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 : 2412904 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 : ['2711237'] with mtr_portfolio_ids : ['21930836'] and first list_photo_ids : [] new path : /proc/2412904/ 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 ofUsing 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 : [] 2025-04-01 02:20:54.517972: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 02:20:54.733260: 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 : 11 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 : 49 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 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 : 56 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 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 : 40 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 : 77 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) None max_time_sub_proc : 3600 parent process len(results) : 11 len(list_Values) 0 process is alive process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10814 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.19245505332946777 nb_pixel_total : 12344 time to create 1 rle with old method : 0.020919084548950195 length of segment : 183 time for calcul the mask position with numpy : 0.14846205711364746 nb_pixel_total : 13897 time to create 1 rle with old method : 0.020519495010375977 length of segment : 171 time for calcul the mask position with numpy : 0.4616541862487793 nb_pixel_total : 30531 time to create 1 rle with old method : 0.046070098876953125 length of segment : 258 time for calcul the mask position with numpy : 0.7098946571350098 nb_pixel_total : 95842 time to create 1 rle with old method : 0.11394953727722168 length of segment : 335 time for calcul the mask position with numpy : 0.211625337600708 nb_pixel_total : 38185 time to create 1 rle with old method : 0.04657769203186035 length of segment : 294 time for calcul the mask position with numpy : 0.20839262008666992 nb_pixel_total : 71199 time to create 1 rle with old method : 0.0878150463104248 length of segment : 645 time for calcul the mask position with numpy : 0.5599374771118164 nb_pixel_total : 191587 time to create 1 rle with new method : 0.016691923141479492 length of segment : 861 time for calcul the mask position with numpy : 0.2916250228881836 nb_pixel_total : 50408 time to create 1 rle with old method : 0.061551570892333984 length of segment : 301 time for calcul the mask position with numpy : 0.6599440574645996 nb_pixel_total : 104280 time to create 1 rle with old method : 0.12184858322143555 length of segment : 468 time for calcul the mask position with numpy : 0.10224723815917969 nb_pixel_total : 95775 time to create 1 rle with old method : 0.1176156997680664 length of segment : 544 time for calcul the mask position with numpy : 0.03376507759094238 nb_pixel_total : 6740 time to create 1 rle with old method : 0.0141754150390625 length of segment : 115 time for calcul the mask position with numpy : 0.004366159439086914 nb_pixel_total : 43664 time to create 1 rle with old method : 0.06033515930175781 length of segment : 202 time for calcul the mask position with numpy : 0.05432581901550293 nb_pixel_total : 5317 time to create 1 rle with old method : 0.009728670120239258 length of segment : 94 time for calcul the mask position with numpy : 0.3808712959289551 nb_pixel_total : 102424 time to create 1 rle with old method : 0.12078404426574707 length of segment : 449 time for calcul the mask position with numpy : 0.5674207210540771 nb_pixel_total : 210412 time to create 1 rle with new method : 0.017636537551879883 length of segment : 490 time for calcul the mask position with numpy : 0.16579866409301758 nb_pixel_total : 23218 time to create 1 rle with old method : 0.029778242111206055 length of segment : 305 time for calcul the mask position with numpy : 0.03964424133300781 nb_pixel_total : 38107 time to create 1 rle with old method : 0.04470014572143555 length of segment : 200 time for calcul the mask position with numpy : 0.12224769592285156 nb_pixel_total : 58195 time to create 1 rle with old method : 0.06981277465820312 length of segment : 270 time for calcul the mask position with numpy : 0.021526813507080078 nb_pixel_total : 15583 time to create 1 rle with old method : 0.02616095542907715 length of segment : 201 time for calcul the mask position with numpy : 0.05802488327026367 nb_pixel_total : 29965 time to create 1 rle with old method : 0.03808403015136719 length of segment : 206 time for calcul the mask position with numpy : 0.30766868591308594 nb_pixel_total : 102800 time to create 1 rle with old method : 0.11948800086975098 length of segment : 352 time for calcul the mask position with numpy : 0.04021143913269043 nb_pixel_total : 48013 time to create 1 rle with old method : 0.06642889976501465 length of segment : 197 time for calcul the mask position with numpy : 0.1311969757080078 nb_pixel_total : 41044 time to create 1 rle with old method : 0.0529789924621582 length of segment : 286 time for calcul the mask position with numpy : 0.04562664031982422 nb_pixel_total : 19617 time to create 1 rle with old method : 0.025156259536743164 length of segment : 210 time for calcul the mask position with numpy : 0.05112600326538086 nb_pixel_total : 22349 time to create 1 rle with old method : 0.02947998046875 length of segment : 269 time for calcul the mask position with numpy : 0.09555983543395996 nb_pixel_total : 17738 time to create 1 rle with old method : 0.024026155471801758 length of segment : 131 time for calcul the mask position with numpy : 0.02693939208984375 nb_pixel_total : 33581 time to create 1 rle with old method : 0.04241323471069336 length of segment : 222 time for calcul the mask position with numpy : 0.015491962432861328 nb_pixel_total : 10961 time to create 1 rle with old method : 0.01726508140563965 length of segment : 153 time for calcul the mask position with numpy : 0.001960277557373047 nb_pixel_total : 8364 time to create 1 rle with old method : 0.009821891784667969 length of segment : 88 time for calcul the mask position with numpy : 0.02787613868713379 nb_pixel_total : 30155 time to create 1 rle with old method : 0.03801560401916504 length of segment : 198 time for calcul the mask position with numpy : 0.028926372528076172 nb_pixel_total : 63847 time to create 1 rle with old method : 0.10266995429992676 length of segment : 350 time for calcul the mask position with numpy : 0.0825648307800293 nb_pixel_total : 125144 time to create 1 rle with old method : 0.153336763381958 length of segment : 636 time for calcul the mask position with numpy : 0.0058596134185791016 nb_pixel_total : 16472 time to create 1 rle with old method : 0.02113819122314453 length of segment : 130 time for calcul the mask position with numpy : 0.0007748603820800781 nb_pixel_total : 5138 time to create 1 rle with old method : 0.006185054779052734 length of segment : 70 time for calcul the mask position with numpy : 0.3786506652832031 nb_pixel_total : 842515 time to create 1 rle with new method : 0.08197379112243652 length of segment : 1175 time for calcul the mask position with numpy : 0.022457361221313477 nb_pixel_total : 101586 time to create 1 rle with old method : 0.11806726455688477 length of segment : 316 time for calcul the mask position with numpy : 0.001737833023071289 nb_pixel_total : 10528 time to create 1 rle with old method : 0.011897563934326172 length of segment : 201 time for calcul the mask position with numpy : 0.003519773483276367 nb_pixel_total : 17571 time to create 1 rle with old method : 0.021132469177246094 length of segment : 152 time for calcul the mask position with numpy : 0.0003345012664794922 nb_pixel_total : 10420 time to create 1 rle with old method : 0.012345314025878906 length of segment : 106 time for calcul the mask position with numpy : 0.03364372253417969 nb_pixel_total : 103671 time to create 1 rle with old method : 0.12411141395568848 length of segment : 483 time for calcul the mask position with numpy : 0.0063402652740478516 nb_pixel_total : 12504 time to create 1 rle with old method : 0.016697168350219727 length of segment : 105 time for calcul the mask position with numpy : 0.03036332130432129 nb_pixel_total : 150208 time to create 1 rle with new method : 0.011595964431762695 length of segment : 535 time for calcul the mask