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 : 407870 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 : ['2718481'] with mtr_portfolio_ids : ['21999409'] and first list_photo_ids : [] new path : /proc/407870/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 13 ; length of list_pids : 13 ; length of list_args : 13 time to download the photos : 2.262444019317627 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 Apr 3 01:20:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-03 01:20:34.782088: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-03 01:20:34.807291: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-03 01:20:34.809248: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7efdd4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-03 01:20:34.809304: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-03 01:20:34.813461: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-03 01:20:34.954449: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3c25240 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-03 01:20:34.954496: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-03 01:20:34.955950: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-03 01:20:34.956355: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-03 01:20:34.959550: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-03 01:20:34.962574: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-03 01:20:34.963132: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-03 01:20:34.965598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-03 01:20:34.966618: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-03 01:20:34.971007: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-03 01:20:34.972501: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-03 01:20:34.972581: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-03 01:20:34.973319: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-03 01:20:34.973334: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-03 01:20:34.973343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-03 01:20:34.974643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9879 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-03 01:20:35.238316: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-03 01:20:35.238427: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-03 01:20:35.238450: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-03 01:20:35.238472: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-03 01:20:35.238492: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-03 01:20:35.238513: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-03 01:20:35.238533: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-03 01:20:35.238553: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-03 01:20:35.240218: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-03 01:20:35.241725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-03 01:20:35.241820: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-03 01:20:35.241840: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-03 01:20:35.241861: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-03 01:20:35.241889: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-03 01:20:35.241913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-03 01:20:35.241931: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-03 01:20:35.241949: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-03 01:20:35.243625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-03 01:20:35.243683: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-03 01:20:35.243696: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-03 01:20:35.243707: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-03 01:20:35.245935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9879 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-03 01:20:47.714103: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-03 01:20:47.935315: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 13 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 55 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 63 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 55 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 72 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 98 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 64 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 21 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 47 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 57 Detection mask done ! Trying to reset tf kernel 408533 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 tf kernel not reseted sub process len(results) : 13 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 13 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10814 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 : 0.0007486343383789062 nb_pixel_total : 20721 time to create 1 rle with old method : 0.025429487228393555 length of segment : 152 time for calcul the mask position with numpy : 0.0004398822784423828 nb_pixel_total : 16367 time to create 1 rle with old method : 0.018517255783081055 length of segment : 188 time for calcul the mask position with numpy : 0.0009000301361083984 nb_pixel_total : 35976 time to create 1 rle with old method : 0.03994417190551758 length of segment : 232 time for calcul the mask position with numpy : 0.0001952648162841797 nb_pixel_total : 7597 time to create 1 rle with old method : 0.00862431526184082 length of segment : 162 time for calcul the mask position with numpy : 0.0007624626159667969 nb_pixel_total : 36172 time to create 1 rle with old method : 0.03865790367126465 length of segment : 198 time for calcul the mask position with numpy : 0.0028753280639648438 nb_pixel_total : 109010 time to create 1 rle with old method : 0.11909317970275879 length of segment : 300 time for calcul the mask position with numpy : 0.0016524791717529297 nb_pixel_total : 57271 time to create 1 rle with old method : 0.06689691543579102 length of segment : 504 time for calcul the mask position with numpy : 0.0011909008026123047 nb_pixel_total : 68319 time to create 1 rle with old method : 0.07643795013427734 length of segment : 332 time for calcul the mask position with numpy : 0.034915924072265625 nb_pixel_total : 780755 time to create 1 rle with new method : 0.07897520065307617 length of segment : 1090 time for calcul the mask position with numpy : 0.00014352798461914062 nb_pixel_total : 3420 time to create 1 rle with old method : 0.004172801971435547 length of segment : 74 time for calcul the mask position with numpy : 0.0006239414215087891 nb_pixel_total : 31749 time to create 1 rle with old method : 0.04085850715637207 length of segment : 175 time for calcul the mask position with numpy : 0.0003170967102050781 nb_pixel_total : 14104 time to create 1 rle with old method : 0.016350507736206055 length of segment : 146 time for calcul the mask position with numpy : 0.0020599365234375 nb_pixel_total : 130571 time to create 1 rle with old method : 0.17337465286254883 length of segment : 366 time for calcul the mask position with numpy : 0.0001900196075439453 nb_pixel_total : 5947 time to create 1 rle with old method : 0.006943941116333008 length of segment : 103 time for calcul the mask position with numpy : 0.002894163131713867 nb_pixel_total : 147634 time to create 1 rle with old method : 0.17899560928344727 length of segment : 286 time for calcul the mask position with numpy : 0.001171112060546875 nb_pixel_total : 42864 time to create 1 rle with old method : 0.048583984375 length of segment : 238 time for calcul the mask position with numpy : 0.00030350685119628906 nb_pixel_total : 15496 time to create 1 rle with old method : 0.0199127197265625 length of segment : 209 time for calcul the mask position with numpy : 0.0017724037170410156 nb_pixel_total : 35761 time to create 1 rle with old method : 0.04507303237915039 length of segment : 369 time for calcul the mask position with numpy : 0.0002803802490234375 nb_pixel_total : 12714 time to create 1 rle with old method : 0.015048742294311523 length of segment : 114 time for calcul the mask position with numpy : 0.003772735595703125 nb_pixel_total : 194176 time to create 1 rle with new method : 0.009535551071166992 length of segment : 527 time for calcul the mask position with numpy : 0.0014460086822509766 nb_pixel_total : 81938 time to create 1 rle with old method : 0.09561324119567871 length of segment : 341 time for calcul the mask position with numpy : 0.0007843971252441406 nb_pixel_total : 16757 time to create 1 rle with old method : 0.019361257553100586 length of segment : 137 time for calcul the mask position with numpy : 0.00038743019104003906 nb_pixel_total : 9480 time to create 1 rle with old method : 0.011305809020996094 length of segment : 78 time for calcul the mask position with numpy : 0.0007088184356689453 nb_pixel_total : 15466 time to create 1 rle with old method : 0.01919722557067871 length of segment : 99 time for calcul the mask position with numpy : 0.03741621971130371 nb_pixel_total : 938431 time to create 1 rle with new method : 0.08328676223754883 length of segment : 975 time for calcul the mask position with numpy : 0.0009210109710693359 nb_pixel_total : 11323 time to create 1 rle with old method : 0.016206741333007812 length of segment : 176 time for calcul the mask position with numpy : 0.002519369125366211 nb_pixel_total : 64483 time to create 1 rle with old method : 0.07386636734008789 length of segment : 387 time for calcul the mask position with numpy : 0.00038361549377441406 nb_pixel_total : 6993 time to create 1 rle with old method : 0.008143186569213867 length of segment : 91 time for calcul the mask position with numpy : 0.004546642303466797 nb_pixel_total : 83419 time to create 1 rle with old method : 0.09863066673278809 length of segment : 336 time for calcul the mask position with numpy : 0.006743907928466797 nb_pixel_total : 153752 time to create 1 rle with new method : 0.008623123168945312 length of segment : 432 time for calcul the mask position with numpy : 0.004462003707885742 nb_pixel_total : 93927 time to create 1 rle with old method : 0.1056663990020752 length of segment : 302 time for calcul the mask position with numpy : 0.0009369850158691406 nb_pixel_total : 14711 time to create 1 rle with old method : 0.017147302627563477 length of segment : 160 time for calcul the mask position with numpy : 0.003679990768432617 nb_pixel_total : 49093 time to create 1 rle with old method : 0.05680990219116211 length of segment : 379 time for calcul the mask position with numpy : 0.0004038810729980469 nb_pixel_total : 4614 time to create 1 rle with old method : 0.005401134490966797 length of segment : 105 time for calcul the mask position with numpy : 0.0016634464263916016 nb_pixel_total : 35457 time to create 1 rle with old method : 0.04155373573303223 length of segment : 175 time for calcul the mask position with numpy : 0.0008060932159423828 nb_pixel_total : 14970 time to create 1 rle with old method : 0.017087936401367188 length of segment : 165 time for calcul the mask position with numpy : 0.0004839897155761719 nb_pixel_total : 6507 time to create 1 rle with old method : 0.0077893733978271484 length of segment : 91 time for calcul the mask position with numpy : 0.003812551498413086 nb_pixel_total : 93821 time to create 1 rle with old method : 0.10479164123535156 length of segment : 290 time for calcul the mask position with numpy : 0.015660762786865234 nb_pixel_total : 349934 time to create 1 rle with new method : 0.028922080993652344 length of segment : 734 time for calcul the mask position with numpy : 0.0006604194641113281 nb_pixel_total : 15590 time to create 1 rle with old method : 0.017780065536499023 length of segment : 175 time for calcul the mask position with numpy : 0.0025751590728759766 nb_pixel_total : 60517 time to create 1 rle with old method : 0.07232999801635742 length of segment : 235 time for calcul the mask position with numpy : 0.0003466606140136719 nb_pixel_total : 8084 time to create 1 rle with old method : 0.009654998779296875 length of segment : 80 time for calcul the mask position with numpy : 0.0008111000061035156 nb_pixel_total : 9582 time to create 1 rle with old method : 0.011374950408935547 length of segment : 171 time for calcul the mask position with numpy : 0.0013306140899658203 nb_pixel_total : 27584 time to create 1 rle with old method : 0.0312652587890625 length of segment : 267 time for calcul the mask position with numpy : 0.0035576820373535156 nb_pixel_total : 75074 time to create 1 rle with old method : 0.09072256088256836 length of segment : 284 time for calcul the mask position with numpy : 0.0003979206085205078 nb_pixel_total : 12183 time to create 1 rle with old method : 0.01405024528503418 length of segment : 152 time for calcul the mask position with numpy : 0.0008983612060546875 nb_pixel_total : 22611 time to create 1 rle with old method : 0.0260162353515625 length of segment : 145 time for calcul the mask position with numpy : 0.0019233226776123047 nb_pixel_total : 52129 time to create 1 rle with old method : 0.06140470504760742 length of segment : 169 time for calcul the mask position with numpy : 0.0005581378936767578 nb_pixel_total : 10404 time to create 1 rle with old method : 0.012281417846679688 length of segment : 130 time for calcul the mask position with numpy : 0.007760763168334961 nb_pixel_total : 192036 time to create 1 rle with new method : 0.012702226638793945 length of segment : 575 time for calcul the mask position with numpy : 0.00315093994140625 nb_pixel_total : 44194 time to create 1 rle with old method : 0.04978680610656738 length of segment : 296 time for calcul the mask position with numpy : 0.013633251190185547 nb_pixel_total : 331360 time to create 1 rle with new method : 0.02616405487060547 length of segment : 757 time for calcul the mask position with numpy : 0.0010013580322265625 nb_pixel_total : 21810 time to create 1 rle with old method : 0.030344009399414062 length of segment : 173 time for calcul the mask position with numpy : 0.00360107421875 nb_pixel_total : 77946 time to create 1 rle with old method : 0.08868932723999023 length of segment : 509 time for calcul the mask position with numpy : 0.007851600646972656 nb_pixel_total : 144259 time to create 1 rle with old method : 0.16270136833190918 length of segment : 485 time for calcul the mask position with numpy : 0.0011582374572753906 nb_pixel_total : 25749 time to create 1 rle with old method : 0.029168367385864258 length of segment : 204 time for calcul the mask position with numpy : 0.0009484291076660156 nb_pixel_total : 16548 time to create 1 rle with old method : 0.01909470558166504 length of segment : 152 time for calcul the mask position with numpy : 0.0029692649841308594 nb_pixel_total : 69236 time to create 1 rle with old method : 0.0784609317779541 length of segment : 453 time for calcul the mask position with numpy : 0.007692098617553711 nb_pixel_total : 156961 time to create 1 rle with new method : 0.01060938835144043 length of segment : 486 time for calcul the mask position with numpy : 0.012659788131713867 nb_pixel_total : 178984 time to create 1 rle with new method : 0.013809442520141602 length of segment : 927 time for calcul the mask position with numpy : 0.0052373409271240234 nb_pixel_total : 44282 time to create 1 rle with old method : 0.05132174491882324 length of segment : 363 time for calcul the mask position with numpy : 0.0021283626556396484 nb_pixel_total : 39873 time to create 1 rle with old method : 0.045954227447509766 length of segment : 488 time for calcul the mask position with numpy : 0.0013866424560546875 nb_pixel_total : 41437 time to create 1 rle with old method : 0.04668736457824707 length of segment : 221 time for calcul the mask position with numpy : 0.0006146430969238281 nb_pixel_total : 33499 time to create 1 rle with old method : 0.03861117362976074 length of segment : 312 time for calcul the mask position with numpy : 0.0008342266082763672 nb_pixel_total : 21018 time to create 1 rle with old method : 0.024281024932861328 length of segment : 126 time for calcul the mask position with numpy : 0.0025110244750976562 nb_pixel_total : 32115 time to create 1 rle with old method : 0.03732800483703613 length of segment : 560 time for calcul the mask position with numpy : 0.0013098716735839844 nb_pixel_total : 33075 time to create 1 rle with old method : 0.03847241401672363 length of segment : 429 time for calcul the mask position with numpy : 0.002611398696899414 nb_pixel_total : 18209 time to create 1 rle with old method : 0.020981788635253906 length of segment : 440 time for calcul the mask position with numpy : 0.002222299575805664 nb_pixel_total : 35162 time to create 1 rle with old method : 0.0401456356048584 length of segment : 199 time for calcul the mask position with numpy : 0.009265899658203125 nb_pixel_total : 165573 time to create 1 rle with new method : 0.011923789978027344 length of segment : 439 time for calcul the mask position with numpy : 0.00033593177795410156 nb_pixel_total : 12876 time to create 1 rle with old method : 0.015057802200317383 length of segment : 200 time for calcul the mask position with numpy : 0.00022172927856445312 nb_pixel_total : 12076 time to create 1 rle with old method : 0.014098405838012695 length of segment : 92 time for calcul the mask position with numpy : 0.0007724761962890625 nb_pixel_total : 13553 time to create 1 rle with old method : 0.015778541564941406 length of segment : 144 time for calcul the mask position with numpy : 0.007935762405395508 nb_pixel_total : 127570 time to create 1 rle with old method : 0.1439800262451172 length of segment : 479 time for calcul the mask position with numpy : 0.0023834705352783203 nb_pixel_total : 52459 time to create 1 rle with old method : 0.06029486656188965 length of segment : 276 time for calcul the mask position with numpy : 0.006312370300292969 nb_pixel_total : 150692 time to create 1 rle with new method : 0.00711822509765625 length of segment : 417 time for calcul the mask position with numpy : 0.016182899475097656 nb_pixel_total : 347402 time to create 1 rle with new method : 0.02161860466003418 length of segment : 912 time for calcul the mask position with numpy : 0.0010464191436767578 nb_pixel_total : 13760 time to create 1 rle with old method : 0.01593184471130371 length of segment : 172 time for calcul the mask position with numpy : 0.0018610954284667969 nb_pixel_total : 73273 time to create 1 rle with old method : 0.08237051963806152 length of segment : 370 time for calcul the mask position with numpy : 0.0008096694946289062 nb_pixel_total : 16781 time to create 1 rle with old method : 0.020859479904174805 length of segment : 147 time for calcul the mask position with numpy : 0.011417388916015625 nb_pixel_total : 196383 time to create 1 rle with new method : 0.019544601440429688 length of segment : 752 time for calcul the mask position with numpy : 0.001501321792602539 nb_pixel_total : 22111 time to create 1 rle with old method : 0.02542734146118164 length of segment : 293 time for calcul the mask position with numpy : 0.0005307197570800781 nb_pixel_total : 5367 time to create 1 rle with old method : 0.006329059600830078 length of segment : 138 time for calcul the mask position with numpy : 0.001931905746459961 nb_pixel_total : 35754 time to create 1 rle with old method : 0.0420994758605957 length of segment : 224 time for calcul the mask position with numpy : 0.0020143985748291016 nb_pixel_total : 27528 time to create 1 rle with old method : 0.03135180473327637 length of segment : 191 time for calcul the mask position with numpy : 0.020991802215576172 nb_pixel_total : 259330 time to create 1 rle with new method : 0.040862083435058594 length of segment : 844 time for calcul the mask position with numpy : 0.0028014183044433594 nb_pixel_total : 45557 time to create 1 rle with old method : 0.05228257179260254 length of segment : 254 time for calcul the mask position with numpy : 0.0028510093688964844 nb_pixel_total : 47675 time to create 1 rle with old method : 0.05460309982299805 length of segment : 300 time for calcul the mask position with numpy : 0.0016367435455322266 nb_pixel_total : 25491 time to create 1 rle with old method : 0.03757357597351074 length of segment : 294 time for calcul the mask position with numpy : 0.0012018680572509766 nb_pixel_total : 18105 time to create 1 rle with old method : 0.020947933197021484 length of segment : 164 time for calcul the mask position with numpy : 0.015174388885498047 nb_pixel_total : 248746 time to create 1 rle with new method : 0.019776582717895508 length of segment : 597 time for calcul the mask position with numpy : 0.0011336803436279297 nb_pixel_total : 18850 time to create 1 rle with old method : 0.022095441818237305 length of segment : 133 time for calcul the mask position with numpy : 0.0008988380432128906 nb_pixel_total : 22385 time to create 1 rle with old method : 0.026638031005859375 length of segment : 130 time for calcul the mask position with numpy : 0.00029921531677246094 nb_pixel_total : 3132 time to create 1 rle with old method : 0.003697633743286133 length of segment : 67 time for calcul the mask position with numpy : 0.0009045600891113281 nb_pixel_total : 11578 time to create 1 rle with old method : 0.013735294342041016 length of segment : 180 time for calcul the mask position with numpy : 0.002789735794067383 nb_pixel_total : 60059 time to create 1 rle with old method : 0.0681905746459961 length of segment : 225 time for calcul the mask position with numpy : 0.0024564266204833984 nb_pixel_total : 47026 time to create 1 rle with old method : 0.0537111759185791 length of segment : 314 time for calcul the mask position with numpy : 0.00018548965454101562 nb_pixel_total : 7156 time to create 1 rle with old method : 0.008507490158081055 length of segment : 82 time for calcul the mask position with numpy : 0.02251744270324707 nb_pixel_total : 515375 time to create 1 rle with new method : 0.03716874122619629 length of segment : 1047 time for calcul the mask position with numpy : 0.0018000602722167969 nb_pixel_total : 19279 time to create 1 rle with old method : 0.021912813186645508 length of segment : 173 time for calcul the mask position with numpy : 0.003924369812011719 nb_pixel_total : 97881 time to create 1 rle with old method : 0.10958623886108398 length of segment : 517 time for calcul the mask position with numpy : 0.0044901371002197266 nb_pixel_total : 70407 time to create 1 rle with old method : 0.07892131805419922 length of segment : 331 time for calcul the mask position with numpy : 0.00019311904907226562 nb_pixel_total : 6965 time to create 1 rle with old method : 0.008510351181030273 length of segment : 58 time for calcul the mask position with numpy : 0.001041412353515625 nb_pixel_total : 18014 time to create 1 rle with old method : 0.021077394485473633 length of segment : 183 time for calcul the mask position with numpy : 0.0020236968994140625 nb_pixel_total : 28909 time to create 1 rle with old method : 0.03312325477600098 length of segment : 181 time for calcul the mask position with numpy : 0.00015616416931152344 nb_pixel_total : 4415 time to create 1 rle with old method : 0.005074739456176758 length of segment : 88 time for calcul the mask position with numpy : 0.0002765655517578125 nb_pixel_total : 7559 time to create 1 rle with old method : 0.008909225463867188 length of segment : 110 time for calcul the mask position with numpy : 0.0014350414276123047 nb_pixel_total : 28265 time to create 1 rle with old method : 0.03243565559387207 length of segment : 213 time for calcul the mask position with numpy : 0.0024194717407226562 nb_pixel_total : 35343 time to create 1 rle with old method : 0.04088902473449707 length of segment : 277 time for calcul the mask position with numpy : 0.0021920204162597656 nb_pixel_total : 36888 time to create 1 rle with old method : 0.05209660530090332 length of segment : 287 time for calcul the mask position with numpy : 0.00087738037109375 nb_pixel_total : 16404 time to create 1 rle with old method : 0.019474267959594727 length of segment : 107 time for calcul the mask position with numpy : 0.0006515979766845703 nb_pixel_total : 22756 time to create 1 rle with old method : 0.02649998664855957 length of segment : 201 time for calcul the mask position with numpy : 0.002234935760498047 nb_pixel_total : 91154 time to create 1 rle with old method : 0.10496997833251953 length of segment : 304 time for calcul the mask position with numpy : 0.003965139389038086 nb_pixel_total : 56235 time to create 1 rle with old method : 0.06268548965454102 length of segment : 444 time for calcul the mask position with numpy : 0.007283687591552734 nb_pixel_total : 182246 time to create 1 rle with new method : 0.012249469757080078 length of segment : 597 time for calcul the mask position with numpy : 0.0002789497375488281 nb_pixel_total : 4738 time to create 1 rle with old method : 0.0054624080657958984 length of segment : 101 time for calcul the mask position with numpy : 0.00036787986755371094 nb_pixel_total : 5746 time to create 1 rle with old method : 0.0066220760345458984 length of segment : 107 time for calcul the mask position with numpy : 0.000537872314453125 nb_pixel_total : 9527 time to create 1 rle with old method : 0.011445283889770508 length of segment : 116 time for calcul the mask position with numpy : 0.002985239028930664 nb_pixel_total : 45918 time to create 1 rle with old method : 0.052992820739746094 length of segment : 261 time for calcul the mask position with numpy : 0.005196809768676758 nb_pixel_total : 77241 time to create 1 rle with old method : 0.08912515640258789 length of segment : 322 time for calcul the mask position with numpy : 0.0008351802825927734 nb_pixel_total : 9965 time to create 1 rle with old method : 0.011510372161865234 length of segment : 164 time for calcul the mask position with numpy : 0.003507375717163086 nb_pixel_total : 47564 time to create 1 rle with old method : 0.05454874038696289 length of segment : 240 time for calcul the mask position with numpy : 0.0009503364562988281 nb_pixel_total : 10974 time to create 1 rle with old method : 0.012886285781860352 length of segment : 122 time for calcul the mask position with numpy : 0.0010104179382324219 nb_pixel_total : 14612 time to create 1 rle with old method : 0.017012834548950195 length of segment : 124 time for calcul the mask position with numpy : 0.0025773048400878906 nb_pixel_total : 33638 time to create 1 rle with old method : 0.03890061378479004 length of segment : 239 time for calcul the mask position with numpy : 0.0008900165557861328 nb_pixel_total : 15654 time to create 1 rle with old method : 0.017940044403076172 length of segment : 147 time for calcul the mask position with numpy : 0.003771066665649414 nb_pixel_total : 58799 time to create 1 rle with old method : 0.06658148765563965 length of segment : 318 time for calcul the mask position with numpy : 0.0019443035125732422 nb_pixel_total : 24698 time to create 1 rle with old method : 0.028068065643310547 length of segment : 133 time for calcul the mask position with numpy : 0.0028104782104492188 nb_pixel_total : 39097 time to create 1 rle with old method : 0.04546189308166504 length of segment : 248 time for calcul the mask position with numpy : 0.023273229598999023 nb_pixel_total : 308592 time to create 1 rle with new method : 0.038686275482177734 length of segment : 666 time for calcul the mask position with numpy : 0.0013718605041503906 nb_pixel_total : 23475 time to create 1 rle with old method : 0.026630401611328125 length of segment : 253 time for calcul the mask position with numpy : 0.0014047622680664062 nb_pixel_total : 21984 time to create 1 rle with old method : 0.02496647834777832 length of segment : 182 time for calcul the mask position with numpy : 0.00048804283142089844 nb_pixel_total : 8099 time to create 1 rle with old method : 0.009387493133544922 length of segment : 96 time for calcul the mask position with numpy : 0.010167598724365234 nb_pixel_total : 109318 time to create 1 rle with old method : 0.15169715881347656 length of segment : 443 time for calcul the mask position with numpy : 0.0018482208251953125 nb_pixel_total : 18400 time to create 1 rle with old method : 0.0219268798828125 length of segment : 207 time for calcul the mask position with numpy : 0.0010387897491455078 nb_pixel_total : 16473 time to create 1 rle with old method : 0.018892765045166016 length of segment : 98 time for calcul the mask position with numpy : 0.007781028747558594 nb_pixel_total : 133344 time to create 1 rle with old method : 0.15035200119018555 length of segment : 396 time for calcul the mask position with numpy : 0.0018377304077148438 nb_pixel_total : 18809 time to create 1 rle with old method : 0.02105879783630371 length of segment : 190 time for calcul the mask position with numpy : 0.001880645751953125 nb_pixel_total : 30763 time to create 1 rle with old method : 0.03441452980041504 length of segment : 218 time for calcul the mask position with numpy : 0.0006361007690429688 nb_pixel_total : 8580 time to create 1 rle with old method : 0.009691476821899414 length of segment : 96 time for calcul the mask position with numpy : 0.003098011016845703 nb_pixel_total : 52276 time to create 1 rle with old method : 0.05774211883544922 length of segment : 226 time for calcul the mask position with numpy : 0.006075382232666016 nb_pixel_total : 116653 time to create 1 rle with old method : 0.12458610534667969 length of segment : 388 time