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 : 803035 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 : ['3759017'] with mtr_portfolio_ids : ['27096242'] and first list_photo_ids : [] new path : /proc/803035/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 34 ; length of list_pids : 34 ; length of list_args : 34 time to download the photos : 4.78110408782959 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 Sep 22 16:10:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10586 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-09-22 16:10:37.448698: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-09-22 16:10:37.472719: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3492910000 Hz 2025-09-22 16:10:37.474658: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f02c4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-09-22 16:10:37.474718: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-09-22 16:10:37.478561: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-09-22 16:10:37.628280: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x85aa3d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-09-22 16:10:37.628354: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-09-22 16:10:37.629654: 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 2025-09-22 16:10:37.630147: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-22 16:10:37.651850: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-22 16:10:37.665967: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-22 16:10:37.666403: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-22 16:10:37.685066: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-22 16:10:37.689034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-22 16:10:37.728261: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-22 16:10:37.730109: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-22 16:10:37.730562: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-22 16:10:37.731512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-22 16:10:37.731531: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-22 16:10:37.731542: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-22 16:10:37.733540: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9805 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. 2025-09-22 16:10:37.985654: 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 2025-09-22 16:10:37.985762: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-22 16:10:37.985779: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-22 16:10:37.985794: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-22 16:10:37.985807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-22 16:10:37.985821: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-22 16:10:37.985834: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-22 16:10:37.985848: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-22 16:10:37.987006: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-22 16:10:37.988154: 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 2025-09-22 16:10:37.988183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-09-22 16:10:37.988197: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-22 16:10:37.988209: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-09-22 16:10:37.988222: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-09-22 16:10:37.988234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-09-22 16:10:37.988246: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-09-22 16:10:37.988259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-09-22 16:10:37.989411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-09-22 16:10:37.989445: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-09-22 16:10:37.989453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-09-22 16:10:37.989460: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-09-22 16:10:37.990648: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9805 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 : [] 2025-09-22 16:10:47.660874: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-09-22 16:10:47.851867: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 34 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 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: 1920.00000 nb d'objets trouves : 14 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 22.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 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: 1920.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 3.00000 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: 1920.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 21.00000 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: 1920.00000 nb d'objets trouves : 6 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 30.00000 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: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 41.00000 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: 1920.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 18 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 31.00000 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: 1920.00000 nb d'objets trouves : 14 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 41.00000 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: 1920.00000 nb d'objets trouves : 13 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 28.00000 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: 1920.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 30.00000 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: 1920.00000 nb d'objets trouves : 10 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 4.00000 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: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 18.00000 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: 1920.00000 nb d'objets trouves : 25 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 35.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 39.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 34.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 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: 1920.00000 nb d'objets trouves : 24 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 24.00000 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: 1920.00000 nb d'objets trouves : 7 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 5 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 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: 1920.00000 nb d'objets trouves : 9 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 29.00000 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: 1920.00000 nb d'objets trouves : 8 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 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: 1920.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 26.00000 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: 1920.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 17 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 32.00000 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: 1920.00000 nb d'objets trouves : 16 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 17.00000 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: 1920.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 33.00000 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: 1920.00000 nb d'objets trouves : 20 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 23.00000 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: 1920.00000 nb d'objets trouves : 11 NEW PHOTO Processing 1 images image shape: (1080, 1920, 3) min: 27.00000 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: 1920.00000 nb d'objets trouves : 11 Detection mask done ! Trying to reset tf kernel 803621 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5294 tf kernel not reseted sub process len(results) : 34 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 34 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 : 10586 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 : 0.0002598762512207031 nb_pixel_total : 6633 time to create 1 rle with old method : 0.008090734481811523 length of segment : 74 time for calcul the mask position with numpy : 0.0002541542053222656 nb_pixel_total : 10672 time to create 1 rle with old method : 0.013817310333251953 length of segment : 147 time for calcul the mask position with numpy : 0.00044655799865722656 nb_pixel_total : 11794 time to create 1 rle with old method : 0.020764827728271484 length of segment : 179 time for calcul the mask position with numpy : 0.004258394241333008 nb_pixel_total : 177400 time to create 1 rle with new method : 0.013553380966186523 length of segment : 787 time for calcul the mask position with numpy : 0.00029015541076660156 nb_pixel_total : 9118 time to create 1 rle with old method : 0.015662431716918945 length of segment : 100 time for calcul the mask position with numpy : 0.00045752525329589844 nb_pixel_total : 22182 time to create 1 rle with old method : 0.03675413131713867 length of segment : 174 time for calcul the mask position with numpy : 0.00011801719665527344 nb_pixel_total : 3510 time to create 1 rle with old method : 0.004340410232543945 length of segment : 71 time for calcul the mask position with numpy : 0.0015375614166259766 nb_pixel_total : 107400 time to create 1 rle with old method : 0.11841821670532227 length of segment : 513 time for calcul the mask position with numpy : 0.00021004676818847656 nb_pixel_total : 11212 time to create 1 rle with old method : 0.012607812881469727 length of segment : 149 time for calcul the mask position with numpy : 0.0002532005310058594 nb_pixel_total : 11011 time to create 1 rle with old method : 0.012476205825805664 length of segment : 197 time for calcul the mask position with numpy : 0.00012445449829101562 nb_pixel_total : 4767 time to create 1 rle with old method : 0.005346059799194336 length of segment : 134 time for calcul the mask position with numpy : 0.019054412841796875 nb_pixel_total : 1005876 time to create 1 rle with new method : 0.051633358001708984 length of segment : 1656 time for calcul the mask position with numpy : 0.00018715858459472656 nb_pixel_total : 11131 time to create 1 rle with old method : 0.01221156120300293 length of segment : 155 time for calcul the mask position with numpy : 0.000217437744140625 nb_pixel_total : 12482 time to create 1 rle with old method : 0.013668060302734375 length of segment : 187 time for calcul the mask position with numpy : 0.00013685226440429688 nb_pixel_total : 6744 time to create 1 rle with old method : 0.007790327072143555 length of segment : 166 time for calcul the mask position with numpy : 0.00034332275390625 nb_pixel_total : 22118 time to create 1 rle with old method : 0.026134252548217773 length of segment : 234 time for calcul the mask position with numpy : 0.0004353523254394531 nb_pixel_total : 23068 time to create 1 rle with old method : 0.025321006774902344 length of segment : 220 time for calcul the mask position with numpy : 0.0003306865692138672 nb_pixel_total : 23897 time to create 1 rle with old method : 0.02632427215576172 length of segment : 228 time for calcul the mask position with numpy : 0.00026726722717285156 nb_pixel_total : 15541 time to create 1 rle with old method : 0.01884174346923828 length of segment : 127 time for calcul the mask position with numpy : 0.00023889541625976562 nb_pixel_total : 12911 time to create 1 rle with old method : 0.014944791793823242 length of segment : 184 time for calcul the mask position with numpy : 0.00010156631469726562 nb_pixel_total : 4932 time to create 1 rle with old method : 0.005956411361694336 length of segment : 75 time for calcul the mask position with numpy : 0.0003981590270996094 nb_pixel_total : 26609 time to create 1 rle with old method : 0.030666589736938477 length of segment : 236 time for calcul the mask position with numpy : 0.00013208389282226562 nb_pixel_total : 6176 time to create 1 rle with old method : 0.007133007049560547 length of segment : 126 time for calcul the mask position with numpy : 0.00013113021850585938 nb_pixel_total : 5754 time to create 1 rle with old method : 0.008280277252197266 length of segment : 109 time for calcul the mask position with numpy : 0.00024127960205078125 nb_pixel_total : 13305 time to create 1 rle with old method : 0.015115976333618164 length of segment : 189 time for calcul the mask position with numpy : 0.00042700767517089844 nb_pixel_total : 31766 time to create 1 rle with old method : 0.036058902740478516 length of segment : 203 time for calcul the mask position with numpy : 0.00020885467529296875 nb_pixel_total : 13472 time to create 1 rle with old method : 0.015423297882080078 length of segment : 124 time for calcul the mask position with numpy : 0.0001685619354248047 nb_pixel_total : 9750 time to create 1 rle with old method : 0.01116180419921875 length of segment : 116 time for calcul the mask position with numpy : 0.0002758502960205078 nb_pixel_total : 15418 time to create 1 rle with old method : 0.0173490047454834 length of segment : 196 time for calcul the mask position with numpy : 0.0002605915069580078 nb_pixel_total : 17301 time to create 1 rle with old method : 0.0207064151763916 length of segment : 147 time for calcul the mask position with numpy : 0.0001380443572998047 nb_pixel_total : 6022 time to create 1 rle with old method : 0.00687718391418457 length of segment : 119 time for calcul the mask position with numpy : 0.00012540817260742188 nb_pixel_total : 7309 time to create 1 rle with old method : 0.008744001388549805 length of segment : 99 time for calcul the mask position with numpy : 0.001676321029663086 nb_pixel_total : 121300 time to create 1 rle with old method : 0.13727641105651855 length of segment : 582 time for calcul the mask position with numpy : 0.00020933151245117188 nb_pixel_total : 6732 time to create 1 rle with old method : 0.0077114105224609375 length of segment : 69 time for calcul the mask position with numpy : 0.0004563331604003906 nb_pixel_total : 13503 time to create 1 rle with old method : 0.01566600799560547 length of segment : 191 time for calcul the mask position with numpy : 0.00018477439880371094 nb_pixel_total : 5045 time to create 1 rle with old method : 0.005953073501586914 length of segment : 70 time for calcul the mask position with numpy : 0.0004849433898925781 nb_pixel_total : 16765 time to create 1 rle with old method : 0.019993305206298828 length of segment : 157 time for calcul the mask position with numpy : 0.002274751663208008 nb_pixel_total : 123691 time to create 1 rle with old method : 0.13850116729736328 length of segment : 549 time for calcul the mask position with numpy : 0.0004899501800537109 nb_pixel_total : 12998 time to create 1 rle with old method : 0.015163421630859375 length of segment : 186 time for calcul the mask position with numpy : 0.00019407272338867188 nb_pixel_total : 3861 time to create 1 rle with old method : 0.004852294921875 length of segment : 69 time for calcul the mask position with numpy : 0.0007598400115966797 nb_pixel_total : 24813 time to create 1 rle with old method : 0.02863454818725586 length of segment : 174 time for calcul the mask position with numpy : 0.0003190040588378906 nb_pixel_total : 7728 time to create 1 rle with old method : 0.009093761444091797 length of segment : 118 time for calcul the mask position with numpy : 0.0003790855407714844 nb_pixel_total : 10382 time to create 1 rle with old method : 0.01222991943359375 length of segment : 126 time for calcul the mask position with numpy : 0.00022172927856445312 nb_pixel_total : 13074 time to create 1 rle with old method : 0.01452493667602539 length of segment : 185 time for calcul the mask position with numpy : 0.0001556873321533203 nb_pixel_total : 7873 time to create 1 rle with old method : 0.009125947952270508 length of segment : 102 time for calcul the mask position with numpy : 0.0006201267242431641 nb_pixel_total : 39969 time to create 1 rle with old method : 0.04527735710144043 length of segment : 356 time for calcul the mask position with numpy : 0.0007109642028808594 nb_pixel_total : 19647 time to create 1 rle with old method : 0.02379441261291504 length of segment : 181 time for calcul the mask position with numpy : 0.00024271011352539062 nb_pixel_total : 5873 time to create 1 rle with old method : 0.006762981414794922 length of segment : 110 time for calcul the mask position with numpy : 0.002493619918823242 nb_pixel_total : 113894 time to create 1 rle with old method : 0.12702727317810059 length of segment : 531 time for calcul the mask position with numpy : 0.0005249977111816406 nb_pixel_total : 18868 time to create 1 rle with old method : 0.021955490112304688 length of segment : 158 time for calcul the mask position with numpy : 0.0006115436553955078 nb_pixel_total : 24264 time to create 1 rle with old method : 0.026971101760864258 length of segment : 178 time for calcul the mask position with numpy : 0.0002751350402832031 nb_pixel_total : 6894 time to create 1 rle with old method : 0.007878303527832031 length of segment : 133 time for calcul the mask position with numpy : 0.00020885467529296875 nb_pixel_total : 12459 time to create 1 rle with old method : 0.013272523880004883 length of segment : 183 time for calcul the mask position with numpy : 0.0005593299865722656 nb_pixel_total : 13129 time to create 1 rle with old method : 0.015342950820922852 length of segment : 342 time for calcul the mask position with numpy : 0.00016689300537109375 nb_pixel_total : 2656 time to create 1 rle with old method : 0.003122568130493164 length of segment : 125 time for calcul the mask position with numpy : 0.0002651214599609375 nb_pixel_total : 12153 time to create 1 rle with old method : 0.013909339904785156 length of segment : 127 time for calcul the mask position with numpy : 0.0025773048400878906 nb_pixel_total : 110779 time to create 1 rle with old method : 0.12450671195983887 length of segment : 519 time for calcul the mask position with numpy : 0.0012221336364746094 nb_pixel_total : 35376 time to create 1 rle with old method : 0.03891253471374512 length of segment : 303 time for calcul the mask position with numpy : 0.0007176399230957031 nb_pixel_total : 22944 time to create 1 rle with old method : 0.02600240707397461 length of segment : 167 time for calcul the mask position with numpy : 0.0004115104675292969 nb_pixel_total : 11459 time to create 1 rle with old method : 0.013025760650634766 length of segment : 175 time for calcul the mask position with numpy : 0.0009338855743408203 nb_pixel_total : 36632 time to create 1 rle with old method : 0.04135870933532715 length of segment : 319 time for calcul the mask position with numpy : 0.00016117095947265625 nb_pixel_total : 3520 time to create 1 rle with old method : 0.004237651824951172 length of segment : 44 time for calcul the mask position with numpy : 0.0002887248992919922 nb_pixel_total : 7000 time to create 1 rle with old method : 0.008091926574707031 length of segment : 120 time for calcul the mask position with numpy : 0.00042057037353515625 nb_pixel_total : 12181 time to create 1 rle with old method : 0.013910293579101562 length of segment : 142 time for calcul the mask position with numpy : 0.0004787445068359375 nb_pixel_total : 12977 time to create 1 rle with old method : 0.015247821807861328 length of segment : 183 time for calcul the mask position with numpy : 0.00032520294189453125 nb_pixel_total : 6642 time to create 1 rle with old method : 0.0077974796295166016 length of segment : 162 time for calcul the mask position with numpy : 0.0003426074981689453 nb_pixel_total : 10158 time to create 1 rle with old method : 0.011858940124511719 length of segment : 126 time for calcul the mask position with numpy : 0.00046062469482421875 nb_pixel_total : 16885 time to create 1 rle with old method : 0.019365549087524414 length