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 : 1470105 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 : ['2734608'] with mtr_portfolio_ids : ['22163334'] and first list_photo_ids : [] new path : /proc/1470105/ 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 of2025-04-09 14:40:33.572317: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 14:40:33.572408: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 14:40:33.572426: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 14:40:33.572442: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 14:40:33.572457: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 14:40:33.572471: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 14:40:33.572485: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 14:40:33.572500: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 14:40:33.573783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 14:40:33.574977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 14:40:33.575008: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 14:40:33.575023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 14:40:33.575037: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 14:40:33.575051: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 14:40:33.575064: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 14:40:33.575078: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 14:40:33.575091: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 14:40:33.576375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 14:40:33.576408: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 14:40:33.576417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 14:40:33.576424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 14:40:33.577743: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9671 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-09 14:40:44.129948: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 14:40:44.345528: 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 : 8 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 72 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 89 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 74 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 75 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 78 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 43 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 62 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 41 Detection mask done ! Trying to reset tf kernel 1470870 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 tf kernel not reseted sub process len(results) : 8 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results)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 : 0.0015306472778320312 nb_pixel_total : 26161 time to create 1 rle with old method : 0.029053449630737305 length of segment : 272 time for calcul the mask position with numpy : 0.00069427490234375 nb_pixel_total : 18619 time to create 1 rle with old method : 0.020838260650634766 length of segment : 152 time for calcul the mask position with numpy : 0.005440235137939453 nb_pixel_total : 150361 time to create 1 rle with new method : 0.013593912124633789 length of segment : 405 time for calcul the mask position with numpy : 0.001657247543334961 nb_pixel_total : 57690 time to create 1 rle with old method : 0.08461594581604004 length of segment : 283 time for calcul the mask position with numpy : 0.0017566680908203125 nb_pixel_total : 52593 time to create 1 rle with old method : 0.05820107460021973 length of segment : 319 time for calcul the mask position with numpy : 0.002254486083984375 nb_pixel_total : 88628 time to create 1 rle with old method : 0.09811162948608398 length of segment : 243 time for calcul the mask position with numpy : 0.0007221698760986328 nb_pixel_total : 17791 time to create 1 rle with old method : 0.019361019134521484 length of segment : 173 time for calcul the mask position with numpy : 0.0006353855133056641 nb_pixel_total : 8888 time to create 1 rle with old method : 0.009963750839233398 length of segment : 250 time for calcul the mask position with numpy : 0.0017075538635253906 nb_pixel_total : 51278 time to create 1 rle with old method : 0.0583195686340332 length of segment : 386 time for calcul the mask position with numpy : 0.0003647804260253906 nb_pixel_total : 10072 time to create 1 rle with old method : 0.011841297149658203 length of segment : 130 time for calcul the mask position with numpy : 0.002140522003173828 nb_pixel_total : 61896 time to create 1 rle with old method : 0.07712268829345703 length of segment : 328 time for calcul the mask position with numpy : 0.0010142326354980469 nb_pixel_total : 33805 time to create 1 rle with old method : 0.039165496826171875 length of segment : 161 time for calcul the mask position with numpy : 0.0016145706176757812 nb_pixel_total : 52580 time to create 1 rle with old method : 0.06073784828186035 length of segment : 369 time for calcul the mask position with numpy : 0.0044171810150146484 nb_pixel_total : 145880 time to create 1 rle with old method : 0.18834519386291504 length of segment : 366 time for calcul the mask position with numpy : 0.0014982223510742188 nb_pixel_total : 45264 time to create 1 rle with old method : 0.05145907402038574 length of segment : 268 time for calcul the mask position with numpy : 0.0009996891021728516 nb_pixel_total : 31082 time to create 1 rle with old method : 0.0362086296081543 length of segment : 168 time for calcul the mask position with numpy : 0.0014221668243408203 nb_pixel_total : 51286 time to create 1 rle with old method : 0.06670880317687988 length of segment : 201 time for calcul the mask position with numpy : 0.0006351470947265625 nb_pixel_total : 19559 time to create 1 rle with old method : 0.022353649139404297 length of segment : 132 time for calcul the mask position with numpy : 0.0004050731658935547 nb_pixel_total : 10643 time to create 1 rle with old method : 0.012728214263916016 length of segment : 115 time for calcul the mask position with numpy : 0.0007071495056152344 nb_pixel_total : 20894 time to create 1 rle with old method : 0.02301788330078125 length of segment : 375 time for calcul the mask position with numpy : 0.00140380859375 nb_pixel_total : 34166 time to create 1 rle with old method : 0.03805184364318848 length of segment : 300 time for calcul the mask position with numpy : 0.005101203918457031 nb_pixel_total : 79085 time to create 1 rle with old method : 0.12591147422790527 length of segment : 330 time for calcul the mask position with numpy : 0.002444744110107422 nb_pixel_total : 25581 time to create 1 rle with old method : 0.03175640106201172 length of segment : 353 time for calcul the mask position with numpy : 0.0015091896057128906 nb_pixel_total : 23203 time to create 1 rle with old method : 0.03217124938964844 length of segment : 173 time for calcul the mask position with numpy : 0.006851673126220703 nb_pixel_total : 106839 time to create 1 rle with old method : 0.1525886058807373 length of segment : 381 time for calcul the mask position with numpy : 0.008638381958007812 nb_pixel_total : 76458 time to create 1 rle with old method : 0.09942030906677246 length of segment : 398 time for calcul the mask position with numpy : 0.002679586410522461 nb_pixel_total : 40135 time to create 1 rle with old method : 0.057085275650024414 length of segment : 215 time for calcul the mask position with numpy : 0.004694223403930664 nb_pixel_total : 62538 time to create 1 rle with old method : 0.08882427215576172 length of segment : 299 time for calcul the mask position with numpy : 0.0019931793212890625 nb_pixel_total : 31134 time to create 1 rle with old method : 0.04437828063964844 length of segment : 272 time for calcul the mask position with numpy : 0.001188516616821289 nb_pixel_total : 16422 time to create 1 rle with old method : 0.024190902709960938 length of segment : 151 time for calcul the mask position with numpy : 0.00627899169921875 nb_pixel_total : 79769 time to create 1 rle with old method : 0.11798977851867676 length of segment : 493 time for calcul the mask position with numpy : 0.002163410186767578 nb_pixel_total : 32985 time to create 1 rle with old method : 0.03821253776550293 length of segment : 212 time for calcul the mask position with numpy : 0.0013489723205566406 nb_pixel_total : 24930 time to create 1 rle with old method : 0.029952526092529297 length of segment : 165 time for calcul the mask position with numpy : 0.002050161361694336 nb_pixel_total : 23705 time to create 1 rle with old method : 0.02812647819519043 length of segment : 197 time for calcul the mask position with numpy : 0.0008730888366699219 nb_pixel_total : 28731 time to create 1 rle with old method : 0.03405308723449707 length of segment : 225 time for calcul the mask position with numpy : 0.0006878376007080078 nb_pixel_total : 9266 time to create 1 rle with old method : 0.011284828186035156 length of segment : 246 time for calcul the mask position with numpy : 0.0005877017974853516 nb_pixel_total : 9528 time to create 1 rle with old method : 0.011291265487670898 length of segment : 89 time for calcul the mask position with numpy : 0.0008265972137451172 nb_pixel_total : 13341 time to create 1 rle with old method : 0.019818544387817383 length of segment : 109 time for calcul the mask position with numpy : 0.002844572067260742 nb_pixel_total : 30422 time to create 1 rle with old method : 0.03501701354980469 length of segment : 263 time for calcul the mask position with numpy : 0.0008904933929443359 nb_pixel_total : 30574 time to create 1 rle with old method : 0.03681135177612305 length of segment : 247 time for calcul the mask position with numpy : 0.0007574558258056641 nb_pixel_total : 7050 time to create 1 rle with old method : 0.011562347412109375 length of segment : 70 time for calcul the mask position with numpy : 0.001508474349975586 nb_pixel_total : 16292 time to create 1 rle with old method : 0.027441978454589844 length of segment : 252 time for calcul the mask position with numpy : 0.0037620067596435547 nb_pixel_total : 41770 time to create 1 rle with old method : 0.09461641311645508 length of segment : 218 time for calcul the mask position with numpy : 0.0016567707061767578 nb_pixel_total : 17383 time to create 1 rle with old method : 0.023273706436157227 length of segment : 144 time for calcul the mask position with numpy : 0.0011239051818847656 nb_pixel_total : 14805 time to create 1 rle with old method : 0.021822214126586914 length of segment : 110 time for calcul the mask position with numpy : 0.0006036758422851562 nb_pixel_total : 10580 time to create 1 rle with old method : 0.016502857208251953 length of segment : 152 time for calcul the mask position with numpy : 0.0071544647216796875 nb_pixel_total : 81988 time to create 1 rle with old method : 0.15239858627319336 length of segment : 373 time for calcul the mask position with numpy : 0.00023484230041503906 nb_pixel_total : 6930 time to create 1 rle with old method : 0.008578062057495117 length of segment : 101 time for calcul the mask position with numpy : 0.0019681453704833984 nb_pixel_total : 28189 time to create 1 rle with old method : 0.03358340263366699 length of segment : 217 time for calcul the mask position with numpy : 0.00046944618225097656 nb_pixel_total : 5955 time to create 1 rle with old method : 0.007277488708496094 length of segment : 115 time for calcul the mask position with numpy : 0.0015497207641601562 nb_pixel_total : 37329 time to create 1 rle with old method : 0.05789637565612793 length of segment : 262 time for calcul the mask position with numpy : 0.0017571449279785156 nb_pixel_total : 21865 time to create 1 rle with old method : 0.03135395050048828 length of segment : 281 time for calcul the mask position with numpy : 0.0005910396575927734 nb_pixel_total : 14869 time to create 1 rle with old method : 0.023813486099243164 length of segment : 173 time for calcul the mask position with numpy : 0.004006862640380859 nb_pixel_total : 80307 time to create 1 rle with old method : 0.11808538436889648 length of segment : 515 time for calcul the mask position with numpy : 0.005888223648071289 nb_pixel_total : 76103 time to create 1 rle with old method : 0.09423613548278809 length of segment : 463 time for calcul the mask position with numpy : 0.005383491516113281 nb_pixel_total : 89749 time to create 1 rle with old method : 0.11815738677978516 length of segment : 518 time for calcul the mask position with numpy : 0.0012812614440917969 nb_pixel_total : 18966 time to create 1 rle with old method : 0.028024911880493164 length of segment : 269 time for calcul the mask position with numpy : 0.0009844303131103516 nb_pixel_total : 6857 time to create 1 rle with old method : 0.009583234786987305 length of segment : 130 time for calcul the mask position with numpy : 0.0004317760467529297 nb_pixel_total : 4066 time to create 1 rle with old method : 0.006421327590942383 length of segment : 101 time for calcul the mask position with numpy : 0.0025246143341064453 nb_pixel_total : 27761 time to create 1 rle with old method : 0.06242489814758301 length of segment : 166 time for calcul the mask position with numpy : 0.008668661117553711 nb_pixel_total : 83285 time to create 1 rle with old method : 0.1102292537689209 length of segment : 343 time for calcul the mask position with numpy : 0.013703107833862305 nb_pixel_total : 110104 time to create 1 rle with old method : 0.1633012294769287 length of segment : 538 time for calcul the mask position with numpy : 0.003752470016479492 nb_pixel_total : 73094 time to create 1 rle with old method : 0.08954596519470215 length of segment : 338 time for calcul the mask position with numpy : 0.0025701522827148438 nb_pixel_total : 31369 time to create 1 rle with old method : 0.052880048751831055 length of segment : 155 time for calcul the mask position with numpy : 0.010906696319580078 nb_pixel_total : 87429 time to create 1 rle with old method : 0.13378000259399414 length of segment : 458 time for calcul the mask position with numpy : 0.0056056976318359375 nb_pixel_total : 74610 time to create 1 rle with old method : 0.08513736724853516 length of segment : 419 time for calcul the mask position with numpy : 0.0016405582427978516 nb_pixel_total : 19566 time to create 1 rle with old method : 0.022096633911132812 length of segment : 197 time for calcul the mask position with numpy : 0.0015745162963867188 nb_pixel_total : 28911 time to create 1 rle with old method : 0.03291726112365723 length of segment : 265 time for calcul the mask position with numpy : 0.0034885406494140625 nb_pixel_total : 35300 time to create 1 rle with old method : 0.0399165153503418 length of segment : 281 time for calcul the mask position with numpy : 0.0016231536865234375 nb_pixel_total : 24869 time to create 1 rle with old method : 0.02866196632385254 length of segment : 188 time for calcul the mask position with numpy : 0.0006992816925048828 nb_pixel_total : 8164 time to create 1 rle with old method : 0.00949549674987793 length of segment : 121 time for calcul the mask position with numpy : 0.0029206275939941406 nb_pixel_total : 45711 time to create 1 rle with old method : 0.05148124694824219 length of segment : 496 time for calcul the mask position with numpy : 0.001687765121459961 nb_pixel_total : 10155 time to create 1 rle with old method : 0.017752647399902344 length of segment : 162 time for calcul the mask position with numpy : 0.0020453929901123047 nb_pixel_total : 24306 time to create 1 rle with old method : 0.0301816463470459 length of segment : 227 time for calcul the mask position with numpy : 0.0030252933502197266 nb_pixel_total : 43040 time to create 1 rle with old method : 0.05130887031555176 length of segment : 257 time for calcul the mask position with numpy : 0.0006368160247802734 nb_pixel_total : 8171 time to create 1 rle with old method : 0.015032529830932617 length of segment : 79 time for calcul the mask position with numpy : 0.0016171932220458984 nb_pixel_total : 14940 time to create 1 rle with old method : 0.022530555725097656 length of segment : 164 time for calcul the mask position with numpy : 0.0010836124420166016 nb_pixel_total : 10105 time to create 1 rle with old method : 0.016427993774414062 length