position with numpy : 0.011964559555053711 nb_pixel_total : 13601 time to create 1 rle with old method : 0.02117180824279785 length of segment : 149 time for calcul the mask position with numpy : 0.004570484161376953 nb_pixel_total : 20888 time to create 1 rle with old method : 0.0259549617767334 length of segment : 253 time for calcul the mask position with numpy : 0.008251667022705078 nb_pixel_total : 13791 time to create 1 rle with old method : 0.018265247344970703 length of segment : 96 time for calcul the mask position with numpy : 0.0026569366455078125 nb_pixel_total : 5234 time to create 1 rle with old method : 0.006030082702636719 length of segment : 120 time for calcul the mask position with numpy : 0.000396728515625 nb_pixel_total : 15516 time to create 1 rle with old method : 0.018140316009521484 length of segment : 143 time for calcul the mask position with numpy : 0.02111220359802246 nb_pixel_total : 94485 time to create 1 rle with old method : 0.10795426368713379 length of segment : 369 time for calcul the mask position with numpy : 0.07030010223388672 nb_pixel_total : 33537 time to create 1 rle with old method : 0.0593266487121582 length of segment : 258 time for calcul the mask position with numpy : 0.011008739471435547 nb_pixel_total : 26946 time to create 1 rle with old method : 0.03611397743225098 length of segment : 172 time for calcul the mask position with numpy : 0.008995771408081055 nb_pixel_total : 51521 time to create 1 rle with old method : 0.07142281532287598 length of segment : 319 time for calcul the mask position with numpy : 0.003359556198120117 nb_pixel_total : 18541 time to create 1 rle with old method : 0.021950721740722656 length of segment : 153 time for calcul the mask position with numpy : 0.009284019470214844 nb_pixel_total : 33855 time to create 1 rle with old method : 0.040685176849365234 length of segment : 297 time for calcul the mask position with numpy : 0.00747227668762207 nb_pixel_total : 33317 time to create 1 rle with old method : 0.042049407958984375 length of segment : 233 time for calcul the mask position with numpy : 0.0017125606536865234 nb_pixel_total : 18628 time to create 1 rle with old method : 0.021358966827392578 length of segment : 188 time for calcul the mask position with numpy : 0.006127595901489258 nb_pixel_total : 99658 time to create 1 rle with old method : 0.1172947883605957 length of segment : 431 time for calcul the mask position with numpy : 0.001976490020751953 nb_pixel_total : 35620 time to create 1 rle with old method : 0.040845632553100586 length of segment : 392 time for calcul the mask position with numpy : 0.011080265045166016 nb_pixel_total : 39310 time to create 1 rle with old method : 0.04604816436767578 length of segment : 339 time for calcul the mask position with numpy : 0.01714801788330078 nb_pixel_total : 28970 time to create 1 rle with old method : 0.03307175636291504 length of segment : 159 time for calcul the mask position with numpy : 0.01297616958618164 nb_pixel_total : 25022 time to create 1 rle with old method : 0.029352188110351562 length of segment : 238 time for calcul the mask position with numpy : 0.020115137100219727 nb_pixel_total : 11548 time to create 1 rle with old method : 0.01911783218383789 length of segment : 239 time for calcul the mask position with numpy : 0.0008485317230224609 nb_pixel_total : 20369 time to create 1 rle with old method : 0.02834773063659668 length of segment : 138 time for calcul the mask position with numpy : 0.008896827697753906 nb_pixel_total : 7850 time to create 1 rle with old method : 0.013725519180297852 length of segment : 131 time for calcul the mask position with numpy : 0.01627492904663086 nb_pixel_total : 35755 time to create 1 rle with old method : 0.058983564376831055 length of segment : 237 time for calcul the mask position with numpy : 0.003662586212158203 nb_pixel_total : 127333 time to create 1 rle with old method : 0.16985106468200684 length of segment : 760 time for calcul the mask position with numpy : 0.0024526119232177734 nb_pixel_total : 18081 time to create 1 rle with old method : 0.022312402725219727 length of segment : 139 time for calcul the mask position with numpy : 0.00019884109497070312 nb_pixel_total : 4846 time to create 1 rle with old method : 0.005753040313720703 length of segment : 83 time for calcul the mask position with numpy : 0.0010373592376708984 nb_pixel_total : 5561 time to create 1 rle with old method : 0.006546735763549805 length of segment : 100 time for calcul the mask position with numpy : 0.014008522033691406 nb_pixel_total : 62705 time to create 1 rle with old method : 0.07137656211853027 length of segment : 385 time for calcul the mask position with numpy : 0.010881185531616211 nb_pixel_total : 29792 time to create 1 rle with old method : 0.037059783935546875 length of segment : 129 time for calcul the mask position with numpy : 0.006028890609741211 nb_pixel_total : 16031 time to create 1 rle with old method : 0.01794719696044922 length of segment : 219 time for calcul the mask position with numpy : 0.010009050369262695 nb_pixel_total : 31655 time to create 1 rle with old method : 0.03584694862365723 length of segment : 215 time for calcul the mask position with numpy : 0.0010609626770019531 nb_pixel_total : 9499 time to create 1 rle with old method : 0.010946273803710938 length of segment : 95 time for calcul the mask position with numpy : 0.005128622055053711 nb_pixel_total : 24791 time to create 1 rle with old method : 0.031919240951538086 length of segment : 303 time for calcul the mask position with numpy : 0.0016913414001464844 nb_pixel_total : 28260 time to create 1 rle with old method : 0.032813072204589844 length of segment : 409 time for calcul the mask position with numpy : 0.0004429817199707031 nb_pixel_total : 7247 time to create 1 rle with old method : 0.008503913879394531 length of segment : 122 time for calcul the mask position with numpy : 0.0013158321380615234 nb_pixel_total : 43538 time to create 1 rle with old method : 0.04965806007385254 length of segment : 248 time for calcul the mask position with numpy : 0.0008542537689208984 nb_pixel_total : 25301 time to create 1 rle with old method : 0.028472185134887695 length of segment : 271 time for calcul the mask position with numpy : 0.0005815029144287109 nb_pixel_total : 21584 time to create 1 rle with old method : 0.024758577346801758 length of segment : 180 time for calcul the mask position with numpy : 0.0007963180541992188 nb_pixel_total : 9600 time to create 1 rle with old method : 0.013035297393798828 length of segment : 128 time for calcul the mask position with numpy : 0.0007572174072265625 nb_pixel_total : 23020 time to create 1 rle with old method : 0.03093719482421875 length of segment : 225 time for calcul the mask position with numpy : 0.0009865760803222656 nb_pixel_total : 13808 time to create 1 rle with old method : 0.01633763313293457 length of segment : 113 time for calcul the mask position with numpy : 0.00026869773864746094 nb_pixel_total : 8994 time to create 1 rle with old method : 0.01382589340209961 length of segment : 128 time for calcul the mask position with numpy : 0.0010151863098144531 nb_pixel_total : 8953 time to create 1 rle with old method : 0.015298604965209961 length of segment : 182 time for calcul the mask position with numpy : 0.00044846534729003906 nb_pixel_total : 12988 time to create 1 rle with old method : 0.015139579772949219 length of segment : 144 time for calcul the mask position with numpy : 0.00032520294189453125 nb_pixel_total : 6039 time to create 1 rle with old method : 0.007305145263671875 length of segment : 104 time for calcul the mask position with numpy : 0.002618551254272461 nb_pixel_total : 75017 time to create 1 rle with old method : 0.08802556991577148 length of segment : 457 time for calcul the mask position with numpy : 0.0006418228149414062 nb_pixel_total : 23159 time to create 1 rle with old method : 0.030646324157714844 length of segment : 313 time for calcul the mask position with numpy : 0.00035953521728515625 nb_pixel_total : 15856 time to create 1 rle with old method : 0.01886606216430664 length of segment : 119 time for calcul the mask position with numpy : 0.00036072731018066406 nb_pixel_total : 13916 time to create 1 rle with old method : 0.01624751091003418 length of segment : 140 time for calcul the mask