of segment : 212 time for calcul the mask position with numpy : 0.00011563301086425781 nb_pixel_total : 2433 time to create 1 rle with old method : 0.0030510425567626953 length of segment : 43 time for calcul the mask position with numpy : 0.00011157989501953125 nb_pixel_total : 1787 time to create 1 rle with old method : 0.0022935867309570312 length of segment : 45 time for calcul the mask position with numpy : 0.0004792213439941406 nb_pixel_total : 12141 time to create 1 rle with old method : 0.01401209831237793 length of segment : 179 time for calcul the mask position with numpy : 0.00021386146545410156 nb_pixel_total : 5180 time to create 1 rle with old method : 0.006120204925537109 length of segment : 75 time for calcul the mask position with numpy : 0.00017452239990234375 nb_pixel_total : 5241 time to create 1 rle with old method : 0.006340742111206055 length of segment : 58 time for calcul the mask position with numpy : 0.00015544891357421875 nb_pixel_total : 6563 time to create 1 rle with old method : 0.0074748992919921875 length of segment : 89 time for calcul the mask position with numpy : 0.0003981590270996094 nb_pixel_total : 14573 time to create 1 rle with old method : 0.016823768615722656 length of segment : 141 time for calcul the mask position with numpy : 0.0003440380096435547 nb_pixel_total : 15583 time to create 1 rle with old method : 0.017650127410888672 length of segment : 119 time for calcul the mask position with numpy : 0.00020241737365722656 nb_pixel_total : 6887 time to create 1 rle with old method : 0.007925033569335938 length of segment : 91 time for calcul the mask position with numpy : 0.0001709461212158203 nb_pixel_total : 7028 time to create 1 rle with old method : 0.007803916931152344 length of segment : 224 time for calcul the mask position with numpy : 0.0014650821685791016 nb_pixel_total : 25103 time to create 1 rle with old method : 0.04101705551147461 length of segment : 495 time for calcul the mask position with numpy : 0.0004165172576904297 nb_pixel_total : 11224 time to create 1 rle with old method : 0.013085365295410156 length of segment : 151 time for calcul the mask position with numpy : 0.00032806396484375 nb_pixel_total : 10037 time to create 1 rle with old method : 0.011595487594604492 length of segment : 102 time for calcul the mask position with numpy : 0.0022525787353515625 nb_pixel_total : 113156 time to create 1 rle with old method : 0.12744688987731934 length of segment : 523 time for calcul the mask position with numpy : 0.00018405914306640625 nb_pixel_total : 6360 time to create 1 rle with old method : 0.008018016815185547 length of segment : 160 time for calcul the mask position with numpy : 0.000396728515625 nb_pixel_total : 12956 time to create 1 rle with old method : 0.015361785888671875 length of segment : 188 time for calcul the mask position with numpy : 0.0005331039428710938 nb_pixel_total : 20329 time to create 1 rle with old method : 0.02416372299194336 length of segment : 207 time for calcul the mask position with numpy : 0.0008225440979003906 nb_pixel_total : 31480 time to create 1 rle with old method : 0.0365750789642334 length of segment : 203 time for calcul the mask position with numpy : 0.0003731250762939453 nb_pixel_total : 18976 time to create 1 rle with old method : 0.022013425827026367 length of segment : 180 time for calcul the mask position with numpy : 0.0002620220184326172 nb_pixel_total : 12044 time to create 1 rle with old method : 0.013863086700439453 length of segment : 144 time for calcul the mask position with numpy : 0.00029778480529785156 nb_pixel_total : 12334 time to create 1 rle with old method : 0.014279842376708984 length of segment : 179 time for calcul the mask position with numpy : 0.0003514289855957031 nb_pixel_total : 19510 time to create 1 rle with old method : 0.022704362869262695 length of segment : 160 time for calcul the mask position with numpy : 0.0002727508544921875 nb_pixel_total : 13844 time to create 1 rle with old method : 0.016344070434570312 length of segment : 125 time for calcul the mask position with numpy : 0.00017976760864257812 nb_pixel_total : 10412 time to create 1 rle with old method : 0.013441801071166992 length of segment : 62 time for calcul the mask position with numpy : 0.0004203319549560547 nb_pixel_total : 20260 time to create 1 rle with old method : 0.022696733474731445 length of segment : 120 time for calcul the mask position with numpy : 0.0019299983978271484 nb_pixel_total : 116642 time to create 1 rle with old method : 0.12540125846862793 length of segment : 542 time for calcul the mask position with numpy : 0.00011706352233886719 nb_pixel_total : 3939 time to create 1 rle with old method : 0.004586219787597656 length of segment : 76 time for calcul the mask position with numpy : 0.0004906654357910156 nb_pixel_total : 12568 time to create 1 rle with old method : 0.013702392578125 length of segment : 189 time for calcul the mask position with numpy : 0.0003123283386230469 nb_pixel_total : 6753 time to create 1 rle with old method : 0.0074939727783203125 length of segment : 123 time for calcul the mask position with numpy : 0.0005156993865966797 nb_pixel_total : 11476 time to create 1 rle with old method : 0.012433528900146484 length of segment : 199 time for calcul the mask position with numpy : 0.0004801750183105469 nb_pixel_total : 12795 time to create 1 rle with old method : 0.013881206512451172 length of segment : 186 time for calcul the mask position with numpy : 0.0007660388946533203 nb_pixel_total : 24198 time to create 1 rle with old method : 0.026647090911865234 length of segment : 278 time for calcul the mask position with numpy : 0.0005400180816650391 nb_pixel_total : 14363 time to create 1 rle with old method : 0.01575636863708496 length of segment : 207 time for calcul the mask position with numpy : 0.00026297569274902344 nb_pixel_total : 6610 time to create 1 rle with old method : 0.010553836822509766 length of segment : 91 time for calcul the mask position with numpy : 0.0004482269287109375 nb_pixel_total : 12580 time to create 1 rle with old method : 0.013549327850341797 length of segment : 183 time for calcul the mask position with numpy : 0.0003352165222167969 nb_pixel_total : 5969 time to create 1 rle with old method : 0.006805896759033203 length of segment : 108 time for calcul the mask position with numpy : 0.0004076957702636719 nb_pixel_total : 8753 time to create 1 rle with old method : 0.010509729385375977 length of segment : 97 time for calcul the mask position with numpy : 0.0005865097045898438 nb_pixel_total : 12362 time to create 1 rle with old method : 0.013661384582519531 length of segment : 190 time for calcul the mask position with numpy : 0.0003724098205566406 nb_pixel_total : 5510 time to create 1 rle with old method : 0.006109952926635742 length of segment : 119 time for calcul the mask position with numpy : 0.0010132789611816406 nb_pixel_total : 19704 time to create 1 rle with old method : 0.02141880989074707 length of segment : 206 time for calcul the mask position with numpy : 0.00041294097900390625 nb_pixel_total : 12647 time to create 1 rle with old method : 0.014248371124267578 length of segment : 139 time for calcul the mask position with numpy : 0.0003476142883300781 nb_pixel_total : 7185 time to create 1 rle with old method : 0.01579737663269043 length of segment : 118 time for calcul the mask position with numpy : 0.0004203319549560547 nb_pixel_total : 11020 time to create 1 rle with old method : 0.012652397155761719 length of segment : 82 time for calcul the mask position with numpy : 0.0005123615264892578 nb_pixel_total : 12708 time to create 1 rle with old method : 0.014932870864868164 length of segment : 200 time for calcul the mask position with numpy : 0.00043654441833496094 nb_pixel_total : 8089 time to create 1 rle with old method : 0.009243249893188477 length of segment : 249 time for calcul the mask position with numpy : 0.00038886070251464844 nb_pixel_total : 12565 time to create 1 rle with old method : 0.01397562026977539 length of segment : 185 time for calcul the mask position with numpy : 0.00018167495727539062 nb_pixel_total : 4399 time to create 1 rle with old method : 0.005330562591552734 length of segment : 69 time for calcul the mask position with numpy : 0.000194549560546875 nb_pixel_total : 4040 time to create 1 rle with old method : 0.00496983528137207 length of segment : 85 time for calcul the mask position with numpy : 0.00047016143798828125 nb_pixel_total : 12473 time to create 1 rle with old method : 0.01420903205871582 length of segment : 185 time for calcul the mask position with numpy : 0.0010464191436767578 nb_pixel_total : 27438 time to create 1 rle with old method : 0.031581878662109375 length of segment : 211 time for calcul the mask position with numpy : 0.00045108795166015625 nb_pixel_total : 12715 time to create 1 rle with old method : 0.01570415496826172 length of segment : 182 time for calcul the mask position with numpy : 0.00026535987854003906 nb_pixel_total : 6268 time to create 1 rle with old method : 0.007619380950927734 length of segment : 109 time for calcul the mask position with numpy : 0.00016951560974121094 nb_pixel_total : 2776 time to create 1 rle with old method : 0.0034301280975341797 length of segment : 75 time for calcul the mask position with numpy : 0.000286102294921875 nb_pixel_total : 10221 time to create 1 rle with old method : 0.01271510124206543 length of segment : 66 time for calcul the mask position with numpy : 0.0019316673278808594 nb_pixel_total : 107301 time to create 1 rle with old method : 0.12243032455444336 length of segment : 513 time for calcul the mask position with numpy : 0.0004870891571044922 nb_pixel_total : 12585 time to create 1 rle with old method : 0.01396322250366211 length of segment : 185 time for calcul the mask position with numpy : 0.00021767616271972656 nb_pixel_total : 3965 time to create 1 rle with old method : 0.004708528518676758 length of segment : 88 time for calcul the mask position with numpy : 0.0004189014434814453 nb_pixel_total : 12888 time to create 1 rle with old method : 0.014559745788574219 length of segment : 180 time for calcul the mask position with numpy : 0.0004038810729980469 nb_pixel_total : 12309 time to create 1 rle with old method : 0.01398158073425293 length of segment : 214 time for calcul the mask position with numpy : 0.0004444122314453125 nb_pixel_total : 13913 time to create 1 rle with old method : 0.01549220085144043 length of segment : 182 time for calcul the mask position with numpy : 0.00027251243591308594 nb_pixel_total : 12717 time to create 1 rle with old method : 0.01468658447265625 length of segment : 130 time for calcul the mask position with numpy : 0.0006723403930664062 nb_pixel_total : 17767 time to create 1 rle with old method : 0.020348310470581055 length of segment : 269 time for calcul the mask position with numpy : 0.0004775524139404297 nb_pixel_total : 12173 time to create 1 rle with old method : 0.013624429702758789 length of segment : 186 time for calcul the mask position with numpy : 0.0005824565887451172 nb_pixel_total : 24306 time to create 1 rle with old method : 0.02795863151550293 length of segment : 174 time for calcul the mask position with numpy : 0.0012271404266357422 nb_pixel_total : 36849 time to create 1 rle with old method : 0.04136085510253906 length of segment : 233 time for calcul the mask position with numpy : 0.0003275871276855469 nb_pixel_total : 9923 time to create 1 rle with old method : 0.01197052001953125 length of segment : 112 time for calcul the mask position with numpy : 0.0001628398895263672 nb_pixel_total : 7413 time to create 1 rle with old method : 0.008675813674926758 length of segment : 101 time for calcul the mask position with numpy : 0.0005018711090087891 nb_pixel_total : 12652 time to create 1 rle with old method : 0.014513254165649414 length of segment : 186 time for calcul the mask position with numpy : 0.0006473064422607422 nb_pixel_total : 25899 time to create 1 rle with old method : 0.030173540115356445 length of segment : 201 time for calcul the mask position with numpy : 0.0008676052093505859 nb_pixel_total : 37539 time to create 1 rle with old method : 0.04294466972351074 length of segment : 172 time for calcul the mask position with numpy : 0.00023317337036132812 nb_pixel_total : 2762 time to create 1 rle with old method : 0.0032749176025390625 length of segment : 100 time for calcul the mask position with numpy : 0.00035881996154785156 nb_pixel_total : 9906 time to create 1 rle with old method : 0.013173580169677734 length of segment : 150 time for calcul the mask position with numpy : 0.0003039836883544922 nb_pixel_total : 7898 time to create 1 rle with old method : 0.013786792755126953 length of segment : 78 time for calcul the mask position with numpy : 0.0031592845916748047 nb_pixel_total : 88596 time to create 1 rle with old method : 0.10483479499816895 length of segment : 580 time for calcul the mask position with numpy : 0.0002872943878173828 nb_pixel_total : 8272 time to create 1 rle with old method : 0.009635210037231445 length of segment : 75 time for calcul the mask position with numpy : 0.00040459632873535156 nb_pixel_total : 16733 time to create 1 rle with old method : 0.01876688003540039 length of segment : 96 time for calcul the mask position with numpy : 0.0007965564727783203 nb_pixel_total : 24999 time to create 1 rle with old method : 0.0283660888671875 length of segment : 252 time for calcul the mask position with numpy : 0.0004665851593017578 nb_pixel_total : 12000 time to create 1 rle with old method : 0.013792276382446289 length of segment : 176 time for calcul the mask position with numpy : 0.0003352165222167969 nb_pixel_total : 9585 time to create 1 rle with old method : 0.011624574661254883 length of segment : 81 time for calcul the mask position with numpy : 0.0003986358642578125 nb_pixel_total : 17023 time to create 1 rle with old method : 0.01988816261291504 length of segment : 120 time for calcul the mask position with numpy : 0.003180980682373047 nb_pixel_total : 55795 time to create 1 rle with old method : 0.0641322135925293 length of segment : 561 time for calcul the mask position with numpy : 0.00032639503479003906 nb_pixel_total : 5998 time to create 1 rle with old method : 0.00719141960144043 length of segment : 187 time for calcul the mask position with numpy : 0.0001609325408935547 nb_pixel_total : 6477 time to create 1 rle with old method : 0.007531881332397461 length of segment : 101 time for calcul the mask position with numpy : 0.0002231597900390625 nb_pixel_total : 8647 time to create 1 rle with old method : 0.009994983673095703 length of segment : 93 time for calcul the mask position with numpy : 0.000469207763671875 nb_pixel_total : 12066 time to create 1 rle with old method : 0.01378178596496582 length of segment : 180 time for calcul the mask position with numpy : 0.0005292892456054688 nb_pixel_total : 16877 time to create 1 rle with old method : 0.019363880157470703 length of segment : 149 time for calcul the mask position with numpy : 0.00041675567626953125 nb_pixel_total : 17301 time to create 1 rle with old method : 0.019295215606689453 length of segment : 168 time for calcul the mask position with numpy : 0.004385232925415039 nb_pixel_total : 108950 time to create 1 rle with old method : 0.11834049224853516 length of segment : 503 time for calcul the mask position with numpy : 0.00023412704467773438 nb_pixel_total : 4905 time to create 1 rle with old method : 0.005593061447143555 length of segment : 81 time for calcul the mask position with numpy : 0.0027756690979003906 nb_pixel_total : 101868 time to create 1 rle with old method : 0.1133420467376709 length of segment : 618 time for calcul the mask position with numpy : 0.0007343292236328125 nb_pixel_total : 19796 time to create 1 rle with old method : 0.023447513580322266 length of segment : 157 time for calcul the mask position with numpy : 0.00048041343688964844 nb_pixel_total : 8694 time to create 1 rle with old method : 0.010510444641113281 length of segment : 164 time for calcul the mask position with numpy : 0.0006821155548095703 nb_pixel_total : 15984 time to create 1 rle with old method : 0.019402503967285156 length of segment : 147 time for calcul the mask position with numpy : 0.0005590915679931641 nb_pixel_total : 13048 time to create 1 rle with old method : 0.01541590690612793 length of segment : 129 time for calcul the mask position with numpy : 0.0004851818084716797 nb_pixel_total : 15171 time to create 1 rle with old method : 0.01835346221923828 length of segment : 115 time for calcul the mask position with numpy : 0.0006597042083740234 nb_pixel_total : 18594 time to create 1 rle with old method : 0.02233576774597168 length of segment : 222 time for calcul the mask position with numpy : 0.0005481243133544922 nb_pixel_total : 18242 time to create 1 rle with old method : 0.022060394287109375 length of segment : 129 time for calcul the mask position with numpy : 0.0005755424499511719 nb_pixel_total : 11685 time to create 1 rle with old method : 0.014462709426879883 length of segment : 171 time for calcul the mask position with numpy : 0.0009589195251464844 nb_pixel_total : 21730 time to create 1 rle with old method : 0.025757789611816406 length of segment : 422 time for calcul the mask position with numpy : 0.00026488304138183594 nb_pixel_total : 15226 time to create 1 rle with old method : 0.017222166061401367 length of segment : 110 time for calcul the mask position with numpy : 0.00028634071350097656 nb_pixel_total : 7561 time to create 1 rle with old method : 0.008551836013793945 length of segment : 90 time for calcul the mask position with numpy : 0.0004661083221435547 nb_pixel_total : 12416 time to create 1 rle with old method : 0.013828754425048828 length of segment : 183 time for calcul the mask position with numpy : 0.0007503032684326172 nb_pixel_total : 38346 time to create 1 rle with old method : 0.04159688949584961 length of segment : 199 time for calcul the mask position with numpy : 0.0007753372192382812 nb_pixel_total : 32164 time to create 1 rle with old method : 0.03672313690185547 length of segment : 233 time for calcul the mask position with numpy : 0.0007319450378417969 nb_pixel_total : 33150 time to create 1 rle with old method : 0.04990243911743164 length of segment : 235 time for calcul the mask position with numpy : 0.0005483627319335938 nb_pixel_total : 11874 time to create 1 rle with old method : 0.01694655418395996 length of segment : 166 time for calcul the mask position with numpy : 0.00018787384033203125 nb_pixel_total : 4310 time to create 1 rle with old method : 0.005589008331298828 length of segment : 76 time for calcul the mask position with numpy : 0.0005478858947753906 nb_pixel_total : 26780 time to create 1 rle with old method : 0.030002593994140625 length of segment : 429 time for calcul the mask position with numpy : 0.0006453990936279297 nb_pixel_total : 22371 time to create 1 rle with old method : 0.03566479682922363 length of segment : 243 time for calcul the mask position with numpy : 0.0005674362182617188 nb_pixel_total : 21825 time to create 1 rle with old method : 0.0275266170501709 length of segment : 283 time for calcul the mask position with numpy : 0.00014829635620117188 nb_pixel_total : 2973 time to create 1 rle with old method : 0.0033257007598876953 length of segment : 66 time spent for convertir_results : 8.639394283294678 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 179 chid ids of type : 3594 Number RLEs to save : 35151 save missing photos in datou_result : time spend for datou_step_exec : 47.23769235610962 time spend to save output : 2.118518829345703 total time spend for step 1 : 49.35621118545532 step2:crop_condition Mon Sep 22 16:11:24 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 34 ! batch 1 Loaded 179 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 ! map_result returned by crop_photo_return_map_crop : length : 82 About to insert : list_path_to_insert length 82 new photo from crops ! About to upload 82 photos upload in portfolio : 3736932 init cache_photo without model_param we have 82 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758550289_803035 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 82 photos in the portfolio 3736932 time of upload the photos Elapsed time : 21.02610468864441 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 ! map_result returned by crop_photo_return_map_crop : length : 13 About to insert : list_path_to_insert length 13 new photo from crops ! About to upload 13 photos upload in portfolio : 3736932 init cache_photo without model_param we have 13 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758550313_803035 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 13 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.7891461849212646 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 4 About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos upload in portfolio : 3736932 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758550318_803035 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 4 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.411940574645996 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 64 About to insert : list_path_to_insert length 64 new photo from crops ! About to upload 64 photos upload in portfolio : 3736932 init cache_photo without model_param we have 64 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758550331_803035 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 64 photos in the portfolio 3736932 time of upload the photos Elapsed time : 15.048298597335815 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 8 About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos upload in portfolio : 3736932 init cache_photo without model_param we have 8 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758550348_803035 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 8 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.3062617778778076 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1758550351_803035 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.2414846420288086 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1758550354_803035 batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! batch_size : 0, verbose : False, strat_bulk_insert : ignore_different_from_first This is a hack ! 