of segment : 138 time for calcul the mask position with numpy : 0.0013244152069091797 nb_pixel_total : 15222 time to create 1 rle with old method : 0.027078628540039062 length of segment : 118 time for calcul the mask position with numpy : 0.0005459785461425781 nb_pixel_total : 9394 time to create 1 rle with old method : 0.01674795150756836 length of segment : 121 time for calcul the mask position with numpy : 0.00223541259765625 nb_pixel_total : 19507 time to create 1 rle with old method : 0.03751420974731445 length of segment : 199 time for calcul the mask position with numpy : 0.0027015209197998047 nb_pixel_total : 38436 time to create 1 rle with old method : 0.05655360221862793 length of segment : 270 time for calcul the mask position with numpy : 0.0011696815490722656 nb_pixel_total : 15158 time to create 1 rle with old method : 0.01785755157470703 length of segment : 150 time for calcul the mask position with numpy : 0.0016019344329833984 nb_pixel_total : 12700 time to create 1 rle with old method : 0.01667332649230957 length of segment : 123 time for calcul the mask position with numpy : 0.0012323856353759766 nb_pixel_total : 9727 time to create 1 rle with old method : 0.016387224197387695 length of segment : 185 time for calcul the mask position with numpy : 0.0010385513305664062 nb_pixel_total : 11778 time to create 1 rle with old method : 0.019506216049194336 length of segment : 148 time for calcul the mask position with numpy : 0.0017333030700683594 nb_pixel_total : 24938 time to create 1 rle with old method : 0.038604021072387695 length of segment : 222 time for calcul the mask position with numpy : 0.004363536834716797 nb_pixel_total : 56372 time to create 1 rle with old method : 0.0718393325805664 length of segment : 279 time for calcul the mask position with numpy : 0.005271434783935547 nb_pixel_total : 68003 time to create 1 rle with old method : 0.08547282218933105 length of segment : 396 time for calcul the mask position with numpy : 0.001008749008178711 nb_pixel_total : 19253 time to create 1 rle with old method : 0.025998353958129883 length of segment : 170 time for calcul the mask position with numpy : 0.0020155906677246094 nb_pixel_total : 29651 time to create 1 rle with old method : 0.03688454627990723 length of segment : 257 time for calcul the mask position with numpy : 0.001909017562866211 nb_pixel_total : 30791 time to create 1 rle with old method : 0.041753292083740234 length of segment : 223 time for calcul the mask position with numpy : 0.0011696815490722656 nb_pixel_total : 16368 time to create 1 rle with old method : 0.02355217933654785 length of segment : 156 time for calcul the mask position with numpy : 0.0019397735595703125 nb_pixel_total : 31860 time to create 1 rle with old method : 0.0423281192779541 length of segment : 161 time for calcul the mask position with numpy : 0.00047135353088378906 nb_pixel_total : 8874 time to create 1 rle with old method : 0.010787010192871094 length of segment : 59 time for calcul the mask position with numpy : 0.006734609603881836 nb_pixel_total : 80425 time to create 1 rle with old method : 0.10107231140136719 length of segment : 442 time for calcul the mask position with numpy : 0.0012316703796386719 nb_pixel_total : 14962 time to create 1 rle with old method : 0.01978588104248047 length of segment : 125 time for calcul the mask position with numpy : 0.0006761550903320312 nb_pixel_total : 8149 time to create 1 rle with old method : 0.01035618782043457 length of segment : 93 time for calcul the mask position with numpy : 0.0016503334045410156 nb_pixel_total : 31993 time to create 1 rle with old method : 0.03828620910644531 length of segment : 191 time for calcul the mask position with numpy : 0.0019290447235107422 nb_pixel_total : 23537 time to create 1 rle with old method : 0.03932023048400879 length of segment : 178 time for calcul the mask position with numpy : 0.0006895065307617188 nb_pixel_total : 5389 time to create 1 rle with old method : 0.006218671798706055 length of segment : 175 time for calcul the mask position with numpy : 0.0014688968658447266 nb_pixel_total : 20877 time to create 1 rle with old method : 0.027734756469726562 length of segment : 222 time for calcul the mask position with numpy : 0.0028142929077148438 nb_pixel_total : 46792 time to create 1 rle with old method : 0.05709123611450195 length of segment : 310 time for calcul the mask position with numpy : 0.0019378662109375 nb_pixel_total : 35356 time to create 1 rle with old method : 0.0486297607421875 length of segment : 193 time for calcul the mask position with numpy : 0.002552509307861328 nb_pixel_total : 35904 time to create 1 rle with old method : 0.04808664321899414 length of segment : 397 time for calcul the mask position with numpy : 0.00408482551574707 nb_pixel_total : 62316 time to create 1 rle with old method : 0.07918572425842285 length of segment : 288 time for calcul the mask position with numpy : 0.0009932518005371094 nb_pixel_total : 30520 time to create 1 rle with old method : 0.04108238220214844 length of segment : 243 time for calcul the mask position with numpy : 0.0030524730682373047 nb_pixel_total : 57461 time to create 1 rle with old method : 0.0706784725189209 length of segment : 346 time for calcul the mask position with numpy : 0.0011897087097167969 nb_pixel_total : 21929 time to create 1 rle with old method : 0.02828192710876465 length of segment : 156 time for calcul the mask position with numpy : 0.0009591579437255859 nb_pixel_total : 16953 time to create 1 rle with old method : 0.02078723907470703 length of segment : 105 time for calcul the mask position with numpy : 0.0077114105224609375 nb_pixel_total : 68057 time to create 1 rle with old method : 0.08730888366699219 length of segment : 428 time for calcul the mask position with numpy : 0.0007908344268798828 nb_pixel_total : 9528 time to create 1 rle with old method : 0.011172771453857422 length of segment : 107 time for calcul the mask position with numpy : 0.0008575916290283203 nb_pixel_total : 15714 time to create 1 rle with old method : 0.023938417434692383 length of segment : 149 time for calcul the mask position with numpy : 0.0016584396362304688 nb_pixel_total : 15727 time to create 1 rle with old method : 0.021721601486206055 length of segment : 165 time for calcul the mask position with numpy : 0.0018148422241210938 nb_pixel_total : 4445 time to create 1 rle with old method : 0.005999326705932617 length of segment : 248 time for calcul the mask position with numpy : 0.0019741058349609375 nb_pixel_total : 22021 time to create 1 rle with old method : 0.026189327239990234 length of segment : 226 time for calcul the mask position with numpy : 0.004182577133178711 nb_pixel_total : 37535 time to create 1 rle with old method : 0.04554390907287598 length of segment : 378 time for calcul the mask position with numpy : 0.0010535717010498047 nb_pixel_total : 14904 time to create 1 rle with old method : 0.018159151077270508 length of segment : 128 time for calcul the mask position with numpy : 0.0022208690643310547 nb_pixel_total : 25621 time to create 1 rle with old method : 0.030949115753173828 length of segment : 242 time for calcul the mask position with numpy : 0.0009489059448242188 nb_pixel_total : 13399 time to create 1 rle with old method : 0.016210556030273438 length of segment : 143 time for calcul the mask position with numpy : 0.001554250717163086 nb_pixel_total : 21986 time to create 1 rle with old method : 0.027794361114501953 length of segment : 187 time for calcul the mask position with numpy : 0.002459287643432617 nb_pixel_total : 38253 time to create 1 rle with old method : 0.05122518539428711 length of segment : 219 time for calcul the mask position with numpy : 0.002811908721923828 nb_pixel_total : 32838 time to create 1 rle with old method : 0.0449986457824707 length of segment : 226 time for calcul the mask position with numpy : 0.0013585090637207031 nb_pixel_total : 14963 time to create 1 rle with old method : 0.021819591522216797 length of segment : 167 time for calcul the mask position with numpy : 0.0016853809356689453 nb_pixel_total : 29677 time to create 1 rle with old method : 0.04045891761779785 length of segment : 195 time for calcul the mask position with numpy : 0.005541563034057617 nb_pixel_total : 91858 time to create 1 rle with old method : 0.12070941925048828 length of segment : 332 time for calcul the mask position with numpy : 0.012544631958007812 nb_pixel_total : 167005 time to create 1 rle with new method : 0.015787839889526367 length of segment : 503 time for calcul the mask position with numpy : 0.0009818077087402344 nb_pixel_total : 12151 time to create 1 rle with old method : 0.018212080001831055 length of segment : 174 time for calcul the mask position with numpy : 0.0024738311767578125 nb_pixel_total : 26729 time to create 1 rle with old method : 0.035149574279785156 length of segment : 223 time for calcul the mask position with numpy : 0.001371145248413086 nb_pixel_total : 12146 time to create 1 rle with old method : 0.018378496170043945 length of segment : 158 time for calcul the mask position with numpy : 0.0018076896667480469 nb_pixel_total : 30803 time to create 1 rle with old method : 0.04125213623046875 length of segment : 214 time for calcul the mask position with numpy : 0.013015985488891602 nb_pixel_total : 187091 time to create 1 rle with new method : 0.02285480499267578 length of segment : 814 time for calcul the mask position with numpy : 0.0022971630096435547 nb_pixel_total : 38918 time to create 1 rle with old method : 0.051007986068725586 length of segment : 237 time for calcul the mask position with numpy : 0.0018124580383300781 nb_pixel_total : 12851 time to create 1 rle with old method : 0.017148494720458984 length of segment : 360 time for calcul the mask position with numpy : 0.0008907318115234375 nb_pixel_total : 11764 time to create 1 rle with old method : 0.0155181884765625 length of segment : 132 time for calcul the mask position with numpy : 0.004214286804199219 nb_pixel_total : 56544 time to create 1 rle with old method : 0.08283662796020508 length of segment : 382 time for calcul the mask position with numpy : 0.0008113384246826172 nb_pixel_total : 11400 time to create 1 rle with old method : 0.013370275497436523 length of segment : 89 time for calcul the mask position with numpy : 0.0024213790893554688 nb_pixel_total : 37594 time to create 1 rle with old method : 0.04915022850036621 length of segment : 171 time for calcul the mask position with numpy : 0.0008833408355712891 nb_pixel_total : 10230 time to create 1 rle with old method : 0.01350712776184082 length of segment : 258 time for calcul the mask position with numpy : 0.0014128684997558594 nb_pixel_total : 15284 time to create 1 rle with old method : 0.02498340606689453 length of segment : 279 time for calcul the mask position with numpy : 0.0016047954559326172 nb_pixel_total : 14861 time to create 1 rle with old method : 0.020625591278076172 length of segment : 199 time for calcul the mask position with numpy : 0.0011968612670898438 nb_pixel_total : 20567 time to create 1 rle with old method : 0.027292251586914062 length of segment : 250 time for calcul the mask position with numpy : 0.001100778579711914 nb_pixel_total : 17070 time to create 1 rle with old method : 0.023006677627563477 length of segment : 154 time for calcul the mask position with numpy : 0.014083147048950195 nb_pixel_total : 194145 time to create 1 rle with new method : 0.023388385772705078 length of segment : 752 time for calcul the mask position with numpy : 0.0007846355438232422 nb_pixel_total : 10179 time to create 1 rle with old method : 0.013450145721435547 length of segment : 120 time for calcul the mask position with numpy : 0.0027256011962890625 nb_pixel_total : 32745 time to create 1 rle with old method : 0.038205862045288086 length of segment : 242 time for calcul the mask position with numpy : 0.0018918514251708984 nb_pixel_total : 31848 time to create 1 rle with old method : 0.0362401008605957 length of segment : 216 time for calcul the mask position with numpy : 0.0007531642913818359 nb_pixel_total : 12362 time to create 1 rle with old method : 0.014533758163452148 length of segment : 131 time for calcul the mask position with numpy : 0.0014503002166748047 nb_pixel_total : 27154 time to create 1 rle with old method : 0.03387165069580078 length of segment : 237 time for calcul the mask position with numpy : 0.0026159286499023438 nb_pixel_total : 38072 time to create 1 rle with old method : 0.054451942443847656 length of segment : 234 time for calcul the mask position with numpy : 0.003799915313720703 nb_pixel_total : 16460 time to create 1 rle with old method : 0.021457433700561523 length of segment : 281 time for calcul the mask position with numpy : 0.002569913864135742 nb_pixel_total : 20466 time to create 1 rle with old method : 0.02526545524597168 length of segment : 314 time for calcul the mask position with numpy : 0.0013093948364257812 nb_pixel_total : 16770 time to create 1 rle with old method : 0.020703554153442383 length of segment : 157 time for calcul the mask position with numpy : 0.0031604766845703125 nb_pixel_total : 28815 time to create 1 rle with old method : 0.045951128005981445 length of segment : 435 time for calcul the mask position with numpy : 0.0021996498107910156 nb_pixel_total : 32963 time to create 1 rle with old method : 0.04457592964172363 length of segment : 243 time for calcul the mask position with numpy : 0.0018911361694335938 nb_pixel_total : 19099 time to create 1 rle with old method : 0.03163933753967285 length of segment : 202 time for calcul the mask position with numpy : 0.0014050006866455078 nb_pixel_total : 23695 time to create 1 rle with old method : 0.026834487915039062 length of segment : 219 time for calcul the mask position with numpy : 0.0008673667907714844 nb_pixel_total : 14279 time to create 1 rle with old method : 0.019255399703979492 length of segment : 122 time for calcul the mask position with numpy : 0.0028116703033447266 nb_pixel_total : 41811 time to create 1 rle with old method : 0.055727243423461914 length of segment : 268 time for calcul the mask position with numpy : 0.007604360580444336 nb_pixel_total : 99200 time to create 1 rle with old method : 0.15091395378112793 length of segment : 333 time for calcul the mask position with numpy : 0.00040531158447265625 nb_pixel_total : 8224 time to create 1 rle with old method : 0.009654760360717773 length of segment : 103 time for calcul the mask position with numpy : 0.010600805282592773 nb_pixel_total : 225420 time to create 1 rle with new method : 0.0163571834564209 length of segment : 681 time for calcul the mask position with numpy : 0.006101846694946289 nb_pixel_total : 56324 time to create 1 rle with old method : 0.08610749244689941 length of segment : 387 time for calcul the mask position with numpy : 0.005578279495239258 nb_pixel_total : 66879 time to create 1 rle with old method : 0.11844110488891602 length of segment : 281 time for calcul the mask position with numpy : 0.00425410270690918 nb_pixel_total : 47027 time to create 1 rle with old method : 0.06305050849914551 length of segment : 266 time for calcul the mask position with numpy : 0.001041412353515625 nb_pixel_total : 15347 time to create 1 rle with old method : 0.019943714141845703 length of segment : 159 time for calcul the mask position with numpy : 0.002862215042114258 nb_pixel_total : 33426 time to create 1 rle with old method : 0.04333829879760742 length of segment : 288 time for calcul the mask position with numpy : 0.007394313812255859 nb_pixel_total : 99591 time to create 1 rle with old method : 0.14070773124694824 length of segment : 508 time for calcul the mask position with numpy : 0.0007193088531494141 nb_pixel_total : 9994 time to create 1 rle with old method : 0.013880729675292969 length of segment : 82 time for calcul the mask position with numpy : 0.0022764205932617188 nb_pixel_total : 23117 time to create 1 rle with old method : 0.030985593795776367 length of segment : 218 time for calcul the mask position with numpy : 0.0006852149963378906 nb_pixel_total : 7836 time to create 1 rle with old method : 0.010961771011352539 length of segment : 121 time for calcul the mask position with numpy : 