for calcul the mask position with numpy : 0.00092315673828125 nb_pixel_total : 10244 time to create 1 rle with old method : 0.012300252914428711 length of segment : 106 time for calcul the mask position with numpy : 0.002864837646484375 nb_pixel_total : 54081 time to create 1 rle with old method : 0.06091594696044922 length of segment : 454 time for calcul the mask position with numpy : 0.0006840229034423828 nb_pixel_total : 9542 time to create 1 rle with old method : 0.011326074600219727 length of segment : 134 time for calcul the mask position with numpy : 0.0009641647338867188 nb_pixel_total : 11127 time to create 1 rle with old method : 0.015832901000976562 length of segment : 182 time for calcul the mask position with numpy : 0.0009872913360595703 nb_pixel_total : 14484 time to create 1 rle with old method : 0.017537593841552734 length of segment : 137 time for calcul the mask position with numpy : 0.0006101131439208984 nb_pixel_total : 6092 time to create 1 rle with old method : 0.007367849349975586 length of segment : 160 time for calcul the mask position with numpy : 0.0008676052093505859 nb_pixel_total : 16299 time to create 1 rle with old method : 0.018730640411376953 length of segment : 226 time for calcul the mask position with numpy : 0.001035928726196289 nb_pixel_total : 13228 time to create 1 rle with old method : 0.016091585159301758 length of segment : 129 time for calcul the mask position with numpy : 0.0016357898712158203 nb_pixel_total : 19804 time to create 1 rle with old method : 0.02324223518371582 length of segment : 192 time for calcul the mask position with numpy : 0.003443479537963867 nb_pixel_total : 49726 time to create 1 rle with old method : 0.0584414005279541 length of segment : 236 time for calcul the mask position with numpy : 0.004407405853271484 nb_pixel_total : 85694 time to create 1 rle with old method : 0.10015678405761719 length of segment : 312 time for calcul the mask position with numpy : 0.0009264945983886719 nb_pixel_total : 10066 time to create 1 rle with old method : 0.016632556915283203 length of segment : 108 time for calcul the mask position with numpy : 0.002009153366088867 nb_pixel_total : 25637 time to create 1 rle with old method : 0.03289484977722168 length of segment : 176 time for calcul the mask position with numpy : 0.0011153221130371094 nb_pixel_total : 12322 time to create 1 rle with old method : 0.014686822891235352 length of segment : 154 time for calcul the mask position with numpy : 0.000400543212890625 nb_pixel_total : 5548 time to create 1 rle with old method : 0.006752729415893555 length of segment : 65 time for calcul the mask position with numpy : 0.0011835098266601562 nb_pixel_total : 15738 time to create 1 rle with old method : 0.01902318000793457 length of segment : 124 time for calcul the mask position with numpy : 0.0004487037658691406 nb_pixel_total : 7436 time to create 1 rle with old method : 0.00913238525390625 length of segment : 90 time for calcul the mask position with numpy : 0.0028820037841796875 nb_pixel_total : 21516 time to create 1 rle with old method : 0.02571725845336914 length of segment : 260 time for calcul the mask position with numpy : 0.0025987625122070312 nb_pixel_total : 28884 time to create 1 rle with old method : 0.03364443778991699 length of segment : 235 time for calcul the mask position with numpy : 0.0010006427764892578 nb_pixel_total : 11899 time to create 1 rle with old method : 0.014154195785522461 length of segment : 129 time for calcul the mask position with numpy : 0.000759124755859375 nb_pixel_total : 14894 time to create 1 rle with old method : 0.017999649047851562 length of segment : 116 time for calcul the mask position with numpy : 0.0019474029541015625 nb_pixel_total : 20395 time to create 1 rle with old method : 0.024835586547851562 length of segment : 185 time for calcul the mask position with numpy : 0.0017638206481933594 nb_pixel_total : 22601 time to create 1 rle with old method : 0.028184890747070312 length of segment : 207 time for calcul the mask position with numpy : 0.00022220611572265625 nb_pixel_total : 1948 time to create 1 rle with old method : 0.002374887466430664 length of segment : 48 time for calcul the mask position with numpy : 0.0065042972564697266 nb_pixel_total : 86014 time to create 1 rle with old method : 0.10748147964477539 length of segment : 539 time for calcul the mask position with numpy : 0.0003952980041503906 nb_pixel_total : 13016 time to create 1 rle with old method : 0.015743732452392578 length of segment : 182 time for calcul the mask position with numpy : 0.003880023956298828 nb_pixel_total : 58107 time to create 1 rle with old method : 0.06648945808410645 length of segment : 297 time for calcul the mask position with numpy : 0.0008876323699951172 nb_pixel_total : 30322 time to create 1 rle with old method : 0.035596370697021484 length of segment : 242 time for calcul the mask position with numpy : 0.0004718303680419922 nb_pixel_total : 18489 time to create 1 rle with old method : 0.02205681800842285 length of segment : 147 time for calcul the mask position with numpy : 0.0012366771697998047 nb_pixel_total : 29747 time to create 1 rle with old method : 0.033843040466308594 length of segment : 229 time for calcul the mask position with numpy : 0.0008907318115234375 nb_pixel_total : 17530 time to create 1 rle with old method : 0.019355297088623047 length of segment : 145 time for calcul the mask position with numpy : 0.0011959075927734375 nb_pixel_total : 34096 time to create 1 rle with old method : 0.03817892074584961 length of segment : 442 time for calcul the mask position with numpy : 0.0008215904235839844 nb_pixel_total : 11594 time to create 1 rle with old method : 0.013849020004272461 length of segment : 145 time for calcul the mask position with numpy : 0.0013132095336914062 nb_pixel_total : 19064 time to create 1 rle with old method : 0.022633790969848633 length of segment : 144 time for calcul the mask position with numpy : 0.00010848045349121094 nb_pixel_total : 2794 time to create 1 rle with old method : 0.003537893295288086 length of segment : 76 time for calcul the mask position with numpy : 0.0014069080352783203 nb_pixel_total : 25681 time to create 1 rle with old method : 0.029273033142089844 length of segment : 262 time for calcul the mask position with numpy : 0.0015273094177246094 nb_pixel_total : 18711 time to create 1 rle with old method : 0.02118372917175293 length of segment : 246 time for calcul the mask position with numpy : 0.0019178390502929688 nb_pixel_total : 20276 time to create 1 rle with old method : 0.02562737464904785 length of segment : 207 time for calcul the mask position with numpy : 0.0007288455963134766 nb_pixel_total : 9836 time to create 1 rle with old method : 0.01150202751159668 length of segment : 94 time for calcul the mask position with numpy : 0.0030486583709716797 nb_pixel_total : 26375 time to create 1 rle with old method : 0.03031635284423828 length of segment : 237 time for calcul the mask position with numpy : 0.0012438297271728516 nb_pixel_total : 20112 time to create 1 rle with old method : 0.023443222045898438 length of segment : 235 time for calcul the mask position with numpy : 0.004688739776611328 nb_pixel_total : 56980 time to create 1 rle with old method : 0.06473970413208008 length of segment : 397 time for calcul the mask position with numpy : 0.0013225078582763672 nb_pixel_total : 15297 time to create 1 rle with old method : 0.0180509090423584 length of segment : 195 time for calcul the mask position with numpy : 0.0025339126586914062 nb_pixel_total : 31606 time to create 1 rle with old method : 0.03684520721435547 length of segment : 178 time for calcul the mask position with numpy : 0.0012488365173339844 nb_pixel_total : 13961 time to create 1 rle with old method : 0.01643824577331543 length of segment : 105 time for calcul the mask position with numpy : 0.0002696514129638672 nb_pixel_total : 2390 time to create 1 rle with old method : 0.003014802932739258 length of segment : 46 time for calcul the mask position with numpy : 0.0011184215545654297 nb_pixel_total : 6103 time to create 1 rle with old method : 0.007250547409057617 length of segment : 161 time for calcul the mask position with numpy : 0.00061798095703125 nb_pixel_total : 2657 time to create 1 rle with old method : 0.0032091140747070312 length of segment : 99 time for calcul the mask position with numpy : 0.0012784004211425781 nb_pixel_total : 20900 time to create 1 rle with old method : 0.024517297744750977 length of segment : 122 time for calcul the mask position with numpy : 0.003295421600341797 nb_pixel_total : 23376 time to create 1 rle with old method : 0.027674198150634766 length of segment : 277 time for calcul the mask position with numpy : 0.0021851062774658203 nb_pixel_total : 26178 time to create 1 rle with old method : 0.031038284301757812 length of segment : 340 time for calcul the mask position with numpy : 0.0008854866027832031 nb_pixel_total : 8171 time to create 1 rle with old method : 0.009924173355102539 length of segment : 167 time for calcul the mask position with numpy : 0.0012974739074707031 nb_pixel_total : 12725 time to create 1 rle with old method : 0.015426397323608398 length of segment : 130 time for calcul the mask position with numpy : 0.0016088485717773438 nb_pixel_total : 13968 time to create 1 rle with old method : 0.016602516174316406 length of segment : 181 time for calcul the mask position with numpy : 0.0005862712860107422 nb_pixel_total : 5339 time to create 1 rle with old method : 0.00656437873840332 length of segment : 105 time for calcul the mask position with numpy : 0.001058340072631836 nb_pixel_total : 11533 time to create 1 rle with old method : 0.01361846923828125 length of segment : 157 time for calcul the mask position with numpy : 0.0018155574798583984 nb_pixel_total : 25165 time to create 1 rle with old method : 0.029557466506958008 length of segment : 244 time for calcul the mask position with numpy : 0.0007984638214111328 nb_pixel_total : 8529 time to create 1 rle with old method : 0.010367870330810547 length of segment : 102 time for calcul the mask position with numpy : 0.0008893013000488281 nb_pixel_total : 6914 time to create 1 rle with old method : 0.008729696273803711 length of segment : 125 time for calcul the mask position with numpy : 0.0027387142181396484 nb_pixel_total : 18203 time to create 1 rle with old method : 0.021771907806396484 length of segment : 394 time for calcul the mask position with numpy : 0.0072002410888671875 nb_pixel_total : 73160 time to create 1 rle with old method : 0.08446717262268066 length of segment : 338 time for calcul the mask position with numpy : 0.0002624988555908203 nb_pixel_total : 3982 time to create 1 rle with old method : 0.004785060882568359 length of segment : 119 time for calcul the mask position with numpy : 0.001708984375 nb_pixel_total : 21869 time to create 1 rle with old method : 0.02573537826538086 length of segment : 202 time for calcul the mask position with numpy : 0.0020623207092285156 nb_pixel_total : 26709 time to create 1 rle with old method : 0.03216123580932617 length of segment : 231 time for calcul the mask position with numpy : 0.0015823841094970703 nb_pixel_total : 20460 time to create 1 rle with old method : 0.023748397827148438 length of segment : 154 time for calcul the mask position with numpy : 0.005393266677856445 nb_pixel_total : 82804 time to create 1 rle with old method : 0.09434676170349121 length of segment : 318 time for calcul the mask position with numpy : 0.0053560733795166016 nb_pixel_total : 63550 time to create 1 rle with old method : 0.0718545913696289 length of segment : 330 time for calcul the mask position with numpy : 0.0014736652374267578 nb_pixel_total : 15955 time to create 1 rle with old method : 0.02560591697692871 length of segment : 140 time for calcul the mask position with numpy : 0.001249074935913086 nb_pixel_total : 15913 time to create 1 rle with old method : 0.019163131713867188 length of segment : 128 time for calcul the mask position with numpy : 0.0006840229034423828 nb_pixel_total : 4793 time to create 1 rle with old method : 0.008497238159179688 length of segment : 100 time for calcul the mask position with numpy : 0.0017781257629394531 nb_pixel_total : 17516 time to create 1 rle with old method : 0.029631853103637695 length of segment : 187 time for calcul the mask position with numpy : 0.006057024002075195 nb_pixel_total : 76339 time to create 1 rle with old method : 0.08568358421325684 length of segment : 413 time for calcul the mask position with numpy : 0.002820253372192383 nb_pixel_total : 32557 time to create 1 rle with old method : 0.03797650337219238 length of segment : 231 time for calcul the mask position with numpy : 0.001430511474609375 nb_pixel_total : 25615 time to create 1 rle with old method : 0.030231714248657227 length of segment : 192 time for calcul the mask position with numpy : 0.0009300708770751953 nb_pixel_total : 13434 time to create 1 rle with old method : 0.015966176986694336 length of segment : 102 time for calcul the mask position with numpy : 0.002278566360473633 nb_pixel_total : 28712 time to create 1 rle with old method : 0.033179283142089844 length of segment : 207 time for calcul the mask position with numpy : 0.0004780292510986328 nb_pixel_total : 4855 time to create 1 rle with old method : 0.0060558319091796875 length of segment : 106 time for calcul the mask position with numpy : 0.0005214214324951172 nb_pixel_total : 5877 time to create 1 rle with old method : 0.006929636001586914 length of segment : 84 time for calcul the mask position with numpy : 0.0034024715423583984 nb_pixel_total : 56776 time to create 1 rle with old method : 0.0679159164428711 length of segment : 314 time for calcul the mask position with numpy : 0.001226663589477539 nb_pixel_total : 23633 time to create 1 rle with old method : 0.02743053436279297 length of segment : 241 time for calcul the mask position with numpy : 0.002249479293823242 nb_pixel_total : 72838 time to create 1 rle with old method : 0.0862424373626709 length of segment : 341 time for calcul the mask position with numpy : 0.0017247200012207031 nb_pixel_total : 22920 time to create 1 rle with old method : 0.0263369083404541 length of segment : 212 time for calcul the mask position with numpy : 0.0011386871337890625 nb_pixel_total : 13009 time to create 1 rle with old method : 0.01566910743713379 length of segment : 203 time for calcul the mask position with numpy : 0.001680135726928711 nb_pixel_total : 18361 time to create 1 rle with old method : 0.021519184112548828 length of segment : 256 time for calcul the mask position with numpy : 0.0024220943450927734 nb_pixel_total : 36120 time to create 1 rle with old method : 0.042493581771850586 length of segment : 237 time for calcul the mask position with numpy : 0.0017015933990478516 nb_pixel_total : 23744 time to create 1 rle with old method : 0.028620243072509766 length of segment : 294 time for calcul the mask position with numpy : 0.0015554428100585938 nb_pixel_total : 20457 time to create 1 rle with old method : 0.024065256118774414 length of segment : 128 time for calcul the mask position with numpy : 0.0013170242309570312 nb_pixel_total : 13994 time to create 1 rle with old method : 0.017029523849487305 length of segment : 99 time for calcul the mask position with numpy : 0.0010993480682373047 nb_pixel_total : 13234 time to create 1 rle with old method : 0.015375137329101562 length of segment : 178 time for calcul the mask position with numpy : 0.0017380714416503906 nb_pixel_total : 34847 time to create 1 rle with old method : 0.03958868980407715 length of segment : 221 time for calcul the mask position with numpy : 0.003814697265625 nb_pixel_total : 49657 time to create 1 rle with old method : 0.05921053886413574 length of segment : 398 time for calcul the mask position with numpy : 0.0017154216766357422 nb_pixel_total : 27196 time to create 1 rle with old method : 0.03126955032348633 length of segment : 262 time for calcul the mask position with numpy : 0.00046896934509277344 nb_pixel_total : 2699 time to create 1 rle with old method : 0.0033800601959228516 length of segment : 83 time for calcul the mask position with numpy : 0.0015316009521484375 nb_pixel_total : 13980 time to create 1 rle with old method : 0.01908397674560547 length of segment : 232 time for calcul the mask position with numpy : 0.0016405582427978516 nb_pixel_total : 11494 time to create 1 rle with old method : 0.014302253723144531 length of segment : 153 time for calcul the mask position with numpy : 0.0013909339904785156 nb_pixel_total : 25810 time to create 1 rle with old method : 0.03232383728027344 length of segment : 247 time for calcul the mask position with numpy : 0.007094621658325195 nb_pixel_total : 81220 time to create 1 rle with old method : 0.09196829795837402 length of segment : 364 time for calcul the mask position with numpy : 0.0003554821014404297 nb_pixel_total : 3079 time to create 1 rle with old method : 0.00363922119140625 length of segment : 111 time for calcul the mask position with numpy : 0.0008835792541503906 nb_pixel_total : 8381 time to create 1 rle with old method : 0.009912252426147461 length of segment : 189 time for calcul the mask position with numpy : 0.0005085468292236328 nb_pixel_total : 5952 time to create 1 rle with old method : 0.007200956344604492 length of segment : 100 time for calcul the mask position with numpy : 0.0040013790130615234 nb_pixel_total : 61906 time to create 1 rle with old method : 0.0715172290802002 length of segment : 398 time for calcul the mask position with numpy : 0.0017964839935302734 nb_pixel_total : 28171 time to create 1 rle with old method : 0.03232288360595703 length of segment : 299 time for calcul the mask position with numpy : 0.001878976821899414 nb_pixel_total : 30766 time to create 1 rle with old method : 0.03611421585083008 length of segment : 190 time for calcul the mask position with numpy : 0.002310037612915039 nb_pixel_total : 37925 time to create 1 rle with old method : 0.04341912269592285 length of segment : 206 time for calcul the mask position with numpy : 0.0007476806640625 nb_pixel_total : 8642 time to create 1 rle with old method : 0.010241031646728516 length of segment : 103 time for calcul the mask position with numpy : 0.0012378692626953125 nb_pixel_total : 12010 time to create 1 rle with old method : 0.014124631881713867 length of segment : 313 time for calcul the mask position with numpy : 0.000881195068359375 nb_pixel_total : 11561 time to create 1 rle with old method : 0.013469457626342773 length of segment : 113 time for calcul the mask position with numpy : 0.001466512680053711 nb_pixel_total : 15410 time to create 1 rle with old method : 0.01912093162536621 length of segment : 171 time for calcul the mask position with numpy : 0.001340627670288086 nb_pixel_total : 17886 time to create 1 rle with old method : 0.021465778350830078 length of segment : 242 time for calcul the mask position with numpy : 0.0043277740478515625 nb_pixel_total : 51429 time to create 1 rle with old method : 0.06090688705444336 length of segment : 333 time for calcul the mask position with numpy : 0.0015153884887695312 nb_pixel_total : 18047 time to create 1 rle with old method : 0.02161121368408203 length of segment : 247 time for calcul the mask position with numpy : 0.0004978179931640625 nb_pixel_total : 5338 time to create 1 rle with old method : 0.006377458572387695 length of segment : 138 time for calcul the mask position with numpy : 0.0011944770812988281 nb_pixel_total : 22863 time to create 1 rle with old method : 0.026719331741333008 length of segment : 258 time for calcul the mask position with numpy : 0.0005314350128173828 nb_pixel_total : 7962 time to create 1 rle with old method : 0.009395360946655273 length of segment : 99 time for calcul the mask position with numpy : 0.0017750263214111328 nb_pixel_total : 12330 time to create 1 rle with old method : 0.015057802200317383 length of segment : 181 time spent for convertir_results : 46.23899865150452 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 516 chid ids of type : 3594 Number RLEs to save : 114808 save missing photos in datou_result : time spend for datou_step_exec : 254.88859629631042 time spend to save output : 231.4199299812317 total time spend for step 1 : 486.3085262775421 step2:crop_condition Thu Feb 6 06:38:36 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 : 12 ! batch 1 Loaded 516 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 ! map_result returned by crop_photo_return_map_crop : length : 395 About to insert : list_path_to_insert length 395 new photo from crops ! About to upload 395 photos upload in portfolio : 3736932 init cache_photo without model_param we have 395 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738820376_2362753 we have uploaded 395 photos in the portfolio 3736932 time of upload the photos Elapsed time : 107.32548785209656 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 ! map_result returned by crop_photo_return_map_crop : length : 78 About to insert : list_path_to_insert length 78 new photo from crops ! About to upload 78 photos upload in portfolio : 3736932 init cache_photo without model_param we have 78 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738820507_2362753 we have uploaded 78 photos in the portfolio 3736932 time of upload the photos Elapsed time : 21.01276421546936 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/1738820531_2362753 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.2251710891723633 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 ! map_result returned by crop_photo_return_map_crop : length : 19 About to insert : list_path_to_insert length 19 new photo from crops ! About to upload 19 photos upload in portfolio : 3736932 init cache_photo without model_param we have 19 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738820543_2362753 we have uploaded 19 photos in the portfolio 3736932 time of upload the photos Elapsed time : 6.461984157562256 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 11 About to insert : list_path_to_insert length 11 new photo from crops ! About to upload 11 photos upload in portfolio : 3736932 init cache_photo without model_param we have 11 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738820553_2362753 we have uploaded 11 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.191206216812134 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 ! map_result returned by crop_photo_return_map_crop : length : 6 About to insert : list_path_to_insert length 6 new photo from crops ! About to upload 6 photos upload in portfolio : 3736932 init cache_photo without model_param we have 6 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738820560_2362753 we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.030670642852783 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 ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1738820565_2362753 we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.3039333820343018 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1335027718, 1335027714, 1335027709, 1335027691, 1335027687, 1335019935, 1335019888, 1335019884, 1335019880, 1335019875, 1335019873, 1335019865] Looping around the photos to save general results len do output : 516 /1335137549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137595Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137620Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137779Didn't retrieve data .Didn't retrieve data .Didn't 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data .Didn't retrieve data . /1335137793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1335137805Didn't retrieve data 