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 : 1.0255928039550781 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 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 [1385461436, 1385461433, 1385461421, 1385461419, 1385461417, 1385461413, 1385461412, 1385461409, 1385461402, 1385461400, 1385461397, 1385461394, 1385461393, 1385461361, 1385461359, 1385461356, 1385461353, 1385461351, 1385461335, 1385461333, 1385461331, 1385461329, 1385461328, 1385461327, 1385461319, 1385461317, 1385461314, 1385461311, 1385461308, 1385461307, 1385461267, 1385461264, 1385461260, 1385461258] Looping around the photos to save general results len do output : 179 /1385482116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482117Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482118Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482119Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482120Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482121Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482123Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482124Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482125Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482126Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482127Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482129Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482130Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482131Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482134Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482135Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482139Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482143Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482147Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482152Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482153Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482154Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482157Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482159Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482160Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482161Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482163Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482164Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482165Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482166Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482167Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482169Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482170Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482173Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482174Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482175Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482177Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482178Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482180Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482182Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482184Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482185Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482186Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482187Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482189Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482190Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482192Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482193Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482197Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482198Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482199Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482202Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482206Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482207Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482210Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482212Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482214Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482217Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482218Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482245Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482246Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482248Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482249Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482250Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482251Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482252Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482253Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482255Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482256Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482257Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482258Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482259Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482260Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482261Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482262Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482264Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482265Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482266Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482268Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482274Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482277Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482278Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482279Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482281Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482286Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482305Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482309Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482314Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1385482376Didn'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, '3759017') ('3318', '27096242', '1385461436', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461433', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461421', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461419', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461417', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461413', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461412', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461409', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461402', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461400', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461397', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461394', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461393', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461361', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461359', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461356', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461353', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461351', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461335', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461333', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461331', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461329', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461328', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461327', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461319', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461317', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461314', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461311', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461308', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461307', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461267', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461264', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461260', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461258', None, None, None, None, None, '3759017') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 571 time used for this insertion : 0.03439497947692871 save_final save missing photos in datou_result : time spend for datou_step_exec : 71.36305713653564 time spend to save output : 0.04061460494995117 total time spend for step 2 : 71.4036717414856 step3:rle_unique_nms_with_priority Mon Sep 22 16:12:35 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 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 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 179 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 9 nb_hashtags : 3 time to prepare the origin masks : 0.40070033073425293 time for calcul the mask position with numpy : 0.07478928565979004 nb_pixel_total : 1731677 time to create 1 rle with new method : 0.27579355239868164 time for calcul the mask position with numpy : 0.006196737289428711 nb_pixel_total : 11212 time to create 1 rle with old method : 0.01237344741821289 time for calcul the mask position with numpy : 0.0106353759765625 nb_pixel_total : 107400 time to create 1 rle with old method : 0.12674331665039062 time for calcul the mask position with numpy : 0.010402679443359375 nb_pixel_total : 3510 time to create 1 rle with old method : 0.0038557052612304688 time for calcul the mask position with numpy : 0.006141185760498047 nb_pixel_total : 22182 time to create 1 rle with old method : 0.025888919830322266 time for calcul the mask position with numpy : 0.005931377410888672 nb_pixel_total : 531 time to create 1 rle with old method : 0.0007135868072509766 time for calcul the mask position with numpy : 0.0075604915618896484 nb_pixel_total : 167989 time to create 1 rle with new method : 0.07841086387634277 time for calcul the mask position with numpy : 0.00655817985534668 nb_pixel_total : 11794 time to create 1 rle with old method : 0.015016555786132812 time for calcul the mask position with numpy : 0.006223440170288086 nb_pixel_total : 10672 time to create 1 rle with old method : 0.012139081954956055 time for calcul the mask position with numpy : 0.0065004825592041016 nb_pixel_total : 6633 time to create 1 rle with old method : 0.0075414180755615234 create new chi : 0.7170610427856445 time to delete rle : 0.0156404972076416 batch 1 Loaded 19 chid ids of type : 3594 +++++++++++Number RLEs to save : 5309 TO DO : save crop sub photo not yet done ! save time : 0.351165771484375 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.12272071838378906 time for calcul the mask position with numpy : 0.01318049430847168 nb_pixel_total : 1056222 time to create 1 rle with new method : 0.031116008758544922 time for calcul the mask position with numpy : 0.013205528259277344 nb_pixel_total : 1001600 time to create 1 rle with new method : 0.029436588287353516 time for calcul the mask position with numpy : 0.006206035614013672 nb_pixel_total : 4767 time to create 1 rle with old method : 0.005391597747802734 time for calcul the mask position with numpy : 0.006491422653198242 nb_pixel_total : 11011 time to create 1 rle with old method : 0.012145280838012695 create new chi : 0.11786556243896484 time to delete rle : 0.0007088184356689453 batch 1 Loaded 8 chid ids of type : 3594 +++++Number RLEs to save : 5001 TO DO : save crop sub photo not yet done ! save time : 0.3083503246307373 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 0.4469170570373535 time for calcul the mask position with numpy : 0.07876467704772949 nb_pixel_total : 1969750 time to create 1 rle with new method : 0.21918725967407227 time for calcul the mask position with numpy : 0.0062503814697265625 nb_pixel_total : 15541 time to create 1 rle with old method : 0.017352581024169922 time for calcul the mask position with numpy : 0.006026029586791992 nb_pixel_total : 23897 time to create 1 rle with old method : 0.026952028274536133 time for calcul the mask position with numpy : 0.0063037872314453125 nb_pixel_total : 23068 time to create 1 rle with old method : 0.02890801429748535 time for calcul the mask position with numpy : 0.007283687591552734 nb_pixel_total : 22118 time to create 1 rle with old method : 0.024627685546875 time for calcul the mask position with numpy : 0.006361484527587891 nb_pixel_total : 6744 time to create 1 rle with old method : 0.007796049118041992 time for calcul the mask position with numpy : 0.006034135818481445 nb_pixel_total : 12482 time to create 1 rle with old method : 0.014081001281738281 create new chi : 0.46623730659484863 time to delete rle : 0.0004742145538330078 batch 1 Loaded 13 chid ids of type : 3594 ++++++++Number RLEs to save : 3404 TO DO : save crop sub photo not yet done ! save time : 0.22959613800048828 nb_obj : 4 nb_hashtags : 3 time to prepare the origin masks : 0.06282496452331543 time for calcul the mask position with numpy : 0.020050525665283203 nb_pixel_total : 2022972 time to create 1 rle with new method : 0.1090080738067627 time for calcul the mask position with numpy : 0.006474494934082031 nb_pixel_total : 6176 time to create 1 rle with old method : 0.007294416427612305 time for calcul the mask position with numpy : 0.006875038146972656 nb_pixel_total : 26609 time to create 1 rle with old method : 0.030588865280151367 time for calcul the mask position with numpy : 0.0061604976654052734 nb_pixel_total : 4932 time to create 1 rle with old method : 0.005817890167236328 time for calcul the mask position with numpy : 0.006203174591064453 nb_pixel_total : 12911 time to create 1 rle with old method : 0.015004396438598633 create new chi : 0.22186803817749023 time to delete rle : 0.00035881996154785156 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 2322 TO DO : save crop sub photo not yet done ! save time : 0.16696906089782715 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.07253360748291016 time for calcul the mask position with numpy : 0.028323650360107422 nb_pixel_total : 2022775 time to create 1 rle with new method : 0.17789626121520996 time for calcul the mask position with numpy : 0.006421327590942383 nb_pixel_total : 31766 time to create 1 rle with old method : 0.03959989547729492 time for calcul the mask position with numpy : 0.0064144134521484375 nb_pixel_total : 13305 time to create 1 rle with old method : 0.015498638153076172 time for calcul the mask position with numpy : 0.006293296813964844 nb_pixel_total : 5754 time to create 1 rle with old method : 0.006775856018066406 create new chi : 0.2958495616912842 time to delete rle : 0.0004899501800537109 batch 1 Loaded 7 chid ids of type : 3594 ++++Number RLEs to save : 2082 TO DO : save crop sub photo not yet done ! save time : 0.16426706314086914 nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 0.27039551734924316 time for calcul the mask position with numpy : 0.17759919166564941 nb_pixel_total : 1883040 time to create 1 rle with new method : 0.08466649055480957 time for calcul the mask position with numpy : 0.006735086441040039 nb_pixel_total : 121288 time to create 1 rle with old method : 0.13091564178466797 time for calcul the mask position with numpy : 0.006145954132080078 nb_pixel_total : 7309 time to create 1 rle with old method : 0.008426666259765625 time for calcul the mask position with numpy : 0.006059885025024414 nb_pixel_total : 6022 time to create 1 rle with old method : 0.006716012954711914 time for calcul the mask position with numpy : 0.006159305572509766 nb_pixel_total : 17301 time to create 1 rle with old method : 0.019738435745239258 time for calcul the mask position with numpy : 0.006311655044555664 nb_pixel_total : 15418 time to create 1 rle with old method : 0.017221450805664062 time for calcul the mask position with numpy : 0.005902528762817383 nb_pixel_total : 9750 time to create 1 rle with old method : 0.010599374771118164 time for calcul the mask position with numpy : 0.0060651302337646484 nb_pixel_total : 13472 time to create 1 rle with old method : 0.014889717102050781 create new chi : 0.5250158309936523 time to delete rle : 0.0006222724914550781 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 3835 TO DO : save crop sub photo not yet done ! save time : 0.27114081382751465 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03240847587585449 time for calcul the mask position with numpy : 0.018712520599365234 nb_pixel_total : 2066868 time to create 1 rle with new method : 0.028006315231323242 time for calcul the mask position with numpy : 0.005915641784667969 nb_pixel_total : 6732 time to create 1 rle with old method : 0.007501125335693359 create new chi : 0.06036686897277832 time to delete rle : 0.00020456314086914062 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1218 TO DO : save crop sub photo not yet done ! save time : 0.12356758117675781 nb_obj : 4 nb_hashtags : 4 time to prepare the origin masks : 0.19329476356506348 time for calcul the mask position with numpy : 0.20284461975097656 nb_pixel_total : 1914596 time to create 1 rle with new method : 0.0821828842163086 time for calcul the mask position with numpy : 0.0067577362060546875 nb_pixel_total : 123691 time to create 1 rle with old method : 0.13482141494750977 time for calcul the mask position with numpy : 0.006151914596557617 nb_pixel_total : 16765 time to create 1 rle with old method : 0.0213015079498291 time for calcul the mask position with numpy : 0.005944967269897461 nb_pixel_total : 5045 time to create 1 rle with old method : 0.005474567413330078 time for calcul the mask position with numpy : 0.006083250045776367 nb_pixel_total : 13503 time to create 1 rle with old method : 0.015067815780639648 