position with numpy : 0.0002498626708984375 nb_pixel_total : 10362 time to create 1 rle with old method : 0.012050867080688477 length of segment : 123 time for calcul the mask position with numpy : 0.0022771358489990234 nb_pixel_total : 67246 time to create 1 rle with old method : 0.07582664489746094 length of segment : 458 time for calcul the mask position with numpy : 0.0005600452423095703 nb_pixel_total : 14511 time to create 1 rle with old method : 0.01648426055908203 length of segment : 198 time for calcul the mask position with numpy : 0.0005629062652587891 nb_pixel_total : 11199 time to create 1 rle with old method : 0.012911081314086914 length of segment : 161 time for calcul the mask position with numpy : 0.0009207725524902344 nb_pixel_total : 34396 time to create 1 rle with old method : 0.03886890411376953 length of segment : 454 time for calcul the mask position with numpy : 0.0007870197296142578 nb_pixel_total : 25084 time to create 1 rle with old method : 0.028381824493408203 length of segment : 202 time for calcul the mask position with numpy : 0.0001819133758544922 nb_pixel_total : 6394 time to create 1 rle with old method : 0.007525205612182617 length of segment : 70 time for calcul the mask position with numpy : 0.002718687057495117 nb_pixel_total : 79552 time to create 1 rle with old method : 0.08893394470214844 length of segment : 663 time for calcul the mask position with numpy : 0.0060770511627197266 nb_pixel_total : 105668 time to create 1 rle with old method : 0.1210176944732666 length of segment : 297 time for calcul the mask position with numpy : 0.0006735324859619141 nb_pixel_total : 20875 time to create 1 rle with old method : 0.02369236946105957 length of segment : 220 time for calcul the mask position with numpy : 0.0008647441864013672 nb_pixel_total : 28575 time to create 1 rle with old method : 0.034776926040649414 length of segment : 345 time for calcul the mask position with numpy : 0.0006780624389648438 nb_pixel_total : 15515 time to create 1 rle with old method : 0.018929243087768555 length of segment : 105 time for calcul the mask position with numpy : 0.0011589527130126953 nb_pixel_total : 16526 time to create 1 rle with old method : 0.02016472816467285 length of segment : 201 time for calcul the mask position with numpy : 0.0025420188903808594 nb_pixel_total : 33575 time to create 1 rle with old method : 0.0432279109954834 length of segment : 332 time for calcul the mask position with numpy : 0.000659942626953125 nb_pixel_total : 25192 time to create 1 rle with old method : 0.03206753730773926 length of segment : 175 time for calcul the mask position with numpy : 0.0013365745544433594 nb_pixel_total : 23189 time to create 1 rle with old method : 0.02988600730895996 length of segment : 195 time for calcul the mask position with numpy : 0.002912759780883789 nb_pixel_total : 74264 time to create 1 rle with old method : 0.08446073532104492 length of segment : 487 time for calcul the mask position with numpy : 0.0006101131439208984 nb_pixel_total : 25859 time to create 1 rle with old method : 0.02990102767944336 length of segment : 145 time for calcul the mask position with numpy : 0.0020780563354492188 nb_pixel_total : 62353 time to create 1 rle with old method : 0.07516002655029297 length of segment : 308 time for calcul the mask position with numpy : 0.00019097328186035156 nb_pixel_total : 5007 time to create 1 rle with old method : 0.006237983703613281 length of segment : 117 time for calcul the mask position with numpy : 0.0009667873382568359 nb_pixel_total : 28846 time to create 1 rle with old method : 0.041028738021850586 length of segment : 287 time for calcul the mask position with numpy : 0.0028188228607177734 nb_pixel_total : 48130 time to create 1 rle with old method : 0.057779550552368164 length of segment : 441 time for calcul the mask position with numpy : 0.0007681846618652344 nb_pixel_total : 18708 time to create 1 rle with old method : 0.02492833137512207 length of segment : 138 time for calcul the mask position with numpy : 0.0004973411560058594 nb_pixel_total : 11225 time to create 1 rle with old method : 0.014139413833618164 length of segment : 238 time for calcul the mask position with numpy : 0.0025856494903564453 nb_pixel_total : 85241 time to create 1 rle with old method : 0.10290408134460449 length of segment : 411 time for calcul the mask position with numpy : 0.0006964206695556641 nb_pixel_total : 15283 time to create 1 rle with old method : 0.02511763572692871 length of segment : 132 time for calcul the mask position with numpy : 0.0010623931884765625 nb_pixel_total : 12013 time to create 1 rle with old method : 0.015816688537597656 length of segment : 113 time for calcul the mask position with numpy : 0.00044989585876464844 nb_pixel_total : 16034 time to create 1 rle with old method : 0.021412372589111328 length of segment : 146 time for calcul the mask position with numpy : 0.0005702972412109375 nb_pixel_total : 13233 time to create 1 rle with old method : 0.01850748062133789 length of segment : 155 time for calcul the mask position with numpy : 0.000949859619140625 nb_pixel_total : 27092 time to create 1 rle with old method : 0.03374433517456055 length of segment : 230 time for calcul the mask position with numpy : 0.000652313232421875 nb_pixel_total : 12256 time to create 1 rle with old method : 0.015744447708129883 length of segment : 211 time for calcul the mask position with numpy : 0.0014336109161376953 nb_pixel_total : 35793 time to create 1 rle with old method : 0.0457608699798584 length of segment : 281 time for calcul the mask position with numpy : 0.000583648681640625 nb_pixel_total : 15179 time to create 1 rle with old method : 0.018174409866333008 length of segment : 252 time for calcul the mask position with numpy : 0.0003228187561035156 nb_pixel_total : 8283 time to create 1 rle with old method : 0.010968446731567383 length of segment : 147 time for calcul the mask position with numpy : 0.00025773048400878906 nb_pixel_total : 8857 time to create 1 rle with old method : 0.011673450469970703 length of segment : 120 time for calcul the mask position with numpy : 0.0004942417144775391 nb_pixel_total : 18084 time to create 1 rle with old method : 0.021103382110595703 length of segment : 150 time for calcul the mask position with numpy : 0.00024175643920898438 nb_pixel_total : 8523 time to create 1 rle with old method : 0.00981903076171875 length of segment : 127 time for calcul the mask position with numpy : 0.00038695335388183594 nb_pixel_total : 15887 time to create 1 rle with old method : 0.01857590675354004 length of segment : 146 time for calcul the mask position with numpy : 0.0005893707275390625 nb_pixel_total : 13260 time to create 1 rle with old method : 0.015774965286254883 length of segment : 139 time for calcul the mask position with numpy : 0.00023603439331054688 nb_pixel_total : 4096 time to create 1 rle with old method : 0.00500035285949707 length of segment : 157 time for calcul the mask position with numpy : 0.00016832351684570312 nb_pixel_total : 5578 time to create 1 rle with old method : 0.006741523742675781 length of segment : 88 time for calcul the mask position with numpy : 0.00014901161193847656 nb_pixel_total : 3773 time to create 1 rle with old method : 0.004608154296875 length of segment : 74 time for calcul the mask position with numpy : 0.0003643035888671875 nb_pixel_total : 13142 time to create 1 rle with old method : 0.015431404113769531 length of segment : 174 time for calcul the mask position with numpy : 0.0003867149353027344 nb_pixel_total : 11934 time to create 1 rle with old method : 0.013831615447998047 length of segment : 156 time for calcul the mask position with numpy : 0.00012874603271484375 nb_pixel_total : 1554 time to create 1 rle with old method : 0.002002239227294922 length of segment : 51 time for calcul the mask position with numpy : 0.00030303001403808594 nb_pixel_total : 10242 time to create 1 rle with old method : 0.011837482452392578 length of segment : 123 time for calcul the mask position with numpy : 0.0015861988067626953 nb_pixel_total : 40322 time to create 1 rle with old method : 0.04622673988342285 length of segment : 355 time for calcul the mask position with numpy : 0.00039577484130859375 nb_pixel_total : 10263 time to create 1 rle with old method : 0.012195348739624023 length of segment : 143 time for calcul the mask position with numpy : 0.0005991458892822266 nb_pixel_total : 13030 time to create 1 rle with old method : 0.01586604118347168 length of segment : 125 time for calcul the mask position with numpy : 0.0007166862487792969 nb_pixel_total : 16082 time to create 1 rle with old method : 0.018957853317260742 length of segment : 217 time for calcul the mask position with numpy : 0.0027246475219726562 nb_pixel_total : 23259 time to create 1 rle with old method : 0.02848672866821289 length of segment : 505 time for calcul the mask position with numpy : 0.0002777576446533203 nb_pixel_total : 6253 time to create 1 rle with old method : 0.007377147674560547 length of segment : 75 time for calcul the mask position with numpy : 0.0001628398895263672 nb_pixel_total : 4903 time to create 1 rle with old method : 0.005784511566162109 length of segment : 110 time for calcul the mask position with numpy : 0.0002903938293457031 nb_pixel_total : 10007 time to create 1 rle with old method : 0.011809110641479492 length of segment : 115 time for calcul the mask position with numpy : 0.00042891502380371094 nb_pixel_total : 10577 time to create 1 rle with old method : 0.012274980545043945 length of segment : 359 time for calcul the mask position with numpy : 0.0014417171478271484 nb_pixel_total : 58475 time to create 1 rle with old method : 0.06842827796936035 length of segment : 383 time for calcul the mask position with numpy : 0.0005924701690673828 nb_pixel_total : 18720 time to create 1 rle with old method : 0.021619796752929688 length of segment : 168 time for calcul the mask position with numpy : 0.00041604042053222656 nb_pixel_total : 13376 time to create 1 rle with old method : 0.015661001205444336 length of segment : 166 time for calcul the mask position with numpy : 0.0007207393646240234 nb_pixel_total : 26157 time to create 1 rle with old method : 0.030253171920776367 length of segment : 233 time for calcul the mask position with numpy : 0.0010166168212890625 nb_pixel_total : 28971 time to create 1 rle with old method : 0.0381317138671875 length of segment : 362 time for calcul the mask position with numpy : 0.0007076263427734375 nb_pixel_total : 21965 time to create 1 rle with old method : 0.02562737464904785 length of segment : 168 time for calcul the mask position with numpy : 0.0004353523254394531 nb_pixel_total : 19650 time to create 1 rle with old method : 0.022743940353393555 length of segment : 173 time for calcul the mask position with numpy : 0.0013172626495361328 nb_pixel_total : 19552 time to create 1 rle with old method : 0.022861480712890625 length of segment : 185 time for calcul the mask position with numpy : 0.0046122074127197266 nb_pixel_total : 66582 time to create 1 rle with old method : 0.0754544734954834 length of segment : 276 time for calcul the mask position with numpy : 0.0009558200836181641 nb_pixel_total : 17098 time to create 1 rle with old method : 0.020011186599731445 length of segment : 104 time for calcul the mask position with numpy : 0.0019397735595703125 nb_pixel_total : 44574 time to create 1 rle with old method : 0.050762176513671875 length of segment : 323 time for calcul the mask position with numpy : 0.0025033950805664062 nb_pixel_total : 35111 time to create 1 rle with old method : 0.03958296775817871 length of segment : 378 time for calcul the mask position with numpy : 0.0024802684783935547 nb_pixel_total : 31618 time to create 1 rle with old method : 0.03665351867675781 length of segment : 413 time for calcul the mask position with numpy : 0.0010595321655273438 nb_pixel_total : 14939 time to create 1 rle with old method : 0.017099380493164062 length of segment : 149 time for calcul the mask position with numpy : 0.002038717269897461 nb_pixel_total : 26436 time to create 1 rle with old method : 0.030163049697875977 length of segment : 263 time for calcul the mask position with numpy : 0.0018849372863769531 nb_pixel_total : 19760 time to create 1 rle with old method : 0.023298025131225586 length of segment : 199 time for calcul the mask position with numpy : 0.0010485649108886719 nb_pixel_total : 16339 time to create 1 rle with old method : 0.0189211368560791 length of segment : 222 time for calcul the mask position with numpy : 0.0025343894958496094 nb_pixel_total : 29438 time to create 1 rle with old method : 0.03472304344177246 length of segment : 222 time for calcul the mask position with numpy : 0.0009682178497314453 nb_pixel_total : 14357 time to create 1 rle with old method : 0.01693582534790039 length of segment : 132 time for calcul the mask position with numpy : 0.0006029605865478516 nb_pixel_total : 11107 time to create 1 rle with old method : 0.01740550994873047 length of segment : 126 time for calcul the mask position with numpy : 0.0010759830474853516 nb_pixel_total : 13971 time to create 1 rle with old method : 0.016530990600585938 length of segment : 148 time for calcul the mask position with numpy : 0.001314401626586914 nb_pixel_total : 18823 time to create 1 rle with old method : 0.021657943725585938 length of segment : 241 time for calcul the mask position with numpy : 0.0008533000946044922 nb_pixel_total : 12476 time to create 1 rle with old method : 0.014406919479370117 length of segment : 173 time for calcul the mask position with numpy : 0.0015721321105957031 nb_pixel_total : 20568 time to create 1 rle with old method : 0.023294448852539062 length of segment : 239 time for calcul the mask position with numpy : 0.0071794986724853516 nb_pixel_total : 114691 time to create 1 rle with old method : 0.13022851943969727 length of segment : 463 time for calcul the mask position with numpy : 0.0037713050842285156 nb_pixel_total : 58814 time to create 1 rle with old method : 0.07227540016174316 length of segment : 350 time for calcul the mask position with numpy : 0.002031087875366211 nb_pixel_total : 38207 time to create 1 rle with old method : 0.04368185997009277 length of segment : 265 time for calcul the mask position with numpy : 0.0007941722869873047 nb_pixel_total : 15811 time to create 1 rle with old method : 0.023905515670776367 length of segment : 156 time for calcul the mask position with numpy : 0.0006785392761230469 nb_pixel_total : 5050 time to create 1 rle with old method : 0.009549379348754883 length of segment : 87 time for calcul the mask position with numpy : 0.00588536262512207 nb_pixel_total : 38893 time to create 1 rle with old method : 0.0756986141204834 length of segment : 200 time for calcul the mask position with numpy : 0.0009675025939941406 nb_pixel_total : 8100 time to create 1 rle with old method : 0.009644031524658203 length of segment : 122 time for calcul the mask position with numpy : 0.0017306804656982422 nb_pixel_total : 24587 time to create 1 rle with old method : 0.02804732322692871 length of segment : 534 time for calcul the mask position with numpy : 0.0020575523376464844 nb_pixel_total : 29837 time to create 1 rle with old method : 0.03994464874267578 length of segment : 256 time for calcul the mask position with numpy : 0.0015554428100585938 nb_pixel_total : 20740 time to create 1 rle with old method : 0.02536916732788086 length of segment : 218 time for calcul the mask position with numpy : 0.004820585250854492 nb_pixel_total : 66205 time to create 1 rle with old method : 0.07453656196594238 length of segment : 376 time for calcul the mask position with numpy : 0.0014154911041259766 nb_pixel_total : 24993 time to create 1 rle with old method : 0.028549909591674805 length of segment : 193 time for calcul the mask position with numpy : 0.0012021064758300781 nb_pixel_total : 17851 time to create 1 rle with old method : 0.020357131958007812 length of segment : 220 time for calcul the mask position with numpy : 0.0005023479461669922 nb_pixel_total : 5208 time to create 1 rle with old method : 0.0060880184173583984 length of segment : 93 time for calcul the mask position with numpy : 0.0018918514251708984 nb_pixel_total : 26239 time to create 1 rle with old method : 0.030562400817871094 length of segment : 154 time for calcul the mask position with numpy : 0.0008342266082763672 nb_pixel_total : 8839 time to create 1 rle with old method : 0.010178327560424805 length of segment : 156 time for calcul the mask position with numpy : 0.0003476142883300781 nb_pixel_total : 8626 time to create 1 rle with old method : 