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None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027709', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027691', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027687', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019935', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019888', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019884', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019880', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019875', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019873', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019865', None, None, None, None, None, '2558005') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1560 time used for this insertion : 0.07851767539978027 save_final save missing photos in datou_result : time spend for datou_step_exec : 249.97808051109314 time spend to save output : 0.08950591087341309 total time spend for step 2 : 250.06758642196655 step3:rle_unique_nms_with_priority Thu Feb 6 06:42:46 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 516 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 59 nb_hashtags : 4 time to prepare the origin masks : 4.207486152648926 time for calcul the mask position with numpy : 0.49753403663635254 nb_pixel_total : 5352709 time to create 1 rle with new method : 0.602492094039917 time for calcul the mask position with numpy : 0.029059648513793945 nb_pixel_total : 9117 time to create 1 rle with old method : 0.010352373123168945 time for calcul the mask position with numpy : 0.02984905242919922 nb_pixel_total : 21492 time to create 1 rle with old method : 0.02420949935913086 time for calcul the mask position with numpy : 0.02922511100769043 nb_pixel_total : 14182 time to create 1 rle with old method : 0.01706218719482422 time for calcul the mask position with numpy : 0.031771183013916016 nb_pixel_total : 39119 time to create 1 rle with old method : 0.05554938316345215 time for calcul the mask position with numpy : 0.03131389617919922 nb_pixel_total : 23799 time to create 1 rle with old method : 0.03025650978088379 time for calcul the mask position with numpy : 0.030621051788330078 nb_pixel_total : 14893 time to create 1 rle with old method : 0.01689291000366211 time for calcul the mask position with numpy : 0.0298306941986084 nb_pixel_total : 86499 time to create 1 rle with old method : 0.10201001167297363 time for calcul the mask position with numpy : 0.03825187683105469 nb_pixel_total : 9152 time to create 1 rle with old method : 0.012889623641967773 time for calcul the mask position with numpy : 0.02979755401611328 nb_pixel_total : 16210 time to create 1 rle with old method : 0.018271207809448242 time for calcul the mask position with numpy : 0.029076099395751953 nb_pixel_total : 14731 time to create 1 rle with old method : 0.017121553421020508 time for calcul the mask position with numpy : 0.029456138610839844 nb_pixel_total : 23678 time to create 1 rle with old method : 0.026700258255004883 time for calcul the mask position with numpy : 0.029392004013061523 nb_pixel_total : 4091 time to create 1 rle with old method : 0.005045890808105469 time for calcul the mask position with numpy : 0.029223203659057617 nb_pixel_total : 41896 time to create 1 rle with old method : 0.04669690132141113 time for calcul the mask position with numpy : 0.029749393463134766 nb_pixel_total : 29547 time to create 1 rle with old method : 0.03346848487854004 time for calcul the mask position with numpy : 0.029625892639160156 nb_pixel_total : 14259 time to create 1 rle with old method : 0.016217708587646484 time for calcul the mask position with numpy : 0.031967878341674805 nb_pixel_total : 26159 time to create 1 rle with old method : 0.030713796615600586 time for calcul the mask position with numpy : 0.03084850311279297 nb_pixel_total : 22128 time to create 1 rle with old method : 0.029700517654418945 time for calcul the mask position with numpy : 0.029237985610961914 nb_pixel_total : 3195 time to create 1 rle with old method : 0.0036814212799072266 time for calcul the mask position with numpy : 0.029690980911254883 nb_pixel_total : 21330 time to create 1 rle with old method : 0.025131702423095703 time for calcul the mask position with numpy : 0.029770374298095703 nb_pixel_total : 3976 time to create 1 rle with old method : 0.004877567291259766 time for calcul the mask position with numpy : 0.03617739677429199 nb_pixel_total : 41406 time to create 1 rle with old method : 0.04577922821044922 time for calcul the mask position with numpy : 0.0298464298248291 nb_pixel_total : 51615 time to create 1 rle with old method : 0.05708622932434082 time for calcul the mask position with numpy : 0.029278278350830078 nb_pixel_total : 12237 time to create 1 rle with old method : 0.01420903205871582 time for calcul the mask position with numpy : 0.029355764389038086 nb_pixel_total : 19371 time to create 1 rle with old method : 0.021257638931274414 time for calcul the mask position with numpy : 0.031047344207763672 nb_pixel_total : 51766 time to create 1 rle with old method : 0.05976271629333496 time for calcul the mask position with numpy : 0.029180049896240234 nb_pixel_total : 11743 time to create 1 rle with old method : 0.01384878158569336 time for calcul the mask position with numpy : 0.02959752082824707 nb_pixel_total : 74063 time to create 1 rle with old method : 0.08314299583435059 time for calcul the mask position with numpy : 0.029347896575927734 nb_pixel_total : 62552 time to create 1 rle with old method : 0.0703427791595459 time for calcul the mask position with numpy : 0.029105186462402344 nb_pixel_total : 2469 time to create 1 rle with old method : 0.0029892921447753906 time for calcul the mask position with numpy : 0.029246091842651367 nb_pixel_total : 79804 time to create 1 rle with old method : 0.08767580986022949 time for calcul the mask position with numpy : 0.029166460037231445 nb_pixel_total : 10482 time to create 1 rle with old method : 0.012296915054321289 time for calcul the mask position with numpy : 0.029451847076416016 nb_pixel_total : 43404 time to create 1 rle with old method : 0.04822397232055664 time for calcul the mask position with numpy : 0.029267072677612305 nb_pixel_total : 9277 time to create 1 rle with old method : 0.011016607284545898 time for calcul the mask position with numpy : 0.029647111892700195 nb_pixel_total : 26953 time to create 1 rle with old method : 0.029859066009521484 time for calcul the mask position with numpy : 0.030031681060791016 nb_pixel_total : 238669 time to create 1 rle with new method : 0.46320509910583496 time for calcul the mask position with numpy : 0.029074668884277344 nb_pixel_total : 26547 time to create 1 rle with old method : 0.02992558479309082 time for calcul the mask position with numpy : 0.02915048599243164 nb_pixel_total : 18513 time to create 1 rle with old method : 0.020524978637695312 time for calcul the mask position with numpy : 0.028899431228637695 nb_pixel_total : 8482 time to create 1 rle with old method : 0.00985097885131836 time for calcul the mask position with numpy : 0.02884960174560547 nb_pixel_total : 2344 time to create 1 rle with old method : 0.0029129981994628906 time for calcul the mask position with numpy : 0.029379606246948242 nb_pixel_total : 27459 time to create 1 rle with old method : 0.044181108474731445 time for calcul the mask position with numpy : 0.03300189971923828 nb_pixel_total : 26760 time to create 1 rle with old method : 0.0315699577331543 time for calcul the mask position with numpy : 0.029445648193359375 nb_pixel_total : 17908 time to create 1 rle with old method : 0.02915787696838379 time for calcul the mask position with numpy : 0.029863595962524414 nb_pixel_total : 29310 time to create 1 rle with old method : 0.03256106376647949 time for calcul the mask position with numpy : 0.029187679290771484 nb_pixel_total : 20689 time to create 1 rle with old method : 0.023680925369262695 time for calcul the mask position with numpy : 0.029232025146484375 nb_pixel_total : 23509 time to create 1 rle with old method : 0.026876449584960938 time for calcul the mask position with numpy : 0.029277563095092773 nb_pixel_total : 13854 time to create 1 rle with old method : 0.01566290855407715 time for calcul the mask position with numpy : 0.029661178588867188 nb_pixel_total : 82847 time to create 1 rle with old method : 0.09091830253601074 time for calcul the mask position with numpy : 0.029227018356323242 nb_pixel_total : 3702 time to create 1 rle with old method : 0.004276275634765625 time for calcul the mask position with numpy : 0.029146909713745117 nb_pixel_total : 20110 time to create 1 rle with old method : 0.023988723754882812 time for calcul the mask position with numpy : 0.02932572364807129 nb_pixel_total : 29864 time to create 1 rle with old method : 0.03584718704223633 time for calcul the mask position with numpy : 0.030099153518676758 nb_pixel_total : 56667 time to create 1 rle with old method : 0.06562447547912598 time for calcul the mask position with numpy : 0.030030250549316406 nb_pixel_total : 45484 time to create 1 rle with old method : 0.05401802062988281 time for calcul the mask position with numpy : 0.030348777770996094 nb_pixel_total : 23904 time to create 1 rle with old method : 0.028544187545776367 time for calcul the mask position with numpy : 0.02989816665649414 nb_pixel_total : 10124 time to create 1 rle with old method : 0.013220548629760742 time for calcul the mask position with numpy : 0.029869556427001953 nb_pixel_total : 18015 time to create 1 rle with old method : 0.02003765106201172 time for calcul the mask position with numpy : 0.030132770538330078 nb_pixel_total : 3608 time to create 1 rle with old method : 0.004121065139770508 time for calcul the mask position with numpy : 0.029135942459106445 nb_pixel_total : 5216 time to create 1 rle with old method : 0.007395267486572266 time for calcul the mask position with numpy : 0.02937173843383789 nb_pixel_total : 5860 time to create 1 rle with old method : 0.006516218185424805 time for calcul the mask position with numpy : 0.029816389083862305 nb_pixel_total : 1491 time to create 1 rle with old method : 0.0018475055694580078 create new chi : 5.10295844078064 time to delete rle : 0.015715599060058594 batch 1 Loaded 119 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26352 TO DO : save crop sub photo not yet done ! save time : 12.983549118041992 nb_obj : 51 nb_hashtags : 5 time to prepare the origin masks : 3.7985544204711914 time for calcul the mask position with numpy : 0.28161048889160156 nb_pixel_total : 5866229 time to create 1 rle with new method : 0.595496654510498 time for calcul the mask position with numpy : 0.029181957244873047 nb_pixel_total : 5660 time to create 1 rle with old method : 0.006475210189819336 time for calcul the mask position with numpy : 0.02912735939025879 nb_pixel_total : 11052 time to create 1 rle with old method : 0.012861251831054688 time for calcul the mask position with numpy : 0.029029130935668945 nb_pixel_total : 18981 time to create 1 rle with old method : 0.021951675415039062 time for calcul the mask position with numpy : 0.029189109802246094 nb_pixel_total : 24308 time to create 1 rle with old method : 0.028699636459350586 time for calcul the mask position with numpy : 0.029188156127929688 nb_pixel_total : 9700 time to create 1 rle with old method : 0.011295557022094727 time for calcul the mask position with numpy : 0.0291745662689209 nb_pixel_total : 7685 time to create 1 rle with old method : 0.009093284606933594 time for calcul the mask position with numpy : 0.029117345809936523 nb_pixel_total : 4252 time to create 1 rle with old method : 0.00506138801574707 time for calcul the mask position with numpy : 0.030660152435302734 nb_pixel_total : 6425 time to create 1 rle with old method : 0.007504701614379883 time for calcul the mask position with numpy : 0.03024435043334961 nb_pixel_total : 13873 time to create 1 rle with old method : 0.03265261650085449 time for calcul the mask position with numpy : 0.03922629356384277 nb_pixel_total : 19078 time to create 1 rle with old method : 0.02239203453063965 time for calcul the mask position with numpy : 0.02983999252319336 nb_pixel_total : 20699 time to create 1 rle with old method : 0.024636268615722656 time for calcul the mask position with numpy : 0.02920389175415039 nb_pixel_total : 10616 time to create 1 rle with old method : 0.011605501174926758 time for calcul the mask position with numpy : 0.029259204864501953 nb_pixel_total : 5265 time to create 1 rle with old method : 0.0063478946685791016 time for calcul the mask position with numpy : 0.029285669326782227 nb_pixel_total : 21055 time to create 1 rle with old method : 0.028625965118408203 time for calcul the mask position with numpy : 0.029282331466674805 nb_pixel_total : 17217 time to create 1 rle with old method : 0.019429445266723633 time for calcul the mask position with numpy : 0.029265642166137695 nb_pixel_total : 26565 time to create 1 rle with old method : 0.030091047286987305 time for calcul the mask position with numpy : 0.030389070510864258 nb_pixel_total : 13576 time to create 1 rle with old method : 0.0166323184967041 time for calcul the mask position with numpy : 0.029034852981567383 nb_pixel_total : 1361 time to create 1 rle with old method : 0.0017421245574951172 time for calcul the mask position with numpy : 0.028935909271240234 nb_pixel_total : 27196 time to create 1 rle with old method : 0.030275344848632812 time for calcul the mask position with numpy : 0.029384374618530273 nb_pixel_total : 59350 time to create 1 rle with old method : 0.06586098670959473 time for calcul the mask position with numpy : 0.028955698013305664 nb_pixel_total : 8202 time to create 1 rle with old method : 0.009109258651733398 time for calcul the mask position with numpy : 0.028896570205688477 nb_pixel_total : 8709 time to create 1 rle with old method : 0.009646415710449219 time for calcul the mask position with numpy : 0.02922964096069336 nb_pixel_total : 30755 time to create 1 rle with old method : 0.0356287956237793 time for calcul the mask position with numpy : 0.029306650161743164 nb_pixel_total : 28862 time to create 1 rle with old method : 0.03272819519042969 time for calcul the mask position with numpy : 0.0294954776763916 nb_pixel_total : 11148 time to create 1 rle with old method : 0.018801450729370117 time for calcul the mask position with numpy : 0.0328524112701416 nb_pixel_total : 23704 time to create 1 rle with old method : 0.03280973434448242 time for calcul the mask position with numpy : 0.029183626174926758 nb_pixel_total : 17992 time to create 1 rle with old method : 0.02113819122314453 time for calcul the mask position with numpy : 0.030212879180908203 nb_pixel_total : 17236 time to create 1 rle with old method : 0.022453784942626953 time for calcul the mask position with numpy : 0.029134750366210938 nb_pixel_total : 17167 time to create 1 rle with old method : 0.01969146728515625 time for calcul the mask position with numpy : 0.02908778190612793 nb_pixel_total : 17621 time to create 1 rle with old method : 0.019702672958374023 time for calcul the mask position with numpy : 0.029547929763793945 nb_pixel_total : 29782 time to create 1 rle with old method : 0.03379535675048828 time for calcul the mask position with numpy : 0.029193639755249023 nb_pixel_total : 47611 time to create 1 rle with old method : 0.052942514419555664 time for calcul the mask position with numpy : 0.0289459228515625 nb_pixel_total : 34940 time to create 1 rle with old method : 0.043753623962402344 time for calcul the mask position with numpy : 0.029747962951660156 nb_pixel_total : 8920 time to create 1 rle with old method : 0.010395288467407227 time for calcul the mask position with numpy : 0.02931380271911621 nb_pixel_total : 21737 time to create 1 rle with old method : 0.024846792221069336 time for calcul the mask position with numpy : 0.03149533271789551 nb_pixel_total : 18651 time to create 1 rle with old method : 0.022010326385498047 time for calcul the mask position with numpy : 0.031245708465576172 nb_pixel_total : 12896 time to create 1 rle with old method : 0.01471257209777832 time for calcul the mask position with numpy : 0.038984060287475586 nb_pixel_total : 93499 time to create 1 rle with old method : 0.10735321044921875 time for calcul the mask position with numpy : 0.029071331024169922 nb_pixel_total : 9437 time to create 1 rle with old method : 0.011052608489990234 time for calcul the mask position with numpy : 0.029118776321411133 nb_pixel_total : 17989 time to create 1 rle with old method : 0.020066261291503906 time for calcul the mask position with numpy : 0.030049562454223633 nb_pixel_total : 186606 time to create 1 rle with new method : 0.3904130458831787 time for calcul the mask position with numpy : 0.029220104217529297 nb_pixel_total : 34138 time to create 1 rle with old method : 0.03775596618652344 time for calcul the mask position with numpy : 0.03073263168334961 nb_pixel_total : 652 time to create 1 rle with old method : 0.0011496543884277344 time for calcul the mask position with numpy : 0.0351412296295166 nb_pixel_total : 25226 time to create 1 rle with old method : 0.02983260154724121 time for calcul the mask position with numpy : 0.030128002166748047 nb_pixel_total : 25796 time to create 1 rle with old method : 0.04206204414367676 time for calcul the mask position with numpy : 0.03207540512084961 nb_pixel_total : 10230 time to create 1 rle with old method : 0.011901140213012695 time for calcul the mask position with numpy : 0.0292208194732666 nb_pixel_total : 24231 time to create 1 rle with old method : 0.02731919288635254 time for calcul the mask position with numpy : 0.029970645904541016 nb_pixel_total : 8367 time to create 1 rle with old method : 0.009602785110473633 time for calcul the mask position with numpy : 0.029494047164916992 nb_pixel_total : 23568 time to create 1 rle with old method : 0.02655506134033203 time for calcul the mask position with numpy : 0.028959035873413086 nb_pixel_total : 15735 time to create 1 rle with old method : 0.017451763153076172 time for calcul the mask position with numpy : 0.029217243194580078 nb_pixel_total : 28690 time to create 1 rle with old method : 0.033463478088378906 create new chi : 4.051766872406006 time to delete rle : 0.003867626190185547 batch 1 Loaded 103 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21742 TO DO : save crop sub photo not yet done ! save time : 8.061832904815674 nb_obj : 41 nb_hashtags : 3 time to prepare the origin masks : 4.0173375606536865 time for calcul the mask position with numpy : 0.35831713676452637 nb_pixel_total : 5878134 time to create 1 rle with new method : 0.9063370227813721 time for calcul the mask position with numpy : 0.029183387756347656 nb_pixel_total : 19518 time to create 1 rle with old method : 0.022190570831298828 time for calcul the mask position with numpy : 0.02965378761291504 nb_pixel_total : 21768 time to create 1 rle with old method : 0.025928974151611328 time for calcul the mask position with numpy : 0.029252052307128906 nb_pixel_total : 23609 time to create 1 rle with old method : 0.026703357696533203 time for calcul the mask position with numpy : 0.02909088134765625 nb_pixel_total : 18167 time to create 1 rle with old method : 0.022642135620117188 time for calcul the mask position with numpy : 0.02956867218017578 nb_pixel_total : 4454 time to create 1 rle with old method : 0.0052182674407958984 time for calcul the mask position with numpy : 0.05068325996398926 nb_pixel_total : 10285 time to create 1 rle with old method : 0.012164592742919922 time for calcul the mask position with numpy : 0.029566049575805664 nb_pixel_total : 24666 time to create 1 rle with old method : 0.0280001163482666 time for calcul the mask position with numpy : 0.02956390380859375 nb_pixel_total : 36530 time to create 1 rle with old method : 0.040529489517211914 time for calcul the mask position with numpy : 0.02909994125366211 nb_pixel_total : 13006 time to create 1 rle with old method : 0.014706134796142578 time for calcul the mask position with numpy : 0.029775142669677734 nb_pixel_total : 12775 time to create 1 rle with old method : 0.015016555786132812 time for calcul the mask position with numpy : 0.02959132194519043 nb_pixel_total : 145261 time to create 1 rle with old method : 0.15989184379577637 time for calcul the mask position with numpy : 0.029202699661254883 nb_pixel_total : 10550 time to create 1 rle with old method : 0.012004852294921875 time for calcul the mask position with numpy : 0.029737472534179688 nb_pixel_total : 64561 time to create 1 rle with old method : 0.07223153114318848 time for calcul the mask position with numpy : 0.029278278350830078 nb_pixel_total : 6480 time to create 1 rle with old method : 0.0072896480560302734 time for calcul the mask position with numpy : 0.029742717742919922 nb_pixel_total : 66822 time to create 1 rle with old method : 0.07495927810668945 time for calcul the mask position with numpy : 0.03167414665222168 nb_pixel_total : 12255 time to create 1 rle with old method : 0.019866466522216797 time for calcul the mask position with numpy : 0.03296232223510742 nb_pixel_total : 9357 time to create 1 rle with old method : 0.01153421401977539 time for calcul the mask position with numpy : 0.02913641929626465 nb_pixel_total : 3471 time to create 1 rle with old method : 0.0040895938873291016 time for calcul the mask position with numpy : 0.029380321502685547 nb_pixel_total : 32280 time to create 1 rle with old method : 0.052849769592285156 time for calcul the mask position with numpy : 0.03275108337402344 nb_pixel_total : 16661 time to create 1 rle with old method : 0.019153594970703125 time for calcul the mask position with numpy : 0.0293276309967041 nb_pixel_total : 19379 time to create 1 rle with old method : 0.021388530731201172 time for calcul the mask position with numpy : 0.029208660125732422 nb_pixel_total : 23319 time to create 1 rle with old method : 0.02570629119873047 time for calcul the mask position with numpy : 0.02973175048828125 nb_pixel_total : 56787 time to create 1 rle with old method : 0.06424784660339355 time for calcul the mask position with numpy : 0.029210329055786133 nb_pixel_total : 10683 time to create 1 rle with old method : 0.013257741928100586 time for calcul the mask position with numpy : 0.029369592666625977 nb_pixel_total : 11703 time to create 1 rle with old method : 0.013288259506225586 time for calcul the mask position with numpy : 0.030508995056152344 nb_pixel_total : 13980 time to create 1 rle with old method : 0.016184568405151367 time for calcul the mask position with numpy : 0.03028583526611328 nb_pixel_total : 10927 time to create 1 rle with old method : 0.012835502624511719 time for calcul the mask position with numpy : 0.030243635177612305 nb_pixel_total : 24728 time to create 1 rle with old method : 0.028734445571899414 time for calcul the mask position with numpy : 0.029576539993286133 nb_pixel_total : 3513 time to create 1 rle with old method : 0.004261970520019531 time for calcul the mask position with numpy : 0.030976295471191406 nb_pixel_total : 15786 time to create 1 rle with old method : 0.021326303482055664 time for calcul the mask position with numpy : 0.030917882919311523 nb_pixel_total : 104998 time to create 1 rle with old method : 0.11659121513366699 time for calcul the mask position with numpy : 0.029419898986816406 nb_pixel_total : 17487 time to create 1 rle with old method : 0.019928693771362305 time for calcul the mask position with numpy : 0.029659748077392578 nb_pixel_total : 15984 time to create 1 rle with old method : 0.018293142318725586 time for calcul the mask position with numpy : 0.03272604942321777 nb_pixel_total : 118707 time to create 1 rle with old method : 0.14142155647277832 time for calcul the mask position with numpy : 0.02918076515197754 nb_pixel_total : 18158 time to create 1 rle with old method : 0.022470951080322266 time for calcul the mask position with numpy : 0.034812211990356445 nb_pixel_total : 23884 time to create 1 rle with old method : 0.03790569305419922 time for calcul the mask position with numpy : 0.0357518196105957 nb_pixel_total : 45390 time to create 1 rle with old method : 0.06400489807128906 time for calcul the mask position with numpy : 0.02963852882385254 nb_pixel_total : 49728 time to create 1 rle with old method : 0.055510520935058594 time for calcul the