create new chi : 0.4972102642059326 time to delete rle : 0.00048351287841796875 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 3014 TO DO : save crop sub photo not yet done ! save time : 0.20904326438903809 nb_obj : 5 nb_hashtags : 3 time to prepare the origin masks : 0.06262063980102539 time for calcul the mask position with numpy : 0.15715765953063965 nb_pixel_total : 2013818 time to create 1 rle with new method : 0.23796463012695312 time for calcul the mask position with numpy : 0.005975961685180664 nb_pixel_total : 10382 time to create 1 rle with old method : 0.011474847793579102 time for calcul the mask position with numpy : 0.005790233612060547 nb_pixel_total : 7728 time to create 1 rle with old method : 0.008348703384399414 time for calcul the mask position with numpy : 0.005998849868774414 nb_pixel_total : 24813 time to create 1 rle with old method : 0.027094602584838867 time for calcul the mask position with numpy : 0.005823850631713867 nb_pixel_total : 3861 time to create 1 rle with old method : 0.004189014434814453 time for calcul the mask position with numpy : 0.005998134613037109 nb_pixel_total : 12998 time to create 1 rle with old method : 0.014511823654174805 create new chi : 0.49981141090393066 time to delete rle : 0.00039315223693847656 batch 1 Loaded 11 chid ids of type : 3594 ++++++Number RLEs to save : 2426 TO DO : save crop sub photo not yet done ! save time : 0.18068742752075195 nb_obj : 3 nb_hashtags : 2 time to prepare the origin masks : 0.050048112869262695 time for calcul the mask position with numpy : 0.018172264099121094 nb_pixel_total : 2012684 time to create 1 rle with new method : 0.16458368301391602 time for calcul the mask position with numpy : 0.00750732421875 nb_pixel_total : 39969 time to create 1 rle with old method : 0.06330442428588867 time for calcul the mask position with numpy : 0.007047891616821289 nb_pixel_total : 7873 time to create 1 rle with old method : 0.011480569839477539 time for calcul the mask position with numpy : 0.006971597671508789 nb_pixel_total : 13074 time to create 1 rle with old method : 0.016759157180786133 create new chi : 0.30489206314086914 time to delete rle : 0.00040221214294433594 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2366 TO DO : save crop sub photo not yet done ! save time : 0.16956186294555664 nb_obj : 6 nb_hashtags : 2 time to prepare the origin masks : 0.10236763954162598 time for calcul the mask position with numpy : 0.12277889251708984 nb_pixel_total : 1884160 time to create 1 rle with new method : 0.08608269691467285 time for calcul the mask position with numpy : 0.0061414241790771484 nb_pixel_total : 6894 time to create 1 rle with old method : 0.0078125 time for calcul the mask position with numpy : 0.005998134613037109 nb_pixel_total : 24264 time to create 1 rle with old method : 0.027126073837280273 time for calcul the mask position with numpy : 0.005978822708129883 nb_pixel_total : 18868 time to create 1 rle with old method : 0.021343231201171875 time for calcul the mask position with numpy : 0.0064008235931396484 nb_pixel_total : 113894 time to create 1 rle with old method : 0.12345290184020996 time for calcul the mask position with numpy : 0.0058667659759521484 nb_pixel_total : 5873 time to create 1 rle with old method : 0.006747722625732422 time for calcul the mask position with numpy : 0.006388664245605469 nb_pixel_total : 19647 time to create 1 rle with old method : 0.02210068702697754 create new chi : 0.4657769203186035 time to delete rle : 0.0006468296051025391 batch 1 Loaded 13 chid ids of type : 3594 ++++++Number RLEs to save : 3662 TO DO : save crop sub photo not yet done ! save time : 0.2506289482116699 nb_obj : 6 nb_hashtags : 2 time to prepare the origin masks : 0.07665777206420898 time for calcul the mask position with numpy : 0.18359899520874023 nb_pixel_total : 1887322 time to create 1 rle with new method : 0.0997464656829834 time for calcul the mask position with numpy : 0.006738424301147461 nb_pixel_total : 35376 time to create 1 rle with old method : 0.039304494857788086 time for calcul the mask position with numpy : 0.007281780242919922 nb_pixel_total : 110779 time to create 1 rle with old method : 0.12000322341918945 time for calcul the mask position with numpy : 0.0065460205078125 nb_pixel_total : 11879 time to create 1 rle with old method : 0.013245344161987305 time for calcul the mask position with numpy : 0.009674787521362305 nb_pixel_total : 2656 time to create 1 rle with old method : 0.0029060840606689453 time for calcul the mask position with numpy : 0.009946346282958984 nb_pixel_total : 13129 time to create 1 rle with old method : 0.014957189559936523 time for calcul the mask position with numpy : 0.010047197341918945 nb_pixel_total : 12459 time to create 1 rle with old method : 0.014247655868530273 create new chi : 0.54931640625 time to delete rle : 0.0006773471832275391 batch 1 Loaded 13 chid ids of type : 3594 +++++++++Number RLEs to save : 4278 TO DO : save crop sub photo not yet done ! save time : 0.3131139278411865 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.1104583740234375 time for calcul the mask position with numpy : 0.21840739250183105 nb_pixel_total : 1992045 time to create 1 rle with new method : 0.09757852554321289 time for calcul the mask position with numpy : 0.0063593387603759766 nb_pixel_total : 7000 time to create 1 rle with old method : 0.008137226104736328 time for calcul the mask position with numpy : 0.0062181949615478516 nb_pixel_total : 3520 time to create 1 rle with old method : 0.004055023193359375 time for calcul the mask position with numpy : 0.006527423858642578 nb_pixel_total : 36632 time to create 1 rle with old method : 0.04192018508911133 time for calcul the mask position with numpy : 0.0063550472259521484 nb_pixel_total : 11459 time to create 1 rle with old method : 0.013154268264770508 time for calcul the mask position with numpy : 0.006268501281738281 nb_pixel_total : 22944 time to create 1 rle with old method : 0.02656388282775879 create new chi : 0.4511392116546631 time to delete rle : 0.0003154277801513672 batch 1 Loaded 11 chid ids of type : 3594 ++++++++Number RLEs to save : 2730 TO DO : save crop sub photo not yet done ! save time : 0.2117607593536377 nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 0.12197327613830566 time for calcul the mask position with numpy : 0.07646799087524414 nb_pixel_total : 2010537 time to create 1 rle with new method : 0.07944607734680176 time for calcul the mask position with numpy : 0.006245136260986328 nb_pixel_total : 1787 time to create 1 rle with old method : 0.0019845962524414062 time for calcul the mask position with numpy : 0.005573272705078125 nb_pixel_total : 2433 time to create 1 rle with old method : 0.002687215805053711 time for calcul the mask position with numpy : 0.0056955814361572266 nb_pixel_total : 16885 time to create 1 rle with old method : 0.01777362823486328 time for calcul the mask position with numpy : 0.005659580230712891 nb_pixel_total : 10158 time to create 1 rle with old method : 0.010864496231079102 time for calcul the mask position with numpy : 0.005671501159667969 nb_pixel_total : 6642 time to create 1 rle with old method : 0.0072672367095947266 time for calcul the mask position with numpy : 0.0057637691497802734 nb_pixel_total : 12977 time to create 1 rle with old method : 0.014186859130859375 time for calcul the mask position with numpy : 0.0057468414306640625 nb_pixel_total : 12181 time to create 1 rle with old method : 0.013051509857177734 create new chi : 0.27476072311401367 time to delete rle : 0.00048041343688964844 batch 1 Loaded 15 chid ids of type : 3594 +++++++Number RLEs to save : 2906 TO DO : save crop sub photo not yet done ! save time : 0.22035574913024902 nb_obj : 8 nb_hashtags : 2 time to prepare the origin masks : 0.09780526161193848 time for calcul the mask position with numpy : 0.19327807426452637 nb_pixel_total : 2000404 time to create 1 rle with new method : 0.08046269416809082 time for calcul the mask position with numpy : 0.0059168338775634766 nb_pixel_total : 7028 time to create 1 rle with old method : 0.007340908050537109 time for calcul the mask position with numpy : 0.005734682083129883 nb_pixel_total : 6887 time to create 1 rle with old method : 0.0074999332427978516 time for calcul the mask position with numpy : 0.005834102630615234 nb_pixel_total : 15583 time to create 1 rle with old method : 0.016976356506347656 time for calcul the mask position with numpy : 0.00603938102722168 nb_pixel_total : 14573 time to create 1 rle with old method : 0.015696287155151367 time for calcul the mask position with numpy : 0.005873680114746094 nb_pixel_total : 6563 time to create 1 rle with old method : 0.007520198822021484 time for calcul the mask position with numpy : 0.0057909488677978516 nb_pixel_total : 5241 time to create 1 rle with old method : 0.005898952484130859 time for calcul the mask position with numpy : 0.0059032440185546875 nb_pixel_total : 5180 time to create 1 rle with old method : 0.005671501159667969 time for calcul the mask position with numpy : 0.005871295928955078 nb_pixel_total : 12141 time to create 1 rle with old method : 0.013425588607788086 create new chi : 0.41144537925720215 time to delete rle : 0.0005564689636230469 batch 1 Loaded 17 chid ids of type : 3594 +++++++++Number RLEs to save : 3032 TO DO : save crop sub photo not yet done ! save time : 0.23024868965148926 nb_obj : 4 nb_hashtags : 4 time to prepare the origin masks : 0.05227828025817871 time for calcul the mask position with numpy : 0.02020096778869629 nb_pixel_total : 1914080 time to create 1 rle with new method : 0.1827239990234375 time for calcul the mask position with numpy : 0.006393909454345703 nb_pixel_total : 113156 time to create 1 rle with old method : 0.12163782119750977 time for calcul the mask position with numpy : 0.005759477615356445 nb_pixel_total : 10037 time to create 1 rle with old method : 0.01053476333618164 time for calcul the mask position with numpy : 0.005718708038330078 nb_pixel_total : 11224 time to create 1 rle with old method : 0.011831998825073242 time for calcul the mask position with numpy : 0.005871295928955078 nb_pixel_total : 25103 time to create 1 rle with old method : 0.027569055557250977 create new chi : 0.40500330924987793 time to delete rle : 0.0005795955657958984 batch 1 Loaded 9 chid ids of type : 3594 +++++Number RLEs to save : 3622 TO DO : save crop sub photo not yet done ! save time : 0.25196027755737305 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 0.32949137687683105 time for calcul the mask position with numpy : 0.13575983047485352 nb_pixel_total : 1972001 time to create 1 rle with new method : 0.09827423095703125 time for calcul the mask position with numpy : 0.005816221237182617 nb_pixel_total : 12044 time to create 1 rle with old method : 0.01266932487487793 time for calcul the mask position with numpy : 0.005629062652587891 nb_pixel_total : 18430 time to create 1 rle with old method : 0.022289037704467773 time for calcul the mask position with numpy : 0.005933046340942383 nb_pixel_total : 31480 time to create 1 rle with old method : 0.034024715423583984 time for calcul the mask position with numpy : 0.0101165771484375 nb_pixel_total : 20329 time to create 1 rle with old method : 0.02254033088684082 time for calcul the mask position with numpy : 0.009892702102661133 nb_pixel_total : 12956 time to create 1 rle with old method : 0.014037370681762695 time for calcul the mask position with numpy : 0.009613037109375 nb_pixel_total : 6360 time to create 1 rle with old method : 0.007042407989501953 create new chi : 0.4042782783508301 time to delete rle : 0.0006594657897949219 batch 1 Loaded 13 chid ids of type : 3594 ++++++++++Number RLEs to save : 3176 TO DO : save crop sub photo not yet done ! save time : 0.23111343383789062 nb_obj : 7 nb_hashtags : 4 time to prepare the origin masks : 0.23731184005737305 time for calcul the mask position with numpy : 0.1634202003479004 nb_pixel_total : 1876782 time to create 1 rle with new method : 0.08282947540283203 time for calcul the mask position with numpy : 0.006226539611816406 nb_pixel_total : 3939 time to create 1 rle with old method : 0.007813692092895508 time for calcul the mask position with numpy : 0.007544755935668945 nb_pixel_total : 116642 time to create 1 rle with old method : 0.12654900550842285 time for calcul the mask position with numpy : 0.0060613155364990234 nb_pixel_total : 20260 time to create 1 rle with old method : 0.022549867630004883 time for calcul the mask position with numpy : 0.00969243049621582 nb_pixel_total : 10412 time to create 1 rle with old method : 0.011504173278808594 time for calcul the mask position with numpy : 0.00959634780883789 nb_pixel_total : 13721 time to create 1 rle with old method : 0.01487278938293457 time for calcul the mask position with numpy : 0.006038188934326172 nb_pixel_total : 19510 time to create 1 rle with old method : 0.021038055419921875 time for calcul the mask position with numpy : 0.006313800811767578 nb_pixel_total : 12334 time to create 1 rle with old method : 0.013378620147705078 create new chi : 0.5263338088989258 time to delete rle : 0.0006451606750488281 batch 1 Loaded 15 chid ids of type : 3594 ++++++++Number RLEs to save : 3573 TO DO : save crop sub photo not yet done ! save time : 0.24329805374145508 nb_obj : 3 nb_hashtags : 3 time to prepare the origin masks : 0.07030797004699707 time for calcul the mask position with numpy : 0.022014379501342773 nb_pixel_total : 2042803 time to create 1 rle with new method : 0.05862760543823242 time for calcul the mask position with numpy : 0.0060024261474609375 nb_pixel_total : 11476 time to create 1 rle with old method : 0.013245820999145508 time for calcul the mask position with numpy : 0.005985260009765625 nb_pixel_total : 6753 time to create 1 rle with old method : 0.007624149322509766 time for calcul the mask position with numpy : 0.006063938140869141 nb_pixel_total : 12568 time to create 1 rle with old method : 0.014318466186523438 create new chi : 0.13422060012817383 time to delete rle : 0.0003459453582763672 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2102 TO DO : save crop sub photo not yet done ! save time : 0.1740429401397705 nb_obj : 4 nb_hashtags : 2 time to prepare the origin masks : 0.06922006607055664 time for calcul the mask position with numpy : 0.1545262336730957 nb_pixel_total : 2015634 time to create 1 rle with new method : 0.0801236629486084 time for calcul the mask position with numpy : 0.009824275970458984 nb_pixel_total : 6610 time to create 1 rle with old method : 0.007172107696533203 time for calcul the mask position with numpy : 0.005789041519165039 nb_pixel_total : 14363 time to create 1 rle with old method : 0.015670061111450195 time for calcul the mask position with numpy : 0.005934715270996094 nb_pixel_total : 24198 time to create 1 rle with old method : 0.026784658432006836 time for calcul the mask position with numpy : 0.00627899169921875 nb_pixel_total : 12795 time to create 1 rle with old method : 0.014069795608520508 create new chi : 0.33511900901794434 time to delete rle : 0.0005526542663574219 batch 1 Loaded 9 chid ids of type : 3594 ++++Number RLEs to save : 2604 TO DO : save crop sub photo not yet done ! save time : 0.19799065589904785 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.04449892044067383 time for calcul the mask position with numpy : 0.02006816864013672 nb_pixel_total : 2061020 time to create 1 rle with new method : 0.059830665588378906 time for calcul the mask position with numpy : 0.006243705749511719 nb_pixel_total : 12580 time to create 1 rle with old method : 0.013834238052368164 create new chi : 0.10022711753845215 time to delete rle : 0.00024890899658203125 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1446 TO DO : save crop sub photo not yet done ! save time : 0.11848044395446777 nb_obj : 8 nb_hashtags : 3 time to prepare the origin masks : 0.22515583038330078 time for calcul the mask position with numpy : 0.13717031478881836 nb_pixel_total : 1990450 time to create 1 rle with new method : 0.20112967491149902 time for calcul the mask position with numpy : 0.006001710891723633 nb_pixel_total : 11020 time to create 1 rle with old method : 0.012087583541870117 time for calcul the mask position with numpy : 0.005957365036010742 nb_pixel_total : 7185 time to create 1 rle with old method : 0.008017301559448242 time for calcul the mask position with numpy : 0.005976200103759766 nb_pixel_total : 12647 time to create 1 rle with old method : 0.01383209228515625 time for calcul the mask position with numpy : 0.005926370620727539 nb_pixel_total : 19704 time to create 1 rle with old method : 0.0212404727935791 time for calcul the mask position with numpy : 0.0058367252349853516 nb_pixel_total : 5510 time to create 1 rle with old method : 0.006342411041259766 time for calcul the mask position with numpy : 0.0059010982513427734 nb_pixel_total : 12362 time to create 1 rle with old method : 0.01315617561340332 time for calcul the mask position with numpy : 0.0060307979583740234 nb_pixel_total : 8753 time to create 1 rle with old method : 0.009527921676635742 time for calcul the mask position with numpy : 0.006041049957275391 nb_pixel_total : 5969 time to create 1 rle with old method : 0.0066375732421875 create new chi : 0.48655176162719727 time to delete rle : 0.0005440711975097656 batch 1 Loaded 17 chid ids of type : 3594 ++++++++Number RLEs to save : 3198 TO DO : save crop sub photo not yet done ! save time : 0.23381280899047852 nb_obj : 1 nb_hashtags : 1 time to prepare the origin masks : 0.03169965744018555 time for calcul the mask position with numpy : 0.01852583885192871 nb_pixel_total : 2060892 time to create 1 rle with new method : 0.027123689651489258 time for calcul the mask position with numpy : 0.005894660949707031 nb_pixel_total : 12708 time to create 1 rle with old method : 0.013472795486450195 create new chi : 0.07487607002258301 time to delete rle : 0.00024080276489257812 batch 1 Loaded 3 chid ids of type : 3594 +Number RLEs to save : 1480 TO DO : save crop sub photo not yet done ! save time : 0.11060762405395508 nb_obj : 3 nb_hashtags : 1 time to prepare the origin masks : 0.06156134605407715 time for calcul the mask position with numpy : 0.06421566009521484 nb_pixel_total : 2048547 time to create 1 rle with new method : 0.14057374000549316 time for calcul the mask position with numpy : 0.005703926086425781 nb_pixel_total : 4399 time to create 1 rle with old method : 0.004919290542602539 time for calcul the mask position with numpy : 0.009485960006713867 nb_pixel_total : 12565 time to create 1 rle with old method : 0.013361930847167969 time for calcul the mask position with numpy : 0.005873680114746094 nb_pixel_total : 8089 time to create 1 rle with old method : 0.008830547332763672 create new chi : 0.26157331466674805 time to delete rle : 0.00038623809814453125 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2086 TO DO : save crop sub photo not yet done ! save time : 0.1700730323791504 nb_obj : 3 nb_hashtags : 1 time to prepare the origin masks : 0.08118295669555664 time for calcul the mask position with numpy : 0.1330711841583252 nb_pixel_total : 2029649 time to create 1 rle with new method : 0.07185888290405273 time for calcul the mask position with numpy : 0.005922555923461914 nb_pixel_total : 27438 time to create 1 rle with old method : 0.029635906219482422 time for calcul the mask position with numpy : 0.005776405334472656 nb_pixel_total : 12473 time to create 1 rle with old method : 0.013229131698608398 time for calcul the mask position with numpy : 0.005608081817626953 nb_pixel_total : 4040 time to create 1 rle with old method : 0.0043146610260009766 create new chi : 0.2782890796661377 time to delete rle : 0.0003535747528076172 batch 1 Loaded 7 chid ids of type : 3594 +++Number RLEs to save : 2042 TO DO : save crop sub photo not yet done ! save time : 0.16817069053649902 nb_obj : 