for calcul the mask position with numpy : 0.004124164581298828 nb_pixel_total : 58001 time to create 1 rle with old method : 0.08396625518798828 length of segment : 250 time for calcul the mask position with numpy : 0.0075266361236572266 nb_pixel_total : 133136 time to create 1 rle with old method : 0.14775967597961426 length of segment : 311 time for calcul the mask position with numpy : 0.0015788078308105469 nb_pixel_total : 21551 time to create 1 rle with old method : 0.025156736373901367 length of segment : 147 time for calcul the mask position with numpy : 0.0010848045349121094 nb_pixel_total : 9016 time to create 1 rle with old method : 0.010338783264160156 length of segment : 146 time for calcul the mask position with numpy : 0.0008749961853027344 nb_pixel_total : 11781 time to create 1 rle with old method : 0.013462543487548828 length of segment : 95 time for calcul the mask position with numpy : 0.0005962848663330078 nb_pixel_total : 8953 time to create 1 rle with old method : 0.010720491409301758 length of segment : 52 time for calcul the mask position with numpy : 0.002135038375854492 nb_pixel_total : 32254 time to create 1 rle with old method : 0.03873944282531738 length of segment : 185 time for calcul the mask position with numpy : 0.0048062801361083984 nb_pixel_total : 85641 time to create 1 rle with old method : 0.09464192390441895 length of segment : 311 time for calcul the mask position with numpy : 0.0049896240234375 nb_pixel_total : 56551 time to create 1 rle with old method : 0.0625307559967041 length of segment : 343 time for calcul the mask position with numpy : 0.0009913444519042969 nb_pixel_total : 14186 time to create 1 rle with old method : 0.01603388786315918 length of segment : 130 time for calcul the mask position with numpy : 0.0005342960357666016 nb_pixel_total : 7223 time to create 1 rle with old method : 0.008296966552734375 length of segment : 156 time for calcul the mask position with numpy : 0.0006158351898193359 nb_pixel_total : 19722 time to create 1 rle with old method : 0.022854328155517578 length of segment : 138 time for calcul the mask position with numpy : 0.000667572021484375 nb_pixel_total : 9546 time to create 1 rle with old method : 0.010622501373291016 length of segment : 126 time for calcul the mask position with numpy : 0.0013408660888671875 nb_pixel_total : 26309 time to create 1 rle with old method : 0.028855562210083008 length of segment : 123 time for calcul the mask position with numpy : 0.0017867088317871094 nb_pixel_total : 15232 time to create 1 rle with old method : 0.01703023910522461 length of segment : 349 time for calcul the mask position with numpy : 0.0008814334869384766 nb_pixel_total : 13284 time to create 1 rle with old method : 0.015454530715942383 length of segment : 94 time for calcul the mask position with numpy : 0.0015151500701904297 nb_pixel_total : 27811 time to create 1 rle with old method : 0.0310819149017334 length of segment : 211 time for calcul the mask position with numpy : 0.002054452896118164 nb_pixel_total : 43978 time to create 1 rle with old method : 0.050778865814208984 length of segment : 182 time for calcul the mask position with numpy : 0.003255128860473633 nb_pixel_total : 59249 time to create 1 rle with old method : 0.06489920616149902 length of segment : 586 time for calcul the mask position with numpy : 0.0018956661224365234 nb_pixel_total : 36963 time to create 1 rle with old method : 0.04146289825439453 length of segment : 243 time for calcul the mask position with numpy : 0.002438068389892578 nb_pixel_total : 56559 time to create 1 rle with old method : 0.06053948402404785 length of segment : 315 time for calcul the mask position with numpy : 0.0007364749908447266 nb_pixel_total : 14166 time to create 1 rle with old method : 0.016008377075195312 length of segment : 193 time for calcul the mask position with numpy : 0.0021283626556396484 nb_pixel_total : 39087 time to create 1 rle with old method : 0.044710397720336914 length of segment : 224 time for calcul the mask position with numpy : 0.002089977264404297 nb_pixel_total : 45480 time to create 1 rle with old method : 0.0495760440826416 length of segment : 270 time for calcul the mask position with numpy : 0.015468358993530273 nb_pixel_total : 315236 time to create 1 rle with new method : 0.17145180702209473 length of segment : 922 time for calcul the mask position with numpy : 0.0011987686157226562 nb_pixel_total : 20823 time to create 1 rle with old method : 0.023148536682128906 length of segment : 158 time for calcul the mask position with numpy : 0.004431486129760742 nb_pixel_total : 93184 time to create 1 rle with old method : 0.10477423667907715 length of segment : 725 time for calcul the mask position with numpy : 0.00569462776184082 nb_pixel_total : 132183 time to create 1 rle with old method : 0.150282621383667 length of segment : 366 time for calcul the mask position with numpy : 0.01788926124572754 nb_pixel_total : 368792 time to create 1 rle with new method : 0.04928112030029297 length of segment : 844 time for calcul the mask position with numpy : 0.004254817962646484 nb_pixel_total : 85006 time to create 1 rle with old method : 0.09399628639221191 length of segment : 316 time for calcul the mask position with numpy : 0.012722969055175781 nb_pixel_total : 172352 time to create 1 rle with new method : 0.16859197616577148 length of segment : 954 time for calcul the mask position with numpy : 0.0003676414489746094 nb_pixel_total : 10114 time to create 1 rle with old method : 0.011044025421142578 length of segment : 138 time for calcul the mask position with numpy : 0.002106189727783203 nb_pixel_total : 48106 time to create 1 rle with old method : 0.053217172622680664 length of segment : 393 time for calcul the mask position with numpy : 0.0025887489318847656 nb_pixel_total : 53546 time to create 1 rle with old method : 0.06096220016479492 length of segment : 422 time for calcul the mask position with numpy : 0.005705118179321289 nb_pixel_total : 155185 time to create 1 rle with new method : 0.0066204071044921875 length of segment : 474 time for calcul the mask position with numpy : 0.0012216567993164062 nb_pixel_total : 14797 time to create 1 rle with old method : 0.016473054885864258 length of segment : 202 time for calcul the mask position with numpy : 0.00027108192443847656 nb_pixel_total : 7814 time to create 1 rle with old method : 0.00890207290649414 length of segment : 111 time for calcul the mask position with numpy : 0.00041604042053222656 nb_pixel_total : 15984 time to create 1 rle with old method : 0.018193960189819336 length of segment : 214 time for calcul the mask position with numpy : 0.0003654956817626953 nb_pixel_total : 4542 time to create 1 rle with old method : 0.005383729934692383 length of segment : 67 time for calcul the mask position with numpy : 0.0017552375793457031 nb_pixel_total : 36673 time to create 1 rle with old method : 0.04078483581542969 length of segment : 132 time for calcul the mask position with numpy : 0.004328727722167969 nb_pixel_total : 121518 time to create 1 rle with old method : 0.13171148300170898 length of segment : 424 time for calcul the mask position with numpy : 0.0033152103424072266 nb_pixel_total : 70397 time to create 1 rle with old method : 0.07733464241027832 length of segment : 363 time for calcul the mask position with numpy : 0.0009717941284179688 nb_pixel_total : 14543 time to create 1 rle with old method : 0.016559123992919922 length of segment : 151 time for calcul the mask position with numpy : 0.0018796920776367188 nb_pixel_total : 30555 time to create 1 rle with old method : 0.03561735153198242 length of segment : 181 time for calcul the mask position with numpy : 0.002759218215942383 nb_pixel_total : 80672 time to create 1 rle with old method : 0.08747172355651855 length of segment : 382 time for calcul the mask position with numpy : 0.0008101463317871094 nb_pixel_total : 30164 time to create 1 rle with old method : 0.03484320640563965 length of segment : 278 time for calcul the mask position with numpy : 0.0005166530609130859 nb_pixel_total : 11675 time to create 1 rle with old method : 0.013360023498535156 length of segment : 163 time for calcul the mask position with numpy : 0.0014693737030029297 nb_pixel_total : 68738 time to create 1 rle with old method : 0.07984066009521484 length of segment : 357 time for calcul the mask position with numpy : 0.001850128173828125 nb_pixel_total : 31421 time to create 1 rle with old method : 0.03716087341308594 length of segment : 243 time for calcul the mask position with numpy : 0.0004267692565917969 nb_pixel_total : 17051 time to create 1 rle with old method : 0.02048015594482422 length of segment : 144 time for calcul the mask position with numpy : 0.0015099048614501953 nb_pixel_total : 55114 time to create 1 rle with old method : 0.06241416931152344 length of segment : 486 time for calcul the mask position with numpy : 0.0004069805145263672 nb_pixel_total : 13718 time to create 1 rle with old method : 0.015703201293945312 length of segment : 234 time for calcul the mask position with numpy : 0.011137723922729492 nb_pixel_total : 436376 time to create 1 rle with new method : 0.02831244468688965 length of segment : 890 time for calcul the mask position with numpy : 0.0025887489318847656 nb_pixel_total : 138279 time to create 1 rle with old method : 0.14999842643737793 length of segment : 534 time for calcul the mask position with numpy : 0.0023636817932128906 nb_pixel_total : 91176 time to create 1 rle with old method : 0.10248804092407227 length of segment : 469 time for calcul the mask position with numpy : 0.0025131702423095703 nb_pixel_total : 123245 time to create 1 rle with old method : 0.13430571556091309 length of segment : 655 time for calcul the mask position with numpy : 0.00044846534729003906 nb_pixel_total : 24458 time to create 1 rle with old method : 0.026499509811401367 length of segment : 182 time for calcul the mask position with numpy : 0.0009720325469970703 nb_pixel_total : 72526 time to create 1 rle with old method : 0.0810999870300293 length of segment : 273 time for calcul the mask position with numpy : 0.0008492469787597656 nb_pixel_total : 51556 time to create 1 rle with old method : 0.05989956855773926 length of segment : 266 time for calcul the mask position with numpy : 0.0013492107391357422 nb_pixel_total : 77277 time to create 1 rle with old method : 0.08570218086242676 length of segment : 387 time for calcul the mask position with numpy : 0.002177715301513672 nb_pixel_total : 78279 time to create 1 rle with old method : 0.08769607543945312 length of segment : 610 time for calcul the mask position with numpy : 0.0029332637786865234 nb_pixel_total : 99469 time to create 1 rle with old method : 0.11098265647888184 length of segment : 304 time for calcul the mask position with numpy : 0.000942230224609375 nb_pixel_total : 30346 time to create 1 rle with old method : 0.05007433891296387 length of segment : 171 time for calcul the mask position with numpy : 0.00383758544921875 nb_pixel_total : 158149 time to create 1 rle with new method : 0.004891395568847656 length of segment : 578 time for calcul the mask position with numpy : 0.003934144973754883 nb_pixel_total : 145403 time to create 1 rle with old method : 0.16056132316589355 length of segment : 516 time for calcul the mask position with numpy : 0.0007677078247070312 nb_pixel_total : 29320 time to create 1 rle with old method : 0.032604217529296875 length of segment : 236 time for calcul the mask position with numpy : 0.03566622734069824 nb_pixel_total : 848078 time to create 1 rle with new method : 0.16148138046264648 length of segment : 1200 time for calcul the mask position with numpy : 0.001772165298461914 nb_pixel_total : 91465 time to create 1 rle with old method : 0.10042905807495117 length of segment : 226 time for calcul the mask position with numpy : 0.0012271404266357422 nb_pixel_total : 59766 time to create 1 rle with old method : 0.06581568717956543 length of segment : 239 time for calcul the mask position with numpy : 0.00047516822814941406 nb_pixel_total : 19862 time to create 1 rle with old method : 0.022609472274780273 length of segment : 246 time for calcul the mask position with numpy : 0.0011136531829833984 nb_pixel_total : 61375 time to create 1 rle with old method : 0.06836247444152832 length of segment : 568 time for calcul the mask position with numpy : 0.002477407455444336 nb_pixel_total : 86075 time to create 1 rle with old method : 0.09328079223632812 length of segment : 467 time for calcul the mask position with numpy : 0.0030052661895751953 nb_pixel_total : 163502 time to create 1 rle with new method : 0.004408836364746094 length of segment : 587 time for calcul the mask position with numpy : 0.0016949176788330078 nb_pixel_total : 86514 time to create 1 rle with old method : 0.09655022621154785 length of segment : 545 time for calcul the mask position with numpy : 0.00170135498046875 nb_pixel_total : 82178 time to create 1 rle with old method : 0.09204435348510742 length of segment : 419 time for calcul the mask position with numpy : 0.001135110855102539 nb_pixel_total : 65001 time to create 1 rle with old method : 0.07391786575317383 length of segment : 526 time for calcul the mask position with numpy : 0.00035572052001953125 nb_pixel_total : 5445 time to create 1 rle with old method : 0.0065610408782958984 length of segment : 100 time for calcul the mask position with numpy : 0.0010936260223388672 nb_pixel_total : 47428 time to create 1 rle with old method : 0.054140329360961914 length of segment : 210 time for calcul the mask position with numpy : 0.0019092559814453125 nb_pixel_total : 94794 time to create 1 rle with old method : 0.10514426231384277 length of segment : 227 time for calcul the mask position with numpy : 0.0027017593383789062 nb_pixel_total : 84894 time to create 1 rle with old method : 0.08834457397460938 length of segment : 654 time for calcul the mask position with numpy : 0.0006184577941894531 nb_pixel_total : 12850 time to create 1 rle with old method : 0.014172554016113281 length of segment : 157 time for calcul the mask position with numpy : 0.003139019012451172 nb_pixel_total : 58085 time to create 1 rle with old method : 0.06196951866149902 length of segment : 519 time for calcul the mask position with numpy : 0.0016922950744628906 nb_pixel_total : 35501 time to create 1 rle with old method : 0.03973126411437988 length of segment : 231 time for calcul the mask position with numpy : 0.0017032623291015625 nb_pixel_total : 56727 time to create 1 rle with old method : 0.06352353096008301 length of segment : 267 time for calcul the mask position with numpy : 0.002298593521118164 nb_pixel_total : 53837 time to create 1 rle with old method : 0.05837392807006836 length of segment : 324 time for calcul the mask position with numpy : 0.0011141300201416016 nb_pixel_total : 21551 time to create 1 rle with old method : 0.024183988571166992 length of segment : 218 time for calcul the mask position with numpy : 0.004292964935302734 nb_pixel_total : 137373 time to create 1 rle with old method : 0.1478888988494873 length of segment : 418 time for calcul the mask position with numpy : 0.001392364501953125 nb_pixel_total : 46365 time to create 1 rle with old method : 0.051840782165527344 length of segment : 230 time for calcul the mask position with numpy : 0.0003650188446044922 nb_pixel_total : 7696 time to create 1 rle with old method : 0.008774280548095703 length of segment : 98 time for calcul the mask position with numpy : 0.0010647773742675781 nb_pixel_total : 25355 time to create 1 rle with old method : 0.027453899383544922 length of segment : 241 time for calcul the mask position with numpy : 0.0021409988403320312 nb_pixel_total : 43490 time to create 1 rle with old method : 0.049458980560302734 length of segment : 309 time for calcul the mask position with numpy : 0.006732463836669922 nb_pixel_total : 198709 time to create 1 rle with new method : 0.00812673568725586 length of segment : 568 time for calcul the mask position with numpy : 0.0011377334594726562 nb_pixel_total : 34426 time to create 1 rle with old method : 0.03698372840881348 length of segment : 496 time for calcul the mask position with numpy : 0.00029754638671875 nb_pixel_total : 7958 time to create 1 rle with old method : 0.009425878524780273 length of segment : 105 time for calcul the mask position with numpy : 0.00208282470703125 nb_pixel_total : 52038 time to create 1 rle with old method : 0.05955910682678223 length of segment : 486 time for calcul the mask position with numpy : 0.0017063617706298828 nb_pixel_total : 52411 time to create 1 rle with old method : 0.059938669204711914 length of segment : 301 time for calcul the mask position with numpy : 0.0015685558319091797 nb_pixel_total : 38684 time to create 1 rle with old method : 0.04478907585144043 length of segment : 225 time for calcul the mask position with numpy : 0.0012316703796386719 nb_pixel_total : 27161 time to create 1 rle with old method : 0.031412363052368164 length of segment : 188 time for calcul the mask position with numpy : 0.0008420944213867188 nb_pixel_total : 24957 time to create 1 rle with old method : 0.028850555419921875 length of segment : 211 time for calcul the mask position with numpy : 0.010111331939697266 nb_pixel_total : 325524 time to create 1 rle with new method : 0.015725374221801758 length of segment : 752 time for calcul the mask position with numpy : 0.005495786666870117 nb_pixel_total : 161661 time to create 1 rle with new method : 0.006890535354614258 length of segment : 656 time for calcul the mask position with numpy : 0.003371715545654297 nb_pixel_total : 78349 time to create 1 rle with old method : 0.08835053443908691 length of segment : 260 time for calcul the mask position with numpy : 0.0014147758483886719 nb_pixel_total : 32808 time to create 1 rle with old method : 0.0371246337890625 length of segment : 199 time for calcul the mask position with numpy : 0.0003871917724609375 nb_pixel_total : 5430 time to create 1 rle with old method : 0.006369829177856445 length of segment : 73 time for calcul the mask position with numpy : 0.007046937942504883 nb_pixel_total : 98831 time to create 1 rle with old method : 0.11612343788146973 length of segment : 541 time for calcul the mask position with numpy : 0.0022056102752685547 nb_pixel_total : 31358 time to create 1 rle with old method : 0.03539276123046875 length of segment : 210 time for calcul the mask position with numpy : 0.0030395984649658203 nb_pixel_total : 46988 time to create 1 rle with old method : 0.05551719665527344 length of segment : 210 time for calcul the mask position with numpy : 0.0013844966888427734 nb_pixel_total : 15243 time to create 1 rle with old method : 0.017301082611083984 length of segment : 192 time for calcul the mask position with numpy : 0.0011103153228759766 nb_pixel_total : 15430 time to create 1 rle with old method : 0.017694950103759766 length of segment : 141 time for calcul the mask position with numpy : 0.0007722377777099609 nb_pixel_total : 9214 time to create 1 rle with old method : 0.011844635009765625 length of segment : 124 time for calcul the mask position with numpy : 0.0029175281524658203 nb_pixel_total : 35688 time to create 1 rle with old method : 0.04060840606689453 length of segment : 342 time for calcul the mask position with numpy : 0.0012154579162597656 nb_pixel_total : 12783 time to create 1 rle with old method : 0.01469278335571289 length of segment : 248 time for calcul the mask position with numpy : 0.002317667007446289 nb_pixel_total : 17838 time to create 1 rle with old method : 0.02071976661682129 length of segment : 273 time for calcul the mask position with numpy : 0.0035173892974853516 nb_pixel_total : 45296 time to create 1 rle with old method : 0.051262855529785156 length of segment : 317 time for calcul the mask position with numpy : 0.0011196136474609375 nb_pixel_total : 14089 time to create 1 rle with old method : 0.016259193420410156 length of segment : 91 time for calcul the mask position with numpy : 0.006878852844238281 nb_pixel_total : 51831 time to create 1 rle with old method : 0.05898451805114746 length of segment : 464 time for calcul the mask position with numpy : 0.00958704948425293 nb_pixel_total : 100879 time to create 1 rle with old method : 0.11211299896240234 length of segment : 430 time for calcul the mask position with numpy : 0.0007250308990478516 nb_pixel_total : 9611 time to create 1 rle with old method : 0.01491093635559082 length of segment : 111 time for calcul the mask position with numpy : 0.00035953521728515625 nb_pixel_total : 9545 time to create 1 rle with old method : 0.015846729278564453 length of segment : 94 time for calcul the mask position with numpy : 0.0018477439880371094 nb_pixel_total : 33110 time to create 1 rle with old method : 0.05708456039428711 length of segment : 312 time for calcul the mask position with numpy : 0.0007503032684326172 nb_pixel_total : 7759 time to create 1 rle with old method : 0.01028299331665039 length of segment : 80 time for calcul the mask position with numpy : 0.001241922378540039 nb_pixel_total : 20096 time to create 1 rle with old method : 0.02339458465576172 length of segment : 145 time for calcul the mask position with numpy : 0.0027985572814941406 nb_pixel_total : 32155 time to create 1 rle with old method : 0.03744387626647949 length of segment : 373 time for calcul the mask position with numpy : 0.0014500617980957031 nb_pixel_total : 24637 time to create 1 rle with old method : 0.037331581115722656 length of segment : 172 time for calcul the mask position with numpy : 0.0005471706390380859 nb_pixel_total : 4635 time to create 1 rle with old method : 0.007794857025146484 length of segment : 87 time for calcul the mask position with numpy : 0.003475189208984375 nb_pixel_total : 61907 time to create 1 rle with old method : 0.07104134559631348 length of segment : 270 time for calcul the mask position with numpy : 0.0027701854705810547 nb_pixel_total : 28415 time to create 1 rle with old method : 0.032283782958984375 length of segment : 238 time for calcul the mask position with numpy : 0.001596212387084961 nb_pixel_total : 23330 time to create 1 rle with old method : 0.02685999870300293 length of segment : 152 time for calcul the mask position with numpy : 0.0014476776123046875 nb_pixel_total : 22033 time to create 1 rle with old method : 0.024970531463623047 length of segment : 206 time for calcul the mask position with numpy : 0.0006313323974609375 nb_pixel_total : 11194 time to create 1 rle with old method : 0.012598514556884766 length of segment : 115 time for calcul the mask position with numpy : 0.0004184246063232422 nb_pixel_total : 5998 time to create 1 rle with old method : 0.007033824920654297 length of segment : 86 time for calcul the mask position with numpy : 0.0019023418426513672 nb_pixel_total : 31761 time to create 1 rle with old method : 0.03499317169189453 length of segment : 225 time for calcul the mask position with numpy : 0.0007770061492919922 nb_pixel_total : 12136 time to create 1 rle with old method : 0.01332855224609375 length of segment : 125 time for calcul the mask position with numpy : 0.0010044574737548828 nb_pixel_total : 12739 time to create 1 rle with old method : 0.013982534408569336 length of segment : 173 time for calcul the mask position with numpy : 0.0023615360260009766 nb_pixel_total : 54591 time to create 1 rle with old method : 0.06425189971923828 length of segment : 153 time for calcul the mask position with numpy : 0.0009539127349853516 nb_pixel_total : 14208 time to create 1 rle with old method : 0.01752328872680664 length of segment : 173 time for calcul the mask position with numpy : 0.0011265277862548828 nb_pixel_total : 22298 time to create 1 rle with old method : 0.02882218360900879 length of segment : 259 time for calcul the mask position with numpy : 0.0013973712921142578 nb_pixel_total : 18382 time to create 1 rle with old method : 0.02306056022644043 length of segment : 150 time for calcul the mask position with numpy : 0.0021584033966064453 nb_pixel_total : 31696 time to create 1 rle with old method : 0.039617061614990234 length of segment : 292 time for calcul the mask position with numpy : 0.0011706352233886719 nb_pixel_total : 22997 time to create 1 rle with old method : 0.028337955474853516 length of segment : 156 time for calcul the mask position with numpy : 0.0009543895721435547 nb_pixel_total : 13531 time to create 1 rle with old method : 0.015944719314575195 length of segment : 92 time for calcul the mask position with numpy : 0.0013427734375 nb_pixel_total : 18813 time to create 1 rle with old method : 0.02250218391418457 length of segment : 170 time for calcul the mask position with numpy : 0.005778789520263672 nb_pixel_total : 70632 time to create 1 rle with old method : 0.08939313888549805 length of segment : 287 time for calcul the mask position with numpy : 0.0009098052978515625 nb_pixel_total : 22688 time to create 1 rle with old method : 0.025046110153198242 length of segment : 177 time for calcul the mask position with numpy : 0.0005407333374023438 nb_pixel_total : 5884 time to create 1 rle with old method : 0.006482601165771484 length of segment : 102 time for calcul the mask position with numpy : 0.0026335716247558594 nb_pixel_total : 39388 time to create 1 rle with old method : 0.04113483428955078 length of segment : 361 time for calcul the mask position with numpy : 0.0013818740844726562 nb_pixel_total : 23148 time to create 1 rle with old method : 0.025491714477539062 length of segment : 283 time for calcul the mask position with numpy : 0.0034477710723876953 nb_pixel_total : 45700 time to create 1 rle with old method : 0.048560380935668945 length of segment : 426 time for calcul the mask position with numpy : 0.0005581378936767578 nb_pixel_total : 11008 time to create 1 rle with old method : 0.012505292892456055 length of segment : 200 time for calcul the mask position with numpy : 0.002813100814819336 nb_pixel_total : 44197 time to create 1 rle with old method : 0.04637408256530762 length of segment : 295 time for calcul the mask position with numpy : 0.0006842613220214844 nb_pixel_total : 25729 time to create 1 rle with old method : 0.027345895767211914 length of segment : 263 time for calcul the mask position with numpy : 0.0011219978332519531 nb_pixel_total : 17524 time to create 1 rle with old method : 0.01970696449279785 length of segment : 180 time for calcul the mask position with numpy : 0.000579833984375 nb_pixel_total : 18221 time to create 1 rle with old method : 0.02001500129699707 length of segment : 141 time for calcul the mask position with numpy : 0.0010633468627929688 nb_pixel_total : 24865 time to create 1 rle with old method : 0.026459693908691406 length of segment : 190 time for calcul the mask position with numpy : 0.0010263919830322266 nb_pixel_total : 20510 time to create 1 rle with old method : 0.02240133285522461 length of segment : 232 time for calcul the mask position with numpy : 0.004224300384521484 nb_pixel_total : 76615 time to create 1 rle with old method : 0.08136391639709473 length of segment : 499 time for calcul the mask position with numpy : 0.0003230571746826172 nb_pixel_total : 7641 time to create 1 rle with old method : 0.008177995681762695 length of segment : 122 time for calcul the mask position with numpy : 0.0008485317230224609 nb_pixel_total : 15541 time to create 1 rle with old method : 0.0172882080078125 length of segment : 288 time for calcul the mask position with numpy : 0.0003857612609863281 nb_pixel_total : 11255 time to create 1 rle with old method : 0.01262354850769043 length of segment : 112 time for calcul the mask position with numpy : 0.0006461143493652344 nb_pixel_total : 14460 time to create 1 rle with old method : 0.015946388244628906 length of segment : 182 time for calcul the mask position with numpy : 0.001697540283203125 nb_pixel_total : 24405 time to create 1 rle with old method : 0.027538299560546875 length of segment : 278 time for calcul the mask position with numpy : 0.0008935928344726562 nb_pixel_total : 15799 time to create 1 rle with old method : 0.01824498176574707 length of segment : 144 time for calcul the mask position with numpy : 0.0007970333099365234 nb_pixel_total : 28982 time to create 1 rle with old method : 0.03369140625 length of segment : 270 time for calcul the mask position with numpy : 0.00654149055480957 nb_pixel_total : 126401 time to create 1 rle with old method : 0.1464402675628662 length of segment : 641 time for calcul the mask position with numpy : 0.0004134178161621094 nb_pixel_total : 5075 time to create 1 rle with old method : 0.0059812068939208984 length of segment : 97 time for calcul the mask position with numpy : 0.002549886703491211 nb_pixel_total : 50784 time to create 1 rle with old method : 0.05813121795654297 length of segment : 289 time for calcul the mask position with numpy : 0.010372400283813477 nb_pixel_total : 107577 time to create 1 rle with old method : 0.14347124099731445 length of segment : 570 time for calcul the mask position with numpy : 0.0004107952117919922 nb_pixel_total : 8651 time to create 1 rle with old method : 0.009873390197753906 length of segment : 164 time for calcul the mask position with numpy : 0.0009672641754150391 nb_pixel_total : 18306 time to create 1 rle with old method : 0.020561933517456055 length of segment : 182 time for calcul the mask position with numpy : 0.0007076263427734375 nb_pixel_total : 21987 time to create 1 rle with old