mask position with numpy : 0.029529094696044922 nb_pixel_total : 30996 time to create 1 rle with old method : 0.03594374656677246 time for calcul the mask position with numpy : 0.029733657836914062 nb_pixel_total : 3487 time to create 1 rle with old method : 0.0041332244873046875 time for calcul the mask position with numpy : 0.0294954776763916 nb_pixel_total : 6 time to create 1 rle with old method : 2.193450927734375e-05 create new chi : 3.9476001262664795 time to delete rle : 0.002382993698120117 batch 1 Loaded 83 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17613 TO DO : save crop sub photo not yet done ! save time : 6.422467231750488 nb_obj : 15 nb_hashtags : 5 time to prepare the origin masks : 6.844249248504639 time for calcul the mask position with numpy : 0.5055460929870605 nb_pixel_total : 5918343 time to create 1 rle with new method : 1.1197547912597656 time for calcul the mask position with numpy : 0.022403240203857422 nb_pixel_total : 22254 time to create 1 rle with old method : 0.024460554122924805 time for calcul the mask position with numpy : 0.02097487449645996 nb_pixel_total : 54965 time to create 1 rle with old method : 0.05859947204589844 time for calcul the mask position with numpy : 0.021039247512817383 nb_pixel_total : 20520 time to create 1 rle with old method : 0.022872209548950195 time for calcul the mask position with numpy : 0.023615598678588867 nb_pixel_total : 424769 time to create 1 rle with new method : 0.563788652420044 time for calcul the mask position with numpy : 0.02106642723083496 nb_pixel_total : 6110 time to create 1 rle with old method : 0.006562709808349609 time for calcul the mask position with numpy : 0.02042675018310547 nb_pixel_total : 7967 time to create 1 rle with old method : 0.008726119995117188 time for calcul the mask position with numpy : 0.020504236221313477 nb_pixel_total : 85853 time to create 1 rle with old method : 0.08960247039794922 time for calcul the mask position with numpy : 0.01973748207092285 nb_pixel_total : 19465 time to create 1 rle with old method : 0.020580291748046875 time for calcul the mask position with numpy : 0.0207517147064209 nb_pixel_total : 73348 time to create 1 rle with old method : 0.07897114753723145 time for calcul the mask position with numpy : 0.022071361541748047 nb_pixel_total : 5333 time to create 1 rle with old method : 0.005586385726928711 time for calcul the mask position with numpy : 0.02216649055480957 nb_pixel_total : 302885 time to create 1 rle with new method : 0.5775249004364014 time for calcul the mask position with numpy : 0.022803783416748047 nb_pixel_total : 16018 time to create 1 rle with old method : 0.018117666244506836 time for calcul the mask position with numpy : 0.021013975143432617 nb_pixel_total : 51076 time to create 1 rle with old method : 0.0545499324798584 time for calcul the mask position with numpy : 0.021785974502563477 nb_pixel_total : 21885 time to create 1 rle with old method : 0.027646541595458984 time for calcul the mask position with numpy : 0.025760889053344727 nb_pixel_total : 19449 time to create 1 rle with old method : 0.022555112838745117 create new chi : 3.606640338897705 time to delete rle : 0.0014111995697021484 batch 1 Loaded 32 chid ids of type : 3594 ++++++++++++++++Number RLEs to save : 10816 TO DO : save crop sub photo not yet done ! save time : 6.93683934211731 nb_obj : 61 nb_hashtags : 6 time to prepare the origin masks : 3.8729586601257324 time for calcul the mask position with numpy : 0.3029208183288574 nb_pixel_total : 5756360 time to create 1 rle with new method : 0.6140289306640625 time for calcul the mask position with numpy : 0.028905153274536133 nb_pixel_total : 19303 time to create 1 rle with old method : 0.021366119384765625 time for calcul the mask position with numpy : 0.02944183349609375 nb_pixel_total : 2535 time to create 1 rle with old method : 0.003052234649658203 time for calcul the mask position with numpy : 0.02877330780029297 nb_pixel_total : 15543 time to create 1 rle with old method : 0.017929792404174805 time for calcul the mask position with numpy : 0.03119349479675293 nb_pixel_total : 16590 time to create 1 rle with old method : 0.018282175064086914 time for calcul the mask position with numpy : 0.028924226760864258 nb_pixel_total : 8849 time to create 1 rle with old method : 0.010104894638061523 time for calcul the mask position with numpy : 0.02882075309753418 nb_pixel_total : 30561 time to create 1 rle with old method : 0.03381657600402832 time for calcul the mask position with numpy : 0.028192758560180664 nb_pixel_total : 25087 time to create 1 rle with old method : 0.027920007705688477 time for calcul the mask position with numpy : 0.02814173698425293 nb_pixel_total : 32409 time to create 1 rle with old method : 0.03452157974243164 time for calcul the mask position with numpy : 0.02770233154296875 nb_pixel_total : 21825 time to create 1 rle with old method : 0.024096250534057617 time for calcul the mask position with numpy : 0.02825331687927246 nb_pixel_total : 9440 time to create 1 rle with old method : 0.010785579681396484 time for calcul the mask position with numpy : 0.028592586517333984 nb_pixel_total : 2204 time to create 1 rle with old method : 0.0025625228881835938 time for calcul the mask position with numpy : 0.028534889221191406 nb_pixel_total : 26119 time to create 1 rle with old method : 0.02880859375 time for calcul the mask position with numpy : 0.028751134872436523 nb_pixel_total : 7048 time to create 1 rle with old method : 0.008019208908081055 time for calcul the mask position with numpy : 0.029091596603393555 nb_pixel_total : 28082 time to create 1 rle with old method : 0.030676841735839844 time for calcul the mask position with numpy : 0.027794837951660156 nb_pixel_total : 10933 time to create 1 rle with old method : 0.011512517929077148 time for calcul the mask position with numpy : 0.028062820434570312 nb_pixel_total : 15922 time to create 1 rle with old method : 0.017727375030517578 time for calcul the mask position with numpy : 0.029552459716796875 nb_pixel_total : 25712 time to create 1 rle with old method : 0.028150320053100586 time for calcul the mask position with numpy : 0.028633594512939453 nb_pixel_total : 30875 time to create 1 rle with old method : 0.03362870216369629 time for calcul the mask position with numpy : 0.028688907623291016 nb_pixel_total : 5958 time to create 1 rle with old method : 0.006893157958984375 time for calcul the mask position with numpy : 0.02857208251953125 nb_pixel_total : 7736 time to create 1 rle with old method : 0.008980751037597656 time for calcul the mask position with numpy : 0.028332948684692383 nb_pixel_total : 39170 time to create 1 rle with old method : 0.043523550033569336 time for calcul the mask position with numpy : 0.02908778190612793 nb_pixel_total : 11269 time to create 1 rle with old method : 0.012917757034301758 time for calcul the mask position with numpy : 0.029074430465698242 nb_pixel_total : 14569 time to create 1 rle with old method : 0.016347169876098633 time for calcul the mask position with numpy : 0.029607057571411133 nb_pixel_total : 7107 time to create 1 rle with old method : 0.008064746856689453 time for calcul the mask position with numpy : 0.028555631637573242 nb_pixel_total : 37143 time to create 1 rle with old method : 0.040744781494140625 time for calcul the mask position with numpy : 0.028923749923706055 nb_pixel_total : 24910 time to create 1 rle with old method : 0.027522802352905273 time for calcul the mask position with numpy : 0.02860569953918457 nb_pixel_total : 4604 time to create 1 rle with old method : 0.0052831172943115234 time for calcul the mask position with numpy : 0.02829885482788086 nb_pixel_total : 54871 time to create 1 rle with old method : 0.0599062442779541 time for calcul the mask position with numpy : 0.02884364128112793 nb_pixel_total : 30069 time to create 1 rle with old method : 0.032810211181640625 time for calcul the mask position with numpy : 0.02887725830078125 nb_pixel_total : 25347 time to create 1 rle with old method : 0.027665138244628906 time for calcul the mask position with numpy : 0.028331518173217773 nb_pixel_total : 14303 time to create 1 rle with old method : 0.01566457748413086 time for calcul the mask position with numpy : 0.028680086135864258 nb_pixel_total : 38134 time to create 1 rle with old method : 0.04346823692321777 time for calcul the mask position with numpy : 0.02876758575439453 nb_pixel_total : 15340 time to create 1 rle with old method : 0.0168001651763916 time for calcul the mask position with numpy : 0.02823805809020996 nb_pixel_total : 15860 time to create 1 rle with old method : 0.016956567764282227 time for calcul the mask position with numpy : 0.028652667999267578 nb_pixel_total : 9455 time to create 1 rle with old method : 0.010450363159179688 time for calcul the mask position with numpy : 0.02831101417541504 nb_pixel_total : 33350 time to create 1 rle with old method : 0.035472869873046875 time for calcul the mask position with numpy : 0.028638362884521484 nb_pixel_total : 23491 time to create 1 rle with old method : 0.025650739669799805 time for calcul the mask position with numpy : 0.029245376586914062 nb_pixel_total : 8256 time to create 1 rle with old method : 0.009732723236083984 time for calcul the mask position with numpy : 0.03145933151245117 nb_pixel_total : 9080 time to create 1 rle with old method : 0.01055598258972168 time for calcul the mask position with numpy : 0.028172731399536133 nb_pixel_total : 12344 time to create 1 rle with old method : 0.013946771621704102 time for calcul the mask position with numpy : 0.02923583984375 nb_pixel_total : 45455 time to create 1 rle with old method : 0.049408674240112305 time for calcul the mask position with numpy : 0.02882528305053711 nb_pixel_total : 7874 time to create 1 rle with old method : 0.008867263793945312 time for calcul the mask position with numpy : 0.029440879821777344 nb_pixel_total : 57028 time to create 1 rle with old method : 0.06143450736999512 time for calcul the mask position with numpy : 0.02920985221862793 nb_pixel_total : 8217 time to create 1 rle with old method : 0.009393692016601562 time for calcul the mask position with numpy : 0.029191970825195312 nb_pixel_total : 45230 time to create 1 rle with old method : 0.04965710639953613 time for calcul the mask position with numpy : 0.028776168823242188 nb_pixel_total : 6283 time to create 1 rle with old method : 0.007227897644042969 time for calcul the mask position with numpy : 0.027940750122070312 nb_pixel_total : 14225 time to create 1 rle with old method : 0.015897035598754883 time for calcul the mask position with numpy : 0.028685808181762695 nb_pixel_total : 17260 time to create 1 rle with old method : 0.018668413162231445 time for calcul the mask position with numpy : 0.028878450393676758 nb_pixel_total : 6398 time to create 1 rle with old method : 0.007258415222167969 time for calcul the mask position with numpy : 0.02954840660095215 nb_pixel_total : 39088 time to create 1 rle with old method : 0.04387688636779785 time for calcul the mask position with numpy : 0.028906583786010742 nb_pixel_total : 44205 time to create 1 rle with old method : 0.0487368106842041 time for calcul the mask position with numpy : 0.028708934783935547 nb_pixel_total : 36435 time to create 1 rle with old method : 0.04036998748779297 time for calcul the mask position with numpy : 0.02963733673095703 nb_pixel_total : 28529 time to create 1 rle with old method : 0.035454750061035156 time for calcul the mask position with numpy : 0.03362226486206055 nb_pixel_total : 3526 time to create 1 rle with old method : 0.004474639892578125 time for calcul the mask position with numpy : 0.02944803237915039 nb_pixel_total : 12487 time to create 1 rle with old method : 0.014253377914428711 time for calcul the mask position with numpy : 0.030458688735961914 nb_pixel_total : 96266 time to create 1 rle with old method : 0.10756325721740723 time for calcul the mask position with numpy : 0.029538869857788086 nb_pixel_total : 20959 time to create 1 rle with old method : 0.023632049560546875 time for calcul the mask position with numpy : 0.031158447265625 nb_pixel_total : 2128 time to create 1 rle with old method : 0.0033910274505615234 time for calcul the mask position with numpy : 0.033945560455322266 nb_pixel_total : 4792 time to create 1 rle with old method : 0.007626533508300781 time for calcul the mask position with numpy : 0.032618045806884766 nb_pixel_total : 17087 time to create 1 rle with old method : 0.019623756408691406 time for calcul the mask position with numpy : 0.03161263465881348 nb_pixel_total : 9005 time to create 1 rle with old method : 0.01416158676147461 create new chi : 4.174013376235962 time to delete rle : 0.0042879581451416016 batch 1 Loaded 123 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24762 TO DO : save crop sub photo not yet done ! save time : 7.377490043640137 nb_obj : 22 nb_hashtags : 5 time to prepare the origin masks : 8.78533387184143 time for calcul the mask position with numpy : 0.5726795196533203 nb_pixel_total : 6019539 time to create 1 rle with new method : 0.7599437236785889 time for calcul the mask position with numpy : 0.037390947341918945 nb_pixel_total : 12088 time to create 1 rle with old method : 0.0140533447265625 time for calcul the mask position with numpy : 0.029326677322387695 nb_pixel_total : 28865 time to create 1 rle with old method : 0.03156757354736328 time for calcul the mask position with numpy : 0.027595996856689453 nb_pixel_total : 17721 time to create 1 rle with old method : 0.019685745239257812 time for calcul the mask position with numpy : 0.03493189811706543 nb_pixel_total : 76006 time to create 1 rle with old method : 0.08335399627685547 time for calcul the mask position with numpy : 0.035301923751831055 nb_pixel_total : 137011 time to create 1 rle with old method : 0.1502668857574463 time for calcul the mask position with numpy : 0.03495907783508301 nb_pixel_total : 8040 time to create 1 rle with old method : 0.009344100952148438 time for calcul the mask position with numpy : 0.03466916084289551 nb_pixel_total : 14698 time to create 1 rle with old method : 0.016360044479370117 time for calcul the mask position with numpy : 0.0340580940246582 nb_pixel_total : 13464 time to create 1 rle with old method : 0.014858484268188477 time for calcul the mask position with numpy : 0.034236907958984375 nb_pixel_total : 78738 time to create 1 rle with old method : 0.08781170845031738 time for calcul the mask position with numpy : 0.03369450569152832 nb_pixel_total : 32128 time to create 1 rle with old method : 0.03435063362121582 time for calcul the mask position with numpy : 0.03263998031616211 nb_pixel_total : 118139 time to create 1 rle with old method : 0.13767027854919434 time for calcul the mask position with numpy : 0.03943181037902832 nb_pixel_total : 6570 time to create 1 rle with old method : 0.011160850524902344 time for calcul the mask position with numpy : 0.03862142562866211 nb_pixel_total : 27245 time to create 1 rle with old method : 0.031751394271850586 time for calcul the mask position with numpy : 0.03746318817138672 nb_pixel_total : 21364 time to create 1 rle with old method : 0.024196147918701172 time for calcul the mask position with numpy : 0.040343284606933594 nb_pixel_total : 10246 time to create 1 rle with old method : 0.013601064682006836 time for calcul the mask position with numpy : 0.03312349319458008 nb_pixel_total : 20791 time to create 1 rle with old method : 0.023509502410888672 time for calcul the mask position with numpy : 0.02131819725036621 nb_pixel_total : 28536 time to create 1 rle with old method : 0.03198099136352539 time for calcul the mask position with numpy : 0.0232698917388916 nb_pixel_total : 152338 time to create 1 rle with new method : 0.5011076927185059 time for calcul the mask position with numpy : 0.03138327598571777 nb_pixel_total : 13585 time to create 1 rle with old method : 0.024577856063842773 time for calcul the mask position with numpy : 0.031224727630615234 nb_pixel_total : 110108 time to create 1 rle with old method : 0.22543978691101074 time for calcul the mask position with numpy : 0.048560380935668945 nb_pixel_total : 23420 time to create 1 rle with old method : 0.04279518127441406 time for calcul the mask position with numpy : 0.04415011405944824 nb_pixel_total : 79600 time to create 1 rle with old method : 0.14919519424438477 create new chi : 3.8476600646972656 time to delete rle : 0.004487276077270508 batch 1 Loaded 45 chid ids of type : 3594 +++++++++++++++++++++++++Number RLEs to save : 12249 TO DO : save crop sub photo not yet done ! save time : 8.89598822593689 nb_obj : 23 nb_hashtags : 4 time to prepare the origin masks : 11.864489316940308 time for calcul the mask position with numpy : 0.619783878326416 nb_pixel_total : 4461823 time to create 1 rle with new method : 0.6719059944152832 time for calcul the mask position with numpy : 0.041506052017211914 nb_pixel_total : 56249 time to create 1 rle with old method : 0.06385922431945801 time for calcul the mask position with numpy : 0.03914785385131836 nb_pixel_total : 100679 time to create 1 rle with old method : 0.14322876930236816 time for calcul the mask position with numpy : 0.0422060489654541 nb_pixel_total : 32485 time to create 1 rle with old method : 0.03618431091308594 time for calcul the mask position with numpy : 0.04660320281982422 nb_pixel_total : 56251 time to create 1 rle with old method : 0.06382513046264648 time for calcul the mask position with numpy : 0.0378265380859375 nb_pixel_total : 135463 time to create 1 rle with old method : 0.15604758262634277 time for calcul the mask position with numpy : 0.03677701950073242 nb_pixel_total : 52057 time to create 1 rle with old method : 0.0580906867980957 time for calcul the mask position with numpy : 0.03172659873962402 nb_pixel_total : 16845 time to create 1 rle with old method : 0.020538806915283203 time for calcul the mask position with numpy : 0.033980607986450195 nb_pixel_total : 293058 time to create 1 rle with new method : 1.3394250869750977 time for calcul the mask position with numpy : 0.028440237045288086 nb_pixel_total : 22501 time to create 1 rle with old method : 0.024756669998168945 time for calcul the mask position with numpy : 0.03778362274169922 nb_pixel_total : 337961 time to create 1 rle with new method : 0.5279524326324463 time for calcul the mask position with numpy : 0.03392314910888672 nb_pixel_total : 116685 time to create 1 rle with old method : 0.13123011589050293 time for calcul the mask position with numpy : 0.02887439727783203 nb_pixel_total : 11504 time to create 1 rle with old method : 0.012892484664916992 time for calcul the mask position with numpy : 0.029383182525634766 nb_pixel_total : 184234 time to create 1 rle with new method : 0.5593416690826416 time for calcul the mask position with numpy : 0.02838730812072754 nb_pixel_total : 33297 time to create 1 rle with old method : 0.03760194778442383 time for calcul the mask position with numpy : 0.030896663665771484 nb_pixel_total : 323799 time to create 1 rle with new method : 0.7216172218322754 time for calcul the mask position with numpy : 0.033872365951538086 nb_pixel_total : 44094 time to create 1 rle with old method : 0.04712247848510742 time for calcul the mask position with numpy : 0.03304290771484375 nb_pixel_total : 67527 time to create 1 rle with old method : 0.0739431381225586 time for calcul the mask position with numpy : 0.035717010498046875 nb_pixel_total : 54352 time to create 1 rle with old method : 0.06644034385681152 time for calcul the mask position with numpy : 0.03613615036010742 nb_pixel_total : 332140 time to create 1 rle with new method : 0.5921525955200195 time for calcul the mask position with numpy : 0.03988981246948242 nb_pixel_total : 52302 time to create 1 rle with old method : 0.05837249755859375 time for calcul the mask position with numpy : 0.03786516189575195 nb_pixel_total : 24061 time to create 1 rle with old method : 0.02777409553527832 time for calcul the mask position with numpy : 0.03545665740966797 nb_pixel_total : 115141 time to create 1 rle with old method : 0.12368917465209961 time for calcul the mask position with numpy : 0.03776907920837402 nb_pixel_total : 125732 time to create 1 rle with old method : 0.13892388343811035 create new chi : 7.30711555480957 time to delete rle : 0.0045049190521240234 batch 1 Loaded 47 chid ids of type : 3594 +++++++++++++++++++++++++++++++++Number RLEs to save : 20231 TO DO : save crop sub photo not yet done ! save time : 13.232462644577026 nb_obj : 19 nb_hashtags : 3 time to prepare the origin masks : 7.082470655441284 time for calcul the mask position with numpy : 0.3117818832397461 nb_pixel_total : 4509567 time to create 1 rle with new method : 0.7667670249938965 time for calcul the mask position with numpy : 0.0352628231048584 nb_pixel_total : 40803 time to create 1 rle with old method : 0.0444486141204834 time for calcul the mask position with numpy : 0.03496909141540527 nb_pixel_total : 9236 time to create 1 rle with old method : 0.011066198348999023 time for calcul the mask position with numpy : 0.03600168228149414 nb_pixel_total : 7088 time to create 1 rle with old method : 0.008242368698120117 time for calcul the mask position with numpy : 0.03389620780944824 nb_pixel_total : 8117 time to create 1 rle with old method : 0.010251283645629883 time for calcul the mask position with numpy : 0.03897809982299805 nb_pixel_total : 282854 time to create 1 rle with new method : 0.7636153697967529 time for calcul the mask position with numpy : 0.036229610443115234 nb_pixel_total : 42653 time to create 1 rle with old method : 0.0460209846496582 time for calcul the mask position with numpy : 0.0345914363861084 nb_pixel_total : 41700 time to create 1 rle with old method : 0.046302080154418945 time for calcul the mask position with numpy : 0.03868818283081055 nb_pixel_total : 58263 time to create 1 rle with old method : 0.0686030387878418 time for calcul the mask position with numpy : 0.037325382232666016 nb_pixel_total : 6329 time to create 1 rle with old method : 0.007305145263671875 time for calcul the mask position with numpy : 0.03603696823120117 nb_pixel_total : 101731 time to create 1 rle with old method : 0.10976886749267578 time for calcul the mask position with numpy : 0.05167508125305176 nb_pixel_total : 794682 time to create 1 rle with new method : 0.6234493255615234 time for calcul the mask position with numpy : 0.03489375114440918 nb_pixel_total : 36201 time to create 1 rle with old method : 0.0377194881439209 time for calcul the mask position with numpy : 0.03161954879760742 nb_pixel_total : 43732 time to create 1 rle with old method : 0.04661130905151367 time for calcul the mask position with numpy : 0.032840728759765625 nb_pixel_total : 41904 time to create 1 rle with old method : 0.046306610107421875 time for calcul the mask position with numpy : 0.033911705017089844 nb_pixel_total : 946058 time to create 1 rle with new method : 0.6994388103485107 time for calcul the mask position with numpy : 0.02172684669494629 nb_pixel_total : 8051 time to create 1 rle with old method : 0.009227514266967773 time for calcul the mask position with numpy : 0.02188420295715332 nb_pixel_total : 11258 time to create 1 rle with old method : 0.013107538223266602 time for calcul the mask position with numpy : 0.021921873092651367 nb_pixel_total : 36995 time to create 1 rle with old method : 0.041867733001708984 time for calcul the mask position with numpy : 0.022554636001586914 nb_pixel_total : 23018 time to create 1 rle with old method : 0.025510549545288086 create new chi : 4.480923652648926 time to delete rle : 0.0035576820373535156 batch 1 Loaded 39 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 15686 TO DO : save crop sub photo not yet done ! save time : 9.36857008934021 nb_obj : 21 nb_hashtags : 3 time to prepare the origin masks : 8.279589653015137 time for calcul the mask position with numpy : 0.6583495140075684 nb_pixel_total : 5780057 time to create 1 rle with new method : 0.7485435009002686 time for calcul the mask