5 nb_hashtags : 2 time to prepare the origin masks : 0.15780258178710938 time for calcul the mask position with numpy : 0.11531257629394531 nb_pixel_total : 1935283 time to create 1 rle with new method : 0.07468032836914062 time for calcul the mask position with numpy : 0.006731986999511719 nb_pixel_total : 107301 time to create 1 rle with old method : 0.11303973197937012 time for calcul the mask position with numpy : 0.005754947662353516 nb_pixel_total : 9257 time to create 1 rle with old method : 0.009685277938842773 time for calcul the mask position with numpy : 0.005577564239501953 nb_pixel_total : 2776 time to create 1 rle with old method : 0.0030989646911621094 time for calcul the mask position with numpy : 0.005618572235107422 nb_pixel_total : 6268 time to create 1 rle with old method : 0.006834983825683594 time for calcul the mask position with numpy : 0.006003141403198242 nb_pixel_total : 12715 time to create 1 rle with old method : 0.013541698455810547 create new chi : 0.37609004974365234 time to delete rle : 0.0004673004150390625 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 2937 TO DO : save crop sub photo not yet done ! save time : 0.20569372177124023 nb_obj : 7 nb_hashtags : 3 time to prepare the origin masks : 0.24066758155822754 time for calcul the mask position with numpy : 0.07202792167663574 nb_pixel_total : 1991367 time to create 1 rle with new method : 0.07735848426818848 time for calcul the mask position with numpy : 0.006145954132080078 nb_pixel_total : 17767 time to create 1 rle with old method : 0.019019126892089844 time for calcul the mask position with numpy : 0.009342670440673828 nb_pixel_total : 12717 time to create 1 rle with old method : 0.013511419296264648 time for calcul the mask position with numpy : 0.0056781768798828125 nb_pixel_total : 13648 time to create 1 rle with old method : 0.015019655227661133 time for calcul the mask position with numpy : 0.005703926086425781 nb_pixel_total : 8663 time to create 1 rle with old method : 0.00922393798828125 time for calcul the mask position with numpy : 0.00585484504699707 nb_pixel_total : 12888 time to create 1 rle with old method : 0.014047861099243164 time for calcul the mask position with numpy : 0.005781412124633789 nb_pixel_total : 3965 time to create 1 rle with old method : 0.004508018493652344 time for calcul the mask position with numpy : 0.005928516387939453 nb_pixel_total : 12585 time to create 1 rle with old method : 0.013780355453491211 create new chi : 0.2935788631439209 time to delete rle : 0.0006456375122070312 batch 1 Loaded 15 chid ids of type : 3594 +++++++++Number RLEs to save : 3408 TO DO : save crop sub photo not yet done ! save time : 0.25119638442993164 nb_obj : 5 nb_hashtags : 4 time to prepare the origin masks : 0.0751185417175293 time for calcul the mask position with numpy : 0.06828069686889648 nb_pixel_total : 1982936 time to create 1 rle with new method : 0.19916558265686035 time for calcul the mask position with numpy : 0.005761861801147461 nb_pixel_total : 7413 time to create 1 rle with old method : 0.007833719253540039 time for calcul the mask position with numpy : 0.005563974380493164 nb_pixel_total : 9923 time to create 1 rle with old method : 0.01050877571105957 time for calcul the mask position with numpy : 0.005991458892822266 nb_pixel_total : 36849 time to create 1 rle with old method : 0.039046525955200195 time for calcul the mask position with numpy : 0.005719184875488281 nb_pixel_total : 24306 time to create 1 rle with old method : 0.025857210159301758 time for calcul the mask position with numpy : 0.005666971206665039 nb_pixel_total : 12173 time to create 1 rle with old method : 0.013016939163208008 create new chi : 0.40085697174072266 time to delete rle : 0.0004673004150390625 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 2692 TO DO : save crop sub photo not yet done ! save time : 0.19336795806884766 nb_obj : 8 nb_hashtags : 4 time to prepare the origin masks : 0.12668943405151367 time for calcul the mask position with numpy : 0.1326463222503662 nb_pixel_total : 1887803 time to create 1 rle with new method : 0.19126486778259277 time for calcul the mask position with numpy : 0.005687236785888672 nb_pixel_total : 545 time to create 1 rle with old method : 0.000736236572265625 time for calcul the mask position with numpy : 0.0076291561126708984 nb_pixel_total : 88596 time to create 1 rle with old method : 0.09520506858825684 time for calcul the mask position with numpy : 0.006279945373535156 nb_pixel_total : 7898 time to create 1 rle with old method : 0.008576631546020508 time for calcul the mask position with numpy : 0.005761623382568359 nb_pixel_total : 9906 time to create 1 rle with old method : 0.010590791702270508 time for calcul the mask position with numpy : 0.005952358245849609 nb_pixel_total : 2762 time to create 1 rle with old method : 0.0030660629272460938 time for calcul the mask position with numpy : 0.005942344665527344 nb_pixel_total : 37539 time to create 1 rle with old method : 0.040441274642944336 time for calcul the mask position with numpy : 0.006135225296020508 nb_pixel_total : 25899 time to create 1 rle with old method : 0.027776002883911133 time for calcul the mask position with numpy : 0.005896806716918945 nb_pixel_total : 12652 time to create 1 rle with old method : 0.013206243515014648 create new chi : 0.5826950073242188 time to delete rle : 0.0006222724914550781 batch 1 Loaded 17 chid ids of type : 3594 ++++++++Number RLEs to save : 4109 TO DO : save crop sub photo not yet done ! save time : 0.26615405082702637 nb_obj : 9 nb_hashtags : 6 time to prepare the origin masks : 0.15169692039489746 time for calcul the mask position with numpy : 0.14820599555969238 nb_pixel_total : 1916343 time to create 1 rle with new method : 0.23673582077026367 time for calcul the mask position with numpy : 0.006174564361572266 nb_pixel_total : 8647 time to create 1 rle with old method : 0.009459495544433594 time for calcul the mask position with numpy : 0.005938053131103516 nb_pixel_total : 6477 time to create 1 rle with old method : 0.007192373275756836 time for calcul the mask position with numpy : 0.00580143928527832 nb_pixel_total : 5998 time to create 1 rle with old method : 0.006629228591918945 time for calcul the mask position with numpy : 0.006270885467529297 nb_pixel_total : 55795 time to create 1 rle with old method : 0.061089277267456055 time for calcul the mask position with numpy : 0.005987405776977539 nb_pixel_total : 17023 time to create 1 rle with old method : 0.019238948822021484 time for calcul the mask position with numpy : 0.005789995193481445 nb_pixel_total : 9585 time to create 1 rle with old method : 0.010874748229980469 time for calcul the mask position with numpy : 0.0059223175048828125 nb_pixel_total : 12000 time to create 1 rle with old method : 0.013190746307373047 time for calcul the mask position with numpy : 0.006189823150634766 nb_pixel_total : 24999 time to create 1 rle with old method : 0.02727222442626953 time for calcul the mask position with numpy : 0.006250858306884766 nb_pixel_total : 16733 time to create 1 rle with old method : 0.01851487159729004 create new chi : 0.6227631568908691 time to delete rle : 0.0007309913635253906 batch 1 Loaded 19 chid ids of type : 3594 ++++++++++Number RLEs to save : 4414 TO DO : save crop sub photo not yet done ! save time : 0.3060002326965332 nb_obj : 6 nb_hashtags : 3 time to prepare the origin masks : 0.10383391380310059 time for calcul the mask position with numpy : 0.023745298385620117 nb_pixel_total : 1811633 time to create 1 rle with new method : 0.03934335708618164 time for calcul the mask position with numpy : 0.006705760955810547 nb_pixel_total : 101868 time to create 1 rle with old method : 0.10755753517150879 time for calcul the mask position with numpy : 0.00589299201965332 nb_pixel_total : 4905 time to create 1 rle with old method : 0.005455970764160156 time for calcul the mask position with numpy : 0.006436586380004883 nb_pixel_total : 108950 time to create 1 rle with old method : 0.11470913887023926 time for calcul the mask position with numpy : 0.006021261215209961 nb_pixel_total : 17301 time to create 1 rle with old method : 0.018826961517333984 time for calcul the mask position with numpy : 0.005805492401123047 nb_pixel_total : 16877 time to create 1 rle with old method : 0.01794886589050293 time for calcul the mask position with numpy : 0.005931854248046875 nb_pixel_total : 12066 time to create 1 rle with old method : 0.013056278228759766 create new chi : 0.37819576263427734 time to delete rle : 0.0007879734039306641 batch 1 Loaded 13 chid ids of type : 3594 ++++++++Number RLEs to save : 4478 TO DO : save crop sub photo not yet done ! save time : 0.29144954681396484 nb_obj : 11 nb_hashtags : 4 time to prepare the origin masks : 0.11804318428039551 time for calcul the mask position with numpy : 0.05689239501953125 nb_pixel_total : 1907869 time to create 1 rle with new method : 0.19833922386169434 time for calcul the mask position with numpy : 0.005944252014160156 nb_pixel_total : 7561 time to create 1 rle with old method : 0.008145809173583984 time for calcul the mask position with numpy : 0.005822896957397461 nb_pixel_total : 15226 time to create 1 rle with old method : 0.01629948616027832 time for calcul the mask position with numpy : 0.005697727203369141 nb_pixel_total : 21730 time to create 1 rle with old method : 0.023391246795654297 time for calcul the mask position with numpy : 0.005881786346435547 nb_pixel_total : 11685 time to create 1 rle with old method : 0.012591123580932617 time for calcul the mask position with numpy : 0.0058727264404296875 nb_pixel_total : 18242 time to create 1 rle with old method : 0.019677400588989258 time for calcul the mask position with numpy : 0.005751371383666992 nb_pixel_total : 18594 time to create 1 rle with old method : 0.019797086715698242 time for calcul the mask position with numpy : 0.005976676940917969 nb_pixel_total : 15171 time to create 1 rle with old method : 0.016537904739379883 time for calcul the mask position with numpy : 0.005608081817626953 nb_pixel_total : 13048 time to create 1 rle with old method : 0.013712882995605469 time for calcul the mask position with numpy : 0.0055751800537109375 nb_pixel_total : 15984 time to create 1 rle with old method : 0.01732635498046875 time for calcul the mask position with numpy : 0.0061337947845458984 nb_pixel_total : 8694 time to create 1 rle with old method : 0.009448528289794922 time for calcul the mask position with numpy : 0.006145477294921875 nb_pixel_total : 19796 time to create 1 rle with old method : 0.022214651107788086 create new chi : 0.5091273784637451 time to delete rle : 0.0008351802825927734 batch 1 Loaded 23 chid ids of type : 3594 ++++++++++++Number RLEs to save : 4792 TO DO : save crop sub photo not yet done ! save time : 0.3179194927215576 nb_obj : 2 nb_hashtags : 2 time to prepare the origin masks : 0.03967690467834473 time for calcul the mask position with numpy : 0.018786191940307617 nb_pixel_total : 2022838 time to create 1 rle with new method : 0.027576923370361328 time for calcul the mask position with numpy : 0.006083488464355469 nb_pixel_total : 38346 time to create 1 rle with old method : 0.04217052459716797 time for calcul the mask position with numpy : 0.005902290344238281 nb_pixel_total : 12416 time to create 1 rle with old method : 0.013550758361816406 create new chi : 0.12706422805786133 time to delete rle : 0.00031638145446777344 batch 1 Loaded 5 chid ids of type : 3594 ++Number RLEs to save : 1844 TO DO : save crop sub photo not yet done ! save time : 0.15302085876464844 nb_obj : 8 nb_hashtags : 3 time to prepare the origin masks : 0.22693991661071777 time for calcul the mask position with numpy : 0.1558518409729004 nb_pixel_total : 1918153 time to create 1 rle with new method : 0.1966407299041748 time for calcul the mask position with numpy : 0.0057413578033447266 nb_pixel_total : 2973 time to create 1 rle with old method : 0.0031380653381347656 time for calcul the mask position with numpy : 0.005577564239501953 nb_pixel_total : 21825 time to create 1 rle with old method : 0.022672653198242188 time for calcul the mask position with numpy : 0.005677700042724609 nb_pixel_total : 22371 time to create 1 rle with old method : 0.02366781234741211 time for calcul the mask position with numpy : 0.0057163238525390625 nb_pixel_total : 26780 time to create 1 rle with old method : 0.0287630558013916 time for calcul the mask position with numpy : 0.006070375442504883 nb_pixel_total : 4310 time to create 1 rle with old method : 0.004858732223510742 time for calcul the mask position with numpy : 0.005733966827392578 nb_pixel_total : 11874 time to create 1 rle with old method : 0.012437582015991211 time for calcul the mask position with numpy : 0.00567173957824707 nb_pixel_total : 33150 time to create 1 rle with old method : 0.03458428382873535 time for calcul the mask position with numpy : 0.006067037582397461 nb_pixel_total : 32164 time to create 1 rle with old method : 0.034476518630981445 create new chi : 0.5727071762084961 time to delete rle : 0.0007181167602539062 batch 1 Loaded 17 chid ids of type : 3594 +++++++++++++++Number RLEs to save : 4542 TO DO : save crop sub photo not yet done ! save time : 0.3181614875793457 map_output_result : {1385461436: (0.0, 'Should be the crop_list due to order', 0), 1385461433: (0.0, 'Should be the crop_list due to order', 0), 1385461421: (0.0, 'Should be the crop_list due to order', 0), 1385461419: (0.0, 'Should be the crop_list due to order', 0), 1385461417: (0.0, 'Should be the crop_list due to order', 0), 1385461413: (0.0, 'Should be the crop_list due to order', 0), 1385461412: (0.0, 'Should be the crop_list due to order', 0), 1385461409: (0.0, 'Should be the crop_list due to order', 0), 1385461402: (0.0, 'Should be the crop_list due to order', 0), 1385461400: (0.0, 'Should be the crop_list due to order', 0), 1385461397: (0.0, 'Should be the crop_list due to order', 0), 1385461394: (0.0, 'Should be the crop_list due to order', 0), 1385461393: (0.0, 'Should be the crop_list due to order', 0), 1385461361: (0.0, 'Should be the crop_list due to order', 0), 1385461359: (0.0, 'Should be the crop_list due to order', 0), 1385461356: (0.0, 'Should be the crop_list due to order', 0), 1385461353: (0.0, 'Should be the crop_list due to order', 0), 1385461351: (0.0, 'Should be the crop_list due to order', 0), 1385461335: (0.0, 'Should be the crop_list due to order', 0), 1385461333: (0.0, 'Should be the crop_list due to order', 0), 1385461331: (0.0, 'Should be the crop_list due to order', 0), 1385461329: (0.0, 'Should be the crop_list due to order', 0), 1385461328: (0.0, 'Should be the crop_list due to order', 0), 1385461327: (0.0, 'Should be the crop_list due to order', 0), 1385461319: (0.0, 'Should be the crop_list due to order', 0), 1385461317: (0.0, 'Should be the crop_list due to order', 0), 1385461314: (0.0, 'Should be the crop_list due to order', 0), 1385461311: (0.0, 'Should be the crop_list due to order', 0), 1385461308: (0.0, 'Should be the crop_list due to order', 0), 1385461307: (0.0, 'Should be the crop_list due to order', 0), 1385461267: (0.0, 'Should be the crop_list due to order', 0), 1385461264: (0.0, 'Should be the crop_list due to order', 0), 1385461260: (0.0, 'Should be the crop_list due to order', 0), 1385461258: (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 [1385461436, 1385461433, 1385461421, 1385461419, 1385461417, 1385461413, 1385461412, 1385461409, 1385461402, 1385461400, 1385461397, 1385461394, 1385461393, 1385461361, 1385461359, 1385461356, 1385461353, 1385461351, 1385461335, 1385461333, 1385461331, 1385461329, 1385461328, 1385461327, 1385461319, 1385461317, 1385461314, 1385461311, 1385461308, 1385461307, 1385461267, 1385461264, 1385461260, 1385461258] Looping around the photos to save general results len do output : 34 /1385461436.Didn't retrieve data . /1385461433.Didn't retrieve data . /1385461421.Didn't retrieve data . /1385461419.Didn't retrieve data . /1385461417.Didn't retrieve data . /1385461413.Didn't retrieve data . /1385461412.Didn't retrieve data . /1385461409.Didn't retrieve data . /1385461402.Didn't retrieve data . /1385461400.Didn't retrieve data . /1385461397.Didn't retrieve data . /1385461394.Didn't retrieve data . /1385461393.Didn't retrieve data . /1385461361.Didn't retrieve data . /1385461359.Didn't retrieve data . /1385461356.Didn't retrieve data . /1385461353.Didn't retrieve data . /1385461351.Didn't retrieve data . /1385461335.Didn't retrieve data . /1385461333.Didn't retrieve data . /1385461331.Didn't retrieve data . /1385461329.Didn't retrieve data . /1385461328.Didn't retrieve data . /1385461327.Didn't retrieve data . /1385461319.Didn't retrieve data . /1385461317.Didn't retrieve data . /1385461314.Didn't retrieve data . /1385461311.Didn't retrieve data . /1385461308.Didn't retrieve data . /1385461307.Didn't retrieve data . /1385461267.Didn't retrieve data . /1385461264.Didn't retrieve data . /1385461260.Didn't retrieve data . /1385461258.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, '3759017') ('3318', '27096242', '1385461436', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461433', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461421', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461419', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461417', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461413', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461412', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461409', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461402', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461400', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461397', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461394', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461393', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461361', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461359', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461356', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461353', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461351', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461335', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461333', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461331', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461329', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461328', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461327', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461319', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461317', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461314', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461311', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461308', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461307', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461267', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461264', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461260', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461258', None, None, None, None, None, '3759017') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 102 time used for this insertion : 0.01775956153869629 save_final save missing photos in datou_result : time spend for datou_step_exec : 25.99903964996338 time spend to save output : 0.01862025260925293 total time spend for step 3 : 26.017659902572632 step4:ventilate_hashtags_in_portfolio Mon Sep 22 16:13:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 27096242 get user id for portfolio 27096242 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`=27096242 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','environnement','flou','mal_croppe','pehd','pet_fonce','autre','metal','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`=27096242 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','environnement','flou','mal_croppe','pehd','pet_fonce','autre','metal','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`=27096242 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','environnement','flou','mal_croppe','pehd','pet_fonce','autre','metal','background','papier')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://marlene.fotonower.com/velours/27096564,27096565,27096566,27096567,27096568,27096569,27096570,27096571,27096572,27096573,27096574?tags=carton,pet_clair,environnement,flou,mal_croppe,pehd,pet_fonce,autre,metal,background,papier Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1385461436, 1385461433, 1385461421, 1385461419, 1385461417, 1385461413, 1385461412, 1385461409, 1385461402, 1385461400, 1385461397, 1385461394, 1385461393, 1385461361, 1385461359, 1385461356, 1385461353, 1385461351, 1385461335, 1385461333, 1385461331, 1385461329, 1385461328, 1385461327, 1385461319, 1385461317, 1385461314, 1385461311, 1385461308, 1385461307, 1385461267, 1385461264, 1385461260, 1385461258] Looping around the photos to save general results len do output : 1 /27096242. 