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 : 1951692 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 : ['3985636'] with mtr_portfolio_ids : ['27900917'] and first list_photo_ids : [] new path : /proc/1951692/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 26 ; length of list_pids : 26 ; length of list_args : 26 time to download the photos : 6.59179425239563 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 Mon Nov 3 14:50:35 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 : 10553 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-11-03 14:50:38.298069: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-11-03 14:50:38.326440: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493010000 Hz 2025-11-03 14:50:38.328666: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f2900000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-11-03 14:50:38.328719: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-11-03 14:50:38.333289: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-11-03 14:50:38.575764: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x167f7c60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-11-03 14:50:38.575813: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-11-03 14:50:38.577082: 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 2025-11-03 14:50:38.577615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-03 14:50:38.582042: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-03 14:50:38.593546: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-03 14:50:38.594511: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-03 14:50:38.601742: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-03 14:50:38.604264: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-03 14:50:38.638179: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-03 14:50:38.639989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-03 14:50:38.640423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-03 14:50:38.641373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-03 14:50:38.641392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-03 14:50:38.641493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-03 14:50:38.643744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9777 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. 2025-11-03 14:50:39.005046: 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 2025-11-03 14:50:39.005144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-03 14:50:39.005172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-03 14:50:39.005197: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-03 14:50:39.005221: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-03 14:50:39.005244: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-03 14:50:39.005268: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-03 14:50:39.005292: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-03 14:50:39.007444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-03 14:50:39.008779: 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 2025-11-03 14:50:39.008810: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-11-03 14:50:39.008825: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-03 14:50:39.008839: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-11-03 14:50:39.008853: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-11-03 14:50:39.008866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-11-03 14:50:39.008880: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-11-03 14:50:39.008893: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-03 14:50:39.010169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-11-03 14:50:39.010201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-11-03 14:50:39.010209: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-11-03 14:50:39.010217: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-11-03 14:50:39.011561: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9777 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 : [] 2025-11-03 14:50:56.156812: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-11-03 14:50:56.376505: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-11-03 14:50:58.207680: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.208249: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.60G (3865470464 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.209389: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 3.24G (3478923264 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.209854: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.92G (3131030784 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.210330: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.62G (2817927680 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.210823: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.36G (2536134912 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.211316: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 2.12G (2282521344 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.211353: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.211832: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.211847: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.293401: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.293440: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.293910: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.293925: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.313417: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.313466: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.313977: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.313994: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 466.56MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.461074: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.461157: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.461875: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.461897: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.467661: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.467704: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.468437: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.468457: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 243.25MiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-11-03 14:50:58.655566: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.656140: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.659450: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.659962: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.808073: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.808641: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.810546: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.811032: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.835809: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.836302: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.837741: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.838393: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.843802: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.844288: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.845845: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.846331: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.856550: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.857041: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.858466: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.858978: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.885216: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.885744: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.886254: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.886776: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.890305: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.890824: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.906393: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.906929: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.907443: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.907949: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.925540: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.926098: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.926619: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.927127: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.931520: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.932040: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.936639: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.937156: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.949307: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.949827: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.953893: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.954422: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.954932: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.955438: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.956152: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.956664: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.967297: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.967828: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.968361: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.968875: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.969385: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.969897: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.970421: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.970962: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.985156: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.985730: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.991995: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:58.992520: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.017687: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.017723: W tensorflow/core/kernels/gpu_utils.cc:49] Failed to allocate memory for convolution redzone checking; skipping this check. This is benign and only means that we won't check cudnn for out-of-bounds reads and writes. This message will only be printed once. 2025-11-03 14:50:59.018244: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.018777: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.026252: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.026797: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.027321: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.027826: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.035752: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.036273: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.056423: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.057028: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.057577: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.058112: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.062195: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.062739: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.063263: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.063814: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.065249: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.075264: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.075820: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.084901: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.085528: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.086450: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.087331: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.088144: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory 2025-11-03 14:50:59.088662: I tensorflow/stream_executor/cuda/cuda_driver.cc:763] failed to allocate 4.00G (4294967296 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY: out of memory local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 26 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 26 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 36 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 32 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 19 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 21 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 45 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 41 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 30 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 14 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 25 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 27 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 29 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 22 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 30 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (2160, 3840, 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: 3840.00000 nb d'objets trouves : 26 Detection mask done ! Trying to reset tf kernel 1952290 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 9360 tf kernel not reseted sub process len(results) : 26 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 26 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 : 10553 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 : 0.0009391307830810547 nb_pixel_total : 20057 time to create 1 rle with old method : 0.028664350509643555 length of segment : 100 time for calcul the mask position with numpy : 0.0005028247833251953 nb_pixel_total : 11143 time to create 1 rle with old method : 0.014646768569946289 length of segment : 130 time for calcul the mask position with numpy : 0.0011692047119140625 nb_pixel_total : 41440 time to create 1 rle with old method : 0.044605255126953125 length of segment : 248 time for calcul the mask position with numpy : 0.0005245208740234375 nb_pixel_total : 18533 time to create 1 rle with old method : 0.020865440368652344 length of segment : 156 time for calcul the mask position with numpy : 0.0004971027374267578 nb_pixel_total : 26088 time to create 1 rle with old method : 0.029253005981445312 length of segment : 147 time for calcul the mask position with numpy : 0.00040411949157714844 nb_pixel_total : 13867 time to create 1 rle with old method : 0.01610708236694336 length of segment : 115 time for calcul the mask position with numpy : 0.0009677410125732422 nb_pixel_total : 32283 time to create 1 rle with old method : 0.036243438720703125 length of segment : 253 time for calcul the mask position with numpy : 0.0003685951232910156 nb_pixel_total : 12988 time to create 1 rle with old method : 0.014936447143554688 length of segment : 158 time for calcul the mask position with numpy : 0.03384232521057129 nb_pixel_total : 1627806 time to create 1 rle with new method : 0.10812616348266602 length of segment : 1806 time for calcul the mask position with numpy : 0.00041413307189941406 nb_pixel_total : 25035 time to create 1 rle with old method : 0.02739119529724121 length of segment : 154 time for calcul the mask position with numpy : 0.004509449005126953 nb_pixel_total : 289593 time to create 1 rle with new method : 0.012636899948120117 length of segment : 1002 time for calcul the mask position with numpy : 0.0018618106842041016 nb_pixel_total : 73205 time to create 1 rle with old method : 0.08167791366577148 length of segment : 306 time for calcul the mask position with numpy : 0.0002627372741699219 nb_pixel_total : 10497 time to create 1 rle with old method : 0.01203298568725586 length of segment : 116 time for calcul the mask position with numpy : 0.0001919269561767578 nb_pixel_total : 4921 time to create 1 rle with old method : 0.0059931278228759766 length of segment : 76 time for calcul the mask position with numpy : 0.0007536411285400391 nb_pixel_total : 33428 time to create 1 rle with old method : 0.03926897048950195 length of segment : 401 time for calcul the mask position with numpy : 0.000823974609375 nb_pixel_total : 36854 time to create 1 rle with old method : 0.04028034210205078 length of segment : 245 time for calcul the mask position with numpy : 0.0003230571746826172 nb_pixel_total : 9292 time to create 1 rle with old method : 0.011008739471435547 length of segment : 98 time for calcul the mask position with numpy : 0.00041675567626953125 nb_pixel_total : 18894 time to create 1 rle with old method : 0.02165818214416504 length of segment : 290 time for calcul the mask position with numpy : 0.000644683837890625 nb_pixel_total : 37912 time to create 1 rle with old method : 0.04363751411437988 length of segment : 172 time for calcul the mask position with numpy : 0.0008859634399414062 nb_pixel_total : 49592 time to create 1 rle with old method : 0.05527663230895996 length of segment : 432 time for calcul the mask position with numpy : 0.0011904239654541016 nb_pixel_total : 47014 time to create 1 rle with old method : 0.051499128341674805 length of segment : 419 time for calcul the mask position with numpy : 0.0007176399230957031 nb_pixel_total : 48162 time to create 1 rle with old method : 0.05361056327819824 length of segment : 348 time for calcul the mask position with numpy : 0.0012085437774658203 nb_pixel_total : 58952 time to create 1 rle with old method : 0.06601786613464355 length of segment : 354 time for calcul the mask position with numpy : 0.0003390312194824219 nb_pixel_total : 17335 time to create 1 rle with old method : 0.02011847496032715 length of segment : 155 time for calcul the mask position with numpy : 0.0008072853088378906 nb_pixel_total : 49132 time to create 1 rle with old method : 0.06081271171569824 length of segment : 286 time for calcul the mask position with numpy : 0.002344369888305664 nb_pixel_total : 102412 time to create 1 rle with old method : 0.11359953880310059 length of segment : 697 time for calcul the mask position with numpy : 0.00031280517578125 nb_pixel_total : 16130 time to create 1 rle with old method : 0.022417545318603516 length of segment : 158 time for calcul the mask position with numpy : 0.00043892860412597656 nb_pixel_total : 16221 time to create 1 rle with old method : 0.026484966278076172 length of segment : 166 time for calcul the mask position with numpy : 0.00025391578674316406 nb_pixel_total : 8909 time to create 1 rle with old method : 0.011389493942260742 length of segment : 161 time for calcul the mask position with numpy : 0.0004885196685791016 nb_pixel_total : 24334 time to create 1 rle with old method : 0.028859853744506836 length of segment : 239 time for calcul the mask position with numpy : 0.0008845329284667969 nb_pixel_total : 49493 time to create 1 rle with old method : 0.05768132209777832 length of segment : 346 time for calcul the mask position with numpy : 0.00035643577575683594 nb_pixel_total : 18315 time to create 1 rle with old method : 0.020938396453857422 length of segment : 190 time for calcul the mask position with numpy : 0.0011699199676513672 nb_pixel_total : 78279 time to create 1 rle with old method : 0.08757472038269043 length of segment : 349 time for calcul the mask position with numpy : 0.00042700767517089844 nb_pixel_total : 18441 time to create 1 rle with old method : 0.030101299285888672 length of segment : 321 time for calcul the mask position with numpy : 0.0009698867797851562 nb_pixel_total : 35019 time to create 1 rle with old method : 0.03981733322143555 length of segment : 166 time for calcul the mask position with numpy : 0.002040386199951172 nb_pixel_total : 53365 time to create 1 rle with old method : 0.061035871505737305 length of segment : 297 time for calcul the mask position with numpy : 0.00089263916015625 nb_pixel_total : 44184 time to create 1 rle with old method : 0.05017733573913574 length of segment : 332 time for calcul the mask position with numpy : 0.0010483264923095703 nb_pixel_total : 53396 time to create 1 rle with old method : 0.05925321578979492 length of segment : 491 time for calcul the mask position with numpy : 0.0008106231689453125 nb_pixel_total : 22258 time to create 1 rle with old method : 0.025823354721069336 length of segment : 153 time for calcul the mask position with numpy : 0.0009005069732666016 nb_pixel_total : 15355 time to create 1 rle with old method : 0.018022537231445312 length of segment : 184 time for calcul the mask position with numpy : 0.0030622482299804688 nb_pixel_total : 100141 time to create 1 rle with old method : 0.10993528366088867 length of segment : 392 time for calcul the mask position with numpy : 0.0008358955383300781 nb_pixel_total : 47946 time to create 1 rle with old method : 0.05254530906677246 length of segment : 236 time for calcul the mask position with numpy : 0.0005218982696533203 nb_pixel_total : 15088 time to create 1 rle with old method : 0.017267227172851562 length of segment : 79 time for calcul the mask position with numpy : 0.0006670951843261719 nb_pixel_total : 13309 time to create 1 rle with old method : 0.015591144561767578 length of segment : 136 time for calcul the mask position with numpy : 0.0015366077423095703 nb_pixel_total : 102445 time to create 1 rle with old method : 0.11328864097595215 length of segment : 285 time for calcul the mask position with numpy : 0.000446319580078125 nb_pixel_total : 16196 time to create 1 rle with old method : 0.018697500228881836 length of segment : 186 time for calcul the mask position with numpy : 0.0005402565002441406 nb_pixel_total : 22704 time to create 1 rle with old method : 0.026322126388549805 length of segment : 176 time for calcul the mask position with numpy : 0.0007169246673583984 nb_pixel_total : 18990 time to create 1 rle with old method : 0.02314591407775879 length of segment : 180 time for calcul the mask position with numpy : 0.0011191368103027344 nb_pixel_total : 51495 time to create 1 rle with old method : 0.06106972694396973 length of segment : 292 time for calcul the mask position with numpy : 0.001447439193725586 nb_pixel_total : 42932 time to create 1 rle with old method : 0.04806637763977051 length of segment : 251 time for calcul the mask position with numpy : 0.0009777545928955078 nb_pixel_total : 28341 time to create 1 rle with old method : 0.03345179557800293 length of segment : 145 time for calcul the mask position with numpy : 0.0020923614501953125 nb_pixel_total : 36090 time to create 1 rle with old method : 0.04377865791320801 length of segment : 319 time for calcul the mask position with numpy : 0.0003452301025390625 nb_pixel_total : 11071 time to create 1 rle with old method : 0.012753486633300781 length of segment : 112 time for calcul the mask position with numpy : 0.00028896331787109375 nb_pixel_total : 15376 time to create 1 rle with old method : 0.017986059188842773 length of segment : 104 time for calcul the mask position with numpy : 0.00048279762268066406 nb_pixel_total : 24020 time to create 1 rle with old method : 0.027435779571533203 length of segment : 202 time for calcul the mask position with numpy : 0.0005867481231689453 nb_pixel_total : 20177 time to create 1 rle with old method : 0.022882938385009766 length of segment : 475 time for calcul the mask position with numpy : 0.0002875328063964844 nb_pixel_total : 13640 time to create 1 rle with old method : 0.01535797119140625 length of segment : 208 time for calcul the mask position with numpy : 0.000782012939453125 nb_pixel_total : 36963 time to create 1 rle with old method : 0.041259050369262695 length of segment : 201 time for calcul the mask position with numpy : 0.0005664825439453125 nb_pixel_total : 17602 time to create 1 rle with old method : 0.020661115646362305 length of segment : 135 time for calcul the mask position with numpy : 0.0002856254577636719 nb_pixel_total : 12560 time to create 1 rle with old method : 0.014183998107910156 length of segment : 186 time for calcul the mask position with numpy : 0.0003726482391357422 nb_pixel_total : 23354 time to create 1 rle with old method : 0.026393413543701172 length of segment : 173 time for calcul the mask position with numpy : 0.008687973022460938 nb_pixel_total : 286659 time to create 1 rle with new method : 0.01277780532836914 length of segment : 656 time for calcul the mask position with numpy : 0.004109382629394531 nb_pixel_total : 152801 time to create 1 rle with new method : 0.006505489349365234 length of segment : 544 time for calcul the mask position with numpy : 0.000457763671875 nb_pixel_total : 24164 time to create 1 rle with old method : 0.027222871780395508 length of segment : 200 time for calcul the mask position with numpy : 0.0016422271728515625 nb_pixel_total : 40148 time to create 1 rle with old method : 0.04602551460266113 length of segment : 304 time for calcul the mask position with numpy : 0.0002162456512451172 nb_pixel_total : 8072 time to create 1 rle with old method : 0.009658575057983398 length of segment : 99 time for calcul the mask position with numpy : 0.001434326171875 nb_pixel_total : 44441 time to create 1 rle with old method : 0.05051398277282715 length of segment : 325 time for calcul the mask position with numpy : 0.0006237030029296875 nb_pixel_total : 20228 time to create 1 rle with old method : 0.023660659790039062 length of segment : 161 time for calcul the mask position with numpy : 0.0003650188446044922 nb_pixel_total : 18883 time to create 1 rle with old method : 0.02167534828186035 length of segment : 151 time for calcul the mask position with numpy : 0.0007219314575195312 nb_pixel_total : 34954 time to create 1 rle with old method : 0.03997397422790527 length of segment : 240 time for calcul the mask position with numpy : 0.001650094985961914 nb_pixel_total : 84939 time to create 1 rle with old method : 0.12085986137390137 length of segment : 337 time for calcul the mask position with numpy : 0.00051116943359375 nb_pixel_total : 28101 time to create 1 rle with old method : 0.03474903106689453 length of segment : 259 time for calcul the mask position with numpy : 0.0005526542663574219 nb_pixel_total : 18656 time to create 1 rle with old method : 0.02124643325805664 length of segment : 432 time for calcul the mask position with numpy : 0.0007219314575195312 nb_pixel_total : 45173 time to create 1 rle with old method : 0.05154252052307129 length of segment : 329 time for calcul the mask position with numpy : 0.0007290840148925781 nb_pixel_total : 49153 time to create 1 rle with old method : 0.05628323554992676 length of segment : 275 time for calcul the mask position with numpy : 0.0025832653045654297 nb_pixel_total : 150406 time to create 1 rle with new method : 0.006921291351318359 length of segment : 417 time for calcul the mask position with numpy : 0.0006589889526367188 nb_pixel_total : 32637 time to create 1 rle with old method : 0.03766274452209473 length of segment : 282 time for calcul the mask position with numpy : 0.0004076957702636719 nb_pixel_total : 26132 time to create 1 rle with old method : 0.029277563095092773 length of segment : 163 time for calcul the mask position with numpy : 0.0010609626770019531 nb_pixel_total : 62293 time to create 1 rle with old method : 0.07352352142333984 length of segment : 379 time for calcul the mask position with numpy : 0.0006561279296875 nb_pixel_total : 41154 time to create 1 rle with old method : 0.04633617401123047 length of segment : 352 time for calcul the mask position with numpy : 0.0005676746368408203 nb_pixel_total : 38711 time to create 1 rle with old method : 0.044342994689941406 length of segment : 230 time for calcul the mask position with numpy : 0.0004515647888183594 nb_pixel_total : 22140 time to create 1 rle with old method : 0.02628016471862793 length of segment : 286 time for calcul the mask position with numpy : 0.00026035308837890625 nb_pixel_total : 14457 time to create 1 rle with old method : 0.016355037689208984 length of segment : 128 time for calcul the mask position with numpy : 0.0008034706115722656 nb_pixel_total : 55382 time to create 1 rle with old method : 0.06180453300476074 length of segment : 234 time for calcul the mask position with numpy : 0.0003056526184082031 nb_pixel_total : 13630 time to create 1 rle with old method : 0.015401363372802734 length of segment : 109 time for calcul the mask position with numpy : 0.0003085136413574219 nb_pixel_total : 17528 time to create 1 rle with old method : 0.026853322982788086 length of segment : 191 time for calcul the mask position with numpy : 0.0005421638488769531 nb_pixel_total : 34550 time to create 1 rle with old method : 0.038494110107421875 length of segment : 271 time for calcul the mask position with numpy : 0.00022745132446289062 nb_pixel_total : 11715 time to create 1 rle with old method : 0.013984203338623047 length of segment : 141 time for calcul the mask position with numpy : 0.0007119178771972656 nb_pixel_total : 53943 time to create 1 rle with old method : 0.061989545822143555 length of segment : 280 time for calcul the mask position with numpy : 0.0021812915802001953 nb_pixel_total : 68884 time to create 1 rle with old method : 0.07758331298828125 length of segment : 325 time for calcul the mask position with numpy : 0.0012812614440917969 nb_pixel_total : 43732 time to create 1 rle with old method : 0.051279544830322266 length of segment : 296 time for calcul the mask position with numpy : 0.0009360313415527344 nb_pixel_total : 33718 time to create 1 rle with old method : 0.037911415100097656 length of segment : 217 time for calcul the mask position with numpy : 0.0021486282348632812 nb_pixel_total : 97249 time to create 1 rle with old method : 0.10969924926757812 length of segment : 365 time for calcul the mask position with numpy : 0.0005388259887695312 nb_pixel_total : 23377 time to create 1 rle with old method : 0.02643299102783203 length of segment : 250 time for calcul the mask position with numpy : 0.0012471675872802734 nb_pixel_total : 50987 time to create 1 rle with old method : 0.05602264404296875 length of segment : 472 time for calcul the mask position with numpy : 0.0015652179718017578 nb_pixel_total : 31992 time to create 1 rle with old method : 0.03966641426086426 length of segment : 456 time for calcul the mask position with numpy : 0.0018296241760253906 nb_pixel_total : 63708 time to create 1 rle with old method : 0.07694244384765625 length of segment : 436 time for calcul the mask position with numpy : 0.0007050037384033203 nb_pixel_total : 53557 time to create 1 rle with old method : 0.060216665267944336 length of segment : 279 time for calcul the mask position with numpy : 0.000644683837890625 nb_pixel_total : 37007 time to create 1 rle with old method : 0.042649269104003906 length of segment : 224 time for calcul the mask position with numpy : 0.0013587474822998047 nb_pixel_total : 81891 time to create 1 rle with old method : 0.09254264831542969 length of segment : 300 time for calcul the mask position with numpy : 0.0005464553833007812 