0.010051727294921875 length of segment : 122 time for calcul the mask position with numpy : 0.0006682872772216797 nb_pixel_total : 10968 time to create 1 rle with old method : 0.013097524642944336 length of segment : 97 time for calcul the mask position with numpy : 0.0012848377227783203 nb_pixel_total : 9866 time to create 1 rle with old method : 0.011714935302734375 length of segment : 115 time for calcul the mask position with numpy : 0.0007109642028808594 nb_pixel_total : 8012 time to create 1 rle with old method : 0.009737491607666016 length of segment : 100 time for calcul the mask position with numpy : 0.0003349781036376953 nb_pixel_total : 4364 time to create 1 rle with old method : 0.005293607711791992 length of segment : 80 time for calcul the mask position with numpy : 0.0027027130126953125 nb_pixel_total : 34859 time to create 1 rle with old method : 0.04068326950073242 length of segment : 266 time for calcul the mask position with numpy : 0.0023560523986816406 nb_pixel_total : 35137 time to create 1 rle with old method : 0.039305925369262695 length of segment : 313 time for calcul the mask position with numpy : 0.0003724098205566406 nb_pixel_total : 8776 time to create 1 rle with old method : 0.01009225845336914 length of segment : 176 time for calcul the mask position with numpy : 0.0009944438934326172 nb_pixel_total : 14257 time to create 1 rle with old method : 0.016385316848754883 length of segment : 194 time for calcul the mask position with numpy : 0.0015685558319091797 nb_pixel_total : 26100 time to create 1 rle with old method : 0.029918193817138672 length of segment : 187 time for calcul the mask position with numpy : 0.0010175704956054688 nb_pixel_total : 18150 time to create 1 rle with old method : 0.024065256118774414 length of segment : 146 time for calcul the mask position with numpy : 0.001619100570678711 nb_pixel_total : 19653 time to create 1 rle with old method : 0.022635459899902344 length of segment : 202 time for calcul the mask position with numpy : 0.003966808319091797 nb_pixel_total : 56420 time to create 1 rle with old method : 0.06287622451782227 length of segment : 347 time for calcul the mask position with numpy : 0.00038743019104003906 nb_pixel_total : 3412 time to create 1 rle with old method : 0.004062175750732422 length of segment : 101 time for calcul the mask position with numpy : 0.0006496906280517578 nb_pixel_total : 15812 time to create 1 rle with old method : 0.01813364028930664 length of segment : 169 time for calcul the mask position with numpy : 0.023294687271118164 nb_pixel_total : 268631 time to create 1 rle with new method : 0.19427037239074707 length of segment : 1178 time for calcul the mask position with numpy : 0.0026323795318603516 nb_pixel_total : 42393 time to create 1 rle with old method : 0.04888129234313965 length of segment : 275 time for calcul the mask position with numpy : 0.00027441978454589844 nb_pixel_total : 7247 time to create 1 rle with old method : 0.008649826049804688 length of segment : 126 time for calcul the mask position with numpy : 0.007432460784912109 nb_pixel_total : 67397 time to create 1 rle with old method : 0.07985377311706543 length of segment : 427 time for calcul the mask position with numpy : 0.0025267601013183594 nb_pixel_total : 34476 time to create 1 rle with old method : 0.04376959800720215 length of segment : 186 time for calcul the mask position with numpy : 0.0004978179931640625 nb_pixel_total : 20047 time to create 1 rle with old method : 0.0230405330657959 length of segment : 184 time for calcul the mask position with numpy : 0.0021042823791503906 nb_pixel_total : 36176 time to create 1 rle with old method : 0.04376649856567383 length of segment : 227 time for calcul the mask position with numpy : 0.0005688667297363281 nb_pixel_total : 12039 time to create 1 rle with old method : 0.015257596969604492 length of segment : 158 time for calcul the mask position with numpy : 0.00044083595275878906 nb_pixel_total : 4664 time to create 1 rle with old method : 0.0055811405181884766 length of segment : 171 time for calcul the mask position with numpy : 0.0011985301971435547 nb_pixel_total : 18521 time to create 1 rle with old method : 0.021631240844726562 length of segment : 258 time for calcul the mask position with numpy : 0.0002090930938720703 nb_pixel_total : 5481 time to create 1 rle with old method : 0.0065479278564453125 length of segment : 129 time for calcul the mask position with numpy : 0.0003769397735595703 nb_pixel_total : 6757 time to create 1 rle with old method : 0.008027076721191406 length of segment : 119 time for calcul the mask position with numpy : 0.00013947486877441406 nb_pixel_total : 3090 time to create 1 rle with old method : 0.003758668899536133 length of segment : 64 time for calcul the mask position with numpy : 0.0003180503845214844 nb_pixel_total : 5993 time to create 1 rle with old method : 0.006967782974243164 length of segment : 99 time for calcul the mask position with numpy : 0.0008819103240966797 nb_pixel_total : 8342 time to create 1 rle with old method : 0.009932994842529297 length of segment : 127 time for calcul the mask position with numpy : 0.00044846534729003906 nb_pixel_total : 9695 time to create 1 rle with old method : 0.011258602142333984 length of segment : 125 time for calcul the mask position with numpy : 0.013612985610961914 nb_pixel_total : 307165 time to create 1 rle with new method : 0.022061586380004883 length of segment : 735 time for calcul the mask position with numpy : 0.0032625198364257812 nb_pixel_total : 51848 time to create 1 rle with old method : 0.05876016616821289 length of segment : 338 time for calcul the mask position with numpy : 0.0009009838104248047 nb_pixel_total : 19159 time to create 1 rle with old method : 0.022507667541503906 length of segment : 197 time for calcul the mask position with numpy : 0.001171112060546875 nb_pixel_total : 23353 time to create 1 rle with old method : 0.026882648468017578 length of segment : 163 time for calcul the mask position with numpy : 0.0066890716552734375 nb_pixel_total : 175075 time to create 1 rle with new method : 0.009634971618652344 length of segment : 664 time for calcul the mask position with numpy : 0.0004146099090576172 nb_pixel_total : 12778 time to create 1 rle with old method : 0.014618635177612305 length of segment : 129 time for calcul the mask position with numpy : 0.0018453598022460938 nb_pixel_total : 67304 time to create 1 rle with old method : 0.07753610610961914 length of segment : 600 time for calcul the mask position with numpy : 0.0002944469451904297 nb_pixel_total : 7670 time to create 1 rle with old method : 0.008820056915283203 length of segment : 125 time for calcul the mask position with numpy : 0.0006110668182373047 nb_pixel_total : 10104 time to create 1 rle with old method : 0.011741161346435547 length of segment : 136 time for calcul the mask position with numpy : 0.0002110004425048828 nb_pixel_total : 7639 time to create 1 rle with old method : 0.009086132049560547 length of segment : 67 time for calcul the mask position with numpy : 0.0003249645233154297 nb_pixel_total : 5777 time to create 1 rle with old method : 0.007017612457275391 length of segment : 87 time for calcul the mask position with numpy : 0.00016736984252929688 nb_pixel_total : 6869 time to create 1 rle with old method : 0.008331775665283203 length of segment : 85 time for calcul the mask position with numpy : 0.00437474250793457 nb_pixel_total : 80173 time to create 1 rle with old method : 0.09000706672668457 length of segment : 423 time for calcul the mask position with numpy : 0.001544952392578125 nb_pixel_total : 50380 time to create 1 rle with old method : 0.05910539627075195 length of segment : 274 time for calcul the mask position with numpy : 0.0015413761138916016 nb_pixel_total : 64227 time to create 1 rle with old method : 0.0760946273803711 length of segment : 383 time for calcul the mask position with numpy : 0.0007147789001464844 nb_pixel_total : 10917 time to create 1 rle with old method : 0.012551307678222656 length of segment : 176 time for calcul the mask position with numpy : 0.000537872314453125 nb_pixel_total : 26261 time to create 1 rle with old method : 0.030083417892456055 length of segment : 207 time for calcul the mask position with numpy : 0.003610849380493164 nb_pixel_total : 67874 time to create 1 rle