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, '3759017') ('3318', '27096242', '1385461436', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461433', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461421', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461419', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461417', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461413', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461412', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461409', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461402', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461400', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461397', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461394', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461393', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461361', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461359', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461356', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461353', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461351', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461335', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461333', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461331', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461329', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461328', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461327', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461319', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461317', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461314', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461311', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461308', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461307', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461267', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461264', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461260', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461258', None, None, None, None, None, '3759017') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 35 time used for this insertion : 0.019706249237060547 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.7420623302459717 time spend to save output : 0.02012157440185547 total time spend for step 4 : 0.7621839046478271 step5:final Mon Sep 22 16:13:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1385461436: ('0.06591898999183013',), 1385461433: ('0.06591898999183013',), 1385461421: ('0.06591898999183013',), 1385461419: ('0.06591898999183013',), 1385461417: ('0.06591898999183013',), 1385461413: ('0.06591898999183013',), 1385461412: ('0.06591898999183013',), 1385461409: ('0.06591898999183013',), 1385461402: ('0.06591898999183013',), 1385461400: ('0.06591898999183013',), 1385461397: ('0.06591898999183013',), 1385461394: ('0.06591898999183013',), 1385461393: ('0.06591898999183013',), 1385461361: ('0.06591898999183013',), 1385461359: ('0.06591898999183013',), 1385461356: ('0.06591898999183013',), 1385461353: ('0.06591898999183013',), 1385461351: ('0.06591898999183013',), 1385461335: ('0.06591898999183013',), 1385461333: ('0.06591898999183013',), 1385461331: ('0.06591898999183013',), 1385461329: ('0.06591898999183013',), 1385461328: ('0.06591898999183013',), 1385461327: ('0.06591898999183013',), 1385461319: ('0.06591898999183013',), 1385461317: ('0.06591898999183013',), 1385461314: ('0.06591898999183013',), 1385461311: ('0.06591898999183013',), 1385461308: ('0.06591898999183013',), 1385461307: ('0.06591898999183013',), 1385461267: ('0.06591898999183013',), 1385461264: ('0.06591898999183013',), 1385461260: ('0.06591898999183013',), 1385461258: ('0.06591898999183013',)} new output for save of step final : {1385461436: ('0.06591898999183013',), 1385461433: ('0.06591898999183013',), 1385461421: ('0.06591898999183013',), 1385461419: ('0.06591898999183013',), 1385461417: ('0.06591898999183013',), 1385461413: ('0.06591898999183013',), 1385461412: ('0.06591898999183013',), 1385461409: ('0.06591898999183013',), 1385461402: ('0.06591898999183013',), 1385461400: ('0.06591898999183013',), 1385461397: ('0.06591898999183013',), 1385461394: ('0.06591898999183013',), 1385461393: ('0.06591898999183013',), 1385461361: ('0.06591898999183013',), 1385461359: ('0.06591898999183013',), 1385461356: ('0.06591898999183013',), 1385461353: ('0.06591898999183013',), 1385461351: ('0.06591898999183013',), 1385461335: ('0.06591898999183013',), 1385461333: ('0.06591898999183013',), 1385461331: ('0.06591898999183013',), 1385461329: ('0.06591898999183013',), 1385461328: ('0.06591898999183013',), 1385461327: ('0.06591898999183013',), 1385461319: ('0.06591898999183013',), 1385461317: ('0.06591898999183013',), 1385461314: ('0.06591898999183013',), 1385461311: ('0.06591898999183013',), 1385461308: ('0.06591898999183013',), 1385461307: ('0.06591898999183013',), 1385461267: ('0.06591898999183013',), 1385461264: ('0.06591898999183013',), 1385461260: ('0.06591898999183013',), 1385461258: ('0.06591898999183013',)} [1385461436, 1385461433, 1385461421, 1385461419, 1385461417, 1385461413, 1385461412, 1385461409, 1385461402, 1385461400, 1385461397, 1385461394, 1385461393, 1385461361, 1385461359, 1385461356, 1385461353, 1385461351, 1385461335, 1385461333, 1385461331, 1385461329, 1385461328, 1385461327, 1385461319, 1385461317, 1385461314, 1385461311, 1385461308, 1385461307, 1385461267, 1385461264, 1385461260, 1385461258] Looping around the photos to save general results len do output : 34 /1385461436.Didn't retrieve data . /1385461433.Didn't retrieve data . /1385461421.Didn't retrieve data . /1385461419.Didn't retrieve data . /1385461417.Didn't retrieve data . /1385461413.Didn't retrieve data . /1385461412.Didn't retrieve data . /1385461409.Didn't retrieve data . /1385461402.Didn't retrieve data . /1385461400.Didn't retrieve data . /1385461397.Didn't retrieve data . /1385461394.Didn't retrieve data . /1385461393.Didn't retrieve data . /1385461361.Didn't retrieve data . /1385461359.Didn't retrieve data . /1385461356.Didn't retrieve data . /1385461353.Didn't retrieve data . /1385461351.Didn't retrieve data . /1385461335.Didn't retrieve data . /1385461333.Didn't retrieve data . /1385461331.Didn't retrieve data . /1385461329.Didn't retrieve data . /1385461328.Didn't retrieve data . /1385461327.Didn't retrieve data . /1385461319.Didn't retrieve data . /1385461317.Didn't retrieve data . /1385461314.Didn't retrieve data . /1385461311.Didn't retrieve data . /1385461308.Didn't retrieve data . /1385461307.Didn't retrieve data . /1385461267.Didn't retrieve data . /1385461264.Didn't retrieve data . /1385461260.Didn't retrieve data . /1385461258.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, '3759017') ('3318', '27096242', '1385461436', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461433', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461421', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461419', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461417', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461413', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461412', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461409', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461402', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461400', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461397', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461394', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461393', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461361', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461359', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461356', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461353', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461351', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461335', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461333', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461331', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461329', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461328', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461327', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461319', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461317', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461314', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461311', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461308', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461307', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461267', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461264', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461260', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461258', None, None, None, None, None, '3759017') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 102 time used for this insertion : 0.016361474990844727 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.1523606777191162 time spend to save output : 0.017728567123413086 total time spend for step 5 : 0.1700892448425293 step6:blur_detection Mon Sep 22 16:13:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab.jpg resize: (1080, 1920) 1385461436 0.3427348242254167 treat image : temp/1758550229_803035_1385461433_96a7d44ea10041d7bdbc273684c7623d.jpg resize: (1080, 1920) 1385461433 1.3088801088319082 treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255.jpg resize: (1080, 1920) 1385461421 1.371715201929634 treat image : temp/1758550229_803035_1385461419_4a4ce228588e1ce26093e5e2c7615273.jpg resize: (1080, 1920) 1385461419 0.6216447420029998 treat image : temp/1758550229_803035_1385461417_8f57ae550bb8d4f441169b5e42a48024.jpg resize: (1080, 1920) 1385461417 1.035410621479173 treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49.jpg resize: (1080, 1920) 1385461413 -3.3208863335931706 treat image : temp/1758550229_803035_1385461412_910860fae65c0401ec4d43ce805378f9.jpg resize: (1080, 1920) 1385461412 -0.12514162383579114 treat image : temp/1758550229_803035_1385461409_db17de8d4f70a7ea1d83be621e37b611.jpg resize: (1080, 1920) 1385461409 1.6126408696979346 treat image : temp/1758550229_803035_1385461402_ae8854b48096e36196a7af8280ff2e55.jpg resize: (1080, 1920) 1385461402 0.3668432315274394 treat image : temp/1758550229_803035_1385461400_a0983c01a90677103c806030c1c94bf6.jpg resize: (1080, 1920) 1385461400 0.5632596708755512 treat image : temp/1758550229_803035_1385461397_ff6308f244e47cec29ce46150e797540.jpg resize: (1080, 1920) 1385461397 0.9152124891471524 treat image : temp/1758550229_803035_1385461394_ff6d3bfe22ac4644f8c106487e386e89.jpg resize: (1080, 1920) 1385461394 1.1945592485929146 treat image : temp/1758550229_803035_1385461393_2bc22f3d17674acc77d72d1c83445f1b.jpg resize: (1080, 1920) 1385461393 0.6034524657724895 treat image : temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282.jpg resize: (1080, 1920) 1385461361 0.6081367902267708 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57.jpg resize: (1080, 1920) 1385461359 1.0126511979016957 treat image : temp/1758550229_803035_1385461356_d1c62fd2cf43cd12a3f110e2436265f7.jpg resize: (1080, 1920) 1385461356 -3.0594097426122584 treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0.jpg resize: (1080, 1920) 1385461353 3.097225570434978 treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7.jpg resize: (1080, 1920) 1385461351 -3.1485449637554113 treat image : temp/1758550229_803035_1385461335_23ad54dacf8c957739b8d2b227062334.jpg resize: (1080, 1920) 1385461335 1.5571019642642943 treat image : temp/1758550229_803035_1385461333_4f4873a17e4e6dbb8f1eb568c1376905.jpg resize: (1080, 1920) 1385461333 0.8320739594439531 treat image : temp/1758550229_803035_1385461331_7567cd136dc5156ec63e1c2d859fa988.jpg resize: (1080, 1920) 1385461331 0.3990137820829352 treat image : temp/1758550229_803035_1385461329_5d16f2a8ff433fce74b4a3e10fddb9c4.jpg resize: (1080, 1920) 1385461329 -2.3413092599472884 treat image : temp/1758550229_803035_1385461328_1389c1f3cce88aa5f07d2584bbe483b0.jpg resize: (1080, 1920) 1385461328 5.879262121876563 treat image : temp/1758550229_803035_1385461327_80a243e028fa2b68a1ab81544d3ca70d.jpg resize: (1080, 1920) 1385461327 -0.06276583398070255 treat image : temp/1758550229_803035_1385461319_cc2e1f0ca152784d4588da6e5c84576f.jpg resize: (1080, 1920) 1385461319 -0.19476750546918448 treat image : temp/1758550229_803035_1385461317_4a192045e56e6f8589ac599f329e6606.jpg resize: (1080, 1920) 1385461317 1.0881499968781385 treat image : temp/1758550229_803035_1385461314_cfa49e45015bece03af0f1d11260d54e.jpg resize: (1080, 1920) 1385461314 0.8861505729405785 treat image : temp/1758550229_803035_1385461311_8af8e08d301abbb75161eea062c8bdbe.jpg resize: (1080, 1920) 1385461311 0.3478235610709353 treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097.jpg resize: (1080, 1920) 1385461308 0.925179237959657 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54.jpg resize: (1080, 1920) 1385461307 -0.08375593938921887 treat image : temp/1758550229_803035_1385461267_5543ab0b9ef231c668f7c91ece797d48.jpg resize: (1080, 1920) 1385461267 0.19443509031535566 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0.jpg resize: (1080, 1920) 1385461264 1.2950945430260858 treat image : temp/1758550229_803035_1385461260_453699025fcfa8b5f3fb5e5625e18b52.jpg resize: (1080, 1920) 1385461260 0.8499129972241347 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b.jpg resize: (1080, 1920) 1385461258 0.6124896414242589 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152962_0.png resize: (72, 127) 1385482116 -0.7778508906831849 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152963_0.png resize: (129, 120) 1385482117 -1.0659991900191432 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152964_0.png resize: (179, 112) 1385482118 -0.9220350850182655 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152968_0.png resize: (70, 97) 1385482119 -2.5389689004759144 treat image : temp/1758550229_803035_1385461433_96a7d44ea10041d7bdbc273684c7623d_rle_crop_3970152971_0.png resize: (183, 113) 1385482120 -1.3611312838787832 treat image : temp/1758550229_803035_1385461433_96a7d44ea10041d7bdbc273684c7623d_rle_crop_3970152972_0.png resize: (134, 50) 1385482121 -0.3946701295015681 treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255_rle_crop_3970152975_0.png resize: (186, 115) 1385482122 -0.8651727450940723 treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255_rle_crop_3970152976_0.png resize: (166, 68) 1385482123 -1.889490059891491 treat image : temp/1758550229_803035_1385461419_4a4ce228588e1ce26093e5e2c7615273_rle_crop_3970152981_0.png resize: (182, 121) 1385482124 -0.7485697101476074 treat image : temp/1758550229_803035_1385461417_8f57ae550bb8d4f441169b5e42a48024_rle_crop_3970152985_0.png resize: (109, 66) 1385482125 0.34520278610631183 treat image : temp/1758550229_803035_1385461417_8f57ae550bb8d4f441169b5e42a48024_rle_crop_3970152986_0.png resize: (187, 119) 1385482126 -0.4337665867664189 treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49_rle_crop_3970152992_0.png resize: (114, 98) 1385482127 -3.0864926076204164 treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49_rle_crop_3970152993_0.png resize: (99, 86) 1385482128 -2.6027602881710217 treat image : temp/1758550229_803035_1385461412_910860fae65c0401ec4d43ce805378f9_rle_crop_3970152995_0.png resize: (68, 121) 1385482129 0.1834677099204134 treat image : temp/1758550229_803035_1385461409_db17de8d4f70a7ea1d83be621e37b611_rle_crop_3970152996_0.png resize: (188, 118) 1385482130 -0.3383983298961019 treat image : temp/1758550229_803035_1385461402_ae8854b48096e36196a7af8280ff2e55_rle_crop_3970153000_0.png resize: (185, 118) 1385482131 -0.26108072231732726 treat image : temp/1758550229_803035_1385461402_ae8854b48096e36196a7af8280ff2e55_rle_crop_3970153001_0.png resize: (69, 87) 1385482132 0.20343650917067507 treat image : temp/1758550229_803035_1385461402_ae8854b48096e36196a7af8280ff2e55_rle_crop_3970153002_0.png resize: (159, 302) 1385482133 -1.1502191026825883 treat image : temp/1758550229_803035_1385461400_a0983c01a90677103c806030c1c94bf6_rle_crop_3970153005_0.png resize: (183, 116) 1385482134 -0.38132297331566894 treat image : temp/1758550229_803035_1385461397_ff6308f244e47cec29ce46150e797540_rle_crop_3970153008_0.png resize: (181, 283) 1385482135 -1.256020084693088 treat image : temp/1758550229_803035_1385461397_ff6308f244e47cec29ce46150e797540_rle_crop_3970153009_0.png resize: (110, 91) 1385482136 -1.9660245794451496 treat image : temp/1758550229_803035_1385461397_ff6308f244e47cec29ce46150e797540_rle_crop_3970153013_0.png resize: (133, 74) 1385482137 -1.599992649356084 treat image : temp/1758550229_803035_1385461394_ff6d3bfe22ac4644f8c106487e386e89_rle_crop_3970153014_0.png resize: (181, 114) 1385482138 -0.3859125461715134 treat image : temp/1758550229_803035_1385461394_ff6d3bfe22ac4644f8c106487e386e89_rle_crop_3970153015_0.png resize: (161, 187) 1385482139 -0.6510260200366114 treat image : temp/1758550229_803035_1385461394_ff6d3bfe22ac4644f8c106487e386e89_rle_crop_3970153016_0.png resize: (125, 42) 1385482140 -1.3632472866914858 treat image : temp/1758550229_803035_1385461394_ff6d3bfe22ac4644f8c106487e386e89_rle_crop_3970153017_0.png resize: (124, 147) 1385482142 0.7009195565993922 treat image : temp/1758550229_803035_1385461393_2bc22f3d17674acc77d72d1c83445f1b_rle_crop_3970153021_0.png resize: (177, 107) 1385482143 -0.5449735528307906 treat image : temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282_rle_crop_3970153025_0.png resize: (142, 149) 1385482144 -0.05035734017280265 treat image : temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282_rle_crop_3970153026_0.png resize: (182, 114) 1385482145 -0.34449356582296514 treat image : temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282_rle_crop_3970153027_0.png resize: (162, 71) 1385482147 -1.868483590253135 treat image : temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282_rle_crop_3970153030_0.png resize: (41, 69) 1385482148 20.0 treat image : temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282_rle_crop_3970153031_0.png resize: (45, 51) 1385482149 5.701335824376978 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57_rle_crop_3970153032_0.png resize: (178, 119) 1385482150 -0.43586876720693724 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57_rle_crop_3970153033_0.png resize: (72, 124) 1385482151 -1.0074757241493444 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57_rle_crop_3970153034_0.png resize: (58, 114) 1385482152 2.087787277379088 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57_rle_crop_3970153035_0.png resize: (88, 89) 1385482153 0.5193204247727166 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57_rle_crop_3970153036_0.png