0.0025148391723632812 nb_pixel_total : 46136 time to create 1 rle with old method : 0.06850242614746094 length of segment : 173 time for calcul the mask position with numpy : 0.0004620552062988281 nb_pixel_total : 2615 time to create 1 rle with old method : 0.004254817962646484 length of segment : 64 time for calcul the mask position with numpy : 0.0014872550964355469 nb_pixel_total : 11196 time to create 1 rle with old method : 0.017645597457885742 length of segment : 93 time for calcul the mask position with numpy : 0.0011556148529052734 nb_pixel_total : 14854 time to create 1 rle with old method : 0.0238039493560791 length of segment : 167 time for calcul the mask position with numpy : 0.0053021907806396484 nb_pixel_total : 50907 time to create 1 rle with old method : 0.07622456550598145 length of segment : 338 time for calcul the mask position with numpy : 0.0017139911651611328 nb_pixel_total : 25269 time to create 1 rle with old method : 0.029387950897216797 length of segment : 183 time for calcul the mask position with numpy : 0.0007603168487548828 nb_pixel_total : 7651 time to create 1 rle with old method : 0.009149551391601562 length of segment : 145 time for calcul the mask position with numpy : 0.005821704864501953 nb_pixel_total : 91566 time to create 1 rle with old method : 0.10478758811950684 length of segment : 487 time for calcul the mask position with numpy : 0.009929180145263672 nb_pixel_total : 147035 time to create 1 rle with old method : 0.18832969665527344 length of segment : 487 time for calcul the mask position with numpy : 0.0009758472442626953 nb_pixel_total : 11691 time to create 1 rle with old method : 0.16663861274719238 length of segment : 132 time for calcul the mask position with numpy : 0.0021555423736572266 nb_pixel_total : 28683 time to create 1 rle with old method : 0.03877401351928711 length of segment : 180 time for calcul the mask position with numpy : 0.0027153491973876953 nb_pixel_total : 32343 time to create 1 rle with old method : 0.03645157814025879 length of segment : 283 time for calcul the mask position with numpy : 0.0016303062438964844 nb_pixel_total : 22554 time to create 1 rle with old method : 0.026293516159057617 length of segment : 275 time for calcul the mask position with numpy : 0.0032401084899902344 nb_pixel_total : 48239 time to create 1 rle with old method : 0.0838477611541748 length of segment : 281 time for calcul the mask position with numpy : 0.0009472370147705078 nb_pixel_total : 18651 time to create 1 rle with old method : 0.023360252380371094 length of segment : 147 time for calcul the mask position with numpy : 0.0009076595306396484 nb_pixel_total : 20050 time to create 1 rle with old method : 0.023056507110595703 length of segment : 271 time for calcul the mask position with numpy : 0.0037233829498291016 nb_pixel_total : 64062 time to create 1 rle with old method : 0.07351040840148926 length of segment : 383 time for calcul the mask position with numpy : 0.0018045902252197266 nb_pixel_total : 25734 time to create 1 rle with old method : 0.030475854873657227 length of segment : 185 time for calcul the mask position with numpy : 0.001157999038696289 nb_pixel_total : 19481 time to create 1 rle with old method : 0.022713184356689453 length of segment : 127 time for calcul the mask position with numpy : 0.003700733184814453 nb_pixel_total : 43993 time to create 1 rle with old method : 0.07138586044311523 length of segment : 276 time for calcul the mask position with numpy : 0.008198738098144531 nb_pixel_total : 43393 time to create 1 rle with old method : 0.05148506164550781 length of segment : 377 time for calcul the mask position with numpy : 0.0011260509490966797 nb_pixel_total : 16003 time to create 1 rle with old method : 0.01835942268371582 length of segment : 173 time for calcul the mask position with numpy : 0.0035789012908935547 nb_pixel_total : 134771 time to create 1 rle with old method : 0.17308855056762695 length of segment : 450 time for calcul the mask position with numpy : 0.0012483596801757812 nb_pixel_total : 11907 time to create 1 rle with old method : 0.020262718200683594 length of segment : 199 time for calcul the mask position with numpy : 0.004098653793334961 nb_pixel_total : 124301 time to create 1 rle with old method : 0.14003467559814453 length of segment : 342 time for calcul the mask position with numpy : 0.08714938163757324 nb_pixel_total : 1180757 time to create 1 rle with new method : 0.11596226692199707 length of segment : 1293 time for calcul the mask position with numpy : 0.0006706714630126953 nb_pixel_total : 14276 time to create 1 rle with old method : 0.017174482345581055 length of segment : 125 time for calcul the mask position with numpy : 0.005058765411376953 nb_pixel_total : 89396 time to create 1 rle with old method : 0.11103296279907227 length of segment : 627 time for calcul the mask position with numpy : 0.004494905471801758 nb_pixel_total : 51514 time to create 1 rle with old method : 0.062135934829711914 length of segment : 418 time for calcul the mask position with numpy : 0.0009069442749023438 nb_pixel_total : 19940 time to create 1 rle with old method : 0.02304387092590332 length of segment : 224 time for calcul the mask position with numpy : 0.02557826042175293 nb_pixel_total : 605442 time to create 1 rle with new method : 0.06984996795654297 length of segment : 1188 time for calcul the mask position with numpy : 0.0006346702575683594 nb_pixel_total : 25020 time to create 1 rle with old method : 0.029781341552734375 length of segment : 247 time for calcul the mask position with numpy : 0.0007500648498535156 nb_pixel_total : 13292 time to create 1 rle with old method : 0.017508268356323242 length of segment : 109 time for calcul the mask position with numpy : 0.006646871566772461 nb_pixel_total : 167031 time to create 1 rle with new method : 0.008015155792236328 length of segment : 544 time for calcul the mask position with numpy : 0.002084016799926758 nb_pixel_total : 36053 time to create 1 rle with old method : 0.05483388900756836 length of segment : 240 time for calcul the mask position with numpy : 0.004050493240356445 nb_pixel_total : 48233 time to create 1 rle with old method : 0.08658695220947266 length of segment : 398 time for calcul the mask position with numpy : 0.003814697265625 nb_pixel_total : 64118 time to create 1 rle with old method : 0.10016226768493652 length of segment : 269 time for calcul the mask position with numpy : 0.0007932186126708984 nb_pixel_total : 24539 time to create 1 rle with old method : 0.029602766036987305 length of segment : 224 time for calcul the mask position with numpy : 0.0006759166717529297 nb_pixel_total : 26369 time to create 1 rle with old method : 0.03413581848144531 length of segment : 205 time for calcul the mask position with numpy : 0.002230405807495117 nb_pixel_total : 43560 time to create 1 rle with old method : 0.06294679641723633 length of segment : 313 time for calcul the mask position with numpy : 0.0008196830749511719 nb_pixel_total : 10181 time to create 1 rle with old method : 0.012859344482421875 length of segment : 100 time for calcul the mask position with numpy : 0.0019071102142333984 nb_pixel_total : 34239 time to create 1 rle with old method : 0.04137253761291504 length of segment : 306 time for calcul the mask position with numpy : 0.0008637905120849609 nb_pixel_total : 19009 time to create 1 rle with old method : 0.03188323974609375 length of segment : 151 time for calcul the mask position with numpy : 0.01688408851623535 nb_pixel_total : 259283 time to create 1 rle with new method : 0.04055666923522949 length of segment : 753 time for calcul the mask position with numpy : 0.0071833133697509766 nb_pixel_total : 108997 time to create 1 rle with old method : 0.1449565887451172 length of segment : 396 time for calcul the mask position with numpy : 0.019082307815551758 nb_pixel_total : 257906 time to create 1 rle with new method : 0.03386282920837402 length of segment : 1412 time for calcul the mask position with numpy : 0.015477895736694336 nb_pixel_total : 286236 time to create 1 rle with new method : 0.01500558853149414 length of segment : 682 time for calcul the mask position with numpy : 0.0012869834899902344 nb_pixel_total : 21788 time to create 1 rle with old method : 0.027898788452148438 length of segment : 181 time for calcul the mask position with numpy : 0.011999845504760742 nb_pixel_total : 236884 time to create 1 rle with new method : 0.01403188705444336 length of segment : 861 time for calcul the mask position with numpy : 0.009819269180297852 nb_pixel_total : 198601 time to create 1 rle with new method : 0.01122903823852539 length of segment : 575 time for calcul the mask position with numpy : 0.0028803348541259766 nb_pixel_total : 54839 time to create 1 rle with old method : 0.07086014747619629 length of segment : 379 time for calcul the mask position with numpy : 0.0015614032745361328 nb_pixel_total : 32768 time to create 1 rle with old method : 0.03966379165649414 length of segment : 201 time for calcul the mask position with numpy : 0.013389110565185547 nb_pixel_total : 311616 time to create 1 rle with new method : 0.018795013427734375 length of segment : 807 time for calcul the mask position with numpy : 0.008391857147216797 nb_pixel_total : 317617 time to create 1 rle with new method : 0.023082971572875977 length of segment : 792 time for calcul the mask position with numpy : 0.0015456676483154297 nb_pixel_total : 31330 time to create 1 rle with old method : 0.0401453971862793 length of segment : 241 time for calcul the mask position with numpy : 0.0033872127532958984 nb_pixel_total : 55615 time to create 1 rle with old method : 0.0849299430847168 length of segment : 463 time for calcul the mask position with numpy : 0.01264333724975586 nb_pixel_total : 176235 time to create 1 rle with new method : 0.017354726791381836 length of segment : 615 time for calcul the mask position with numpy : 0.0013685226440429688 nb_pixel_total : 31174 time to create 1 rle with old method : 0.04045557975769043 length of segment : 256 time for calcul the mask position with numpy : 0.002535581588745117 nb_pixel_total : 49887 time to create 1 rle with old method : 0.06155037879943848 length of segment : 351 time for calcul the mask position with numpy : 0.005053281784057617 nb_pixel_total : 135065 time to create 1 rle with old method : 0.178725004196167 length of segment : 446 time for calcul the mask position with numpy : 0.0005595684051513672 nb_pixel_total : 9665 time to create 1 rle with old method : 0.012141704559326172 length of segment : 155 time for calcul the mask position with numpy : 0.0008015632629394531 nb_pixel_total : 12853 time to create 1 rle with old method : 0.01710963249206543 length of segment : 91 time for calcul the mask position with numpy : 0.0003342628479003906 nb_pixel_total : 7793 time to create 1 rle with old method : 0.009776592254638672 length of segment : 82 time for calcul the mask position with numpy : 0.004940032958984375 nb_pixel_total : 92308 time to create 1 rle with old method : 0.12420940399169922 length of segment : 590 time for calcul the mask position with numpy : 0.0009725093841552734 nb_pixel_total : 14346 time to create 1 rle with old method : 0.020537376403808594 length of segment : 149 time for calcul the mask position with numpy : 0.0010173320770263672 nb_pixel_total : 34135 time to create 1 rle with old method : 0.04542970657348633 length of segment : 266 time for calcul the mask position with numpy : 0.0005204677581787109 nb_pixel_total : 13241 time to create 1 rle with old method : 0.01612234115600586 length of segment : 110 time for calcul the mask position with numpy : 0.0007586479187011719 nb_pixel_total : 19455 time to create 1 rle with old method : 0.024294614791870117 length of segment : 255 time for calcul the mask position with numpy : 0.0014414787292480469 nb_pixel_total : 20140 time to create 1 rle with old method : 0.02657938003540039 length of segment : 280 time for calcul the mask position with numpy : 0.00459599494934082 nb_pixel_total : 104457 time to create 1 rle with old method : 0.13835906982421875 length of segment : 577 time for calcul the mask position with numpy : 0.0017957687377929688 nb_pixel_total : 35150 time to create 1 rle with old method : 0.04501819610595703 length of segment : 239 time for calcul the mask position with numpy : 0.001180410385131836 nb_pixel_total : 21719 time to create 1 rle with old method : 0.028842449188232422 length of segment : 224 time for calcul the mask position with numpy : 0.00038933753967285156 nb_pixel_total : 8320 time to create 1 rle with old method : 0.010760068893432617 length of segment : 64 time for calcul the mask position with numpy : 0.003576993942260742 nb_pixel_total : 100429 time to create 1 rle with old method : 0.12916183471679688 length of segment : 461 time for calcul the mask position with numpy : 0.001791238784790039 nb_pixel_total : 34041 time to create 1 rle with old method : 0.04215741157531738 length of segment : 320 time for calcul the mask position with numpy : 0.008589506149291992 nb_pixel_total : 172899 time to create 1 rle with new method : 0.009893417358398438 length of segment : 763 time for calcul the mask position with numpy : 0.0029201507568359375 nb_pixel_total : 45020 time to create 1 rle with old method : 0.0611727237701416 length of segment : 243 time for calcul the mask position with numpy : 0.0028574466705322266 nb_pixel_total : 63549 time to create 1 rle with old method : 0.08099079132080078 length of segment : 347 time for calcul the mask position with numpy : 0.003609895706176758 nb_pixel_total : 60350 time to create 1 rle with old method : 0.08917903900146484 length of segment : 323 time for calcul the mask position with numpy : 0.00854802131652832 nb_pixel_total : 198069 time to create 1 rle with new method : 0.011954069137573242 length of segment : 572 time for calcul the mask position with numpy : 0.0023756027221679688 nb_pixel_total : 64454 time to create 1 rle with old method : 0.07372236251831055 length of segment : 403 time for calcul the mask position with numpy : 0.003360271453857422 nb_pixel_total : 81916 time to create 1 rle with old method : 0.0934135913848877 length of segment : 265 time for calcul the mask position with numpy : 0.00033783912658691406 nb_pixel_total : 7854 time to create 1 rle with old method : 0.00928497314453125 length of segment : 99 time for calcul the mask position with numpy : 0.0015347003936767578 nb_pixel_total : 38663 time to create 1 rle with old method : 0.043497323989868164 length of segment : 254 time for calcul the mask position with numpy : 0.0037789344787597656 nb_pixel_total : 134174 time to create 1 rle with old method : 0.1514902114868164 length of segment : 330 time for calcul the mask position with numpy : 0.0012471675872802734 nb_pixel_total : 44878 time to create 1 rle with old method : 0.06023716926574707 length of segment : 316 time for calcul the mask position with numpy : 0.0035843849182128906 nb_pixel_total : 75169 time to create 1 rle with old method : 0.09601092338562012 length of segment : 414 time spent for convertir_results : 26.375182151794434 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 258 chid ids of type : 3594 Number RLEs to save : 72910 save missing photos in datou_result : time spend for datou_step_exec : 128.05925679206848 time spend to save output : 8.81470513343811 total time spend for step 1 : 136.8739619255066 step2:crop_condition Wed Apr 9 14:42:46 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 8 ! batch 1 Loaded 258 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 187 About to insert : list_path_to_insert length 187 new photo from crops ! About to upload 187 photos upload in portfolio : 3736932 init cache_photo without model_param we have 187 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202616_1470105 we have uploaded 187 photos in the portfolio 3736932 time of upload the photos Elapsed time : 81.1774091720581 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 33 About to insert : list_path_to_insert length 33 new photo from crops ! About to upload 33 photos upload in portfolio : 3736932 init cache_photo without model_param we have 33 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202711_1470105 we have uploaded 33 photos in the portfolio 3736932 time of upload the photos Elapsed time : 14.536351680755615 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 1 About to insert : list_path_to_insert length 1 new photo from crops ! About to upload 1 photos upload in portfolio : 3736932 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202727_1470105 we have uploaded 1 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.9833950996398926 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 ! map_result returned by crop_photo_return_map_crop : length : 25 About to insert : list_path_to_insert length 25 new photo from crops ! About to upload 25 photos upload in portfolio : 3736932 init cache_photo without model_param we have 25 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202741_1470105 we have uploaded 25 photos in the portfolio 3736932 time of upload the photos Elapsed time : 9.467606782913208 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 ! map_result returned by crop_photo_return_map_crop : length : 7 About to insert : list_path_to_insert length 7 new photo from crops ! About to upload 7 photos upload in portfolio : 3736932 init cache_photo without model_param we have 7 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202754_1470105 we have uploaded 7 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.747044801712036 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 ! 