nb_pixel_total : 22438 time to create 1 rle with old method : 0.034716129302978516 length of segment : 230 time for calcul the mask position with numpy : 0.000293731689453125 nb_pixel_total : 16790 time to create 1 rle with old method : 0.0193326473236084 length of segment : 153 time for calcul the mask position with numpy : 0.0016162395477294922 nb_pixel_total : 112418 time to create 1 rle with old method : 0.1247251033782959 length of segment : 465 time for calcul the mask position with numpy : 0.0008797645568847656 nb_pixel_total : 54380 time to create 1 rle with old method : 0.06060194969177246 length of segment : 306 time for calcul the mask position with numpy : 0.0007386207580566406 nb_pixel_total : 47922 time to create 1 rle with old method : 0.05692148208618164 length of segment : 262 time for calcul the mask position with numpy : 0.0003921985626220703 nb_pixel_total : 7241 time to create 1 rle with old method : 0.008927583694458008 length of segment : 211 time for calcul the mask position with numpy : 0.00027871131896972656 nb_pixel_total : 15031 time to create 1 rle with old method : 0.016969680786132812 length of segment : 110 time for calcul the mask position with numpy : 0.0009813308715820312 nb_pixel_total : 33137 time to create 1 rle with old method : 0.03763127326965332 length of segment : 218 time for calcul the mask position with numpy : 0.004072427749633789 nb_pixel_total : 86468 time to create 1 rle with old method : 0.09842276573181152 length of segment : 595 time for calcul the mask position with numpy : 0.0018978118896484375 nb_pixel_total : 67661 time to create 1 rle with old method : 0.07735800743103027 length of segment : 275 time for calcul the mask position with numpy : 0.0007781982421875 nb_pixel_total : 19628 time to create 1 rle with old method : 0.024882793426513672 length of segment : 224 time for calcul the mask position with numpy : 0.0019867420196533203 nb_pixel_total : 63663 time to create 1 rle with old method : 0.07579565048217773 length of segment : 282 time for calcul the mask position with numpy : 0.0008566379547119141 nb_pixel_total : 27257 time to create 1 rle with old method : 0.03066277503967285 length of segment : 286 time for calcul the mask position with numpy : 0.0005872249603271484 nb_pixel_total : 17522 time to create 1 rle with old method : 0.020540475845336914 length of segment : 130 time for calcul the mask position with numpy : 0.0008780956268310547 nb_pixel_total : 23257 time to create 1 rle with old method : 0.026343584060668945 length of segment : 207 time for calcul the mask position with numpy : 0.0008678436279296875 nb_pixel_total : 11683 time to create 1 rle with old method : 0.013740062713623047 length of segment : 344 time for calcul the mask position with numpy : 0.0013747215270996094 nb_pixel_total : 46097 time to create 1 rle with old method : 0.05229520797729492 length of segment : 232 time for calcul the mask position with numpy : 0.0027115345001220703 nb_pixel_total : 141687 time to create 1 rle with old method : 0.18305706977844238 length of segment : 519 time for calcul the mask position with numpy : 0.0016107559204101562 nb_pixel_total : 87841 time to create 1 rle with old method : 0.09787511825561523 length of segment : 459 time for calcul the mask position with numpy : 0.00079345703125 nb_pixel_total : 39961 time to create 1 rle with old method : 0.04526257514953613 length of segment : 253 time for calcul the mask position with numpy : 0.00017642974853515625 nb_pixel_total : 5590 time to create 1 rle with old method : 0.006552219390869141 length of segment : 141 time for calcul the mask position with numpy : 0.0006935596466064453 nb_pixel_total : 39776 time to create 1 rle with old method : 0.04896259307861328 length of segment : 649 time for calcul the mask position with numpy : 0.0037963390350341797 nb_pixel_total : 226253 time to create 1 rle with new method : 0.009766101837158203 length of segment : 463 time for calcul the mask position with numpy : 0.0005633831024169922 nb_pixel_total : 24549 time to create 1 rle with old method : 0.038810014724731445 length of segment : 215 time for calcul the mask position with numpy : 0.0007703304290771484 nb_pixel_total : 43619 time to create 1 rle with old method : 0.04905200004577637 length of segment : 331 time for calcul the mask position with numpy : 0.0025017261505126953 nb_pixel_total : 125889 time to create 1 rle with old method : 0.16213464736938477 length of segment : 384 time for calcul the mask position with numpy : 0.0001881122589111328 nb_pixel_total : 7878 time to create 1 rle with old method : 0.00940394401550293 length of segment : 64 time for calcul the mask position with numpy : 0.00017905235290527344 nb_pixel_total : 8474 time to create 1 rle with old method : 0.009901285171508789 length of segment : 120 time for calcul the mask position with numpy : 0.004055976867675781 nb_pixel_total : 255755 time to create 1 rle with new method : 0.010286808013916016 length of segment : 706 time for calcul the mask position with numpy : 0.00030159950256347656 nb_pixel_total : 15875 time to create 1 rle with old method : 0.018438100814819336 length of segment : 131 time for calcul the mask position with numpy : 0.0003731250762939453 nb_pixel_total : 10857 time to create 1 rle with old method : 0.01265859603881836 length of segment : 270 time for calcul the mask position with numpy : 0.0004553794860839844 nb_pixel_total : 20378 time to create 1 rle with old method : 0.023131370544433594 length of segment : 281 time for calcul the mask position with numpy : 0.00036525726318359375 nb_pixel_total : 20561 time to create 1 rle with old method : 0.023558378219604492 length of segment : 161 time for calcul the mask position with numpy : 0.0005593299865722656 nb_pixel_total : 28875 time to create 1 rle with old method : 0.03229641914367676 length of segment : 242 time for calcul the mask position with numpy : 0.0016505718231201172 nb_pixel_total : 81664 time to create 1 rle with old method : 0.09070181846618652 length of segment : 628 time for calcul the mask position with numpy : 0.0006785392761230469 nb_pixel_total : 25925 time to create 1 rle with old method : 0.028833627700805664 length of segment : 305 time for calcul the mask position with numpy : 0.00045299530029296875 nb_pixel_total : 23204 time to create 1 rle with old method : 0.027246713638305664 length of segment : 240 time for calcul the mask position with numpy : 0.00027751922607421875 nb_pixel_total : 13114 time to create 1 rle with old method : 0.014766931533813477 length of segment : 159 time for calcul the mask position with numpy : 0.0023806095123291016 nb_pixel_total : 142130 time to create 1 rle with old method : 0.15826106071472168 length of segment : 599 time for calcul the mask position with numpy : 0.0005922317504882812 nb_pixel_total : 30993 time to create 1 rle with old method : 0.035315513610839844 length of segment : 252 time for calcul the mask position with numpy : 0.00028014183044433594 nb_pixel_total : 7774 time to create 1 rle with old method : 0.009011507034301758 length of segment : 154 time for calcul the mask position with numpy : 0.0005805492401123047 nb_pixel_total : 37675 time to create 1 rle with old method : 0.043546438217163086 length of segment : 219 time for calcul the mask position with numpy : 0.0012319087982177734 nb_pixel_total : 58026 time to create 1 rle with old method : 0.06616687774658203 length of segment : 358 time for calcul the mask position with numpy : 0.0005040168762207031 nb_pixel_total : 20744 time to create 1 rle with old method : 0.024050474166870117 length of segment : 172 time for calcul the mask position with numpy : 0.0007083415985107422 nb_pixel_total : 40435 time to create 1 rle with old method : 0.04630160331726074 length of segment : 275 time for calcul the mask position with numpy : 0.00020313262939453125 nb_pixel_total : 10829 time to create 1 rle with old method : 0.012085676193237305 length of segment : 131 time for calcul the mask position with numpy : 0.0002472400665283203 nb_pixel_total : 8658 time to create 1 rle with old method : 0.01062321662902832 length of segment : 83 time for calcul the mask position with numpy : 0.0011889934539794922 nb_pixel_total : 53048 time to create 1 rle with old method : 0.06002616882324219 length of segment : 312 time for calcul the mask position with numpy : 0.0028100013732910156 nb_pixel_total : 151826 time to create 1 rle with new method : 0.010449647903442383 length of segment : 383 time for calcul the mask position with numpy : 0.0015006065368652344 nb_pixel_total : 53659 time to create 1 rle with old method : 0.06032061576843262 length of segment : 375 time for calcul the mask position with numpy : 0.0002288818359375 nb_pixel_total : 10152 time to create 1 rle with old method : 0.01176595687866211 length of segment : 77 time for calcul the mask position with numpy : 0.0002796649932861328 nb_pixel_total : 10396 time to create 1 rle with old method : 0.012179136276245117 length of segment : 118 time for calcul the mask position with numpy : 0.00047707557678222656 nb_pixel_total : 18587 time to create 1 rle with old method : 0.02080225944519043 length of segment : 226 time for calcul the mask position with numpy : 0.0016143321990966797 nb_pixel_total : 86207 time to create 1 rle with old method : 0.1087961196899414 length of segment : 541 time for calcul the mask position with numpy : 0.0005390644073486328 nb_pixel_total : 20982 time to create 1 rle with old method : 0.02452683448791504 length of segment : 157 time for calcul the mask position with numpy : 0.0029058456420898438 nb_pixel_total : 125514 time to create 1 rle with old method : 0.1410834789276123 length of segment : 424 time for calcul the mask position with numpy : 0.0011641979217529297 nb_pixel_total : 31123 time to create 1 rle with old method : 0.03843188285827637 length of segment : 244 time for calcul the mask position with numpy : 0.00044846534729003906 nb_pixel_total : 20024 time to create 1 rle with old method : 0.023180484771728516 length of segment : 113 time for calcul the mask position with numpy : 0.0006113052368164062 nb_pixel_total : 23576 time to create 1 rle with old method : 0.02688121795654297 length of segment : 184 time for calcul the mask position with numpy : 0.0012390613555908203 nb_pixel_total : 63199 time to create 1 rle with old method : 0.07196950912475586 length of segment : 195 time for calcul the mask position with numpy : 0.0018970966339111328 nb_pixel_total : 86795 time to create 1 rle with old method : 0.09819436073303223 length of segment : 412 time for calcul the mask position with numpy : 0.0024673938751220703 nb_pixel_total : 120570 time to create 1 rle with old method : 0.1464369297027588 length of segment : 360 time for calcul the mask position with numpy : 0.001394033432006836 nb_pixel_total : 71701 time to create 1 rle with old method : 0.08736634254455566 length of segment : 326 time for calcul the mask position with numpy : 0.0031325817108154297 nb_pixel_total : 129174 time to create 1 rle with old method : 0.14614653587341309 length of segment : 468 time for calcul the mask position with numpy : 0.0006971359252929688 nb_pixel_total : 29319 time to create 1 rle with old method : 0.03325319290161133 length of segment : 293 time for calcul the mask position with numpy : 0.0010216236114501953 nb_pixel_total : 37809 time to create 1 rle with old method : 0.04619264602661133 length of segment : 382 time for calcul the mask position with numpy : 0.0006663799285888672 nb_pixel_total : 31372 time to create 1 rle with old method : 0.04142498970031738 length of segment : 247 time for calcul the mask position with numpy : 0.0002834796905517578 nb_pixel_total : 9079 time to create 1 rle with old method : 0.011488676071166992 length of segment : 122 time for calcul the mask position with numpy : 0.00022220611572265625 nb_pixel_total : 10824 time to create 1 rle with old method : 0.013715982437133789 length of segment : 89 time for calcul the mask position with numpy : 0.0002779960632324219 nb_pixel_total : 10578 time to create 1 rle with old method : 0.013580083847045898 length of segment : 88 time for calcul the mask position with numpy : 0.002958059310913086 nb_pixel_total : 163136 time to create 1 rle with new method : 0.006534099578857422 length of segment : 377 time for calcul the mask position with numpy : 0.0013644695281982422 nb_pixel_total : 55608 time to create 1 rle with old method : 0.06405115127563477 length of segment : 348 time for calcul the mask position with numpy : 0.000362396240234375 nb_pixel_total : 10323 time to create 1 rle with old method : 0.012560367584228516 length of segment : 110 time for calcul the mask position with numpy : 0.0006017684936523438 nb_pixel_total : 19633 time to create 1 rle with old method : 0.024646759033203125 length of segment : 226 time for calcul the mask position with numpy : 0.0020036697387695312 nb_pixel_total : 90081 time to create 1 rle with old method : 0.10045075416564941 length of segment : 511 time for calcul the mask position with numpy : 0.0009963512420654297 nb_pixel_total : 38992 time to create 1 rle with old method : 0.058115243911743164 length of segment : 317 time for calcul the mask position with numpy : 0.0019550323486328125 nb_pixel_total : 85196 time to create 1 rle with old method : 0.1167452335357666 length of segment : 415 time for calcul the mask position with numpy : 0.0007987022399902344 nb_pixel_total : 29228 time to create 1 rle with old method : 0.04672551155090332 length of segment : 145 time for calcul the mask position with numpy : 0.0016636848449707031 nb_pixel_total : 67236 time to create 1 rle with old method : 0.07648277282714844 length of segment : 321 time for calcul the mask position with numpy : 0.0011420249938964844 nb_pixel_total : 39754 time to create 1 rle with old method : 0.04542970657348633 length of segment : 306 time for calcul the mask position with numpy : 0.001125335693359375 nb_pixel_total : 50494 time to create 1 rle with old method : 0.05662846565246582 length of segment : 311 time for calcul the mask position with numpy : 0.0005500316619873047 nb_pixel_total : 23285 time to create 1 rle with old method : 0.026329994201660156 length of segment : 154 time for calcul the mask position with numpy : 0.0027053356170654297 nb_pixel_total : 113289 time to create 1 rle with old method : 0.14397621154785156 length of segment : 429 time for calcul the mask position with numpy : 0.0007243156433105469 nb_pixel_total : 30204 time to create 1 rle with old method : 0.03336834907531738 length of segment : 296 time for calcul the mask position with numpy : 0.0025370121002197266 nb_pixel_total : 132157 time to create 1 rle with old method : 0.14985251426696777 length of segment : 345 time for calcul the mask position with numpy : 0.0005252361297607422 nb_pixel_total : 8511 time to create 1 rle with old method : 0.010298013687133789 length of segment : 161 time for calcul the mask position with numpy : 0.0015692710876464844 nb_pixel_total : 84185 time to create 1 rle with old method : 0.09640884399414062 length of segment : 430 time for calcul the mask position with numpy : 0.0003254413604736328 nb_pixel_total : 15107 time to create 1 rle with old method : 0.019169330596923828 length of segment : 121 time for calcul the mask position with numpy : 0.00047278404235839844 nb_pixel_total : 21534 time to create 1 rle with old method : 0.02507185935974121 length of segment : 131 time for calcul the mask position with numpy : 0.00032019615173339844 nb_pixel_total : 8463 time to create 1 rle with old method : 0.009776592254638672 length of segment : 140 time for calcul the mask position with numpy : 0.0004086494445800781 nb_pixel_total : 20200 time to create 1 rle with old method : 0.023589134216308594 length of segment : 154 time for calcul the mask position with numpy : 0.0004286766052246094 nb_pixel_total : 19076 time to create 1 rle with old method : 0.022057771682739258 length of segment : 183 time for calcul the mask position with numpy : 0.0005435943603515625 nb_pixel_total : 25255 time to create 1 rle with old method : 0.03210282325744629 length of segment : 284 time for calcul the mask position with numpy : 0.0007891654968261719 nb_pixel_total : 36970 time to create 1 rle with old method : 0.04210042953491211 length of segment : 281 time for calcul the mask position with numpy : 0.007934808731079102 nb_pixel_total : 275862 time to create 1 rle with new method : 0.03234577178955078 length of segment : 1134 time for calcul the mask position with numpy : 0.0042726993560791016 nb_pixel_total : 210040 time to create 1 rle with new method : 0.01137995719909668 length of segment : 561 time for calcul the mask position with numpy : 0.0013675689697265625 nb_pixel_total : 78071 time to create 1 rle with old method : 0.08815264701843262 length of segment : 274 time for calcul the mask position with numpy : 0.00038361549377441406 nb_pixel_total : 22409 time to create 1 rle with old method : 0.025828123092651367 length of segment : 218 time for calcul the mask position with numpy : 0.0006022453308105469 nb_pixel_total : 24827 time to create 1 rle with old method : 0.028472900390625 length of segment : 232 time for calcul the mask position with numpy : 0.0021736621856689453 nb_pixel_total : 126120 time to create 1 rle with old method : 0.14213228225708008 length of segment : 431 time for calcul the mask position with numpy : 0.00024008750915527344 nb_pixel_total : 7158 time to create 1 rle with old method : 0.008455276489257812 length of segment : 129 time for calcul the mask position with numpy : 0.0011217594146728516 nb_pixel_total : 33699 time to create 1 rle with old method : 0.04260659217834473 length of segment : 171 time for calcul the mask position with numpy : 0.001081228256225586 nb_pixel_total : 53127 time to create 1 rle with old method : 0.058740854263305664 length of segment : 245 time for calcul the mask position with numpy : 0.0021996498107910156 nb_pixel_total : 84323 time to create 1 rle with old method : 0.09594273567199707 length of segment : 355 time for calcul the mask position with numpy : 0.00060272216796875 nb_pixel_total : 35357 time to create 1 rle with old method : 0.039981842041015625 length of segment : 221 time for calcul the mask position with numpy : 0.0011746883392333984 nb_pixel_total : 69377 time to create 1 rle with old method : 0.09897851943969727 length of segment : 232 time for calcul the mask position with numpy : 0.0005917549133300781 nb_pixel_total : 24162 time to create 1 rle with old method : 0.02771759033203125 length of segment : 218 time for calcul the mask position with numpy : 0.00039505958557128906 nb_pixel_total : 25428 time to create 1 rle with old method : 0.02869868278503418 length of segment : 234 time for calcul the mask position with numpy : 0.0002880096435546875 nb_pixel_total : 14096 time to create 1 rle with old method : 0.016086816787719727 length of segment : 175 time for calcul the mask position with numpy : 0.0008394718170166016 nb_pixel_total : 48285 time to create 1 rle with old method : 0.05332350730895996 length of segment : 344 time for calcul the mask position with numpy : 0.0005850791931152344 nb_pixel_total : 24497 time to create 1 rle with old method : 0.030688762664794922 length of segment : 215 time for calcul the mask position with numpy : 0.007131814956665039 nb_pixel_total : 152885 time to create 1 rle with new method : 0.06661128997802734 length of segment : 936 time for calcul the mask position with numpy : 0.00047397613525390625 nb_pixel_total : 28200 time to create 1 rle with old method : 0.031286001205444336 length of segment : 201 time for calcul the mask position with numpy : 0.001684427261352539 nb_pixel_total : 105117 time to create 1 rle with old method : 0.1175689697265625 length of segment : 444 time for calcul the mask position with numpy : 0.0005366802215576172 nb_pixel_total : 37970 time to create 1 rle with old method : 0.04237937927246094 length of segment : 184 time for calcul the mask position with numpy : 0.0009849071502685547 nb_pixel_total : 65073 time to create 1 rle with old method : 0.0737006664276123 length of segment : 297 time for calcul the mask position with numpy : 0.0012862682342529297 nb_pixel_total : 74748 time to create 1 rle with old method : 0.08388996124267578 length of segment : 299 time for calcul the mask position with numpy : 0.001102447509765625 nb_pixel_total : 41324 time to create 1 rle with old method : 0.048146724700927734 length of segment : 333 time for calcul the mask position with numpy : 0.004006624221801758 nb_pixel_total : 216051 time to create 1 rle with new method : 0.011680841445922852 length of segment : 725 time for calcul the mask position with numpy : 0.0005543231964111328 nb_pixel_total : 32278 time to create 1 rle with old method : 0.03684401512145996 length of segment : 223 time for calcul the mask position with numpy : 0.0004596710205078125 nb_pixel_total : 31103 time to create 1 rle with old method : 0.03482460975646973 length of segment : 183 time for calcul the mask position with numpy : 0.0004611015319824219 nb_pixel_total : 27641 time to create 1 rle with old method : 0.03140878677368164 length of segment : 184 time for calcul the mask position with numpy : 0.0004935264587402344 nb_pixel_total : 36583 time to create 1 rle with old method : 0.04142260551452637 length of segment : 194 time for calcul the mask position with numpy : 0.0007872581481933594 nb_pixel_total : 53164 time to create 1 rle with old method : 0.060164690017700195 length of segment : 303 time for calcul the mask position with numpy : 0.0004730224609375 nb_pixel_total : 7586 time to create 1 rle with old method : 0.009473323822021484 length of segment : 204 time for calcul the mask position with numpy : 0.00018167495727539062 nb_pixel_total : 7463 time to create 1 rle with old method : 0.012626886367797852 length of segment : 102 time for calcul the mask position with numpy : 0.0004303455352783203 nb_pixel_total : 23663 time to create 1 rle with old method : 0.03328990936279297 length of segment : 241 time for calcul the mask position with numpy : 0.0017423629760742188 nb_pixel_total : 100742 time to create 1 rle with old method : 0.13657331466674805 length of segment : 407 time for calcul the mask position with numpy : 0.0004508495330810547 nb_pixel_total : 29264 time to create 1 rle with old method : 0.03296923637390137 length of segment : 167 time for calcul the mask position with numpy : 0.004289865493774414 nb_pixel_total : 252235 time to create 1 rle with new method : 0.011960029602050781 length of segment : 709 time for calcul the mask position with numpy : 0.0013501644134521484 nb_pixel_total : 74992 time to create 1 rle with old method : 0.0830223560333252 length of segment : 353 time for calcul the mask position with numpy : 0.0008189678192138672 nb_pixel_total : 63825 time to create 1 rle with old method : 0.07211828231811523 length of segment : 288 time for calcul the mask position with numpy : 0.0007987022399902344 nb_pixel_total : 46963 time to create 1 rle with old method : 0.05292963981628418 length of segment : 291 time for calcul the mask position with numpy : 0.0005915164947509766 nb_pixel_total : 39009 time to create 1 rle with old method : 0.04464888572692871 length of segment : 180 time for calcul the mask position with numpy : 0.0006761550903320312 nb_pixel_total : 34713 time to create 1 rle with old method : 0.052904367446899414 length of segment : 233 time for calcul the mask position with numpy : 0.0004711151123046875 nb_pixel_total : 25997 time to create 1 rle with old method : 0.029908180236816406 length of segment : 185 time for calcul the mask position with numpy : 0.00023794174194335938 nb_pixel_total : 14880 time to create 1 rle with old method : 0.017514467239379883 length of segment : 123 time for calcul the mask position with numpy : 0.0022661685943603516 nb_pixel_total : 163373 time to create 1 rle with new method : 0.005801200866699219 length of segment : 1145 time for calcul the mask position with numpy : 0.0016889572143554688 nb_pixel_total : 100190 time to create 1 rle with old method : 0.11400651931762695 length of segment : 349 time for calcul the mask position with numpy : 0.0003941059112548828 nb_pixel_total : 14913 time to create 1 rle with old method : 0.016822338104248047 length of segment : 165 time for calcul the mask position with numpy : 0.0014843940734863281 nb_pixel_total : 66366 time to create 1 rle with old method : 0.07917547225952148 length of segment : 313 time for calcul the mask position with numpy : 0.000732421875 nb_pixel_total : 38398 time to create 1 rle with old method : 0.04495835304260254 length of segment : 290 time for calcul the mask position with numpy : 0.0003490447998046875 nb_pixel_total : 22593 time to create 1 rle with old method : 0.025285720825195312 length of segment : 238 time for calcul the mask position with numpy : 0.0004904270172119141 nb_pixel_total : 22744 time to create 1 rle with old method : 0.025758743286132812 length of segment : 268 time for calcul the mask position with numpy : 0.0012898445129394531 nb_pixel_total : 69445 time to create 1 rle with old method : 0.07692146301269531 length of segment : 566 time for calcul the mask position with numpy : 0.0013127326965332031 nb_pixel_total : 94075 time to create 1 rle with old method : 0.10661482810974121 length of segment : 386 time for calcul the mask position with numpy : 0.0011937618255615234 nb_pixel_total : 54872 time to create 1 rle with old method : 0.06237292289733887 length of segment : 326 time for calcul the mask position with numpy : 0.001264333724975586 nb_pixel_total : 64903 time to create 1 rle with old method : 0.09362673759460449 length of segment : 474 time for calcul the mask position with numpy : 0.00421452522277832 nb_pixel_total : 299605 time to create 1 rle with new method : 0.010553121566772461 length of segment : 1060 time for calcul the mask position with numpy : 0.00044655799865722656 nb_pixel_total : 25861 time to create 1 rle with old method : 0.029366016387939453 length of segment : 173 time for calcul the mask position with numpy : 0.0008213520050048828 nb_pixel_total : 47214 time to create 1 rle with old method : 0.05330514907836914 length of segment : 259 time for calcul the mask position with numpy : 0.0023059844970703125 nb_pixel_total : 154016 time to create 1 rle with new method : 0.0055887699127197266 length of segment : 496 time for calcul the mask position with numpy : 0.0007658004760742188 nb_pixel_total : 53289 time to create 1 rle with old method : 0.05880856513977051 length of segment : 325 time for calcul the mask position with numpy : 0.0008494853973388672 nb_pixel_total : 51189 time to create 1 rle with old method : 0.05819201469421387 length of segment : 216 time for calcul the mask position with numpy : 0.0038995742797851562 nb_pixel_total : 235909 time to create 1 rle with new method : 0.009984731674194336 length of segment : 836 time for calcul the mask position with numpy : 0.0004837512969970703 nb_pixel_total : 21714 time to create 1 rle with old method : 0.02504277229309082 length of segment : 172 time for calcul the mask position with numpy : 0.0003674030303955078 nb_pixel_total : 18488 time to create 1 rle with old method : 0.021323680877685547 length of segment : 141 time for calcul the mask position with numpy : 0.0003116130828857422 nb_pixel_total : 11544 time to create 1 rle with old method : 0.013119935989379883 length of segment : 170 time for calcul the mask position with numpy : 0.001132965087890625 nb_pixel_total : 79574 time to create 1 rle with old method : 0.0851125717163086 length of segment : 374 time for calcul the mask position with numpy : 0.0013701915740966797 nb_pixel_total : 95843 time to create 1 rle with old method : 0.10461163520812988 length of segment : 367 time for calcul the mask position with numpy : 0.0012416839599609375 nb_pixel_total : 60838 time to create 1 rle with old method : 0.06598424911499023 length of segment : 323 time for calcul the mask position with numpy : 0.0003333091735839844 nb_pixel_total : 16976 time to create 1 rle with old method : 0.019247770309448242 length of segment : 154 time for calcul the mask position with numpy : 0.0016856193542480469 nb_pixel_total : 93985 time to create 1 rle with old method : 0.10504269599914551 length of segment : 782 time for calcul the mask position with numpy : 0.0003006458282470703 nb_pixel_total : 16247 time to create 1 rle with old method : 0.017897367477416992 length of segment : 204 time for calcul the mask position with numpy : 0.0004985332489013672 nb_pixel_total : 28521 time to create 1 rle with old method : 0.03141212463378906 length of segment : 266 time for calcul the mask position with numpy : 0.0009212493896484375 nb_pixel_total : 63931 time to create 1 rle with old method : 0.07092523574829102 length of segment : 342 time for calcul the mask position with numpy : 0.0008246898651123047 nb_pixel_total : 57108 time to create 1 rle with old method : 0.06308889389038086 length of segment : 194 time for calcul the mask position with numpy : 0.0002503395080566406 nb_pixel_total : 15356 time to create 1 rle with old method : 0.018185138702392578 length of segment : 94 time for calcul the mask position with numpy : 0.00193023681640625 nb_pixel_total : 108282 time to create 1 rle with old method : 0.1279909610748291 length of segment : 395 time for calcul the mask position with numpy : 0.0005035400390625 nb_pixel_total : 20722 time to create 1 rle with old method : 0.02357006072998047 length of segment : 216 time for calcul the mask position with numpy : 0.0012195110321044922 nb_pixel_total : 88072 time to create 1 rle with old method : 0.09944319725036621 length of segment : 426 time for calcul the mask position with numpy : 0.0004432201385498047 nb_pixel_total : 22127 time to create 1 rle with old method : 0.02461385726928711 length of segment : 217 time for calcul the mask position with numpy : 0.0018167495727539062 nb_pixel_total : 102902 time to create 1 rle with old method : 0.1118772029876709 length of segment : 453 time for calcul the mask position with numpy : 0.0014023780822753906 nb_pixel_total : 90119 time to create 1 rle with old method : 0.12499332427978516 length of segment : 407 time for calcul the mask position with numpy : 0.0007045269012451172 nb_pixel_total : 38791 time to create 1 rle with old method : 0.045137643814086914 length of segment : 300 time for calcul the mask position with numpy : 0.0036783218383789062 nb_pixel_total : 179095 time to create 1 rle with new method : 0.011603593826293945 length of segment : 586 time for calcul the mask position with numpy : 0.0004642009735107422 nb_pixel_total : 28308 time to create 1 rle with old method : 0.0329287052154541 length of segment : 226 time for calcul the mask position with numpy : 0.0010225772857666016 nb_pixel_total : 59788 time to create 1 rle with old method : 0.06824421882629395 length of segment : 554 time for calcul the mask position with numpy : 0.0037839412689208984 nb_pixel_total : 175454 time to create 1 rle with new method : 0.011239767074584961 length of segment : 713 time for calcul the mask position with numpy : 0.0005724430084228516 nb_pixel_total : 30840 time to create 1 rle with old method : 0.03434348106384277 length of segment : 235 time for calcul the mask position with numpy : 0.00032591819763183594 nb_pixel_total : 16951 time to create 1 rle with old method : 0.019179821014404297 length of segment : 190 time for calcul the mask position with numpy : 0.0002543926239013672 nb_pixel_total : 12252 time to create 1 rle with old method : 0.013481616973876953 length of segment : 170 time for calcul the mask position with numpy : 0.000865936279296875 nb_pixel_total : 48935 time to create 1 rle with old method : 0.05503487586975098 length of segment : 241 time for calcul the mask position with numpy : 0.0006098747253417969 nb_pixel_total : 32375 time to create 1 rle with old method : 0.04117584228515625 length of segment : 259 time for calcul the mask position with numpy : 0.0018398761749267578 nb_pixel_total : 106025 time to create 1 rle with old method : 0.1385970115661621 length of segment : 326 time for calcul the mask position with numpy : 0.0005888938903808594 nb_pixel_total : 43537 time to create 1 rle with old method : 0.0500645637512207 length of segment : 240 time for calcul the mask position with numpy : 0.0012638568878173828 nb_pixel_total : 63170 time to create 1 rle with old method : 0.06920528411865234 length of segment : 466 time for calcul the mask position with numpy : 0.0024547576904296875 nb_pixel_total : 141364 time to create 1 rle with old method : 0.18393611907958984 length of segment : 630 time for calcul the mask position with numpy : 0.0008664131164550781 nb_pixel_total : 56831 time to create 1 rle with old method : 0.06355571746826172 length of segment : 321 time for calcul the mask position with numpy : 0.0011353492736816406 nb_pixel_total : 66215 time to create 1 rle with old method : 0.0736846923828125 length of segment : 669 time for calcul the mask position with numpy : 0.0007016658782958984 nb_pixel_total : 36724 time to create 1 rle with old method : 0.042584896087646484 length of segment : 233 time for calcul the mask position with numpy : 0.0013515949249267578 nb_pixel_total : 96680 time to create 1 rle with old method : 0.10888242721557617 length of segment : 310 time for calcul the mask position with numpy : 0.00020194053649902344 nb_pixel_total : 10225 time to create 1 rle with old method : 0.012157678604125977 length of segment : 83 time for calcul the mask position with numpy : 0.0033936500549316406 nb_pixel_total : 180005 time to create 1 rle with new method : 0.009632110595703125 length of segment : 793 time for calcul the mask position with numpy : 0.0009722709655761719 nb_pixel_total : 72952 time to create 1 rle with old method : 0.08086562156677246 length of segment : 381 time for calcul the mask position with numpy : 0.0008628368377685547 nb_pixel_total : 47395 time to create 1 rle with old method : 0.05781912803649902 length of segment : 287 time for calcul the mask position with numpy : 0.0008273124694824219 nb_pixel_total : 65844 time to create 1 rle with old method : 0.0753931999206543 length of segment : 353 time for calcul the mask position with numpy : 0.0004754066467285156 nb_pixel_total : 27746 time to create 1 rle with old method : 0.03246331214904785 length of segment : 179 time for calcul the mask position with numpy : 0.00031876564025878906 nb_pixel_total : 18130 time to create 1 rle with old method : 0.021698951721191406 length of segment : 106 time for calcul the mask position with numpy : 0.002805948257446289 nb_pixel_total : 95143 time to create 1 rle with old method : 0.10794925689697266 length of segment : 330 time for calcul the mask position with numpy : 0.0021245479583740234 nb_pixel_total : 92291 time to create 1 rle with old method : 0.10238456726074219 length of segment : 730 time for calcul the mask position with numpy : 0.0015099048614501953 nb_pixel_total : 25190 time to create 1 rle with old method : 0.027739286422729492 length of segment : 414 time for calcul the mask position with numpy : 0.00035119056701660156 nb_pixel_total : 17206 time to create 1 rle with old method : 0.019295692443847656 length of segment : 149 time for calcul the mask position with numpy : 0.0018982887268066406 nb_pixel_total : 27651 time to create 1 rle with old method : 0.031476497650146484 length of segment : 514 time for calcul the mask position with numpy : 0.0032799243927001953 nb_pixel_total : 223297 time to create 1 rle with new method : 0.008780241012573242 length of segment : 541 time for calcul the mask position with numpy : 0.0006239414215087891 nb_pixel_total : 28132 time to create 1 rle with old method : 0.0317683219909668 length of segment : 180 time for calcul the mask position with numpy : 0.0026018619537353516 nb_pixel_total : 155441 time to create 1 rle with new method : 0.006865024566650391 length of segment : 488 time for calcul the mask position with numpy : 0.00042057037353515625 nb_pixel_total : 18243 time to create 1 rle with old method : 0.020461559295654297 length of segment : 142 time for calcul the mask position with numpy : 0.0010902881622314453 nb_pixel_total : 46638 time to create 1 rle with old method : 0.05225181579589844 length of segment : 265 time for calcul the mask position with numpy : 0.0002086162567138672 nb_pixel_total : 9577 time to create 1 rle with old method : 0.010839223861694336 length of segment : 112 time for calcul the mask position with numpy : 0.0015208721160888672 nb_pixel_total : 56589 time to create 1 rle with old method : 0.06340789794921875 length of segment : 444 time for calcul the mask position with numpy : 0.0008475780487060547 nb_pixel_total : 52726 time to create 1 rle with old method : 0.056169748306274414 length of segment : 239 time for calcul the mask position with numpy : 0.0002231597900390625 nb_pixel_total : 13353 time to create 1 rle with old method : 0.014761209487915039 length of segment : 89 time for calcul the mask position with numpy : 0.0011439323425292969 nb_pixel_total : 45208 time to create 1 rle with old method : 0.050755977630615234 length of segment : 265 time for calcul the mask position with numpy : 0.003640413284301758 nb_pixel_total : 142729 time to create 1 rle with old method : 0.15886759757995605 length of segment : 423 time for calcul the mask position with numpy : 0.0015721321105957031 nb_pixel_total : 75971 time to create 1 rle with old method : 0.09854531288146973 length of segment : 308 time for calcul the mask position with numpy : 0.002382040023803711 nb_pixel_total : 98689 time to create 1 rle with old method : 0.10952091217041016 length of segment : 393 time for calcul the mask position with numpy : 0.0011680126190185547 nb_pixel_total : 57888 time to create 1 rle with old method : 0.06542587280273438 length of segment : 260 time for calcul the mask position with numpy : 0.0006210803985595703 nb_pixel_total : 17610 time to create 1 rle with old method : 0.019695520401000977 length of segment : 262 time for calcul the mask position with numpy : 0.0030841827392578125 nb_pixel_total : 118715 time to create 1 rle with old method : 0.15513372421264648 length of segment : 471 time for calcul the mask position with numpy : 0.0007967948913574219 nb_pixel_total : 21273 time to create 1 rle with old method : 0.024399995803833008 length of segment : 168 time for calcul the mask position with numpy : 0.00093841552734375 nb_pixel_total : 20941 time to create 1 rle with old method : 0.03413081169128418 length of segment : 222 time for calcul the mask position with numpy : 0.008101463317871094 nb_pixel_total : 332914 time to create 1 rle with new method : 0.014544486999511719 length of segment : 899 time for calcul the mask position with numpy : 0.0010991096496582031 nb_pixel_total : 52953 time to create 1 rle with old method : 0.059705257415771484 length of segment : 412 time for calcul the mask position with numpy : 0.0017778873443603516 nb_pixel_total : 50771 time to create 1 rle with old method : 0.0566098690032959 length of segment : 284 time for calcul the mask position with numpy : 0.0013110637664794922 nb_pixel_total : 40213 time to create 1 rle with old method : 0.04567742347717285 length of segment : 211 time for calcul the mask position with numpy : 0.0008873939514160156 nb_pixel_total : 19430 time to create 1 rle with old method : 0.031870365142822266 length of segment : 131 time for calcul the mask position with numpy : 0.0010859966278076172 nb_pixel_total : 28169 time to create 1 rle with old method : 0.03191947937011719 length of segment : 215 time for calcul the mask position with numpy : 0.0010657310485839844 nb_pixel_total : 29598 time to create 1 rle with old method : 0.034152984619140625 length of segment : 217 time for calcul the mask position with numpy : 0.0011565685272216797 nb_pixel_total : 37477 time to create 1 rle with old method : 0.04219841957092285 length of segment : 220 time for calcul the mask position with numpy : 0.005897045135498047 nb_pixel_total : 149984 time to create 1 rle with old method : 0.16852855682373047 length of segment : 518 time for calcul the mask position with numpy : 0.015619754791259766 nb_pixel_total : 594769 time to create 1 rle with new method : 0.022662878036499023 length of segment : 834 time for calcul the mask position with numpy : 0.0009379386901855469 nb_pixel_total : 33622 time to create 1 rle with old method : 0.03822946548461914 length of segment : 213 time for calcul the mask position with numpy : 0.0017762184143066406 nb_pixel_total : 53811 time to create 1 rle with old method : 0.0612330436706543 length of segment : 340 time for calcul the mask position with numpy : 0.0007567405700683594 nb_pixel_total : 22445 time to create 1 rle with old method : 0.037366390228271484 length of segment : 159 time spent for convertir_results : 32.27107524871826 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 369 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 102084 save missing photos in datou_result : time spend for datou_step_exec : 189.66757345199585 time spend to save output : 6.196335792541504 total time spend for step 1 : 195.86390924453735 step2:crop_condition Mon Nov 3 14:53:50 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 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 : 26 ! batch 1 Loaded 369 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 284 About to insert : list_path_to_insert length 284 new photo from crops ! About to upload 284 photos upload in portfolio : 3736932 init cache_photo without model_param we have 284 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1762178117_1951692 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905912_0.png', 0, 119, 130, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905913_0.png', 0, 319, 200, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905919_0.png', 0, 189, 119, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905920_0.png', 0, 207, 153, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905921_0.png', 0, 846, 585, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905924_0.png', 0, 344, 243, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905927_0.png', 0, 170, 93, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905928_0.png', 0, 182, 218, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4003028082_0.png', 0, 1494, 1528, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905929_0.png', 0, 341, 172, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905930_0.png', 0, 174, 408, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905931_0.png', 0, 298, 375, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905932_0.png', 0, 178, 347, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905933_0.png', 0, 437, 229, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905934_0.png', 0, 181, 152, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905936_0.png', 0, 547, 519, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905938_0.png', 0, 153, 157, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905939_0.png', 0, 66, 161, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905940_0.png', 0, 214, 239, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905941_0.png', 0, 318, 227, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905942_0.png', 0, 152, 190, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905943_0.png', 0, 327, 349, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905944_0.png', 0, 245, 183, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4016612017_0.png', 0, 157, 158, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905945_0.png', 0, 317, 165, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905946_0.png', 0, 299, 297, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905948_0.png', 0, 176, 439, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905949_0.png', 0, 194, 153, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905950_0.png', 0, 157, 175, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905951_0.png', 0, 461, 379, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905954_0.png', 0, 221, 116, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905955_0.png', 0, 526, 252, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905956_0.png', 0, 140, 186, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4016612018_0.png', 0, 206, 316, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4016612019_0.png', 0, 258, 233, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4016612020_0.png', 0, 182, 176, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905959_0.png', 0, 305, 292, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905960_0.png', 0, 263, 251, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905961_0.png', 0, 299, 145, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905963_0.png', 0, 131, 112, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905964_0.png', 0, 201, 101, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905966_0.png', 0, 163, 201, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905967_0.png', 0, 146, 293, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905968_0.png', 0, 91, 208, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905969_0.png', 0, 164, 135, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905970_0.png', 0, 93, 184, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905971_0.png', 0, 166, 173, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905973_0.png', 0, 440, 510, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905974_0.png', 0, 178, 183, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905975_0.png', 0, 249, 302, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905976_0.png', 0, 158, 156, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905977_0.png', 0, 114, 98, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4003028084_0.png', 0, 153, 317, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4003028085_0.png', 0, 229, 194, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4003028086_0.png', 0, 736, 609, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905979_0.png', 0, 177, 151, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905980_0.png', 0, 350, 234, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905981_0.png', 0, 451, 331, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905982_0.png', 0, 165, 255, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905983_0.png', 0, 229, 266, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905985_0.png', 0, 245, 273, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905987_0.png', 0, 301, 231, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905988_0.png', 0, 217, 152, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4003028087_0.png', 0, 592, 396, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905989_0.png', 0, 332, 335, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905990_0.png', 0, 172, 352, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905991_0.png', 0, 251, 230, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905993_0.png', 0, 155, 128, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905994_0.png', 0, 357, 233, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905996_0.png', 0, 121, 191, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905997_0.png', 0, 208, 243, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905998_0.png', 0, 140, 141, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4003028088_0.png', 0, 158, 108, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906000_0.png', 0, 362, 305, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906001_0.png', 0, 245, 287, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906002_0.png', 0, 252, 214, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906004_0.png', 0, 614, 278, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906005_0.png', 0, 284, 303, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906006_0.png', 0, 298, 290, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4003028089_0.png', 0, 240, 280, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4003028090_0.png', 0, 216, 250, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4003028091_0.png', 0, 463, 284, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906009_0.png', 0, 240, 279, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906010_0.png', 0, 273, 224, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906011_0.png', 0, 603, 218, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906012_0.png', 0, 211, 230, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906013_0.png', 0, 165, 153, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906014_0.png', 0, 337, 444, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906015_0.png', 0, 286, 295, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906016_0.png', 0, 254, 261, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906017_0.png', 0, 247, 139, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906018_0.png', 0, 203, 110, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906020_0.png', 0, 360, 522, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906022_0.png', 0, 125, 215, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906024_0.png', 0, 142, 262, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906026_0.png', 0, 211, 198, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4003028092_0.png', 0, 191, 217, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4003028093_0.png', 0, 304, 282, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4003028094_0.png', 0, 160, 204, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4003028095_0.png', 0, 328, 232, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906030_0.png', 0, 562, 519, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906031_0.png', 0, 406, 374, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906032_0.png', 0, 360, 183, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906033_0.png', 0, 53, 140, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906034_0.png', 0, 164, 374, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906035_0.png', 0, 991, 310, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906037_0.png', 0, 289, 260, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906038_0.png', 0, 655, 343, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906039_0.png', 0, 152, 62, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906040_0.png', 0, 87, 116, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906041_0.png', 0, 642, 579, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906042_0.png', 0, 150, 129, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906043_0.png', 0, 139, 195, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906044_0.png', 0, 149, 281, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906045_0.png', 0, 173, 160, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906047_0.png', 0, 296, 562, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906048_0.png', 0, 112, 304, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906049_0.png', 0, 131, 240, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906050_0.png', 0, 130, 158, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906051_0.png', 0, 485, 502, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906052_0.png', 0, 277, 199, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906053_0.png', 0, 136, 151, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906054_0.png', 0, 211, 219, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906055_0.png', 0, 282, 334, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906056_0.png', 0, 355, 206, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906058_0.png', 0, 98, 131, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906060_0.png', 0, 305, 286, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906061_0.png', 0, 505, 382, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906062_0.png', 0, 413, 226, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906063_0.png', 0, 147, 77, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906064_0.png', 0, 129, 117, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906065_0.png', 0, 149, 226, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906066_0.png', 0, 217, 537, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906067_0.png', 0, 279, 137, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906068_0.png', 0, 470, 410, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906069_0.png', 0, 276, 226, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906070_0.png', 0, 239, 113, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906073_0.png', 0, 450, 309, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906075_0.png', 0, 312, 325, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906076_0.png', 0, 502, 436, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906079_0.png', 0, 107, 122, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4003028098_0.png', 0, 210, 313, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906081_0.png', 0, 132, 89, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906082_0.png', 0, 177, 88, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906083_0.png', 0, 541, 372, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906084_0.png', 0, 337, 288, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906085_0.png', 0, 129, 110, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906086_0.png', 0, 152, 226, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906087_0.png', 0, 230, 511, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906088_0.png', 0, 254, 246, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906089_0.png', 0, 397, 312, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906090_0.png', 0, 265, 141, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906091_0.png', 0, 319, 314, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906092_0.png', 0, 300, 253, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906093_0.png', 0, 294, 274, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906094_0.png', 0, 264, 142, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906096_0.png', 0, 165, 280, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906098_0.png', 0, 198, 165, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4016612021_0.png', 0, 436, 419, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906100_0.png', 0, 181, 121, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906101_0.png', 0, 267, 121, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906103_0.png', 0, 232, 154, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906104_0.png', 0, 155, 183, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906105_0.png', 0, 131, 267, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906106_0.png', 0, 249, 258, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906107_0.png', 0, 869, 789, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906108_0.png', 0, 577, 561, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906109_0.png', 0, 127, 214, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906110_0.png', 0, 509, 228, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906112_0.png', 0, 494, 411, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906113_0.png', 0, 112, 128, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906114_0.png', 0, 343, 224, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906115_0.png', 0, 335, 146, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4003028099_0.png', 0, 376, 357, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4003028100_0.png', 0, 244, 177, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906116_0.png', 0, 558, 355, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906118_0.png', 0, 455, 232, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906119_0.png', 0, 245, 208, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906121_0.png', 0, 184, 154, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906123_0.png', 0, 299, 206, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906124_0.png', 0, 361, 860, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4003028101_0.png', 0, 213, 221, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906125_0.png', 0, 172, 198, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906126_0.png', 0, 442, 408, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906127_0.png', 0, 309, 178, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906128_0.png', 0, 283, 296, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906129_0.png', 0, 427, 306, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906130_0.png', 0, 325, 289, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906131_0.png', 0, 697, 553, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906132_0.png', 0, 228, 223, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906133_0.png', 0, 215, 183, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906134_0.png', 0, 220, 168, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906137_0.png', 0, 243, 137, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906139_0.png', 0, 119, 240, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906140_0.png', 0, 450, 376, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906143_0.png', 0, 420, 343, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906144_0.png', 0, 264, 288, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906146_0.png', 0, 313, 179, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906147_0.png', 0, 232, 233, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906148_0.png', 0, 243, 164, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4016612022_0.png', 0, 215, 165, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4016612023_0.png', 0, 873, 573, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906150_0.png', 0, 409, 603, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906151_0.png', 0, 558, 329, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906152_0.png', 0, 144, 150, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906153_0.png', 0, 416, 296, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906154_0.png', 0, 205, 256, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906155_0.png', 0, 122, 234, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906156_0.png', 0, 183, 257, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906157_0.png', 0, 305, 448, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906160_0.png', 0, 298, 382, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906161_0.png', 0, 449, 991, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906162_0.png', 0, 226, 173, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906163_0.png', 0, 335, 219, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906164_0.png', 0, 474, 457, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906165_0.png', 0, 250, 300, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906168_0.png', 0, 194, 172, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906169_0.png', 0, 166, 141, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906170_0.png', 0, 158, 170, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906171_0.png', 0, 309, 373, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906173_0.png', 0, 398, 323, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906174_0.png', 0, 177, 154, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906175_0.png', 0, 249, 603, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906177_0.png', 0, 233, 334, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906180_0.png', 0, 248, 90, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906181_0.png', 0, 495, 376, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906182_0.png', 0, 154, 216, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906183_0.png', 0, 283, 418, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906184_0.png', 0, 153, 210, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906185_0.png', 0, 422, 403, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906186_0.png', 0, 340, 368, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906187_0.png', 0, 199, 298, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906188_0.png', 0, 687, 493, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906189_0.png', 0, 178, 221, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906191_0.png', 0, 581, 710, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906192_0.png', 0, 212, 216, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906193_0.png', 0, 136, 190, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906194_0.png', 0, 99, 163, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906195_0.png', 0, 335, 236, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906197_0.png', 0, 552, 313, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906199_0.png', 0, 283, 463, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906200_0.png', 0, 496, 531, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906201_0.png', 0, 314, 311, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906202_0.png', 0, 269, 495, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906204_0.png', 0, 