position with numpy : 0.030016183853149414 nb_pixel_total : 56011 time to create 1 rle with old method : 0.08355927467346191 time for calcul the mask position with numpy : 0.022446632385253906 nb_pixel_total : 11729 time to create 1 rle with old method : 0.019969463348388672 time for calcul the mask position with numpy : 0.02261519432067871 nb_pixel_total : 39038 time to create 1 rle with old method : 0.04778003692626953 time for calcul the mask position with numpy : 0.021568775177001953 nb_pixel_total : 10520 time to create 1 rle with old method : 0.011820554733276367 time for calcul the mask position with numpy : 0.02192211151123047 nb_pixel_total : 21086 time to create 1 rle with old method : 0.023123741149902344 time for calcul the mask position with numpy : 0.022214889526367188 nb_pixel_total : 120374 time to create 1 rle with old method : 0.14195775985717773 time for calcul the mask position with numpy : 0.027901649475097656 nb_pixel_total : 14958 time to create 1 rle with old method : 0.02682209014892578 time for calcul the mask position with numpy : 0.02617502212524414 nb_pixel_total : 201983 time to create 1 rle with new method : 0.4590647220611572 time for calcul the mask position with numpy : 0.02129983901977539 nb_pixel_total : 2811 time to create 1 rle with old method : 0.0034148693084716797 time for calcul the mask position with numpy : 0.022993803024291992 nb_pixel_total : 164018 time to create 1 rle with new method : 0.5268838405609131 time for calcul the mask position with numpy : 0.023886680603027344 nb_pixel_total : 9474 time to create 1 rle with old method : 0.011282682418823242 time for calcul the mask position with numpy : 0.021902084350585938 nb_pixel_total : 20222 time to create 1 rle with old method : 0.022650957107543945 time for calcul the mask position with numpy : 0.02195572853088379 nb_pixel_total : 13801 time to create 1 rle with old method : 0.01611018180847168 time for calcul the mask position with numpy : 0.022978544235229492 nb_pixel_total : 36314 time to create 1 rle with old method : 0.040342092514038086 time for calcul the mask position with numpy : 0.022266387939453125 nb_pixel_total : 18401 time to create 1 rle with old method : 0.020761489868164062 time for calcul the mask position with numpy : 0.022820472717285156 nb_pixel_total : 204204 time to create 1 rle with new method : 0.4777796268463135 time for calcul the mask position with numpy : 0.02133655548095703 nb_pixel_total : 131556 time to create 1 rle with old method : 0.14136767387390137 time for calcul the mask position with numpy : 0.02120351791381836 nb_pixel_total : 27753 time to create 1 rle with old method : 0.03210878372192383 time for calcul the mask position with numpy : 0.02310800552368164 nb_pixel_total : 136272 time to create 1 rle with old method : 0.14974141120910645 time for calcul the mask position with numpy : 0.022381067276000977 nb_pixel_total : 13008 time to create 1 rle with old method : 0.014474630355834961 time for calcul the mask position with numpy : 0.02213144302368164 nb_pixel_total : 16650 time to create 1 rle with old method : 0.018459558486938477 create new chi : 4.295581579208374 time to delete rle : 0.0019495487213134766 batch 1 Loaded 43 chid ids of type : 3594 ++++++++++++++++++++++++++++++++Number RLEs to save : 12508 TO DO : save crop sub photo not yet done ! save time : 4.97868013381958 nb_obj : 49 nb_hashtags : 4 time to prepare the origin masks : 3.9443042278289795 time for calcul the mask position with numpy : 0.5569920539855957 nb_pixel_total : 6009400 time to create 1 rle with new method : 0.9865524768829346 time for calcul the mask position with numpy : 0.03357839584350586 nb_pixel_total : 17730 time to create 1 rle with old method : 0.0205690860748291 time for calcul the mask position with numpy : 0.029989004135131836 nb_pixel_total : 12873 time to create 1 rle with old method : 0.015041112899780273 time for calcul the mask position with numpy : 0.02952718734741211 nb_pixel_total : 7181 time to create 1 rle with old method : 0.008453369140625 time for calcul the mask position with numpy : 0.029251813888549805 nb_pixel_total : 27811 time to create 1 rle with old method : 0.032521724700927734 time for calcul the mask position with numpy : 0.029999494552612305 nb_pixel_total : 9167 time to create 1 rle with old method : 0.010252952575683594 time for calcul the mask position with numpy : 0.03191065788269043 nb_pixel_total : 33856 time to create 1 rle with old method : 0.03732562065124512 time for calcul the mask position with numpy : 0.02949213981628418 nb_pixel_total : 25022 time to create 1 rle with old method : 0.028657913208007812 time for calcul the mask position with numpy : 0.029141902923583984 nb_pixel_total : 21068 time to create 1 rle with old method : 0.023180246353149414 time for calcul the mask position with numpy : 0.029693126678466797 nb_pixel_total : 37309 time to create 1 rle with old method : 0.04228973388671875 time for calcul the mask position with numpy : 0.02927541732788086 nb_pixel_total : 19805 time to create 1 rle with old method : 0.022435665130615234 time for calcul the mask position with numpy : 0.029384851455688477 nb_pixel_total : 9709 time to create 1 rle with old method : 0.011263847351074219 time for calcul the mask position with numpy : 0.029103994369506836 nb_pixel_total : 49880 time to create 1 rle with old method : 0.055162668228149414 time for calcul the mask position with numpy : 0.0292966365814209 nb_pixel_total : 17631 time to create 1 rle with old method : 0.020008563995361328 time for calcul the mask position with numpy : 0.029517650604248047 nb_pixel_total : 13431 time to create 1 rle with old method : 0.015708446502685547 time for calcul the mask position with numpy : 0.029807090759277344 nb_pixel_total : 25091 time to create 1 rle with old method : 0.02819657325744629 time for calcul the mask position with numpy : 0.030112266540527344 nb_pixel_total : 34751 time to create 1 rle with old method : 0.0402379035949707 time for calcul the mask position with numpy : 0.03145599365234375 nb_pixel_total : 23874 time to create 1 rle with old method : 0.02752089500427246 time for calcul the mask position with numpy : 0.029858112335205078 nb_pixel_total : 43510 time to create 1 rle with old method : 0.04863715171813965 time for calcul the mask position with numpy : 0.0296018123626709 nb_pixel_total : 15080 time to create 1 rle with old method : 0.017870426177978516 time for calcul the mask position with numpy : 0.03159928321838379 nb_pixel_total : 24648 time to create 1 rle with old method : 0.02751898765563965 time for calcul the mask position with numpy : 0.029651403427124023 nb_pixel_total : 8612 time to create 1 rle with old method : 0.009867191314697266 time for calcul the mask position with numpy : 0.02959418296813965 nb_pixel_total : 49269 time to create 1 rle with old method : 0.0540921688079834 time for calcul the mask position with numpy : 0.029572010040283203 nb_pixel_total : 22088 time to create 1 rle with old method : 0.0254364013671875 time for calcul the mask position with numpy : 0.02986598014831543 nb_pixel_total : 19132 time to create 1 rle with old method : 0.021081924438476562 time for calcul the mask position with numpy : 0.030500411987304688 nb_pixel_total : 37911 time to create 1 rle with old method : 0.041857242584228516 time for calcul the mask position with numpy : 0.03176283836364746 nb_pixel_total : 23299 time to create 1 rle with old method : 0.0260467529296875 time for calcul the mask position with numpy : 0.029883384704589844 nb_pixel_total : 11437 time to create 1 rle with old method : 0.01335287094116211 time for calcul the mask position with numpy : 0.030398130416870117 nb_pixel_total : 41349 time to create 1 rle with old method : 0.04950356483459473 time for calcul the mask position with numpy : 0.028900146484375 nb_pixel_total : 21132 time to create 1 rle with old method : 0.02338385581970215 time for calcul the mask position with numpy : 0.02924060821533203 nb_pixel_total : 11457 time to create 1 rle with old method : 0.012729406356811523 time for calcul the mask position with numpy : 0.03041553497314453 nb_pixel_total : 7318 time to create 1 rle with old method : 0.008374929428100586 time for calcul the mask position with numpy : 0.031350135803222656 nb_pixel_total : 26805 time to create 1 rle with old method : 0.030185699462890625 time for calcul the mask position with numpy : 0.029300212860107422 nb_pixel_total : 20900 time to create 1 rle with old method : 0.02346181869506836 time for calcul the mask position with numpy : 0.029478073120117188 nb_pixel_total : 12204 time to create 1 rle with old method : 0.01350712776184082 time for calcul the mask position with numpy : 0.0289764404296875 nb_pixel_total : 30793 time to create 1 rle with old method : 0.03341937065124512 time for calcul the mask position with numpy : 0.028559207916259766 nb_pixel_total : 4303 time to create 1 rle with old method : 0.005124330520629883 time for calcul the mask position with numpy : 0.028543710708618164 nb_pixel_total : 33665 time to create 1 rle with old method : 0.03689289093017578 time for calcul the mask position with numpy : 0.028528690338134766 nb_pixel_total : 23530 time to create 1 rle with old method : 0.025292396545410156 time for calcul the mask position with numpy : 0.028469085693359375 nb_pixel_total : 28492 time to create 1 rle with old method : 0.031107425689697266 time for calcul the mask position with numpy : 0.028617143630981445 nb_pixel_total : 5476 time to create 1 rle with old method : 0.006377696990966797 time for calcul the mask position with numpy : 0.028879404067993164 nb_pixel_total : 15120 time to create 1 rle with old method : 0.016759634017944336 time for calcul the mask position with numpy : 0.027857542037963867 nb_pixel_total : 13994 time to create 1 rle with old method : 0.015427112579345703 time for calcul the mask position with numpy : 0.02779102325439453 nb_pixel_total : 9890 time to create 1 rle with old method : 0.011348485946655273 time for calcul the mask position with numpy : 0.028835296630859375 nb_pixel_total : 8641 time to create 1 rle with old method : 0.014017820358276367 time for calcul the mask position with numpy : 0.03270530700683594 nb_pixel_total : 33274 time to create 1 rle with old method : 0.04741930961608887 time for calcul the mask position with numpy : 0.02874135971069336 nb_pixel_total : 18041 time to create 1 rle with old method : 0.01993870735168457 time for calcul the mask position with numpy : 0.02809429168701172 nb_pixel_total : 23544 time to create 1 rle with old method : 0.025899887084960938 time for calcul the mask position with numpy : 0.0278472900390625 nb_pixel_total : 5706 time to create 1 rle with old method : 0.006414175033569336 time for calcul the mask position with numpy : 0.028134822845458984 nb_pixel_total : 4031 time to create 1 rle with old method : 0.004569530487060547 create new chi : 4.228294610977173 time to delete rle : 0.0034668445587158203 batch 1 Loaded 99 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23440 TO DO : save crop sub photo not yet done ! save time : 11.630517959594727 nb_obj : 91 nb_hashtags : 3 time to prepare the origin masks : 4.2787017822265625 time for calcul the mask position with numpy : 0.7926545143127441 nb_pixel_total : 5179010 time to create 1 rle with new method : 0.6713385581970215 time for calcul the mask position with numpy : 0.028537988662719727 nb_pixel_total : 13018 time to create 1 rle with old method : 0.014738798141479492 time for calcul the mask position with numpy : 0.028755664825439453 nb_pixel_total : 13228 time to create 1 rle with old method : 0.015546321868896484 time for calcul the mask position with numpy : 0.028371810913085938 nb_pixel_total : 11899 time to create 1 rle with old method : 0.013499736785888672 time for calcul the mask position with numpy : 0.029621362686157227 nb_pixel_total : 26375 time to create 1 rle with old method : 0.029376983642578125 time for calcul the mask position with numpy : 0.029485225677490234 nb_pixel_total : 2315 time to create 1 rle with old method : 0.002764463424682617 time for calcul the mask position with numpy : 0.029677867889404297 nb_pixel_total : 20112 time to create 1 rle with old method : 0.023400545120239258 time for calcul the mask position with numpy : 0.03231072425842285 nb_pixel_total : 16929 time to create 1 rle with old method : 0.01950526237487793 time for calcul the mask position with numpy : 0.029093503952026367 nb_pixel_total : 17577 time to create 1 rle with old method : 0.020091772079467773 time for calcul the mask position with numpy : 0.029094934463500977 nb_pixel_total : 28187 time to create 1 rle with old method : 0.031713247299194336 time for calcul the mask position with numpy : 0.029181241989135742 nb_pixel_total : 58107 time to create 1 rle with old method : 0.06492328643798828 time for calcul the mask position with numpy : 0.02909255027770996 nb_pixel_total : 28884 time to create 1 rle with old method : 0.0328524112701416 time for calcul the mask position with numpy : 0.029135465621948242 nb_pixel_total : 20395 time to create 1 rle with old method : 0.02357792854309082 time for calcul the mask position with numpy : 0.030877351760864258 nb_pixel_total : 20276 time to create 1 rle with old method : 0.033269405364990234 time for calcul the mask position with numpy : 0.0342714786529541 nb_pixel_total : 9836 time to create 1 rle with old method : 0.011133432388305664 time for calcul the mask position with numpy : 0.02914881706237793 nb_pixel_total : 10244 time to create 1 rle with old method : 0.013024568557739258 time for calcul the mask position with numpy : 0.02917194366455078 nb_pixel_total : 12259 time to create 1 rle with old method : 0.013699531555175781 time for calcul the mask position with numpy : 0.02970123291015625 nb_pixel_total : 78439 time to create 1 rle with old method : 0.08585929870605469 time for calcul the mask position with numpy : 0.029242277145385742 nb_pixel_total : 11440 time to create 1 rle with old method : 0.01329350471496582 time for calcul the mask position with numpy : 0.029319047927856445 nb_pixel_total : 20454 time to create 1 rle with old method : 0.02327561378479004 time for calcul the mask position with numpy : 0.030856609344482422 nb_pixel_total : 18711 time to create 1 rle with old method : 0.021903514862060547 time for calcul the mask position with numpy : 0.029330968856811523 nb_pixel_total : 10289 time to create 1 rle with old method : 0.011969566345214844 time for calcul the mask position with numpy : 0.029634714126586914 nb_pixel_total : 17592 time to create 1 rle with old method : 0.020052194595336914 time for calcul the mask position with numpy : 0.03307962417602539 nb_pixel_total : 19804 time to create 1 rle with old method : 0.03177237510681152 time for calcul the mask position with numpy : 0.0328214168548584 nb_pixel_total : 10066 time to create 1 rle with old method : 0.011799097061157227 time for calcul the mask position with numpy : 0.029265165328979492 nb_pixel_total : 31606 time to create 1 rle with old method : 0.03587174415588379 time for calcul the mask position with numpy : 0.029129505157470703 nb_pixel_total : 25637 time to create 1 rle with old method : 0.03006124496459961 time for calcul the mask position with numpy : 0.029276132583618164 nb_pixel_total : 85906 time to create 1 rle with old method : 0.09525370597839355 time for calcul the mask position with numpy : 0.029428958892822266 nb_pixel_total : 6103 time to create 1 rle with old method : 0.0070040225982666016 time for calcul the mask position with numpy : 0.029168367385864258 nb_pixel_total : 13961 time to create 1 rle with old method : 0.016260862350463867 time for calcul the mask position with numpy : 0.029326200485229492 nb_pixel_total : 16196 time to create 1 rle with old method : 0.018067121505737305 time for calcul the mask position with numpy : 0.02936077117919922 nb_pixel_total : 56979 time to create 1 rle with old method : 0.06384468078613281 time for calcul the mask position with numpy : 0.029297590255737305 nb_pixel_total : 86014 time to create 1 rle with old method : 0.09713554382324219 time for calcul the mask position with numpy : 0.02917623519897461 nb_pixel_total : 11697 time to create 1 rle with old method : 0.015194177627563477 time for calcul the mask position with numpy : 0.02904534339904785 nb_pixel_total : 28430 time to create 1 rle with old method : 0.031189680099487305 time for calcul the mask position with numpy : 0.029233932495117188 nb_pixel_total : 23376 time to create 1 rle with old method : 0.02613067626953125 time for calcul the mask position with numpy : 0.03009772300720215 nb_pixel_total : 20900 time to create 1 rle with old method : 0.023039579391479492 time for calcul the mask position with numpy : 0.029103517532348633 nb_pixel_total : 22884 time to create 1 rle with old method : 0.025442838668823242 time for calcul the mask position with numpy : 0.029266834259033203 nb_pixel_total : 4042 time to create 1 rle with old method : 0.0046999454498291016 time for calcul the mask position with numpy : 0.029040813446044922 nb_pixel_total : 8313 time to create 1 rle with old method : 0.009329795837402344 time for calcul the mask position with numpy : 0.02909374237060547 nb_pixel_total : 29532 time to create 1 rle with old method : 0.03275585174560547 time for calcul the mask position with numpy : 0.02937006950378418 nb_pixel_total : 15293 time to create 1 rle with old method : 0.016973495483398438 time for calcul the mask position with numpy : 0.02933025360107422 nb_pixel_total : 19064 time to create 1 rle with old method : 0.021136999130249023 time for calcul the mask position with numpy : 0.02898406982421875 nb_pixel_total : 5167 time to create 1 rle with old method : 0.006114006042480469 time for calcul the mask position with numpy : 0.02909708023071289 nb_pixel_total : 6092 time to create 1 rle with old method : 0.007170677185058594 time for calcul the mask position with numpy : 0.02911829948425293 nb_pixel_total : 9542 time to create 1 rle with old method : 0.011122703552246094 time for calcul the mask position with numpy : 0.02904486656188965 nb_pixel_total : 6525 time to create 1 rle with old method : 0.00751185417175293 time for calcul the mask position with numpy : 0.029466629028320312 nb_pixel_total : 2450 time to create 1 rle with old method : 0.0028748512268066406 time for calcul the mask position with numpy : 0.030431509017944336 nb_pixel_total : 29360 time to create 1 rle with old method : 0.03463149070739746 time for calcul the mask position with numpy : 0.029844999313354492 nb_pixel_total : 29183 time to create 1 rle with old method : 0.03221440315246582 time for calcul the mask position with numpy : 0.028959274291992188 nb_pixel_total : 6323 time to create 1 rle with old method : 0.007431507110595703 time for calcul the mask position with numpy : 0.0290377140045166 nb_pixel_total : 7276 time to create 1 rle with old method : 0.008488893508911133 time for calcul the mask position with numpy : 0.02907562255859375 nb_pixel_total : 17725 time to create 1 rle with old method : 0.01979827880859375 time for calcul the mask position with numpy : 0.028996944427490234 nb_pixel_total : 1948 time to create 1 rle with old method : 0.0023200511932373047 time for calcul the mask position with numpy : 0.0289459228515625 nb_pixel_total : 2790 time to create 1 rle with old method : 0.0033371448516845703 time for calcul the mask position with numpy : 0.028943777084350586 nb_pixel_total : 7436 time to create 1 rle with old method : 0.008648872375488281 time for calcul the mask position with numpy : 0.02898693084716797 nb_pixel_total : 6144 time to create 1 rle with old method : 0.007270097732543945 time for calcul the mask position with numpy : 0.029056549072265625 nb_pixel_total : 54081 time to create 1 rle with old method : 0.060471534729003906 time for calcul the mask position with numpy : 0.029138565063476562 nb_pixel_total : 22601 time to create 1 rle with old method : 0.025815486907958984 time for calcul the mask position with numpy : 0.029079675674438477 nb_pixel_total : 45694 time to create 1 rle with old method : 0.05111193656921387 time for calcul the mask position with numpy : 0.029143571853637695 nb_pixel_total : 4634 time to create 1 rle with old method : 0.005389690399169922 time for calcul the mask position with numpy : 0.0291898250579834 nb_pixel_total : 25134 time to create 1 rle with old method : 0.03334927558898926 time for calcul the mask position with numpy : 0.03228259086608887 nb_pixel_total : 17530 time to create 1 rle with old method : 0.021743297576904297 time for calcul the mask position with numpy : 0.03145265579223633 nb_pixel_total : 14484 time to create 1 rle with old method : 0.01614236831665039 time for calcul the mask position with numpy : 0.028975248336791992 nb_pixel_total : 10792 time to create 1 rle with old method : 0.012724161148071289 time for calcul the mask position with numpy : 0.02897787094116211 nb_pixel_total : 25681 time to create 1 rle with old method : 0.030434370040893555 time for calcul the mask position with numpy : 0.028995037078857422 nb_pixel_total : 14562 time to create 1 rle with old method : 0.018400192260742188 time for calcul the mask position with numpy : 0.03062891960144043 nb_pixel_total : 30324 time to create 1 rle with old method : 0.0332486629486084 time for calcul the mask position with numpy : 0.029018878936767578 nb_pixel_total : 2786 time to create 1 rle with old method : 0.0032711029052734375 time for calcul the mask position with numpy : 0.028945446014404297 nb_pixel_total : 8171 time to create 1 rle with old method : 0.009595632553100586 time for calcul the mask position with numpy : 0.029262781143188477 nb_pixel_total : 32290 time to create 1 rle with old method : 0.036527156829833984 time for calcul the mask position with numpy : 0.029139995574951172 nb_pixel_total : 2414 time to create 1 rle with old method : 0.00293731689453125 time for calcul the mask position with numpy : 0.02898430824279785 nb_pixel_total : 17639 time to create 1 rle with old method : 0.0195009708404541 time for calcul the mask position with numpy : 0.029177427291870117 nb_pixel_total : 25313 time to create 1 rle with old method : 0.02865147590637207 time for calcul the mask position with numpy : 0.03313159942626953 nb_pixel_total : 7285 time to create 1 rle with old method : 0.008194684982299805 time for calcul the mask position with numpy : 0.02905440330505371 nb_pixel_total : 14650 time to create 1 rle with old method : 0.016449689865112305 time for calcul the mask position with numpy : 0.02901911735534668 nb_pixel_total : 7604 time to create 1 rle with old method : 0.008891582489013672 time for calcul the mask position with numpy : 0.029160499572753906 nb_pixel_total : 34633 time to create 1 rle with old method : 0.04084420204162598 time for calcul the mask position with numpy : 0.03126406669616699 nb_pixel_total : 12322 time to create 1 rle with old method : 0.013696432113647461 time for calcul the mask position with numpy : 0.029238462448120117 nb_pixel_total : 3591 time to create 1 rle with old method : 0.004214286804199219 time for calcul the mask position with numpy : 0.02903604507446289 nb_pixel_total : 37711 time to create 1 rle with old method : 0.04097104072570801 time for calcul the mask position with numpy : 0.02947211265563965 nb_pixel_total : 15738 time to create 1 rle with old method : 0.017746448516845703 time for calcul the mask position with numpy : 0.029239892959594727 nb_pixel_total : 5636 time to create 1 rle with old method : 0.00652003288269043 time for calcul the mask position with numpy : 0.02913832664489746 nb_pixel_total : 5548 time to create 1 rle with old method : 0.007596731185913086 time for calcul the mask position with numpy : 0.02916741371154785 nb_pixel_total : 7243 time to create 1 rle with old method : 0.008502006530761719 time for calcul the mask position with numpy : 0.029999494552612305 nb_pixel_total : 29747 time to create 1 rle with old method : 0.034505605697631836 time for calcul the mask position with numpy : 