with old method : 0.07649421691894531 length of segment : 467 time for calcul the mask position with numpy : 0.0013048648834228516 nb_pixel_total : 25585 time to create 1 rle with old method : 0.02903604507446289 length of segment : 230 time for calcul the mask position with numpy : 0.0017139911651611328 nb_pixel_total : 39811 time to create 1 rle with old method : 0.05953359603881836 length of segment : 292 time for calcul the mask position with numpy : 0.0007741451263427734 nb_pixel_total : 10101 time to create 1 rle with old method : 0.011758804321289062 length of segment : 140 time for calcul the mask position with numpy : 0.0005564689636230469 nb_pixel_total : 9897 time to create 1 rle with old method : 0.011527776718139648 length of segment : 121 time for calcul the mask position with numpy : 0.0005035400390625 nb_pixel_total : 3811 time to create 1 rle with old method : 0.0045757293701171875 length of segment : 135 time for calcul the mask position with numpy : 0.0008122920989990234 nb_pixel_total : 23483 time to create 1 rle with old method : 0.027085542678833008 length of segment : 180 time for calcul the mask position with numpy : 0.0005185604095458984 nb_pixel_total : 22901 time to create 1 rle with old method : 0.02659153938293457 length of segment : 187 time for calcul the mask position with numpy : 0.0013267993927001953 nb_pixel_total : 33694 time to create 1 rle with old method : 0.03761434555053711 length of segment : 318 time for calcul the mask position with numpy : 0.0006682872772216797 nb_pixel_total : 21737 time to create 1 rle with old method : 0.02499842643737793 length of segment : 203 time for calcul the mask position with numpy : 0.001688241958618164 nb_pixel_total : 64179 time to create 1 rle with old method : 0.07746243476867676 length of segment : 421 time for calcul the mask position with numpy : 0.00016069412231445312 nb_pixel_total : 5828 time to create 1 rle with old method : 0.007027149200439453 length of segment : 81 time for calcul the mask position with numpy : 0.0009355545043945312 nb_pixel_total : 14959 time to create 1 rle with old method : 0.01745915412902832 length of segment : 141 time for calcul the mask position with numpy : 0.0009357929229736328 nb_pixel_total : 21554 time to create 1 rle with old method : 0.02464771270751953 length of segment : 182 time for calcul the mask position with numpy : 0.0009520053863525391 nb_pixel_total : 13339 time to create 1 rle with old method : 0.015388727188110352 length of segment : 191 time for calcul the mask position with numpy : 0.0005223751068115234 nb_pixel_total : 11226 time to create 1 rle with old method : 0.01291203498840332 length of segment : 120 time for calcul the mask position with numpy : 0.0008754730224609375 nb_pixel_total : 14531 time to create 1 rle with old method : 0.01721501350402832 length of segment : 147 time for calcul the mask position with numpy : 0.00040602684020996094 nb_pixel_total : 4938 time to create 1 rle with old method : 0.0058596134185791016 length of segment : 108 time for calcul the mask position with numpy : 0.0011413097381591797 nb_pixel_total : 22745 time to create 1 rle with old method : 0.025833606719970703 length of segment : 242 time for calcul the mask position with numpy : 0.0008609294891357422 nb_pixel_total : 21846 time to create 1 rle with old method : 0.02451634407043457 length of segment : 193 time for calcul the mask position with numpy : 0.0015909671783447266 nb_pixel_total : 42461 time to create 1 rle with old method : 0.04817771911621094 length of segment : 230 time for calcul the mask position with numpy : 0.01186823844909668 nb_pixel_total : 383002 time to create 1 rle with new method : 0.17397046089172363 length of segment : 763 time for calcul the mask position with numpy : 0.0011925697326660156 nb_pixel_total : 17801 time to create 1 rle with old method : 0.020724058151245117 length of segment : 168 time for calcul the mask position with numpy : 0.0006959438323974609 nb_pixel_total : 13024 time to create 1 rle with old method : 0.014716386795043945 length of segment : 167 time for calcul the mask position with numpy : 0.00046539306640625 nb_pixel_total : 9748 time to create 1 rle with old method : 0.011367559432983398 length of segment : 112 time for calcul the mask position with numpy : 0.0029573440551757812 nb_pixel_total : 76868 time to create 1 rle with old method : 0.08712959289550781 length of segment : 350 time for calcul the mask position with numpy : 0.0007107257843017578 nb_pixel_total : 16635 time to create 1 rle with old method : 0.018826961517333984 length of segment : 223 time for calcul the mask position with numpy : 0.0016100406646728516 nb_pixel_total : 40838 time to create 1 rle with old method : 0.047342777252197266 length of segment : 225 time for calcul the mask position with numpy : 0.0006463527679443359 nb_pixel_total : 21027 time to create 1 rle with old method : 0.024131298065185547 length of segment : 177 time for calcul the mask position with numpy : 0.0003726482391357422 nb_pixel_total : 9656 time to create 1 rle with old method : 0.011098623275756836 length of segment : 107 time for calcul the mask position with numpy : 0.0001423358917236328 nb_pixel_total : 2034 time to create 1 rle with old method : 0.0024874210357666016 length of segment : 46 time for calcul the mask position with numpy : 0.004169940948486328 nb_pixel_total : 130604 time to create 1 rle with old method : 0.145188570022583 length of segment : 723 time for calcul the mask position with numpy : 0.0012054443359375 nb_pixel_total : 26361 time to create 1 rle with old method : 0.03143191337585449 length of segment : 444 time for calcul the mask position with numpy : 0.002216339111328125 nb_pixel_total : 76572 time to create 1 rle with old method : 0.08664059638977051 length of segment : 353 time for calcul the mask position with numpy : 0.0005311965942382812 nb_pixel_total : 20110 time to create 1 rle with old method : 0.023171186447143555 length of segment : 182 time for calcul the mask position with numpy : 0.0006642341613769531 nb_pixel_total : 13151 time to create 1 rle with old method : 0.014909982681274414 length of segment : 141 time for calcul the mask position with numpy : 0.0008504390716552734 nb_pixel_total : 22753 time to create 1 rle with old method : 0.02645564079284668 length of segment : 130 time for calcul the mask position with numpy : 0.0006492137908935547 nb_pixel_total : 11544 time to create 1 rle with old method : 0.01357412338256836 length of segment : 146 time for calcul the mask position with numpy : 0.0007619857788085938 nb_pixel_total : 15019 time to create 1 rle with old method : 0.017174720764160156 length of segment : 148 time for calcul the mask position with numpy : 0.0003440380096435547 nb_pixel_total : 7433 time to create 1 rle with old method : 0.008625507354736328 length of segment : 82 time for calcul the mask position with numpy : 0.0009279251098632812 nb_pixel_total : 19175 time to create 1 rle with old method : 0.021783113479614258 length of segment : 212 time for calcul the mask position with numpy : 0.0005688667297363281 nb_pixel_total : 13111 time to create 1 rle with old method : 0.014978170394897461 length of segment : 179 time for calcul the mask position with numpy : 0.00119781494140625 nb_pixel_total : 33302 time to create 1 rle with old method : 0.037339210510253906 length of segment : 334 time for calcul the mask position with numpy : 0.00044727325439453125 nb_pixel_total : 14525 time to create 1 rle with old method : 0.016567468643188477 length of segment : 130 time for calcul the mask position with numpy : 0.0006809234619140625 nb_pixel_total : 17329 time to create 1 rle with old method : 0.020130634307861328 length of segment : 176 time for calcul the mask position with numpy : 0.0008313655853271484 nb_pixel_total : 22511 time to create 1 rle with old method : 0.025666475296020508 length of segment : 222 time for calcul the mask position with numpy : 0.0005426406860351562 nb_pixel_total : 15177 time to create 1 rle with old method : 0.01713109016418457 length of segment : 224 time for calcul the mask position with numpy : 0.014397382736206055 nb_pixel_total : 475696 time to create 1 rle with new method : 0.024174213409423828 length of segment : 1292 time for calcul the mask position with numpy : 0.0006716251373291016 nb_pixel_total : 24276 time to create 1 rle with old method : 0.027126789093017578 length of segment : 254 time for calcul the mask position with numpy : 0.00046706199645996094 nb_pixel_total : 10701 time to create 1 rle with old method : 0.01224517822265625 length of segment : 95 time for calcul the mask position with numpy : 0.00046062469482421875 nb_pixel_total : 11169 time to create 1 rle with old method : 0.013034343719482422 length of segment : 105 time for calcul the mask position with numpy : 0.0019881725311279297 nb_pixel_total : 52307 time to create 1 rle with old method : 0.059188127517700195 length of segment : 380 time for calcul the mask position with numpy : 0.0007526874542236328 nb_pixel_total : 16620 time to create 1 rle with old method : 0.018718719482421875 length of segment : 194 time for calcul the mask position with numpy : 0.0019626617431640625 nb_pixel_total : 48788 time to create 1 rle with old method : 0.05517578125 length of segment : 313 time for calcul the mask position with numpy : 0.0003314018249511719 nb_pixel_total : 4830 time to create 1 rle with old method : 0.005650758743286133 length of segment : 71 time for calcul the mask position with numpy : 0.002651691436767578 nb_pixel_total : 48723 time to create 1 rle with old method : 0.05504798889160156 length of segment : 345 time for calcul the mask position with numpy : 0.003762960433959961 nb_pixel_total : 70986 time to create 1 rle with old method : 0.0795295238494873 length of segment : 441 time for calcul the mask position with numpy : 0.011013269424438477 nb_pixel_total : 226614 time to create 1 rle with new method : 0.015323638916015625 length of segment : 387 time for calcul the mask position with numpy : 0.001211404800415039 nb_pixel_total : 15644 time to create 1 rle with old method : 0.026186466217041016 length of segment : 170 time for calcul the mask position with numpy : 0.0019021034240722656 nb_pixel_total : 21375 time to create 1 rle with old method : 0.037511348724365234 length of segment : 213 time for calcul the mask position with numpy : 0.0010821819305419922 nb_pixel_total : 13551 time to create 1 rle with old method : 0.026514768600463867 length of segment : 126 time for calcul the mask position with numpy : 0.01040196418762207 nb_pixel_total : 97772 time to create 1 rle with old method : 0.11035466194152832 length of segment : 508 time for calcul the mask position with numpy : 0.0009851455688476562 nb_pixel_total : 11446 time to create 1 rle with old method : 0.013124465942382812 length of segment : 157 time for calcul the mask position with numpy : 0.0014519691467285156 nb_pixel_total : 20284 time to create 1 rle with old method : 0.023141145706176758 length of segment : 224 time for calcul the mask position with numpy : 0.012478828430175781 nb_pixel_total : 125581 time to create 1 rle with old method : 0.1661362648010254 length of segment : 390 time for calcul the mask position with numpy : 0.013252496719360352 nb_pixel_total : 147801 time to create 1 rle with old method : 0.16345548629760742 length of segment : 681 time for calcul the mask position with numpy : 0.0006690025329589844 nb_pixel_total : 9134 time to create 1 rle with old method : 0.010389566421508789 length of segment : 155 time for calcul the mask position with numpy : 0.0008642673492431641 nb_pixel_total : 15332 time to create 1 rle with old method : 0.017943859100341797 length of segment : 93 time for calcul the mask position with numpy : 0.008890151977539062 nb_pixel_total : 160569 time to create 1 rle with new method : 0.01123809814453125 length of segment : 500 time for calcul the mask position with numpy : 0.006201028823852539 nb_pixel_total : 29474 time to create 1 rle with old method : 0.03367209434509277 length of segment : 330 time for calcul the mask position with numpy : 0.0016567707061767578 nb_pixel_total : 42258 time to create 1 rle with old method : 0.04771995544433594 length of segment : 295 time for calcul the mask position with numpy : 0.0016002655029296875 nb_pixel_total : 20944 time to create 1 rle with old method : 0.023705720901489258 length of segment : 199 time for calcul the mask position with numpy : 0.006619930267333984 nb_pixel_total : 112629 time to create 1 rle with old method : 0.1251826286315918 length of segment : 508 time for calcul the mask position with numpy : 0.0005207061767578125 nb_pixel_total : 14874 time to create 1 rle with old method : 0.017154693603515625 length of segment : 105 time for calcul the mask position with numpy : 0.0008385181427001953 nb_pixel_total : 22522 time to create 1 rle with old method : 0.026288270950317383 length of segment : 153 time for calcul the mask position with numpy : 0.0015370845794677734 nb_pixel_total : 19297 time to create 1 rle with old method : 0.02182459831237793 length of segment : 191 time for calcul the mask position with numpy : 0.0017328262329101562 nb_pixel_total : 31486 time to create 1 rle with old method : 0.03822207450866699 length of segment : 305 time for calcul the mask position with numpy : 0.0006182193756103516 nb_pixel_total : 12050 time to create 1 rle with old method : 0.01373291015625 length of segment : 249 time for calcul the mask position with numpy : 0.0005314350128173828 nb_pixel_total : 15950 time to create 1 rle with old method : 0.02037644386291504 length of segment : 198 time for calcul the mask position with numpy : 0.00043702125549316406 nb_pixel_total : 6903 time to create 1 rle with old method : 0.007929086685180664 length of segment : 92 time for calcul the mask position with numpy : 0.0005671977996826172 nb_pixel_total : 16027 time to create 1 rle with old method : 0.018483400344848633 length of segment : 161 time for calcul the mask position with numpy : 0.0004582405090332031 nb_pixel_total : 14896 time to create 1 rle with old method : 0.01899886131286621 length of segment : 248 time for calcul the mask position with numpy : 0.003521442413330078 nb_pixel_total : 74952 time to create 1 rle with old method : 0.08578944206237793 length of segment : 388 time for calcul the mask position with numpy : 0.0013895034790039062 nb_pixel_total : 11218 time to create 1 rle with old method : 0.013281583786010742 length of segment : 203 time for calcul the mask position with numpy : 0.0024657249450683594 nb_pixel_total : 49300 time to create 1 rle with old method : 0.05669260025024414 length of segment : 248 time for calcul the mask position with numpy : 0.0004916191101074219 nb_pixel_total : 6054 time to create 1 rle with old method : 0.006993770599365234 length of segment : 89 time for calcul the mask position with numpy : 0.0033469200134277344 nb_pixel_total : 43813 time to create 1 rle with old method : 0.05368924140930176 length of segment : 280 time for calcul the mask position with numpy : 0.00452876091003418 nb_pixel_total : 66989 time to create 1 rle with old method : 0.07514142990112305 length of segment : 330 time for calcul the mask position with numpy : 0.00574493408203125 nb_pixel_total : 108909 time to create 1 rle with old method : 0.12478446960449219 length of segment : 438 time for calcul the mask position with numpy : 0.0006005764007568359 nb_pixel_total : 14559 time to create 1 rle with old method : 0.016750574111938477 length of segment : 166 time for calcul the mask position with numpy : 0.0005981922149658203 nb_pixel_total : 18261 time to create 1 rle with old method : 0.02070331573486328 length of segment : 148 time for calcul the mask position with numpy : 0.0007519721984863281 nb_pixel_total : 11459 time to create 1 rle with old method : 0.014739990234375 length of segment : 115 time for calcul the mask position with numpy : 0.0005655288696289062 nb_pixel_total : 18553 time to create 1 rle with old method : 0.02189469337463379 length of segment : 104 time for calcul the mask position with numpy : 0.0005195140838623047 nb_pixel_total : 17637 time to create 1 rle with old method : 0.02075672149658203 length of segment : 171 time for calcul the mask position with numpy : 0.009337663650512695 nb_pixel_total : 309560 time to create 1 rle with new method : 0.01505899429321289 length of segment : 545 time for calcul the mask position with numpy : 0.0012569427490234375 nb_pixel_total : 45630 time to create 1 rle with old method : 0.05269360542297363 length of segment : 179 time for calcul the mask position with numpy : 0.0020706653594970703 