resize: (137, 164) 1385482154 0.11540557255044662 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57_rle_crop_3970153038_0.png resize: (82, 138) 1385482155 -0.8422659410327163 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57_rle_crop_3970153039_0.png resize: (195, 54) 1385482156 -1.2210784389199862 treat image : temp/1758550229_803035_1385461356_d1c62fd2cf43cd12a3f110e2436265f7_rle_crop_3970153040_0.png resize: (494, 119) 1385482157 -2.0883368123228356 treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153044_0.png resize: (144, 121) 1385482158 -2.174227555464507 treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153045_0.png resize: (186, 118) 1385482159 -0.2679085072251459 treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153047_0.png resize: (176, 321) 1385482160 0.5214945496078145 treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7_rle_crop_3970153050_0.png resize: (178, 121) 1385482161 -0.5610129954102563 treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7_rle_crop_3970153054_0.png resize: (115, 281) 1385482162 -4.292200293754945 treat image : temp/1758550229_803035_1385461335_23ad54dacf8c957739b8d2b227062334_rle_crop_3970153057_0.png resize: (188, 115) 1385482163 -0.7593874179996194 treat image : temp/1758550229_803035_1385461333_4f4873a17e4e6dbb8f1eb568c1376905_rle_crop_3970153060_0.png resize: (185, 115) 1385482164 -0.3366404802699651 treat image : temp/1758550229_803035_1385461333_4f4873a17e4e6dbb8f1eb568c1376905_rle_crop_3970153063_0.png resize: (76, 114) 1385482165 1.721977295616737 treat image : temp/1758550229_803035_1385461331_7567cd136dc5156ec63e1c2d859fa988_rle_crop_3970153064_0.png resize: (183, 115) 1385482166 -0.3237836976292786 treat image : temp/1758550229_803035_1385461329_5d16f2a8ff433fce74b4a3e10fddb9c4_rle_crop_3970153066_0.png resize: (95, 139) 1385482167 -2.1369438630000195 treat image : temp/1758550229_803035_1385461329_5d16f2a8ff433fce74b4a3e10fddb9c4_rle_crop_3970153067_0.png resize: (188, 108) 1385482169 -0.4854983980282487 treat image : temp/1758550229_803035_1385461329_5d16f2a8ff433fce74b4a3e10fddb9c4_rle_crop_3970153068_0.png resize: (109, 98) 1385482170 -2.8083761788481625 treat image : temp/1758550229_803035_1385461329_5d16f2a8ff433fce74b4a3e10fddb9c4_rle_crop_3970153070_0.png resize: (115, 161) 1385482171 -1.7136260623427844 treat image : temp/1758550229_803035_1385461329_5d16f2a8ff433fce74b4a3e10fddb9c4_rle_crop_3970153071_0.png resize: (112, 127) 1385482172 -2.120033498088837 treat image : temp/1758550229_803035_1385461328_1389c1f3cce88aa5f07d2584bbe483b0_rle_crop_3970153073_0.png resize: (184, 133) 1385482173 -1.3545102099417357 treat image : temp/1758550229_803035_1385461327_80a243e028fa2b68a1ab81544d3ca70d_rle_crop_3970153074_0.png resize: (249, 59) 1385482174 -2.541592207275706 treat image : temp/1758550229_803035_1385461327_80a243e028fa2b68a1ab81544d3ca70d_rle_crop_3970153075_0.png resize: (184, 113) 1385482175 -0.4718596656756132 treat image : temp/1758550229_803035_1385461327_80a243e028fa2b68a1ab81544d3ca70d_rle_crop_3970153076_0.png resize: (68, 115) 1385482176 -2.0017296435456617 treat image : temp/1758550229_803035_1385461319_cc2e1f0ca152784d4588da6e5c84576f_rle_crop_3970153077_0.png resize: (85, 60) 1385482177 8.21549582377911 treat image : temp/1758550229_803035_1385461319_cc2e1f0ca152784d4588da6e5c84576f_rle_crop_3970153078_0.png resize: (182, 116) 1385482178 -0.4414809077923324 treat image : temp/1758550229_803035_1385461319_cc2e1f0ca152784d4588da6e5c84576f_rle_crop_3970153079_0.png resize: (207, 168) 1385482179 -0.627168434846798 treat image : temp/1758550229_803035_1385461317_4a192045e56e6f8589ac599f329e6606_rle_crop_3970153080_0.png resize: (180, 118) 1385482180 -0.375434459344484 treat image : temp/1758550229_803035_1385461317_4a192045e56e6f8589ac599f329e6606_rle_crop_3970153081_0.png resize: (109, 89) 1385482181 -1.9526110262914167 treat image : temp/1758550229_803035_1385461317_4a192045e56e6f8589ac599f329e6606_rle_crop_3970153082_0.png resize: (75, 54) 1385482182 -0.8071660308007774 treat image : temp/1758550229_803035_1385461317_4a192045e56e6f8589ac599f329e6606_rle_crop_3970153083_0.png resize: (64, 248) 1385482183 -0.3719660621474166 treat image : temp/1758550229_803035_1385461314_cfa49e45015bece03af0f1d11260d54e_rle_crop_3970153085_0.png resize: (184, 114) 1385482184 -0.47734184028966004 treat image : temp/1758550229_803035_1385461314_cfa49e45015bece03af0f1d11260d54e_rle_crop_3970153091_0.png resize: (183, 210) 1385482185 -0.7272146044710134 treat image : temp/1758550229_803035_1385461311_8af8e08d301abbb75161eea062c8bdbe_rle_crop_3970153092_0.png resize: (186, 114) 1385482186 -0.8349764661869578 treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153097_0.png resize: (185, 116) 1385482187 -0.4532740895364134 treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153098_0.png resize: (192, 171) 1385482188 -0.8688257226877169 treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153100_0.png resize: (100, 61) 1385482189 -2.6229013783848023 treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153101_0.png resize: (150, 89) 1385482190 0.08001563578612796 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153107_0.png resize: (175, 115) 1385482192 -0.5066921081343131 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153111_0.png resize: (182, 74) 1385482193 -2.315761754851564 treat image : temp/1758550229_803035_1385461267_5543ab0b9ef231c668f7c91ece797d48_rle_crop_3970153114_0.png resize: (178, 114) 1385482194 -0.809277590674281 treat image : temp/1758550229_803035_1385461267_5543ab0b9ef231c668f7c91ece797d48_rle_crop_3970153115_0.png resize: (149, 155) 1385482195 1.2341125844348941 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153120_0.png resize: (157, 210) 1385482196 -1.4924726631040306 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153121_0.png resize: (164, 121) 1385482197 -1.8765544950724344 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153127_0.png resize: (169, 108) 1385482198 -0.41791232557222907 treat image : temp/1758550229_803035_1385461260_453699025fcfa8b5f3fb5e5625e18b52_rle_crop_3970153131_0.png resize: (182, 117) 1385482199 -0.4410473896122531 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153135_0.png resize: (162, 112) 1385482200 -0.37079533916583657 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153136_0.png resize: (76, 89) 1385482201 -1.2842825685924477 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152970_0.png resize: (149, 100) 1385482202 -0.9945172251748496 treat image : temp/1758550229_803035_1385461433_96a7d44ea10041d7bdbc273684c7623d_rle_crop_3970152974_0.png resize: (155, 99) 1385482203 -1.220080061955798 treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49_rle_crop_3970152990_0.png resize: (186, 143) 1385482204 -2.297620591324067 treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49_rle_crop_3970152994_0.png resize: (576, 364) 1385482205 -0.21943656135937523 treat image : temp/1758550229_803035_1385461409_db17de8d4f70a7ea1d83be621e37b611_rle_crop_3970152997_0.png resize: (70, 103) 1385482206 0.12104663940756268 treat image : temp/1758550229_803035_1385461356_d1c62fd2cf43cd12a3f110e2436265f7_rle_crop_3970153043_0.png resize: (522, 365) 1385482207 -0.0650102836610109 treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7_rle_crop_3970153055_0.png resize: (541, 362) 1385482208 -0.17423157074661344 treat image : temp/1758550229_803035_1385461335_23ad54dacf8c957739b8d2b227062334_rle_crop_3970153058_0.png resize: (122, 85) 1385482209 -1.7079854891007056 treat image : temp/1758550229_803035_1385461329_5d16f2a8ff433fce74b4a3e10fddb9c4_rle_crop_3970153065_0.png resize: (107, 81) 1385482210 -1.186604227746565 treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153103_0.png resize: (553, 331) 1385482211 -0.7445046803507455 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153113_0.png resize: (93, 134) 1385482212 -1.1851208756158647 treat image : temp/1758550229_803035_1385461267_5543ab0b9ef231c668f7c91ece797d48_rle_crop_3970153119_0.png resize: (517, 342) 1385482213 -0.2920592226764998 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153128_0.png resize: (289, 174) 1385482214 -1.554782445175143 treat image : temp/1758550229_803035_1385461314_cfa49e45015bece03af0f1d11260d54e_rle_crop_3970153090_0.png resize: (130, 123) 1385482215 -1.5544966934173463 treat image : temp/1758550229_803035_1385461311_8af8e08d301abbb75161eea062c8bdbe_rle_crop_3970153095_0.png resize: (112, 117) 1385482216 -1.3264280254264533 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153130_0.png resize: (90, 99) 1385482217 -1.7937815492931533 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153140_0.png resize: (66, 58) 1385482218 0.2678218885782082 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152965_0.png resize: (590, 584) 1385482245 -1.8315154401273153 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152966_0.png resize: (100, 133) 1385482246 -1.359249286997957 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152967_0.png resize: (173, 162) 1385482247 -1.9890404545771965 treat image : temp/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab_rle_crop_3970152969_0.png resize: (508, 310) 1385482248 0.7058447188274207 treat image : temp/1758550229_803035_1385461433_96a7d44ea10041d7bdbc273684c7623d_rle_crop_3970152973_0.png resize: (1004, 1409) 1385482249 -1.0558517779965848 treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255_rle_crop_3970152977_0.png resize: (164, 202) 1385482250 -1.6677109147143157 treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255_rle_crop_3970152978_0.png resize: (176, 269) 1385482251 -1.0466831081021026 treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255_rle_crop_3970152979_0.png resize: (155, 222) 1385482252 -0.9790311825890701 treat image : temp/1758550229_803035_1385461419_4a4ce228588e1ce26093e5e2c7615273_rle_crop_3970152982_0.png resize: (73, 82) 1385482253 0.8546366200776393 treat image : temp/1758550229_803035_1385461419_4a4ce228588e1ce26093e5e2c7615273_rle_crop_3970152984_0.png resize: (121, 69) 1385482255 -0.2317564140448817 treat image : temp/1758550229_803035_1385461417_8f57ae550bb8d4f441169b5e42a48024_rle_crop_3970152987_0.png resize: (156, 345) 1385482256 -0.2561279763772156 treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49_rle_crop_3970152988_0.png resize: (124, 139) 1385482257 -4.2496074735573 treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49_rle_crop_3970152989_0.png resize: (113, 123) 1385482258 -3.9219018551197187 treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49_rle_crop_3970152991_0.png resize: (142, 161) 1385482259 -3.1854154383723094 treat image : temp/1758550229_803035_1385461409_db17de8d4f70a7ea1d83be621e37b611_rle_crop_3970152999_0.png resize: (548, 371) 1385482260 0.10238809984154731 treat image : temp/1758550229_803035_1385461402_ae8854b48096e36196a7af8280ff2e55_rle_crop_3970153004_0.png resize: (107, 140) 1385482261 -1.886575896400364 treat image : temp/1758550229_803035_1385461400_a0983c01a90677103c806030c1c94bf6_rle_crop_3970153006_0.png resize: (91, 124) 1385482262 -1.0291514264937596 treat image : temp/1758550229_803035_1385461400_a0983c01a90677103c806030c1c94bf6_rle_crop_3970153007_0.png resize: (355, 189) 1385482264 -1.144710380992091 treat image : temp/1758550229_803035_1385461397_ff6308f244e47cec29ce46150e797540_rle_crop_3970153010_0.png resize: (526, 352) 1385482265 0.13481647848044465 treat image : temp/1758550229_803035_1385461397_ff6308f244e47cec29ce46150e797540_rle_crop_3970153011_0.png resize: (133, 236) 1385482266 -1.284546100869233 treat image : temp/1758550229_803035_1385461397_ff6308f244e47cec29ce46150e797540_rle_crop_3970153012_0.png resize: (177, 182) 1385482268 -0.9067505259145561 treat image : 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temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282_rle_crop_3970153029_0.png resize: (182, 163) 1385482275 -1.6765225271125508 treat image : temp/1758550229_803035_1385461359_56bb4daf2b31f14c63fdda429ef59b57_rle_crop_3970153037_0.png resize: (113, 186) 1385482276 -0.32234893140342125 treat image : temp/1758550229_803035_1385461356_d1c62fd2cf43cd12a3f110e2436265f7_rle_crop_3970153041_0.png resize: (144, 127) 1385482277 -2.5815316903391685 treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7_rle_crop_3970153051_0.png resize: (119, 234) 1385482278 -2.9082912121077142 treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7_rle_crop_3970153052_0.png resize: (123, 167) 1385482279 -3.525120098451039 treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7_rle_crop_3970153053_0.png resize: (59, 202) 1385482280 -2.7569658169837483 treat image : 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temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153099_0.png resize: (160, 349) 1385482295 -1.759035944816059 treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153104_0.png resize: (75, 135) 1385482296 0.6340141952278647 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153105_0.png resize: (92, 206) 1385482297 1.5513045967050394 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153110_0.png resize: (490, 513) 1385482298 -1.0675376130495524 treat image : temp/1758550229_803035_1385461267_5543ab0b9ef231c668f7c91ece797d48_rle_crop_3970153116_0.png resize: (166, 150) 1385482300 -1.262468797135414 treat image : temp/1758550229_803035_1385461267_5543ab0b9ef231c668f7c91ece797d48_rle_crop_3970153117_0.png resize: (412, 617) 1385482301 -1.1343453647928827 treat image : temp/1758550229_803035_1385461267_5543ab0b9ef231c668f7c91ece797d48_rle_crop_3970153118_0.png resize: (80, 101) 1385482302 -3.30522655680389 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153122_0.png resize: (147, 140) 1385482304 -1.20595238426966 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153123_0.png resize: (108, 177) 1385482305 -1.5786336061571267 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153124_0.png resize: (115, 194) 1385482306 -1.0169471304388602 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153125_0.png resize: (222, 134) 1385482308 -1.227415246324125 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153126_0.png resize: (125, 181) 1385482309 0.2675241082398333 treat image : temp/1758550229_803035_1385461264_69d489b84ecc6717ee5e64e3026544d0_rle_crop_3970153129_0.png resize: (105, 199) 1385482310 -1.9580930142545465 treat image : temp/1758550229_803035_1385461260_453699025fcfa8b5f3fb5e5625e18b52_rle_crop_3970153132_0.png resize: (198, 251) 1385482312 -0.8971945339355211 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153133_0.png resize: (232, 187) 1385482313 -0.6625878312151962 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153134_0.png resize: (228, 194) 1385482314 -0.6651107783222957 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153137_0.png resize: (295, 169) 1385482316 -2.488521568363292 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153138_0.png resize: (167, 250) 1385482317 0.35086819221643384 treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153139_0.png resize: (177, 189) 1385482319 -1.2538324972113732 treat image : temp/1758550229_803035_1385461419_4a4ce228588e1ce26093e5e2c7615273_rle_crop_3970152983_0.png resize: (236, 149) 1385482342 -0.6737310236316121 treat image : temp/1758550229_803035_1385461409_db17de8d4f70a7ea1d83be621e37b611_rle_crop_3970152998_0.png resize: (147, 198) 1385482344 -0.3503189053194676 treat image : temp/1758550229_803035_1385461402_ae8854b48096e36196a7af8280ff2e55_rle_crop_3970153003_0.png resize: (118, 97) 1385482345 -0.8343225560480954 treat image : temp/1758550229_803035_1385461356_d1c62fd2cf43cd12a3f110e2436265f7_rle_crop_3970153042_0.png resize: (97, 123) 1385482346 -0.5868522382022442 treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153049_0.png resize: (144, 139) 1385482348 -1.2843339086273624 treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7_rle_crop_3970153056_0.png resize: (76, 62) 1385482349 5.4213078931077865 treat image : temp/1758550229_803035_1385461311_8af8e08d301abbb75161eea062c8bdbe_rle_crop_3970153096_0.png resize: (99, 107) 1385482350 -1.3243789634899854 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153106_0.png resize: (252, 208) 1385482351 -1.000092868466563 treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255_rle_crop_3970152980_0.png resize: (125, 178) 1385482363 0.23737561018945527 treat image : temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282_rle_crop_3970153028_0.png resize: (123, 118) 1385482364 -1.092433595932959 treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153046_0.png resize: (207, 137) 1385482365 -0.8913508470080911 treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153048_0.png resize: (178, 141) 1385482366 -0.5183663705863214 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153112_0.png resize: (101, 81) 1385482367 0.8937935377149749 treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153102_0.png resize: (78, 121) 1385482374 0.8716196966064904 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153108_0.png resize: (70, 163) 1385482375 -0.5547792917686823 treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153109_0.png resize: (113, 213) 1385482376 -1.5255026760970227 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 : 213 time used for this insertion : 0.0205686092376709 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 213 time used for this insertion : 0.045881032943725586 save missing photos in datou_result : time spend for datou_step_exec : 28.35058331489563 time spend to save output : 0.07187008857727051 total time spend for step 6 : 28.4224534034729 step7:brightness Mon Sep 22 16:13:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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/1758550229_803035_1385461436_f04dbaafb4be779d1868f766933010ab.jpg treat image : temp/1758550229_803035_1385461433_96a7d44ea10041d7bdbc273684c7623d.jpg treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255.jpg treat image : temp/1758550229_803035_1385461419_4a4ce228588e1ce26093e5e2c7615273.jpg treat image : temp/1758550229_803035_1385461417_8f57ae550bb8d4f441169b5e42a48024.jpg treat image : temp/1758550229_803035_1385461413_77897eb0d4bdcbc08c43fb1a4fcd5f49.jpg treat image : temp/1758550229_803035_1385461412_910860fae65c0401ec4d43ce805378f9.jpg treat image : temp/1758550229_803035_1385461409_db17de8d4f70a7ea1d83be621e37b611.jpg treat image : temp/1758550229_803035_1385461402_ae8854b48096e36196a7af8280ff2e55.jpg treat image : temp/1758550229_803035_1385461400_a0983c01a90677103c806030c1c94bf6.jpg treat image : 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temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153137_0.png treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153138_0.png treat image : temp/1758550229_803035_1385461258_ff40436b026a78c3890b46048ed2147b_rle_crop_3970153139_0.png treat image : temp/1758550229_803035_1385461419_4a4ce228588e1ce26093e5e2c7615273_rle_crop_3970152983_0.png treat image : temp/1758550229_803035_1385461409_db17de8d4f70a7ea1d83be621e37b611_rle_crop_3970152998_0.png treat image : temp/1758550229_803035_1385461402_ae8854b48096e36196a7af8280ff2e55_rle_crop_3970153003_0.png treat image : temp/1758550229_803035_1385461356_d1c62fd2cf43cd12a3f110e2436265f7_rle_crop_3970153042_0.png treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153049_0.png treat image : temp/1758550229_803035_1385461351_01036fc565ab5ffdd9a4cb9bfdce6be7_rle_crop_3970153056_0.png treat image : temp/1758550229_803035_1385461311_8af8e08d301abbb75161eea062c8bdbe_rle_crop_3970153096_0.png treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153106_0.png treat image : temp/1758550229_803035_1385461421_8196139d16325931a30cafc3cf16f255_rle_crop_3970152980_0.png treat image : temp/1758550229_803035_1385461361_2dc94fa7d8e1686a5451e5257343a282_rle_crop_3970153028_0.png treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153046_0.png treat image : temp/1758550229_803035_1385461353_6cbe615239a8b10de1af4e2d9a6e44b0_rle_crop_3970153048_0.png treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153112_0.png treat image : temp/1758550229_803035_1385461308_6d4fbb54aa4840d369c8f06b73b86097_rle_crop_3970153102_0.png treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153108_0.png treat image : temp/1758550229_803035_1385461307_76b3b3207a6c2c14c658aa1770e06d54_rle_crop_3970153109_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 : 213 time used for this insertion : 0.02393317222595215 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 213 time used for this insertion : 0.0551605224609375 save missing photos in datou_result : time spend for datou_step_exec : 8.195433378219604 time spend to save output : 0.08539795875549316 total time spend for step 7 : 8.280831336975098 step8:velours_tree Mon Sep 22 16:13:39 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 : 0.08257198333740234 time spend to save output : 7.009506225585938e-05 total time spend for step 8 : 0.0826420783996582 step9:send_mail_cod Mon Sep 22 16:13:39 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_P27096242_22-09-2025_16_13_39.pdf 27096564 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette270965641758550419 27096565 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette270965651758550420 27096567 imagette270965671758550421 27096568 imagette270965681758550421 27096569 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette270965691758550421 27096570 change filename to text .change filename to text .change filename to text .imagette270965701758550422 27096571 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette270965711758550422 27096572 change filename to text .change filename to text .change filename to text .change filename to text .imagette270965721758550423 27096573 imagette270965731758550423 27096574 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette270965741758550423 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=27096242 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://marlene.fotonower.com/velours/27096564,27096565,27096566,27096567,27096568,27096569,27096570,27096571,27096572,27096573,27096574?tags=carton,pet_clair,environnement,flou,mal_croppe,pehd,pet_fonce,autre,metal,background,papier args[1385461436] : ((1385461436, 0.3427348242254167, 492688767), (1385461436, 0.40155701829057006, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461433] : ((1385461433, 1.3088801088319082, 492688767), (1385461433, 0.3225868384962149, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461421] : ((1385461421, 1.371715201929634, 492688767), (1385461421, 0.45562473368046597, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461419] : ((1385461419, 0.6216447420029998, 492688767), (1385461419, 0.6848705444162075, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461417] : ((1385461417, 1.035410621479173, 492688767), (1385461417, 0.5076393369215625, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461413] : ((1385461413, -3.3208863335931706, 492609224), (1385461413, 0.23773619987636446, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461412] : ((1385461412, -0.12514162383579114, 492688767), (1385461412, 0.6669602184449152, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461409] : ((1385461409, 1.6126408696979346, 492688767), (1385461409, 0.5610160818757268, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461402] : ((1385461402, 0.3668432315274394, 492688767), (1385461402, 0.7601937544968492, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461400] : ((1385461400, 0.5632596708755512, 492688767), (1385461400, 0.6750543244284541, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461397] : ((1385461397, 0.9152124891471524, 492688767), (1385461397, 0.5523020582806758, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461394] : ((1385461394, 1.1945592485929146, 492688767), (1385461394, 0.9381810983843943, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461393] : ((1385461393, 0.6034524657724895, 492688767), (1385461393, 0.7406055683590588, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461361] : ((1385461361, 0.6081367902267708, 492688767), (1385461361, 0.5069088514285675, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461359] : ((1385461359, 1.0126511979016957, 492688767), (1385461359, 0.3773759647461307, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461356] : ((1385461356, -3.0594097426122584, 492609224), (1385461356, 0.33036131615009934, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461353] : ((1385461353, 3.097225570434978, 492688767), (1385461353, 0.7126557693359845, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461351] : ((1385461351, -3.1485449637554113, 492609224), (1385461351, 0.2831446964406452, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461335] : ((1385461335, 1.5571019642642943, 492688767), (1385461335, 0.4844708117414239, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461333] : ((1385461333, 0.8320739594439531, 492688767), (1385461333, 0.7683987934975853, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461331] : ((1385461331, 0.3990137820829352, 492688767), (1385461331, 0.6651417035334486, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461329] : ((1385461329, -2.3413092599472884, 492609224), (1385461329, 0.31429123093516054, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461328] : ((1385461328, 5.879262121876563, 492688767), (1385461328, 0.2744725059765485, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461327] : ((1385461327, -0.06276583398070255, 492688767), (1385461327, 0.4494079444345299, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461319] : ((1385461319, -0.19476750546918448, 492688767), (1385461319, 0.7722611189190465, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461317] : ((1385461317, 1.0881499968781385, 492688767), (1385461317, 0.5341513552148638, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461314] : ((1385461314, 0.8861505729405785, 492688767), (1385461314, 0.4896881399640655, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461311] : ((1385461311, 0.3478235610709353, 492688767), (1385461311, 0.47841022951344137, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461308] : ((1385461308, 0.925179237959657, 492688767), (1385461308, 0.4441768795432713, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461307] : ((1385461307, -0.08375593938921887, 492688767), (1385461307, 0.7533893488578033, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461267] : ((1385461267, 0.19443509031535566, 492688767), (1385461267, 0.48886279016763867, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461264] : ((1385461264, 1.2950945430260858, 492688767), (1385461264, 0.3985481418026077, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461260] : ((1385461260, 0.8499129972241347, 492688767), (1385461260, 0.6648404293158667, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com args[1385461258] : ((1385461258, 0.6124896414242589, 492688767), (1385461258, 0.5164342178865233, 2107752395), '0.06591898999183013') We are sending mail with results at report@fotonower.com refus_total : 0.06591898999183013 2022-04-13 10:29:59 0 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=27096242 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_P27096242_22-09-2025_16_13_39.pdf results_Auto_P27096242_22-09-2025_16_13_39.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27096242_22-09-2025_16_13_39.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','27096242','results_Auto_P27096242_22-09-2025_16_13_39.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27096242_22-09-2025_16_13_39.pdf','pdf','','0.45','0.06591898999183013') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/27096242

https://www.fotonower.com/image?json=false&list_photos_id=1385461436
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461433
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.3088801088319082)
https://www.fotonower.com/image?json=false&list_photos_id=1385461421
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.371715201929634)
https://www.fotonower.com/image?json=false&list_photos_id=1385461419
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461417
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.035410621479173)
https://www.fotonower.com/image?json=false&list_photos_id=1385461413
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461412
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461409
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.6126408696979346)
https://www.fotonower.com/image?json=false&list_photos_id=1385461402
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461400
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461397
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461394
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.1945592485929146)
https://www.fotonower.com/image?json=false&list_photos_id=1385461393
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461361
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461359
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.0126511979016957)
https://www.fotonower.com/image?json=false&list_photos_id=1385461356
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461353
La photo est trop floue, merci de reprendre une photo.(avec le score = 3.097225570434978)
https://www.fotonower.com/image?json=false&list_photos_id=1385461351
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461335
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.5571019642642943)
https://www.fotonower.com/image?json=false&list_photos_id=1385461333
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461331
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461329
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461328
La photo est trop floue, merci de reprendre une photo.(avec le score = 5.879262121876563)
https://www.fotonower.com/image?json=false&list_photos_id=1385461327
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461319
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461317
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.0881499968781385)
https://www.fotonower.com/image?json=false&list_photos_id=1385461314
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461311
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461308
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461307
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461267
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461264
La photo est trop floue, merci de reprendre une photo.(avec le score = 1.2950945430260858)
https://www.fotonower.com/image?json=false&list_photos_id=1385461260
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1385461258
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 6.59%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/27096564?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/27096565?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/27096569?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/27096570?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/27096571?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/27096572?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/27096574?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27096242_22-09-2025_16_13_39.pdf.

Lien vers velours :https://marlene.fotonower.com/velours/27096564,27096565,27096566,27096567,27096568,27096569,27096570,27096571,27096572,27096573,27096574?tags=carton,pet_clair,environnement,flou,mal_croppe,pehd,pet_fonce,autre,metal,background,papier.


L'équipe Fotonower 202 b'' Server: nginx Date: Mon, 22 Sep 2025 14:13:47 GMT Content-Length: 0 Connection: close X-Message-Id: IOR3kvR3RsS6kXx5ZH14HQ 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 [1385461436, 1385461433, 1385461421, 1385461419, 1385461417, 1385461413, 1385461412, 1385461409, 1385461402, 1385461400, 1385461397, 1385461394, 1385461393, 1385461361, 1385461359, 1385461356, 1385461353, 1385461351, 1385461335, 1385461333, 1385461331, 1385461329, 1385461328, 1385461327, 1385461319, 1385461317, 1385461314, 1385461311, 1385461308, 1385461307, 1385461267, 1385461264, 1385461260, 1385461258] 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, '3759017') ('3318', '27096242', '1385461436', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461433', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461421', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461419', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461417', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461413', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461412', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461409', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461402', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461400', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461397', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461394', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461393', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461361', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461359', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461356', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461353', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461351', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461335', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461333', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461331', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461329', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461328', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461327', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461319', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461317', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461314', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461311', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461308', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461307', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461267', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461264', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461260', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461258', None, None, None, None, None, '3759017') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 34 time used for this insertion : 0.02013540267944336 save_final save missing photos in datou_result : time spend for datou_step_exec : 8.298202991485596 time spend to save output : 0.020519018173217773 total time spend for step 9 : 8.318722009658813 step10:split_time_score Mon Sep 22 16:13:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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'}] (('13', 34),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 22092025 27096242 Nombre de photos uploadées : 34 / 23040 (0%) 22092025 27096242 Nombre de photos taguées (types de déchets): 0 / 34 (0%) 22092025 27096242 Nombre de photos taguées (volume) : 0 / 34 (0%) elapsed_time : load_data_split_time_score 5.245208740234375e-06 elapsed_time : order_list_meta_photo_and_scores 7.3909759521484375e-06 ?????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0016219615936279297 Catched exception ! Connect or reconnect ! Catched exception ! Connect or reconnect ! elapsed_time : insert_dashboard_record_day_entry 0.21479582786560059 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 : 0.05209852430555556 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27081911_22-09-2025_09_51_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27081911 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`=27081911 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27086455 order by id desc limit 1 Qualite : 0.11064838927469141 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27086457_22-09-2025_10_51_50.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27086457 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`=27086457 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27096228 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27096232 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27096240 order by id desc limit 1 Qualite : 0.06591898999183013 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P27096242_22-09-2025_16_13_39.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 27096242 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`=27096242 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'22092025': {'nb_upload': 34, '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 [1385461436, 1385461433, 1385461421, 1385461419, 1385461417, 1385461413, 1385461412, 1385461409, 1385461402, 1385461400, 1385461397, 1385461394, 1385461393, 1385461361, 1385461359, 1385461356, 1385461353, 1385461351, 1385461335, 1385461333, 1385461331, 1385461329, 1385461328, 1385461327, 1385461319, 1385461317, 1385461314, 1385461311, 1385461308, 1385461307, 1385461267, 1385461264, 1385461260, 1385461258] Looping around the photos to save general results len do output : 1 /27096242Didn'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, '3759017') ('3318', '27096242', '1385461436', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461433', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461421', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461419', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461417', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461413', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461412', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461409', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461402', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461400', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461397', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461394', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461393', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461361', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461359', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461356', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461353', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461351', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461335', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461333', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461331', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461329', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461328', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461327', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461319', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461317', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461314', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461311', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461308', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461307', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461267', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461264', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461260', None, None, None, None, None, '3759017') ('3318', None, None, None, None, None, None, None, '3759017') ('3318', '27096242', '1385461258', None, None, None, None, None, '3759017') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 35 time used for this insertion : 0.019422292709350586 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.4975974559783936 time spend to save output : 0.01980900764465332 total time spend for step 10 : 4.517406463623047 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 34 set_done_treatment 104.89user 41.45system 3:26.69elapsed 70%CPU (0avgtext+0avgdata 3880840maxresident)k 1367056inputs+47632outputs (4736major+3907023minor)pagefaults 0swaps