method : 0.024660825729370117 length of segment : 216 time for calcul the mask position with numpy : 0.0008590221405029297 nb_pixel_total : 11787 time to create 1 rle with old method : 0.01361989974975586 length of segment : 113 time for calcul the mask position with numpy : 0.000896453857421875 nb_pixel_total : 18898 time to create 1 rle with old method : 0.021372079849243164 length of segment : 163 time for calcul the mask position with numpy : 0.0005671977996826172 nb_pixel_total : 12451 time to create 1 rle with old method : 0.01396489143371582 length of segment : 287 time for calcul the mask position with numpy : 0.0011241436004638672 nb_pixel_total : 17795 time to create 1 rle with old method : 0.020632505416870117 length of segment : 123 time for calcul the mask position with numpy : 0.0024869441986083984 nb_pixel_total : 121385 time to create 1 rle with old method : 0.13264107704162598 length of segment : 633 time for calcul the mask position with numpy : 0.0024933815002441406 nb_pixel_total : 29799 time to create 1 rle with old method : 0.03732609748840332 length of segment : 225 time for calcul the mask position with numpy : 0.0024559497833251953 nb_pixel_total : 48347 time to create 1 rle with old method : 0.05449652671813965 length of segment : 190 time for calcul the mask position with numpy : 0.0007603168487548828 nb_pixel_total : 10686 time to create 1 rle with old method : 0.012303352355957031 length of segment : 117 time for calcul the mask position with numpy : 0.0013544559478759766 nb_pixel_total : 18487 time to create 1 rle with old method : 0.021246671676635742 length of segment : 188 time for calcul the mask position with numpy : 0.0017502307891845703 nb_pixel_total : 22547 time to create 1 rle with old method : 0.0256502628326416 length of segment : 168 time for calcul the mask position with numpy : 0.002160787582397461 nb_pixel_total : 34261 time to create 1 rle with old method : 0.038013458251953125 length of segment : 206 time for calcul the mask position with numpy : 0.00334930419921875 nb_pixel_total : 39349 time to create 1 rle with old method : 0.0456693172454834 length of segment : 218 time for calcul the mask position with numpy : 0.001012563705444336 nb_pixel_total : 14606 time to create 1 rle with old method : 0.016996145248413086 length of segment : 144 time for calcul the mask position with numpy : 0.00096893310546875 nb_pixel_total : 14694 time to create 1 rle with old method : 0.017002344131469727 length of segment : 113 time for calcul the mask position with numpy : 0.002603769302368164 nb_pixel_total : 30644 time to create 1 rle with old method : 0.03589987754821777 length of segment : 267 time for calcul the mask position with numpy : 0.0033898353576660156 nb_pixel_total : 30171 time to create 1 rle with old method : 0.05029582977294922 length of segment : 219 time for calcul the mask position with numpy : 0.0025169849395751953 nb_pixel_total : 41218 time to create 1 rle with old method : 0.04673123359680176 length of segment : 206 time for calcul the mask position with numpy : 0.0020475387573242188 nb_pixel_total : 25918 time to create 1 rle with old method : 0.030071735382080078 length of segment : 356 time for calcul the mask position with numpy : 0.0013751983642578125 nb_pixel_total : 17341 time to create 1 rle with old method : 0.020150184631347656 length of segment : 148 time for calcul the mask position with numpy : 0.0021207332611083984 nb_pixel_total : 37392 time to create 1 rle with old method : 0.04381155967712402 length of segment : 127 time for calcul the mask position with numpy : 0.003171205520629883 nb_pixel_total : 45714 time to create 1 rle with old method : 0.05169677734375 length of segment : 217 time for calcul the mask position with numpy : 0.0009672641754150391 nb_pixel_total : 14493 time to create 1 rle with old method : 0.016942739486694336 length of segment : 146 time for calcul the mask position with numpy : 0.0008156299591064453 nb_pixel_total : 8670 time to create 1 rle with old method : 0.010047674179077148 length of segment : 90 time for calcul the mask position with numpy : 0.002381563186645508 nb_pixel_total : 29019 time to create 1 rle with old method : 0.03279590606689453 length of segment : 203 time for calcul the mask position with numpy : 0.0019843578338623047 nb_pixel_total : 23567 time to create 1 rle with old method : 0.027016162872314453 length of segment : 176 time for calcul the mask position with numpy : 0.0019588470458984375 nb_pixel_total : 34017 time to create 1 rle with old method : 0.03908348083496094 length of segment : 256 time for calcul the mask position with numpy : 0.003963947296142578 nb_pixel_total : 66871 time to create 1 rle with old method : 0.07467818260192871 length of segment : 516 time for calcul the mask position with numpy : 0.0009198188781738281 nb_pixel_total : 10395 time to create 1 rle with old method : 0.012282609939575195 length of segment : 166 time for calcul the mask position with numpy : 0.007361650466918945 nb_pixel_total : 69916 time to create 1 rle with old method : 0.0812234878540039 length of segment : 280 time for calcul the mask position with numpy : 0.001905679702758789 nb_pixel_total : 28964 time to create 1 rle with old method : 0.03360414505004883 length of segment : 185 time for calcul the mask position with numpy : 0.00046062469482421875 nb_pixel_total : 6873 time to create 1 rle with old method : 0.007653951644897461 length of segment : 107 time for calcul the mask position with numpy : 0.001962900161743164 nb_pixel_total : 31765 time to create 1 rle with old method : 0.036149024963378906 length of segment : 175 time for calcul the mask position with numpy : 0.00017905235290527344 nb_pixel_total : 6081 time to create 1 rle with old method : 0.007001161575317383 length of segment : 77 time for calcul the mask position with numpy : 0.00090789794921875 nb_pixel_total : 13669 time to create 1 rle with old method : 0.01551055908203125 length of segment : 122 time for calcul the mask position with numpy : 0.0008668899536132812 nb_pixel_total : 11455 time to create 1 rle with old method : 0.013192176818847656 length of segment : 82 time for calcul the mask position with numpy : 0.0005457401275634766 nb_pixel_total : 5332 time to create 1 rle with old method : 0.009183406829833984 length of segment : 101 time for calcul the mask position with numpy : 0.0018513202667236328 nb_pixel_total : 17069 time to create 1 rle with old method : 0.02846670150756836 length of segment : 184 time for calcul the mask position with numpy : 0.0064716339111328125 nb_pixel_total : 90950 time to create 1 rle with old method : 0.1037139892578125 length of segment : 310 time for calcul the mask position with numpy : 0.0021409988403320312 nb_pixel_total : 29029 time to create 1 rle with old method : 0.033410072326660156 length of segment : 209 time for calcul the mask position with numpy : 0.0006837844848632812 nb_pixel_total : 16535 time to create 1 rle with old method : 0.01925516128540039 length of segment : 129 time for calcul the mask position with numpy : 0.006218671798706055 nb_pixel_total : 68319 time to create 1 rle with old method : 0.0782010555267334 length of segment : 426 time for calcul the mask position with numpy : 0.01497340202331543 nb_pixel_total : 198766 time to create 1 rle with new method : 0.014992475509643555 length of segment : 495 time for calcul the mask position with numpy : 0.0018048286437988281 nb_pixel_total : 19316 time to create 1 rle with old method : 0.02221989631652832 length of segment : 175 time for calcul the mask position with numpy : 0.0056304931640625 nb_pixel_total : 54241 time to create 1 rle with old method : 0.09729719161987305 length of segment : 234 time for calcul the mask position with numpy : 0.0013704299926757812 nb_pixel_total : 17018 time to create 1 rle with old method : 0.030842065811157227 length of segment : 138 time for calcul the mask position with numpy : 0.0010988712310791016 nb_pixel_total : 13600 time to create 1 rle with old method : 0.02505016326904297 length of segment : 122 time for calcul the mask position with numpy : 0.001611948013305664 nb_pixel_total : 17491 time to create 1 rle with old method : 0.031778573989868164 length of segment : 181 time for calcul the mask position with numpy : 0.001421213150024414 nb_pixel_total : 15086 time to create 1 rle with old method : 0.0272371768951416 length of segment : 128 time for calcul the mask position with numpy : 0.0002701282501220703 nb_pixel_total : 3584 time to create 1 rle with old method : 0.006764888763427734 length of segment : 58 time for calcul the mask position with numpy : 0.0012934207916259766 nb_pixel_total : 12995 time to create 1 rle with old method : 0.023556947708129883 length of segment : 120 time for calcul the mask position with numpy : 0.0012786388397216797 nb_pixel_total : 19067 time to create 1 rle with old method : 0.03368806838989258 length of segment : 184 time for calcul the mask position with numpy : 0.0026450157165527344 nb_pixel_total : 33153 time to create 1 rle with old method : 0.05778360366821289 length of segment : 214 time for calcul the mask position with numpy : 0.0020275115966796875 nb_pixel_total : 25203 time to create 1 rle with old method : 0.02857494354248047 length of segment : 221 time for calcul the mask position with numpy : 0.0004868507385253906 nb_pixel_total : 8454 time to create 1 rle with old method : 0.009917974472045898 length of segment : 80 time for calcul the mask position with numpy : 0.0021843910217285156 nb_pixel_total : 35163 time to create 1 rle with old method : 0.040106773376464844 length of segment : 300 time for calcul the mask position with numpy : 0.00508880615234375 nb_pixel_total : 37579 time to create 1 rle with old method : 0.04336118698120117 length of segment : 295 time for calcul the mask position with numpy : 0.0004775524139404297 nb_pixel_total : 8598 time to create 1 rle with old method : 0.009934425354003906 length of segment : 160 time for calcul the mask position with numpy : 0.0007832050323486328 nb_pixel_total : 9033 time to create 1 rle with old method : 0.010956287384033203 length of segment : 68 time for calcul the mask position with numpy : 0.005208253860473633 nb_pixel_total : 80485 time to create 1 rle with old method : 0.09080147743225098 length of segment : 554 time for calcul the mask position with numpy : 0.006663799285888672 nb_pixel_total : 150911 time to create 1 rle with new method : 0.00715184211730957 length of segment : 451 time for calcul the mask position with numpy : 0.002952098846435547 nb_pixel_total : 30507 time to create 1 rle with old method : 0.03496050834655762 length of segment : 342 time for calcul the mask position with numpy : 0.002728700637817383 nb_pixel_total : 60986 time to create 1 rle with old method : 0.0704033374786377 length of segment : 244 time for calcul the mask position with numpy : 0.0010797977447509766 nb_pixel_total : 23178 time to create 1 rle with old method : 0.026851654052734375 length of segment : 254 time for calcul the mask position with numpy : 0.008859634399414062 nb_pixel_total : 145724 time to create 1 rle with old method : 0.164719820022583 length of segment : 365 time for calcul the mask position with numpy : 0.0013818740844726562 nb_pixel_total : 20246 time to create 1 rle with old method : 0.02333235740661621 length of segment : 234 time for calcul the mask position with numpy : 0.0008504390716552734 nb_pixel_total : 21385 time to create 1 rle with old method : 0.025003910064697266 length of segment : 117 time for calcul the mask position with numpy : 0.0012307167053222656 nb_pixel_total : 19173 time to create 1 rle with old method : 0.02194070816040039 length of segment : 220 time for calcul the mask position with numpy : 0.009295940399169922 nb_pixel_total : 163845 time to create 1 rle with new method : 0.010420799255371094 length of segment : 536 time for calcul the mask position with numpy : 0.0021915435791015625 nb_pixel_total : 38539 time to create 1 rle with old method : 0.049269676208496094 length of segment : 213 time for calcul the mask position with numpy : 0.001165628433227539 nb_pixel_total : 24526 time to create 1 rle with old method : 0.028073787689208984 length of segment : 209 time for calcul the mask position with numpy : 0.004987239837646484 nb_pixel_total : 114435 time to create 1 rle with old method : 0.12939834594726562 length of segment : 335 time for calcul the mask position with numpy : 0.004244327545166016 nb_pixel_total : 94142 time to create 1 rle with old method : 0.10660123825073242 length of segment : 614 time for calcul the mask position with numpy : 0.0006859302520751953 nb_pixel_total : 19210 time to create 1 rle with old method : 0.022642135620117188 length of segment : 167 time for calcul the mask position with numpy : 0.016796350479125977 nb_pixel_total : 452006 time to create 1 rle with new method : 0.16399145126342773 length of segment : 598 time for calcul the mask position with numpy : 0.0004420280456542969 nb_pixel_total : 15172 time to create 1 rle with old method : 0.017679691314697266 length of segment : 117 time for calcul the mask position with numpy : 0.0004010200500488281 nb_pixel_total : 8206 time to create 1 rle with old method : 0.009624004364013672 length of segment : 112 time for calcul the mask position with numpy : 0.0004630088806152344 nb_pixel_total : 12305 time to create 1 rle with old method : 0.014510393142700195 length of segment : 161 time for calcul the mask position with numpy : 0.003422260284423828 nb_pixel_total : 83861 time to create 1 rle with old method : 0.0937652587890625 length of segment : 478 time for calcul the mask position with numpy : 0.0009381771087646484 nb_pixel_total : 23575 time to create 1 rle with old method : 0.026968955993652344 length of segment : 253 time for calcul the mask position with numpy : 0.002666473388671875 nb_pixel_total : 35985 time to create 1 rle with old method : 0.03991055488586426 length of segment : 479 time for calcul the mask position with numpy : 0.0007162094116210938 nb_pixel_total : 9795 time to create 1 rle with old method : 0.010901212692260742 length of segment : 168 time for calcul the mask position with numpy : 0.0012583732604980469 nb_pixel_total : 22005 time to create 1 rle with old method : 0.024667978286743164 length of segment : 257 time for calcul the mask position with numpy : 0.00023055076599121094 nb_pixel_total : 9547 time to create 1 rle with old method : 0.010776758193969727 length of segment : 162 time for calcul the mask position with numpy : 0.00044035911560058594 nb_pixel_total : 17571 time to create 1 rle with old method : 0.019960403442382812 length of segment : 129 time for calcul the mask position with numpy : 0.006593465805053711 nb_pixel_total : 176529 time to create 1 rle with new method : 0.01335906982421875 length of segment : 650 time for calcul the mask position with numpy : 0.00046896934509277344 nb_pixel_total : 13815 time to create 1 rle with old method : 0.015899658203125 length of segment : 126 time for calcul the mask position with numpy : 0.0021147727966308594 nb_pixel_total : 45829 time to create 1 rle with old method : 0.0507810115814209 length of segment : 233 time for calcul the mask position with numpy : 0.0039675235748291016 nb_pixel_total : 80045 time to create 1 rle with old method : 0.08807253837585449 length of segment : 449 time for calcul the mask position with numpy : 0.0005598068237304688 nb_pixel_total : 14153 time to create 1 rle with old method : 0.015644311904907227 length of segment : 130 time spent for convertir_results : 36.839242458343506 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 703 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 84872 save missing photos in datou_result : time spend for datou_step_exec : 178.52608346939087 time spend to save output : 13.768248796463013 total time spend for step 1 : 192.29433226585388 step2:crop_condition Thu Apr 3 01:23:44 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 ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 13 ! batch 1 Loaded 703 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 ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 453 About to insert : list_path_to_insert length 453 new photo from crops ! About to upload 453 photos upload in portfolio : 3736932 init cache_photo without model_param we have 453 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743636323_407870 we have uploaded 453 photos in the portfolio 3736932 time of upload the photos Elapsed time : 152.53522324562073 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 126 About to insert : list_path_to_insert length 126 new photo from crops ! About to upload 126 photos upload in portfolio : 3736932 init cache_photo without model_param we have 126 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743636511_407870 we have uploaded 126 photos in the portfolio 3736932 time of upload the photos Elapsed time : 43.10167169570923 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 8 About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos upload in portfolio : 3736932 init cache_photo without model_param we have 8 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743636557_407870 we have uploaded 8 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.304295301437378 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 77 About to insert : list_path_to_insert length 77 new photo from crops ! About to upload 77 photos upload in portfolio : 3736932 init cache_photo without model_param we have 77 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743636603_407870 we have uploaded 77 photos in the portfolio 3736932 time of upload the photos Elapsed time : 23.295774221420288 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 16 About to insert : list_path_to_insert length 16 new photo from crops ! About to upload 16 photos upload in portfolio : 3736932 init cache_photo without model_param we have 16 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743636631_407870 we have uploaded 16 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.230294227600098 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! 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/1743636639_407870 we have uploaded 6 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.285133123397827 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 ! 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/1743636644_407870 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.95121169090271 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349664067, 1349664062, 1349664053, 1349664048, 1349517656, 1349517577, 1349516100, 1349516073, 1349516053, 1349516024, 1349515759, 1349515733, 1349515697] Looping around the photos to save general results len do output : 689 /1349689701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689724Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689789Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689862Didn't retrieve data 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/1349689889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689894Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689901Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689902Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689903Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689904Didn't retrieve data .Didn't retrieve data .Didn't 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data .Didn't retrieve data . /1349689918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689922Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689926Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349689930Didn't retrieve data 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/1349690025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690031Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690036Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690037Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690038Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690040Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690043Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690048Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690049Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690053Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690054Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690056Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690057Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690059Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690063Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690064Didn't retrieve data 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/1349690732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690760Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690770Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690802Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349690835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664067', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664062', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664048', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517656', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517577', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516100', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516073', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516024', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515759', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515733', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515697', None, None, None, None, None, '2718481') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 2080 time used for this insertion : 5.586474657058716 save_final save missing photos in datou_result : time spend for datou_step_exec : 420.54545307159424 time spend to save output : 5.6026389598846436 total time spend for step 2 : 426.1480920314789 step3:rle_unique_nms_with_priority Thu Apr 3 01:30:50 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 703 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 31 nb_hashtags : 3 time to prepare the origin masks : 5.275485038757324 time for calcul the mask position with numpy : 0.2086625099182129 nb_pixel_total : 3949429 time to create 1 rle with new method : 0.545182466506958 time for calcul the mask position with numpy : 0.03169083595275879 nb_pixel_total : 68015 time to create 1 rle with old method : 0.07901668548583984 time for calcul the mask position with numpy : 0.030973434448242188 nb_pixel_total : 12714 time to create 1 rle with old method : 0.015745162963867188 time for calcul the mask position with numpy : 0.029630661010742188 nb_pixel_total : 35976 time to create 1 rle with old method : 0.04584002494812012 time for calcul the mask position with numpy : 0.03727316856384277 nb_pixel_total : 7150 time to create 1 rle with old method : 0.01387786865234375 time for calcul the mask position with numpy : 0.037656307220458984 nb_pixel_total : 14104 time to create 1 rle with old method : 0.026903152465820312 time for calcul the mask position with numpy : 0.038703203201293945 nb_pixel_total : 147634 time to create 1 rle with old method : 0.22007036209106445 time for calcul the mask position with numpy : 0.035986900329589844 nb_pixel_total : 31749 time to create 1 rle with old method : 0.03643035888671875 time for calcul the mask position with numpy : 0.031017065048217773 nb_pixel_total : 83390 time to create 1 rle with old method : 0.09565114974975586 time for calcul the mask position with numpy : 0.030342578887939453 nb_pixel_total : 109010 time to create 1 rle with old method : 0.1247406005859375 time for calcul the mask position with numpy : 0.03005671501159668 nb_pixel_total : 35761 time to create 1 rle with old method : 0.040456533432006836 time for calcul the mask position with numpy : 0.029721975326538086 nb_pixel_total : 3376 time to create 1 rle with old method : 0.004130363464355469 time for calcul the mask position with numpy : 0.02929234504699707 nb_pixel_total : 2149 time to create 1 rle with old method : 0.0024826526641845703 time for calcul the mask position with numpy : 0.03139615058898926 nb_pixel_total : 194176 time to create 1 rle with new method : 0.40190911293029785 time for calcul the mask position with numpy : 0.10828113555908203 nb_pixel_total : 938325 time to create 1 rle with new method : 0.4293999671936035 time for calcul the mask position with numpy : 0.032985687255859375 nb_pixel_total : 20721 time to create 1 rle with old method : 0.025473833084106445 time for calcul the mask position with numpy : 0.03190469741821289 nb_pixel_total : 16757 time to create 1 rle with old method : 0.022072792053222656 time for calcul the mask position with numpy : 0.03135991096496582 nb_pixel_total : 57271 time to create 1 rle with old method : 0.06911110877990723 time for calcul the mask position with numpy : 0.030197858810424805 nb_pixel_total : 93927 time to create 1 rle with old method : 0.1180119514465332 time for calcul the mask position with numpy : 0.03792381286621094 nb_pixel_total : 780755 time to create 1 rle with new method : 0.3333401679992676 time for calcul the mask position with numpy : 0.030339717864990234 nb_pixel_total : 153752 time to create 1 rle with new method : 0.3567619323730469 time for calcul the mask position with numpy : 0.029434680938720703 nb_pixel_total : 11323 time to create 1 rle with old method : 0.012906789779663086 time for calcul the mask position with numpy : 0.03087306022644043 nb_pixel_total : 42864 time to create 1 rle with old method : 0.04941821098327637 time for calcul the mask position with numpy : 0.02994823455810547 nb_pixel_total : 15496 time to create 1 rle with old method : 0.021143674850463867 time for calcul the mask position with numpy : 0.0298154354095459 nb_pixel_total : 5947 time to create 1 rle with old method : 0.006842136383056641 time for calcul the mask position with numpy : 0.02998971939086914 nb_pixel_total : 36172 time to create 1 rle with old method : 0.04134702682495117 time for calcul the mask position with numpy : 0.029478073120117188 nb_pixel_total : 6993 time to create 1 rle with old method : 0.00800466537475586 time for calcul the mask position with numpy : 0.0300142765045166 nb_pixel_total : 130571 time to create 1 rle with old method : 0.15439176559448242 time for calcul the mask position with numpy : 0.02954268455505371 nb_pixel_total : 16367 time to create 1 rle with old method : 0.01818537712097168 time for calcul the mask position with numpy : 0.02934885025024414 nb_pixel_total : 15466 time to create 1 rle with old method : 0.017364501953125 time for calcul the mask position with numpy : 0.02912139892578125 nb_pixel_total : 3420 time to create 1 rle with old method : 0.0039064884185791016 time for calcul the mask position with numpy : 0.029036283493041992 nb_pixel_total : 9480 time to create 1 rle with old method : 0.010598182678222656 create new chi : 4.760791301727295 time to delete rle : 0.030477523803710938 batch 1 Loaded 63 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19262 TO DO : save crop sub photo not yet done ! save time : 7.797943592071533 nb_obj : 25 nb_hashtags : 4 time to prepare the origin masks : 11.291838645935059 time for calcul the mask position with numpy : 0.24911713600158691 nb_pixel_total : 5350021 time to create 1 rle with new method : 0.7567934989929199 time for calcul the mask position with numpy : 0.044820308685302734 nb_pixel_total : 25749 time to create 1 rle with old method : 0.030724525451660156 time for calcul the mask position with numpy : 0.04487872123718262 nb_pixel_total : 144259 time to create 1 rle with old method : 0.1607804298400879 time for calcul the mask position with numpy : 0.0420839786529541 nb_pixel_total : 77946 time to create 1 rle with old method : 0.10028719902038574 time for calcul the mask position with numpy : 0.04966163635253906 nb_pixel_total : 21810 time to create 1 rle with old method : 0.024986743927001953 time for calcul the mask position with numpy : 0.048660993576049805 nb_pixel_total : 331360 time to create 1 rle with new method : 0.908189058303833 time for calcul the mask position with numpy : 0.0440068244934082 nb_pixel_total : 44194 time to create 1 rle with old method : 0.05221390724182129 time for calcul the mask position with numpy : 0.05237722396850586 nb_pixel_total : 192036 time to create 1 rle with new method : 0.4075593948364258 time for calcul the mask position with numpy : 0.04402589797973633 nb_pixel_total : 10404 time to create 1 rle with old method : 0.01203608512878418 time for calcul the mask position with numpy : 0.057206153869628906 nb_pixel_total : 52129 time to create 1 rle with old method : 0.05937910079956055 time for calcul the mask position with numpy : 0.04510354995727539 nb_pixel_total : 22611 time to create 1 rle with old method : 0.02802443504333496 time for calcul the mask position with numpy : 0.04219675064086914 nb_pixel_total : 12183 time to create 1 rle with old method : 0.013892412185668945 time for calcul the mask position with numpy : 0.04792499542236328 nb_pixel_total : 75074 time to create 1 rle with old method : 0.0838932991027832 time for calcul the mask position with numpy : 0.0428922176361084 nb_pixel_total : 27584 time to create 1 rle with old method : 0.03149127960205078 time for calcul the mask position with numpy : 0.04448843002319336 nb_pixel_total : 9582 time to create 1 rle with old method : 0.010986566543579102 time for calcul