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/1744202758_1470105 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.429220199584961 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 ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744202762_1470105 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.4281837940216064 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 Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350768794, 1350768790, 1350765356, 1350765310, 1350765238, 1350765167, 1350765131, 1350765095] Looping around the photos to save general results len do output : 258 /1350785683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785719Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785734Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785756Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785759Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785761Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785766Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785773Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785785Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785789Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785798Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785801Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785804Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785808Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785861Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785883Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785894Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785898Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785899Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785900Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785920Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785921Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785923Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785924Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785925Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785927Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785928Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785929Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785931Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785932Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785933Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785934Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785941Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785946Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785950Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785974Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350785999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350786023Didn'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, '2734608') ('3318', '22163334', '1350768794', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350768790', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765356', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765310', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765238', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765167', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765131', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765095', None, None, None, None, None, '2734608') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 782 time used for this insertion : 0.052056074142456055 save_final save missing photos in datou_result : time spend for datou_step_exec : 196.99990797042847 time spend to save output : 0.05818295478820801 total time spend for step 2 : 197.05809092521667 step3:rle_unique_nms_with_priority Wed Apr 9 14:46:03 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 VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 258 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 37 nb_hashtags : 5 time to prepare the origin masks : 4.647381782531738 time for calcul the mask position with numpy : 1.3211042881011963 nb_pixel_total : 5428483 time to create 1 rle with new method : 0.4967632293701172 time for calcul the mask position with numpy : 0.029328107833862305 nb_pixel_total : 31082 time to create 1 rle with old method : 0.03461909294128418 time for calcul the mask position with numpy : 0.029053688049316406 nb_pixel_total : 31134 time to create 1 rle with old method : 0.03457164764404297 time for calcul the mask position with numpy : 0.029469013214111328 nb_pixel_total : 16422 time to create 1 rle with old method : 0.018467426300048828 time for calcul the mask position with numpy : 0.02923440933227539 nb_pixel_total : 8888 time to create 1 rle with old method : 0.012777566909790039 time for calcul the mask position with numpy : 0.033627986907958984 nb_pixel_total : 52593 time to create 1 rle with old method : 0.07161736488342285 time for calcul the mask position with numpy : 0.03157234191894531 nb_pixel_total : 150361 time to create 1 rle with new method : 0.9018292427062988 time for calcul the mask position with numpy : 0.028287172317504883 nb_pixel_total : 9528 time to create 1 rle with old method : 0.010727167129516602 time for calcul the mask position with numpy : 0.029039382934570312 nb_pixel_total : 23705 time to create 1 rle with old method : 0.026645898818969727 time for calcul the mask position with numpy : 0.028983354568481445 nb_pixel_total : 24930 time to create 1 rle with old method : 0.028138160705566406 time for calcul the mask position with numpy : 0.028947830200195312 nb_pixel_total : 25581 time to create 1 rle with old method : 0.028881072998046875 time for calcul the mask position with numpy : 0.02903127670288086 nb_pixel_total : 17791 time to create 1 rle with old method : 0.019811630249023438 time for calcul the mask position with numpy : 0.032675743103027344 nb_pixel_total : 79769 time to create 1 rle with old method : 0.1024162769317627 time for calcul the mask position with numpy : 0.03192710876464844 nb_pixel_total : 40135 time to create 1 rle with old method : 0.04787111282348633 time for calcul the mask position with numpy : 0.03137516975402832 nb_pixel_total : 47636 time to create 1 rle with old method : 0.05601096153259277 time for calcul the mask position with numpy : 0.029515981674194336 nb_pixel_total : 57690 time to create 1 rle with old method : 0.06928253173828125 time for calcul the mask position with numpy : 0.03063201904296875 nb_pixel_total : 51278 time to create 1 rle with old method : 0.05939030647277832 time for calcul the mask position with numpy : 0.03059840202331543 nb_pixel_total : 61896 time to create 1 rle with old method : 0.07176971435546875 time for calcul the mask position with numpy : 0.029844284057617188 nb_pixel_total : 51286 time to create 1 rle with old method : 0.061205387115478516 time for calcul the mask position with numpy : 0.03207874298095703 nb_pixel_total : 10643 time to create 1 rle with old method : 0.012402057647705078 time for calcul the mask position with numpy : 0.03020310401916504 nb_pixel_total : 79085 time to create 1 rle with old method : 0.09369039535522461 time for calcul the mask position with numpy : 0.029911518096923828 nb_pixel_total : 20894 time to create 1 rle with old method : 0.023581504821777344 time for calcul the mask position with numpy : 0.03095531463623047 nb_pixel_total : 62538 time to create 1 rle with old method : 0.0714254379272461 time for calcul the mask position with numpy : 0.03101515769958496 nb_pixel_total : 9266 time to create 1 rle with old method : 0.01060032844543457 time for calcul the mask position with numpy : 0.03118443489074707 nb_pixel_total : 76458 time to create 1 rle with old method : 0.08861017227172852 time for calcul the mask position with numpy : 0.030704975128173828 nb_pixel_total : 34166 time to create 1 rle with old method : 0.038510799407958984 time for calcul the mask position with numpy : 0.03384041786193848 nb_pixel_total : 106839 time to create 1 rle with old method : 0.12174463272094727 time for calcul the mask position with numpy : 0.029909610748291016 nb_pixel_total : 23203 time to create 1 rle with old method : 0.030086040496826172 time for calcul the mask position with numpy : 0.032182931900024414 nb_pixel_total : 32985 time to create 1 rle with old method : 0.040487051010131836 time for calcul the mask position with numpy : 0.03991293907165527 nb_pixel_total : 4060 time to create 1 rle with old method : 0.004798173904418945 time for calcul the mask position with numpy : 0.030245065689086914 nb_pixel_total : 45264 time to create 1 rle with old method : 0.054598331451416016 time for calcul the mask position with numpy : 0.029430389404296875 nb_pixel_total : 1999 time to create 1 rle with old method : 0.0031375885009765625 time for calcul the mask position with numpy : 0.0332949161529541 nb_pixel_total : 18619 time to create 1 rle with old method : 0.021377086639404297 time for calcul the mask position with numpy : 0.030050992965698242 nb_pixel_total : 26161 time to create 1 rle with old method : 0.029413461685180664 time for calcul the mask position with numpy : 0.032952308654785156 nb_pixel_total : 145880 time to create 1 rle with old method : 0.21819639205932617 time for calcul the mask position with numpy : 0.029694080352783203 nb_pixel_total : 88628 time to create 1 rle with old method : 0.1009056568145752 time for calcul the mask position with numpy : 0.02923274040222168 nb_pixel_total : 33805 time to create 1 rle with old method : 0.03734850883483887 time for calcul the mask position with numpy : 0.029598474502563477 nb_pixel_total : 19559 time to create 1 rle with old method : 0.021929264068603516 create new chi : 5.694874048233032 time to delete rle : 0.01727890968322754 batch 1 Loaded 75 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 20730 TO DO : save crop sub photo not yet done ! save time : 1.387080192565918 nb_obj : 48 nb_hashtags : 4 time to prepare the origin masks : 4.5502824783325195 time for calcul the mask position with numpy : 0.8152658939361572 nb_pixel_total : 5590219 time to create 1 rle with new method : 0.6312530040740967 time for calcul the mask position with numpy : 0.03027200698852539 nb_pixel_total : 15222 time to create 1 rle with old method : 0.018440961837768555 time for calcul the mask position with numpy : 0.03667449951171875 nb_pixel_total : 8164 time to create 1 rle with old method : 0.009229183197021484 time for calcul the mask position with numpy : 0.03174328804016113 nb_pixel_total : 37329 time to create 1 rle with old method : 0.05340099334716797 time for calcul the mask position with numpy : 0.03351616859436035 nb_pixel_total : 30574 time to create 1 rle with old method : 0.05015897750854492 time for calcul the mask position with numpy : 0.04308128356933594 nb_pixel_total : 1048 time to create 1 rle with old method : 0.0013632774353027344 time for calcul the mask position with numpy : 0.03055739402770996 nb_pixel_total : 75648 time to create 1 rle with old method : 0.09655308723449707 time for calcul the mask position with numpy : 0.030877113342285156 nb_pixel_total : 16292 time to create 1 rle with old method : 0.018915414810180664 time for calcul the mask position with numpy : 0.031196117401123047 nb_pixel_total : 4066 time to create 1 rle with old method : 0.004652500152587891 time for calcul the mask position with numpy : 0.02987527847290039 nb_pixel_total : 74610 time to create 1 rle with old method : 0.08500075340270996 time for calcul the mask position with numpy : 0.02953195571899414 nb_pixel_total : 24802 time to create 1 rle with old method : 0.030057668685913086 time for calcul the mask position with numpy : 0.030358314514160156 nb_pixel_total : 80307 time to create 1 rle with old method : 0.09179997444152832 time for calcul the mask position with numpy : 0.02936244010925293 nb_pixel_total : 27401 time to create 1 rle with old method : 0.030780315399169922 time for calcul the mask position with numpy : 0.0292360782623291 nb_pixel_total : 10155 time to create 1 rle with old method : 0.01143956184387207 time for calcul the mask position with numpy : 0.029384851455688477 nb_pixel_total : 35300 time to create 1 rle with old method : 0.0407712459564209 time for calcul the mask position with numpy : 0.029010534286499023 nb_pixel_total : 10580 time to create 1 rle with old method : 0.011788368225097656 time for calcul the mask position with numpy : 0.029509782791137695 nb_pixel_total : 19507 time to create 1 rle with old method : 0.022361278533935547 time for calcul the mask position with numpy : 0.03016519546508789 nb_pixel_total : 1015 time to create 1 rle with old method : 0.001257181167602539 time for calcul the mask position with numpy : 0.028963088989257812 nb_pixel_total : 14869 time to create 1 rle with old method : 0.016655921936035156 time for calcul the mask position with numpy : 0.029143810272216797 nb_pixel_total : 88314 time to create 1 rle with old method : 0.10448861122131348 time for calcul the mask position with numpy : 0.033460378646850586 nb_pixel_total : 14940 time to create 1 rle with old method : 0.024436235427856445 time for calcul the mask position with numpy : 0.03059864044189453 nb_pixel_total : 28189 time to create 1 rle with old method : 0.03167557716369629 time for calcul the mask position with numpy : 0.029696941375732422 nb_pixel_total : 24869 time to create 1 rle with old method : 0.027948856353759766 time for calcul the mask position with numpy : 0.029636383056640625 nb_pixel_total : 21865 time to create 1 rle with old method : 0.024615764617919922 time for calcul the mask position with numpy : 0.029379844665527344 nb_pixel_total : 19566 time to create 1 rle with old method : 0.02189493179321289 time for calcul the mask position with numpy : 0.029074668884277344 nb_pixel_total : 24306 time to create 1 rle with old method : 0.026942968368530273 time for calcul the mask position with numpy : 0.02878737449645996 nb_pixel_total : 12700 time to create 1 rle with old method : 0.014216899871826172 time for calcul the mask position with numpy : 0.0293426513671875 nb_pixel_total : 15158 time to create 1 rle with old method : 0.01684856414794922 time for calcul the mask position with numpy : 0.029252290725708008 nb_pixel_total : 83285 time to create 1 rle with old method : 0.09469199180603027 time for calcul the mask position with numpy : 0.03122091293334961 nb_pixel_total : 14805 time to create 1 rle with old method : 0.016628026962280273 time for calcul the mask position with numpy : 0.02900218963623047 nb_pixel_total : 17383 time to create 1 rle with old method : 0.019373178482055664 time for calcul the mask position with numpy : 0.029030561447143555 nb_pixel_total : 10105 time to create 1 rle with old method : 0.01128697395324707 time for calcul the mask position with numpy : 0.030582427978515625 nb_pixel_total : 370 time to create 1 rle