476, 306, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906205_0.png', 0, 194, 68, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906207_0.png', 0, 288, 364, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906208_0.png', 0, 303, 260, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906209_0.png', 0, 213, 348, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906210_0.png', 0, 240, 179, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906211_0.png', 0, 273, 105, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4016612025_0.png', 0, 620, 575, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906212_0.png', 0, 478, 326, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906213_0.png', 0, 264, 601, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906214_0.png', 0, 180, 331, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906215_0.png', 0, 185, 149, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906218_0.png', 0, 275, 180, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906219_0.png', 0, 520, 486, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906220_0.png', 0, 194, 138, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906221_0.png', 0, 266, 255, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906222_0.png', 0, 136, 112, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906223_0.png', 0, 361, 411, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906224_0.png', 0, 363, 239, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906225_0.png', 0, 183, 89, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906226_0.png', 0, 290, 235, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906227_0.png', 0, 563, 409, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906228_0.png', 0, 320, 276, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906229_0.png', 0, 343, 393, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906230_0.png', 0, 317, 257, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906231_0.png', 0, 84, 262, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906232_0.png', 0, 479, 397, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906233_0.png', 0, 171, 166, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906234_0.png', 0, 169, 222, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906235_0.png', 0, 835, 736, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906236_0.png', 0, 188, 382, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906237_0.png', 0, 289, 284, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906238_0.png', 0, 275, 129, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906239_0.png', 0, 308, 209, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906240_0.png', 0, 188, 215, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906241_0.png', 0, 215, 217, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906243_0.png', 0, 899, 332, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906245_0.png', 0, 1178, 665, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906246_0.png', 0, 256, 340, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178177), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4003028104_0.png', 0, 232, 126, 0, 1762178177,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 284 photos in the portfolio 3736932 time of upload the photos Elapsed time : 72.69420337677002 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 ! map_result returned by crop_photo_return_map_crop : length : 28 About to insert : list_path_to_insert length 28 new photo from crops ! About to upload 28 photos upload in portfolio : 3736932 init cache_photo without model_param we have 28 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1762178201_1951692 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905911_0.png', 0, 224, 97, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905915_0.png', 0, 232, 144, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905917_0.png', 0, 215, 253, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905926_0.png', 0, 389, 156, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905935_0.png', 0, 281, 249, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905958_0.png', 0, 154, 162, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905978_0.png', 0, 208, 293, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905984_0.png', 0, 192, 329, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905992_0.png', 0, 180, 270, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906025_0.png', 0, 172, 130, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906036_0.png', 0, 163, 215, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906046_0.png', 0, 170, 242, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906059_0.png', 0, 256, 70, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906072_0.png', 0, 474, 188, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906077_0.png', 0, 143, 293, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906080_0.png', 0, 172, 226, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906102_0.png', 0, 102, 137, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906120_0.png', 0, 159, 229, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906122_0.png', 0, 380, 280, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906136_0.png', 0, 234, 299, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906145_0.png', 0, 229, 288, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906167_0.png', 0, 649, 577, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906196_0.png', 0, 186, 259, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906198_0.png', 0, 261, 232, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4016612024_0.png', 0, 262, 231, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906216_0.png', 0, 213, 285, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906217_0.png', 0, 721, 438, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178207), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906244_0.png', 0, 193, 209, 0, 1762178207,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 28 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.828933238983154 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 ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1762178213_1951692 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178213), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906138_0.png', 0, 88, 102, 0, 1762178213,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.6349086761474609 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1762178221_1951692 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905914_0.png', 0, 185, 156, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905916_0.png', 0, 141, 115, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905922_0.png', 0, 379, 293, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905923_0.png', 0, 91, 73, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905953_0.png', 0, 239, 77, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906021_0.png', 0, 381, 272, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906057_0.png', 0, 204, 134, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906074_0.png', 0, 528, 339, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906097_0.png', 0, 560, 345, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906159_0.png', 0, 302, 290, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906166_0.png', 0, 459, 195, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906172_0.png', 0, 376, 348, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906178_0.png', 0, 199, 236, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906179_0.png', 0, 417, 179, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906190_0.png', 0, 310, 332, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178225), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906242_0.png', 0, 249, 197, 0, 1762178225,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 16 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.532788991928101 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1762178229_1951692 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178230), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4003028083_0.png', 0, 113, 116, 0, 1762178230,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178230), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906158_0.png', 0, 321, 385, 0, 1762178230,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178230), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906176_0.png', 0, 93, 203, 0, 1762178230,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.1922643184661865 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 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1762178234_1951692 INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178234), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906071_0.png', 0, 248, 156, 0, 1762178234,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178234), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906135_0.png', 0, 246, 190, 0, 1762178234,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! INSERT INTO MTRBack.photos (`timeStamp`, `latitude`, `longitude`, `right_categories`, `tags`, `speed`, `size`, `text`, `altitude`, `width`, `height`, `score`, `created_at`,`source_id`,`place_id`) VALUES (FROM_UNIXTIME(1762178234), 0.0, 0.0, 14, '', 0, 0, '1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906149_0.png', 0, 161, 123, 0, 1762178234,'0',0) batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.0955827236175537 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles 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 [1389748410, 1389748404, 1389748383, 1389748380, 1389748377, 1389748373, 1389748366, 1389748359, 1389748330, 1389748323, 1389748317, 1389748305, 1389748297, 1389748285, 1389748281, 1389748273, 1389748271, 1389748264, 1389748249, 1389748240, 1389748234, 1389748226, 1389748218, 1389748200, 1389748196, 1389748192] Looping around the photos to save general results len do output : 335 /1392063468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063485Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063486Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063508Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063532Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063544Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063592Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063625Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063639Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063705Didn't retrieve data .Didn't retrieve data .Didn't 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1392063865Didn'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, '3985636') ('3318', '27900917', '1389748410', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748404', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748383', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748380', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748377', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748373', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748366', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748359', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748330', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748323', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748317', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748305', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748297', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748285', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748281', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748273', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748271', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748264', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748249', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748240', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748234', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748226', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748218', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748200', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748196', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748192', None, None, None, None, None, '3985636') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1031 time used for this insertion : 0.09643101692199707 save_final save missing photos in datou_result : time spend for datou_step_exec : 203.79210233688354 time spend to save output : 0.10410833358764648 total time spend for step 2 : 203.8962106704712 step3:rle_unique_nms_with_priority Mon Nov 3 14:57:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array 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 369 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 20 nb_hashtags : 4 time to prepare the origin masks : 15.053332567214966 time for calcul the mask position with numpy : 0.4588358402252197 nb_pixel_total : 6323250 time to create 1 rle with new method : 0.5731885433197021 time for calcul the mask position with numpy : 0.041504859924316406 nb_pixel_total : 1386 time to create 1 rle with old method : 0.0016734600067138672 time for calcul the mask position with numpy : 0.03834176063537598 nb_pixel_total : 9212 time to create 1 rle with old method : 0.010632514953613281 time for calcul the mask position with numpy : 0.028185606002807617 nb_pixel_total : 36505 time to create 1 rle with old method : 0.04155397415161133 time for calcul the mask position with numpy : 0.024944543838500977 nb_pixel_total : 10 time to create 1 rle with old method : 4.315376281738281e-05 time for calcul the mask position with numpy : 0.024931669235229492 nb_pixel_total : 4902 time to create 1 rle with old method : 0.005496025085449219 time for calcul the mask position with numpy : 0.02494359016418457 nb_pixel_total : 10497 time to create 1 rle with old method : 0.011834144592285156 time for calcul the mask position with numpy : 0.025453805923461914 nb_pixel_total : 72924 time to create 1 rle with old method : 0.08189964294433594 time for calcul the mask position with numpy : 0.027080297470092773 nb_pixel_total : 595 time to create 1 rle with old method : 0.0010204315185546875 time for calcul the mask position with numpy : 0.025654315948486328 nb_pixel_total : 25012 time to create 1 rle with old method : 0.028061389923095703 time for calcul the mask position with numpy : 0.025144577026367188 nb_pixel_total : 12864 time to create 1 rle with old method : 0.014709949493408203 time for calcul the mask position with numpy : 0.025351524353027344 nb_pixel_total : 7066 time to create 1 rle with old method : 0.009507894515991211 time for calcul the mask position with numpy : 0.06633138656616211 nb_pixel_total : 1626662 time to create 1 rle with new method : 0.6012458801269531 time for calcul the mask position with numpy : 0.02817392349243164 nb_pixel_total : 32184 time to create 1 rle with old method : 0.03635668754577637 time for calcul the mask position with numpy : 0.02672100067138672 nb_pixel_total : 13826 time to create 1 rle with old method : 0.01593804359436035 time for calcul the mask position with numpy : 0.025808095932006836 nb_pixel_total : 26017 time to create 1 rle with old method : 0.028666257858276367 time for calcul the mask position with numpy : 0.024113893508911133 nb_pixel_total : 18502 time to create 1 rle with old method : 0.020358562469482422 time for calcul the mask position with numpy : 0.02477884292602539 nb_pixel_total : 368 time to create 1 rle with old method : 0.0007014274597167969 time for calcul the mask position with numpy : 0.024942636489868164 nb_pixel_total : 41482 time to create 1 rle with old method : 0.046365976333618164 time for calcul the mask position with numpy : 0.025214195251464844 nb_pixel_total : 11100 time to create 1 rle with old method : 0.012405872344970703 time for calcul the mask position with numpy : 0.02520751953125 nb_pixel_total : 20036 time to create 1 rle with old method : 0.021855831146240234 create new chi : 2.672879695892334 time to delete rle : 0.022135496139526367 batch 1 Loaded 42 chid ids of type : 3594 ++++++++++++++++++++++++++++++++Number RLEs to save : 11917 TO DO : save crop sub photo not yet done ! save time : 0.7243211269378662 nb_obj : 17 nb_hashtags : 2 time to prepare the origin masks : 10.550011396408081 time for calcul the mask position with numpy : 0.932152509689331 nb_pixel_total : 7655768 time to create 1 rle with new method : 0.7540206909179688 time for calcul the mask position with numpy : 0.03642606735229492 nb_pixel_total : 16900 time to create 1 rle with old method : 0.019316673278808594 time for calcul the mask position with numpy : 0.039946556091308594 nb_pixel_total : 78300 time to create 1 rle with old method : 0.08501029014587402 time for calcul the mask position with numpy : 0.037865400314331055 nb_pixel_total : 18293 time to create 1 rle with old method : 0.01966238021850586 time for calcul the mask position with numpy : 0.03804492950439453 nb_pixel_total : 49464 time to create 1 rle with old method : 0.05389046669006348 time for calcul the mask position with numpy : 0.03880167007446289 nb_pixel_total : 24269 time to create 1 rle with old method : 0.026926517486572266 time for calcul the mask position with numpy : 0.03886055946350098 nb_pixel_total : 8893 time to create 1 rle with old method : 0.009740829467773438 time for calcul the mask position with numpy : 0.04377579689025879 nb_pixel_total : 16185 time to create 1 rle with old method : 0.017854690551757812 time for calcul the mask position with numpy : 0.040970802307128906 nb_pixel_total : 187 time to create 1 rle with old method : 0.0005421638488769531 time for calcul the mask position with numpy : 0.043872833251953125 nb_pixel_total : 15979 time to create 1 rle with old method : 0.02576279640197754 time for calcul the mask position with numpy : 0.03671717643737793 nb_pixel_total : 102064 time to create 1 rle with old method : 0.10751748085021973 time for calcul the mask position with numpy : 0.023363828659057617 nb_pixel_total : 48957 time to create 1 rle with old method : 0.05123639106750488 time for calcul the mask position with numpy : 0.024003982543945312 nb_pixel_total : 17286 time to create 1 rle with old method : 0.0184018611907959 time for calcul the mask position with numpy : 0.024544954299926758 nb_pixel_total : 59655 time to create 1 rle with old method : 0.0634162425994873 time for calcul the mask position with numpy : 0.04368257522583008 nb_pixel_total : 48097 time to create 1 rle with old method : 0.0522153377532959 time for calcul the mask position with numpy : 0.03870344161987305 nb_pixel_total : 46787 time to create 1 rle with old method : 0.051894426345825195 time for calcul the mask position with numpy : 0.0407407283782959 nb_pixel_total : 49568 time to create 1 rle with old method : 0.052411556243896484 time for calcul the mask position with numpy : 0.03864026069641113 nb_pixel_total : 37748 time to create 1 rle with old method : 0.04069018363952637 create new chi : 3.0572643280029297 time to delete rle : 0.0016276836395263672 batch 1 Loaded 35 chid ids of type : 3594 +++++++++++++++++++++++++++Number RLEs to save : 11737 TO DO : save crop sub photo not yet done ! save time : 0.6631467342376709 nb_obj : 17 nb_hashtags : 3 time to prepare the origin masks : 8.518208026885986 time for calcul the mask position with numpy : 0.5961349010467529 nb_pixel_total : 7734968 time to create 1 rle with new method : 0.7447700500488281 time for calcul the mask position with numpy : 0.023635387420654297 nb_pixel_total : 18948 time to create 1 rle with old method : 0.02106165885925293 time for calcul the mask position with numpy : 0.04019045829772949 nb_pixel_total : 152 time to create 1 rle with old method : 0.00032806396484375 time for calcul the mask position with numpy : 0.039748191833496094 nb_pixel_total : 22704 time to create 1 rle with old method : 0.025616884231567383 time for calcul the mask position with numpy : 0.03910088539123535 nb_pixel_total : 16184 time to create 1 rle with old method : 0.01800537109375 time for calcul the mask position with numpy : 0.0393826961517334 nb_pixel_total : 101887 time to create 1 rle with old method : 0.11224246025085449 time for calcul the mask position with numpy : 0.040264129638671875 nb_pixel_total : 13231 time to create 1 rle with old method : 0.01469278335571289 time for calcul the mask position with numpy : 0.03766489028930664 nb_pixel_total : 15014 time to create 1 rle with old method : 0.0169064998626709 time for calcul the mask position with numpy : 0.02490234375 nb_pixel_total : 313 time to create 1 rle with old method : 0.0006313323974609375 time for calcul the mask position with numpy : 0.026081085205078125 nb_pixel_total : 47697 time to create 1 rle with old method : 0.05352640151977539 time for calcul the mask position with numpy : 0.026110410690307617 nb_pixel_total : 100202 time to create 1 rle with old method : 0.1119379997253418 time for calcul the mask position with numpy : 0.025469064712524414 nb_pixel_total : 15344 time to create 1 rle with old method : 0.01694631576538086 time for calcul the mask position with numpy : 0.026161909103393555 nb_pixel_total : 22193 time to create 1 rle with old method : 0.024770736694335938 time for calcul the mask position with numpy : 0.027696609497070312 nb_pixel_total : 53172 time to create 1 rle with old method : 0.05855560302734375 time for calcul the mask position with numpy : 0.02450275421142578 nb_pixel_total : 180 time to create 1 rle with old method : 0.00040459632873535156 time for calcul the mask position with numpy : 0.024309396743774414 nb_pixel_total : 44184 time to create 1 rle with old method : 0.04818272590637207 time for calcul the mask position with numpy : 0.025263547897338867 nb_pixel_total : 53180 time to create 1 rle with old method : 0.05843710899353027 time for calcul the mask position with numpy : 0.027854442596435547 nb_pixel_total : 34847 time to create 1 rle with old method : 0.03693556785583496 create new chi : 2.523646831512451 time to delete rle : 0.00133514404296875 batch 1 Loaded 35 chid ids of type : 3594 +++++++++++++++++Number RLEs to save : 9221 TO DO : save crop sub photo not yet done ! save time : 0.5592565536499023 nb_obj : 23 nb_hashtags : 2 time to prepare the origin masks : 13.089632272720337 time for calcul the mask position with numpy : 0.881779670715332 nb_pixel_total : 7380658 time to create 1 rle with new method : 0.9008808135986328 time for calcul the mask position with numpy : 0.025025129318237305 nb_pixel_total : 44306 time to create 1 rle with old method : 0.04912710189819336 time for calcul the mask position with numpy : 0.04260540008544922 nb_pixel_total : 8033 time to create 1 rle with old method : 0.008792638778686523 time for calcul the mask position with numpy : 0.04104137420654297 nb_pixel_total : 20274 time to create 1 rle with old method : 0.022752046585083008 time for calcul the mask position with numpy : 0.04158902168273926 nb_pixel_total : 40035 time to create 1 rle with old method : 0.044190406799316406 time for calcul the mask position with numpy : 0.039650917053222656 nb_pixel_total : 24220 time to create 1 rle with old method : 0.026658296585083008 time for calcul the mask position with numpy : 0.04130816459655762 nb_pixel_total : 152353 time to create 1 rle with new method : 1.1070294380187988 time for calcul the mask position with numpy : 0.04644322395324707 nb_pixel_total : 1869 time to create 1 rle with old method : 0.0042877197265625 time for calcul the mask position with numpy : 0.04772615432739258 nb_pixel_total : 285113 time to create 1 rle with new method : 0.5466203689575195 time for calcul the mask position with numpy : 0.0406041145324707 nb_pixel_total : 23336 time to create 1 rle with old method : 0.02619194984436035 time for calcul the mask position with numpy : 0.03956270217895508 nb_pixel_total : 12548 time to create 1 rle with old method : 0.014102697372436523 time for calcul the mask position with numpy : 0.03985714912414551 nb_pixel_total : 17547 time to create 1 rle with old method : 0.01971149444580078 time for calcul the mask position with numpy : 0.040618896484375 nb_pixel_total : 391 time to create 1 rle with old method : 0.0006499290466308594 time for calcul the mask position with numpy : 0.041556358337402344 nb_pixel_total : 13617 time to create 1 rle with old method : 0.015386104583740234 time for calcul the mask position with numpy : 0.04028511047363281 nb_pixel_total : 23348 time to create 1 rle with old method : 0.0260617733001709 time for calcul the mask position with numpy : 0.04029107093811035 nb_pixel_total : 23922 time to create 1 rle with old method : 0.02660679817199707 time for calcul the mask position with numpy : 0.04343605041503906 nb_pixel_total : 37612 time to create 1 rle with old method : 0.04156136512756348 time for calcul the mask position with numpy : 0.026322126388549805 nb_pixel_total : 15332 time to create 1 rle with old method : 0.01760101318359375 time for calcul the mask position with numpy : 0.02745985984802246 nb_pixel_total : 11088 time to create 1 rle with old method : 0.012705802917480469 time for calcul the mask position with numpy : 0.02803325653076172 nb_pixel_total : 1182 time to create 1 rle with old method : 0.0015668869018554688 time for calcul the mask position with numpy : 0.027689695358276367 nb_pixel_total : 35133 time to create 1 rle with old method : 0.039862632751464844 time for calcul the mask position with numpy : 0.026497364044189453 nb_pixel_total : 28172 time to create 1 rle with old method : 0.03145956993103027 time for calcul the mask position with numpy : 0.02496480941772461 nb_pixel_total : 42852 time to create 1 rle with old method : 0.048388004302978516 time for calcul the mask position with numpy : 0.02628159523010254 nb_pixel_total : 51459 time to create 1 rle with old method : 0.0579066276550293 create new chi : 4.914264678955078 time to delete rle : 0.003040790557861328 batch 1 Loaded 47 chid ids of type : 3594 +++++++++++++++++++++++++++Number RLEs to save : 13216 TO DO : save crop sub photo not yet done ! save time : 0.7936947345733643 nb_obj : 11 nb_hashtags : 2 time to prepare the origin masks : 9.374270915985107 time for calcul the mask position with numpy : 0.7197713851928711 nb_pixel_total : 7805988 time to create 1 rle with new method : 1.210205316543579 time for calcul the mask position with numpy : 0.04283761978149414 nb_pixel_total : 26063 time to create 1 rle with old method : 0.032570838928222656 time for calcul the mask position with numpy : 0.0325465202331543 nb_pixel_total : 32417 time to create 1 rle with old method : 0.036767005920410156 time for calcul the mask position with numpy : 0.03130793571472168 nb_pixel_total : 369 time to create 1 rle with old method : 0.00164794921875 time for calcul the mask position with numpy : 0.0319671630859375 nb_pixel_total : 150406 time to create 1 rle with new method : 0.7181751728057861 time for calcul the mask position with numpy : 0.046156883239746094 nb_pixel_total : 49055 time to create 1 rle with old method : 0.0542757511138916 time for calcul the mask position with numpy : 0.044332027435302734 nb_pixel_total : 45146 time to create 1 rle with old method : 0.050035953521728516 time for calcul the mask position with numpy : 0.04436612129211426 nb_pixel_total : 18544 time to create 1 rle with old method : 0.022081375122070312 time for calcul the mask position with numpy : 0.04695701599121094 nb_pixel_total : 28093 time to create 1 rle with old method : 0.03407549858093262 time for calcul the mask position with numpy : 0.04040718078613281 nb_pixel_total : 84687 time to create 1 rle with old method : 0.09951639175415039 time for calcul the mask position with numpy : 0.040706634521484375 nb_pixel_total : 34795 time to create 1 rle with old method : 0.03890800476074219 time for calcul the mask position with numpy : 0.03251004219055176 nb_pixel_total : 18837 time to create 1 rle with old method : 0.02114391326904297 create new chi : 3.553539991378784 time to delete rle : 0.0020678043365478516 batch 1 Loaded 23 chid ids of type : 3594 +++++++++++++Number RLEs to save : 8146 TO DO : save crop sub photo not yet done ! save time : 0.5188336372375488 nb_obj : 11 nb_hashtags : 2 time to prepare the origin masks : 7.138695955276489 time for calcul the mask position with numpy : 0.6018815040588379 nb_pixel_total : 7983247 time to create 1 rle with new method : 0.796436071395874 time for calcul the mask position with numpy : 0.04849410057067871 nb_pixel_total : 11678 time to create 1 rle with old method : 0.012923002243041992 time for calcul the mask position with numpy : 0.04641270637512207 nb_pixel_total : 34797 time to create 1 rle with old method : 0.03803753852844238 time for calcul the mask position with numpy : 0.04826235771179199 nb_pixel_total : 17474 time to create 1 rle with old method : 0.019137859344482422 time for calcul the mask position with numpy : 0.047325849533081055 nb_pixel_total : 657 time to create 1 rle with old method : 0.0008547306060791016 time for calcul the mask position with numpy : 0.047208309173583984 nb_pixel_total : 13009 time to create 1 rle with old method : 0.014149665832519531 time for calcul the mask position with numpy : 0.04614686965942383 nb_pixel_total : 55259 time to create 1 rle with old method : 0.05894184112548828 time for calcul the mask position with numpy : 0.050047874450683594 nb_pixel_total : 14399 time to create 1 rle with old method : 0.020259618759155273 time for calcul the mask position with numpy : 0.039769649505615234 nb_pixel_total : 22031 time to create 1 rle with old method : 0.02499103546142578 time for calcul the mask position with numpy : 0.05280113220214844 nb_pixel_total : 38606 time to create 1 rle with old method : 0.04984235763549805 time for calcul the mask position with numpy : 0.05314135551452637 nb_pixel_total : 41126 time to create 1 rle with old method : 0.057352542877197266 time for calcul the mask position with numpy : 0.05364680290222168 nb_pixel_total : 62117 time to create 1 rle with old method : 0.08821988105773926 create new chi : 2.3706741333007812 time to delete rle : 0.002149820327758789 batch 1 Loaded 23 chid ids of type : 3594 ++++++++++++++Number RLEs to save : 6831 TO DO : save crop sub photo not yet done ! save time : 0.4405076503753662 nb_obj : 13 nb_hashtags : 1 time to prepare the origin masks : 9.70666766166687 time for calcul the mask position with numpy : 0.4940178394317627 nb_pixel_total : 7803543 time to create 1 rle with new method : 0.7265763282775879 