the mask position with numpy : 0.04231834411621094 nb_pixel_total : 8084 time to create 1 rle with old method : 0.009401082992553711 time for calcul the mask position with numpy : 0.04737687110900879 nb_pixel_total : 60517 time to create 1 rle with old method : 0.06943607330322266 time for calcul the mask position with numpy : 0.04613065719604492 nb_pixel_total : 15590 time to create 1 rle with old method : 0.017760276794433594 time for calcul the mask position with numpy : 0.046604156494140625 nb_pixel_total : 349934 time to create 1 rle with new method : 0.8851673603057861 time for calcul the mask position with numpy : 0.04906606674194336 nb_pixel_total : 93821 time to create 1 rle with old method : 0.11659979820251465 time for calcul the mask position with numpy : 0.053708553314208984 nb_pixel_total : 6507 time to create 1 rle with old method : 0.007421255111694336 time for calcul the mask position with numpy : 0.05412483215332031 nb_pixel_total : 14970 time to create 1 rle with old method : 0.02065110206604004 time for calcul the mask position with numpy : 0.04273343086242676 nb_pixel_total : 35457 time to create 1 rle with old method : 0.04574275016784668 time for calcul the mask position with numpy : 0.053071022033691406 nb_pixel_total : 4614 time to create 1 rle with old method : 0.00554656982421875 time for calcul the mask position with numpy : 0.05150461196899414 nb_pixel_total : 49093 time to create 1 rle with old method : 0.06309247016906738 time for calcul the mask position with numpy : 0.05485033988952637 nb_pixel_total : 14711 time to create 1 rle with old method : 0.02275848388671875 create new chi : 5.525976657867432 time to delete rle : 0.004627227783203125 batch 1 Loaded 51 chid ids of type : 3594 ++++++++++++++++++++++++++++Number RLEs to save : 15972 TO DO : save crop sub photo not yet done ! save time : 5.895244121551514 nb_obj : 27 nb_hashtags : 2 time to prepare the origin masks : 4.481006383895874 time for calcul the mask position with numpy : 0.6642374992370605 nb_pixel_total : 5211435 time to create 1 rle with new method : 1.3502423763275146 time for calcul the mask position with numpy : 0.027252197265625 nb_pixel_total : 4223 time to create 1 rle with old method : 0.004719972610473633 time for calcul the mask position with numpy : 0.02709364891052246 nb_pixel_total : 18209 time to create 1 rle with old method : 0.020416975021362305 time for calcul the mask position with numpy : 0.028409719467163086 nb_pixel_total : 16548 time to create 1 rle with old method : 0.018494129180908203 time for calcul the mask position with numpy : 0.028078079223632812 nb_pixel_total : 32115 time to create 1 rle with old method : 0.03602170944213867 time for calcul the mask position with numpy : 0.029734373092651367 nb_pixel_total : 5367 time to create 1 rle with old method : 0.008841276168823242 time for calcul the mask position with numpy : 0.03309297561645508 nb_pixel_total : 22111 time to create 1 rle with old method : 0.0325160026550293 time for calcul the mask position with numpy : 0.027962923049926758 nb_pixel_total : 8062 time to create 1 rle with old method : 0.009034156799316406 time for calcul the mask position with numpy : 0.029720544815063477 nb_pixel_total : 13760 time to create 1 rle with old method : 0.014413118362426758 time for calcul the mask position with numpy : 0.02711176872253418 nb_pixel_total : 39873 time to create 1 rle with old method : 0.04368901252746582 time for calcul the mask position with numpy : 0.029883384704589844 nb_pixel_total : 178984 time to create 1 rle with new method : 0.6334700584411621 time for calcul the mask position with numpy : 0.02850651741027832 nb_pixel_total : 31952 time to create 1 rle with old method : 0.03506636619567871 time for calcul the mask position with numpy : 0.028488874435424805 nb_pixel_total : 156961 time to create 1 rle with new method : 0.5233049392700195 time for calcul the mask position with numpy : 0.028586149215698242 nb_pixel_total : 33075 time to create 1 rle with old method : 0.03566932678222656 time for calcul the mask position with numpy : 0.027149677276611328 nb_pixel_total : 52459 time to create 1 rle with old method : 0.05693221092224121 time for calcul the mask position with numpy : 0.027309417724609375 nb_pixel_total : 41437 time to create 1 rle with old method : 0.0444180965423584 time for calcul the mask position with numpy : 0.02959442138671875 nb_pixel_total : 347402 time to create 1 rle with new method : 0.8248152732849121 time for calcul the mask position with numpy : 0.028708457946777344 nb_pixel_total : 69236 time to create 1 rle with old method : 0.07815790176391602 time for calcul the mask position with numpy : 0.02848219871520996 nb_pixel_total : 21018 time to create 1 rle with old method : 0.0234982967376709 time for calcul the mask position with numpy : 0.028606176376342773 nb_pixel_total : 149831 time to create 1 rle with old method : 0.16522574424743652 time for calcul the mask position with numpy : 0.02884364128112793 nb_pixel_total : 44282 time to create 1 rle with old method : 0.05049872398376465 time for calcul the mask position with numpy : 0.02860569953918457 nb_pixel_total : 13553 time to create 1 rle with old method : 0.015148401260375977 time for calcul the mask position with numpy : 0.029026508331298828 nb_pixel_total : 191822 time to create 1 rle with new method : 1.2430756092071533 time for calcul the mask position with numpy : 0.02776336669921875 nb_pixel_total : 16781 time to create 1 rle with old method : 0.018862009048461914 time for calcul the mask position with numpy : 0.028683185577392578 nb_pixel_total : 165573 time to create 1 rle with new method : 0.6160571575164795 time for calcul the mask position with numpy : 0.029101133346557617 nb_pixel_total : 35162 time to create 1 rle with old method : 0.041188955307006836 time for calcul the mask position with numpy : 0.029645681381225586 nb_pixel_total : 127570 time to create 1 rle with old method : 0.14266657829284668 time for calcul the mask position with numpy : 0.027778148651123047 nb_pixel_total : 1439 time to create 1 rle with old method : 0.001689910888671875 create new chi : 7.6766440868377686 time to delete rle : 0.003047943115234375 batch 1 Loaded 55 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21192 TO DO : save crop sub photo not yet done ! save time : 5.04291296005249 nb_obj : 35 nb_hashtags : 3 time to prepare the origin masks : 4.583397626876831 time for calcul the mask position with numpy : 0.550849437713623 nb_pixel_total : 4931346 time to create 1 rle with new method : 0.923837423324585 time for calcul the mask position with numpy : 0.032502174377441406 nb_pixel_total : 15658 time to create 1 rle with old method : 0.017993927001953125 time for calcul the mask position with numpy : 0.0289306640625 nb_pixel_total : 18014 time to create 1 rle with old method : 0.02074575424194336 time for calcul the mask position with numpy : 0.02905559539794922 nb_pixel_total : 47675 time to create 1 rle with old method : 0.05517005920410156 time for calcul the mask position with numpy : 0.033080101013183594 nb_pixel_total : 515375 time to create 1 rle with new method : 1.487262487411499 time for calcul the mask position with numpy : 0.02893662452697754 nb_pixel_total : 60059 time to create 1 rle with old method : 0.06922769546508789 time for calcul the mask position with numpy : 0.028893232345581055 nb_pixel_total : 35754 time to create 1 rle with old method : 0.04114341735839844 time for calcul the mask position with numpy : 0.02893543243408203 nb_pixel_total : 27528 time to create 1 rle with old method : 0.03080892562866211 time for calcul the mask position with numpy : 0.028726577758789062 nb_pixel_total : 6433 time to create 1 rle with old method : 0.007541656494140625 time for calcul the mask position with numpy : 0.02920675277709961 nb_pixel_total : 19279 time to create 1 rle with old method : 0.022615909576416016 time for calcul the mask position with numpy : 0.028963088989257812 nb_pixel_total : 56235 time to create 1 rle with old method : 0.06487822532653809 time for calcul the mask position with numpy : 0.028957366943359375 nb_pixel_total : 36888 time to create 1 rle with old method : 0.04282355308532715 time for calcul the mask position with numpy : 0.02913975715637207 nb_pixel_total : 16404 time to create 1 rle with old method : 0.01854562759399414 time for calcul the mask position with numpy : 0.02888202667236328 nb_pixel_total : 18850 time to create 1 rle with old method : 0.0215451717376709 time for calcul the mask position with numpy : 0.029189109802246094 nb_pixel_total : 45557 time to create 1 rle with old method : 0.05144858360290527 time for calcul the mask position with numpy : 0.029564380645751953 nb_pixel_total : 91154 time to create 1 rle with old method : 0.10608673095703125 time for calcul the mask position with numpy : 0.03047013282775879 nb_pixel_total : 182246 time to create 1 rle with new method : 0.568781852722168 time for calcul the mask position with numpy : 0.035233259201049805 nb_pixel_total : 259330 time to create 1 rle with new method : 0.660038948059082 time for calcul the mask position with numpy : 0.029276609420776367 nb_pixel_total : 47026 time to create 1 rle with old method : 0.05392813682556152 time for calcul the mask position with numpy : 0.02879810333251953 nb_pixel_total : 3132 time to create 1 rle with old method : 0.0036668777465820312 time for calcul the mask position with numpy : 0.02887701988220215 nb_pixel_total : 28265 time to create 1 rle with old method : 0.03236699104309082 time for calcul the mask position with numpy : 0.029564619064331055 nb_pixel_total : 97881 time to create 1 rle with old method : 0.11165881156921387 time for calcul the mask position with numpy : 0.030184268951416016 nb_pixel_total : 35343 time to create 1 rle with old method : 0.0402529239654541 time for calcul the mask position with numpy : 0.02929377555847168 nb_pixel_total : 22756 time to create 1 rle with old method : 0.025780916213989258 time for calcul the mask position with numpy : 0.029433250427246094 nb_pixel_total : 70407 time to create 1 rle with old method : 0.07780027389526367 time for calcul the mask position with numpy : 0.03002476692199707 nb_pixel_total : 248746 time to create 1 rle with new method : 0.6809000968933105 time for calcul the mask position with numpy : 0.029224395751953125 nb_pixel_total : 22385 time to create 1 rle with old method : 0.025644302368164062 time for calcul the mask position with numpy : 0.02911210060119629 nb_pixel_total : 5746 time to create 1 rle with old method : 0.00947117805480957 time for calcul the mask position with numpy : 0.028830528259277344 nb_pixel_total : 18105 time to create 1 rle with old method : 0.020673513412475586 time for calcul the mask position with numpy : 0.028792858123779297 nb_pixel_total : 11578 time to create 1 rle with old method : 0.013142824172973633 time for calcul the mask position with numpy : 0.028690576553344727 nb_pixel_total : 151 time to create 1 rle with old method : 0.0002117156982421875 time for calcul the mask position with numpy : 0.0287322998046875 nb_pixel_total : 9313 time to create 1 rle with old method : 0.01074671745300293 time for calcul the mask position with numpy : 0.02866983413696289 nb_pixel_total : 7559 time to create 1 rle with old method : 0.008712530136108398 time for calcul the mask position with numpy : 0.028763771057128906 nb_pixel_total : 28909 time to create 1 rle with old method : 0.03293037414550781 time for calcul the mask position with numpy : 0.0287783145904541 nb_pixel_total : 4738 time to create 1 rle with old method : 0.005424976348876953 time for calcul the mask position with numpy : 0.02877211570739746 nb_pixel_total : 4415 time to create 1 rle with old method : 0.005040884017944336 create new chi : 7.090250015258789 time to delete rle : 0.0029990673065185547 batch 1 Loaded 71 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20579 TO DO : save crop sub photo not yet done ! save time : 5.258096933364868 nb_obj : 42 nb_hashtags : 5 time to prepare the origin masks : 8.416867971420288 time for calcul the mask position with numpy : 0.42580199241638184 nb_pixel_total : 5254705 time to create 1 rle with new method : 0.8914062976837158 time for calcul the mask position with numpy : 0.029105424880981445 nb_pixel_total : 24696 time to create 1 rle with old method : 0.027718067169189453 time for calcul the mask position with numpy : 0.02888631820678711 nb_pixel_total : 9550 time to create 1 rle with old method : 0.010917186737060547 time for calcul the mask position with numpy : 0.029091358184814453 nb_pixel_total : 26308 time to create 1 rle with old method : 0.029818058013916016 time for calcul the mask position with numpy : 0.028815031051635742 nb_pixel_total : 15226 time to create 1 rle with old method : 0.017417430877685547 time for calcul the mask position with numpy : 0.02898716926574707 nb_pixel_total : 27810 time to create 1 rle with old method : 0.03151345252990723 time for calcul the mask position with numpy : 0.028288841247558594 nb_pixel_total : 32254 time to create 1 rle with old method : 0.036393165588378906 time for calcul the mask position with numpy : 0.02929067611694336 nb_pixel_total : 85631 time to create 1 rle with old method : 0.09550094604492188 time for calcul the mask position with numpy : 0.02910327911376953 nb_pixel_total : 18811 time to create 1 rle with old method : 0.021324872970581055 time for calcul the mask position with numpy : 0.029532194137573242 nb_pixel_total : 52292 time to create 1 rle with old method : 0.05933499336242676 time for calcul the mask position with numpy : 0.03020453453063965 nb_pixel_total : 21551 time to create 1 rle with old method : 0.024463415145874023 time for calcul the mask position with numpy : 0.02942037582397461 nb_pixel_total : 109304 time to create 1 rle with old method : 0.12348031997680664 time for calcul the mask position with numpy : 0.02951502799987793 nb_pixel_total : 133463 time to create 1 rle with old method : 0.1491384506225586 time for calcul the mask position with numpy : 0.02786421775817871 nb_pixel_total : 13283 time to create 1 rle with old method : 0.014898300170898438 time for calcul the mask position with numpy : 0.030086517333984375 nb_pixel_total : 19725 time to create 1 rle with old method : 0.025937795639038086 time for calcul the mask position with numpy : 0.030699729919433594 nb_pixel_total : 14612 time to create 1 rle with old method : 0.016423463821411133 time for calcul the mask position with numpy : 0.02947831153869629 nb_pixel_total : 18411 time to create 1 rle with old method : 0.02175116539001465 time for calcul the mask position with numpy : 0.02956104278564453 nb_pixel_total : 133137 time to create 1 rle with old method : 0.1771688461303711 time for calcul the mask position with numpy : 0.02995753288269043 nb_pixel_total : 58801 time to create 1 rle with old method : 0.06701779365539551 time for calcul the mask position with numpy : 0.02910780906677246 nb_pixel_total : 15654 time to create 1 rle with old method : 0.01750493049621582 time for calcul the mask position with numpy : 0.0286102294921875 nb_pixel_total : 9963 time to create 1 rle with old method : 0.011169910430908203 time for calcul the mask position with numpy : 0.02918839454650879 nb_pixel_total : 30763 time to create 1 rle with old method : 0.0340423583984375 time for calcul the mask position with numpy : 0.029132604598999023 nb_pixel_total : 8584 time to create 1 rle with old method : 0.009476184844970703 time for calcul the mask position with numpy : 0.028834819793701172 nb_pixel_total : 14187 time to create 1 rle with old method : 0.016561031341552734 time for calcul the mask position with numpy : 0.029912471771240234 nb_pixel_total : 116648 time to create 1 rle with old method : 0.13335752487182617 time for calcul the mask position with numpy : 0.03067159652709961 nb_pixel_total : 33647 time to create 1 rle with old method : 0.041356563568115234 time for calcul the mask position with numpy : 0.029511451721191406 nb_pixel_total : 21984 time to create 1 rle with old method : 0.02480602264404297 time for calcul the mask position with numpy : 0.029048442840576172 nb_pixel_total : 11779 time to create 1 rle with old method : 0.013193607330322266 time for calcul the mask position with numpy : 0.02923297882080078 nb_pixel_total : 39116 time to create 1 rle with old method : 0.042955636978149414 time for calcul the mask position with numpy : 0.02937006950378418 nb_pixel_total : 23477 time to create 1 rle with old method : 0.02673196792602539 time for calcul the mask position with numpy : 0.02875208854675293 nb_pixel_total : 9017 time to create 1 rle with old method : 0.010408878326416016 time for calcul the mask position with numpy : 0.02935004234313965 nb_pixel_total : 8098 time to create 1 rle with old method : 0.009057760238647461 time for calcul the mask position with numpy : 0.02955937385559082 nb_pixel_total : 47568 time to create 1 rle with old method : 0.054297685623168945 time for calcul the mask position with numpy : 0.030362606048583984 nb_pixel_total : 308577 time to create 1 rle with new method : 0.9814281463623047 time for calcul the mask position with numpy : 0.029433727264404297 nb_pixel_total : 77258 time to create 1 rle with old method : 0.08837437629699707 time for calcul the mask position with numpy : 0.02943134307861328 nb_pixel_total : 16475 time to create 1 rle with old method : 0.01966094970703125 time for calcul the mask position with numpy : 0.02879166603088379 nb_pixel_total : 7225 time to create 1 rle with old method : 0.008795976638793945 time for calcul the mask position with numpy : 0.029213666915893555 nb_pixel_total : 58003 time to create 1 rle with old method : 0.06419515609741211 time for calcul the mask position with numpy : 0.029065370559692383 nb_pixel_total : 56795 time to create 1 rle with old method : 0.06386184692382812 time for calcul the mask position with numpy : 0.029358386993408203 nb_pixel_total : 45920 time to create 1 rle with old method : 0.053071022033691406 time for calcul the mask position with numpy : 0.02864217758178711 nb_pixel_total : 10978 time to create 1 rle with old method : 0.013139963150024414 time for calcul the mask position with numpy : 0.030283689498901367 nb_pixel_total : 8954 time to create 1 rle with old method : 0.010052204132080078 create new chi : 5.307744979858398 time to delete rle : 0.0028455257415771484 batch 1 Loaded 84 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.15809965133666992 nb_obj : 35 nb_hashtags : 6 time to prepare the origin masks : 7.994786977767944 time for calcul the mask position with numpy : 0.6706159114837646 nb_pixel_total : 4812993 time to create 1 rle with new method : 1.770312786102295 time for calcul the mask position with numpy : 0.02867293357849121 nb_pixel_total : 36962 time to create 1 rle with old method : 0.040505409240722656 time for calcul the mask position with numpy : 0.02825450897216797 nb_pixel_total : 14800 time to create 1 rle with old method : 0.016421794891357422 time for calcul the mask position with numpy : 0.028374433517456055 nb_pixel_total : 31420 time to create 1 rle with old method : 0.03529977798461914 time for calcul the mask position with numpy : 0.028113603591918945 nb_pixel_total : 14543 time to create 1 rle with old method : 0.015993833541870117 time for calcul the mask position with numpy : 0.027902841567993164 nb_pixel_total : 45480 time to create 1 rle with old method : 0.048830270767211914 time for calcul the mask position with numpy : 0.0276792049407959 nb_pixel_total : 48107 time to create 1 rle with old method : 0.05233168601989746 time for calcul the mask position with numpy : 0.027725696563720703 nb_pixel_total : 20823 time to create 1 rle with old method : 0.023190975189208984 time for calcul the mask position with numpy : 0.028060436248779297 nb_pixel_total : 17056 time to create 1 rle with old method : 0.018678665161132812 time for calcul the mask position with numpy : 0.02849888801574707 nb_pixel_total : 80685 time to create 1 rle with old method : 0.08770370483398438 time for calcul the mask position with numpy : 0.0286407470703125 nb_pixel_total : 56556 time to create 1 rle with old method : 0.06658148765563965 time for calcul the mask position with numpy : 0.029294729232788086 nb_pixel_total : 93242 time to create 1 rle with old method : 0.10548996925354004 time for calcul the mask position with numpy : 0.029313325881958008 nb_pixel_total : 7813 time to create 1 rle with old method : 0.008586406707763672 time for calcul the mask position with numpy : 0.028666019439697266 nb_pixel_total : 10113 time to create 1 rle with old method : 0.011289834976196289 time for calcul the mask position with numpy : 0.030498504638671875 nb_pixel_total : 315226 time to create 1 rle with new method : 1.6381886005401611 time for calcul the mask position with numpy : 0.030686378479003906 nb_pixel_total : 70395 time to create 1 rle with old method : 0.08161258697509766 time for calcul the mask position with numpy : 0.03088068962097168 nb_pixel_total : 1919 time to create 1 rle with old method : 0.0024051666259765625 time for calcul the mask position with numpy : 0.030184030532836914 nb_pixel_total : 30059 time to create 1 rle with old method : 0.03633618354797363 time for calcul the mask position with numpy : 0.029608488082885742 nb_pixel_total : 4541 time to create 1 rle with old method : 0.0054700374603271484 time for calcul the mask position with numpy : 0.030818462371826172 nb_pixel_total : 59264 time to create 1 rle with old method : 0.06817197799682617 time for calcul the mask position with numpy : 0.029161453247070312 nb_pixel_total : 11674 time to create 1 rle with old method : 0.013586759567260742 time for calcul the mask position with numpy : 0.034538984298706055 nb_pixel_total : 172510 time to create 1 rle with new method : 0.801154613494873 time for calcul the mask position with numpy : 0.029818058013916016 nb_pixel_total : 39088 time to create 1 rle with old method : 0.0442347526550293 time for calcul the mask position with numpy : 0.02949810028076172 nb_pixel_total : 43977 time to create 1 rle with old method : 0.052013397216796875 time for calcul the mask position with numpy : 0.029548168182373047 nb_pixel_total : 14165 time to create 1 rle with old method : 0.016717195510864258 time for calcul the mask position with numpy : 0.03191781044006348 nb_pixel_total : 368811 time to create 1 rle with new method : 0.35285186767578125 time for calcul the mask position with numpy : 0.03687882423400879 nb_pixel_total : 155159 time to create 1 rle with new method : 1.5129790306091309 time for calcul the mask position with numpy : 0.03187131881713867 nb_pixel_total : 53522 time to create 1 rle with old method : 0.07601070404052734 time for calcul the mask position with numpy : 0.02945709228515625 nb_pixel_total : 85004 time to create 1 rle with old method : 0.10451483726501465 time for calcul the mask position with numpy : 0.038236379623413086 nb_pixel_total : 14659 time to create 1 rle with old method : 0.026946306228637695 time for calcul the mask position with numpy : 0.0378270149230957 nb_pixel_total : 121502 time to create 1 rle with old method : 0.178924560546875 time for calcul the mask position with numpy : 0.031524658203125 nb_pixel_total : 132177 time to create 1 rle with old method : 0.16345739364624023 time for calcul the mask position with numpy : 0.03089118003845215 nb_pixel_total : 29349 time to create 1 rle with old method : 0.03377079963684082 time for calcul the mask position with numpy : 0.028629779815673828 nb_pixel_total : 36643 time to create 1 rle with old method : 0.053679466247558594 time for calcul the mask position with numpy : 0.036522626876831055 nb_pixel_total : 3 time to create 1 rle with old method : 8.678436279296875e-05 create new chi : 9.44351577758789 time to delete rle : 0.004937648773193359 batch 1 Loaded 68 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 3 TO DO : save crop sub photo not yet done ! save time : 0.19796347618103027 nb_obj : 12 nb_hashtags : 3 time to prepare the origin masks : 12.186485767364502 time for calcul the mask position with numpy : 0.033956050872802734 nb_pixel_total : 4998 time to create 1 rle with old method : 0.009545564651489258 time for calcul the mask position with numpy : 0.03574180603027344 nb_pixel_total : 51567 time to create 1 rle with old method : 0.0585169792175293 time for calcul the mask position with numpy : 0.03438973426818848 nb_pixel_total : 10484 time to create 1 rle with old method : 0.012089252471923828 time for calcul the mask position with numpy : 0.0335841178894043 nb_pixel_total : 24461 time to create 1 rle with old method : 0.02778935432434082 time for calcul the mask position with numpy : 0.03463625907897949 nb_pixel_total : 123193 time to create 1 rle with old method : 0.13542962074279785 time for calcul the mask position with numpy : 0.03239011764526367 nb_pixel_total : 1 time to create 1 rle with old method : 2.2172927856445312e-05 time for calcul the mask position with numpy : 0.03321528434753418 nb_pixel_total : 91165 time to create 1 rle with old method : 0.10300469398498535 time for calcul the mask position with numpy : 0.03353548049926758 nb_pixel_total : 138280 time to create 1 rle with old method : 0.16086077690124512 time for calcul the mask position with numpy : 0.04416775703430176 nb_pixel_total : 436553 time to create 1 rle with new method : 1.444849967956543 time for calcul the mask position with numpy : 0.0350341796875 nb_pixel_total : 13697 time to create 1 rle with old method : 0.015045166015625 time for calcul the mask position with numpy : 0.03401064872741699 nb_pixel_total : 55113 time to create 1 rle with old method : 0.06327152252197266 time for calcul the mask position with numpy : 0.889096736907959 nb_pixel_total : 6100728 time to create 1 rle with new method : 0.8521015644073486 create new chi : 4.254414319992065 time to delete rle : 0.001962900161743164 batch 1 Loaded 22 chid ids of type : 3594 +++++++++++++Number RLEs to save : 1 TO DO : save crop sub photo not yet done ! save time : 0.13043427467346191 nb_obj : 12 nb_hashtags : 4 time to prepare the origin masks : 11.210822105407715 time for calcul the mask position with numpy : 0.034140825271606445 nb_pixel_total : 135 time to create 1 rle with old method : 0.0004775524139404297 time for calcul the mask position with numpy : 0.034560203552246094 nb_pixel_total : 19866 time to create 1 rle with old method : 0.03350210189819336 time for calcul the mask position with numpy : 0.0391995906829834 nb_pixel_total : 59765 time to create 1 rle with old method : 0.06693387031555176 time for calcul the mask position with numpy : 0.03470945358276367 nb_pixel_total : 91463 time to create 1 rle with old method : 0.11108636856079102 time for calcul the mask position with numpy : 0.051207542419433594 nb_pixel_total : 848239 time to create 1 rle with new method : 0.8235962390899658 time for calcul the mask position with numpy : 0.033629417419433594 nb_pixel_total : 29320 time to create 1 rle with old method : 0.03713703155517578 time for calcul the mask position with numpy : 0.024926185607910156 nb_pixel_total : 145441 time to create 1 rle with old method : 0.15889334678649902 time for calcul the mask position with numpy : 0.022188663482666016 nb_pixel_total : 158151 time to create 1 rle with new method : 0.9689168930053711 time for calcul the mask position with numpy : 0.022084712982177734 nb_pixel_total : 30349 time to create 1 rle with old method : 0.03417229652404785 time for calcul the mask position with numpy : 0.021438121795654297 nb_pixel_total : 99475 time to create 1 rle with old method : 0.10923504829406738 time for calcul the mask position with numpy : 0.021693706512451172 nb_pixel_total : 78280 time to create 1 rle with old method : 0.08711910247802734 time for calcul the mask position with numpy : 1.3162238597869873 nb_pixel_total : 5489756 time to create 1 rle with new method : 0.6882612705230713 create new chi : 4.899663209915161 time to delete rle : 0.0034914016723632812 batch 1 Loaded 25 chid ids of type : 3594 ++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.11121392250061035 nb_obj : 10 nb_hashtags : 4 time to prepare the origin masks : 7.8543877601623535 time for calcul the mask position with numpy : 0.03468012809753418 nb_pixel_total : 84893 time to create 1 rle with old method : 0.09102225303649902 time for calcul the mask position with numpy : 0.034223318099975586 nb_pixel_total : 94795 time to create 1 rle with old method : 0.10394740104675293 time for calcul the mask position with numpy : 0.035395145416259766 nb_pixel_total : 47434 time to create 1 rle with old method : 0.06625175476074219 time for calcul the mask position with numpy : 0.021340608596801758 nb_pixel_total : 5445 time to create 1 rle with old method : 0.0059549808502197266 time for calcul the mask position with numpy : 0.021861791610717773 nb_pixel_total : 62730 time to create 1 rle with old method : 0.06958794593811035 time for calcul the mask position with numpy : 0.021810293197631836 nb_pixel_total : 82182 time to create 1 rle with old method : 0.09457731246948242 time for calcul the mask position with numpy : 0.024297475814819336 nb_pixel_total : 86468 time to create 1 rle with old method : 0.09815692901611328 time for calcul the mask position with numpy : 0.02396106719970703 nb_pixel_total : 163512 time to create 1 rle with new method : 1.8840575218200684 time for calcul the mask position with numpy : 0.02397322654724121 nb_pixel_total : 86076 time to create 1 rle with old method : 0.10607123374938965 time for calcul the mask position with numpy : 1.9752376079559326 nb_pixel_total : 6336705 time to create 1 rle with new method : 0.7908906936645508 create new chi : 5.623114824295044 time to delete rle : 0.0029685497283935547 batch 1 Loaded 19 chid ids of type : 3594 +++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.09884834289550781 nb_obj : 24 nb_hashtags : 5 time to prepare the origin masks : 7.883613348007202 time for calcul the mask position with numpy : 0.4780571460723877 nb_pixel_total : 5537006 time to create 1 rle with new method : 0.8400099277496338 time for calcul the mask position with numpy : 0.029339075088500977 nb_pixel_total : 56728 time to create 1 rle with old method : 0.06433892250061035 time for calcul the mask position with numpy : 0.029779434204101562 nb_pixel_total : 137356 time to create 1 rle with old method : 0.1565258502960205 time for calcul the mask position with numpy : 0.02930736541748047 nb_pixel_total : 58095 time to create 1 rle with old method : 0.06608843803405762 time for calcul the mask position with numpy : 0.0297393798828125 nb_pixel_total : 161701 time to create 1 rle with new method : 0.684544563293457 time for calcul the mask position with numpy : 0.02826714515686035 nb_pixel_total : 12851 time to create 1 rle with old method : 0.014711856842041016 time for calcul the mask position with numpy : 0.028241395950317383 nb_pixel_total : 52351 time to create 1 rle with old method : 0.058937788009643555 time for calcul the mask position with numpy : 0.031106233596801758 nb_pixel_total : 305184 time to create 1 rle with new method : 0.7977230548858643 time for calcul the mask position with numpy : 0.02960038185119629 nb_pixel_total : 24957 time to create 1 rle with old method : 0.02986741065979004 time for calcul the mask position with numpy : 0.030064821243286133 nb_pixel_total : 35501 time to create 1 rle with old method : 0.04518270492553711 time for calcul the mask position with numpy : 0.028931140899658203 nb_pixel_total : 7956 time to create 1 rle with old method : 0.009191036224365234 time for calcul the mask position with numpy : 0.028898000717163086 nb_pixel_total : 21555 time to create 1 rle with old method : 0.024745941162109375 time for calcul the mask position with numpy : 0.02896881103515625 nb_pixel_total : 34426 time to create 1 rle with old method : 0.039961814880371094 time for calcul the mask position with numpy : 0.03157925605773926 nb_pixel_total : 52096 time to create 1 rle with old method : 0.06010127067565918 time for calcul the mask position with numpy : 0.029093503952026367 nb_pixel_total : 198726 time to create 1 rle with new method : 0.5811796188354492 time for calcul the mask position with numpy : 0.0280606746673584 nb_pixel_total : 43495 time to create 1 rle with old method : 0.04869556427001953 time for calcul the mask position with numpy : 0.028271198272705078 nb_pixel_total : 25357 time to create 1 rle with old method : 0.028078079223632812 time for calcul the mask position with numpy : 0.028325796127319336 nb_pixel_total : 27168 time to create 1 rle with old method : 0.030291080474853516 time for calcul the mask position with numpy : 0.02875232696533203 nb_pixel_total : 78341 time to create 1 rle with old method : 0.08928179740905762 time for calcul the mask position with numpy : 0.028635740280151367 nb_pixel_total : 32809 time to create 1 rle with old method : 0.0365910530090332 time for calcul the mask position with numpy : 0.02725839614868164 nb_pixel_total : 38682 time to create 1 rle with old method : 0.04261517524719238 time for calcul the mask position with numpy : 0.02924036979675293 nb_pixel_total : 53838 time to create 1 rle with old method : 0.0610957145690918 time for calcul the mask position with numpy : 0.02862071990966797 nb_pixel_total : 46364 time to create 1 rle with old method : 0.051959991455078125 time for calcul the mask position with numpy : 0.028343915939331055 nb_pixel_total : 7697 time to create 1 rle with old method : 0.008625507354736328 create new chi : 5.154294490814209 time to delete rle : 0.0033309459686279297 batch 1 Loaded 47 chid ids of type : 3594 ++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.1401219367980957 nb_obj : 80 nb_hashtags : 5 time to prepare the origin masks : 8.366122007369995 time for calcul the mask position with numpy : 0.6941795349121094 nb_pixel_total : 5049796 time to create 1 rle with new method : 1.1791789531707764 time for calcul the mask position with numpy : 0.030838489532470703 nb_pixel_total : 18382 time to create 1 rle with old method : 0.02003622055053711 time for calcul the mask position with numpy : 0.029087543487548828 nb_pixel_total : 15239 time to create 1 rle with old method : 0.016980648040771484 time for calcul the mask position with numpy : 0.02915477752685547 nb_pixel_total : 31697 time to create 1 rle with old method : 0.03528428077697754 time for calcul the mask position with numpy : 0.027661800384521484 nb_pixel_total : 12783 time to create 1 rle with old method : 0.013710737228393555 time for calcul the mask position with numpy : 0.029216289520263672 nb_pixel_total : 11009 time to create 1 rle with old method : 0.018671035766601562 time for calcul the mask position with numpy : 0.029891252517700195 nb_pixel_total : 11783 time to create 1 rle with old method : 0.013410568237304688 time for calcul the mask position with numpy : 0.02875828742980957 nb_pixel_total : 5885 time to create 1 rle with old method : 0.006870269775390625 time for calcul the mask position with numpy : 0.03011012077331543 nb_pixel_total : 22998 time to create 1 rle with old method : 0.02791428565979004 time for calcul the mask position with numpy : 0.030533313751220703 nb_pixel_total : 126397 time to create 1 rle with old method : 0.17806315422058105 time for calcul the mask position with numpy : 0.03823685646057129 nb_pixel_total : 22028 time to create 1 rle with old method : 0.024749040603637695 time for calcul the mask position with numpy : 0.029291152954101562 nb_pixel_total : 15800 time to create 1 rle with old method : 0.018955469131469727 time for calcul the mask position with numpy : 0.02889728546142578 nb_pixel_total : 11824 time to create 1 rle with old method : 0.015305042266845703 time for calcul the mask position with numpy : 0.029186248779296875 nb_pixel_total : 1303 time to create 1 rle with old method : 0.0016410350799560547 time for calcul the mask position with numpy : 0.03000187873840332 nb_pixel_total : 11034 time to create 1 rle with old method : 0.012824773788452148 time for calcul the mask position with numpy : 0.02902841567993164 nb_pixel_total : 7759 time to create 1 rle with old method : 0.009313583374023438 time for calcul the mask position with numpy : 0.02910470962524414 nb_pixel_total : 9214 time to create 1 rle with old method : 0.011307477951049805 time for calcul the mask position with numpy : 0.029059171676635742 nb_pixel_total : 23330 time to create 1 rle with old method : 0.026778459548950195 time for calcul the mask position with numpy : 0.028757572174072266 nb_pixel_total : 100747 time to create 1 rle with old method : 0.11382007598876953 time for calcul the mask position with numpy : 0.03049635887145996 nb_pixel_total : 7 time to create 1 rle with old method : 0.00024175643920898438 time for calcul the mask position with numpy : 0.028797388076782227 nb_pixel_total : 27 time to create 1 rle with old method : 0.00029850006103515625 time for calcul the mask position with numpy : 0.028113603591918945 nb_pixel_total : 24871 time to create 1 rle with old method : 0.027069091796875 time for calcul the mask position with numpy : 0.028515338897705078 nb_pixel_total : 12135 time to create 1 rle with old method : 0.013382673263549805 time for calcul the mask position with numpy : 0.028850555419921875 nb_pixel_total : 31760 time to create 1 rle with old method : 0.0392298698425293 time for calcul the mask position with numpy : 0.03419899940490723 nb_pixel_total : 31359 time to create 1 rle with old method : 0.04968976974487305 time for calcul the mask position with numpy : 0.028955459594726562 nb_pixel_total : 9205 time to create 1 rle with old method : 0.010376930236816406 time for calcul the mask position with numpy : 0.027898311614990234 nb_pixel_total : 39352 time to create 1 rle with old method : 0.04236721992492676 time for calcul the mask position with numpy : 0.030193328857421875 nb_pixel_total : 21989 time to create 1 rle with old method : 0.027541160583496094 time for calcul the mask position with numpy : 0.032883405685424805 nb_pixel_total : 10536 time to create 1 rle with old method : 0.013167619705200195 time for calcul the mask position with numpy : 0.03299713134765625 nb_pixel_total : 32159 time to create 1 rle with old method : 0.03506112098693848 time for calcul the mask position with numpy : 0.028398990631103516 nb_pixel_total : 107622 time to create 1 rle with old method : 0.12213015556335449 time for calcul the mask position with numpy : 0.028677701950073242 nb_pixel_total : 8492 time to create 1 rle with old method : 0.00923299789428711 time for calcul the mask position with numpy : 0.0289762020111084 nb_pixel_total : 98890 time to create 1 rle with old method : 0.10594844818115234 time for calcul the mask position with numpy : 0.02924799919128418 nb_pixel_total : 12735 time to create 1 rle with old method : 0.013978719711303711 time for calcul the mask position with numpy : 0.02858567237854004 nb_pixel_total : 18306 time to create 1 rle with old method : 0.02044510841369629 time for calcul the mask position with numpy : 0.028003931045532227 nb_pixel_total : 24401 time to create 1 rle with old method : 0.02703571319580078 time for calcul the mask position with numpy : 0.02865147590637207 nb_pixel_total : 17845 time to create 1 rle with old method : 0.02053236961364746 time for calcul the mask position with numpy : 0.03155398368835449 nb_pixel_total : 30107 time to create 1 rle with old method : 0.037271738052368164 time for calcul the mask position with numpy : 0.028374910354614258 nb_pixel_total : 24637 time to create 1 rle with old method : 0.026350736618041992 time for calcul the mask position with numpy : 0.029590129852294922 nb_pixel_total : 51836 time to create 1 rle with old method : 0.08442831039428711 time for calcul the mask position with numpy : 0.029419660568237305 nb_pixel_total : 45546 time to create 1 rle with old method : 0.05830550193786621 time for calcul the mask position with numpy : 0.029157161712646484 nb_pixel_total : 28444 time to create 1 rle with old method : 0.033544301986694336 time for calcul the mask position with numpy : 0.031317710876464844 nb_pixel_total : 18221 time to create 1 rle with old method : 0.021112680435180664 time for calcul the mask position with numpy : 0.0305631160736084 nb_pixel_total : 22688 time to create 1 rle with old method : 0.026528120040893555 time for calcul the mask position with numpy : 0.029567718505859375 nb_pixel_total : 20512 time to create 1 rle with old method : 0.0342860221862793 time for calcul the mask position with numpy : 0.03300142288208008 nb_pixel_total : 35673 time to create 1 rle with old method : 0.04203391075134277 time for calcul the mask position with numpy : 0.03035140037536621 nb_pixel_total : 9610 time to create 1 rle with old method : 0.010808706283569336 time for calcul the mask position with numpy : 0.02946305274963379 nb_pixel_total : 70619 time to create 1 rle with old method : 0.07961034774780273 time for calcul the mask position with numpy : 0.02912425994873047 nb_pixel_total : 14446 time to create 1 rle with old method : 0.01901555061340332 time for calcul the mask position with numpy : 0.02963566780090332 nb_pixel_total : 23147 time to create 1 rle with old method : 0.028306961059570312 time for calcul the mask position with numpy : 0.030468225479125977 nb_pixel_total : 18898 time to create 1 rle with old method : 0.021722078323364258 time for calcul the mask position with numpy : 0.02950882911682129 nb_pixel_total : 7629 time to create 1 rle with old method : 0.008700847625732422 time for calcul the mask position with numpy : 0.02970600128173828 nb_pixel_total : 29800 time to create 1 rle with old method : 0.03380560874938965 time for calcul the mask position with numpy : 0.029732942581176758 nb_pixel_total : 434 time to create 1 rle with old method : 0.0010533332824707031 time for calcul the mask position with numpy : 0.033829450607299805 nb_pixel_total : 43221 time to create 1 rle with old method : 0.06045937538146973 time for calcul the mask position with numpy : 0.029450654983520508 nb_pixel_total : 3406 time to create 1 rle with old method : 0.004175901412963867 time for calcul the mask position with numpy : 0.030015945434570312 nb_pixel_total : 76952 time to create 1 rle with old method : 0.08688735961914062 time for calcul the mask position with numpy : 0.028522491455078125 nb_pixel_total : 9545 time to create 1 rle with old method : 0.010975122451782227 time for calcul the mask position with numpy : 0.028717994689941406 nb_pixel_total : 37245 time to create 1 rle with old method : 0.04204154014587402 time for calcul the mask position with numpy : 0.029937028884887695 nb_pixel_total : 14090 time to create 1 rle with old method : 0.015830516815185547 time for calcul the mask position with numpy : 0.02870464324951172 nb_pixel_total : 14206 time to create 1 rle with old method : 0.015706539154052734 time for calcul the mask position with numpy : 0.028743743896484375 nb_pixel_total : 15533 time to create 1 rle with old method : 0.01747870445251465 time for calcul the mask position with numpy : 0.02817392349243164 nb_pixel_total : 18820 time to create 1 rle with old method : 0.022587299346923828 time for calcul the mask position with numpy : 0.028416872024536133 nb_pixel_total : 5075 time to create 1 rle with old method : 0.005735874176025391 time for calcul the mask position with numpy : 0.028916597366333008 nb_pixel_total : 10689 time to create 1 rle with old method : 0.01209712028503418 time for calcul the mask position with numpy : 0.028515338897705078 nb_pixel_total : 46981 time to create 1 rle with old method : 0.0518336296081543 time for calcul the mask position with numpy : 0.0280458927154541 nb_pixel_total : 12901 time to create 1 rle with old method : 0.014506101608276367 time for calcul the mask position with numpy : 0.05112290382385254 nb_pixel_total : 24 time to create 1 rle with old method : 0.00011301040649414062 time for calcul the mask position with numpy : 0.02815389633178711 nb_pixel_total : 17798 time to create 1 rle with old method : 0.019655466079711914 time for calcul the mask position with numpy : 0.02829456329345703 nb_pixel_total : 18490 time to create 1 rle with old method : 0.020774364471435547 time for calcul the mask position with numpy : 0.02881765365600586 nb_pixel_total : 6065 time to create 1 rle with old method : 0.0068018436431884766 time for calcul the mask position with numpy : 0.028939247131347656 nb_pixel_total : 45326 time to create 1 rle with old method : 0.04909515380859375 time for calcul the mask position with numpy : 0.028085708618164062 nb_pixel_total : 20098 time to create 1 rle with old method : 0.022208690643310547 time for calcul the mask position with numpy : 0.028020143508911133 nb_pixel_total : 17525 time to create 1 rle with old method : 0.019490718841552734 time for calcul the mask position with numpy : 0.028123855590820312 nb_pixel_total : 62021 time to create 1 rle with old method : 0.06828927993774414 time for calcul the mask position with numpy : 0.028839111328125 nb_pixel_total : 4636 time to create 1 rle with old method : 0.005115509033203125 time for calcul the mask position with numpy : 0.028539419174194336 nb_pixel_total : 54592 time to create 1 rle with old method : 0.06198430061340332 time for calcul the mask position with numpy : 0.028592348098754883 nb_pixel_total : 15430 time to create 1 rle with old method : 0.01696610450744629 time for calcul the mask position with numpy : 0.0286405086517334 nb_pixel_total : 11193 time to create 1 rle with old method : 0.012435436248779297 time for calcul the mask position with numpy : 0.028182029724121094 nb_pixel_total : 5432 time to create 1 rle with old method : 0.006086826324462891 create new chi : 6.6774702072143555 time to delete rle : 0.005439281463623047 batch 1 Loaded 162 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 523 TO DO : save crop sub photo not yet done ! save time : 0.26829028129577637 nb_obj : 51 nb_hashtags : 6 time to prepare the origin masks : 7.774264335632324 time for calcul the mask position with numpy : 0.3917372226715088 nb_pixel_total : 5514789 time to create 1 rle with new method : 0.8455934524536133 time for calcul the mask position with numpy : 0.028127193450927734 nb_pixel_total : 37392 time to create 1 rle with old method : 0.0409388542175293 time for calcul the mask position with numpy : 0.028173446655273438 nb_pixel_total : 41217 time to create 1 rle with old method : 0.04512619972229004 time for calcul the mask position with numpy : 0.028077125549316406 nb_pixel_total : 29030 time to create 1 rle with old method : 0.032149314880371094 time for calcul the mask position with numpy : 0.028627395629882812 nb_pixel_total : 22549 time to create 1 rle with old method : 0.025304079055786133 time for calcul the mask position with numpy : 0.028557538986206055 nb_pixel_total : 6874 time to create 1 rle with old method : 0.007942676544189453 time for calcul the mask position with numpy : 0.028889179229736328 nb_pixel_total : 23568 time to create 1 rle with old method : 0.02662944793701172 time for calcul the mask position with numpy : 0.028992176055908203 nb_pixel_total : 8664 time to create 1 rle with old method : 0.009955644607543945 time for calcul the mask position with numpy : 0.029191970825195312 nb_pixel_total : 16536 time to create 1 rle with old method : 0.018076181411743164 time for calcul the mask position with numpy : 0.02918243408203125 nb_pixel_total : 30655 time to create 1 rle with old method : 0.03396177291870117 time for calcul the mask position with numpy : 0.02794814109802246 nb_pixel_total : 17340 time to create 1 rle with old method : 0.018743276596069336 time for calcul the mask position with numpy : 0.02715754508972168 nb_pixel_total : 17069 time to create 1 rle with old method : 0.017939329147338867 time for calcul the mask position with numpy : 0.02750539779663086 nb_pixel_total : 15087 time to create 1 rle with old method : 0.01653432846069336 time for calcul the mask position with numpy : 0.027835607528686523 nb_pixel_total : 37598 time to create 1 rle with old method : 0.04203963279724121 time for calcul the mask position with numpy : 0.02863287925720215 nb_pixel_total : 33157 time to create 1 rle with old method : 0.03700613975524902 time for calcul the mask position with numpy : 0.028975248336791992 nb_pixel_total : 34018 time to create 1 rle with old method : 0.03802824020385742 time for calcul the mask position with numpy : 0.02962636947631836 nb_pixel_total : 67370 time to create 1 rle with old method : 0.07640433311462402 time for calcul the mask position with numpy : 0.0315699577331543 nb_pixel_total : 13598 time to create 1 rle with old method : 0.015293359756469727 time for calcul the mask position with numpy : 0.029172420501708984 nb_pixel_total : 34262 time to create 1 rle with old method : 0.045708656311035156 time for calcul the mask position with numpy : 0.03282785415649414 nb_pixel_total : 8454 time to create 1 rle with old method : 0.014199495315551758 time for calcul the mask position with numpy : 0.030420541763305664 nb_pixel_total : 3584 time to create 1 rle with old method : 0.004156351089477539 time for calcul the mask position with numpy : 0.03183126449584961 nb_pixel_total : 198783 time to create 1 rle with new method : 1.029775619506836 time for calcul the mask position with numpy : 0.029005050659179688 nb_pixel_total : 39349 time to create 1 rle with old method : 0.04370594024658203 time for calcul the mask position with numpy : 0.029327869415283203 nb_pixel_total : 17491 time to create 1 rle with old method : 0.020012855529785156 time for calcul the mask position with numpy : 0.03044295310974121 nb_pixel_total : 31763 time to create 1 rle with old method : 0.03594970703125 time for calcul the mask position with numpy : 0.029410839080810547 nb_pixel_total : 90954 time to create 1 rle with old method : 0.10019898414611816 time for calcul the mask position with numpy : 0.028571128845214844 nb_pixel_total : 19315 time to create 1 rle with old method : 0.025577783584594727 time for calcul the mask position with numpy : 0.029335498809814453 nb_pixel_total : 13667 time to create 1 rle with old method : 0.015164375305175781 time for calcul the mask position with numpy : 0.028495073318481445 nb_pixel_total : 54246 time to create 1 rle with old method : 0.05965852737426758 time for calcul the mask position with numpy : 0.02835679054260254 nb_pixel_total : 29022 time to create 1 rle with old method : 0.03203940391540527 time for calcul the mask position with numpy : 0.027940034866333008 nb_pixel_total : 707 time to create 1 rle with old method : 0.000827789306640625 time for calcul the mask position with numpy : 0.028206348419189453 nb_pixel_total : 10398 time to create 1 rle with old method : 0.011794567108154297 time for calcul the mask position with numpy : 0.028562307357788086 nb_pixel_total : 5332 time to create 1 rle with old method : 0.0062601566314697266 time for calcul the mask position with numpy : 0.02860236167907715 nb_pixel_total : 17016 time to create 1 rle with old method : 0.019402265548706055 time for calcul the mask position with numpy : 0.02873063087463379 nb_pixel_total : 11454 time to create 1 rle with old method : 0.013316154479980469 time for calcul the mask position with numpy : 0.029943227767944336 nb_pixel_total : 14493 time to create 1 rle with old method : 0.0169522762298584 time for calcul the mask position with numpy : 0.028969287872314453 nb_pixel_total : 77545 time to create 1 rle with old method : 0.08424687385559082 time for calcul the mask position with numpy : 0.027588605880737305 nb_pixel_total : 25920 time to create 1 rle with old method : 0.02788686752319336 time for calcul the mask position with numpy : 0.027697324752807617 nb_pixel_total : 30172 time to create 1 rle with old method : 0.03344011306762695 time for calcul the mask position with numpy : 0.02806854248046875 nb_pixel_total : 14606 time to create 1 rle with old method : 0.015776395797729492 time for calcul the mask position with numpy : 0.027493953704833984 nb_pixel_total : 28966 time to create 1 rle with old method : 0.032195091247558594 time for calcul the mask position with numpy : 0.028281450271606445 nb_pixel_total : 68331 time to create 1 rle with old method : 0.07503485679626465 time for calcul the mask position with numpy : 0.027718305587768555 nb_pixel_total : 12996 time to create 1 rle with old method : 0.014203548431396484 time for calcul the mask position with numpy : 0.027466297149658203 nb_pixel_total : 35176 time to create 1 rle with old method : 0.039308786392211914 time for calcul the mask position with numpy : 0.028192520141601562 nb_pixel_total : 45717 time to create 1 rle with old method : 0.04886770248413086 time for calcul the mask position with numpy : 0.02882218360900879 nb_pixel_total : 69927 time to create 1 rle with old method : 0.07671356201171875 time for calcul the mask position with numpy : 0.028008222579956055 nb_pixel_total : 19071 time to create 1 rle with old method : 0.02141404151916504 time for calcul the mask position with numpy : 0.02728438377380371 nb_pixel_total : 14694 time to create 1 rle with old method : 0.015882015228271484 time for calcul the mask position with numpy : 0.026830673217773438 nb_pixel_total : 25202 time to create 1 rle with old method : 0.027181625366210938 time for calcul the mask position with numpy : 0.02827930450439453 nb_pixel_total : 6082 time to create 1 rle with old method : 0.007002592086791992 time for calcul the mask position with numpy : 0.028729677200317383 nb_pixel_total : 9034 time to create 1 rle with old method : 0.010334253311157227 create new chi : 5.2914769649505615 time to delete rle : 0.005137920379638672 batch 1 Loaded 102 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.16264700889587402 nb_obj : 32 nb_hashtags : 6 time to prepare the origin masks : 7.832155227661133 time for calcul the mask position with numpy : 0.47736525535583496 nb_pixel_total : 4937098 time to create 1 rle with new method : 0.6169331073760986 time for calcul the mask position with numpy : 0.027062416076660156 nb_pixel_total : 21386 time to create 1 rle with old method : 0.023447036743164062 time for calcul the mask position with numpy : 0.02867436408996582 nb_pixel_total : 12305 time to create 1 rle with old method : 0.013722896575927734 time for calcul the mask position with numpy : 0.027102231979370117 nb_pixel_total : 2318 time to create 1 rle with old method : 0.0026214122772216797 time for calcul the mask position with numpy : 0.027115583419799805 nb_pixel_total : 9773 time to create 1 rle with old method : 0.011043548583984375 time for calcul the mask position with numpy : 0.029252052307128906 nb_pixel_total : 150899 time to create 1 rle with new method : 0.7125964164733887 time for calcul the mask position with numpy : 0.027777671813964844 nb_pixel_total : 60980 time to create 1 rle with old method : 0.06830406188964844 time for calcul the mask position with numpy : 0.028490781784057617 nb_pixel_total : 13816 time to create 1 rle with old method : 0.01587224006652832 time for calcul the mask position with numpy : 0.028914928436279297 nb_pixel_total : 45827 time to create 1 rle with old method : 0.051294565200805664 time for calcul the mask position with numpy : 0.029263734817504883 nb_pixel_total : 145724 time to create 1 rle with old method : 0.15917730331420898 time for calcul the mask position with numpy : 0.028388023376464844 nb_pixel_total : 22004 time to create 1 rle with old method : 0.024662494659423828 time for calcul the mask position with numpy : 0.029294490814208984 nb_pixel_total : 176565 time to create 1 rle with new method : 0.5724210739135742 time for calcul the mask position with numpy : 0.02909541130065918 nb_pixel_total : 23575 time to create 1 rle with old method : 0.0254669189453125 time for calcul the mask position with numpy : 0.027808666229248047 nb_pixel_total : 20245 time to create 1 rle with old method : 0.023392677307128906 time for calcul the mask position with numpy : 0.031327009201049805 nb_pixel_total : 15170 time to create 1 rle with old method : 0.01698613166809082 time for calcul the mask position with numpy : 0.028455018997192383 nb_pixel_total : 80005 time to create 1 rle with old method : 0.08942484855651855 time for calcul the mask position with numpy : 0.028525114059448242 nb_pixel_total : 30509 time to create 1 rle with old method : 0.03430771827697754 time for calcul the mask position with numpy : 0.028417587280273438 nb_pixel_total : 19175 time to create 1 rle with old method : 0.021878480911254883 time for calcul the mask position with numpy : 0.02859640121459961 nb_pixel_total : 83819 time to create 1 rle with old method : 0.09368777275085449 time for calcul the mask position with numpy : 0.028523683547973633 nb_pixel_total : 173698 time to create 1 rle with new method : 0.5021805763244629 time for calcul the mask position with numpy : 0.0288236141204834 nb_pixel_total : 114430 time to create 1 rle with old method : 0.12485980987548828 time for calcul the mask position with numpy : 0.027925968170166016 nb_pixel_total : 23180 time to create 1 rle with old method : 0.025438308715820312 time for calcul the mask position with numpy : 0.027249574661254883 nb_pixel_total : 14154 time to create 1 rle with old method : 0.015199422836303711 time for calcul the mask position with numpy : 0.026705026626586914 nb_pixel_total : 24524 time to create 1 rle with old method : 0.026693105697631836 time for calcul the mask position with numpy : 0.027631282806396484 nb_pixel_total : 8206 time to create 1 rle with old method : 0.008945465087890625 time for calcul the mask position with numpy : 0.029443740844726562 nb_pixel_total : 18794 time to create 1 rle with old method : 0.02122044563293457 time for calcul the mask position with numpy : 0.03207898139953613 nb_pixel_total : 451994 time to create 1 rle with new method : 0.6123313903808594 time for calcul the mask position with numpy : 0.028995513916015625 nb_pixel_total : 163854 time to create 1 rle with new method : 0.5867838859558105 time for calcul the mask position with numpy : 0.02852654457092285 nb_pixel_total : 38537 time to create 1 rle with old method : 0.04360342025756836 time for calcul the mask position with numpy : 0.028873205184936523 nb_pixel_total : 94121 time to create 1 rle with old method : 0.10392618179321289 time for calcul the mask position with numpy : 0.028326034545898438 nb_pixel_total : 35984 time to create 1 rle with old method : 0.04017162322998047 time for calcul the mask position with numpy : 0.028293848037719727 nb_pixel_total : 17571 time to create 1 rle with old method : 0.01961064338684082 create new chi : 6.255500793457031 time to delete rle : 0.003326416015625 batch 1 Loaded 63 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 2.5254831314086914 map_output_result : {1349664067: (0.0, 'Should be the crop_list due to order', 0), 1349664062: (0.0, 'Should be the crop_list due to order', 0), 1349664053: (0.0, 'Should be the crop_list due to order', 0), 1349664048: (0.0, 'Should be the crop_list due to order', 0), 1349517656: (0.0, 'Should be the crop_list due to order', 0), 1349517577: (0.0, 'Should be the crop_list due to order', 0), 1349516100: (0.0, 'Should be the crop_list due to order', 0), 1349516073: (0.0, 'Should be the crop_list due to order', 0), 1349516053: (0.0, 'Should be the crop_list due to order', 0), 1349516024: (0.0, 'Should be the crop_list due to order', 0), 1349515759: (0.0, 'Should be the crop_list due to order', 0), 1349515733: (0.0, 'Should be the crop_list due to order', 0), 1349515697: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : rle_unique_nms_with_priority we use saveGeneral [1349664067, 1349664062, 1349664053, 1349664048, 1349517656, 1349517577, 1349516100, 1349516073, 1349516053, 1349516024, 1349515759, 1349515733, 1349515697] Looping around the photos to save general results len do output : 13 /1349664067.Didn't retrieve data . /1349664062.Didn't retrieve data . /1349664053.Didn't retrieve data . /1349664048.Didn't retrieve data . /1349517656.Didn't retrieve data . /1349517577.Didn't retrieve data . /1349516100.Didn't retrieve data . /1349516073.Didn't retrieve data . /1349516053.Didn't retrieve data . /1349516024.Didn't retrieve data . /1349515759.Didn't retrieve data . /1349515733.Didn't retrieve data . /1349515697.