with old method : 0.0005257129669189453 time for calcul the mask position with numpy : 0.03062605857849121 nb_pixel_total : 73094 time to create 1 rle with old method : 0.10608911514282227 time for calcul the mask position with numpy : 0.029213428497314453 nb_pixel_total : 5955 time to create 1 rle with old method : 0.0066928863525390625 time for calcul the mask position with numpy : 0.03210139274597168 nb_pixel_total : 31369 time to create 1 rle with old method : 0.03504681587219238 time for calcul the mask position with numpy : 0.029532909393310547 nb_pixel_total : 87429 time to create 1 rle with old method : 0.09805130958557129 time for calcul the mask position with numpy : 0.029362916946411133 nb_pixel_total : 45711 time to create 1 rle with old method : 0.05299639701843262 time for calcul the mask position with numpy : 0.0310366153717041 nb_pixel_total : 110104 time to create 1 rle with old method : 0.1256568431854248 time for calcul the mask position with numpy : 0.03417181968688965 nb_pixel_total : 18966 time to create 1 rle with old method : 0.03252243995666504 time for calcul the mask position with numpy : 0.030364990234375 nb_pixel_total : 13341 time to create 1 rle with old method : 0.014975786209106445 time for calcul the mask position with numpy : 0.02937793731689453 nb_pixel_total : 41770 time to create 1 rle with old method : 0.04983401298522949 time for calcul the mask position with numpy : 0.030011415481567383 nb_pixel_total : 81988 time to create 1 rle with old method : 0.09270262718200684 time for calcul the mask position with numpy : 0.02964496612548828 nb_pixel_total : 30422 time to create 1 rle with old method : 0.03686785697937012 time for calcul the mask position with numpy : 0.031356096267700195 nb_pixel_total : 28911 time to create 1 rle with old method : 0.032405853271484375 time for calcul the mask position with numpy : 0.033049821853637695 nb_pixel_total : 6139 time to create 1 rle with old method : 0.006955862045288086 time for calcul the mask position with numpy : 0.02920985221862793 nb_pixel_total : 6857 time to create 1 rle with old method : 0.007751941680908203 time for calcul the mask position with numpy : 0.02985548973083496 nb_pixel_total : 7050 time to create 1 rle with old method : 0.00796055793762207 time for calcul the mask position with numpy : 0.029332637786865234 nb_pixel_total : 8171 time to create 1 rle with old method : 0.009433269500732422 create new chi : 4.709470510482788 time to delete rle : 0.0035843849182128906 batch 1 Loaded 97 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23388 TO DO : save crop sub photo not yet done ! save time : 1.6486270427703857 nb_obj : 37 nb_hashtags : 5 time to prepare the origin masks : 4.446465253829956 time for calcul the mask position with numpy : 0.6044995784759521 nb_pixel_total : 5972599 time to create 1 rle with new method : 0.6150691509246826 time for calcul the mask position with numpy : 0.03069019317626953 nb_pixel_total : 31860 time to create 1 rle with old method : 0.04805326461791992 time for calcul the mask position with numpy : 0.03475546836853027 nb_pixel_total : 24938 time to create 1 rle with old method : 0.02821969985961914 time for calcul the mask position with numpy : 0.029327869415283203 nb_pixel_total : 4445 time to create 1 rle with old method : 0.0055217742919921875 time for calcul the mask position with numpy : 0.030025005340576172 nb_pixel_total : 21929 time to create 1 rle with old method : 0.02564239501953125 time for calcul the mask position with numpy : 0.03022003173828125 nb_pixel_total : 35356 time to create 1 rle with old method : 0.04124116897583008 time for calcul the mask position with numpy : 0.03014230728149414 nb_pixel_total : 14962 time to create 1 rle with old method : 0.017550945281982422 time for calcul the mask position with numpy : 0.029688358306884766 nb_pixel_total : 13399 time to create 1 rle with old method : 0.015182971954345703 time for calcul the mask position with numpy : 0.02965545654296875 nb_pixel_total : 5389 time to create 1 rle with old method : 0.006095170974731445 time for calcul the mask position with numpy : 0.029576539993286133 nb_pixel_total : 31993 time to create 1 rle with old method : 0.03577280044555664 time for calcul the mask position with numpy : 0.030039072036743164 nb_pixel_total : 25621 time to create 1 rle with old method : 0.030153274536132812 time for calcul the mask position with numpy : 0.03053450584411621 nb_pixel_total : 16953 time to create 1 rle with old method : 0.020013093948364258 time for calcul the mask position with numpy : 0.0313262939453125 nb_pixel_total : 46792 time to create 1 rle with old method : 0.05525946617126465 time for calcul the mask position with numpy : 0.029604673385620117 nb_pixel_total : 14904 time to create 1 rle with old method : 0.016642332077026367 time for calcul the mask position with numpy : 0.03030872344970703 nb_pixel_total : 62316 time to create 1 rle with old method : 0.0695796012878418 time for calcul the mask position with numpy : 0.030530691146850586 nb_pixel_total : 68003 time to create 1 rle with old method : 0.07680296897888184 time for calcul the mask position with numpy : 0.029589176177978516 nb_pixel_total : 9528 time to create 1 rle with old method : 0.010853290557861328 time for calcul the mask position with numpy : 0.029677629470825195 nb_pixel_total : 19253 time to create 1 rle with old method : 0.021569490432739258 time for calcul the mask position with numpy : 0.02997112274169922 nb_pixel_total : 30520 time to create 1 rle with old method : 0.040160417556762695 time for calcul the mask position with numpy : 0.033734798431396484 nb_pixel_total : 21986 time to create 1 rle with old method : 0.03565073013305664 time for calcul the mask position with numpy : 0.029708385467529297 nb_pixel_total : 38253 time to create 1 rle with old method : 0.04259300231933594 time for calcul the mask position with numpy : 0.029663801193237305 nb_pixel_total : 35904 time to create 1 rle with old method : 0.040456533432006836 time for calcul the mask position with numpy : 0.030005693435668945 nb_pixel_total : 15727 time to create 1 rle with old method : 0.022951126098632812 time for calcul the mask position with numpy : 0.02938103675842285 nb_pixel_total : 8149 time to create 1 rle with old method : 0.00970315933227539 time for calcul the mask position with numpy : 0.03188824653625488 nb_pixel_total : 68057 time to create 1 rle with old method : 0.08137249946594238 time for calcul the mask position with numpy : 0.03161334991455078 nb_pixel_total : 57461 time to create 1 rle with old method : 0.07625126838684082 time for calcul the mask position with numpy : 0.033315420150756836 nb_pixel_total : 56372 time to create 1 rle with old method : 0.08354854583740234 time for calcul the mask position with numpy : 0.03194546699523926 nb_pixel_total : 80425 time to create 1 rle with old method : 0.0908205509185791 time for calcul the mask position with numpy : 0.03072810173034668 nb_pixel_total : 23537 time to create 1 rle with old method : 0.0374295711517334 time for calcul the mask position with numpy : 0.030994415283203125 nb_pixel_total : 15714 time to create 1 rle with old method : 0.018700838088989258 time for calcul the mask position with numpy : 0.030450820922851562 nb_pixel_total : 30791 time to create 1 rle with old method : 0.03645825386047363 time for calcul the mask position with numpy : 0.030612707138061523 nb_pixel_total : 29651 time to create 1 rle with old method : 0.037835121154785156 time for calcul the mask position with numpy : 0.030704021453857422 nb_pixel_total : 37535 time to create 1 rle with old method : 0.043917179107666016 time for calcul the mask position with numpy : 0.03025650978088379 nb_pixel_total : 11778 time to create 1 rle with old method : 0.01395106315612793 time for calcul the mask position with numpy : 0.03040766716003418 nb_pixel_total : 22021 time to create 1 rle with old method : 0.025969266891479492 time for calcul the mask position with numpy : 0.03047490119934082 nb_pixel_total : 20877 time to create 1 rle with old method : 0.02430129051208496 time for calcul the mask position with numpy : 0.030219316482543945 nb_pixel_total : 16368 time to create 1 rle with old method : 0.0188448429107666 time for calcul the mask position with numpy : 0.0311129093170166 nb_pixel_total : 8874 time to create 1 rle with old method : 0.010417699813842773 create new chi : 3.7066779136657715 time to delete rle : 0.00400996208190918 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18470 TO DO : save crop sub photo not yet done ! save time : 2.2206010818481445 nb_obj : 39 nb_hashtags : 4 time to prepare the origin masks : 4.717447996139526 time for calcul the mask position with numpy : 0.23761630058288574 nb_pixel_total : 5365211 time to create 1 rle with new method : 0.45274901390075684 time for calcul the mask position with numpy : 0.03063035011291504 nb_pixel_total : 10179 time to create 1 rle with old method : 0.013538837432861328 time for calcul the mask position with numpy : 0.032456398010253906 nb_pixel_total : 14279 time to create 1 rle with old method : 0.018105506896972656 time for calcul the mask position with numpy : 0.03087306022644043 nb_pixel_total : 18400 time to create 1 rle with old method : 0.023369550704956055 time for calcul the mask position with numpy : 0.03400778770446777 nb_pixel_total : 12151 time to create 1 rle with old method : 0.014548063278198242 time for calcul the mask position with numpy : 0.02982926368713379 nb_pixel_total : 8215 time to create 1 rle with old method : 0.009325981140136719 time for calcul the mask position with numpy : 0.029121875762939453 nb_pixel_total : 14963 time to create 1 rle with old method : 0.017600536346435547 time for calcul the mask position with numpy : 0.029638290405273438 nb_pixel_total : 12146 time to create 1 rle with old method : 0.013768196105957031 time for calcul the mask position with numpy : 0.02936553955078125 nb_pixel_total : 28815 time to create 1 rle with old method : 0.03356432914733887 time for calcul the mask position with numpy : 0.02918529510498047 nb_pixel_total : 32963 time to create 1 rle with old method : 0.03715705871582031 time for calcul the mask position with numpy : 0.029155254364013672 nb_pixel_total : 20567 time to create 1 rle with old method : 0.023339033126831055 time for calcul the mask position with numpy : 0.02917313575744629 nb_pixel_total : 19099 time to create 1 rle with old method : 0.02187633514404297 time for calcul the mask position with numpy : 0.02985858917236328 nb_pixel_total : 12851 time to create 1 rle with old method : 0.014888286590576172 time for calcul the mask position with numpy : 0.02947854995727539 nb_pixel_total : 37594 time to create 1 rle with old method : 0.05116438865661621 time for calcul the mask position with numpy : 0.033889055252075195 nb_pixel_total : 10230 time to create 1 rle with old method : 0.012172698974609375 time for calcul the mask position with numpy : 0.03157830238342285 nb_pixel_total : 187091 time to create 1 rle with new method : 0.38164472579956055 time for calcul the mask position with numpy : 0.029568910598754883 nb_pixel_total : 56544 time to create 1 rle with old method : 0.06395316123962402 time for calcul the mask position with numpy : 0.031002283096313477 nb_pixel_total : 26729 time to create 1 rle with old method : 0.03205370903015137 time for calcul the mask position with numpy : 0.029687166213989258 nb_pixel_total : 27154 time to create 1 rle with old method : 0.03547859191894531 time for calcul the mask position with numpy : 0.033972978591918945 nb_pixel_total : 11764 time to create 1 rle with old method : 0.018185853958129883 time for calcul the mask position with numpy : 0.03476715087890625 nb_pixel_total : 91858 time to create 1 rle with old method : 0.12405824661254883 time for calcul the mask position with numpy : 0.03144049644470215 nb_pixel_total : 1142 time to create 1 rle with old method : 0.001468658447265625 time for calcul the mask position with numpy : 0.030076026916503906 nb_pixel_total : 17070 time to create 1 rle with old method : 0.02151036262512207 time for calcul the mask position with numpy : 0.03206014633178711 nb_pixel_total : 15284 time to create 1 rle with old method : 0.018666744232177734 time for calcul the mask position with numpy : 0.0323944091796875 nb_pixel_total : 14861 time to create 1 rle with old method : 0.0181427001953125 time for calcul the mask position with numpy : 0.03160524368286133 nb_pixel_total : 99200 time to create 1 rle with old method : 0.12175488471984863 time for calcul the mask position with numpy : 0.030052661895751953 nb_pixel_total : 23695 time to create 1 rle with old method : 0.02819061279296875 time for calcul the mask position with numpy : 0.0322725772857666 nb_pixel_total : 32745 time to create 1 rle with old method : 0.03829646110534668 time for calcul the mask position with numpy : 0.032611846923828125 nb_pixel_total : 167005 time to create 1 rle with new method : 0.3364894390106201 time for calcul the mask position with numpy : 0.029421091079711914 nb_pixel_total : 41811 time to create 1 rle with old method : 0.05459117889404297 time for calcul the mask position with numpy : 0.029677152633666992 nb_pixel_total : 38918 time to create 1 rle with old method : 0.05154109001159668 time for calcul the mask position with numpy : 0.030604839324951172 nb_pixel_total : 30803 time to create 1 rle with old method : 0.03771829605102539 time for calcul the mask position with numpy : 0.031203031539916992 nb_pixel_total : 31848 time to create 1 rle with old method : 0.03981971740722656 time for calcul the mask position with numpy : 0.030920028686523438 nb_pixel_total : 29677 time to create 1 rle with old method : 0.0361783504486084 time for calcul the mask position with numpy : 0.031584739685058594 nb_pixel_total : 162878 time to create 1 rle with new method : 0.31704092025756836 time for calcul the mask position with numpy : 0.032279253005981445 nb_pixel_total : 225420 time to create 1 rle with new method : 0.33823442459106445 time for calcul the mask position with numpy : 0.029870986938476562 nb_pixel_total : 32838 time to create 1 rle with old method : 0.03757047653198242 time for calcul the mask position with numpy : 0.0314488410949707 nb_pixel_total : 16770 time to create 1 rle with old method : 0.0194854736328125 time for calcul the mask position with numpy : 0.029931306838989258 nb_pixel_total : 38072 time to create 1 rle with old method : 0.04326510429382324 time for calcul the mask position with numpy : 0.02977466583251953 nb_pixel_total : 11400 time to create 1 rle with old method : 0.013286828994750977 create new chi : 4.561313629150391 time to delete rle : 0.0038492679595947266 batch 1 Loaded 80 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22837 TO DO : save crop sub photo not yet done ! save time : 2.7577295303344727 nb_obj : 32 nb_hashtags : 4 time to prepare the origin masks : 4.249988794326782 time for calcul the mask position with numpy : 0.3373851776123047 nb_pixel_total : 5920085 time to create 1 rle with new method : 0.7245442867279053 time for calcul the mask position with numpy : 0.029727935791015625 nb_pixel_total : 9994 time to create 1 rle with old method : 0.011893272399902344 time for calcul the mask position with numpy : 0.029703855514526367 nb_pixel_total : 11691 time to create 1 rle with old method : 0.013748407363891602 time for calcul the mask position with numpy : 0.030255556106567383 nb_pixel_total : 50907 time to create 1 rle with old method : 0.059041500091552734 time for calcul the mask position with numpy : 0.029880046844482422 nb_pixel_total : 2615 time to create 1 rle with old method : 0.0031342506408691406 time for calcul the mask position with numpy : 0.029788970947265625 nb_pixel_total : 7836 time to create 1 rle with old method : 0.009205341339111328 time for calcul the mask position with numpy : 0.029665231704711914 nb_pixel_total : 2355 time to create 1 rle with old method : 0.0031087398529052734 time for calcul the mask position with numpy : 0.03090524673461914 nb_pixel_total : 147035 time to create 1 rle with old method : 0.17222070693969727 time for calcul the mask position with numpy : 0.029815196990966797 nb_pixel_total : 11196 time to create 1 rle with