time for calcul the mask position with numpy : 0.027420520782470703 nb_pixel_total : 16373 time to create 1 rle with old method : 0.017647743225097656 time for calcul the mask position with numpy : 0.02811455726623535 nb_pixel_total : 962 time to create 1 rle with old method : 0.0016448497772216797 time for calcul the mask position with numpy : 0.02716684341430664 nb_pixel_total : 63184 time to create 1 rle with old method : 0.07064127922058105 time for calcul the mask position with numpy : 0.029024839401245117 nb_pixel_total : 31792 time to create 1 rle with old method : 0.03516221046447754 time for calcul the mask position with numpy : 0.028596878051757812 nb_pixel_total : 57327 time to create 1 rle with old method : 0.06228017807006836 time for calcul the mask position with numpy : 0.02914118766784668 nb_pixel_total : 1942 time to create 1 rle with old method : 0.0024013519287109375 time for calcul the mask position with numpy : 0.030182838439941406 nb_pixel_total : 97003 time to create 1 rle with old method : 0.10609078407287598 time for calcul the mask position with numpy : 0.029610157012939453 nb_pixel_total : 21637 time to create 1 rle with old method : 0.024625539779663086 time for calcul the mask position with numpy : 0.03067755699157715 nb_pixel_total : 33677 time to create 1 rle with old method : 0.03718280792236328 time for calcul the mask position with numpy : 0.032813310623168945 nb_pixel_total : 43658 time to create 1 rle with old method : 0.04888629913330078 time for calcul the mask position with numpy : 0.02829766273498535 nb_pixel_total : 68513 time to create 1 rle with old method : 0.0746910572052002 time for calcul the mask position with numpy : 0.026804208755493164 nb_pixel_total : 846 time to create 1 rle with old method : 0.0011725425720214844 time for calcul the mask position with numpy : 0.028887271881103516 nb_pixel_total : 53943 time to create 1 rle with old method : 0.05908203125 create new chi : 2.1917784214019775 time to delete rle : 0.0020422935485839844 batch 1 Loaded 27 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 9093 TO DO : save crop sub photo not yet done ! save time : 0.600672721862793 nb_obj : 10 nb_hashtags : 1 time to prepare the origin masks : 5.695021629333496 time for calcul the mask position with numpy : 0.5724480152130127 nb_pixel_total : 7847370 time to create 1 rle with new method : 0.5549399852752686 time for calcul the mask position with numpy : 0.040578603744506836 nb_pixel_total : 14947 time to create 1 rle with old method : 0.016713619232177734 time for calcul the mask position with numpy : 0.04500532150268555 nb_pixel_total : 6841 time to create 1 rle with old method : 0.008041620254516602 time for calcul the mask position with numpy : 0.042002201080322266 nb_pixel_total : 47771 time to create 1 rle with old method : 0.053143978118896484 time for calcul the mask position with numpy : 0.041770219802856445 nb_pixel_total : 54235 time to create 1 rle with old method : 0.06214118003845215 time for calcul the mask position with numpy : 0.042011260986328125 nb_pixel_total : 112220 time to create 1 rle with old method : 0.12567138671875 time for calcul the mask position with numpy : 0.035539865493774414 nb_pixel_total : 16743 time to create 1 rle with old method : 0.02262282371520996 time for calcul the mask position with numpy : 0.032645225524902344 nb_pixel_total : 22426 time to create 1 rle with old method : 0.030613183975219727 time for calcul the mask position with numpy : 0.03243732452392578 nb_pixel_total : 81481 time to create 1 rle with old method : 0.12355351448059082 time for calcul the mask position with numpy : 0.041208744049072266 nb_pixel_total : 36917 time to create 1 rle with old method : 0.04152393341064453 time for calcul the mask position with numpy : 0.04119420051574707 nb_pixel_total : 53449 time to create 1 rle with old method : 0.05933570861816406 create new chi : 2.1128668785095215 time to delete rle : 0.0012845993041992188 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++Number RLEs to save : 7264 TO DO : save crop sub photo not yet done ! save time : 0.5041751861572266 nb_obj : 15 nb_hashtags : 3 time to prepare the origin masks : 8.873991250991821 time for calcul the mask position with numpy : 0.5447618961334229 nb_pixel_total : 7893309 time to create 1 rle with new method : 0.7459611892700195 time for calcul the mask position with numpy : 0.02261209487915039 nb_pixel_total : 4636 time to create 1 rle with old method : 0.004948854446411133 time for calcul the mask position with numpy : 0.02232670783996582 nb_pixel_total : 1255 time to create 1 rle with old method : 0.0016803741455078125 time for calcul the mask position with numpy : 0.023058176040649414 nb_pixel_total : 45076 time to create 1 rle with old method : 0.04834723472595215 time for calcul the mask position with numpy : 0.029525279998779297 nb_pixel_total : 31 time to create 1 rle with old method : 0.00013399124145507812 time for calcul the mask position with numpy : 0.02352428436279297 nb_pixel_total : 11658 time to create 1 rle with old method : 0.012451410293579102 time for calcul the mask position with numpy : 0.025761842727661133 nb_pixel_total : 23257 time to create 1 rle with old method : 0.025815486907958984 time for calcul the mask position with numpy : 0.02361917495727539 nb_pixel_total : 17480 time to create 1 rle with old method : 0.019127607345581055 time for calcul the mask position with numpy : 0.04112744331359863 nb_pixel_total : 27213 time to create 1 rle with old method : 0.029621362686157227 time for calcul the mask position with numpy : 0.037473440170288086 nb_pixel_total : 315 time to create 1 rle with old method : 0.0006463527679443359 time for calcul the mask position with numpy : 0.039698123931884766 nb_pixel_total : 63663 time to create 1 rle with old method : 0.07470226287841797 time for calcul the mask position with numpy : 0.02480292320251465 nb_pixel_total : 19604 time to create 1 rle with old method : 0.025872230529785156 time for calcul the mask position with numpy : 0.026542186737060547 nb_pixel_total : 67408 time to create 1 rle with old method : 0.0741889476776123 time for calcul the mask position with numpy : 0.025104284286499023 nb_pixel_total : 86290 time to create 1 rle with old method : 0.09441876411437988 time for calcul the mask position with numpy : 0.024424076080322266 nb_pixel_total : 68 time to create 1 rle with old method : 0.00022363662719726562 time for calcul the mask position with numpy : 0.028720617294311523 nb_pixel_total : 33137 time to create 1 rle with old method : 0.036701202392578125 create new chi : 2.1990270614624023 time to delete rle : 0.001398324966430664 batch 1 Loaded 30 chid ids of type : 3594 ++++++++++++++++++Number RLEs to save : 8433 TO DO : save crop sub photo not yet done ! save time : 0.5898656845092773 nb_obj : 16 nb_hashtags : 2 time to prepare the origin masks : 10.393874883651733 time for calcul the mask position with numpy : 0.4097120761871338 nb_pixel_total : 7301414 time to create 1 rle with new method : 0.840463399887085 time for calcul the mask position with numpy : 0.036092281341552734 nb_pixel_total : 20516 time to create 1 rle with old method : 0.023318052291870117 time for calcul the mask position with numpy : 0.041159629821777344 nb_pixel_total : 17677 time to create 1 rle with old method : 0.020054101943969727 time for calcul the mask position with numpy : 0.04409933090209961 nb_pixel_total : 1213 time to create 1 rle with old method : 0.0015099048614501953 time for calcul the mask position with numpy : 0.0413815975189209 nb_pixel_total : 15667 time to create 1 rle with old method : 0.017373085021972656 time for calcul the mask position with numpy : 0.04118680953979492 nb_pixel_total : 222316 time to create 1 rle with new method : 1.3299891948699951 time for calcul the mask position with numpy : 0.04087424278259277 nb_pixel_total : 8462 time to create 1 rle with old method : 0.009040594100952148 time for calcul the mask position with numpy : 0.03896069526672363 nb_pixel_total : 7833 time to create 1 rle with old method : 0.008378028869628906 time for calcul the mask position with numpy : 0.03918862342834473 nb_pixel_total : 125317 time to create 1 rle with old method : 0.13251852989196777 time for calcul the mask position with numpy : 0.038919925689697266 nb_pixel_total : 43547 time to create 1 rle with old method : 0.04603457450866699 time for calcul the mask position with numpy : 0.040283203125 nb_pixel_total : 24489 time to create 1 rle with old method : 0.026215076446533203 time for calcul the mask position with numpy : 0.04003572463989258 nb_pixel_total : 189556 time to create 1 rle with new method : 0.6477115154266357 time for calcul the mask position with numpy : 0.039018869400024414 nb_pixel_total : 41862 time to create 1 rle with old method : 0.046102285385131836 time for calcul the mask position with numpy : 0.03823113441467285 nb_pixel_total : 5588 time to create 1 rle with old method : 0.006036043167114258 time for calcul the mask position with numpy : 0.03861188888549805 nb_pixel_total : 39793 time to create 1 rle with old method : 0.04298973083496094 time for calcul the mask position with numpy : 0.03780245780944824 nb_pixel_total : 87561 time to create 1 rle with old method : 0.09540867805480957 time for calcul the mask position with numpy : 0.026089191436767578 nb_pixel_total : 141589 time to create 1 rle with old method : 0.15100908279418945 create new chi : 4.5760204792022705 time to delete rle : 0.0028150081634521484 batch 1 Loaded 33 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 11254 TO DO : save crop sub photo not yet done ! save time : 0.6729047298431396 nb_obj : 16 nb_hashtags : 3 time to prepare the origin masks : 10.463244676589966 time for calcul the mask position with numpy : 0.6988542079925537 nb_pixel_total : 7703638 time to create 1 rle with new method : 0.7281975746154785 time for calcul the mask position with numpy : 0.02659463882446289 nb_pixel_total : 8560 time to create 1 rle with old method : 0.008888483047485352 time for calcul the mask position with numpy : 0.03715920448303223 nb_pixel_total : 10823 time to create 1 rle with old method : 0.011352300643920898 time for calcul the mask position with numpy : 0.036917924880981445 nb_pixel_total : 61982 time to create 1 rle with old method : 0.06344079971313477 time for calcul the mask position with numpy : 0.022650957107543945 nb_pixel_total : 20625 time to create 1 rle with old method : 0.022009611129760742 time for calcul the mask position with numpy : 0.025084257125854492 nb_pixel_total : 40202 time to create 1 rle with old method : 0.04344296455383301 time for calcul the mask position with numpy : 0.02382683753967285 nb_pixel_total : 57975 time to create 1 rle with old method : 0.06077456474304199 time for calcul the mask position with numpy : 0.023636817932128906 nb_pixel_total : 37616 time to create 1 rle with old method : 0.03996706008911133 time for calcul the mask position with numpy : 0.02446889877319336 nb_pixel_total : 7678 time to create 1 rle with old method : 0.008203744888305664 time for calcul the mask position with numpy : 0.024388551712036133 nb_pixel_total : 30785 time to create 1 rle with old method : 0.03374648094177246 time for calcul the mask position with numpy : 0.025298595428466797 nb_pixel_total : 142242 time to create 1 rle with old method : 0.15169835090637207 time for calcul the mask position with numpy : 0.02385568618774414 nb_pixel_total : 13080 time to create 1 rle with old method : 0.01388859748840332 time for calcul the mask position with numpy : 0.024013042449951172 nb_pixel_total : 23143 time to create 1 rle with old method : 0.02482771873474121 time for calcul the mask position with numpy : 0.024434566497802734 nb_pixel_total : 25899 time to create 1 rle with old method : 0.027396202087402344 time for calcul the mask position with numpy : 0.024791955947875977 nb_pixel_total : 81213 time to create 1 rle with old method : 0.09035634994506836 time for calcul the mask position with numpy : 0.02480173110961914 nb_pixel_total : 398 time to create 1 rle with old method : 0.0007464885711669922 time for calcul the mask position with numpy : 0.028673171997070312 nb_pixel_total : 28541 time to create 1 rle with old method : 0.03064584732055664 create new chi : 2.519810199737549 time to delete rle : 0.001750946044921875 batch 1 Loaded 33 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 10787 TO DO : save crop sub photo not yet done ! save time : 0.665546178817749 nb_obj : 22 nb_hashtags : 4 time to prepare the origin masks : 12.644968032836914 time for calcul the mask position with numpy : 0.5902748107910156 nb_pixel_total : 7247622 time to create 1 rle with new method : 0.8606011867523193 time for calcul the mask position with numpy : 0.033471107482910156 nb_pixel_total : 31372 time to create 1 rle with old method : 0.03373217582702637 time for calcul the mask position with numpy : 0.03822040557861328 nb_pixel_total : 9023 time to create 1 rle with old method : 0.009227752685546875 time for calcul the mask position with numpy : 0.032653093338012695 nb_pixel_total : 1062 time to create 1 rle with old method : 0.0013439655303955078 time for calcul the mask position with numpy : 0.030777931213378906 nb_pixel_total : 37602 time to create 1 rle with old method : 0.03913688659667969 time for calcul the mask position with numpy : 0.023576021194458008 nb_pixel_total : 29319 time to create 1 rle with old method : 0.030336380004882812 time for calcul the mask position with numpy : 0.02297806739807129 nb_pixel_total : 8188 time to create 1 rle with old method : 0.008414268493652344 time for calcul the mask position with numpy : 0.024041175842285156 nb_pixel_total : 71578 time to create 1 rle with old method : 0.07338476181030273 time for calcul the mask position with numpy : 0.023236989974975586 nb_pixel_total : 120241 time to create 1 rle with old method : 0.12723660469055176 time for calcul the mask position with numpy : 0.025403261184692383 nb_pixel_total : 86664 time to create 1 rle with old method : 0.09283089637756348 time for calcul the mask position with numpy : 0.02422022819519043 nb_pixel_total : 62898 time to create 1 rle with old method : 0.0679616928100586 time for calcul the mask position with numpy : 0.024234771728515625 nb_pixel_total : 8912 time to create 1 rle with old method : 0.009809732437133789 time for calcul the mask position with numpy : 0.02454686164855957 nb_pixel_total : 19963 time to create 1 rle with old method : 0.021364212036132812 time for calcul the mask position with numpy : 0.02492046356201172 nb_pixel_total : 30960 time to create 1 rle with old method : 0.03385353088378906 time for calcul the mask position with numpy : 0.025119781494140625 nb_pixel_total : 125356 time to create 1 rle with old method : 0.12791013717651367 time for calcul the mask position with numpy : 0.022974252700805664 nb_pixel_total : 20810 time to create 1 rle with old method : 0.02159905433654785 time for calcul the mask position with numpy : 0.024834156036376953 nb_pixel_total : 86089 time to create 1 rle with old method : 0.08986902236938477 time for calcul the mask position with numpy : 0.024253129959106445 nb_pixel_total : 18562 time to create 1 rle with old method : 0.029924392700195312 time for calcul the mask position with numpy : 0.026851415634155273 nb_pixel_total : 10357 time to create 1 rle with old method : 0.016773462295532227 time for calcul the mask position with numpy : 0.02650761604309082 nb_pixel_total : 10132 time to create 1 rle with old method : 0.01109766960144043 time for calcul the mask position with numpy : 0.024763822555541992 nb_pixel_total : 53172 time to create 1 rle with old method : 0.05782032012939453 time for calcul the mask position with numpy : 0.025275230407714844 nb_pixel_total : 151619 time to create 1 rle with new method : 1.823610782623291 time for calcul the mask position with numpy : 0.028253555297851562 nb_pixel_total : 52899 time to create 1 rle with old method : 0.062406301498413086 create new chi : 4.8903748989105225 time to delete rle : 0.0035750865936279297 batch 1 Loaded 45 chid ids of type : 3594 +++++++++++++++++++++++++++Number RLEs to save : 13433 TO DO : save crop sub photo not yet done ! save time : 0.768376350402832 nb_obj : 19 nb_hashtags : 2 time to prepare the origin masks : 7.781319618225098 time for calcul the mask position with numpy : 0.6993997097015381 nb_pixel_total : 7315615 time to create 1 rle with new method : 0.8730137348175049 time for calcul the mask position with numpy : 0.029781818389892578 nb_pixel_total : 8511 time to create 1 rle with old method : 0.009754180908203125 time for calcul the mask position with numpy : 0.04264020919799805 nb_pixel_total : 132157 time to create 1 rle with old method : 0.1459183692932129 time for calcul the mask position with numpy : 0.04113602638244629 nb_pixel_total : 30204 time to create 1 rle with old method : 0.03383684158325195 time for calcul the mask position with numpy : 0.04153180122375488 nb_pixel_total : 762 time to create 1 rle with old method : 0.001340627670288086 time for calcul the mask position with numpy : 0.04267716407775879 nb_pixel_total : 112783 time to create 1 rle with old method : 0.1256091594696045 time for calcul the mask position with numpy : 0.04335188865661621 nb_pixel_total : 23285 time to create 1 rle with old method : 0.0294950008392334 time for calcul the mask position with numpy : 0.04173159599304199 nb_pixel_total : 50494 time to create 1 rle with old method : 0.05651593208312988 time for calcul the mask position with numpy : 0.041764020919799805 nb_pixel_total : 39754 time to create 1 rle with old method : 0.04518389701843262 time for calcul the mask position with numpy : 0.04114508628845215 nb_pixel_total : 67236 time to create 1 rle with old method : 0.07515954971313477 time for calcul the mask position with numpy : 0.04105877876281738 nb_pixel_total : 29228 time to create 1 rle with old method : 0.03200411796569824 time for calcul the mask position with numpy : 0.041060686111450195 nb_pixel_total : 85196 time to create 1 rle with old method : 0.12185096740722656 time for calcul the mask position with numpy : 0.0428469181060791 nb_pixel_total : 38992 time to create 1 rle with old method : 0.04386591911315918 time for calcul the mask position with numpy : 0.04107356071472168 nb_pixel_total : 90081 time to create 1 rle with old method : 0.10268378257751465 time for calcul the mask position with numpy : 0.04330253601074219 nb_pixel_total : 19633 time to create 1 rle with old method : 0.03163743019104004 time for calcul the mask position with numpy : 0.03794240951538086 nb_pixel_total : 10323 time to create 1 rle with old method : 0.011548995971679688 time for calcul the mask position with numpy : 0.03981137275695801 nb_pixel_total : 55608 time to create 1 rle with old method : 0.06256437301635742 time for calcul the mask position with numpy : 0.04389810562133789 nb_pixel_total : 163136 time to create 1 rle with new method : 0.7421693801879883 time for calcul the mask position with numpy : 0.03531289100646973 nb_pixel_total : 10578 time to create 1 rle with old method : 0.016956806182861328 time for calcul the mask position with numpy : 0.03459286689758301 nb_pixel_total : 10824 time to create 1 rle with old method : 0.012326717376708984 create new chi : 4.115121364593506 time to delete rle : 0.0030794143676757812 batch 1 Loaded 39 chid ids of type : 3594 ++++++++++++++++++++++++++Number RLEs to save : 12379 TO DO : save crop sub photo not yet done ! save time : 0.7581424713134766 nb_obj : 19 nb_hashtags : 2 time to prepare the origin masks : 7.061835527420044 time for calcul the mask position with numpy : 0.3009929656982422 nb_pixel_total : 7252820 time to create 1 rle with new method : 0.48943662643432617 time for calcul the mask position with numpy : 0.026767492294311523 nb_pixel_total : 33699 time to create 1 rle with old method : 0.044956207275390625 time for calcul the mask position with numpy : 0.03235626220703125 nb_pixel_total : 53127 time to create 1 rle with old method : 0.06342935562133789 time for calcul the mask position with numpy : 0.024286508560180664 nb_pixel_total : 7158 time to create 1 rle with old method : 0.008008956909179688 time for calcul the mask position with numpy : 0.02556896209716797 nb_pixel_total : 125962 time to create 1 rle with old method : 0.13775348663330078 time for calcul the mask position with numpy : 0.02554178237915039 nb_pixel_total : 318 time to create 1 rle with old method : 0.0006017684936523438 time for calcul the mask position with numpy : 0.02570796012878418 nb_pixel_total : 24631 time to create 1 rle with old method : 0.027300119400024414 time for calcul the mask position with numpy : 0.024547100067138672 nb_pixel_total : 78071 time to create 1 rle with old method : 0.08537149429321289 time for calcul the mask position with numpy : 0.0247194766998291 nb_pixel_total : 22409 time to create 1 rle with old method : 0.029228687286376953 time for calcul the mask position with numpy : 0.02864670753479004 nb_pixel_total : 209591 time to create 1 rle with new method : 1.1763155460357666 time for calcul the mask position with numpy : 0.04297924041748047 nb_pixel_total : 254378 time to create 1 rle with new method : 1.4229061603546143 time for calcul the mask position with numpy : 0.027875661849975586 nb_pixel_total : 36970 time to create 1 rle with old method : 0.09077906608581543 time for calcul the mask position with numpy : 0.029378890991210938 nb_pixel_total : 25255 time to create 1 rle with old method : 0.031375885009765625 time for calcul the mask position with numpy : 0.030889034271240234 nb_pixel_total : 19076 time to create 1 rle with old method : 0.021397829055786133 time for calcul the mask position with numpy : 0.026638507843017578 nb_pixel_total : 20200 time to create 1 rle with old method : 0.02303028106689453 time for calcul the mask position with numpy : 0.028232336044311523 nb_pixel_total : 8463 time to create 1 rle with old method : 0.009859800338745117 time for calcul the mask position with numpy : 0.0271146297454834 nb_pixel_total : 21534 time to create 1 rle with old method : 0.027131080627441406 time for calcul the mask position with numpy : 0.02726578712463379 nb_pixel_total : 15107 time to create 1 rle with old method : 0.016938209533691406 time for calcul the mask position with numpy : 0.028020858764648438 nb_pixel_total : 306 time to create 1 rle with old method : 0.0006949901580810547 time for calcul the mask position with numpy : 0.04940676689147949 nb_pixel_total : 85325 time to create 1 rle with old method : 0.09596061706542969 create new chi : 4.779808282852173 time to delete rle : 0.0034537315368652344 batch 1 Loaded 39 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 12358 TO DO : save crop sub photo not yet done ! save time : 0.7152047157287598 nb_obj : 10 nb_hashtags : 2 time to prepare the origin masks : 3.8571932315826416 time for calcul the mask position with numpy : 0.5005190372467041 nb_pixel_total : 7815530 time to create 1 rle with new method : 0.7926108837127686 time for calcul the mask position with numpy : 0.02980494499206543 nb_pixel_total : 152885 time to create 1 rle with new method : 0.6440062522888184 time for calcul the mask position with numpy : 0.04131364822387695 nb_pixel_total : 24497 time to create 1 rle with old method : 0.027577638626098633 time for calcul the mask position with numpy : 0.04701495170593262 nb_pixel_total : 48285 time to create 1 rle with old method : 0.059812307357788086 time for calcul the mask position with numpy : 0.03912520408630371 nb_pixel_total : 14096 time to create 1 rle with old method : 0.01565098762512207 time for calcul the mask position with numpy : 0.040053367614746094 nb_pixel_total : 25428 time to create 1 rle with old method : 0.04177546501159668 time for calcul the mask position with numpy : 0.03759050369262695 nb_pixel_total : 24162 time to create 1 rle with old method : 0.027307510375976562 time for calcul the mask position with numpy : 0.030352115631103516 nb_pixel_total : 69377 time to create 1 rle with old method : 0.08710169792175293 time for calcul the mask position with numpy : 0.025683879852294922 nb_pixel_total : 220 time to create 1 rle with old method : 0.0004718303680419922 time for calcul the mask position with numpy : 0.028119802474975586 nb_pixel_total : 35597 time to create 1 rle with old method : 0.03969001770019531 time for calcul the mask position with numpy : 0.026099681854248047 nb_pixel_total : 84323 time to create 1 rle with old method : 0.09493517875671387 create new chi : 2.749868392944336 time to delete rle : 0.0014138221740722656 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++Number RLEs to save : 8147 TO DO : save crop sub photo not yet done ! save time : 0.5000152587890625 nb_obj : 15 nb_hashtags : 4 time to prepare the origin masks : 5.916465520858765 time for calcul the mask position with numpy : 0.803638219833374 nb_pixel_total : 7506766 time to create 1 rle with new method : 0.491527795791626 time for calcul the mask position with numpy : 0.03803825378417969 nb_pixel_total : 23663 time to create 1 rle with old method : 0.030260562896728516 time for calcul the mask position with numpy : 0.027635574340820312 nb_pixel_total : 7463 time to create 1 rle with old method : 0.009005069732666016 time for calcul the mask position with numpy : 0.026818275451660156 nb_pixel_total : 7586 time to create 1 rle with old method : 0.009831428527832031 time for calcul the mask position with numpy : 0.029303789138793945 nb_pixel_total : 53164 time to create 1 rle with old method : 0.06627202033996582 time for calcul the mask position with numpy : 0.03144073486328125 nb_pixel_total : 36583 time to create 1 rle with old method : 0.04076886177062988 time for calcul the mask position with numpy : 0.026932239532470703 nb_pixel_total : 27641 time to create 1 rle with old method : 0.031219959259033203 time for calcul the mask position with numpy : 0.02572798728942871 nb_pixel_total : 31103 time to create 1 rle with old method : 0.03549456596374512 time for calcul the mask position with numpy : 0.02630019187927246 nb_pixel_total : 31948 time to create 1 rle with old method : 0.03829026222229004 time for calcul the mask position with numpy : 0.02898097038269043 nb_pixel_total : 216051 time to create 1 rle with new method : 0.7110812664031982 time for calcul the mask position with numpy : 0.026914596557617188 nb_pixel_total : 41324 time to create 1 rle with old method : 0.04666757583618164 time for calcul the mask position with numpy : 0.027518510818481445 nb_pixel_total : 74748 time to create 1 rle with old method : 0.08255314826965332 time for calcul the mask position with numpy : 0.02591848373413086 nb_pixel_total : 65073 time to create 1 rle with old method : 0.07237911224365234 time for calcul the mask position with numpy : 0.03023838996887207 nb_pixel_total : 37970 time to create 1 rle with old method : 0.042536258697509766 time for calcul the mask position with numpy : 0.04256033897399902 nb_pixel_total : 105117 time to create 1 rle with old method : 0.12915444374084473 time for calcul the mask position with numpy : 0.038726091384887695 nb_pixel_total : 28200 time to create 1 rle with old method : 0.0319976806640625 create new chi : 3.203901529312134 time to delete rle : 0.0018086433410644531 batch 1 Loaded 31 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 10376 TO DO : save crop sub photo not yet done ! save time : 0.6222577095031738 nb_obj : 13 nb_hashtags : 3 time to prepare the origin masks : 8.140528917312622 time for calcul the mask position with numpy : 0.43726158142089844 nb_pixel_total : 7594487 time to create 1 rle with new method : 0.818795919418335 time for calcul the mask position with numpy : 0.02728581428527832 nb_pixel_total : 14880 time to create 1 rle with old method : 0.023945331573486328 time for calcul the mask position with numpy : 0.028191328048706055 nb_pixel_total : 25996 time to create 1 rle with old method : 0.0301058292388916 time for calcul the mask position with numpy : 0.026240110397338867 nb_pixel_total : 34713 time to create 1 rle with old method : 0.03946638107299805 time for calcul the mask position with numpy : 0.02689075469970703 nb_pixel_total : 39009 time to create 1 rle with old method : 0.04417777061462402 time for calcul the mask position with numpy : 0.027039766311645508 nb_pixel_total : 46963 time to create 1 rle with old method : 0.05340075492858887 time for calcul the mask position with numpy : 0.0282742977142334 nb_pixel_total : 63825 time to create 1 rle with old method : 0.0715188980102539 time for calcul the mask position with numpy : 0.028815031051635742 nb_pixel_total : 74992 time to create 1 rle with old method : 0.08787322044372559 time for calcul the mask position with numpy : 0.02824091911315918 nb_pixel_total : 2534 time to create 1 rle with old method : 0.0037851333618164062 time for calcul the mask position with numpy : 0.027652263641357422 nb_pixel_total : 14521 time to create 1 rle with old method : 0.01754927635192871 time for calcul the mask position with numpy : 0.02789163589477539 nb_pixel_total : 252235 time to create 1 rle with new method : 0.42595791816711426 time for calcul the mask position with numpy : 0.03259897232055664 nb_pixel_total : 343 time to create 1 rle with old method : 0.0006439685821533203 time for calcul the mask position with numpy : 0.028348922729492188 nb_pixel_total : 29160 time to create 1 rle with old method : 0.03447890281677246 time for calcul the mask position with numpy : 0.03169369697570801 nb_pixel_total : 100742 time to create 1 rle with old method : 0.1308915615081787 create new chi : 2.6668787002563477 time to delete rle : 0.003289937973022461 batch 1 Loaded 27 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 9776 TO DO : save crop sub photo not yet done ! save time : 0.5972771644592285 nb_obj : 10 nb_hashtags : 3 time to prepare the origin masks : 7.011808633804321 time for calcul the mask position with numpy : 0.6453092098236084 nb_pixel_total : 7647431 time to create 1 rle with new method : 0.9635677337646484 time for calcul the mask position with numpy : 0.03842878341674805 nb_pixel_total : 54872 time to create 1 rle with old method : 0.06299757957458496 time for calcul the mask position with numpy : 0.04723548889160156 nb_pixel_total : 94075 time to create 1 rle with old method : 0.10551619529724121 time for calcul the mask position with numpy : 0.042305707931518555 nb_pixel_total : 69445 time to create 1 rle with old method : 0.08289718627929688 time for calcul the mask position with numpy : 0.04859209060668945 nb_pixel_total : 22744 time to create 1 rle with