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, '2718481') ('3318', '21999409', '1349664067', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664062', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664048', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517656', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517577', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516100', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516073', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516024', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515759', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515733', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515697', None, None, None, None, None, '2718481') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 39 time used for this insertion : 0.01443028450012207 save_final save missing photos in datou_result : time spend for datou_step_exec : 213.05956482887268 time spend to save output : 0.015194892883300781 total time spend for step 3 : 213.07475972175598 step4:ventilate_hashtags_in_portfolio Thu Apr 3 01:34:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 21999409 get user id for portfolio 21999409 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`=21999409 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','carton','pehd','autre','papier','pet_clair','pet_fonce','flou','metal','mal_croppe','background')) 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`=21999409 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','carton','pehd','autre','papier','pet_clair','pet_fonce','flou','metal','mal_croppe','background')) AND mptpi.`min_score`=0.5 To do 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`=21999409 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','carton','pehd','autre','papier','pet_clair','pet_fonce','flou','metal','mal_croppe','background')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21999972,21999973,21999974,21999975,21999976,21999977,21999978,21999979,21999980,21999981,21999982?tags=environnement,carton,pehd,autre,papier,pet_clair,pet_fonce,flou,metal,mal_croppe,background Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349664067, 1349664062, 1349664053, 1349664048, 1349517656, 1349517577, 1349516100, 1349516073, 1349516053, 1349516024, 1349515759, 1349515733, 1349515697] Looping around the photos to save general results len do output : 1 /21999409. 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, '2718481') ('3318', '21999409', '1349664067', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664062', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664048', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517656', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517577', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516100', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516073', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516024', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515759', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515733', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515697', None, None, None, None, None, '2718481') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 14 time used for this insertion : 0.014714241027832031 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.7901206016540527 time spend to save output : 0.015002727508544922 total time spend for step 4 : 0.8051233291625977 step5:final Thu Apr 3 01:34:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1349664067: ('0.25288078572775285',), 1349664062: ('0.25288078572775285',), 1349664053: ('0.25288078572775285',), 1349664048: ('0.25288078572775285',), 1349517656: ('0.25288078572775285',), 1349517577: ('0.25288078572775285',), 1349516100: ('0.25288078572775285',), 1349516073: ('0.25288078572775285',), 1349516053: ('0.25288078572775285',), 1349516024: ('0.25288078572775285',), 1349515759: ('0.25288078572775285',), 1349515733: ('0.25288078572775285',), 1349515697: ('0.25288078572775285',)} new output for save of step final : {1349664067: ('0.25288078572775285',), 1349664062: ('0.25288078572775285',), 1349664053: ('0.25288078572775285',), 1349664048: ('0.25288078572775285',), 1349517656: ('0.25288078572775285',), 1349517577: ('0.25288078572775285',), 1349516100: ('0.25288078572775285',), 1349516073: ('0.25288078572775285',), 1349516053: ('0.25288078572775285',), 1349516024: ('0.25288078572775285',), 1349515759: ('0.25288078572775285',), 1349515733: ('0.25288078572775285',), 1349515697: ('0.25288078572775285',)} [1349664067, 1349664062, 1349664053, 1349664048, 1349517656, 1349517577, 1349516100, 1349516073, 1349516053, 1349516024, 1349515759, 1349515733, 1349515697] Looping around the photos to save general results len do output : 13 /1349664067.Didn't retrieve data . /1349664062.Didn't retrieve data . /1349664053.Didn't retrieve data . /1349664048.Didn't retrieve data . /1349517656.Didn't retrieve data . /1349517577.Didn't retrieve data . /1349516100.Didn't retrieve data . /1349516073.Didn't retrieve data . /1349516053.Didn't retrieve data . /1349516024.Didn't retrieve data . /1349515759.Didn't retrieve data . /1349515733.Didn't retrieve data . /1349515697.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, '2718481') ('3318', '21999409', '1349664067', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664062', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664048', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517656', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517577', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516100', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516073', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516024', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515759', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515733', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515697', None, None, None, None, None, '2718481') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 39 time used for this insertion : 0.11240148544311523 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.12104010581970215 time spend to save output : 0.11314034461975098 total time spend for step 5 : 0.23418045043945312 step6:blur_detection Thu Apr 3 01:34:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03.jpg resize: (2160, 3264) 1349664067 -1.2145045573588766 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82.jpg resize: (2160, 3264) 1349664062 -3.192048905318694 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19.jpg resize: (2160, 3264) 1349664053 -4.182975378143321 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82.jpg resize: (2160, 3264) 1349664048 -4.302976869299141 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3.jpg resize: (2160, 3264) 1349517656 -1.8607156811533523 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b.jpg resize: (2160, 3264) 1349517577 -3.0462269582397807 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0.jpg resize: (2160, 3264) 1349516100 -1.764021058547955 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f.jpg resize: (2160, 3264) 1349516073 -2.2990199814560324 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46.jpg resize: (2160, 3264) 1349516053 -3.168569000626637 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9.jpg resize: (2160, 3264) 1349516024 -2.0005299106125483 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74.jpg resize: (2160, 3264) 1349515759 -5.462228613291413 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b.jpg resize: (2160, 3264) 1349515733 -5.309034200847762 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3.jpg resize: (2160, 3264) 1349515697 -3.8003748089530713 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425407_0.png resize: (498, 218) 1349689701 -2.523423793303564 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425428_0.png resize: (89, 95) 1349689702 0.02335763741680944 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425408_0.png resize: (321, 264) 1349689703 -1.170415889257581 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425417_0.png resize: (209, 107) 1349689704 -1.685629774229742 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425404_0.png resize: (162, 72) 1349689705 -2.2063071432471846 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425403_0.png resize: (219, 235) 1349689706 -0.17973979175254942 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425414_0.png resize: (101, 98) 1349689707 -2.5369950334083704 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425429_0.png resize: (335, 486) 1349689708 -0.8075847140242628 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425412_0.png resize: (142, 141) 1349689709 -0.07234754245986448 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425405_0.png resize: (198, 233) 1349689710 -0.9464932597629214 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425402_0.png resize: (188, 140) 1349689711 -2.5108822860518436 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425410_0.png resize: (68, 77) 1349689713 -2.271025814622255 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425424_0.png resize: (99, 240) 1349689714 -2.6050809468217113 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425415_0.png resize: (271, 768) 1349689715 -0.5346426565087827 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425419_0.png resize: (107, 150) 1349689716 -0.38451992134152285 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425406_0.png resize: (266, 602) 1349689717 -2.396372449862055 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425418_0.png resize: (294, 310) 1349689718 -0.33367266989081756 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425420_0.png resize: (527, 640) 1349689719 -1.306302110979748 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425411_0.png resize: (154, 288) 1349689720 -2.819094861520857 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425423_0.png resize: (76, 153) 1349689721 -1.4943207910618121 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425436_0.png resize: (165, 118) 1349689722 -1.2422843368740564 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425454_0.png resize: (509, 215) 1349689723 -2.2772416999350895 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425434_0.png resize: (105, 74) 1349689724 -2.3070632149198484 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425446_0.png resize: (152, 115) 1349689725 -2.1442577037439516 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425438_0.png resize: (268, 432) 1349689726 -0.9172635611119084 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425453_0.png resize: (173, 204) 1349689727 -2.007460139334826 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425442_0.png resize: (80, 144) 1349689728 -1.5473590281936982 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425432_0.png resize: (156, 146) 1349689729 -2.0336343863921162 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425456_0.png resize: (195, 197) 1349689730 -2.0487414045882915 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425440_0.png resize: (174, 133) 1349689731 -1.6727322409992533 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425451_0.png resize: (311, 336) 1349689732 -1.7688629989403684 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425435_0.png resize: (161, 279) 1349689733 -2.5566266822658785 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425443_0.png resize: (171, 124) 1349689734 -2.4673210855109193 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425444_0.png resize: (265, 158) 1349689735 -2.710695432389488 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425437_0.png resize: (82, 125) 1349689736 -2.742493089290766 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425449_0.png resize: (122, 150) 1349689737 -2.398078116713444 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425447_0.png resize: (145, 194) 1349689738 -2.762288793925852 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425467_0.png resize: (418, 114) 1349689739 -1.6190067866152058 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425460_0.png resize: (510, 681) 1349689740 -2.316867589941688 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425457_0.png resize: (152, 166) 1349689741 -3.6349943403065907 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425482_0.png resize: (293, 138) 1349689742 -2.5863120446378707 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425465_0.png resize: (119, 259) 1349689743 -2.295105155549199 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425479_0.png resize: (298, 402) 1349689744 -3.0058076032522716 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425464_0.png resize: (201, 248) 1349689745 -3.0890960262601816 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425483_0.png resize: (138, 103) 1349689746 -3.082694553997055 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425478_0.png resize: (169, 184) 1349689747 -2.056898221788802 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425458_0.png resize: (342, 302) 1349689748 -3.907776531484094 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425475_0.png resize: (270, 258) 1349689749 -3.844823477362062 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425470_0.png resize: (439, 569) 1349689750 -3.4955851921003562 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425473_0.png resize: (144, 136) 1349689751 -1.8173606802888613 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425480_0.png resize: (112, 233) 1349689753 -1.637459832711073 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425462_0.png resize: (330, 324) 1349689754 -2.2516029746927346 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425459_0.png resize: (484, 483) 1349689755 -3.0945288299646445 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425477_0.png resize: (593, 912) 1349689756 -3.830486001274649 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425468_0.png resize: (474, 144) 1349689757 -1.6393121434367057 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425472_0.png resize: (92, 152) 1349689758 -2.1821482962682 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425495_0.png resize: (180, 148) 1349689759 -2.3206716791320496 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425504_0.png resize: (176, 169) 1349689760 -2.3704993518218016 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425517_0.png resize: (107, 69) 1349689762 -0.4550417582853667 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425516_0.png resize: (101, 56) 1349689763 20.0 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425490_0.png resize: (158, 165) 1349689764 -2.075317345779464 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425497_0.png resize: (313, 224) 1349689765 -2.2933920850873615 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425505_0.png resize: (175, 263) 1349689766 -2.3243637571314992 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425485_0.png resize: (191, 270) 1349689767 -2.1848387600063703 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425518_0.png resize: (84, 179) 1349689768 -3.421072955288133 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425507_0.png resize: (106, 101) 1349689769 -1.6772429841691983 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425494_0.png resize: (67, 59) 1349689770 -0.14260944225685254 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425508_0.png resize: (195, 277) 1349689771 -2.9930709095851227 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425493_0.png resize: (110, 287) 1349689772 -2.6186695103563715 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425506_0.png resize: (88, 65) 1349689773 -0.05448211708174964 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425491_0.png resize: (470, 749) 1349689776 -3.25401007730312 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425512_0.png resize: (181, 163) 1349689777 -1.79326395894996 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425503_0.png resize: (56, 165) 1349689778 -2.0629441763584864 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425496_0.png resize: (220, 397) 1349689779 -2.536000916321913 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425501_0.png resize: (377, 399) 1349689780 -3.971178457634379 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425502_0.png resize: (278, 374) 1349689781 -3.328225363171627 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425489_0.png resize: (225, 281) 1349689782 -3.4703026731086046 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425515_0.png resize: (597, 469) 1349689783 -2.537169612843427 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425499_0.png resize: (1046, 699) 1349689784 -1.8989410934814068 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425509_0.png resize: (255, 265) 1349689785 -3.5265249921067374 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472288_0.png resize: (232, 301) 1349689786 -1.6303856076054506 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472290_0.png resize: (124, 153) 1349689787 -1.5224105035999704 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476636_0.png resize: (124, 153) 1349689788 -1.5224105035999704 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476653_0.png resize: (232, 301) 1349689789 -1.6303856076054506 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472295_0.png resize: (226, 374) 1349689790 -2.444980068440222 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476649_0.png resize: (226, 374) 1349689791 -2.443604583005136 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472314_0.png resize: (93, 201) 1349689792 -2.4641145291480875 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476648_0.png resize: (93, 201) 1349689793 -2.4641145291480875 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472315_0.png resize: (50, 226) 1349689794 -0.7531649784689853 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476662_0.png resize: (50, 226) 1349689795 -0.7531649784689853 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476661_0.png resize: (113, 147) 1349689796 -2.626588384074353 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472289_0.png resize: (113, 147) 1349689797 -2.641631283984636 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472312_0.png resize: (143, 256) 1349689798 -2.469809034274052 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476631_0.png resize: (143, 256) 1349689800 -2.469809034274052 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472293_0.png resize: (287, 321) 1349689801 -1.5612386513023317 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476639_0.png resize: (287, 321) 1349689802 -1.5607958395612078 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472306_0.png resize: (93, 126) 1349689803 -1.050947392149725 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476643_0.png resize: (93, 126) 1349689804 -1.0512387002132222 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472285_0.png resize: (248, 273) 1349689805 -1.3283395704709213 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472320_0.png resize: (138, 188) 1349689806 -1.9135658308454015 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476660_0.png resize: (248, 273) 1349689807 -1.3285831901337588 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476635_0.png resize: (138, 188) 1349689808 -1.8941346916913582 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472287_0.png resize: (163, 94) 1349689809 -0.8800889404864886 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476641_0.png resize: (163, 94) 1349689810 -0.8800889404864886 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472301_0.png resize: (184, 212) 1349689811 -1.9683940898637171 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476637_0.png resize: (184, 212) 1349689812 -1.9685753862202133 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472298_0.png resize: (182, 155) 1349689813 -0.38225183587093337 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476647_0.png resize: (182, 155) 1349689814 -0.38225183587093337 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472318_0.png resize: (125, 165) 1349689815 -1.3487197597144536 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476644_0.png resize: (125, 165) 1349689816 -1.3491658972529077 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472291_0.png resize: (225, 243) 1349689817 -1.604127669625629 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476646_0.png resize: (225, 243) 1349689818 -1.6060196777900315 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472304_0.png resize: (189, 209) 1349689819 -1.2043734647315096 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476629_0.png resize: (189, 209) 1349689820 -1.2055594652232313 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472319_0.png resize: (127, 103) 1349689821 -3.0036299158518807 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476657_0.png resize: (127, 103) 1349689822 -3.003322985057563 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476656_0.png resize: (98, 224) 1349689823 -1.442050835357895 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472302_0.png resize: (98, 224) 1349689824 -1.442050835357895 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472321_0.png resize: (124, 114) 1349689825 1.2273493978828405 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476623_0.png resize: (124, 114) 1349689826 1.2170969666342137 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472292_0.png resize: (147, 127) 1349689827 1.0530798721477719 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476640_0.png resize: (147, 127) 1349689828 1.0530798721477719 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472307_0.png resize: (226, 319) 1349689829 -4.288300090173293 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476630_0.png resize: (226, 319) 1349689830 -4.288056233422906 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472323_0.png resize: (274, 136) 1349689831 -1.3503177096423642 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476625_0.png resize: (274, 135) 1349689832 -1.3356611366389501 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472341_0.png resize: (391, 189) 1349689833 -1.8539183915611284 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476791_0.png resize: (391, 189) 1349689834 -1.8541026652796953 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476799_0.png resize: (834, 565) 1349689835 -1.34212656801656 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472333_0.png resize: (834, 565) 1349689836 -1.342701945927984 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472346_0.png resize: (162, 197) 1349689837 -2.8643130051492443 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476814_0.png resize: (162, 197) 1349689838 -2.9181641266898293 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476786_0.png resize: (243, 255) 1349689839 0.015149440466054001 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472328_0.png resize: (243, 255) 1349689840 0.014185010639273282 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472340_0.png resize: (138, 84) 1349689841 -2.49198692272895 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476798_0.png resize: (138, 84) 1349689843 -2.49198692272895 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472335_0.png resize: (462, 287) 1349689844 -2.3894594945247194 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476796_0.png resize: (462, 287) 1349689845 -2.385572810522761 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472334_0.png resize: (146, 216) 1349689846 -2.225738116306587 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476792_0.png resize: (146, 216) 1349689848 -2.225738116306587 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472343_0.png resize: (415, 511) 1349689849 -1.8034659655193457 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476811_0.png resize: (415, 510) 1349689850 -1.790101169237141 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472349_0.png resize: (413, 511) 1349689851 -2.757577387486206 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476815_0.png resize: (413, 511) 1349689852 -2.756519870859422 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472339_0.png resize: (525, 758) 1349689853 -1.412077194151203 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476806_0.png resize: (525, 758) 1349689854 -1.4159765123539996 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472350_0.png resize: (336, 282) 1349689855 -1.1110630261931256 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476800_0.png resize: (336, 282) 1349689856 -1.1110281356501552 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476790_0.png resize: (270, 243) 1349689858 -0.6721260407300836 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472332_0.png resize: (270, 243) 1349689861 -0.6681415148457868 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472356_0.png resize: (162, 110) 1349689862 -0.759512088780418 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476805_0.png resize: (162, 110) 1349689863 -0.7601478506504334 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472368_0.png resize: (387, 304) 1349689864 -1.0830987863330024 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476871_0.png resize: (346, 263) 1349689865 0.22655287984581038 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472362_0.png resize: (522, 324) 1349689866 -3.53560958894508 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476877_0.png resize: (522, 324) 1349689867 -3.5294914937592026 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472365_0.png resize: (182, 157) 1349689868 -0.2972394045224581 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476874_0.png resize: (182, 157) 1349689869 -0.30767321083681015 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472359_0.png resize: (482, 181) 1349689870 -2.9909961352434795 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476880_0.png resize: (482, 181) 1349689871 -2.9908054965052693 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472360_0.png resize: (234, 126) 1349689872 -3.1910784139037283 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472361_0.png resize: (863, 748) 1349689873 -2.466293778046877 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476879_0.png resize: (234, 126) 1349689874 -3.1910982950156472 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476873_0.png resize: (257, 391) 1349689875 4.445488913822451 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472366_0.png resize: (267, 391) 1349689876 3.0377240725596377 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476878_0.png resize: (863, 748) 1349689877 -2.467960942261039 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472364_0.png resize: (649, 259) 1349689878 -0.973180769774673 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476875_0.png resize: (649, 259) 1349689879 -0.9675847137313695 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472378_0.png resize: (245, 114) 1349689880 -0.7985708627869562 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476926_0.png resize: (245, 114) 1349689881 -0.7954384905001147 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3745425521_0.png resize: (376, 375) 1349689883 -3.593249685147817 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476925_0.png resize: (364, 314) 1349689884 5.914613312903735 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3745425520_0.png resize: (1200, 994) 1349689885 -2.3365038951357415 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476929_0.png resize: (1203, 995) 1349689886 -2.3561651269988384 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476935_0.png resize: (468, 262) 1349689887 -1.3130843718518979 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472369_0.png resize: (468, 262) 1349689888 -1.3143605085568946 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472372_0.png resize: (576, 338) 1349689889 -3.877625551949437 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476932_0.png resize: (576, 338) 1349689890 -3.877711717747333 