old method : 0.013127565383911133 time for calcul the mask position with numpy : 0.03000020980834961 nb_pixel_total : 28683 time to create 1 rle with old method : 0.03857922554016113 time for calcul the mask position with numpy : 0.032880306243896484 nb_pixel_total : 23117 time to create 1 rle with old method : 0.03069758415222168 time for calcul the mask position with numpy : 0.029284238815307617 nb_pixel_total : 15347 time to create 1 rle with old method : 0.018021106719970703 time for calcul the mask position with numpy : 0.02968144416809082 nb_pixel_total : 14854 time to create 1 rle with old method : 0.017194747924804688 time for calcul the mask position with numpy : 0.02971339225769043 nb_pixel_total : 64062 time to create 1 rle with old method : 0.07475900650024414 time for calcul the mask position with numpy : 0.029540300369262695 nb_pixel_total : 32343 time to create 1 rle with old method : 0.038168907165527344 time for calcul the mask position with numpy : 0.029741764068603516 nb_pixel_total : 18651 time to create 1 rle with old method : 0.021858930587768555 time for calcul the mask position with numpy : 0.030002593994140625 nb_pixel_total : 99591 time to create 1 rle with old method : 0.11024904251098633 time for calcul the mask position with numpy : 0.028986215591430664 nb_pixel_total : 33426 time to create 1 rle with old method : 0.03731679916381836 time for calcul the mask position with numpy : 0.031278371810913086 nb_pixel_total : 21600 time to create 1 rle with old method : 0.024576902389526367 time for calcul the mask position with numpy : 0.03112959861755371 nb_pixel_total : 43993 time to create 1 rle with old method : 0.04999899864196777 time for calcul the mask position with numpy : 0.03221702575683594 nb_pixel_total : 56324 time to create 1 rle with old method : 0.06558966636657715 time for calcul the mask position with numpy : 0.02888655662536621 nb_pixel_total : 25734 time to create 1 rle with old method : 0.028733491897583008 time for calcul the mask position with numpy : 0.028971433639526367 nb_pixel_total : 48239 time to create 1 rle with old method : 0.0544283390045166 time for calcul the mask position with numpy : 0.02960681915283203 nb_pixel_total : 25269 time to create 1 rle with old method : 0.029529571533203125 time for calcul the mask position with numpy : 0.02954411506652832 nb_pixel_total : 17996 time to create 1 rle with old method : 0.021031618118286133 time for calcul the mask position with numpy : 0.029199838638305664 nb_pixel_total : 7651 time to create 1 rle with old method : 0.008524417877197266 time for calcul the mask position with numpy : 0.028904438018798828 nb_pixel_total : 22554 time to create 1 rle with old method : 0.02629828453063965 time for calcul the mask position with numpy : 0.029741764068603516 nb_pixel_total : 19481 time to create 1 rle with old method : 0.02292323112487793 time for calcul the mask position with numpy : 0.029878616333007812 nb_pixel_total : 46136 time to create 1 rle with old method : 0.05437922477722168 time for calcul the mask position with numpy : 0.030354738235473633 nb_pixel_total : 91566 time to create 1 rle with old method : 0.10802626609802246 time for calcul the mask position with numpy : 0.03011345863342285 nb_pixel_total : 66879 time to create 1 rle with old method : 0.07832193374633789 time for calcul the mask position with numpy : 0.030017375946044922 nb_pixel_total : 16003 time to create 1 rle with old method : 0.022954463958740234 time for calcul the mask position with numpy : 0.03019094467163086 nb_pixel_total : 47027 time to create 1 rle with old method : 0.05463528633117676 create new chi : 3.3807365894317627 time to delete rle : 0.0023643970489501953 batch 1 Loaded 65 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16968 TO DO : save crop sub photo not yet done ! save time : 5.1344993114471436 nb_obj : 20 nb_hashtags : 5 time to prepare the origin masks : 9.98214840888977 time for calcul the mask position with numpy : 0.463930606842041 nb_pixel_total : 4447717 time to create 1 rle with new method : 0.8785629272460938 time for calcul the mask position with numpy : 0.03439521789550781 nb_pixel_total : 19009 time to create 1 rle with old method : 0.02216649055480957 time for calcul the mask position with numpy : 0.023797988891601562 nb_pixel_total : 34239 time to create 1 rle with old method : 0.03965497016906738 time for calcul the mask position with numpy : 0.025936126708984375 nb_pixel_total : 8919 time to create 1 rle with old method : 0.010222673416137695 time for calcul the mask position with numpy : 0.023580551147460938 nb_pixel_total : 43560 time to create 1 rle with old method : 0.049973249435424805 time for calcul the mask position with numpy : 0.02337193489074707 nb_pixel_total : 25504 time to create 1 rle with old method : 0.029452085494995117 time for calcul the mask position with numpy : 0.023030519485473633 nb_pixel_total : 21735 time to create 1 rle with old method : 0.024813175201416016 time for calcul the mask position with numpy : 0.02434062957763672 nb_pixel_total : 64118 time to create 1 rle with old method : 0.07318854331970215 time for calcul the mask position with numpy : 0.02411627769470215 nb_pixel_total : 48233 time to create 1 rle with old method : 0.06080174446105957 time for calcul the mask position with numpy : 0.025352001190185547 nb_pixel_total : 36053 time to create 1 rle with old method : 0.05619001388549805 time for calcul the mask position with numpy : 0.02545619010925293 nb_pixel_total : 165308 time to create 1 rle with new method : 0.5837440490722656 time for calcul the mask position with numpy : 0.02418684959411621 nb_pixel_total : 13292 time to create 1 rle with old method : 0.017876625061035156 time for calcul the mask position with numpy : 0.025000810623168945 nb_pixel_total : 25020 time to create 1 rle with old method : 0.03378796577453613 time for calcul the mask position with numpy : 0.030823707580566406 nb_pixel_total : 605442 time to create 1 rle with new method : 0.6292269229888916 time for calcul the mask position with numpy : 0.022693872451782227 nb_pixel_total : 19940 time to create 1 rle with old method : 0.02796339988708496 time for calcul the mask position with numpy : 0.02515125274658203 nb_pixel_total : 51514 time to create 1 rle with old method : 0.057653188705444336 time for calcul the mask position with numpy : 0.024785995483398438 nb_pixel_total : 89396 time to create 1 rle with old method : 0.10570669174194336 time for calcul the mask position with numpy : 0.023223400115966797 nb_pixel_total : 14276 time to create 1 rle with old method : 0.016739606857299805 time for calcul the mask position with numpy : 0.03451108932495117 nb_pixel_total : 1180757 time to create 1 rle with new method : 0.575573205947876 time for calcul the mask position with numpy : 0.021966934204101562 nb_pixel_total : 124301 time to create 1 rle with old method : 0.13779139518737793 time for calcul the mask position with numpy : 0.022769689559936523 nb_pixel_total : 11907 time to create 1 rle with old method : 0.014007329940795898 create new chi : 4.526290416717529 time to delete rle : 0.0023069381713867188 batch 1 Loaded 41 chid ids of type : 3594 +++++++++++++++++++++++Number RLEs to save : 16764 TO DO : save crop sub photo not yet done ! save time : 4.237167835235596 nb_obj : 28 nb_hashtags : 4 time to prepare the origin masks : 5.559516906738281 time for calcul the mask position with numpy : 0.288804292678833 nb_pixel_total : 4468842 time to create 1 rle with new method : 0.6275708675384521 time for calcul the mask position with numpy : 0.0339810848236084 nb_pixel_total : 12853 time to create 1 rle with old method : 0.02113509178161621 time for calcul the mask position with numpy : 0.031926631927490234 nb_pixel_total : 7793 time to create 1 rle with old method : 0.009110212326049805 time for calcul the mask position with numpy : 0.032093048095703125 nb_pixel_total : 286236 time to create 1 rle with new method : 0.6032938957214355 time for calcul the mask position with numpy : 0.031487226486206055 nb_pixel_total : 55546 time to create 1 rle with old method : 0.06426143646240234 time for calcul the mask position with numpy : 0.03331804275512695 nb_pixel_total : 259283 time to create 1 rle with new method : 0.45660948753356934 time for calcul the mask position with numpy : 0.029736995697021484 nb_pixel_total : 21788 time to create 1 rle with old method : 0.025446176528930664 time for calcul the mask position with numpy : 0.030736684799194336 nb_pixel_total : 10542 time to create 1 rle with old method : 0.015003442764282227 time for calcul the mask position with numpy : 0.03473854064941406 nb_pixel_total : 311616 time to create 1 rle with new method : 0.5098743438720703 time for calcul the mask position with numpy : 0.03094005584716797 nb_pixel_total : 135065 time to create 1 rle with old method : 0.1527259349822998 time for calcul the mask position with numpy : 0.03194236755371094 nb_pixel_total : 257906 time to create 1 rle with new method : 0.35605907440185547 time for calcul the mask position with numpy : 0.03096938133239746 nb_pixel_total : 198601 time to create 1 rle with new method : 0.4124879837036133 time for calcul the mask position with numpy : 0.03346896171569824 nb_pixel_total : 54839 time to create 1 rle with old method : 0.06147265434265137 time for calcul the mask position with numpy : 0.029420137405395508 nb_pixel_total : 2959 time to create 1 rle with old method : 0.003705263137817383 time for calcul the mask position with numpy : 0.032917022705078125 nb_pixel_total : 31330 time to create 1 rle with old method : 0.04973936080932617 time for calcul the mask position with numpy : 0.03515458106994629 nb_pixel_total : 14346 time to create 1 rle with old method : 0.01675271987915039 time for calcul the mask position with numpy : 0.02985525131225586 nb_pixel_total : 29986 time to create 1 rle with old method : 0.03510570526123047 time for calcul the mask position with numpy : 0.03035569190979004 nb_pixel_total : 92308 time to create 1 rle with old method : 0.10704517364501953 time for calcul the mask position with numpy : 0.031328678131103516 nb_pixel_total : 236884 time to create 1 rle with new method : 0.40442490577697754 time for calcul the mask position with numpy : 0.030459165573120117 nb_pixel_total : 104457 time to create 1 rle with old method : 0.1205747127532959 time for calcul the mask position with numpy : 0.029949426651000977 nb_pixel_total : 19368 time to create 1 rle with old method : 0.023467063903808594 time for calcul the mask position with numpy : 0.0307157039642334 nb_pixel_total : 108997 time to create 1 rle with old method : 0.1279301643371582 time for calcul the mask position with numpy : 0.0317387580871582 nb_pixel_total : 35150 time to create 1 rle with old method : 0.04097318649291992 time for calcul the mask position with numpy : 0.03158307075500488 nb_pixel_total : 176235 time to create 1 rle with new method : 0.5602002143859863 time for calcul the mask position with numpy : 0.030155658721923828 nb_pixel_total : 13241 time to create 1 rle with old method : 0.015308380126953125 time for calcul the mask position with numpy : 0.03042006492614746 nb_pixel_total : 49887 time to create 1 rle with old method : 0.05757284164428711 time for calcul the mask position with numpy : 0.030209064483642578 nb_pixel_total : 20140 time to create 1 rle with old method : 0.023408889770507812 time for calcul the mask position with numpy : 0.03003406524658203 nb_pixel_total : 32768 time to create 1 rle with old method : 0.03768777847290039 time for calcul the mask position with numpy : 0.029768705368041992 nb_pixel_total : 1274 time to create 1 rle with old method : 0.0015871524810791016 create new chi : 6.326584815979004 time to delete rle : 0.005959510803222656 batch 1 Loaded 57 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 25426 TO DO : save crop sub photo not yet done ! save time : 3.4013848304748535 nb_obj : 16 nb_hashtags : 3 time to prepare the origin masks : 5.408849477767944 time for calcul the mask position with numpy : 0.48343539237976074 nb_pixel_total : 5907406 time to create 1 rle with new method : 0.8180949687957764 time for calcul the mask position with numpy : 0.03700590133666992 nb_pixel_total : 75169 time to create 1 rle with old method : 0.08517575263977051 time for calcul the mask position with numpy : 0.03830409049987793 nb_pixel_total : 36208 time to create 1 rle with old method : 0.043364524841308594 time for calcul the mask position with numpy : 0.0502321720123291 nb_pixel_total : 134174 time to create 1 rle with old method : 0.18438935279846191 time for calcul the mask position with numpy : 0.04045677185058594 nb_pixel_total : 38663 time to create 1 rle with old method : 0.05137181282043457 time for calcul the mask position with numpy : 0.039871931076049805 nb_pixel_total : 7854 time to create 1 rle with old method : 0.01147603988647461 time for calcul the mask position with numpy : 0.04104781150817871 nb_pixel_total : 81916 time to create 1 rle with old method : 0.11630749702453613 time for calcul the mask position with numpy : 0.04105710983276367 nb_pixel_total : 64454 time to create 1 rle with old method : 0.07613325119018555 time for calcul the mask position with numpy : 0.03783726692199707 nb_pixel_total : 198069 time to create 1 rle with new method : 0.6850261688232422 time for calcul the mask position with numpy : 0.02277541160583496 nb_pixel_total : 60350 time to create 1 rle with old method : 0.07377052307128906 time for calcul the mask position with numpy : 0.026418209075927734 nb_pixel_total : 63549 time to create 1 rle with old method : 0.0820465087890625 time for calcul the mask position with numpy : 0.024701595306396484 nb_pixel_total : 45020 time to create 1 rle with old method : 0.07091546058654785 time for calcul the mask position with numpy : 0.0265655517578125 nb_pixel_total : 172899 time to create 1 rle with new method : 0.8955225944519043 time for calcul the mask position with numpy : 0.02336430549621582 nb_pixel_total : 34041 time to create 1 rle with old method : 0.03943181037902832 time for calcul the mask position with numpy : 0.023631811141967773 nb_pixel_total : 100429 time to create 1 rle with old method : 0.11610245704650879 time for calcul the mask position with numpy : 0.023276567459106445 nb_pixel_total : 8320 time to create 1 rle with old method : 0.009811878204345703 time for calcul the mask position with numpy : 0.023311376571655273 nb_pixel_total : 21719 time to create 1 rle with old method : 0.025215625762939453 create new chi : 4.473167181015015 time to delete rle : 0.0018875598907470703 batch 1 Loaded 33 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 12781 TO DO : save crop sub photo not yet done ! save time : 2.134730577468872 map_output_result : {1350768794: (0.0, 'Should be the crop_list due to order', 0), 1350768790: (0.0, 'Should be the crop_list due to order', 0), 1350765356: (0.0, 'Should be the crop_list due to order', 0), 1350765310: (0.0, 'Should be the crop_list due to order', 0), 1350765238: (0.0, 'Should be the crop_list due to order', 0), 1350765167: (0.0, 'Should be the crop_list due to order', 0), 1350765131: (0.0, 'Should be the crop_list due to order', 0), 1350765095: (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 [1350768794, 1350768790, 1350765356, 1350765310, 1350765238, 1350765167, 1350765131, 1350765095] Looping around the photos to save general results len do output : 8 /1350768794.Didn't retrieve data . /1350768790.Didn't retrieve data . /1350765356.Didn't retrieve data . /1350765310.Didn't retrieve data . /1350765238.Didn't retrieve data . /1350765167.Didn't retrieve data . /1350765131.Didn't retrieve data . /1350765095.