old method : 0.02732086181640625 time for calcul the mask position with numpy : 0.042386770248413086 nb_pixel_total : 22593 time to create 1 rle with old method : 0.02741408348083496 time for calcul the mask position with numpy : 0.048272132873535156 nb_pixel_total : 38398 time to create 1 rle with old method : 0.06302356719970703 time for calcul the mask position with numpy : 0.05037260055541992 nb_pixel_total : 66366 time to create 1 rle with old method : 0.07857227325439453 time for calcul the mask position with numpy : 0.03892922401428223 nb_pixel_total : 14913 time to create 1 rle with old method : 0.017073869705200195 time for calcul the mask position with numpy : 0.0358736515045166 nb_pixel_total : 100190 time to create 1 rle with old method : 0.11780500411987305 time for calcul the mask position with numpy : 0.04429483413696289 nb_pixel_total : 163373 time to create 1 rle with new method : 1.2719368934631348 create new chi : 3.994044303894043 time to delete rle : 0.003232240676879883 batch 1 Loaded 21 chid ids of type : 3594 ++++++++++++++++++Number RLEs to save : 10252 TO DO : save crop sub photo not yet done ! save time : 0.7024650573730469 nb_obj : 8 nb_hashtags : 3 time to prepare the origin masks : 4.8241071701049805 time for calcul the mask position with numpy : 0.5051479339599609 nb_pixel_total : 7362414 time to create 1 rle with new method : 0.9936883449554443 time for calcul the mask position with numpy : 0.043523550033569336 nb_pixel_total : 235909 time to create 1 rle with new method : 0.4292476177215576 time for calcul the mask position with numpy : 0.05246090888977051 nb_pixel_total : 51189 time to create 1 rle with old method : 0.05782485008239746 time for calcul the mask position with numpy : 0.05180764198303223 nb_pixel_total : 53289 time to create 1 rle with old method : 0.060861825942993164 time for calcul the mask position with numpy : 0.05330920219421387 nb_pixel_total : 154016 time to create 1 rle with new method : 0.6172084808349609 time for calcul the mask position with numpy : 0.05027198791503906 nb_pixel_total : 47214 time to create 1 rle with old method : 0.053044795989990234 time for calcul the mask position with numpy : 0.05128335952758789 nb_pixel_total : 25861 time to create 1 rle with old method : 0.02907872200012207 time for calcul the mask position with numpy : 0.05260062217712402 nb_pixel_total : 299605 time to create 1 rle with new method : 0.8042948246002197 time for calcul the mask position with numpy : 0.03777313232421875 nb_pixel_total : 64903 time to create 1 rle with old method : 0.07301640510559082 create new chi : 4.167172908782959 time to delete rle : 0.0035028457641601562 batch 1 Loaded 17 chid ids of type : 3594 ++++++++++Number RLEs to save : 9838 TO DO : save crop sub photo not yet done ! save time : 0.6958177089691162 nb_obj : 12 nb_hashtags : 3 time to prepare the origin masks : 6.995783805847168 time for calcul the mask position with numpy : 0.7938611507415771 nb_pixel_total : 7729631 time to create 1 rle with new method : 0.4563901424407959 time for calcul the mask position with numpy : 0.051825761795043945 nb_pixel_total : 57108 time to create 1 rle with old method : 0.07492637634277344 time for calcul the mask position with numpy : 0.06091022491455078 nb_pixel_total : 28521 time to create 1 rle with old method : 0.03607821464538574 time for calcul the mask position with numpy : 0.05148601531982422 nb_pixel_total : 63931 time to create 1 rle with old method : 0.09581255912780762 time for calcul the mask position with numpy : 0.05236697196960449 nb_pixel_total : 16247 time to create 1 rle with old method : 0.018827199935913086 time for calcul the mask position with numpy : 0.050338029861450195 nb_pixel_total : 93985 time to create 1 rle with old method : 0.10790824890136719 time for calcul the mask position with numpy : 0.04913187026977539 nb_pixel_total : 16976 time to create 1 rle with old method : 0.01970076560974121 time for calcul the mask position with numpy : 0.044376373291015625 nb_pixel_total : 60838 time to create 1 rle with old method : 0.06812691688537598 time for calcul the mask position with numpy : 0.03302621841430664 nb_pixel_total : 95843 time to create 1 rle with old method : 0.1101691722869873 time for calcul the mask position with numpy : 0.029992103576660156 nb_pixel_total : 79574 time to create 1 rle with old method : 0.08876585960388184 time for calcul the mask position with numpy : 0.03177046775817871 nb_pixel_total : 11544 time to create 1 rle with old method : 0.01822495460510254 time for calcul the mask position with numpy : 0.033719778060913086 nb_pixel_total : 18488 time to create 1 rle with old method : 0.021085023880004883 time for calcul the mask position with numpy : 0.03139042854309082 nb_pixel_total : 21714 time to create 1 rle with old method : 0.02447962760925293 create new chi : 2.625826120376587 time to delete rle : 0.0026831626892089844 batch 1 Loaded 25 chid ids of type : 3594 ++++++++++++++Number RLEs to save : 9138 TO DO : save crop sub photo not yet done ! save time : 0.5977239608764648 nb_obj : 15 nb_hashtags : 2 time to prepare the origin masks : 6.1622748374938965 time for calcul the mask position with numpy : 0.3984997272491455 nb_pixel_total : 7305354 time to create 1 rle with new method : 0.541823148727417 time for calcul the mask position with numpy : 0.034198760986328125 nb_pixel_total : 12252 time to create 1 rle with old method : 0.014129877090454102 time for calcul the mask position with numpy : 0.04396677017211914 nb_pixel_total : 16951 time to create 1 rle with old method : 0.019439220428466797 time for calcul the mask position with numpy : 0.04286551475524902 nb_pixel_total : 30840 time to create 1 rle with old method : 0.045143842697143555 time for calcul the mask position with numpy : 0.05086636543273926 nb_pixel_total : 175454 time to create 1 rle with new method : 0.5347108840942383 time for calcul the mask position with numpy : 0.04513096809387207 nb_pixel_total : 59788 time to create 1 rle with old method : 0.06821346282958984 time for calcul the mask position with numpy : 0.04550480842590332 nb_pixel_total : 28308 time to create 1 rle with old method : 0.0323638916015625 time for calcul the mask position with numpy : 0.05134320259094238 nb_pixel_total : 179095 time to create 1 rle with new method : 0.46283578872680664 time for calcul the mask position with numpy : 0.051862239837646484 nb_pixel_total : 38778 time to create 1 rle with old method : 0.04534029960632324 time for calcul the mask position with numpy : 0.05737757682800293 nb_pixel_total : 90119 time to create 1 rle with old method : 0.10712623596191406 time for calcul the mask position with numpy : 0.04927492141723633 nb_pixel_total : 102902 time to create 1 rle with old method : 0.11342692375183105 time for calcul the mask position with numpy : 0.05006718635559082 nb_pixel_total : 22127 time to create 1 rle with old method : 0.02513718605041504 time for calcul the mask position with numpy : 0.0561065673828125 nb_pixel_total : 88072 time to create 1 rle with old method : 0.10741138458251953 time for calcul the mask position with numpy : 0.05224466323852539 nb_pixel_total : 20722 time to create 1 rle with old method : 0.028885602951049805 time for calcul the mask position with numpy : 0.053750038146972656 nb_pixel_total : 108282 time to create 1 rle with old method : 0.12156987190246582 time for calcul the mask position with numpy : 0.05042862892150879 nb_pixel_total : 15356 time to create 1 rle with old method : 0.01758551597595215 create new chi : 3.5248069763183594 time to delete rle : 0.002940654754638672 batch 1 Loaded 31 chid ids of type : 3594 ++++++++++++++++++++++Number RLEs to save : 12509 TO DO : save crop sub photo not yet done ! save time : 0.7226400375366211 nb_obj : 8 nb_hashtags : 2 time to prepare the origin masks : 2.9875388145446777 time for calcul the mask position with numpy : 0.551680326461792 nb_pixel_total : 7735948 time to create 1 rle with new method : 0.6968989372253418 time for calcul the mask position with numpy : 0.03073430061340332 nb_pixel_total : 66215 time to create 1 rle with old method : 0.07050633430480957 time for calcul the mask position with numpy : 0.03496098518371582 nb_pixel_total : 56831 time to create 1 rle with old method : 0.06148409843444824 time for calcul the mask position with numpy : 0.03513169288635254 nb_pixel_total : 141364 time to create 1 rle with old method : 0.15738487243652344 time for calcul the mask position with numpy : 0.03277015686035156 nb_pixel_total : 63170 time to create 1 rle with old method : 0.06924676895141602 time for calcul the mask position with numpy : 0.03260350227355957 nb_pixel_total : 43537 time to create 1 rle with old method : 0.04729604721069336 time for calcul the mask position with numpy : 0.03184151649475098 nb_pixel_total : 106025 time to create 1 rle with old method : 0.1137089729309082 time for calcul the mask position with numpy : 0.0347287654876709 nb_pixel_total : 32375 time to create 1 rle with old method : 0.03522181510925293 time for calcul the mask position with numpy : 0.05082297325134277 nb_pixel_total : 48935 time to create 1 rle with old method : 0.05420231819152832 create new chi : 2.1920008659362793 time to delete rle : 0.0021429061889648438 batch 1 Loaded 17 chid ids of type : 3594 +++++++++++++Number RLEs to save : 8464 TO DO : save crop sub photo not yet done ! save time : 0.5195205211639404 nb_obj : 11 nb_hashtags : 2 time to prepare the origin masks : 5.827696084976196 time for calcul the mask position with numpy : 0.48503637313842773 nb_pixel_total : 7729612 time to create 1 rle with new method : 0.4073622226715088 time for calcul the mask position with numpy : 0.023386716842651367 nb_pixel_total : 18130 time to create 1 rle with old method : 0.020233869552612305 time for calcul the mask position with numpy : 0.025905132293701172 nb_pixel_total : 27746 time to create 1 rle with old method : 0.029898643493652344 time for calcul the mask position with numpy : 0.02570509910583496 nb_pixel_total : 65844 time to create 1 rle with old method : 0.06938958168029785 time for calcul the mask position with numpy : 0.027227163314819336 nb_pixel_total : 47395 time to create 1 rle with old method : 0.052019357681274414 time for calcul the mask position with numpy : 0.02590012550354004 nb_pixel_total : 72952 time to create 1 rle with old method : 0.07715249061584473 time for calcul the mask position with numpy : 0.023428916931152344 nb_pixel_total : 3322 time to create 1 rle with old method : 0.004147052764892578 time for calcul the mask position with numpy : 0.02433943748474121 nb_pixel_total : 185677 time to create 1 rle with new method : 0.4903273582458496 time for calcul the mask position with numpy : 0.026122331619262695 nb_pixel_total : 10225 time to create 1 rle with old method : 0.011323690414428711 time for calcul the mask position with numpy : 0.024838685989379883 nb_pixel_total : 96680 time to create 1 rle with old method : 0.10290980339050293 time for calcul the mask position with numpy : 0.023580551147460938 nb_pixel_total : 555 time to create 1 rle with old method : 0.0008339881896972656 time for calcul the mask position with numpy : 0.024681806564331055 nb_pixel_total : 36262 time to create 1 rle with old method : 0.03870677947998047 create new chi : 2.1363365650177 time to delete rle : 0.001169443130493164 batch 1 Loaded 23 chid ids of type : 3594 ++++++++++++++++++++Number RLEs to save : 8197 TO DO : save crop sub photo not yet done ! save time : 0.5263903141021729 nb_obj : 15 nb_hashtags : 2 time to prepare the origin masks : 3.737156391143799 time for calcul the mask position with numpy : 0.39806175231933594 nb_pixel_total : 7448443 time to create 1 rle with new method : 0.449474573135376 time for calcul the mask position with numpy : 0.026940584182739258 nb_pixel_total : 13353 time to create 1 rle with old method : 0.015236854553222656 time for calcul the mask position with numpy : 0.027236461639404297 nb_pixel_total : 52726 time to create 1 rle with old method : 0.059526920318603516 time for calcul the mask position with numpy : 0.02780604362487793 nb_pixel_total : 39968 time to create 1 rle with old method : 0.04735207557678223 time for calcul the mask position with numpy : 0.025259733200073242 nb_pixel_total : 9577 time to create 1 rle with old method : 0.010868310928344727 time for calcul the mask position with numpy : 0.025261878967285156 nb_pixel_total : 46638 time to create 1 rle with old method : 0.05281639099121094 time for calcul the mask position with numpy : 0.026184797286987305 nb_pixel_total : 18243 time to create 1 rle with old method : 0.02204132080078125 time for calcul the mask position with numpy : 0.03187441825866699 nb_pixel_total : 155441 time to create 1 rle with new method : 0.5766477584838867 time for calcul the mask position with numpy : 0.026775836944580078 nb_pixel_total : 28132 time to create 1 rle with old method : 0.034844398498535156 time for calcul the mask position with numpy : 0.02711176872253418 nb_pixel_total : 891 time to create 1 rle with old method : 0.0019040107727050781 time for calcul the mask position with numpy : 0.02818894386291504 nb_pixel_total : 223507 time to create 1 rle with new method : 1.0539188385009766 time for calcul the mask position with numpy : 0.03925585746765137 nb_pixel_total : 27651 time to create 1 rle with old method : 0.03754401206970215 time for calcul the mask position with numpy : 0.02672553062438965 nb_pixel_total : 17206 time to create 1 rle with old method : 0.019664764404296875 time for calcul the mask position with numpy : 0.025188446044921875 nb_pixel_total : 25190 time to create 1 rle with old method : 0.02858710289001465 time for calcul the mask position with numpy : 0.026679277420043945 nb_pixel_total : 92291 time to create 1 rle with old method : 0.10901570320129395 time for calcul the mask position with numpy : 0.02614283561706543 nb_pixel_total : 95143 time to create 1 rle with old method : 0.11678767204284668 create new chi : 3.5552008152008057 time to delete rle : 0.0020449161529541016 batch 1 Loaded 31 chid ids of type : 3594 ++++++++++++++++++++++++++Number RLEs to save : 11563 TO DO : save crop sub photo not yet done ! save time : 0.7145037651062012 nb_obj : 11 nb_hashtags : 1 time to prepare the origin masks : 4.106090068817139 time for calcul the mask position with numpy : 0.7630610466003418 nb_pixel_total : 7309509 time to create 1 rle with new method : 2.6261277198791504 time for calcul the mask position with numpy : 0.02866816520690918 nb_pixel_total : 52953 time to create 1 rle with old method : 0.06027936935424805 time for calcul the mask position with numpy : 0.030939340591430664 nb_pixel_total : 332914 time to create 1 rle with new method : 0.5344476699829102 time for calcul the mask position with numpy : 0.027103662490844727 nb_pixel_total : 20941 time to create 1 rle with old method : 0.025713682174682617 time for calcul the mask position with numpy : 0.02882838249206543 nb_pixel_total : 21273 time to create 1 rle with old method : 0.025139570236206055 time for calcul the mask position with numpy : 0.02747058868408203 nb_pixel_total : 118715 time to create 1 rle with old method : 0.14113283157348633 time for calcul the mask position with numpy : 0.026973724365234375 nb_pixel_total : 17610 time to create 1 rle with old method : 0.019952774047851562 time for calcul the mask position with numpy : 0.02607583999633789 nb_pixel_total : 57888 time to create 1 rle with old method : 0.06577515602111816 time for calcul the mask position with numpy : 0.027214527130126953 nb_pixel_total : 98689 time to create 1 rle with old method : 0.12592840194702148 time for calcul the mask position with numpy : 0.028632402420043945 nb_pixel_total : 75971 time to create 1 rle with old method : 0.08493375778198242 time for calcul the mask position with numpy : 0.02786397933959961 nb_pixel_total : 142729 time to create 1 rle with old method : 0.16303062438964844 time for calcul the mask position with numpy : 0.04772663116455078 nb_pixel_total : 45208 time to create 1 rle with old method : 0.05074739456176758 create new chi : 5.092795372009277 time to delete rle : 0.0019145011901855469 batch 1 Loaded 23 chid ids of type : 3594 +++++++++++++Number RLEs to save : 10326 TO DO : save crop sub photo not yet done ! save time : 0.6507744789123535 nb_obj : 11 nb_hashtags : 3 time to prepare the origin masks : 4.903681516647339 time for calcul the mask position with numpy : 0.41672468185424805 nb_pixel_total : 7234111 time to create 1 rle with new method : 1.1578538417816162 time for calcul the mask position with numpy : 0.04765176773071289 nb_pixel_total : 22445 time to create 1 rle with old method : 0.025302410125732422 time for calcul the mask position with numpy : 0.03941917419433594 nb_pixel_total : 53811 time to create 1 rle with old method : 0.06289339065551758 time for calcul the mask position with numpy : 0.03048396110534668 nb_pixel_total : 594769 time to create 1 rle with new method : 1.2565550804138184 time for calcul the mask position with numpy : 0.03016209602355957 nb_pixel_total : 33622 time to create 1 rle with old method : 0.04021167755126953 time for calcul the mask position with numpy : 0.02808833122253418 nb_pixel_total : 149984 time to create 1 rle with old method : 0.1846916675567627 time for calcul the mask position with numpy : 0.027810096740722656 nb_pixel_total : 37477 time to create 1 rle with old method : 0.04882216453552246 time for calcul the mask position with numpy : 0.027422189712524414 nb_pixel_total : 29598 time to create 1 rle with old method : 0.03319907188415527 time for calcul the mask position with numpy : 0.026169776916503906 nb_pixel_total : 28169 time to create 1 rle with old method : 0.03530168533325195 time for calcul the mask position with numpy : 0.026211023330688477 nb_pixel_total : 40213 time to create 1 rle with old method : 0.04934883117675781 time for calcul the mask position with numpy : 0.026267528533935547 nb_pixel_total : 19430 time to create 1 rle with old method : 0.02562570571899414 time for calcul the mask position with numpy : 0.02793145179748535 nb_pixel_total : 50771 time to create 1 rle with old method : 0.06504130363464355 create new chi : 3.82076358795166 time to delete rle : 0.0014488697052001953 batch 1 Loaded 23 chid ids of type : 3594 ++++++++++++++++Number RLEs to save : 8844 TO DO : save crop sub photo not yet done ! save time : 0.5239076614379883 map_output_result : {1389748410: (0.0, 'Should be the crop_list due to order', 0), 1389748404: (0.0, 'Should be the crop_list due to order', 0), 1389748383: (0.0, 'Should be the crop_list due to order', 0), 1389748380: (0.0, 'Should be the crop_list due to order', 0), 1389748377: (0.0, 'Should be the crop_list due to order', 0), 1389748373: (0.0, 'Should be the crop_list due to order', 0), 1389748366: (0.0, 'Should be the crop_list due to order', 0), 1389748359: (0.0, 'Should be the crop_list due to order', 0), 1389748330: (0.0, 'Should be the crop_list due to order', 0), 1389748323: (0.0, 'Should be the crop_list due to order', 0), 1389748317: (0.0, 'Should be the crop_list due to order', 0), 1389748305: (0.0, 'Should be the crop_list due to order', 0), 1389748297: (0.0, 'Should be the crop_list due to order', 0), 1389748285: (0.0, 'Should be the crop_list due to order', 0), 1389748281: (0.0, 'Should be the crop_list due to order', 0), 1389748273: (0.0, 'Should be the crop_list due to order', 0), 1389748271: (0.0, 'Should be the crop_list due to order', 0), 1389748264: (0.0, 'Should be the crop_list due to order', 0), 1389748249: (0.0, 'Should be the crop_list due to order', 0), 1389748240: (0.0, 'Should be the crop_list due to order', 0), 1389748234: (0.0, 'Should be the crop_list due to order', 0), 1389748226: (0.0, 'Should be the crop_list due to order', 0), 1389748218: (0.0, 'Should be the crop_list due to order', 0), 1389748200: (0.0, 'Should be the crop_list due to order', 0), 1389748196: (0.0, 'Should be the crop_list due to order', 0), 1389748192: (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 [1389748410, 1389748404, 1389748383, 1389748380, 1389748377, 1389748373, 1389748366, 1389748359, 1389748330, 1389748323, 1389748317, 1389748305, 1389748297, 1389748285, 1389748281, 1389748273, 1389748271, 1389748264, 1389748249, 1389748240, 1389748234, 1389748226, 1389748218, 1389748200, 1389748196, 1389748192] Looping around the photos to save general results len do output : 26 /1389748410.Didn't retrieve data . /1389748404.Didn't retrieve data . /1389748383.Didn't retrieve data . /1389748380.Didn't retrieve data . /1389748377.Didn't retrieve data . /1389748373.Didn't retrieve data . /1389748366.Didn't retrieve data . /1389748359.Didn't retrieve data . /1389748330.Didn't retrieve data . /1389748323.Didn't retrieve data . /1389748317.Didn't retrieve data . /1389748305.Didn't retrieve data . /1389748297.Didn't retrieve data . /1389748285.Didn't retrieve data . /1389748281.Didn't retrieve data . /1389748273.Didn't retrieve data . /1389748271.Didn't retrieve data . /1389748264.Didn't retrieve data . /1389748249.Didn't retrieve data . /1389748240.Didn't retrieve data . /1389748234.Didn't retrieve data . /1389748226.Didn't retrieve data . /1389748218.Didn't retrieve data . /1389748200.Didn't retrieve data . /1389748196.Didn't retrieve data . /1389748192.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, '3985636') ('3318', '27900917', '1389748410', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748404', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748383', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748380', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748377', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748373', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748366', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748359', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748330', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748323', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748317', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748305', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748297', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748285', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748281', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748273', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748271', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748264', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748249', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748240', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748234', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748226', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748218', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748200', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748196', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748192', None, None, None, None, None, '3985636') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 78 time used for this insertion : 0.022122621536254883 save_final save missing photos in datou_result : time spend for datou_step_exec : 307.1383044719696 time spend to save output : 0.2496333122253418 total time spend for step 3 : 307.38793778419495 step4:ventilate_hashtags_in_portfolio Mon Nov 3 15:02:22 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 : 27900917 get user id for portfolio 27900917 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`=27900917 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pehd','flou','pet_fonce','metal','environnement','autre','mal_croppe','carton','background','papier')) 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`=27900917 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pehd','flou','pet_fonce','metal','environnement','autre','mal_croppe','carton','background','papier')) 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`=27900917 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','pehd','flou','pet_fonce','metal','environnement','autre','mal_croppe','carton','background','papier')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/28099877,28099878,28099879,28099880,28099881,28099882,28099883,28099884,28099885,28099886,28099887?tags=pet_clair,pehd,flou,pet_fonce,metal,environnement,autre,mal_croppe,carton,background,papier Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1389748410, 1389748404, 1389748383, 1389748380, 1389748377, 1389748373, 1389748366, 1389748359, 1389748330, 1389748323, 1389748317, 1389748305, 1389748297, 1389748285, 1389748281, 1389748273, 1389748271, 1389748264, 1389748249, 1389748240, 1389748234, 1389748226, 1389748218, 1389748200, 1389748196, 1389748192] Looping around the photos to save general results len do output : 1 /27900917. 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, '3985636') ('3318', '27900917', '1389748410', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748404', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748383', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748380', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748377', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748373', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748366', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748359', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748330', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748323', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748317', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748305', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748297', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748285', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748281', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748273', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748271', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748264', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748249', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748240', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748234', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748226', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748218', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748200', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748196', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748192', None, None, None, None, None, '3985636') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 27 time used for this insertion : 0.02104973793029785 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.8833451271057129 time spend to save output : 0.021534204483032227 total time spend for step 4 : 0.9048793315887451 step5:final Mon Nov 3 15:02:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1389748410: ('0.09267584616868467',), 1389748404: ('0.09267584616868467',), 1389748383: ('0.09267584616868467',), 1389748380: ('0.09267584616868467',), 1389748377: ('0.09267584616868467',), 1389748373: ('0.09267584616868467',), 1389748366: ('0.09267584616868467',), 1389748359: ('0.09267584616868467',), 1389748330: ('0.09267584616868467',), 1389748323: ('0.09267584616868467',), 1389748317: ('0.09267584616868467',), 1389748305: ('0.09267584616868467',), 1389748297: ('0.09267584616868467',), 1389748285: ('0.09267584616868467',), 1389748281: ('0.09267584616868467',), 1389748273: ('0.09267584616868467',), 1389748271: ('0.09267584616868467',), 1389748264: ('0.09267584616868467',), 1389748249: ('0.09267584616868467',), 1389748240: ('0.09267584616868467',), 1389748234: ('0.09267584616868467',), 1389748226: ('0.09267584616868467',), 1389748218: ('0.09267584616868467',), 1389748200: ('0.09267584616868467',), 1389748196: ('0.09267584616868467',), 1389748192: ('0.09267584616868467',)} new output for save of step final : {1389748410: ('0.09267584616868467',), 1389748404: ('0.09267584616868467',), 1389748383: ('0.09267584616868467',), 1389748380: ('0.09267584616868467',), 1389748377: ('0.09267584616868467',), 1389748373: ('0.09267584616868467',), 1389748366: ('0.09267584616868467',), 1389748359: ('0.09267584616868467',), 1389748330: ('0.09267584616868467',), 1389748323: ('0.09267584616868467',), 1389748317: ('0.09267584616868467',), 1389748305: ('0.09267584616868467',), 1389748297: ('0.09267584616868467',), 1389748285: ('0.09267584616868467',), 1389748281: ('0.09267584616868467',), 1389748273: ('0.09267584616868467',), 1389748271: ('0.09267584616868467',), 1389748264: ('0.09267584616868467',), 1389748249: ('0.09267584616868467',), 1389748240: ('0.09267584616868467',), 1389748234: ('0.09267584616868467',), 1389748226: ('0.09267584616868467',), 1389748218: ('0.09267584616868467',), 1389748200: ('0.09267584616868467',), 1389748196: ('0.09267584616868467',), 1389748192: ('0.09267584616868467',)} [1389748410, 1389748404, 1389748383, 1389748380, 1389748377, 1389748373, 1389748366, 1389748359, 1389748330, 1389748323, 1389748317, 1389748305, 1389748297, 1389748285, 1389748281, 1389748273, 1389748271, 1389748264, 1389748249, 1389748240, 1389748234, 1389748226, 1389748218, 1389748200, 1389748196, 1389748192] Looping around the photos to save general results len do output : 26 /1389748410.Didn't retrieve data . /1389748404.Didn't retrieve data . /1389748383.Didn't retrieve data . /1389748380.Didn't retrieve data . /1389748377.Didn't retrieve data . /1389748373.Didn't retrieve data . /1389748366.Didn't retrieve data . /1389748359.Didn't retrieve data . /1389748330.Didn't retrieve data . /1389748323.Didn't retrieve data . /1389748317.Didn't retrieve data . /1389748305.Didn't retrieve data . /1389748297.Didn't retrieve data . /1389748285.Didn't retrieve data . /1389748281.Didn't retrieve data . /1389748273.Didn't retrieve data . /1389748271.Didn't retrieve data . /1389748264.Didn't retrieve data . /1389748249.Didn't retrieve data . /1389748240.Didn't retrieve data . /1389748234.Didn't retrieve data . /1389748226.Didn't retrieve data . /1389748218.Didn't retrieve data . /1389748200.Didn't retrieve data . /1389748196.Didn't retrieve data . /1389748192.