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472380_0.png resize: (449, 330) 1349689893 -1.1770802723214375 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476980_0.png resize: (449, 330) 1349689894 -1.1755672029155866 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472384_0.png resize: (302, 343) 1349689895 -2.3853102998964575 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476976_0.png resize: (297, 343) 1349689896 -2.082473917636971 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472386_0.png resize: (201, 322) 1349689897 -2.435069618510816 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476974_0.png resize: (201, 322) 1349689899 -2.4312943048235023 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472385_0.png resize: (100, 93) 1349689900 -1.8777314533172422 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476975_0.png resize: (100, 93) 1349689901 -1.8786537805387034 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476978_0.png resize: (490, 268) 1349689902 -0.818629505974325 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472382_0.png resize: (490, 268) 1349689903 -0.8197892453818084 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472383_0.png resize: (419, 291) 1349689904 -1.0984710234527246 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476977_0.png resize: (419, 291) 1349689905 -1.0976222179251283 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472381_0.png resize: (577, 345) 1349689906 -4.118134287498075 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476979_0.png resize: (577, 345) 1349689907 -4.119025505366212 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472389_0.png resize: (157, 104) 1349689908 -0.918008330054358 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477125_0.png resize: (157, 104) 1349689909 -0.918008330054358 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472395_0.png resize: (417, 446) 1349689910 -1.4100957423994085 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477119_0.png resize: (417, 446) 1349689911 -1.4115639574389094 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472407_0.png resize: (194, 173) 1349689912 -3.2262829869611944 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477107_0.png resize: (194, 173) 1349689913 -3.2268918427141378 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472399_0.png resize: (277, 260) 1349689914 -2.541269991555595 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477115_0.png resize: (277, 260) 1349689915 -2.5430346794409737 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477117_0.png resize: (98, 100) 1349689916 -0.8125979388170451 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472397_0.png resize: (98, 100) 1349689917 -0.8125979388170451 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472403_0.png resize: (290, 287) 1349689918 -2.895363866264775 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472394_0.png resize: (181, 230) 1349689919 -2.6791284144093335 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477120_0.png resize: (181, 230) 1349689920 -2.6801804412283743 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477111_0.png resize: (290, 287) 1349689921 -2.894395288540462 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472402_0.png resize: (105, 112) 1349689922 -0.6646554431369711 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477112_0.png resize: (105, 112) 1349689923 -0.645080048130152 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472391_0.png resize: (218, 310) 1349689924 -0.6305529110887742 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477123_0.png resize: (218, 310) 1349689925 -0.6301556136324143 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477103_0.png resize: (191, 269) 1349689926 -1.1285738152449005 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472411_0.png resize: (191, 269) 1349689927 -1.1290695082695388 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472392_0.png resize: (264, 242) 1349689928 -0.9847917086755237 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477122_0.png resize: (264, 242) 1349689929 -0.9851223780940134 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472390_0.png resize: (366, 337) 1349689930 -2.6085781557458887 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477124_0.png resize: (366, 337) 1349689931 -2.608380724525071 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472401_0.png resize: (321, 155) 1349689932 -2.4694912125557993 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477113_0.png resize: (321, 155) 1349689933 -2.46944183224855 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472398_0.png resize: (229, 145) 1349689934 -2.8560985620351005 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477116_0.png resize: (230, 145) 1349689935 -2.8423205432040684 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472538_0.png resize: (225, 191) 1349689936 -2.558073191726595 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472468_0.png resize: (203, 233) 1349689937 -3.1284348868684586 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479255_0.png resize: (203, 233) 1349689938 -3.128997169500149 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479283_0.png resize: (225, 191) 1349689939 -2.558073191726595 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472485_0.png resize: (224, 298) 1349689940 -2.885659462523783 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479260_0.png resize: (224, 298) 1349689941 -2.8860255958137997 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472509_0.png resize: (198, 172) 1349689945 -2.9680116747753815 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479280_0.png resize: (198, 172) 1349689946 -2.9680116747753815 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472483_0.png resize: (78, 131) 1349689947 -4.335810546002385 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479248_0.png resize: (78, 131) 1349689948 -4.335810546002385 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472523_0.png resize: (140, 149) 1349689949 -3.8462688276874037 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479244_0.png resize: (140, 149) 1349689950 -3.8462688276874037 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472514_0.png resize: (138, 188) 1349689951 -3.91218203369162 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479273_0.png resize: (138, 188) 1349689952 -3.91218203369162 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472484_0.png resize: (142, 186) 1349689953 -1.875896354282905 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479301_0.png resize: (142, 186) 1349689954 -1.8756757485752598 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472478_0.png resize: (573, 301) 1349689956 -2.174585549971696 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479270_0.png resize: (573, 301) 1349689957 -2.1743186477080534 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472495_0.png resize: (125, 125) 1349689958 -3.605350023868811 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479253_0.png resize: (125, 125) 1349689959 -3.606769308357766 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472507_0.png resize: (101, 82) 1349689960 -3.2381544150201305 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479240_0.png resize: (101, 82) 1349689961 -3.2381544150201305 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472482_0.png resize: (312, 178) 1349689962 -3.0243873164349404 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472497_0.png resize: (173, 148) 1349689963 -3.4063027562554273 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479268_0.png resize: (312, 178) 1349689964 -2.87891167501238 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472540_0.png resize: (172, 161) 1349689965 -2.8413312428828394 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479290_0.png resize: (173, 148) 1349689966 -3.4083474190617133 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479298_0.png resize: (172, 161) 1349689967 -2.8413312428828394 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472481_0.png resize: (94, 126) 1349689968 -1.7296638729433864 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479287_0.png resize: (94, 126) 1349689969 -1.7296638729433864 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472487_0.png resize: (84, 78) 1349689970 -1.8121620110512484 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479304_0.png resize: (84, 78) 1349689971 -1.8121620110512484 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472471_0.png resize: (140, 150) 1349689972 -1.8927895428120343 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479249_0.png resize: (124, 109) 1349689973 -1.6466368865386023 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472472_0.png resize: (124, 109) 1349689974 -1.6466368865386023 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472534_0.png resize: (138, 197) 1349689975 -2.421287635616587 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479281_0.png resize: (138, 197) 1349689976 -2.421287635616587 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472506_0.png resize: (177, 185) 1349689977 -2.730221906254799 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479274_0.png resize: (177, 185) 1349689978 -2.730221906254799 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479306_0.png resize: (140, 150) 1349689979 -1.8925536759040873 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479265_0.png resize: (181, 135) 1349689980 -2.287797108759836 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472531_0.png resize: (181, 135) 1349689981 -2.287797108759836 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472486_0.png resize: (171, 194) 1349689982 -3.7986190457275044 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479269_0.png resize: (171, 194) 1349689983 -3.7986190457275044 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472494_0.png resize: (225, 171) 1349689984 -3.3240327641797203 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479254_0.png resize: (225, 171) 1349689985 -3.3240327641797203 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472496_0.png resize: (171, 98) 1349689986 -1.227351046795206 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479264_0.png resize: (171, 98) 1349689987 -1.227351046795206 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472527_0.png resize: (97, 81) 1349689988 -1.208381145785617 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479293_0.png resize: (97, 81) 1349689989 -1.208381145785617 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472528_0.png resize: (208, 366) 1349689990 -4.9332145180828375 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479288_0.png resize: (208, 366) 1349689991 -4.870254691018532 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472469_0.png resize: (201, 311) 1349689992 -3.893282230925824 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479295_0.png resize: (201, 311) 1349689993 -3.8934959681072656 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3745425525_0.png resize: (378, 253) 1349689994 -3.476677999078647 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472510_0.png resize: (374, 253) 1349689995 -3.4529555051371323 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479271_0.png resize: (374, 253) 1349689996 -3.4529555051371323 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472477_0.png resize: (87, 236) 1349689997 -4.332599700609415 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479289_0.png resize: (87, 236) 1349689998 -4.332599700609415 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472539_0.png resize: (117, 131) 1349689999 -3.6357273889719597 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479294_0.png resize: (117, 131) 1349690000 -3.6357273889719597 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472529_0.png resize: (557, 363) 1349690001 -3.2998228307993176 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479261_0.png resize: (557, 363) 1349690002 -3.2998228307993176 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472476_0.png resize: (316, 245) 1349690003 -3.4426766476709023 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479300_0.png resize: (316, 245) 1349690004 -3.448448069082139 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472536_0.png resize: (123, 262) 1349690005 -2.9575181767969316 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479297_0.png resize: (123, 262) 1349690006 -2.9575181767969316 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472501_0.png resize: (197, 145) 1349690007 -4.001356116939755 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479236_0.png resize: (290, 157) 1349690008 -3.852955737964116 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472473_0.png resize: (280, 192) 1349690009 -3.6810644431185637 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472467_0.png resize: (394, 409) 1349690010 -3.763112945912327 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479276_0.png resize: (280, 192) 1349690011 -3.670153780854538 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479263_0.png resize: (394, 409) 1349690012 -3.7662133551133077 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472474_0.png resize: (247, 77) 1349690013 -4.24275564644413 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479237_0.png resize: (247, 77) 1349690014 -4.24275564644413 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472518_0.png resize: (215, 140) 1349690016 -3.29315630238429 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479291_0.png resize: (215, 140) 1349690017 -3.29315630238429 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472511_0.png resize: (200, 92) 1349690018 -3.154079977172878 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479238_0.png resize: (200, 92) 1349690019 -3.154079977172878 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472535_0.png resize: (227, 116) 1349690020 -3.4218040103670835 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472526_0.png resize: (208, 241) 1349690021 -3.3663475783416157 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479245_0.png resize: (206, 116) 1349690022 -3.512515855007825 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479285_0.png resize: (201, 241) 1349690023 -1.6410871152942157 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472519_0.png resize: (232, 140) 1349690024 -2.3518263276503344 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479275_0.png resize: (232, 140) 1349690025 -2.3518263276503344 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472491_0.png resize: (178, 230) 1349690026 -3.2881715100696898 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472515_0.png resize: (187, 203) 1349690027 -2.8852925218025436 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479243_0.png resize: (178, 230) 1349690029 -3.2866060196631866 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479234_0.png resize: (149, 175) 1349690030 -2.98808650073898 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472500_0.png resize: (149, 175) 1349690031 -2.98808650073898 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479252_0.png resize: (187, 203) 1349690032 -2.8852925218025436 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3745425522_0.png resize: (420, 485) 1349690033 -3.692575002688349 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472533_0.png resize: (113, 157) 1349690034 -2.951480145598925 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479239_0.png resize: (113, 157) 1349690035 -2.951480145598925 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472532_0.png resize: (216, 166) 1349690036 -2.561405311776773 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479258_0.png resize: (216, 166) 1349690037 -2.561405311776773 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472524_0.png resize: (122, 87) 1349690038 -3.8104276306436597 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479282_0.png resize: (122, 87) 1349690039 -3.8104276306436597 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479251_0.png resize: (420, 433) 1349690040 -3.8632885309312495 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3745425526_0.png resize: (277, 228) 1349690041 -4.478627056927888 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472517_0.png resize: (110, 148) 1349690042 -2.9113958477220794 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472520_0.png resize: (278, 223) 1349690043 -4.463063102372423 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479284_0.png resize: (278, 223) 1349690044 -4.463063102372423 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479247_0.png resize: (110, 148) 1349690045 -3.174192735149285 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3745425524_0.png resize: (92, 196) 1349690046 -2.7865998521788664 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472503_0.png resize: (92, 186) 1349690047 -2.7611942586625644 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479296_0.png resize: (92, 186) 1349690048 -2.7611942586625644 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472470_0.png resize: (192, 133) 1349690049 -1.3695238654251758 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479235_0.png resize: (192, 133) 1349690050 -1.3586578231531377 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472493_0.png resize: (115, 123) 1349690051 -1.263267389405468 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479307_0.png resize: (115, 123) 1349690052 -1.254102651043082 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472502_0.png resize: (146, 229) 1349690053 -3.2293046026006733 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479241_0.png resize: (146, 229) 1349690054 -3.2293046026006733 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472475_0.png resize: (226, 182) 1349690055 -4.046418309452288 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479267_0.png resize: (226, 182) 1349690056 -4.046576580982254 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472499_0.png resize: (228, 176) 1349690057 -2.3049652324001286 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479256_0.png resize: (219, 175) 1349690058 -0.9765510718540072 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472521_0.png resize: (162, 158) 1349690059 -3.6104388637012677 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479279_0.png resize: (162, 158) 1349690060 -3.6104388637012677 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479308_0.png resize: (73, 88) 1349690061 -1.7925534768538447 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472466_0.png resize: (73, 88) 1349690062 -1.7884188894615907 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3745425523_0.png resize: (86, 89) 1349690063 -3.0041525472593382 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479299_0.png resize: (87, 89) 1349690064 -3.044457524362394 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472512_0.png resize: (258, 140) 1349690065 -3.4456055361172377 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479259_0.png resize: (258, 137) 1349690066 -2.643900745392304 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472504_0.png resize: (155, 168) 1349690067 -3.3532498124489396 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479292_0.png resize: (155, 168) 1349690068 -3.3532498124489396 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472508_0.png resize: (304, 207) 1349690069 -2.687492693677804 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479257_0.png resize: (304, 207) 1349690071 -2.687492693677804 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472537_0.png resize: (559, 276) 1349690072 -4.678633841772333 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472530_0.png resize: (137, 112) 1349690073 -3.0917376144710818 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479262_0.png resize: (137, 112) 1349690074 -2.681070755437178 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479246_0.png resize: (343, 168) 1349690075 -0.0713189499514314 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472513_0.png resize: (180, 138) 1349690076 -3.2299448162785787 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479302_0.png resize: (180, 138) 1349690077 -3.2299448162785787 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472554_0.png resize: (84, 144) 1349690078 -3.0379941457730135 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479633_0.png resize: (84, 144) 1349690080 -3.0379941457730135 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472587_0.png resize: (228, 310) 1349690081 -3.548550844997713 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472551_0.png resize: (123, 388) 1349690082 -1.3892895849742979 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479627_0.png resize: (123, 388) 1349690083 -1.3892895849742979 treat image : 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temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425481_0.png resize: (625, 678) 1349690334 -3.388097621210807 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425461_0.png resize: (288, 511) 1349690335 -3.4085276964738194 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425469_0.png resize: (144, 382) 1349690336 -2.7674963257759067 treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19_rle_crop_3745425471_0.png resize: (149, 154) 1349690337 -2.3095929822318357 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425514_0.png resize: (431, 189) 1349690338 -1.9846651375995226 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425492_0.png resize: (132, 207) 1349690339 -3.207140797290923 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425487_0.png resize: (224, 302) 1349690340 -2.2481072888797247 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425510_0.png resize: (188, 305) 1349690341 -2.7124606841361967 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425500_0.png resize: (171, 195) 1349690342 -2.8012842143131005 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425486_0.png resize: (581, 994) 1349690343 -1.8997818745481545 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425513_0.png resize: (255, 535) 1349690344 -3.165948618754865 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425484_0.png resize: (185, 332) 1349690345 -3.9021215650743124 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425488_0.png resize: (221, 385) 1349690347 -3.538047337241876 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425498_0.png resize: (82, 119) 1349690348 -1.515079641427659 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472305_0.png resize: (216, 191) 1349690349 -1.2993696424224033 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476642_0.png resize: (216, 191) 1349690350 -1.301604173152977 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472313_0.png resize: (143, 118) 1349690351 -3.4165373364152187 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476651_0.png resize: (143, 118) 1349690352 -3.4165373364152187 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3745425519_0.png resize: (306, 442) 1349690353 -1.5814737705657835 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476659_0.png resize: (308, 439) 1349690354 -1.5461336788928677 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472286_0.png resize: (236, 509) 1349690355 -1.9625041706374928 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476655_0.png resize: (236, 509) 1349690356 -1.9626153544691136 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472294_0.png resize: (127, 294) 1349690357 -0.1365273315644045 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476622_0.png resize: (127, 294) 1349690358 -0.1344003482842767 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472324_0.png resize: (93, 222) 1349690359 -1.4417474963368382 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476634_0.png resize: (93, 222) 1349690360 -1.4414514656990696 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472310_0.png resize: (241, 363) 1349690361 -2.031228587035049 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476658_0.png resize: (241, 363) 1349690362 -2.0312685941422406 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472297_0.png resize: (253, 153) 1349690363 -2.7841185888643913 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476650_0.png resize: (253, 153) 1349690364 -2.7825012302599634 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472309_0.png resize: (386, 387) 1349690365 -1.1148879816623882 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476645_0.png resize: (386, 387) 1349690366 -1.1160912841513906 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472296_0.png resize: (644, 874) 1349690367 0.3505194793882831 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476654_0.png resize: (644, 874) 1349690368 0.3505314125320735 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472325_0.png resize: (207, 189) 1349690370 -2.5323663684730584 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476626_0.png resize: (207, 189) 1349690371 -2.5337520978918935 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472354_0.png resize: (278, 209) 1349690372 -2.175738653030463 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476802_0.png resize: (278, 209) 1349690373 -2.182547578315343 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472331_0.png resize: (222, 342) 1349690374 -1.7748841853392525 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476807_0.png resize: (222, 342) 1349690375 -1.7748841853392525 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472344_0.png resize: (199, 143) 1349690376 -3.179249949241536 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476787_0.png resize: (199, 143) 1349690377 -3.17926548087479 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472326_0.png resize: (181, 381) 1349690378 -2.187668902340738 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476808_0.png resize: (181, 381) 1349690379 -2.1874405842302744 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472342_0.png resize: (405, 300) 1349690380 -2.8306439316152616 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476812_0.png resize: (405, 300) 1349690381 -2.825077711021333 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472338_0.png resize: (316, 419) 1349690382 -1.0707003029838091 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476813_0.png resize: (316, 419) 1349690383 -1.0703075833584288 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472358_0.png resize: (133, 171) 1349690384 -1.6830813551326724 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476793_0.png resize: (133, 171) 1349690385 -1.6801465005956036 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472327_0.png resize: (421, 264) 1349690386 -2.2588475636526213 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476804_0.png resize: (421, 264) 1349690388 -2.2585686507020424 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472347_0.png resize: (67, 135) 1349690389 -0.8927488075745783 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476803_0.png resize: (67, 135) 1349690390 -0.891800553945536 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472352_0.png resize: (177, 355) 1349690391 -2.2781922384854494 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476817_0.png resize: (177, 355) 1349690392 -2.4207388549206463 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472348_0.png resize: (103, 573) 1349690393 -2.4341172296754845 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476818_0.png resize: (103, 573) 1349690394 -2.4298175910099835 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472355_0.png resize: (330, 270) 1349690395 -1.0437406220179346 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476801_0.png resize: (322, 255) 1349690396 -0.935461102070669 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472330_0.png resize: (193, 82) 1349690397 0.14369893271607684 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476809_0.png resize: (193, 82) 1349690398 0.14318656168313765 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472374_0.png resize: (236, 177) 1349690399 -3.895703456319152 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476930_0.png resize: (236, 177) 1349690400 -3.895703456319152 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472387_0.png resize: (215, 543) 1349690401 -3.038949486349035 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476973_0.png resize: (215, 544) 1349690402 -3.0430428203592634 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472406_0.png resize: (124, 405) 1349690403 -2.4665045128265968 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477108_0.png resize: (124, 405) 1349690404 -2.4601198966383913 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472405_0.png resize: (220, 239) 1349690405 -1.7076931153396404 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477109_0.png resize: (220, 239) 1349690406 -1.708107340189251 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472400_0.png resize: (568, 465) 1349690407 -0.8388924845153926 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477114_0.png resize: (568, 465) 1349690408 -0.8379136074157522 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472522_0.png resize: (268, 142) 1349690409 -2.5398385004100517 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479266_0.png resize: (268, 142) 1349690410 -2.5398385004100517 