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, '2734608') ('3318', '22163334', '1350768794', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350768790', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765356', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765310', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765238', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765167', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765131', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765095', None, None, None, None, None, '2734608') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.04901933670043945 save_final save missing photos in datou_result : time spend for datou_step_exec : 105.56434798240662 time spend to save output : 0.04957413673400879 total time spend for step 3 : 105.61392211914062 step4:ventilate_hashtags_in_portfolio Wed Apr 9 14:47:49 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 : 22163334 get user id for portfolio 22163334 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`=22163334 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','metal','autre','environnement','background','papier','pehd','pet_fonce','mal_croppe','flou')) 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`=22163334 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','metal','autre','environnement','background','papier','pehd','pet_fonce','mal_croppe','flou')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22163334 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pet_clair','metal','autre','environnement','background','papier','pehd','pet_fonce','mal_croppe','flou')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22164345,22164346,22164347,22164348,22164349,22164350,22164351,22164352,22164353,22164354,22164355?tags=carton,pet_clair,metal,autre,environnement,background,papier,pehd,pet_fonce,mal_croppe,flou Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350768794, 1350768790, 1350765356, 1350765310, 1350765238, 1350765167, 1350765131, 1350765095] Looping around the photos to save general results len do output : 1 /22163334. 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, '2734608') ('3318', '22163334', '1350768794', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350768790', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765356', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765310', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765238', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765167', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765131', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765095', None, None, None, None, None, '2734608') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.04179668426513672 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.033305644989014 time spend to save output : 0.042182207107543945 total time spend for step 4 : 9.075487852096558 step5:final Wed Apr 9 14:47:58 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 : {1350768794: ('0.23583165253948815',), 1350768790: ('0.23583165253948815',), 1350765356: ('0.23583165253948815',), 1350765310: ('0.23583165253948815',), 1350765238: ('0.23583165253948815',), 1350765167: ('0.23583165253948815',), 1350765131: ('0.23583165253948815',), 1350765095: ('0.23583165253948815',)} new output for save of step final : {1350768794: ('0.23583165253948815',), 1350768790: ('0.23583165253948815',), 1350765356: ('0.23583165253948815',), 1350765310: ('0.23583165253948815',), 1350765238: ('0.23583165253948815',), 1350765167: ('0.23583165253948815',), 1350765131: ('0.23583165253948815',), 1350765095: ('0.23583165253948815',)} [1350768794, 1350768790, 1350765356, 1350765310, 1350765238, 1350765167, 1350765131, 1350765095] Looping around the photos to save general results len do output : 8 /1350768794.Didn't retrieve data . /1350768790.Didn't retrieve data . /1350765356.Didn't retrieve data . /1350765310.Didn't retrieve data . /1350765238.Didn't retrieve data . /1350765167.Didn't retrieve data . /1350765131.Didn't retrieve data . /1350765095.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, '2734608') ('3318', '22163334', '1350768794', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350768790', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765356', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765310', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765238', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765167', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765131', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765095', None, None, None, None, None, '2734608') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 24 time used for this insertion : 0.04321932792663574 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.33241701126098633 time spend to save output : 0.04366016387939453 total time spend for step 5 : 0.37607717514038086 step6:blur_detection Wed Apr 9 14:47:58 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/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972.jpg resize: (2160, 3264) 1350768794 -3.8460696130196066 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe.jpg resize: (2160, 3264) 1350768790 -4.196678751116197 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96.jpg resize: (2160, 3264) 1350765356 -4.153970598232593 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5.jpg resize: (2160, 3264) 1350765310 -3.81265767548402 treat image : temp/1744202428_1470105_1350765238_86d73cd6c00af6d06494e56929e80f9d.jpg resize: (2160, 3264) 1350765238 -4.402790953787279 treat image : temp/1744202428_1470105_1350765167_5dc528bd92bceffeae7c510e0e090473.jpg resize: (2160, 3264) 1350765167 -3.2334376428794065 treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a.jpg resize: (2160, 3264) 1350765131 -2.839649965248579 treat image : temp/1744202428_1470105_1350765095_da56e9846e1721c5deb97b716a061c33.jpg resize: (2160, 3264) 1350765095 -3.6733618380353907 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593566_0.png resize: (264, 299) 1350785683 -1.8400902148588048 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593583_0.png resize: (291, 216) 1350785685 -2.3812247931945114 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593573_0.png resize: (323, 304) 1350785686 -1.5042064081532627 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593588_0.png resize: (401, 305) 1350785687 -2.3150406815665514 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593597_0.png resize: (161, 330) 1350785688 -2.776394419627604 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593595_0.png resize: (160, 192) 1350785689 -2.2209196510904587 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593564_0.png resize: (152, 214) 1350785690 -2.163941199315916 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593586_0.png resize: (164, 192) 1350785692 -1.8194422919013753 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593574_0.png resize: (155, 263) 1350785693 -2.3690741839038925 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593571_0.png resize: (351, 217) 1350785694 -1.0684985516279635 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593572_0.png resize: (126, 105) 1350785695 -1.6167586463903096 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593563_0.png resize: (252, 215) 1350785696 -1.9634029483250266 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593576_0.png resize: (274, 726) 1350785697 -3.210350264967876 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593590_0.png resize: (277, 412) 1350785698 -1.4575124258470946 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593570_0.png resize: (165, 157) 1350785701 -2.869266127443977 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593581_0.png resize: (115, 123) 1350785702 1.5789838807573666 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593575_0.png resize: (279, 305) 1350785703 -1.6375344861396566 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593569_0.png resize: (173, 152) 1350785704 -1.036422234929418 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593591_0.png resize: (226, 176) 1350785705 -1.6663351109179356 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593599_0.png resize: (87, 160) 1350785706 -1.858045112146768 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593585_0.png resize: (341, 128) 1350785707 -1.7404507666008826 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593589_0.png resize: (215, 249) 1350785708 -1.9404204132087646 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593587_0.png resize: (365, 509) 1350785709 -2.004017014016926 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593567_0.png resize: (294, 229) 1350785710 -2.120645982397207 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593580_0.png resize: (131, 181) 1350785711 -0.20142690083214654 treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593582_0.png resize: (368, 68) 1350785712 -2.3452601814275336 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593618_0.png resize: (495, 241) 1350785713 -3.143004250014166 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593609_0.png resize: (310, 403) 1350785714 -2.4226253825101964 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593615_0.png resize: (173, 122) 1350785715 -1.7669857350732752 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593646_0.png resize: (116, 165) 1350785716 -2.3061048186893447 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593617_0.png resize: (365, 367) 1350785717 -2.687512761145807 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593625_0.png resize: (336, 343) 1350785718 -3.11976683679859 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593613_0.png resize: (162, 314) 1350785719 -1.7355210779455128 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593623_0.png resize: (317, 427) 1350785721 -2.2603189629242078 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593641_0.png resize: (107, 187) 1350785723 -1.122421557446946 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593624_0.png resize: (537, 429) 1350785724 -2.3464529456983536 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593614_0.png resize: (190, 176) 1350785725 -2.379680428471308 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593616_0.png resize: (472, 287) 1350785726 -2.8878411387648293 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593644_0.png resize: (207, 268) 1350785727 -2.946941623116146 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593647_0.png resize: (132, 143) 1350785729 -1.7845124318075338 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593621_0.png resize: (90, 58) 1350785730 -0.9686349926479874 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593601_0.png resize: (261, 265) 1350785733 -1.8924245275379583 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593639_0.png resize: (145, 181) 1350785734 -2.1072327409904217 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593608_0.png resize: (151, 121) 1350785735 -2.790524257786939 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593622_0.png resize: (151, 223) 1350785737 -1.9961190201596941 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593633_0.png resize: (116, 81) 1350785738 -0.21982234728266037 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593610_0.png resize: (100, 109) 1350785739 -1.4680068838852152 treat image : 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temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593606_0.png resize: (144, 167) 1350785753 -2.235245242731343 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593629_0.png resize: (196, 146) 1350785754 -1.3120874589669551 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593626_0.png resize: (145, 340) 1350785756 -2.204430196944921 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593604_0.png resize: (222, 129) 1350785757 -2.0268913643484847 treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593619_0.png resize: (265, 88) 1350785758 -2.633494591862473 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593666_0.png resize: (192, 211) 1350785759 0.07097741470945745 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593670_0.png resize: (334, 234) 1350785761 -1.6732097113926816 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593668_0.png resize: (283, 335) 1350785762 -2.806159457374617 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593662_0.png resize: (170, 206) 1350785764 -2.128732409154218 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593660_0.png resize: (90, 130) 1350785765 -2.2582493420046648 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593656_0.png resize: (156, 245) 1350785766 0.1230851582484905 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593671_0.png resize: (154, 230) 1350785767 -1.7468697355311311 treat image : 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temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593659_0.png resize: (112, 227) 1350785776 -2.147242823317236 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593653_0.png resize: (222, 170) 1350785777 -2.7228415500417937 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593679_0.png resize: (295, 341) 1350785778 -2.6618327254466956 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593673_0.png resize: (324, 460) 1350785779 -2.8589170150041445 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593669_0.png resize: (202, 260) 1350785780 -1.2969543250891857 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593684_0.png resize: (211, 316) 1350785782 -3.152256636364739 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593652_0.png resize: (170, 174) 1350785784 -2.252023538508212 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593674_0.png resize: (107, 139) 1350785785 -0.9341467135103831 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593663_0.png resize: (175, 59) 1350785786 -2.589237642847167 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593655_0.png resize: (156, 150) 1350785787 -1.5983806333978878 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593665_0.png resize: (288, 246) 1350785788 -2.544555938154112 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593650_0.png resize: (268, 341) 1350785789 -3.0887078274535544 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593657_0.png resize: (56, 204) 1350785790 -0.6330047864222986 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593677_0.png resize: (205, 188) 1350785791 -0.5004110302122005 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593654_0.png resize: (222, 187) 1350785792 -1.752152019946053 treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593664_0.png resize: (222, 168) 1350785793 -1.0244164448283914 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593704_0.png resize: (250, 97) 1350785794 -0.3267029766763083 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593701_0.png resize: (163, 101) 1350785796 -3.914845399563572 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593724_0.png resize: (552, 517) 1350785797 -2.559587143652949 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593690_0.png resize: (167, 93) 1350785798 -0.528270908564426 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593703_0.png resize: (199, 117) 1350785799 -1.5079152375658909 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593711_0.png resize: (179, 230) 1350785800 -2.5487685816939782 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593688_0.png resize: (289, 455) 1350785801 -0.9460332480423417 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593706_0.png resize: (584, 614) 1350785803 -3.1010340479618375 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593696_0.png resize: (228, 122) 1350785804 -3.2450836279648816 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593685_0.png resize: (226, 208) 1350785806 -1.4091565932453731 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593707_0.png resize: (120, 131) 1350785807 -1.1187936218247179 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593714_0.png resize: (258, 306) 1350785808 -1.204350138194677 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593694_0.png resize: (737, 431) 1350785809 -0.8321168196762877 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593686_0.png resize: (167, 146) 1350785810 -1.9186472294187817 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593713_0.png resize: (245, 242) 1350785811 -1.6420091799035335 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593712_0.png resize: (216, 287) 1350785812 -3.321351412269418 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593721_0.png resize: (147, 430) 1350785813 -2.9199173175730175 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593710_0.png resize: (131, 128) 1350785814 -0.4263545718214006 