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, '3985636') ('3318', '27900917', '1389748410', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748404', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748383', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748380', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748377', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748373', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748366', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748359', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748330', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748323', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748317', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748305', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748297', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748285', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748281', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748273', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748271', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748264', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748249', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748240', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748234', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748226', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748218', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748200', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748196', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748192', None, None, None, None, None, '3985636') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 78 time used for this insertion : 0.01894664764404297 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.15541410446166992 time spend to save output : 0.020429134368896484 total time spend for step 5 : 0.1758432388305664 step6:blur_detection Mon Nov 3 15:02:23 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 inside step blur_detection methode: ratio et variance treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4.jpg resize: (2160, 3840) 1389748410 -5.867310206046132 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575.jpg resize: (2160, 3840) 1389748404 -6.897693294336126 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669.jpg resize: (2160, 3840) 1389748383 -6.969195672929006 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0.jpg resize: (2160, 3840) 1389748380 -6.909041637188264 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238.jpg resize: (2160, 3840) 1389748377 -6.873071565597251 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd.jpg resize: (2160, 3840) 1389748373 -6.923142172913038 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8.jpg resize: (2160, 3840) 1389748366 -6.940568738519625 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2.jpg resize: (2160, 3840) 1389748359 -6.9210620667313645 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b.jpg resize: (2160, 3840) 1389748330 -6.886665361097833 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5.jpg resize: (2160, 3840) 1389748323 -6.810717651969815 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d.jpg resize: (2160, 3840) 1389748317 -6.691049952875845 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1.jpg resize: (2160, 3840) 1389748305 -7.034119276088728 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb.jpg resize: (2160, 3840) 1389748297 -7.037780069325714 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75.jpg resize: (2160, 3840) 1389748285 -6.9783735389352355 treat image : temp/1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061.jpg resize: (2160, 3840) 1389748281 -6.639740856749241 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9.jpg resize: (2160, 3840) 1389748273 -6.968356906853137 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6.jpg resize: (2160, 3840) 1389748271 -7.004160515189696 treat image : temp/1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a.jpg resize: (2160, 3840) 1389748264 -6.991417633863455 treat image : temp/1762177828_1951692_1389748249_5852d0d064df597a140439a760660212.jpg resize: (2160, 3840) 1389748249 -6.8178560205413685 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019.jpg resize: (2160, 3840) 1389748240 -6.932047140178858 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a.jpg resize: (2160, 3840) 1389748234 -6.680653720325641 treat image : temp/1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8.jpg resize: (2160, 3840) 1389748226 -6.950875279006611 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98.jpg resize: (2160, 3840) 1389748218 -6.93116503502784 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774.jpg resize: (2160, 3840) 1389748200 -6.762416252508783 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a.jpg resize: (2160, 3840) 1389748196 -6.846770994905227 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e.jpg resize: (2160, 3840) 1389748192 -5.9057524058001 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905912_0.png resize: (130, 119) 1392063468 -1.5430449134769888 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905913_0.png resize: (200, 319) 1392063469 0.29498320149553653 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905919_0.png resize: (119, 189) 1392063470 -4.244001459450059 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905920_0.png resize: (153, 207) 1392063471 -2.9186479471941524 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905921_0.png resize: (585, 846) 1392063472 -4.953795784036077 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905924_0.png resize: (243, 344) 1392063473 -2.2749882705857756 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905927_0.png resize: (93, 170) 1392063474 -1.6678259585379376 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905928_0.png resize: (218, 182) 1392063475 -3.863389903419758 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4003028082_0.png resize: (1528, 1494) 1392063476 -5.91692075016385 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905929_0.png resize: (172, 341) 1392063477 -0.30131414476896634 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905930_0.png resize: (408, 174) 1392063478 -3.4463316295695505 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905931_0.png resize: (375, 298) 1392063479 -3.6241390008079457 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905932_0.png resize: (347, 178) 1392063480 -2.250808061847555 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905933_0.png resize: (229, 437) 1392063481 -2.205665261231192 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905934_0.png resize: (152, 181) 1392063482 -2.673551353972657 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905936_0.png resize: (519, 547) 1392063483 -3.8581872855558146 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905938_0.png resize: (157, 153) 1392063484 -2.4890059542249436 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905939_0.png resize: (161, 66) 1392063485 -1.2087985571893989 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905940_0.png resize: (239, 214) 1392063486 -2.9648063119448316 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905941_0.png resize: (227, 318) 1392063487 -4.341402914310246 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905942_0.png resize: (190, 152) 1392063488 -1.766941591386471 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905943_0.png resize: (349, 327) 1392063489 -4.2153364735601615 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905944_0.png resize: (183, 245) 1392063490 -4.081206097973539 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4016612017_0.png resize: (158, 157) 1392063491 -1.8861702439412762 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905945_0.png resize: (165, 317) 1392063492 -2.267907609640529 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905946_0.png resize: (297, 299) 1392063493 0.44401589898752414 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905948_0.png resize: (439, 176) 1392063495 -4.1377879199884395 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905949_0.png resize: (153, 194) 1392063496 -2.8765616146695803 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905950_0.png resize: (175, 157) 1392063497 -3.4305405348875375 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905951_0.png resize: (379, 461) 1392063498 -4.524202642394013 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905954_0.png resize: (116, 221) 1392063499 -2.080170687471276 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905955_0.png resize: (252, 526) 1392063500 -4.198164850843562 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905956_0.png resize: (186, 140) 1392063502 -2.9165214313567613 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4016612018_0.png resize: (316, 206) 1392063503 -3.801266819897622 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4016612019_0.png resize: (233, 258) 1392063504 -2.182199234490461 treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4016612020_0.png resize: (176, 182) 1392063505 -2.0473370989210298 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905959_0.png resize: (292, 305) 1392063506 -2.300535300828749 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905960_0.png resize: (251, 263) 1392063507 0.7933104924858513 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905961_0.png resize: (145, 299) 1392063508 -2.581335637609425 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905963_0.png resize: (112, 131) 1392063509 -4.324160283435587 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905964_0.png resize: (101, 201) 1392063510 -0.08680440323919879 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905966_0.png resize: (201, 163) 1392063511 -0.6612185839959652 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905967_0.png resize: (293, 146) 1392063512 -3.0247853301505265 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905968_0.png resize: (208, 91) 1392063513 -1.2329516369252855 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905969_0.png resize: (135, 164) 1392063514 -4.192022762233038 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905970_0.png resize: (184, 93) 1392063515 -4.03196082962915 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905971_0.png resize: (173, 166) 1392063516 -0.7187052431331032 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905973_0.png resize: (510, 440) 1392063517 -3.601490616733061 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905974_0.png resize: (183, 178) 1392063518 -1.1539510202454677 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905975_0.png resize: (302, 249) 1392063519 -4.354050271581129 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905976_0.png resize: (156, 158) 1392063520 1.3501989635451672 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4002905977_0.png resize: (98, 114) 1392063521 -1.9585758335999721 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4003028084_0.png resize: (317, 153) 1392063522 -4.418421035753983 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4003028085_0.png resize: (194, 229) 1392063523 -3.0590403032432003 treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0_rle_crop_4003028086_0.png resize: (609, 736) 1392063524 -3.8916555544098705 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905979_0.png resize: (151, 177) 1392063525 -1.7867357816498504 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905980_0.png resize: (234, 350) 1392063526 -3.045112118533036 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905981_0.png resize: (331, 451) 1392063527 -2.571847195555114 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905982_0.png resize: (255, 165) 1392063528 -3.7320531809550928 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905983_0.png resize: (266, 229) 1392063529 -3.2333763270705753 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905985_0.png resize: (273, 245) 1392063530 0.43616918938514176 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905987_0.png resize: (231, 301) 1392063531 -0.9464453645085594 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4002905988_0.png resize: (152, 217) 1392063532 -2.4852289301904236 treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238_rle_crop_4003028087_0.png resize: (396, 592) 1392063533 -0.33445274925287416 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905989_0.png resize: (335, 332) 1392063534 -0.47601298140269727 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905990_0.png resize: (352, 172) 1392063535 -0.940281813102123 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905991_0.png resize: (230, 251) 1392063536 -0.7420139519545094 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905993_0.png resize: (128, 155) 1392063537 -3.0439059698779656 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905994_0.png resize: (233, 357) 1392063538 -4.29456964468903 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905996_0.png resize: (191, 121) 1392063539 -5.012635275507494 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905997_0.png resize: (243, 208) 1392063540 -3.642020935475976 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4002905998_0.png resize: (141, 140) 1392063541 -2.618615339304979 treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd_rle_crop_4003028088_0.png resize: (108, 158) 1392063542 -4.77693815908349 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906000_0.png resize: (305, 362) 1392063543 -4.066790962850093 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906001_0.png resize: (287, 245) 1392063544 -4.62945611780408 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906002_0.png resize: (214, 252) 1392063545 0.5814432883536205 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906004_0.png resize: (278, 614) 1392063546 -2.5587807449895945 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906005_0.png resize: (303, 284) 1392063547 5.760307053022258 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4002906006_0.png resize: (290, 298) 1392063548 -2.2361872373164373 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4003028089_0.png resize: (280, 240) 1392063549 2.0233996223326676 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4003028090_0.png resize: (250, 216) 1392063550 -2.4024847380811902 treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8_rle_crop_4003028091_0.png resize: (284, 463) 1392063551 -2.314361473541392 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906009_0.png resize: (279, 240) 1392063552 2.372686959571503 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906010_0.png resize: (224, 273) 1392063553 -0.49014680421961854 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906011_0.png resize: (218, 603) 1392063554 -2.745376054678304 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906012_0.png resize: (230, 211) 1392063555 -2.316585419178572 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906013_0.png resize: (153, 165) 1392063556 -3.4216711117978984 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906014_0.png resize: (444, 337) 1392063557 -4.123165509265578 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906015_0.png resize: (295, 286) 1392063558 -4.566684849664353 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906016_0.png resize: (261, 254) 1392063560 8.808368439608754 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906017_0.png resize: (139, 247) 1392063561 1.0091630889251666 treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2_rle_crop_4002906018_0.png resize: (110, 203) 1392063562 -3.302854919259206 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906020_0.png resize: (522, 360) 1392063563 -2.439855089386518 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906022_0.png resize: (215, 125) 1392063564 0.13928575746219998 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906024_0.png resize: (262, 142) 1392063565 -1.7380356215633168 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906026_0.png resize: (198, 211) 1392063566 3.7966113071145866 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4003028092_0.png resize: (217, 191) 1392063567 -1.2901056559857582 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4003028093_0.png resize: (282, 304) 1392063568 -2.2732852191565076 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4003028094_0.png resize: (204, 160) 1392063569 -4.123943774868068 treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4003028095_0.png resize: (232, 328) 1392063570 -2.11503503230481 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906030_0.png resize: (519, 562) 1392063571 -1.7630881121048965 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906031_0.png resize: (374, 406) 1392063572 -3.09125855087689 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906032_0.png resize: (183, 360) 1392063573 -3.614893565816379 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906033_0.png resize: (140, 53) 1392063574 -2.95202670431367 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906034_0.png resize: (374, 164) 1392063575 -0.18537771162623273 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906035_0.png resize: (310, 991) 1392063576 -5.048507910942498 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906037_0.png resize: (260, 289) 1392063577 -0.1827359886483104 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906038_0.png resize: (343, 655) 1392063578 -3.2680974187326317 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906039_0.png resize: (62, 152) 1392063579 -0.657897637318615 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906040_0.png resize: (116, 87) 1392063580 -3.354973640763599 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906041_0.png resize: (579, 642) 1392063581 -3.897625879606539 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906042_0.png resize: (129, 150) 1392063582 -2.072427912658348 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906043_0.png resize: (195, 139) 1392063583 -2.9881473084940904 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906044_0.png resize: (281, 149) 1392063584 -2.5350386679486725 treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5_rle_crop_4002906045_0.png resize: (160, 173) 1392063585 -0.398160323269245 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906047_0.png resize: (562, 296) 1392063586 -3.2167484251169056 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906048_0.png resize: (304, 112) 1392063587 -3.9509209129504064 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906049_0.png resize: (240, 131) 1392063588 -3.9661981223545584 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906050_0.png resize: (158, 130) 1392063589 -1.8630055761576492 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906051_0.png resize: (502, 485) 1392063590 -1.5758056670000224 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906052_0.png resize: (199, 277) 1392063591 -3.8529914373782495 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906053_0.png resize: (151, 136) 1392063592 -1.248880039441619 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906054_0.png resize: (219, 211) 1392063593 0.3682061715191836 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906055_0.png resize: (334, 282) 1392063594 -2.3596759364364632 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906056_0.png resize: (206, 355) 1392063596 -3.4017349507376764 treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906058_0.png resize: (131, 98) 1392063597 -1.6644734932940435 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906060_0.png resize: (286, 305) 1392063598 -4.222109024572322 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906061_0.png resize: (382, 505) 1392063600 -4.9115819942912 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906062_0.png resize: (226, 413) 1392063601 2.6279466744068873 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906063_0.png resize: (77, 147) 1392063602 -2.904039264418523 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906064_0.png resize: (117, 129) 1392063603 -3.57404927822484 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906065_0.png resize: (226, 149) 1392063604 -1.1817502480333653 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906066_0.png resize: (537, 217) 1392063605 -3.715150311523421 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906067_0.png resize: (137, 279) 1392063606 -3.2403098720851546 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906068_0.png resize: (410, 470) 1392063608 -2.9335389538670538 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906069_0.png resize: (226, 276) 1392063609 -1.5654606858198234 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906070_0.png resize: (113, 239) 1392063610 -4.222296121656535 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906073_0.png resize: (309, 450) 1392063611 -3.826510773651354 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906075_0.png resize: (325, 312) 1392063612 -3.5047605894186487 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906076_0.png resize: (436, 502) 1392063613 -1.7609222360441976 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906079_0.png resize: (122, 107) 1392063614 -5.046607327425723 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4003028098_0.png resize: (313, 210) 1392063615 -3.47421985960329 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906081_0.png resize: (89, 132) 1392063616 -2.4342621392945443 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906082_0.png resize: (88, 177) 1392063617 -1.0646174480470507 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906083_0.png resize: (372, 541) 1392063618 -4.959686067302604 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906084_0.png resize: (288, 337) 1392063619 -4.1529521319536755 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906085_0.png resize: (110, 129) 1392063621 -3.4819361006935368 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906086_0.png resize: (226, 152) 1392063622 -1.4269543035314682 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906087_0.png resize: (511, 230) 1392063623 -4.220049243963545 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906088_0.png resize: (246, 254) 1392063624 -3.637801599716789 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906089_0.png resize: (312, 397) 1392063625 -3.935811841682974 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906090_0.png resize: (141, 265) 1392063626 -4.798236449473182 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906091_0.png resize: (314, 319) 1392063627 -3.361346620055253 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906092_0.png resize: (253, 300) 1392063628 -3.1235097988860048 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906093_0.png resize: (274, 294) 1392063629 -1.2600259552148425 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906094_0.png resize: (142, 264) 1392063630 -3.1773352792594753 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906096_0.png resize: (280, 165) 1392063631 -4.354853995950389 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906098_0.png resize: (165, 198) 1392063632 -0.62587335401994 treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4016612021_0.png resize: (419, 436) 1392063633 -2.5763755167228912 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906100_0.png resize: (121, 181) 1392063634 1.8196197636171267 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906101_0.png resize: (121, 267) 1392063635 -1.3921602624057687 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906103_0.png resize: (154, 232) 1392063636 0.046778451818107074 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906104_0.png resize: (183, 155) 1392063637 1.3118364924272239 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906105_0.png resize: (267, 131) 1392063638 -4.498482601457056 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906106_0.png resize: (258, 249) 1392063639 -2.8965823658754424 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906107_0.png resize: (789, 869) 1392063640 -4.2649696334936325 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906108_0.png resize: (561, 577) 1392063641 -4.187791299394795 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906109_0.png resize: (214, 127) 1392063643 -2.6842345176186098 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906110_0.png resize: (228, 509) 1392063644 -0.47254294211217396 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906112_0.png resize: (411, 494) 1392063645 -0.22759777517574198 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906113_0.png resize: (128, 112) 1392063646 -2.8007864425430355 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906114_0.png resize: (224, 343) 1392063647 -4.364045631325154 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4002906115_0.png resize: (146, 335) 1392063648 -3.927378971213798 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4003028099_0.png resize: (357, 376) 1392063649 -2.4682771900390605 treat image : temp/1762177828_1951692_1389748285_74e243b4107195d1e05730a3d62c8e75_rle_crop_4003028100_0.png resize: (177, 244) 1392063650 -3.697430763217108 treat image : temp/1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906116_0.png resize: (355, 558) 1392063651 -0.2747235561966557 treat image : temp/1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906118_0.png resize: (232, 455) 1392063652 -1.6659024148762769 treat image : temp/1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906119_0.png resize: (208, 245) 1392063653 -2.257467844969027 treat image : temp/1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906121_0.png resize: (154, 184) 1392063654 -2.505628999440246 treat image : temp/1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906123_0.png resize: (206, 299) 1392063655 -1.067891919214548 treat image : temp/1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4002906124_0.png resize: (860, 361) 1392063656 -2.370751559518703 treat image : temp/1762177828_1951692_1389748281_0706b2be74bd3c0d158665dfce669061_rle_crop_4003028101_0.png resize: (221, 213) 1392063657 -3.0094676089279675 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906125_0.png resize: (198, 172) 1392063658 -3.4843129845899727 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906126_0.png resize: (408, 442) 1392063659 0.3332973721273301 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906127_0.png resize: (178, 309) 1392063660 2.974890280809643 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906128_0.png resize: (296, 283) 1392063661 -0.366772005586068 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906129_0.png resize: (306, 427) 1392063662 -3.0848801582665493 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906130_0.png resize: (289, 325) 1392063663 -2.552471246921658 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906131_0.png resize: (553, 697) 1392063664 -4.118877535808669 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906132_0.png resize: (223, 228) 1392063665 0.34357601886063 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906133_0.png resize: (183, 215) 1392063667 -2.652583882220669 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906134_0.png resize: (168, 220) 1392063668 -2.1120069130981127 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906137_0.png resize: (137, 243) 1392063669 -2.982227500610075 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906139_0.png resize: (240, 119) 1392063670 1.7686118584087542 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906140_0.png resize: (376, 450) 1392063671 0.7378220535832539 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906143_0.png resize: (343, 420) 1392063672 -0.6269259789690214 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906144_0.png resize: (288, 264) 1392063673 -1.6352950391640972 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906146_0.png resize: (179, 313) 1392063674 2.282743613335293 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906147_0.png resize: (233, 232) 1392063675 -2.4609290146479283 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906148_0.png resize: (164, 243) 1392063676 -4.176749850269805 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4016612022_0.png resize: (165, 215) 1392063677 -2.5164324907794398 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4016612023_0.png resize: (573, 873) 1392063678 -4.151870342942511 treat image : temp/1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906150_0.png resize: (603, 409) 1392063679 -3.675802413730684 treat image : temp/1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906151_0.png resize: (329, 558) 1392063680 -2.8083732573532982 treat image : temp/1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906152_0.png resize: (150, 144) 1392063681 -0.2096818793987977 treat image : 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temp/1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906161_0.png resize: (991, 449) 1392063688 -4.359193050406395 treat image : temp/1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906162_0.png resize: (173, 226) 1392063689 -2.2996827632151056 treat image : temp/1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906163_0.png resize: (219, 335) 1392063690 -2.7880344403513213 treat image : temp/1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906164_0.png resize: (457, 474) 1392063691 -4.163012378330524 treat image : temp/1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906165_0.png resize: (300, 250) 1392063692 -3.486392128420129 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906168_0.png resize: (172, 194) 1392063693 -2.0420049407756173 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906169_0.png resize: (141, 166) 1392063694 -1.0502006083835127 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906170_0.png resize: (170, 158) 1392063695 -3.493942286839086 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906171_0.png resize: (373, 309) 1392063697 -3.2077800140105825 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906173_0.png resize: (323, 398) 1392063698 1.334401837153775 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906174_0.png resize: (154, 177) 1392063699 -3.2893238022060447 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906175_0.png resize: (603, 249) 1392063700 -3.7462119764404798 treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906177_0.png resize: (334, 233) 1392063701 -1.1067307775086623 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906180_0.png resize: (90, 248) 1392063702 -3.4035507459690755 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906181_0.png resize: (376, 495) 1392063703 -2.962200565439371 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906182_0.png resize: (216, 154) 1392063704 -3.7244436011596926 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906183_0.png resize: (418, 283) 1392063705 -2.395230829550088 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906184_0.png resize: (210, 153) 1392063706 -3.880406458537413 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906185_0.png resize: (403, 422) 1392063707 -3.3161794190123524 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906186_0.png resize: (368, 340) 1392063708 -4.307159775351035 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906187_0.png resize: (298, 199) 1392063709 -4.600561106059499 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906188_0.png resize: (493, 687) 1392063710 -4.7525026843681095 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906189_0.png resize: (221, 178) 1392063711 -2.3064036546328293 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906191_0.png resize: (710, 581) 1392063712 -4.413554962679242 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906192_0.png resize: (216, 212) 1392063713 -3.055762113183705 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906193_0.png resize: (190, 136) 1392063714 -1.9275806836695304 treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906194_0.png resize: (163, 99) 1392063715 -2.30324979664164 treat image : temp/1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906195_0.png resize: (236, 335) 1392063716 1.9229733372477156 treat image : temp/1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906197_0.png resize: (313, 552) 1392063717 -3.257991658320338 treat image : temp/1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906199_0.png resize: (463, 283) 1392063718 -3.32274449233658 treat image : temp/1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906200_0.png resize: (531, 496) 1392063719 -4.390183990260745 treat image : temp/1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906201_0.png resize: (311, 314) 1392063720 2.0136784642261856 treat image : temp/1762177828_1951692_1389748226_4b8aaf06323dc064faa7ba19d3e426e8_rle_crop_4002906202_0.png resize: (495, 269) 1392063721 -4.010701186441537 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906204_0.png resize: (306, 476) 1392063722 -2.8363369376167498 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906205_0.png resize: (68, 194) 1392063723 -3.0877539514147245 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906207_0.png resize: (364, 288) 1392063724 1.9563990445269814 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906208_0.png resize: (260, 303) 1392063725 -3.6465667457877706 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906209_0.png resize: (348, 213) 1392063726 1.6430180024234946 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906210_0.png resize: (179, 240) 1392063727 -1.7006853009266432 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4002906211_0.png resize: (105, 273) 1392063728 -1.4923720842591548 treat image : temp/1762177828_1951692_1389748218_5d1263aee4e63134047c4b0a247f3b98_rle_crop_4016612025_0.png resize: (575, 620) 1392063730 -3.6439336897448316 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906212_0.png resize: (326, 478) 1392063731 -3.9680709165138026 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906213_0.png resize: (601, 264) 1392063732 -3.640136063003992 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906214_0.png resize: (331, 180) 1392063733 -2.9834346677799335 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906215_0.png resize: (149, 185) 1392063734 -2.989454738892622 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906218_0.png resize: (180, 275) 1392063735 -2.1301755276403496 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906219_0.png resize: (486, 520) 1392063736 1.5298991159984578 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906220_0.png resize: (138, 194) 1392063739 -4.22495889226479 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906221_0.png resize: (255, 266) 1392063740 -0.8904111960863232 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906222_0.png resize: (112, 136) 1392063741 -3.591469007119385 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906223_0.png resize: (411, 361) 1392063742 -3.4343711881142553 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906224_0.png resize: (239, 363) 1392063743 -3.5812077175104005 treat image : temp/1762177828_1951692_1389748200_34164b202130cda7d017728264c4b774_rle_crop_4002906225_0.png resize: (89, 183) 1392063744 -2.5827618207808882 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906226_0.png resize: (235, 290) 1392063745 -1.9056109225071318 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906227_0.png resize: (409, 563) 1392063746 -3.763421609939892 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906228_0.png resize: (276, 320) 1392063747 -3.348897462330367 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906229_0.png resize: (393, 343) 1392063749 2.0316600790639088 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906230_0.png resize: (257, 317) 1392063750 -2.7479804489259276 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906231_0.png resize: (262, 84) 1392063751 -3.412581771570703 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906232_0.png resize: (397, 479) 1392063752 -4.113636793766566 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906233_0.png resize: (166, 171) 1392063753 -2.7909661310468756 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906234_0.png resize: (222, 169) 1392063754 -3.287716262506468 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906235_0.png resize: (736, 835) 1392063755 -5.272692338368448 treat image : temp/1762177828_1951692_1389748196_ced5150fefd4e4c97346c31deacabe9a_rle_crop_4002906236_0.png resize: (382, 188) 1392063756 -3.630582611966542 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906237_0.png resize: (284, 289) 1392063757 -3.9474164716899223 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906238_0.png resize: (129, 275) 1392063758 -4.253697309722057 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906239_0.png resize: (209, 308) 1392063759 -2.887030530820574 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906240_0.png resize: (215, 188) 1392063760 -2.1277295670190246 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906241_0.png resize: (217, 215) 1392063761 -2.7715963408962003 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906243_0.png resize: (332, 899) 1392063762 -4.219944947695901 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906245_0.png resize: (665, 1178) 1392063763 -5.017224950255509 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906246_0.png resize: (340, 256) 1392063764 -2.043354431018626 treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4003028104_0.png resize: (126, 232) 1392063766 -4.468437728360238 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905911_0.png resize: (97, 224) 1392063804 -4.107198288734105 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905915_0.png resize: (144, 232) 1392063805 -3.4246937468335616 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905917_0.png resize: (253, 215) 1392063806 -3.1011031187020266 treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905926_0.png resize: (156, 389) 1392063807 -4.525981475203158 treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575_rle_crop_4002905935_0.png resize: (249, 281) 1392063808 -3.2253083359397126 treat image : 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temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906176_0.png resize: (203, 93) 1392063858 -4.43489382290934 treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906071_0.png resize: (156, 248) 1392063863 -3.8751217775027316 treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906135_0.png resize: (190, 246) 1392063864 -4.689959793074689 treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906149_0.png resize: (123, 161) 1392063865 -2.933320826602753 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 : 361 time used for this insertion : 0.038593292236328125 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 361 time used for this insertion : 0.06869387626647949 save missing photos in datou_result : time spend for datou_step_exec : 101.53897309303284 time spend to save output : 0.11498737335205078 total time spend for step 6 : 101.65396046638489 step7:brightness Mon Nov 3 15:04:04 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/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4.jpg treat image : temp/1762177828_1951692_1389748404_de31a8ec5f3d454d76252c97c3eaa575.jpg treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669.jpg treat image : temp/1762177828_1951692_1389748380_be262f6dea1660602791866d9d0b35a0.jpg treat image : temp/1762177828_1951692_1389748377_480a0e478d796883dc93dd3ad039f238.jpg treat image : temp/1762177828_1951692_1389748373_6376a6bf0285b2d008781f694abd1dbd.jpg treat image : temp/1762177828_1951692_1389748366_65c731273ce9c7735f2536fe1230f8f8.jpg treat image : temp/1762177828_1951692_1389748359_0ff3dab461c47487e64d9a2c6942d9a2.jpg treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b.jpg treat image : temp/1762177828_1951692_1389748323_d94a48cd87836ca49d41bebfc1e64ed5.jpg treat image : 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temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906244_0.png treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906138_0.png treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905914_0.png treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905916_0.png treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905922_0.png treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4002905923_0.png treat image : temp/1762177828_1951692_1389748383_a502e136980c7150260b13a9311b7669_rle_crop_4002905953_0.png treat image : temp/1762177828_1951692_1389748330_0b4dd8f95dce2d4d0912ff6515fc6f7b_rle_crop_4002906021_0.png treat image : temp/1762177828_1951692_1389748317_fec8638673444abed666e3144ea7ce2d_rle_crop_4002906057_0.png treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906074_0.png treat image : temp/1762177828_1951692_1389748297_657639f36720f0e776f3e4f857c10fdb_rle_crop_4002906097_0.png treat image : temp/1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906159_0.png treat image : temp/1762177828_1951692_1389748249_5852d0d064df597a140439a760660212_rle_crop_4002906166_0.png treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906172_0.png treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906178_0.png treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906179_0.png treat image : temp/1762177828_1951692_1389748234_314680078ede2b4e4edf41cee788194a_rle_crop_4002906190_0.png treat image : temp/1762177828_1951692_1389748192_31b32dce5dd80fc9935d60630799123e_rle_crop_4002906242_0.png treat image : temp/1762177828_1951692_1389748410_4a73fb1c068337949c992b1a84016cf4_rle_crop_4003028083_0.png treat image : temp/1762177828_1951692_1389748264_17c3485492997bfb1c91362fe36b550a_rle_crop_4002906158_0.png treat image : temp/1762177828_1951692_1389748240_d766660713fdce26ee06815708a6e019_rle_crop_4002906176_0.png treat image : temp/1762177828_1951692_1389748305_87cf59bb88295fd03fd50541c9b768f1_rle_crop_4002906071_0.png treat image : temp/1762177828_1951692_1389748273_36c30c2dc3b9d339ec19e821514e9ce9_rle_crop_4002906135_0.png treat image : temp/1762177828_1951692_1389748271_e64a52949dcb0ce4ef0845da383848e6_rle_crop_4002906149_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 : 361 time used for this insertion : 0.03640174865722656 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 361 time used for this insertion : 0.0839080810546875 save missing photos in datou_result : time spend for datou_step_exec : 27.42746663093567 time spend to save output : 0.12750577926635742 total time spend for step 7 : 27.554972410202026 step8:velours_tree Mon Nov 3 15:04:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.41021037101745605 time spend to save output : 0.0003762245178222656 total time spend for step 8 : 0.4105865955352783 step9:send_mail_cod Mon Nov 3 15:04:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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_P27900917_03-11-2025_15_04_34.pdf 28099877 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette280998771762178674 28099878 imagette280998781762178675 28099879 imagette280998791762178675 28099880 change filename to text .change filename to text .change filename to text .imagette280998801762178675 28099881 change filename to text .imagette280998811762178676 28099883 change filename to text .change filename to text .change filename to text .imagette280998831762178676 28099884 imagette280998841762178676 28099885 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette280998851762178676 28099886 imagette280998861762178678 28099887 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette280998871762178678 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=27900917 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/28099877,28099878,28099879,28099880,28099881,28099882,28099883,28099884,28099885,28099886,28099887?tags=pet_clair,pehd,flou,pet_fonce,metal,environnement,autre,mal_croppe,carton,background,papier args[1389748410] : ((1389748410, -5.867310206046132, 492609224), (1389748410, 0.8811830480357193, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748404] : ((1389748404, -6.897693294336126, 492609224), (1389748404, 1.8516838549521923, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748383] : ((1389748383, -6.969195672929006, 492609224), (1389748383, 1.612812211206837, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748380] : ((1389748380, -6.909041637188264, 492609224), (1389748380, 1.4099629706692385, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748377] : ((1389748377, -6.873071565597251, 492609224), (1389748377, 1.5614013437569303, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748373] : ((1389748373, -6.923142172913038, 492609224), (1389748373, 1.6703954955412703, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748366] : ((1389748366, -6.940568738519625, 492609224), (1389748366, 1.8845563178320799, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748359] : ((1389748359, -6.9210620667313645, 492609224), (1389748359, 1.9051238931049814, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748330] : ((1389748330, -6.886665361097833, 492609224), (1389748330, 2.276426884150155, 492649241), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748323] : ((1389748323, -6.810717651969815, 492609224), (1389748323, 1.583951451700927, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748317] : ((1389748317, -6.691049952875845, 492609224), (1389748317, 1.7141408585494555, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748305] : ((1389748305, -7.034119276088728, 492609224), (1389748305, 2.1784695875143747, 492649241), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748297] : ((1389748297, -7.037780069325714, 492609224), (1389748297, 2.177993974746006, 492649241), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748285] : ((1389748285, -6.9783735389352355, 492609224), (1389748285, 1.7394558470465744, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748281] : ((1389748281, -6.639740856749241, 492609224), (1389748281, 1.746413607311716, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748273] : ((1389748273, -6.968356906853137, 492609224), (1389748273, 1.4254359728576065, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748271] : ((1389748271, -7.004160515189696, 492609224), (1389748271, 1.4265581580600821, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748264] : ((1389748264, -6.991417633863455, 492609224), (1389748264, 1.9528548323348622, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748249] : ((1389748249, -6.8178560205413685, 492609224), (1389748249, 1.880310925233681, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748240] : ((1389748240, -6.932047140178858, 492609224), (1389748240, 1.9186805591482263, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748234] : ((1389748234, -6.680653720325641, 492609224), (1389748234, 1.2016863146267993, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748226] : ((1389748226, -6.950875279006611, 492609224), (1389748226, 1.7287227262421654, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748218] : ((1389748218, -6.93116503502784, 492609224), (1389748218, 2.0350088822983667, 492649241), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748200] : ((1389748200, -6.762416252508783, 492609224), (1389748200, 1.3427970836228993, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748196] : ((1389748196, -6.846770994905227, 492609224), (1389748196, 1.6478366693285862, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com args[1389748192] : ((1389748192, -5.9057524058001, 492609224), (1389748192, 0.7626019033015754, 2107752395), '0.09267584616868467') We are sending mail with results at report@fotonower.com refus_total : 0.09267584616868467 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos_view ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=27900917 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900917_03-11-2025_15_04_34.pdf results_Auto_P27900917_03-11-2025_15_04_34.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900917_03-11-2025_15_04_34.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','27900917','results_Auto_P27900917_03-11-2025_15_04_34.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900917_03-11-2025_15_04_34.pdf','pdf','','1.3','0.09267584616868467') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/27900917

https://www.fotonower.com/image?json=false&list_photos_id=1389748410
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748404
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748383
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748380
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748377
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748373
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748366
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748359
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748330
La photo est trop lumineuse, merci de reprendre une photo.(avec le score = 2.276426884150155,le score doit entre -2 et 2)
https://www.fotonower.com/image?json=false&list_photos_id=1389748323
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748317
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748305
La photo est trop lumineuse, merci de reprendre une photo.(avec le score = 2.1784695875143747,le score doit entre -2 et 2)
https://www.fotonower.com/image?json=false&list_photos_id=1389748297
La photo est trop lumineuse, merci de reprendre une photo.(avec le score = 2.177993974746006,le score doit entre -2 et 2)
https://www.fotonower.com/image?json=false&list_photos_id=1389748285
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748281
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748273
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748271
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748264
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748249
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748240
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748234
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748226
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748218
La photo est trop lumineuse, merci de reprendre une photo.(avec le score = 2.0350088822983667,le score doit entre -2 et 2)
https://www.fotonower.com/image?json=false&list_photos_id=1389748200
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748196
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1389748192
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 9.27%
Veuillez trouver les photos des contaminants.

exemples de contaminants: pet_clair: https://www.fotonower.com/view/28099877?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/28099880?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/28099881?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/28099883?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/28099885?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/28099887?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900917_03-11-2025_15_04_34.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/28099877,28099878,28099879,28099880,28099881,28099882,28099883,28099884,28099885,28099886,28099887?tags=pet_clair,pehd,flou,pet_fonce,metal,environnement,autre,mal_croppe,carton,background,papier.


L'équipe Fotonower 202 b'' Server: nginx Date: Mon, 03 Nov 2025 14:04:45 GMT Content-Length: 0 Connection: close X-Message-Id: 3a1GA_IkSrmHcE4iE0JtLw 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 [1389748410, 1389748404, 1389748383, 1389748380, 1389748377, 1389748373, 1389748366, 1389748359, 1389748330, 1389748323, 1389748317, 1389748305, 1389748297, 1389748285, 1389748281, 1389748273, 1389748271, 1389748264, 1389748249, 1389748240, 1389748234, 1389748226, 1389748218, 1389748200, 1389748196, 1389748192] 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, '3985636') ('3318', '27900917', '1389748410', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748404', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748383', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748380', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748377', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748373', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748366', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748359', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748330', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748323', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748317', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748305', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748297', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748285', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748281', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748273', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748271', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748264', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748249', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748240', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748234', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748226', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748218', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748200', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748196', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748192', None, None, None, None, None, '3985636') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 26 time used for this insertion : 0.021335601806640625 save_final save missing photos in datou_result : time spend for datou_step_exec : 12.741284370422363 time spend to save output : 0.021784305572509766 total time spend for step 9 : 12.763068675994873 step10:split_time_score Mon Nov 3 15:04:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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'}] (('12', 26),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 16102025 27900917 Nombre de photos uploadées : 26 / 23040 (0%) 16102025 27900917 Nombre de photos taguées (types de déchets): 0 / 26 (0%) 16102025 27900917 Nombre de photos taguées (volume) : 0 / 26 (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 0.0013175010681152344 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.21640372276306152 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 : 0.029187932358634608 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27861244_16-10-2025_07_22_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27861244 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`=27861244 AND mptpi.`type`=3726 To do Qualite : 0.014203875804198495 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27861249_16-10-2025_07_16_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27861249 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`=27861249 AND mptpi.`type`=3726 To do Qualite : 0.03395851486180369 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900874_18-10-2025_11_41_44.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27900874 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`=27900874 AND mptpi.`type`=3726 To do Qualite : 0.01840036713885808 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900898_18-10-2025_09_55_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27900898 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`=27900898 AND mptpi.`type`=3726 To do Qualite : 0.03457118685661461 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900901_29-10-2025_16_27_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27900901 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`=27900901 AND mptpi.`type`=3726 To do Qualite : 0.09267584616868467 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900917_03-11-2025_15_04_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27900917 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`=27900917 AND mptpi.`type`=3594 To do Qualite : 0.09000460436875367 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900923_29-10-2025_16_49_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27900923 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`=27900923 AND mptpi.`type`=3594 To do Qualite : 0.09063382303942201 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900927_29-10-2025_16_35_17.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27900927 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`=27900927 AND mptpi.`type`=3594 To do Qualite : 0.11539575197073881 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27883564_17-10-2025_14_57_16.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27883564 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`=27883564 AND mptpi.`type`=3726 To do Qualite : 0.05682449738511661 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27883565_17-10-2025_15_24_04.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27883565 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`=27883565 AND mptpi.`type`=3594 To do Qualite : 0.09289645222479426 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27883593_17-10-2025_15_13_22.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27883593 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`=27883593 AND mptpi.`type`=3594 To do Qualite : 0.08176132301879085 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27883596_17-10-2025_15_08_20.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27883596 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`=27883596 AND mptpi.`type`=3594 To do Qualite : 0.026251479202423232 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27900938_30-10-2025_17_42_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27900938 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`=27900938 AND mptpi.`type`=3726 To do Qualite : 0.10726960358796293 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28056026_01-11-2025_09_33_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28056026 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`=28056026 AND mptpi.`type`=3594 To do Qualite : 0.07806623746141975 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28056072_01-11-2025_09_25_20.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28056072 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`=28056072 AND mptpi.`type`=3594 To do Qualite : 0.09821116735320613 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28056075_01-11-2025_09_47_32.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28056075 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`=28056075 AND mptpi.`type`=3726 To do Qualite : 0.008941362819152698 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P28056098_01-11-2025_11_09_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 28056098 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`=28056098 AND mptpi.`type`=3726 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'16102025': {'nb_upload': 26, '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 [1389748410, 1389748404, 1389748383, 1389748380, 1389748377, 1389748373, 1389748366, 1389748359, 1389748330, 1389748323, 1389748317, 1389748305, 1389748297, 1389748285, 1389748281, 1389748273, 1389748271, 1389748264, 1389748249, 1389748240, 1389748234, 1389748226, 1389748218, 1389748200, 1389748196, 1389748192] Looping around the photos to save general results len do output : 1 /27900917Didn'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, '3985636') ('3318', '27900917', '1389748410', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748404', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748383', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748380', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748377', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748373', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748366', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748359', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748330', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748323', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748317', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748305', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748297', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748285', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748281', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748273', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748271', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748264', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748249', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748240', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748234', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748226', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748218', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748200', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748196', None, None, None, None, None, '3985636') ('3318', None, None, None, None, None, None, None, '3985636') ('3318', '27900917', '1389748192', None, None, None, None, None, '3985636') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 27 time used for this insertion : 0.01879429817199707 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.4949054718017578 time spend to save output : 0.019130229949951172 total time spend for step 10 : 1.514035701751709 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 26 set_done_treatment 425.66user 338.18system 14:22.50elapsed 88%CPU (0avgtext+0avgdata 7831304maxresident)k 1785376inputs+339056outputs (35848major+37418763minor)pagefaults 0swaps