0.02917957305908203 nb_pixel_total : 21516 time to create 1 rle with old method : 0.025130748748779297 time for calcul the mask position with numpy : 0.03406357765197754 nb_pixel_total : 25770 time to create 1 rle with old method : 0.028949737548828125 time for calcul the mask position with numpy : 0.02989649772644043 nb_pixel_total : 9699 time to create 1 rle with old method : 0.01118612289428711 time for calcul the mask position with numpy : 0.029936552047729492 nb_pixel_total : 85694 time to create 1 rle with old method : 0.0927588939666748 time for calcul the mask position with numpy : 0.02806234359741211 nb_pixel_total : 49726 time to create 1 rle with old method : 0.05401039123535156 time for calcul the mask position with numpy : 0.028830289840698242 nb_pixel_total : 2657 time to create 1 rle with old method : 0.0030210018157958984 create new chi : 6.341984987258911 time to delete rle : 0.008717536926269531 batch 1 Loaded 183 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 35905 TO DO : save crop sub photo not yet done ! save time : 13.318671226501465 nb_obj : 63 nb_hashtags : 4 time to prepare the origin masks : 4.0011186599731445 time for calcul the mask position with numpy : 0.42191219329833984 nb_pixel_total : 5569890 time to create 1 rle with new method : 0.6220231056213379 time for calcul the mask position with numpy : 0.02930903434753418 nb_pixel_total : 20460 time to create 1 rle with old method : 0.022531747817993164 time for calcul the mask position with numpy : 0.03221845626831055 nb_pixel_total : 3079 time to create 1 rle with old method : 0.003479480743408203 time for calcul the mask position with numpy : 0.02751612663269043 nb_pixel_total : 13980 time to create 1 rle with old method : 0.014955759048461914 time for calcul the mask position with numpy : 0.0287930965423584 nb_pixel_total : 63550 time to create 1 rle with old method : 0.07116866111755371 time for calcul the mask position with numpy : 0.028037309646606445 nb_pixel_total : 15410 time to create 1 rle with old method : 0.0171811580657959 time for calcul the mask position with numpy : 0.028033971786499023 nb_pixel_total : 61906 time to create 1 rle with old method : 0.06638860702514648 time for calcul the mask position with numpy : 0.02851390838623047 nb_pixel_total : 11533 time to create 1 rle with old method : 0.012527227401733398 time for calcul the mask position with numpy : 0.027753353118896484 nb_pixel_total : 18203 time to create 1 rle with old method : 0.020001888275146484 time for calcul the mask position with numpy : 0.02829742431640625 nb_pixel_total : 18361 time to create 1 rle with old method : 0.019982099533081055 time for calcul the mask position with numpy : 0.028998374938964844 nb_pixel_total : 18047 time to create 1 rle with old method : 0.019796133041381836 time for calcul the mask position with numpy : 0.028406620025634766 nb_pixel_total : 12010 time to create 1 rle with old method : 0.013383626937866211 time for calcul the mask position with numpy : 0.03097248077392578 nb_pixel_total : 27196 time to create 1 rle with old method : 0.029300928115844727 time for calcul the mask position with numpy : 0.028405189514160156 nb_pixel_total : 23744 time to create 1 rle with old method : 0.029762983322143555 time for calcul the mask position with numpy : 0.029377222061157227 nb_pixel_total : 1231 time to create 1 rle with old method : 0.0018146038055419922 time for calcul the mask position with numpy : 0.028917312622070312 nb_pixel_total : 13234 time to create 1 rle with old method : 0.015302896499633789 time for calcul the mask position with numpy : 0.030231475830078125 nb_pixel_total : 73160 time to create 1 rle with old method : 0.09190011024475098 time for calcul the mask position with numpy : 0.029539108276367188 nb_pixel_total : 28171 time to create 1 rle with old method : 0.031174659729003906 time for calcul the mask position with numpy : 0.029677152633666992 nb_pixel_total : 34847 time to create 1 rle with old method : 0.03968048095703125 time for calcul the mask position with numpy : 0.029758214950561523 nb_pixel_total : 32557 time to create 1 rle with old method : 0.03663825988769531 time for calcul the mask position with numpy : 0.029502391815185547 nb_pixel_total : 37925 time to create 1 rle with old method : 0.04219698905944824 time for calcul the mask position with numpy : 0.02968454360961914 nb_pixel_total : 36120 time to create 1 rle with old method : 0.0398862361907959 time for calcul the mask position with numpy : 0.02923440933227539 nb_pixel_total : 30766 time to create 1 rle with old method : 0.033850669860839844 time for calcul the mask position with numpy : 0.03212285041809082 nb_pixel_total : 13968 time to create 1 rle with old method : 0.015463590621948242 time for calcul the mask position with numpy : 0.030473947525024414 nb_pixel_total : 13434 time to create 1 rle with old method : 0.015125274658203125 time for calcul the mask position with numpy : 0.03207063674926758 nb_pixel_total : 5877 time to create 1 rle with old method : 0.006890535354614258 time for calcul the mask position with numpy : 0.028626441955566406 nb_pixel_total : 25615 time to create 1 rle with old method : 0.027940750122070312 time for calcul the mask position with numpy : 0.029011249542236328 nb_pixel_total : 82804 time to create 1 rle with old method : 0.09041142463684082 time for calcul the mask position with numpy : 0.02870345115661621 nb_pixel_total : 12725 time to create 1 rle with old method : 0.014626264572143555 time for calcul the mask position with numpy : 0.02931976318359375 nb_pixel_total : 22920 time to create 1 rle with old method : 0.02594280242919922 time for calcul the mask position with numpy : 0.029296159744262695 nb_pixel_total : 23633 time to create 1 rle with old method : 0.0261080265045166 time for calcul the mask position with numpy : 0.02960371971130371 nb_pixel_total : 56776 time to create 1 rle with old method : 0.06422853469848633 time for calcul the mask position with numpy : 0.02938532829284668 nb_pixel_total : 12330 time to create 1 rle with old method : 0.01428675651550293 time for calcul the mask position with numpy : 0.029850006103515625 nb_pixel_total : 17516 time to create 1 rle with old method : 0.01986098289489746 time for calcul the mask position with numpy : 0.030226707458496094 nb_pixel_total : 4793 time to create 1 rle with old method : 0.005744457244873047 time for calcul the mask position with numpy : 0.030238866806030273 nb_pixel_total : 8642 time to create 1 rle with old method : 0.010483264923095703 time for calcul the mask position with numpy : 0.029592514038085938 nb_pixel_total : 15913 time to create 1 rle with old method : 0.017807722091674805 time for calcul the mask position with numpy : 0.02973341941833496 nb_pixel_total : 28712 time to create 1 rle with old method : 0.03202533721923828 time for calcul the mask position with numpy : 0.029927492141723633 nb_pixel_total : 76339 time to create 1 rle with old method : 0.08603739738464355 time for calcul the mask position with numpy : 0.033553361892700195 nb_pixel_total : 11561 time to create 1 rle with old method : 0.012953758239746094 time for calcul the mask position with numpy : 0.0291440486907959 nb_pixel_total : 17886 time to create 1 rle with old method : 0.02037215232849121 time for calcul the mask position with numpy : 0.029181480407714844 nb_pixel_total : 8381 time to create 1 rle with old method : 0.010931015014648438 time for calcul the mask position with numpy : 0.029714345932006836 nb_pixel_total : 51225 time to create 1 rle with old method : 0.05657196044921875 time for calcul the mask position with numpy : 0.02868199348449707 nb_pixel_total : 4855 time to create 1 rle with old method : 0.005666017532348633 time for calcul the mask position with numpy : 0.02908802032470703 nb_pixel_total : 13994 time to create 1 rle with old method : 0.02031230926513672 time for calcul the mask position with numpy : 0.032735347747802734 nb_pixel_total : 15955 time to create 1 rle with old method : 0.02619004249572754 time for calcul the mask position with numpy : 0.02995753288269043 nb_pixel_total : 20457 time to create 1 rle with old method : 0.03243541717529297 time for calcul the mask position with numpy : 0.033115386962890625 nb_pixel_total : 5952 time to create 1 rle with old method : 0.010210990905761719 time for calcul the mask position with numpy : 0.029695987701416016 nb_pixel_total : 25165 time to create 1 rle with old method : 0.028732776641845703 time for calcul the mask position with numpy : 0.030165910720825195 nb_pixel_total : 25810 time to create 1 rle with old method : 0.03273153305053711 time for calcul the mask position with numpy : 0.02920222282409668 nb_pixel_total : 22863 time to create 1 rle with old method : 0.026217222213745117 time for calcul the mask position with numpy : 0.029392480850219727 nb_pixel_total : 81220 time to create 1 rle with old method : 0.09172606468200684 time for calcul the mask position with numpy : 0.029662609100341797 nb_pixel_total : 13009 time to create 1 rle with old method : 0.017671823501586914 time for calcul the mask position with numpy : 0.02879619598388672 nb_pixel_total : 2699 time to create 1 rle with old method : 0.003177642822265625 time for calcul the mask position with numpy : 0.02848958969116211 nb_pixel_total : 7962 time to create 1 rle with old method : 0.00924825668334961 time for calcul the mask position with numpy : 0.029458045959472656 nb_pixel_total : 26709 time to create 1 rle with old method : 0.032032012939453125 time for calcul the mask position with numpy : 0.02828526496887207 nb_pixel_total : 5532 time to create 1 rle with old method : 0.006357431411743164 time for calcul the mask position with numpy : 0.029134273529052734 nb_pixel_total : 8529 time to create 1 rle with old method : 0.009359598159790039 time for calcul the mask position with numpy : 0.02875518798828125 nb_pixel_total : 6914 time to create 1 rle with old method : 0.007763862609863281 time for calcul the mask position with numpy : 0.027977943420410156 nb_pixel_total : 49657 time to create 1 rle with old method : 0.05710148811340332 time for calcul the mask position with numpy : 0.028586864471435547 nb_pixel_total : 5338 time to create 1 rle with old method : 0.005842447280883789 time for calcul the mask position with numpy : 0.02913045883178711 nb_pixel_total : 21869 time to create 1 rle with old method : 0.035384416580200195 time for calcul the mask position with numpy : 0.032935142517089844 nb_pixel_total : 3982 time to create 1 rle with old method : 0.006720781326293945 time for calcul the mask position with numpy : 0.030796527862548828 nb_pixel_total : 5339 time to create 1 rle with old method : 0.006266593933105469 create new chi : 4.665703058242798 time to delete rle : 0.0045130252838134766 batch 1 Loaded 127 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 28002 TO DO : save crop sub photo not yet done ! save time : 4.078401803970337 map_output_result : {1335027718: (0.0, 'Should be the crop_list due to order', 0.0), 1335027714: (0.0, 'Should be the crop_list due to order', 0.0), 1335027709: (0.0, 'Should be the crop_list due to order', 0.0), 1335027691: (0.0, 'Should be the crop_list due to order', 0.0), 1335027687: (0.0, 'Should be the crop_list due to order', 0.0), 1335019935: (0.0, 'Should be the crop_list due to order', 0.0), 1335019888: (0.0, 'Should be the crop_list due to order', 0.0), 1335019884: (0.0, 'Should be the crop_list due to order', 0.0), 1335019880: (0.0, 'Should be the crop_list due to order', 0.0), 1335019875: (0.0, 'Should be the crop_list due to order', 0.0), 1335019873: (0.0, 'Should be the crop_list due to order', 0.0), 1335019865: (0.0, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1335027718, 1335027714, 1335027709, 1335027691, 1335027687, 1335019935, 1335019888, 1335019884, 1335019880, 1335019875, 1335019873, 1335019865] Looping around the photos to save general results len do output : 12 /1335027718.Didn't retrieve data . /1335027714.Didn't retrieve data . /1335027709.Didn't retrieve data . /1335027691.Didn't retrieve data . /1335027687.Didn't retrieve data . /1335019935.Didn't retrieve data . /1335019888.Didn't retrieve data . /1335019884.Didn't retrieve data . /1335019880.Didn't retrieve data . /1335019875.Didn't retrieve data . /1335019873.Didn't retrieve data . /1335019865.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, '2558005') ('3318', '20286245', '1335027718', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027714', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027709', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027691', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027687', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019935', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019888', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019884', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019880', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019875', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019873', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019865', None, None, None, None, None, '2558005') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.01709151268005371 save_final save missing photos in datou_result : time spend for datou_step_exec : 235.98638272285461 time spend to save output : 0.018727779388427734 total time spend for step 3 : 236.00511050224304 step4:ventilate_hashtags_in_portfolio Thu Feb 6 06:46:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 20286245 get user id for portfolio 20286245 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`=20286245 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','flou','papier','pet_fonce','pet_clair','carton','metal','pehd','mal_croppe','environnement','autre')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20286245 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','flou','papier','pet_fonce','pet_clair','carton','metal','pehd','mal_croppe','environnement','autre')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=20286245 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('background','flou','papier','pet_fonce','pet_clair','carton','metal','pehd','mal_croppe','environnement','autre')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20289607,20289608,20289609,20289610,20289611,20289612,20289613,20289614,20289615,20289616,20289617?tags=background,flou,papier,pet_fonce,pet_clair,carton,metal,pehd,mal_croppe,environnement,autre Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1335027718, 1335027714, 1335027709, 1335027691, 1335027687, 1335019935, 1335019888, 1335019884, 1335019880, 1335019875, 1335019873, 1335019865] Looping around the photos to save general results len do output : 1 /20286245. 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, '2558005') ('3318', '20286245', '1335027718', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027714', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027709', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027691', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027687', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019935', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019888', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019884', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019880', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019875', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019873', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019865', None, None, None, None, None, '2558005') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.017101526260375977 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.7730093002319336 time spend to save output : 0.017453670501708984 total time spend for step 4 : 1.7904629707336426 step5:final Thu Feb 6 06:46:44 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 : {1335027718: ('0.2163261936236685',), 1335027714: ('0.2163261936236685',), 1335027709: ('0.2163261936236685',), 1335027691: ('0.2163261936236685',), 1335027687: ('0.2163261936236685',), 1335019935: ('0.2163261936236685',), 1335019888: ('0.2163261936236685',), 1335019884: ('0.2163261936236685',), 1335019880: ('0.2163261936236685',), 1335019875: ('0.2163261936236685',), 1335019873: ('0.2163261936236685',), 1335019865: ('0.2163261936236685',)} new output for save of step final : {1335027718: ('0.2163261936236685',), 1335027714: ('0.2163261936236685',), 1335027709: ('0.2163261936236685',), 1335027691: ('0.2163261936236685',), 1335027687: ('0.2163261936236685',), 1335019935: ('0.2163261936236685',), 1335019888: ('0.2163261936236685',), 1335019884: ('0.2163261936236685',), 1335019880: ('0.2163261936236685',), 1335019875: ('0.2163261936236685',), 1335019873: ('0.2163261936236685',), 1335019865: ('0.2163261936236685',)} [1335027718, 1335027714, 1335027709, 1335027691, 1335027687, 1335019935, 1335019888, 1335019884, 1335019880, 1335019875, 1335019873, 1335019865] Looping around the photos to save general results len do output : 12 /1335027718.Didn't retrieve data . /1335027714.Didn't retrieve data . /1335027709.Didn't retrieve data . /1335027691.Didn't retrieve data . /1335027687.Didn't retrieve data . /1335019935.Didn't retrieve data . /1335019888.Didn't retrieve data . /1335019884.Didn't retrieve data . /1335019880.Didn't retrieve data . /1335019875.Didn't retrieve data . /1335019873.Didn't retrieve data . /1335019865.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, '2558005') ('3318', '20286245', '1335027718', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027714', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027709', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027691', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027687', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019935', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019888', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019884', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019880', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019875', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019873', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019865', None, None, None, None, None, '2558005') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.015659332275390625 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.10422253608703613 time spend to save output : 0.0162811279296875 total time spend for step 5 : 0.12050366401672363 step6:blur_detection Thu Feb 6 06:46:44 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/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c.jpg resize: (2160, 3264) 1335027718 -6.187358682574875 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c.jpg resize: (2160, 3264) 1335027714 -6.825873796033706 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640.jpg resize: (2160, 3264) 1335027709 -6.322509568432731 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec.jpg resize: (2160, 3264) 1335027691 -3.705793804813742 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c.jpg resize: (2160, 3264) 1335027687 -6.424992267850785 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa.jpg resize: (2160, 3264) 1335019935 -5.2914561578008525 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19.jpg resize: (2160, 3264) 1335019888 -3.3692559069950976 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d.jpg resize: (2160, 3264) 1335019884 -2.0120456270608966 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549.jpg resize: (2160, 3264) 1335019880 -4.472338483727959 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6.jpg resize: (2160, 3264) 1335019875 -6.657349520263892 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751.jpg resize: (2160, 3264) 1335019873 -6.826251451924046 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372.jpg resize: (2160, 3264) 1335019865 -6.303306953982627 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270107_0.png resize: (148, 104) 1335137549 -3.6913673202867137 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270116_0.png resize: (135, 236) 1335137550 -3.9681414621787003 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270091_0.png resize: (182, 337) 1335137551 -3.566599620746242 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270137_0.png resize: (288, 278) 1335137552 -4.513000370047402 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270142_0.png resize: (176, 263) 1335137553 -4.502751750482344 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270129_0.png resize: (147, 95) 1335137554 -2.3071124371206926 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270103_0.png resize: (150, 131) 1335137555 -2.9504953003749894 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270120_0.png resize: (60, 88) 1335137556 -4.266530637735902 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270119_0.png resize: (125, 88) 1335137557 -2.925973366771608 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270125_0.png resize: (148, 186) 1335137558 -4.358431654972885 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270099_0.png resize: (295, 345) 1335137559 -3.230039946449432 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270115_0.png resize: (106, 163) 1335137560 -3.443379698783104 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270111_0.png resize: (106, 206) 1335137561 -3.7291886314207843 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270096_0.png resize: (116, 244) 1335137562 -4.5036223026169795 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270130_0.png resize: (105, 161) 1335137564 -4.723188053829667 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270114_0.png resize: (228, 251) 1335137565 -2.5178957540651403 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270135_0.png resize: (162, 192) 1335137566 -4.035173861167927 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270133_0.png resize: (94, 251) 1335137567 -4.241372037677862 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270108_0.png resize: (268, 188) 1335137568 -3.1436098007214683 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270124_0.png resize: (350, 404) 1335137569 -3.3155157091745373 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270121_0.png resize: (146, 210) 1335137570 -3.2762341096003844 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270118_0.png resize: (106, 60) 1335137571 -2.432302573379397 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270112_0.png resize: (64, 71) 1335137572 -4.52744313185137 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270090_0.png resize: (83, 60) 1335137573 -1.4387009479911337 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270101_0.png resize: (250, 435) 1335137574 -5.053174101273524 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270104_0.png resize: (187, 171) 1335137575 -2.798235113949421 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270113_0.png resize: (201, 165) 1335137576 -2.315332636255034 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270139_0.png resize: (165, 230) 1335137577 -4.319801409056557 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270094_0.png resize: (275, 376) 1335137578 -4.163150567897202 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270128_0.png resize: (127, 135) 1335137579 -4.3053043897246 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270131_0.png resize: (185, 164) 1335137580 -3.8697844462630617 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270163_0.png resize: (556, 513) 1335137581 -4.13650236022471 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270165_0.png resize: (101, 190) 1335137582 -4.694461332103161 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270188_0.png resize: (166, 220) 1335137583 -3.769312083184865 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270160_0.png resize: (150, 351) 1335137584 -3.632570944758338 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270155_0.png resize: (222, 253) 1335137585 -3.8274363936993017 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270153_0.png resize: (173, 240) 1335137586 -5.144167545535049 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270180_0.png resize: (177, 244) 1335137587 -4.11087492307811 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270196_0.png resize: (358, 387) 1335137588 -4.963504294965267 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270147_0.png resize: (117, 122) 1335137589 -1.6850819039711629 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270170_0.png resize: (232, 273) 1335137590 -4.113989698862976 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270171_0.png resize: (116, 177) 1335137591 -4.64858475809669 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270166_0.png resize: (296, 287) 1335137592 -4.6169712990205 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270194_0.png resize: (109, 143) 1335137593 -3.0109480252084255 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270176_0.png resize: (164, 251) 1335137594 -4.54215413289371 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270197_0.png resize: (188, 183) 1335137595 -3.8613832161230595 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270152_0.png resize: (143, 97) 1335137596 -2.9537423801794906 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270178_0.png resize: (121, 160) 1335137597 -2.4712213589757845 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270150_0.png resize: (332, 111) 1335137598 -3.4084659362345215 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270158_0.png resize: (125, 117) 1335137599 -3.3372846051129432 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270187_0.png resize: (210, 212) 1335137600 -3.8632661022787405 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270151_0.png resize: (203, 164) 1335137601 -4.420491264797884 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270161_0.png resize: (251, 183) 1335137602 -3.4559259349460874 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270185_0.png resize: (75, 97) 1335137603 0.5864738171281925 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270149_0.png resize: (219, 131) 1335137604 -4.501712285487301 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270186_0.png resize: (134, 106) 1335137605 -4.082253650821701 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270156_0.png resize: (245, 179) 1335137606 -3.058293691157615 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270174_0.png resize: (218, 160) 1335137607 -5.392564158409991 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270154_0.png resize: (124, 126) 1335137608 -3.5899623817635558 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270148_0.png resize: (196, 181) 1335137609 -2.8728502066309165 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270172_0.png resize: (186, 193) 1335137610 -5.011213567349697 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270162_0.png resize: (175, 214) 1335137611 -3.923250478360946 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270190_0.png resize: (164, 167) 1335137612 -2.2692224366737364 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270173_0.png resize: (192, 157) 1335137613 -4.593570775513395 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270175_0.png resize: (143, 133) 1335137614 -3.962069554433352 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270157_0.png resize: (160, 98) 1335137615 -3.279693309236861 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270169_0.png resize: (139, 157) 1335137616 -2.518438997553149 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270191_0.png resize: (130, 102) 1335137617 -4.592472256055264 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270182_0.png resize: (247, 219) 1335137618 -4.539976337933418 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270181_0.png resize: (176, 223) 1335137619 -4.320747077613981 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270159_0.png resize: (267, 244) 1335137620 -3.424708159878083 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270167_0.png resize: (95, 88) 1335137621 -3.206197148016705 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270168_0.png resize: (174, 278) 1335137622 -4.795817408386927 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270177_0.png resize: (60, 73) 1335137623 -1.2036757029737881 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270164_0.png resize: (150, 113) 1335137625 -4.563547781355963 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270215_0.png resize: (126, 46) 1335137626 -3.749594111012494 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270230_0.png resize: (131, 113) 1335137627 -3.9091195161610894 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270232_0.png resize: (238, 317) 1335137628 -4.093482069897185 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270229_0.png resize: (115, 260) 1335137629 -3.713393845245616 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270204_0.png resize: (147, 511) 1335137630 -4.427200453445629 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270221_0.png resize: (204, 666) 1335137631 -3.4422299750963563 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270223_0.png resize: (74, 101) 1335137632 -4.807441643049468 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270213_0.png resize: (51, 127) 1335137633 -3.556648693519389 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270201_0.png resize: (403, 652) 1335137634 -2.4523435147244306 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270208_0.png resize: (307, 246) 1335137635 -3.52016630260928 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270203_0.png resize: (106, 251) 1335137636 -3.9568069038530838 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270211_0.png resize: (170, 229) 1335137637 -3.1629839828905184 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270236_0.png resize: (119, 138) 1335137638 -4.562028040457453 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270235_0.png resize: (44, 99) 1335137639 -1.5905274160055987 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270219_0.png resize: (135, 147) 1335137640 -3.3069527353220853 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270238_0.png resize: (238, 446) 1335137641 -4.075797128160949 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270200_0.png resize: (153, 126) 1335137642 -0.4782973777832794 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270199_0.png resize: (152, 165) 1335137643 -3.3145471967194715 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270231_0.png resize: (127, 373) 1335137644 -4.003693284390462 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270216_0.png resize: (145, 272) 1335137645 -3.6073255389464833 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270226_0.png resize: (134, 159) 1335137646 -4.020507231276278 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270234_0.png resize: (74, 63) 1335137647 -1.0574901344494703 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270233_0.png resize: (95, 228) 1335137648 -4.001256224100128 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270214_0.png resize: (146, 91) 1335137649 -0.8286164143115525 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270198_0.png resize: (206, 236) 1335137650 -4.74142053924761 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270205_0.png resize: (189, 153) 1335137651 -4.647216604848083 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270202_0.png resize: (200, 153) 1335137652 -4.507906165971082 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270206_0.png resize: (222, 149) 1335137653 -4.183896840374917 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270220_0.png resize: (160, 192) 1335137654 -4.698720508063499 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270228_0.png resize: (136, 212) 1335137655 -4.4912801480405005 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270222_0.png resize: (91, 95) 1335137656 -3.57976752660399 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270212_0.png resize: (136, 187) 1335137657 -4.808021236771439 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270224_0.png resize: (113, 129) 1335137658 -3.342577766125552 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270237_0.png resize: (104, 211) 1335137659 -5.165677466640568 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270207_0.png resize: (180, 191) 1335137660 -3.0279627750272726 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270227_0.png resize: (126, 137) 1335137661 -4.030661521849411 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270210_0.png resize: (191, 108) 1335137662 -3.2253091665704097 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270254_0.png resize: (122, 165) 1335137663 -3.9983035556232176 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270248_0.png resize: (55, 177) 1335137664 -1.6512381200835113 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270249_0.png resize: (78, 103) 1335137665 -2.8272761546728833 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270240_0.png resize: (185, 162) 1335137667 -2.0283259219095573 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270239_0.png resize: (142, 189) 1335137668 0.10968506279634549 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270246_0.png resize: (192, 181) 1335137669 -2.899896232058399 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270245_0.png resize: (407, 293) 1335137670 -3.775538342299823 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270244_0.png resize: (84, 83) 1335137671 -2.782186515619209 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270247_0.png resize: (283, 362) 1335137672 -3.6481215249268657 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270275_0.png resize: (153, 104) 1335137673 -3.208362116738093 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270264_0.png resize: (173, 242) 1335137674 -4.299874737801505 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270263_0.png resize: (211, 231) 1335137675 -3.015131725312602 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270283_0.png resize: (48, 62) 1335137676 -0.9310701397039388 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270308_0.png resize: (290, 501) 1335137677 -4.0740560551366585 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270260_0.png resize: (150, 203) 1335137678 -4.081043742633997 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270289_0.png resize: (76, 148) 1335137679 -3.2409572250723446 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270294_0.png resize: (162, 261) 1335137680 -4.778355568589643 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270293_0.png resize: (202, 259) 1335137681 -4.336252363782345 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270262_0.png resize: (148, 206) 1335137682 -3.0075278307106794 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270303_0.png resize: (48, 64) 1335137683 -2.0352334976015873 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270278_0.png resize: (136, 126) 1335137684 -3.7522063524702984 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270256_0.png resize: (293, 206) 1335137685 -4.135839932522878 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270302_0.png resize: (177, 130) 1335137686 -0.5376294194210111 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270298_0.png resize: (273, 203) 1335137687 -4.462068360443789 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270305_0.png resize: (98, 101) 1335137688 -3.4361237434834413 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270284_0.png resize: (252, 163) 1335137689 -3.9825296097189136 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270265_0.png resize: (95, 93) 1335137690 -3.896736183174774 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270266_0.png resize: (232, 223) 1335137691 -2.911647454922356 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270314_0.png resize: (292, 450) 1335137692 -5.070488617049325 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270271_0.png resize: (202, 230) 1335137693 -2.7648446200124273 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270277_0.png resize: (162, 70) 1335137694 -4.241264602531991 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270307_0.png resize: (137, 160) 1335137695 -4.870974252514858 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270311_0.png resize: (159, 142) 1335137696 -0.3806871418966034 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270272_0.png resize: (198, 122) 1335137697 -5.378088339846183 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270292_0.png resize: (132, 96) 1335137698 -4.008346443444878 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270257_0.png resize: (229, 285) 1335137699 -4.780645241911884 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270315_0.png resize: (269, 118) 1335137700 -4.43391791653796 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270296_0.png resize: (191, 177) 1335137701 -2.630221261510015 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270312_0.png resize: (158, 175) 1335137702 -3.450387701921257 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270285_0.png resize: (105, 137) 1335137703 -2.9440565887059287 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270274_0.png resize: (172, 193) 1335137704 -4.380567162210609 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270273_0.png resize: (128, 281) 1335137705 -1.6814694628853462 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270300_0.png resize: (81, 64) 1335137706 -3.3990474920149305 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270313_0.png resize: (129, 112) 1335137707 -4.38701908222538 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270299_0.png resize: (130, 207) 1335137708 -5.3028180545049315 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270287_0.png resize: (77, 88) 1335137709 -1.93492251873294 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270286_0.png resize: (139, 155) 1335137710 -1.9815151942924623 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270259_0.png resize: (106, 100) 1335137711 -3.435498991414903 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270304_0.png resize: (151, 101) 1335137712 -3.3298048920089554 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270297_0.png resize: (266, 210) 1335137713 -4.246388487743444 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270301_0.png resize: (106, 88) 1335137714 -2.40020828254683 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270270_0.png resize: (93, 131) 1335137715 -4.7660600079164475 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270280_0.png resize: (84, 69) 1335137716 -2.3308648026543004 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270261_0.png resize: (309, 169) 1335137717 -4.268123969556737 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270281_0.png resize: (127, 82) 1335137718 -2.076087602550285 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270268_0.png resize: (165, 162) 1335137719 -3.735523513509767 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270269_0.png resize: (96, 88) 1335137720 -4.580232951561425 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270267_0.png resize: (265, 223) 1335137721 -3.9393842190556665 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270309_0.png resize: (104, 113) 1335137722 -3.281518134302397 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270290_0.png resize: (52, 58) 1335137723 -3.1874167721365123 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270320_0.png resize: (400, 646) 1335137724 -3.127566937063018 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270333_0.png resize: (332, 577) 1335137725 -3.486603420235412 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270332_0.png resize: (115, 99) 1335137726 -1.962356460963436 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270336_0.png resize: (178, 282) 1335137727 -3.5438570630796913 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270328_0.png resize: (266, 237) 1335137728 -3.0010753011125573 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270326_0.png resize: (93, 92) 1335137729 -0.4088558468861972 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270317_0.png resize: (163, 203) 1335137730 -4.377389514092108 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270330_0.png resize: (127, 151) 1335137731 -2.363330010714668 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270323_0.png resize: (124, 145) 1335137732 -3.012983697208901 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270331_0.png resize: (124, 150) 1335137733 -1.8069125380340556 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270335_0.png resize: (141, 196) 1335137734 -2.4223462784645347 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270325_0.png resize: (137, 266) 1335137735 -3.331499479077735 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270337_0.png resize: (113, 175) 1335137736 -3.4574164762238233 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270319_0.png resize: (173, 116) 1335137737 -4.279518437102827 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270344_0.png resize: (239, 369) 1335137738 -0.4047881761127378 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270340_0.png resize: (431, 103) 1335137739 -2.7116211968483634 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270345_0.png resize: (582, 154) 1335137740 -2.7086956237464532 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270346_0.png resize: (779, 1066) 1335137741 -2.892083857990029 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270338_0.png resize: (319, 656) 1335137742 -3.264071110612959 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270347_0.png resize: (267, 269) 1335137743 -2.1117792071270802 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270339_0.png resize: (321, 543) 1335137744 -1.7015326240192212 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270354_0.png resize: (281, 93) 1335137745 -4.357994922760647 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270356_0.png resize: (375, 767) 1335137746 -2.52327920427636 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270349_0.png resize: (121, 175) 1335137747 -2.7269180903377124 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270348_0.png resize: (488, 631) 1335137748 -1.9719877944383246 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270353_0.png resize: (659, 817) 1335137749 -2.3960217784421793 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270358_0.png resize: (253, 187) 1335137750 -3.3108945466780026 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270368_0.png resize: (286, 176) 1335137751 -1.9718719922511614 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270361_0.png resize: (249, 125) 1335137752 -2.8213355888226026 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270375_0.png resize: (361, 1031) 1335137753 -1.3062396741778355 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270371_0.png resize: (114, 122) 1335137754 -3.184551885453805 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270379_0.png resize: (222, 276) 1335137755 -2.6445291107335547 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270364_0.png resize: (213, 77) 1335137756 -3.2541943866421237 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270372_0.png resize: (201, 486) 1335137757 -3.6289933745588034 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270392_0.png resize: (56, 59) 1335137758 6.47793844082204 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270381_0.png resize: (100, 183) 1335137759 -1.8875274970134812 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270388_0.png resize: (126, 154) 1335137760 -2.8987382981768057 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270394_0.png resize: (107, 166) 1335137761 -2.672341423317675 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270389_0.png resize: (156, 195) 1335137762 -2.890293077261746 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270400_0.png resize: (341, 268) 1335137763 -1.008339533467168 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270398_0.png resize: (238, 347) 1335137764 -1.8830359970468546 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270391_0.png resize: (540, 433) 1335137765 -5.0884481232991785 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270383_0.png resize: (228, 202) 1335137766 -2.33578758900891 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270387_0.png resize: (211, 251) 1335137767 -4.702865345410948 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270399_0.png resize: (147, 119) 1335137768 -2.151977521146983 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270397_0.png resize: (166, 79) 1335137769 -1.9430718893762002 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270390_0.png resize: (135, 108) 1335137770 -3.577336644456507 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270405_0.png resize: (231, 174) 1335137771 -4.651936870632261 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270415_0.png resize: (347, 129) 1335137772 -2.0172271390600476 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270423_0.png resize: (132, 116) 1335137773 -2.871210102819044 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270401_0.png resize: (199, 323) 1335137774 -4.034915944203338 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270418_0.png resize: (253, 164) 1335137775 -3.8567926021950214 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270407_0.png resize: (130, 92) 1335137776 -0.702042114970796 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270409_0.png resize: (102, 149) 1335137777 -3.279331164518658 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270444_0.png resize: (147, 169) 1335137778 -3.027751647161818 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270441_0.png resize: (188, 240) 1335137779 -4.456300374308212 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270425_0.png resize: (294, 168) 1335137780 -4.842994990755158 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270402_0.png resize: (124, 116) 1335137781 -1.935036073159816 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270447_0.png resize: (247, 221) 1335137782 -3.105983019000292 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270440_0.png resize: (302, 157) 1335137783 -5.509523611873541 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270436_0.png resize: (107, 64) 1335137784 -3.8606436527501047 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270443_0.png resize: (161, 162) 1335137785 -3.6559563177686467 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270416_0.png resize: (77, 108) 1335137786 -4.462202796983777 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270430_0.png resize: (264, 165) 1335137787 -4.096640842105269 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270421_0.png resize: (178, 118) 1335137788 -3.5437867917671277 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270410_0.png resize: (222, 163) 1335137789 -4.458360052913188 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270435_0.png resize: (163, 230) 1335137790 -4.316099077579803 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270446_0.png resize: (264, 293) 1335137791 -3.7536377700407106 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270413_0.png resize: (112, 130) 1335137792 -2.275598095109599 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270404_0.png resize: (134, 232) 1335137793 -2.9165602179498236 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270420_0.png resize: (154, 302) 1335137794 -4.7794834394056975 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270442_0.png resize: (313, 334) 1335137795 -4.359464369981117 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270411_0.png resize: (158, 176) 1335137796 -3.688344908013088 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270406_0.png resize: (265, 232) 1335137797 -3.9833262427937504 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270431_0.png resize: (141, 117) 1335137798 -5.199712065498344 