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593719_0.png resize: (218, 175) 1350785816 -1.8153937498827755 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593687_0.png resize: (195, 215) 1350785817 -2.003585315577223 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593695_0.png resize: (237, 263) 1350785818 -2.8494960240120624 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593691_0.png resize: (194, 262) 1350785819 -0.1637361654753886 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593705_0.png resize: (142, 197) 1350785820 -1.9777110948488494 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593697_0.png resize: (132, 143) 1350785821 -0.34917811092848305 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593720_0.png resize: (119, 156) 1350785823 -3.089250599833272 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593722_0.png resize: (332, 457) 1350785824 -2.5285119518704784 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593723_0.png resize: (102, 115) 1350785825 -2.8117396689092518 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593717_0.png resize: (198, 257) 1350785826 -2.6016179785808573 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593715_0.png resize: (155, 192) 1350785827 -1.5930631434946112 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593693_0.png resize: (214, 189) 1350785828 -0.07957439570558257 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593709_0.png resize: (215, 250) 1350785829 -1.743512270173856 treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593692_0.png resize: (158, 145) 1350785830 -3.6295809292185783 treat image : 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temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593672_0.png resize: (98, 238) 1350786023 -1.3525358079449754 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 : 266 time used for this insertion : 0.06721830368041992 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 266 time used for this insertion : 0.11122298240661621 save missing photos in datou_result : time spend for datou_step_exec : 36.71760106086731 time spend to save output : 0.19931578636169434 total time spend for step 6 : 36.916916847229004 step7:brightness Wed Apr 9 14:48:35 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 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/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972.jpg treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe.jpg treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96.jpg treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5.jpg treat image : temp/1744202428_1470105_1350765238_86d73cd6c00af6d06494e56929e80f9d.jpg treat image : temp/1744202428_1470105_1350765167_5dc528bd92bceffeae7c510e0e090473.jpg treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a.jpg treat image : temp/1744202428_1470105_1350765095_da56e9846e1721c5deb97b716a061c33.jpg treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593566_0.png treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593583_0.png 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temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a_rle_crop_3751593786_0.png treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a_rle_crop_3751593804_0.png treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a_rle_crop_3751593780_0.png treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a_rle_crop_3751593782_0.png treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a_rle_crop_3751593799_0.png treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a_rle_crop_3751593792_0.png treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a_rle_crop_3751593777_0.png treat image : temp/1744202428_1470105_1350765095_da56e9846e1721c5deb97b716a061c33_rle_crop_3751593805_0.png treat image : temp/1744202428_1470105_1350765095_da56e9846e1721c5deb97b716a061c33_rle_crop_3751593818_0.png treat image : 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temp/1744202428_1470105_1350765095_da56e9846e1721c5deb97b716a061c33_rle_crop_3751593814_0.png treat image : temp/1744202428_1470105_1350768790_4c8a57b6c08a2a0596c445f736415afe_rle_crop_3751593612_0.png treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593651_0.png treat image : temp/1744202428_1470105_1350765310_f1add3715384cbc4cfacc39cbf7c49d5_rle_crop_3751593698_0.png treat image : temp/1744202428_1470105_1350765238_86d73cd6c00af6d06494e56929e80f9d_rle_crop_3751593734_0.png treat image : temp/1744202428_1470105_1350765238_86d73cd6c00af6d06494e56929e80f9d_rle_crop_3751593748_0.png treat image : temp/1744202428_1470105_1350765238_86d73cd6c00af6d06494e56929e80f9d_rle_crop_3751593744_0.png treat image : temp/1744202428_1470105_1350765131_7a95d7cee62f84b323fe8edcc93ad38a_rle_crop_3751593800_0.png treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593598_0.png treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593592_0.png treat image : temp/1744202428_1470105_1350765167_5dc528bd92bceffeae7c510e0e090473_rle_crop_3751593768_0.png treat image : temp/1744202428_1470105_1350768794_10d99e11377b98c18860bd5966b4b972_rle_crop_3751593579_0.png treat image : temp/1744202428_1470105_1350765356_80258737e03e9af6f21e03d56e1d3a96_rle_crop_3751593672_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 : 266 time used for this insertion : 0.0699000358581543 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 266 time used for this insertion : 0.13485217094421387 save missing photos in datou_result : time spend for datou_step_exec : 8.788957118988037 time spend to save output : 0.22538232803344727 total time spend for step 7 : 9.014339447021484 step8:velours_tree Wed Apr 9 14:48:44 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.5470726490020752 time spend to save output : 5.1021575927734375e-05 total time spend for step 8 : 0.5471236705780029 step9:send_mail_cod Wed Apr 9 14:48:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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_P22163334_09-04-2025_14_48_45.pdf 22164345 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 .imagette221643451744202925 22164346 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 .imagette221643461744202926 22164347 change filename to text .imagette221643471744202928 22164348 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 .imagette221643481744202928 22164350 imagette221643501744202928 22164351 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 .imagette221643511744202928 22164352 change filename to text .change filename to text .change filename to text .imagette221643521744202930 22164353 change filename to text .change filename to text .imagette221643531744202930 22164354 imagette221643541744202931 22164355 imagette221643551744202931 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22163334 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22164345,22164346,22164347,22164348,22164349,22164350,22164351,22164352,22164353,22164354,22164355?tags=carton,pet_clair,metal,autre,environnement,background,papier,pehd,pet_fonce,mal_croppe,flou args[1350768794] : ((1350768794, -3.8460696130196066, 492609224), (1350768794, 0.09001621193285014, 2107752395), '0.23583165253948815') We are sending mail with results at report@fotonower.com args[1350768790] : ((1350768790, -4.196678751116197, 492609224), (1350768790, 0.14755521678366196, 2107752395), '0.23583165253948815') We are sending mail with results at report@fotonower.com args[1350765356] : ((1350765356, -4.153970598232593, 492609224), (1350765356, -0.0522755086382565, 2107752395), '0.23583165253948815') We are sending mail with results at report@fotonower.com args[1350765310] : ((1350765310, -3.81265767548402, 492609224), (1350765310, 0.05487645048695831, 2107752395), '0.23583165253948815') We are sending mail with results at report@fotonower.com args[1350765238] : ((1350765238, -4.402790953787279, 492609224), (1350765238, -0.20883018605273734, 496442774), '0.23583165253948815') We are sending mail with results at report@fotonower.com args[1350765167] : ((1350765167, -3.2334376428794065, 492609224), (1350765167, -0.22473237195531917, 496442774), '0.23583165253948815') We are sending mail with results at report@fotonower.com args[1350765131] : ((1350765131, -2.839649965248579, 492609224), (1350765131, -0.16367613878123832, 496442774), '0.23583165253948815') We are sending mail with results at report@fotonower.com args[1350765095] : ((1350765095, -3.6733618380353907, 492609224), (1350765095, -0.3113517264466515, 496442774), '0.23583165253948815') We are sending mail with results at report@fotonower.com refus_total : 0.23583165253948815 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=22163334 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350765095,1350765238,1350765310,1350765356,1350768790,1350765131,1350765167,1350768794) Found this number of photos: 8 begin to download photo : 1350765095 begin to download photo : 1350765310 begin to download photo : 1350768790 begin to download photo : 1350765167 download finish for photo 1350765167 begin to download photo : 1350768794 download finish for photo 1350768790 begin to download photo : 1350765131 download finish for photo 1350765095 begin to download photo : 1350765238 download finish for photo 1350765310 begin to download photo : 1350765356 download finish for photo 1350765238 download finish for photo 1350768794 download finish for photo 1350765356 download finish for photo 1350765131 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163334_09-04-2025_14_48_45.pdf results_Auto_P22163334_09-04-2025_14_48_45.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163334_09-04-2025_14_48_45.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','22163334','results_Auto_P22163334_09-04-2025_14_48_45.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163334_09-04-2025_14_48_45.pdf','pdf','','0.88','0.23583165253948815') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22163334

https://www.fotonower.com/image?json=false&list_photos_id=1350768794
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350768790
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350765356
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350765310
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350765238
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350765167
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350765131
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350765095
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 23.58%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/22164345?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22164346?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22164347?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22164348?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/22164351?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22164352?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22164353?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163334_09-04-2025_14_48_45.pdf.

Lien vers velours :https://www.fotonower.com/velours/22164345,22164346,22164347,22164348,22164349,22164350,22164351,22164352,22164353,22164354,22164355?tags=carton,pet_clair,metal,autre,environnement,background,papier,pehd,pet_fonce,mal_croppe,flou.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 12:48:54 GMT Content-Length: 0 Connection: close X-Message-Id: TPNWEE96RpyTkxNAfScVvw 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 [1350768794, 1350768790, 1350765356, 1350765310, 1350765238, 1350765167, 1350765131, 1350765095] 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, '2734608') ('3318', '22163334', '1350768794', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350768790', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765356', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765310', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765238', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765167', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765131', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765095', None, None, None, None, None, '2734608') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.049027204513549805 save_final save missing photos in datou_result : time spend for datou_step_exec : 9.298529386520386 time spend to save output : 0.04921722412109375 total time spend for step 9 : 9.34774661064148 step10:split_time_score Wed Apr 9 14:48:54 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', 8),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 09042025 22163334 Nombre de photos uploadées : 8 / 23040 (0%) 09042025 22163334 Nombre de photos taguées (types de déchets): 0 / 8 (0%) 09042025 22163334 Nombre de photos taguées (volume) : 0 / 8 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 4.0531158447265625e-06 ???????? elapsed_time : fill_and_build_computed_from_old_data 0.0003628730773925781 elapsed_time : insert_dashboard_record_day_entry 0.049573659896850586 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 : 0.24394142078851233 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161064_09-04-2025_12_55_46.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161064 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`=22161064 AND mptpi.`type`=3594 To do Qualite : 0.04609045052441947 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161067_09-04-2025_12_22_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161067 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161067 AND mptpi.`type`=3726 To do Qualite : 0.1886047378056162 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161073_09-04-2025_12_52_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161073 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`=22161073 AND mptpi.`type`=3594 To do Qualite : 0.22379874656749282 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161075_09-04-2025_12_27_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161075 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`=22161075 AND mptpi.`type`=3594 To do Qualite : 0.0944780203179495 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161654_09-04-2025_13_05_02.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161654 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161654 AND mptpi.`type`=3726 To do Qualite : 0.23583165253948815 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163334_09-04-2025_14_48_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22163334 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`=22163334 AND mptpi.`type`=3594 To do Qualite : 0.16103178757035222 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163336_09-04-2025_14_36_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22163336 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`=22163336 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'09042025': {'nb_upload': 8, '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 [1350768794, 1350768790, 1350765356, 1350765310, 1350765238, 1350765167, 1350765131, 1350765095] Looping around the photos to save general results len do output : 1 /22163334Didn'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, '2734608') ('3318', '22163334', '1350768794', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350768790', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765356', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765310', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765238', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765167', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765131', None, None, None, None, None, '2734608') ('3318', None, None, None, None, None, None, None, '2734608') ('3318', '22163334', '1350765095', None, None, None, None, None, '2734608') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 9 time used for this insertion : 0.046215057373046875 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.080627202987671 time spend to save output : 0.0464324951171875 total time spend for step 10 : 2.1270596981048584 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 8 set_done_treatment 240.07user 116.03system 8:30.86elapsed 69%CPU (0avgtext+0avgdata 7025192maxresident)k 1031824inputs+172960outputs (21160major+20397602minor)pagefaults 0swaps