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472488_0.png resize: (206, 410) 1349690412 -3.5816475431461314 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479303_0.png resize: (206, 410) 1349690413 -3.567530937312176 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3745425527_0.png resize: (184, 317) 1349690414 -2.104278352330731 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472490_0.png resize: (238, 257) 1349690415 -2.169113670618825 treat image : 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temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479674_0.png resize: (221, 148) 1349690422 -1.9023108349415354 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472568_0.png resize: (184, 150) 1349690423 -2.3878042251717706 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479637_0.png resize: (184, 150) 1349690424 -2.3878042251717706 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472579_0.png resize: (121, 168) 1349690425 -3.8424352172604688 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479638_0.png resize: (121, 168) 1349690426 -3.8424352172604688 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472550_0.png resize: (147, 180) 1349690427 -1.884613273073587 treat image : 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temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479628_0.png resize: (201, 252) 1349690435 0.16824942637209392 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472559_0.png resize: (161, 98) 1349690436 -3.578805179779273 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479657_0.png resize: (161, 98) 1349690438 -3.578805179779273 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479805_0.png resize: (219, 134) 1349690439 -2.2974890302052087 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472598_0.png resize: (219, 134) 1349690440 -2.2974890302052087 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472599_0.png resize: (534, 699) 1349690441 -1.117485150301556 treat image : 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temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479792_0.png resize: (150, 145) 1349690448 -3.057498246895658 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472603_0.png resize: (380, 407) 1349690449 -3.487356265919588 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479817_0.png resize: (380, 407) 1349690450 -3.487356265919588 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472565_0.png resize: (122, 140) 1349690498 -2.939435984753297 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479653_0.png resize: (122, 140) 1349690500 -2.939435984753297 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472576_0.png resize: (136, 164) 1349690502 -3.500854932148001 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479659_0.png resize: (136, 164) 1349690504 -3.500854932148001 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479789_0.png resize: (104, 270) 1349690505 -2.663197391012351 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472597_0.png resize: (104, 270) 1349690506 -2.663197391012351 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472614_0.png resize: (111, 185) 1349690507 -3.5468033731033652 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479791_0.png resize: (88, 134) 1349690509 -2.744667404592088 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425413_0.png resize: (363, 437) 1349690693 -1.1239169999734064 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425430_0.png resize: (402, 561) 1349690694 -1.0987603773353944 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425409_0.png resize: (929, 1253) 1349690695 -2.1920268398580838 treat image : temp/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03_rle_crop_3745425425_0.png resize: (959, 1358) 1349690696 -0.7038334322454758 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425455_0.png resize: (376, 608) 1349690697 -1.1376130908135322 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425441_0.png resize: (227, 348) 1349690698 -3.6174737363461413 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425445_0.png resize: (284, 392) 1349690699 -3.034405628973335 treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82_rle_crop_3745425511_0.png resize: (107, 216) 1349690700 -3.7550383976796984 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472311_0.png resize: (294, 567) 1349690701 -1.7823499252826855 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476638_0.png resize: (294, 567) 1349690702 -1.782390565999284 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472300_0.png resize: (384, 533) 1349690703 -1.7004372876802938 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476632_0.png resize: (383, 533) 1349690704 -1.7017771187626027 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472316_0.png resize: (168, 270) 1349690705 -2.207025448459386 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476627_0.png resize: (168, 270) 1349690706 -2.207025448459386 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472303_0.png resize: (284, 608) 1349690707 -1.0552148787131719 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472322_0.png resize: (103, 316) 1349690708 -0.9118099967530571 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476624_0.png resize: (103, 316) 1349690709 -0.9118099967530571 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476633_0.png resize: (284, 608) 1349690710 -1.0491953378922974 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472337_0.png resize: (680, 982) 1349690711 -2.215324701741373 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476810_0.png resize: (680, 982) 1349690712 -2.2161369205010524 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472353_0.png resize: (365, 312) 1349690713 -1.9797092324744192 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476794_0.png resize: (365, 312) 1349690714 -1.9763220350616642 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472336_0.png resize: (234, 793) 1349690715 -2.413425085596046 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476816_0.png resize: (234, 793) 1349690716 -2.4171888350838535 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472329_0.png resize: (313, 252) 1349690717 -2.983054557382817 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476795_0.png resize: (313, 252) 1349690718 -2.9826790173476376 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472357_0.png resize: (169, 268) 1349690719 -2.8499220001456798 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476788_0.png resize: (169, 268) 1349690720 -2.8501133705763864 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472363_0.png resize: (451, 296) 1349690721 -1.4411292494295616 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476876_0.png resize: (451, 296) 1349690723 -1.4410514195038329 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744472367_0.png resize: (247, 255) 1349690724 -0.7233227213282614 treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0_rle_crop_3744476872_0.png resize: (247, 255) 1349690726 -0.7241093211525496 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472377_0.png resize: (239, 310) 1349690727 -2.2665295858512007 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476927_0.png resize: (239, 310) 1349690728 -2.2665295858512007 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472370_0.png resize: (290, 407) 1349690729 -2.230837672952418 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476934_0.png resize: (290, 407) 1349690730 -2.2299579887151437 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472373_0.png resize: (516, 414) 1349690731 -3.6423680422578975 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476931_0.png resize: (516, 414) 1349690732 -3.638437404113886 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472371_0.png resize: (167, 207) 1349690733 -1.8397437293657009 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476933_0.png resize: (167, 207) 1349690734 -1.8397437293657009 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744472376_0.png resize: (207, 527) 1349690735 -2.783431504022303 treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f_rle_crop_3744476928_0.png resize: (207, 527) 1349690736 -2.783431504022303 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744472388_0.png resize: (653, 216) 1349690737 -3.76309828958015 treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46_rle_crop_3744476972_0.png resize: (653, 216) 1349690738 -3.7629923689304814 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472409_0.png resize: (636, 334) 1349690739 -1.5688007626113563 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477105_0.png resize: (636, 334) 1349690740 -1.573348434106641 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472404_0.png resize: (250, 274) 1349690741 -3.4349730035639467 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477110_0.png resize: (250, 274) 1349690742 -3.451027197706987 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472410_0.png resize: (241, 463) 1349690743 -3.2545686482769884 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477104_0.png resize: (241, 463) 1349690744 -3.255380515634734 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472408_0.png resize: (749, 586) 1349690745 -1.6384161420150245 treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477106_0.png resize: (749, 586) 1349690746 -1.5678158171357446 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472516_0.png resize: (498, 240) 1349690747 -3.4654222932803878 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479286_0.png resize: (498, 240) 1349690748 -3.4654222932803878 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472505_0.png resize: (278, 391) 1349690749 -4.146868725203193 treat image : 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temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472583_0.png resize: (169, 267) 1349690756 -3.7629583149235444 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479640_0.png resize: (169, 267) 1349690757 -3.7629583149235444 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472594_0.png resize: (274, 959) 1349690758 -1.8779932199951404 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479797_0.png resize: (274, 959) 1349690759 -1.8779932199951404 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472611_0.png resize: (361, 253) 1349690760 -3.669816169914886 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472593_0.png resize: (243, 334) 1349690761 -3.0223565667415397 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479818_0.png resize: (361, 253) 1349690762 -3.669816169914886 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479794_0.png resize: (243, 334) 1349690763 -3.0223565667415397 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472605_0.png resize: (553, 1072) 1349690764 -2.718878912208328 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479814_0.png resize: (553, 1072) 1349690765 -2.718878912208328 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472617_0.png resize: (644, 466) 1349690766 -2.4193496342928547 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479799_0.png resize: (644, 466) 1349690767 -2.4193496342928547 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472600_0.png resize: (213, 266) 1349690768 -3.555600105107857 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479816_0.png resize: (213, 266) 1349690769 -3.555600105107857 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744472619_0.png resize: (439, 312) 1349690770 -2.8085636139745933 treat image : temp/1743636029_407870_1349515697_37a8cf8263997763fe3084260faba4e3_rle_crop_3744479803_0.png resize: (439, 312) 1349690771 -2.8085636139745933 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472317_0.png resize: (311, 389) 1349690796 -0.8633573063280998 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476628_0.png resize: (311, 389) 1349690797 -0.8636735264326004 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744476652_0.png resize: (96, 103) 1349690798 -0.015976857784339996 treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3_rle_crop_3744472299_0.png resize: (96, 103) 1349690799 -0.015976857784339996 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476797_0.png resize: (104, 97) 1349690800 -1.0584156814157473 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472345_0.png resize: (104, 97) 1349690801 -1.0584156814157473 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744472545_0.png resize: (113, 163) 1349690802 -1.2515828698595866 treat image : temp/1743636029_407870_1349515733_622c5199ba6644aa31a163a3e40ea56b_rle_crop_3744479673_0.png resize: (113, 163) 1349690803 -1.2515828698595866 treat image : 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temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472480_0.png resize: (102, 128) 1349690823 -3.2912871791840983 treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479277_0.png resize: (102, 128) 1349690824 -3.2912871791840983 treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425448_0.png resize: (166, 399) 1349690833 -5.52611684938336 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472351_0.png resize: (148, 152) 1349690834 -2.849458590576752 treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476789_0.png resize: (148, 152) 1349690835 -2.849458590576752 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 : 702 time used for this insertion : 0.05617403984069824 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 702 time used for this insertion : 0.1622633934020996 save missing photos in datou_result : time spend for datou_step_exec : 61.48455476760864 time spend to save output : 0.22690320014953613 total time spend for step 6 : 61.71145796775818 step7:brightness Thu Apr 3 01:35:26 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/1743636029_407870_1349664067_ad7d8e3d6bfcf40f0d52bd1fd688aa03.jpg treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82.jpg treat image : temp/1743636029_407870_1349664053_968fc06a21db4d8caec984f716074d19.jpg treat image : temp/1743636029_407870_1349664048_2afe8e74de145ea1f15cbe9a3e9dca82.jpg treat image : temp/1743636029_407870_1349517656_3d668b8e72e164fc433adffcfcbac4d3.jpg treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b.jpg treat image : temp/1743636029_407870_1349516100_024d045e246a3e93cc183e09d9b717f0.jpg treat image : temp/1743636029_407870_1349516073_d9d80631deb89022f0e905cc61064b9f.jpg treat image : temp/1743636029_407870_1349516053_5a096eded25c48fff04d644108689d46.jpg treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9.jpg treat image : 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temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744472393_0.png treat image : temp/1743636029_407870_1349516024_48620ca480a041da3d4e1f06b855fdd9_rle_crop_3744477121_0.png treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744472480_0.png treat image : temp/1743636029_407870_1349515759_2e0a1b1df778c90dc4b545d3187c7b74_rle_crop_3744479277_0.png treat image : temp/1743636029_407870_1349664062_f7e0c055407e677a57697f4ff50a8e82_rle_crop_3745425448_0.png treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744472351_0.png treat image : temp/1743636029_407870_1349517577_42092a0aecfdf7cb8c9b0efe5d2b723b_rle_crop_3744476789_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 : 702 time used for this insertion : 0.05876445770263672 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 702 time used for this insertion : 0.28978681564331055 save missing photos in datou_result : time spend for datou_step_exec : 17.463978052139282 time spend to save output : 0.35589051246643066 total time spend for step 7 : 17.819868564605713 step8:velours_tree Thu Apr 3 01:35:44 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 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 : 1.8680698871612549 time spend to save output : 3.647804260253906e-05 total time spend for step 8 : 1.8681063652038574 step9:send_mail_cod Thu Apr 3 01:35:46 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 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_P21999409_03-04-2025_01_35_46.pdf 21999973 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219999731743636946 21999974 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219999741743636947 21999975 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219999751743636948 21999976 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219999761743636949 21999977 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219999771743636950 21999978 change filename to text .change filename to text .change filename to text .imagette219999781743636952 21999979 imagette219999791743636952 21999980 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette219999801743636952 21999981 imagette219999811743636953 21999982 imagette219999821743636953 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21999409 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21999972,21999973,21999974,21999975,21999976,21999977,21999978,21999979,21999980,21999981,21999982?tags=environnement,carton,pehd,autre,papier,pet_clair,pet_fonce,flou,metal,mal_croppe,background args[1349664067] : ((1349664067, -1.2145045573588766, 492688767), (1349664067, -0.16352967326704235, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349664062] : ((1349664062, -3.192048905318694, 492609224), (1349664062, -0.18226002499714222, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349664053] : ((1349664053, -4.182975378143321, 492609224), (1349664053, -0.14215036818657628, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349664048] : ((1349664048, -4.302976869299141, 492609224), (1349664048, -0.2944655460423324, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349517656] : ((1349517656, -1.8607156811533523, 492688767), (1349517656, -0.2566184442081278, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349517577] : ((1349517577, -3.0462269582397807, 492609224), (1349517577, 0.08840360716012553, 2107752395), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349516100] : ((1349516100, -1.764021058547955, 492688767), (1349516100, -0.37024996122781967, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349516073] : ((1349516073, -2.2990199814560324, 492609224), (1349516073, -0.0161733448695183, 2107752395), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349516053] : ((1349516053, -3.168569000626637, 492609224), (1349516053, 0.09657919997456979, 2107752395), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349516024] : ((1349516024, -2.0005299106125483, 492609224), (1349516024, -0.15412244843643458, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349515759] : ((1349515759, -5.462228613291413, 492609224), (1349515759, -0.06473506795577648, 2107752395), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349515733] : ((1349515733, -5.309034200847762, 492609224), (1349515733, -0.27613798717844634, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com args[1349515697] : ((1349515697, -3.8003748089530713, 492609224), (1349515697, -0.15997283024066836, 496442774), '0.25288078572775285') We are sending mail with results at report@fotonower.com refus_total : 0.25288078572775285 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=21999409 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349515697,1349664067,1349515759,1349664062,1349515733,1349516024,1349516053,1349516073,1349516100,1349517577,1349517656,1349664048,1349664053) Found this number of photos: 13 begin to download photo : 1349515697 begin to download photo : 1349664062 begin to download photo : 1349516053 begin to download photo : 1349517577 begin to download photo : 1349664053 download finish for photo 1349664053 download finish for photo 1349517577 begin to download photo : 1349517656 download finish for photo 1349664062 begin to download photo : 1349515733 download finish for photo 1349515697 begin to download photo : 1349664067 download finish for photo 1349516053 begin to download photo : 1349516073 download finish for photo 1349515733 begin to download photo : 1349516024 download finish for photo 1349517656 begin to download photo : 1349664048 download finish for photo 1349664067 begin to download photo : 1349515759 download finish for photo 1349516024 download finish for photo 1349664048 download finish for photo 1349516073 begin to download photo : 1349516100 download finish for photo 1349515759 download finish for photo 1349516100 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21999409_03-04-2025_01_35_46.pdf results_Auto_P21999409_03-04-2025_01_35_46.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21999409_03-04-2025_01_35_46.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','21999409','results_Auto_P21999409_03-04-2025_01_35_46.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21999409_03-04-2025_01_35_46.pdf','pdf','','1.14','0.25288078572775285') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21999409

https://www.fotonower.com/image?json=false&list_photos_id=1349664067
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349664062
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349664053
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349664048
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349517656
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349517577
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349516100
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349516073
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349516053
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349516024
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349515759
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349515733
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349515697
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 25.29%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/21999973?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/21999974?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21999975?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/21999976?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21999977?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21999978?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21999980?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21999409_03-04-2025_01_35_46.pdf.

Lien vers velours :https://www.fotonower.com/velours/21999972,21999973,21999974,21999975,21999976,21999977,21999978,21999979,21999980,21999981,21999982?tags=environnement,carton,pehd,autre,papier,pet_clair,pet_fonce,flou,metal,mal_croppe,background.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 02 Apr 2025 23:35:57 GMT Content-Length: 0 Connection: close X-Message-Id: Dye0hvobQA6jsRXUahP_AA 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 [1349664067, 1349664062, 1349664053, 1349664048, 1349517656, 1349517577, 1349516100, 1349516073, 1349516053, 1349516024, 1349515759, 1349515733, 1349515697] 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, '2718481') ('3318', '21999409', '1349664067', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664062', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664048', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517656', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517577', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516100', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516073', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516024', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515759', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515733', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515697', None, None, None, None, None, '2718481') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.014673709869384766 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.26321792602539 time spend to save output : 0.015012264251708984 total time spend for step 9 : 11.2782301902771 step10:split_time_score Thu Apr 3 01:35:57 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'}] (('17', 13),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 02042025 21999409 Nombre de photos uploadées : 13 / 23040 (0%) 02042025 21999409 Nombre de photos taguées (types de déchets): 0 / 13 (0%) 02042025 21999409 Nombre de photos taguées (volume) : 0 / 13 (0%) elapsed_time : load_data_split_time_score 1.9073486328125e-06 elapsed_time : order_list_meta_photo_and_scores 5.9604644775390625e-06 ????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0006966590881347656 elapsed_time : insert_dashboard_record_day_entry 0.022637605667114258 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 : 0.18285560775236032 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979633_02-04-2025_19_06_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979633 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`=21979633 AND mptpi.`type`=3594 To do Qualite : 0.2209257494149923 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21993432_02-04-2025_23_29_14.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21993432 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`=21993432 AND mptpi.`type`=3594 To do Qualite : 0.18835891481078024 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21993462_02-04-2025_23_18_00.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21993462 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`=21993462 AND mptpi.`type`=3594 To do Qualite : 0.07143447116002825 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979679_02-04-2025_18_13_07.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979679 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`=21979679 AND mptpi.`type`=3726 To do Qualite : 0.19510886263337235 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21996606_03-04-2025_01_04_26.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21996606 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`=21996606 AND mptpi.`type`=3594 To do Qualite : 0.3020745190437286 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979695_02-04-2025_18_45_59.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979695 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`=21979695 AND mptpi.`type`=3594 To do Qualite : 0.1668441074346405 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21979697_02-04-2025_19_37_40.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21979697 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`=21979697 AND mptpi.`type`=3594 To do Qualite : 0.25288078572775285 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21999409_03-04-2025_01_35_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21999409 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`=21999409 AND mptpi.`type`=3594 To do Qualite : 0.20617435609393334 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21996613_03-04-2025_00_45_00.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21996613 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`=21996613 AND mptpi.`type`=3594 To do Qualite : 0.22920264061932644 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21986466_02-04-2025_20_37_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21986466 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`=21986466 AND mptpi.`type`=3594 To do Qualite : 0.06889783056580229 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21993543_02-04-2025_23_13_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21993543 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`=21993543 AND mptpi.`type`=3726 To do Qualite : 0.24937878507884653 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21996619_03-04-2025_00_39_10.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21996619 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`=21996619 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'02042025': {'nb_upload': 13, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1349664067, 1349664062, 1349664053, 1349664048, 1349517656, 1349517577, 1349516100, 1349516073, 1349516053, 1349516024, 1349515759, 1349515733, 1349515697] Looping around the photos to save general results len do output : 1 /21999409Didn'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, '2718481') ('3318', '21999409', '1349664067', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664062', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349664048', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517656', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349517577', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516100', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516073', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516053', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349516024', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515759', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515733', None, None, None, None, None, '2718481') ('3318', None, None, None, None, None, None, None, '2718481') ('3318', '21999409', '1349515697', None, None, None, None, None, '2718481') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 14 time used for this insertion : 0.05058145523071289 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.9734334945678711 time spend to save output : 0.05080008506774902 total time spend for step 10 : 1.0242335796356201 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 13 set_done_treatment 456.34user 203.60system 15:33.18elapsed 70%CPU (0avgtext+0avgdata 7898128maxresident)k 1905144inputs+375096outputs (49857major+33195290minor)pagefaults 0swaps