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270403_0.png resize: (219, 194) 1335137799 -2.7106854863961347 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270449_0.png resize: (133, 131) 1335137800 -3.6929176295728374 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270417_0.png resize: (144, 85) 1335137801 -1.5812133809855413 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270437_0.png resize: (184, 207) 1335137802 -5.201905250839629 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270419_0.png resize: (134, 122) 1335137803 -3.000664741106827 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270408_0.png resize: (268, 118) 1335137804 -3.7791324054350817 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270434_0.png resize: (368, 223) 1335137805 -4.445184366139531 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270438_0.png resize: (180, 210) 1335137806 -4.2035399329175185 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270433_0.png resize: (281, 89) 1335137807 -4.586506237871185 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270412_0.png resize: (185, 138) 1335137808 -3.7677040766612557 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270445_0.png resize: (242, 148) 1335137809 -3.9025391932709863 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270424_0.png resize: (159, 113) 1335137810 -3.1177040421721127 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270439_0.png resize: (81, 91) 1335137811 -3.920160491320201 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270448_0.png resize: (113, 87) 1335137812 -3.698348494537604 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270414_0.png resize: (110, 135) 1335137813 -2.6188360181920434 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270499_0.png resize: (261, 406) 1335137814 -3.055016935948689 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270516_0.png resize: (233, 170) 1335137815 -3.894389894154534 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270502_0.png resize: (122, 162) 1335137816 -4.514481010323703 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270517_0.png resize: (147, 198) 1335137817 -2.888564544368318 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270484_0.png resize: (356, 308) 1335137818 -3.6346824341747364 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270509_0.png resize: (91, 236) 1335137819 -3.890087606433689 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270461_0.png resize: (156, 164) 1335137820 -3.2760038213107596 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270501_0.png resize: (175, 190) 1335137821 -5.137008208523876 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270515_0.png resize: (297, 273) 1335137822 -3.8539216337369986 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270478_0.png resize: (309, 235) 1335137823 -3.499598544931573 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270469_0.png resize: (148, 164) 1335137824 -4.325213656879913 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270521_0.png resize: (138, 128) 1335137825 -3.0657282293909573 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270471_0.png resize: (143, 284) 1335137827 -3.889236331038964 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270500_0.png resize: (108, 132) 1335137828 -3.70328630867019 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270463_0.png resize: (219, 144) 1335137829 -1.9982160399704174 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270462_0.png resize: (379, 126) 1335137830 -2.4064181356627863 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270467_0.png resize: (194, 218) 1335137831 -4.453517763098696 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270485_0.png resize: (209, 218) 1335137832 -2.9214906382376613 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270490_0.png resize: (454, 177) 1335137833 -3.843424176048597 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270539_0.png resize: (249, 252) 1335137834 -3.6748506373785843 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270535_0.png resize: (135, 128) 1335137835 -3.70235911523068 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270513_0.png resize: (457, 305) 1335137836 -4.485441564643139 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270507_0.png resize: (233, 230) 1335137837 -4.235613618052416 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270497_0.png resize: (191, 172) 1335137838 -2.913831097329961 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270524_0.png resize: (242, 170) 1335137840 -3.905569948917904 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270537_0.png resize: (118, 212) 1335137841 -3.4033121714290675 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270526_0.png resize: (200, 193) 1335137842 -4.014502991814122 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270538_0.png resize: (185, 230) 1335137843 -4.639096955645961 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270533_0.png resize: (99, 222) 1335137844 -3.5149356832093566 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270511_0.png resize: (177, 203) 1335137845 -3.8444060221277447 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270503_0.png resize: (65, 112) 1335137846 -3.5605348795299294 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270492_0.png resize: (142, 127) 1335137847 -2.6056402003959063 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270481_0.png resize: (156, 113) 1335137848 -3.2197054863485817 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270505_0.png resize: (90, 118) 1335137849 -2.8995981877402146 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270458_0.png resize: (145, 136) 1335137850 -3.772192085891527 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270468_0.png resize: (223, 221) 1335137851 -4.168888538260783 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270482_0.png resize: (226, 158) 1335137852 -3.547770834783895 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270536_0.png resize: (95, 66) 1335137853 -4.60154705800525 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270465_0.png resize: (220, 175) 1335137854 -2.155636979713302 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270514_0.png resize: (150, 127) 1335137855 -3.6052151359482005 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270488_0.png resize: (130, 209) 1335137856 -4.086964574994871 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270474_0.png resize: (94, 82) 1335137857 -3.051804289668619 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270531_0.png resize: (184, 145) 1335137859 -2.958016859492585 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270452_0.png resize: (256, 191) 1335137860 -3.1197697097566843 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270479_0.png resize: (87, 85) 1335137861 -2.3918739393065382 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270525_0.png resize: (217, 120) 1335137862 -4.280881205322063 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270540_0.png resize: (167, 66) 1335137863 -3.5235631189929912 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270518_0.png resize: (180, 207) 1335137864 -3.7064558077715155 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270459_0.png resize: (112, 74) 1335137865 -2.488408090947293 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270494_0.png resize: (146, 81) 1335137866 -4.817855546178748 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270486_0.png resize: (162, 116) 1335137867 -3.783286103426852 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270523_0.png resize: (75, 57) 1335137868 -2.079417711324924 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270476_0.png resize: (481, 290) 1335137869 -4.981537911558661 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270483_0.png resize: (81, 73) 1335137870 -3.280972474962957 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270475_0.png resize: (80, 113) 1335137871 -3.1197430444560523 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270506_0.png resize: (211, 316) 1335137872 -3.311680570627189 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270512_0.png resize: (48, 47) 1335137873 -0.35554967871717186 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270472_0.png resize: (111, 91) 1335137874 -3.8790068611889703 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270457_0.png resize: (92, 108) 1335137875 -4.166427124016588 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270520_0.png resize: (294, 207) 1335137876 -4.8081271814283575 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270464_0.png resize: (312, 343) 1335137877 -4.13209282003363 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270519_0.png resize: (145, 162) 1335137878 -3.3437396331876204 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270456_0.png resize: (108, 102) 1335137880 -3.6908802906583458 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270495_0.png resize: (207, 121) 1335137881 -4.934678304900097 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270454_0.png resize: (191, 173) 1335137882 -2.850866846939719 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270487_0.png resize: (162, 146) 1335137883 -3.0153559178840355 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270493_0.png resize: (134, 145) 1335137884 -4.5366239783588425 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270508_0.png resize: (128, 136) 1335137885 -4.099968276418718 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270534_0.png resize: (45, 72) 1335137886 -0.6573336708534804 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270530_0.png resize: (226, 424) 1335137887 -4.722715380707003 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270528_0.png resize: (182, 283) 1335137888 -4.544824180305989 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270466_0.png resize: (192, 105) 1335137889 -2.3375022677589103 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270491_0.png resize: (134, 102) 1335137890 -3.798634787375204 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270510_0.png resize: (177, 242) 1335137891 -3.757097774432991 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270455_0.png resize: (112, 74) 1335137892 -0.5002049475003421 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270470_0.png resize: (69, 79) 1335137893 -3.502157718077896 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270460_0.png resize: (124, 53) 1335137894 -3.9811074878666353 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270477_0.png resize: (70, 73) 1335137895 -4.1337470251605515 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270453_0.png resize: (75, 104) 1335137896 -1.2109896225429009 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270504_0.png resize: (122, 195) 1335137897 -4.7257409997044935 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270522_0.png resize: (137, 191) 1335137898 -3.8057533132615577 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270451_0.png resize: (146, 187) 1335137899 -2.165028308497906 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270529_0.png resize: (232, 95) 1335137900 -4.2049165861300235 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270551_0.png resize: (202, 156) 1335137901 -3.5788001025624006 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270553_0.png resize: (147, 179) 1335137902 -2.134174309352376 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270591_0.png resize: (186, 245) 1335137903 -3.337886721435586 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270571_0.png resize: (203, 104) 1335137904 -3.150498858642182 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270555_0.png resize: (271, 362) 1335137905 -4.1051224108838635 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270554_0.png resize: (233, 514) 1335137906 -4.575446588678329 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270560_0.png resize: (356, 362) 1335137907 -4.050174906690482 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270585_0.png resize: (328, 328) 1335137908 -5.071712271602689 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270592_0.png resize: (199, 318) 1335137909 -3.63756251227452 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270580_0.png resize: (204, 189) 1335137910 -4.226259558449119 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270562_0.png resize: (189, 218) 1335137911 -4.0028063172103625 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270542_0.png resize: (181, 152) 1335137912 -3.160732296811703 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270541_0.png resize: (130, 169) 1335137913 -2.575738228875199 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270549_0.png resize: (333, 452) 1335137915 -1.9152391635972905 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270568_0.png resize: (241, 139) 1335137916 -3.047515234276476 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270548_0.png resize: (334, 190) 1335137917 -3.7597399306870125 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270586_0.png resize: (91, 63) 1335137918 -5.481243273494895 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270544_0.png resize: (153, 98) 1335137919 -3.8074583182785453 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270564_0.png resize: (187, 230) 1335137920 -3.216425277915075 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270579_0.png resize: (294, 299) 1335137921 -3.542201709143194 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270572_0.png resize: (243, 128) 1335137922 -4.3516683901687685 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270570_0.png resize: (211, 168) 1335137923 -3.8949308569421426 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270599_0.png resize: (189, 153) 1335137924 -4.279967467723535 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270559_0.png resize: (187, 151) 1335137925 -4.158175360330182 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270590_0.png resize: (297, 141) 1335137926 -3.3521788667537025 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270600_0.png resize: (138, 55) 1335137927 -2.7065537236916866 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270573_0.png resize: (237, 213) 1335137928 -3.9929641518348027 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270546_0.png resize: (102, 119) 1335137929 -3.0476137608457337 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270583_0.png resize: (171, 186) 1335137930 -3.817230572393096 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270581_0.png resize: (80, 73) 1335137931 -5.0956109310303965 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270547_0.png resize: (125, 126) 1335137932 -3.795900349364626 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270584_0.png resize: (191, 191) 1335137933 -5.088139270400474 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270563_0.png resize: (100, 196) 1335137934 -4.437058006727981 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270550_0.png resize: (91, 56) 1335137935 -1.627193096008589 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270543_0.png resize: (83, 107) 1335137937 -4.045495952341716 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270552_0.png resize: (231, 164) 1335137938 -1.86706768869004 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270545_0.png resize: (244, 127) 1335137939 -3.1494645324103248 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270593_0.png resize: (101, 126) 1335137940 -3.9685239757016504 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270602_0.png resize: (95, 125) 1335137941 -2.463047864786634 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270577_0.png resize: (176, 108) 1335137942 -4.928192898832427 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270578_0.png resize: (208, 235) 1335137943 -4.413864527233965 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270596_0.png resize: (171, 163) 1335137944 -4.347321466655193 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270557_0.png resize: (114, 181) 1335137945 -3.692658738420083 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270561_0.png resize: (231, 223) 1335137946 -1.9018611345386067 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270594_0.png resize: (185, 124) 1335137947 -3.9744772379431654 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270588_0.png resize: (100, 85) 1335137948 -3.088219320060745 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270565_0.png resize: (90, 81) 1335137949 -3.5492368564725933 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270558_0.png resize: (99, 84) 1335137950 -3.877277809503765 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270587_0.png resize: (153, 111) 1335137951 -4.04568741186576 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270566_0.png resize: (84, 91) 1335137952 -1.5299174998815166 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270095_0.png resize: (769, 410) 1335138010 -2.800071761154307 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270117_0.png resize: (199, 360) 1335138011 -4.764991505210282 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270105_0.png resize: (211, 136) 1335138012 -2.176182642287466 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270092_0.png resize: (152, 291) 1335138013 -4.419969461394257 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270088_0.png resize: (207, 219) 1335138014 -2.535975442524778 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270109_0.png resize: (160, 134) 1335138015 -3.2916069805126846 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270097_0.png resize: (145, 235) 1335138016 -4.995937221830327 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270132_0.png resize: (129, 207) 1335138017 -1.9679759244026118 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270093_0.png resize: (228, 225) 1335138019 -4.483384104615955 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270098_0.png resize: (282, 169) 1335138020 -2.8070568958063955 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270089_0.png resize: (291, 321) 1335138021 -3.475625030940143 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270146_0.png resize: (95, 92) 1335138022 -4.064133828960949 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270136_0.png resize: (266, 285) 1335138023 -3.3982118795922327 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270141_0.png resize: (273, 467) 1335138024 -5.076939246826726 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270122_0.png resize: (175, 205) 1335138025 -3.56681511435955 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270145_0.png resize: (180, 249) 1335138026 -5.154565553558212 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270106_0.png resize: (232, 285) 1335138027 -4.18452614472863 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270140_0.png resize: (123, 160) 1335138028 -4.532809303964943 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270102_0.png resize: (137, 213) 1335138029 -2.202621370696245 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270123_0.png resize: (138, 119) 1335138030 -3.7518188399808423 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270100_0.png resize: (272, 252) 1335138031 -4.492638456990419 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270144_0.png resize: (276, 304) 1335138032 -3.453276478292478 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270195_0.png resize: (119, 182) 1335138033 -3.957280867777104 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270189_0.png resize: (145, 202) 1335138034 -4.179461005066789 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270183_0.png resize: (118, 98) 1335138035 -3.545918451971992 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270193_0.png resize: (107, 135) 1335138036 -3.021667909016374 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270209_0.png resize: (213, 258) 1335138037 -1.8354332646089353 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270217_0.png resize: (260, 391) 1335138038 -4.883346835714616 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270225_0.png resize: (169, 260) 1335138039 -3.3021502653807557 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270253_0.png resize: (268, 101) 1335138040 -2.613319295305234 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270310_0.png resize: (234, 230) 1335138041 -5.202694912215269 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270276_0.png resize: (164, 198) 1335138042 -4.161102728462428 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270295_0.png resize: (159, 160) 1335138043 -4.629764693243547 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270258_0.png resize: (245, 252) 1335138044 -4.959340203052689 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270322_0.png resize: (196, 143) 1335138045 -3.764190597598711 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270316_0.png resize: (327, 332) 1335138046 -3.0177104675304154 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270343_0.png resize: (216, 438) 1335138047 -2.5136514035238364 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270352_0.png resize: (178, 251) 1335138048 -2.90355732861321 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270342_0.png resize: (510, 890) 1335138049 -1.824266862614373 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270357_0.png resize: (286, 400) 1335138050 -2.841214504365653 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270351_0.png resize: (680, 890) 1335138051 -4.6325411836372705 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270365_0.png resize: (1141, 1673) 1335138052 -1.398281983219283 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270373_0.png resize: (143, 444) 1335138053 -3.4111401059324864 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270378_0.png resize: (116, 110) 1335138054 -1.3838591461763219 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270370_0.png resize: (570, 358) 1335138055 -2.952123614966939 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270376_0.png resize: (47, 246) 1335138056 -3.8100920378609597 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270374_0.png resize: (264, 389) 1335138057 -2.4776693226917503 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270366_0.png resize: (293, 302) 1335138058 -2.965048993676219 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270363_0.png resize: (83, 173) 1335138059 -1.0674452013961155 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270362_0.png resize: (314, 189) 1335138060 -3.077382113001481 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270369_0.png resize: (1161, 1089) 1335138061 -2.3179469553265855 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270367_0.png resize: (314, 194) 1335138062 -2.3102713775128123 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270386_0.png resize: (128, 196) 1335138063 -2.0388750237670195 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270393_0.png resize: (386, 713) 1335138064 -3.9372199025023615 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270380_0.png resize: (131, 197) 1335138065 -1.5011693432404243 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270384_0.png resize: (312, 628) 1335138066 -2.976890779473061 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270396_0.png resize: (153, 207) 1335138067 -3.4668597470574185 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270432_0.png resize: (136, 176) 1335138068 -3.3315645974179438 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270427_0.png resize: (238, 245) 1335138069 -4.592946379294936 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270426_0.png resize: (153, 206) 1335138070 -4.954061469371919 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270422_0.png resize: (219, 145) 1335138071 -2.832388116593802 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270498_0.png resize: (215, 329) 1335138072 -3.3562957091667784 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270450_0.png resize: (84, 213) 1335138073 -1.2013397893216684 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270480_0.png resize: (292, 261) 1335138074 -4.3587304141944125 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270496_0.png resize: (128, 144) 1335138075 -2.3096316704543876 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270527_0.png resize: (93, 138) 1335138076 -6.499822051187704 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270489_0.png resize: (106, 141) 1335138077 -3.039380100014595 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270473_0.png resize: (153, 110) 1335138078 -4.616472324809659 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270601_0.png resize: (254, 138) 1335138079 -3.3330807794100386 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270582_0.png resize: (207, 143) 1335138080 -2.54560420578001 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270574_0.png resize: (163, 216) 1335138081 -2.9539345083126984 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270569_0.png resize: (336, 455) 1335138082 -1.9939841482171028 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270575_0.png resize: (126, 232) 1335138083 -4.179967460516904 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270576_0.png resize: (95, 220) 1335138084 -4.370134910844375 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270598_0.png resize: (330, 250) 1335138085 -3.643296180525516 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270603_0.png resize: (180, 179) 1335138086 -3.543048710750876 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270556_0.png resize: (136, 185) 1335138087 -4.194167125822646 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270597_0.png resize: (206, 129) 1335138088 -3.7848023492835616 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270179_0.png resize: (161, 122) 1335138091 -3.8504904938980666 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270242_0.png resize: (136, 143) 1335138092 -3.2611650488951396 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270288_0.png resize: (99, 90) 1335138093 -4.275005644537439 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270134_0.png resize: (379, 321) 1335138108 -3.3802238371811537 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270243_0.png resize: (507, 761) 1335138109 -3.9883570436690237 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270241_0.png resize: (173, 402) 1335138110 -2.9832083759141157 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270252_0.png resize: (301, 266) 1335138111 -3.741369256040134 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270250_0.png resize: (1210, 518) 1335138112 -1.0381719005355932 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270279_0.png resize: (270, 268) 1335138113 -4.396145517372525 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270255_0.png resize: (221, 646) 1335138114 -2.12755526799695 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270327_0.png resize: (310, 468) 1335138115 -3.3993724321434686 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270318_0.png resize: (294, 525) 1335138116 -5.347151843036663 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270334_0.png resize: (393, 265) 1335138117 -5.1818305036438215 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270359_0.png resize: (224, 602) 1335138119 -2.1902449989293524 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270355_0.png resize: (221, 348) 1335138120 -1.737129109256444 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270341_0.png resize: (234, 286) 1335138121 -3.544446928116829 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270350_0.png resize: (373, 451) 1335138122 -4.022801521232706 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270395_0.png resize: (524, 349) 1335138123 -4.496514226548958 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270385_0.png resize: (289, 1104) 1335138124 -2.1849794384731305 treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549_rle_crop_3657270382_0.png resize: (343, 490) 1335138125 -1.8052935018673377 treat image : temp/1738819827_2362753_1335019873_43c2b282b486d3830ae221c45740f751_rle_crop_3657270532_0.png resize: (178, 252) 1335138126 -3.0904828798966606 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270589_0.png resize: (397, 193) 1335138127 -4.311418050020549 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270143_0.png resize: (128, 52) 1335138130 -4.362423087671114 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270127_0.png resize: (114, 232) 1335138131 -3.0740409472304004 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270110_0.png resize: (122, 101) 1335138132 -0.9648041827179092 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270138_0.png resize: (338, 427) 1335138133 -4.58226862354847 treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c_rle_crop_3657270126_0.png resize: (255, 314) 1335138134 -3.8713309656389288 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270184_0.png resize: (152, 139) 1335138135 -2.0129699568307493 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270282_0.png resize: (191, 211) 1335138136 -4.388124538301842 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270329_0.png resize: (244, 537) 1335138137 -2.7124930790951196 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270324_0.png resize: (195, 130) 1335138138 -2.768819622731175 treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d_rle_crop_3657270377_0.png resize: (95, 99) 1335138139 -2.9084306528978487 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270429_0.png resize: (237, 170) 1335138140 -3.5867985243802223 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270291_0.png resize: (199, 158) 1335138144 -4.871045826641599 treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c_rle_crop_3657270306_0.png resize: (348, 267) 1335138145 -3.2317988704181038 treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa_rle_crop_3657270321_0.png resize: (218, 167) 1335138146 -3.4750223816661268 treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19_rle_crop_3657270360_0.png resize: (577, 1042) 1335138147 -3.2134937122087837 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270567_0.png resize: (310, 218) 1335138148 -3.5024664373986623 treat image : temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270595_0.png resize: (113, 134) 1335138149 -2.6029347524356212 treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270192_0.png resize: (110, 97) 1335138151 -3.187847323710843 treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270218_0.png resize: (300, 536) 1335138152 -2.04720344305601 treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270251_0.png resize: (214, 117) 1335138154 -3.7578298874511704 treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270428_0.png resize: (169, 153) 1335138155 -3.266368990834529 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 : 528 time used for this insertion : 0.048159122467041016 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 528 time used for this insertion : 0.10152435302734375 save missing photos in datou_result : time spend for datou_step_exec : 47.89946126937866 time spend to save output : 0.15701746940612793 total time spend for step 6 : 48.05647873878479 step7:brightness Thu Feb 6 06:47:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1738819827_2362753_1335027718_38a8bf470e480d310d939c65524b399c.jpg treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c.jpg treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640.jpg treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec.jpg treat image : temp/1738819827_2362753_1335027687_04833d54af063c29d3dcae68698a8e2c.jpg treat image : temp/1738819827_2362753_1335019935_019cd8955b584b545e0cd57cfa961afa.jpg treat image : temp/1738819827_2362753_1335019888_f6ddbc90141df12dcea4d3dde7e51a19.jpg treat image : temp/1738819827_2362753_1335019884_0a3a1244783034db17962cf95987df3d.jpg treat image : temp/1738819827_2362753_1335019880_66811f1cd65fb4510805e9ec2f362549.jpg treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6.jpg treat image : 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temp/1738819827_2362753_1335019865_37c16be165fba87e77d0c93ed8b99372_rle_crop_3657270595_0.png treat image : temp/1738819827_2362753_1335027714_b04cf2436db6b22e664ba0b5ffcf076c_rle_crop_3657270192_0.png treat image : temp/1738819827_2362753_1335027709_b95e3cb6ab2be3d181768a6042b93640_rle_crop_3657270218_0.png treat image : temp/1738819827_2362753_1335027691_711c5abb1ad6ddcec4ab4de421faa1ec_rle_crop_3657270251_0.png treat image : temp/1738819827_2362753_1335019875_d144b64a66ade4812abcd435d2837db6_rle_crop_3657270428_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 : 528 time used for this insertion : 0.04671502113342285 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 528 time used for this insertion : 0.5211009979248047 save missing photos in datou_result : time spend for datou_step_exec : 14.361627578735352 time spend to save output : 0.57440185546875 total time spend for step 7 : 14.936029434204102 step8:velours_tree Thu Feb 6 06:47:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.19054388999938965 time spend to save output : 3.337860107421875e-05 total time spend for step 8 : 0.19057726860046387 step9:send_mail_cod Thu Feb 6 06:47:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 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_P20286245_06-02-2025_06_47_47.pdf 20289607 imagette202896071738820867 20289608 imagette202896081738820867 20289609 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette202896091738820867 20289610 change filename to text .change filename to text .change filename to text .change filename to text .imagette202896101738820869 20289611 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette202896111738820869 20289612 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette202896121738820870 20289613 change filename to text .change filename to text .change filename to text .imagette202896131738820872 20289614 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette202896141738820872 20289615 imagette202896151738820872 20289617 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette202896171738820872 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20286245 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20289607,20289608,20289609,20289610,20289611,20289612,20289613,20289614,20289615,20289616,20289617?tags=background,flou,papier,pet_fonce,pet_clair,carton,metal,pehd,mal_croppe,environnement,autre args[1335027718] : ((1335027718, -6.187358682574875, 492609224), (1335027718, -0.226545767726356, 496442774), '0.2163261936236685') apple ((1335027718, -6.187358682574875, 492609224), (1335027718, -0.226545767726356, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335027714] : ((1335027714, -6.825873796033706, 492609224), (1335027714, -0.06064420310671671, 2107752395), '0.2163261936236685') apple ((1335027714, -6.825873796033706, 492609224), (1335027714, -0.06064420310671671, 2107752395), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335027709] : ((1335027709, -6.322509568432731, 492609224), (1335027709, -0.1992245154125845, 496442774), '0.2163261936236685') apple ((1335027709, -6.322509568432731, 492609224), (1335027709, -0.1992245154125845, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335027691] : ((1335027691, -3.705793804813742, 492609224), (1335027691, -0.15446967060592487, 496442774), '0.2163261936236685') apple ((1335027691, -3.705793804813742, 492609224), (1335027691, -0.15446967060592487, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335027687] : ((1335027687, -6.424992267850785, 492609224), (1335027687, -0.3123570927689113, 496442774), '0.2163261936236685') apple ((1335027687, -6.424992267850785, 492609224), (1335027687, -0.3123570927689113, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335019935] : ((1335019935, -5.2914561578008525, 492609224), (1335019935, -0.23626917065722577, 496442774), '0.2163261936236685') apple ((1335019935, -5.2914561578008525, 492609224), (1335019935, -0.23626917065722577, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335019888] : ((1335019888, -3.3692559069950976, 492609224), (1335019888, -0.23034351619666368, 496442774), '0.2163261936236685') apple ((1335019888, -3.3692559069950976, 492609224), (1335019888, -0.23034351619666368, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335019884] : ((1335019884, -2.0120456270608966, 492609224), (1335019884, -0.31112401468835993, 496442774), '0.2163261936236685') apple ((1335019884, -2.0120456270608966, 492609224), (1335019884, -0.31112401468835993, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335019880] : ((1335019880, -4.472338483727959, 492609224), (1335019880, 0.06767324418961661, 2107752395), '0.2163261936236685') apple ((1335019880, -4.472338483727959, 492609224), (1335019880, 0.06767324418961661, 2107752395), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335019875] : ((1335019875, -6.657349520263892, 492609224), (1335019875, -0.12662263343810004, 496442774), '0.2163261936236685') apple ((1335019875, -6.657349520263892, 492609224), (1335019875, -0.12662263343810004, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335019873] : ((1335019873, -6.826251451924046, 492609224), (1335019873, -0.03095229844583186, 2107752395), '0.2163261936236685') apple ((1335019873, -6.826251451924046, 492609224), (1335019873, -0.03095229844583186, 2107752395), '0.2163261936236685') We are sending mail with results at report@fotonower.com args[1335019865] : ((1335019865, -6.303306953982627, 492609224), (1335019865, -0.0735000674204905, 496442774), '0.2163261936236685') apple ((1335019865, -6.303306953982627, 492609224), (1335019865, -0.0735000674204905, 496442774), '0.2163261936236685') We are sending mail with results at report@fotonower.com refus_total : 0.2163261936236685 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=20286245 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1335019884,1335019888,1335019873,1335027718,1335019865,1335019875,1335019880,1335019935,1335027687,1335027691,1335027709,1335027714) Found this number of photos: 12 begin to download photo : 1335019884 begin to download photo : 1335027718 begin to download photo : 1335019880 begin to download photo : 1335027691 download finish for photo 1335027691 begin to download photo : 1335027709 download finish for photo 1335019884 begin to download photo : 1335019888 download finish for photo 1335019880 begin to download photo : 1335019935 download finish for photo 1335027709 begin to download photo : 1335027714 download finish for photo 1335019888 begin to download photo : 1335019873 download finish for photo 1335019935 begin to download photo : 1335027687 download finish for photo 1335027718 begin to download photo : 1335019865 download finish for photo 1335027714 download finish for photo 1335027687 download finish for photo 1335019865 begin to download photo : 1335019875 download finish for photo 1335019873 download finish for photo 1335019875 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20286245_06-02-2025_06_47_47.pdf results_Auto_P20286245_06-02-2025_06_47_47.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20286245_06-02-2025_06_47_47.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','20286245','results_Auto_P20286245_06-02-2025_06_47_47.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20286245_06-02-2025_06_47_47.pdf','pdf','','1.27','0.2163261936236685') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/20286245

https://www.fotonower.com/image?json=false&list_photos_id=1335027718
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335027714
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335027709
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335027691
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335027687
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335019935
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335019888
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335019884
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335019880
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335019875
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335019873
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1335019865
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 21.63%
Veuillez trouver les photos des contaminants.

exemples de contaminants: papier: https://www.fotonower.com/view/20289609?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/20289610?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/20289611?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/20289612?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/20289613?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/20289614?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/20289617?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20286245_06-02-2025_06_47_47.pdf.

Lien vers velours :https://www.fotonower.com/velours/20289607,20289608,20289609,20289610,20289611,20289612,20289613,20289614,20289615,20289616,20289617?tags=background,flou,papier,pet_fonce,pet_clair,carton,metal,pehd,mal_croppe,environnement,autre.


L'équipe Fotonower 202 b'' Server: nginx Date: Thu, 06 Feb 2025 05:47:57 GMT Content-Length: 0 Connection: close X-Message-Id: zMculu4CRWyv0qTA_dSvTg 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 [1335027718, 1335027714, 1335027709, 1335027691, 1335027687, 1335019935, 1335019888, 1335019884, 1335019880, 1335019875, 1335019873, 1335019865] 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, '2558005') ('3318', '20286245', '1335027718', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027714', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027709', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027691', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027687', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019935', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019888', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019884', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019880', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019875', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019873', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019865', None, None, None, None, None, '2558005') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.03687763214111328 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.039095640182495 time spend to save output : 0.037206172943115234 total time spend for step 9 : 10.07630181312561 step10:split_time_score Thu Feb 6 06:47:57 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', 12),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 05022025 20286245 Nombre de photos uploadées : 12 / 23040 (0%) 05022025 20286245 Nombre de photos taguées (types de déchets): 0 / 12 (0%) 05022025 20286245 Nombre de photos taguées (volume) : 0 / 12 (0%) elapsed_time : load_data_split_time_score 2.1457672119140625e-06 elapsed_time : order_list_meta_photo_and_scores 6.67572021484375e-06 ???????????? elapsed_time : fill_and_build_computed_from_old_data 0.0006008148193359375 elapsed_time : insert_dashboard_record_day_entry 0.025249004364013672 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 : 0.22049059616263506 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277502_06-02-2025_02_23_52.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277502 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`=20277502 AND mptpi.`type`=3594 To do Qualite : 0.24811212809776687 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277503_06-02-2025_02_17_05.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277503 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`=20277503 AND mptpi.`type`=3594 To do Qualite : 0.07663699265928005 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20286225_06-02-2025_05_33_17.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20286225 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`=20286225 AND mptpi.`type`=3726 To do Qualite : 0.21787861122458246 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20286226_06-02-2025_05_57_11.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20286226 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`=20286226 AND mptpi.`type`=3594 To do Qualite : 0.2311395498592955 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277511_06-02-2025_02_48_24.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277511 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`=20277511 AND mptpi.`type`=3594 To do Qualite : 0.2571360868566177 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20286232 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`=20286232 AND mptpi.`type`=3594 To do Qualite : 0.1770747284253208 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277523_06-02-2025_01_29_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277523 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`=20277523 AND mptpi.`type`=3594 To do Qualite : 0.06769655641860853 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277527_06-02-2025_00_59_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277527 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`=20277527 AND mptpi.`type`=3726 To do Qualite : 0.21480488134682896 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277530_06-02-2025_01_27_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277530 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`=20277530 AND mptpi.`type`=3594 To do Qualite : 0.180145604972313 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277549_06-02-2025_00_58_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277549 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`=20277549 AND mptpi.`type`=3594 To do Qualite : 0.09609489658538523 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20277551_06-02-2025_02_21_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20277551 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`=20277551 AND mptpi.`type`=3726 To do Qualite : 0.2163261936236685 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20286245_06-02-2025_06_47_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20286245 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`=20286245 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'05022025': {'nb_upload': 12, '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 [1335027718, 1335027714, 1335027709, 1335027691, 1335027687, 1335019935, 1335019888, 1335019884, 1335019880, 1335019875, 1335019873, 1335019865] Looping around the photos to save general results len do output : 1 /20286245Didn'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, '2558005') ('3318', '20286245', '1335027718', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027714', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027709', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027691', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335027687', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019935', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019888', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019884', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019880', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019875', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019873', None, None, None, None, None, '2558005') ('3318', None, None, None, None, None, None, None, '2558005') ('3318', '20286245', '1335019865', None, None, None, None, None, '2558005') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.014261960983276367 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.606740951538086 time spend to save output : 0.014501094818115234 total time spend for step 10 : 1.6212420463562012 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 12 set_done_treatment 334.65user 196.71system 17:34.28elapsed 50%CPU (0avgtext+0avgdata 6418372maxresident)k 15220200inputs+291080outputs (396560major+34951075minor)pagefaults 0swaps