nb_pixel_total : 58106 time to create 1 rle with old method : 0.06635570526123047 length of segment : 428 time for calcul the mask position with numpy : 0.0013339519500732422 nb_pixel_total : 45953 time to create 1 rle with old method : 0.05121421813964844 length of segment : 273 time for calcul the mask position with numpy : 0.0007834434509277344 nb_pixel_total : 25277 time to create 1 rle with old method : 0.029751300811767578 length of segment : 187 time for calcul the mask position with numpy : 0.005760908126831055 nb_pixel_total : 271378 time to create 1 rle with new method : 0.007691860198974609 length of segment : 411 time for calcul the mask position with numpy : 0.0002484321594238281 nb_pixel_total : 6421 time to create 1 rle with old method : 0.0078089237213134766 length of segment : 61 time for calcul the mask position with numpy : 0.0005221366882324219 nb_pixel_total : 10728 time to create 1 rle with old method : 0.013173341751098633 length of segment : 98 time for calcul the mask position with numpy : 0.0012385845184326172 nb_pixel_total : 33063 time to create 1 rle with old method : 0.03832578659057617 length of segment : 260 time for calcul the mask position with numpy : 0.00035119056701660156 nb_pixel_total : 8364 time to create 1 rle with old method : 0.009655237197875977 length of segment : 112 time for calcul the mask position with numpy : 0.0002803802490234375 nb_pixel_total : 6677 time to create 1 rle with old method : 0.0080108642578125 length of segment : 84 time for calcul the mask position with numpy : 0.0024755001068115234 nb_pixel_total : 92078 time to create 1 rle with old method : 0.10316109657287598 length of segment : 340 time for calcul the mask position with numpy : 0.0008718967437744141 nb_pixel_total : 18547 time to create 1 rle with old method : 0.021219968795776367 length of segment : 170 time for calcul the mask position with numpy : 0.0010151863098144531 nb_pixel_total : 14638 time to create 1 rle with old method : 0.01792144775390625 length of segment : 179 time for calcul the mask position with numpy : 0.0007715225219726562 nb_pixel_total : 12773 time to create 1 rle with old method : 0.014928579330444336 length of segment : 209 time for calcul the mask position with numpy : 0.000820159912109375 nb_pixel_total : 13328 time to create 1 rle with old method : 0.016062259674072266 length of segment : 176 time for calcul the mask position with numpy : 0.0007834434509277344 nb_pixel_total : 15589 time to create 1 rle with old method : 0.018145084381103516 length of segment : 145 time for calcul the mask position with numpy : 0.0017518997192382812 nb_pixel_total : 31757 time to create 1 rle with old method : 0.03609609603881836 length of segment : 260 time for calcul the mask position with numpy : 0.0006275177001953125 nb_pixel_total : 14419 time to create 1 rle with old method : 0.016947507858276367 length of segment : 129 time for calcul the mask position with numpy : 0.0007071495056152344 nb_pixel_total : 25231 time to create 1 rle with old method : 0.028848886489868164 length of segment : 271 time for calcul the mask position with numpy : 0.0011341571807861328 nb_pixel_total : 24610 time to create 1 rle with old method : 0.028243064880371094 length of segment : 196 time for calcul the mask position with numpy : 0.010994434356689453 nb_pixel_total : 218817 time to create 1 rle with new method : 0.015693187713623047 length of segment : 1210 time for calcul the mask position with numpy : 0.0025222301483154297 nb_pixel_total : 53036 time to create 1 rle with old method : 0.05970144271850586 length of segment : 400 time for calcul the mask position with numpy : 0.0006663799285888672 nb_pixel_total : 20905 time to create 1 rle with old method : 0.024674415588378906 length of segment : 356 time for calcul the mask position with numpy : 0.0009169578552246094 nb_pixel_total : 17169 time to create 1 rle with old method : 0.02023005485534668 length of segment : 153 time for calcul the mask position with numpy : 0.0005881786346435547 nb_pixel_total : 14344 time to create 1 rle with old method : 0.01717209815979004 length of segment : 89 time for calcul the mask position with numpy : 0.0005753040313720703 nb_pixel_total : 9556 time to create 1 rle with old method : 0.010946989059448242 length of segment : 157 time for calcul the mask position with numpy : 0.0037751197814941406 nb_pixel_total : 27393 time to create 1 rle with old method : 0.03212547302246094 length of segment : 389 time for calcul the mask position with numpy : 0.00542902946472168 nb_pixel_total : 72335 time to create 1 rle with old method : 0.08287405967712402 length of segment : 657 time for calcul the mask position with numpy : 0.0010192394256591797 nb_pixel_total : 16402 time to create 1 rle with old method : 0.01861882209777832 length of segment : 190 time for calcul the mask position with numpy : 0.0010068416595458984 nb_pixel_total : 14924 time to create 1 rle with old method : 0.01773667335510254 length of segment : 510 time for calcul the mask position with numpy : 0.0011005401611328125 nb_pixel_total : 26384 time to create 1 rle with old method : 0.02953815460205078 length of segment : 192 time for calcul the mask position with numpy : 0.009033203125 nb_pixel_total : 218388 time to create 1 rle with new method : 0.011488676071166992 length of segment : 577 time for calcul the mask position with numpy : 0.0004143714904785156 nb_pixel_total : 10188 time to create 1 rle with old method : 0.011722087860107422 length of segment : 170 time for calcul the mask position with numpy : 0.000858306884765625 nb_pixel_total : 24331 time to create 1 rle with old method : 0.028332948684692383 length of segment : 178 time for calcul the mask position with numpy : 0.002826690673828125 nb_pixel_total : 62082 time to create 1 rle with old method : 0.07094097137451172 length of segment : 444 time for calcul the mask position with numpy : 0.0020008087158203125 nb_pixel_total : 16212 time to create 1 rle with old method : 0.019411802291870117 length of segment : 196 time for calcul the mask position with numpy : 0.0005035400390625 nb_pixel_total : 11006 time to create 1 rle with old method : 0.012887001037597656 length of segment : 133 time for calcul the mask position with numpy : 0.0025501251220703125 nb_pixel_total : 75449 time to create 1 rle with old method : 0.08661794662475586 length of segment : 386 time for calcul the mask position with numpy : 0.0006418228149414062 nb_pixel_total : 18572 time to create 1 rle with old method : 0.021213769912719727 length of segment : 85 time for calcul the mask position with numpy : 0.00039005279541015625 nb_pixel_total : 9120 time to create 1 rle with old method : 0.010930538177490234 length of segment : 106 time for calcul the mask position with numpy : 0.0005419254302978516 nb_pixel_total : 13100 time to create 1 rle with old method : 0.015509605407714844 length of segment : 104 time for calcul the mask position with numpy : 0.00934290885925293 nb_pixel_total : 262776 time to create 1 rle with new method : 0.016381025314331055 length of segment : 899 time for calcul the mask position with numpy : 0.0020685195922851562 nb_pixel_total : 51573 time to create 1 rle with old method : 0.057500600814819336 length of segment : 391 time for calcul the mask position with numpy : 0.00023508071899414062 nb_pixel_total : 5819 time to create 1 rle with old method : 0.0070149898529052734 length of segment : 59 time for calcul the mask position with numpy : 0.0014307498931884766 nb_pixel_total : 27647 time to create 1 rle with old method : 0.031694889068603516 length of segment : 394 time for calcul the mask position with numpy : 0.00023698806762695312 nb_pixel_total : 10622 time to create 1 rle with old method : 0.012464046478271484 length of segment : 99 time for calcul the mask position with numpy : 0.002784252166748047 nb_pixel_total : 95148 time to create 1 rle with old method : 0.10854458808898926 length of segment : 378 time for calcul the mask position with numpy : 0.00020194053649902344 nb_pixel_total : 1673 time to create 1 rle with old method : 0.002009868621826172 length of segment : 65 time for calcul the mask position with numpy : 0.001074075698852539 nb_pixel_total : 34945 time to create 1 rle with old method : 0.04310488700866699 length of segment : 189 time for calcul the mask position with numpy : 0.00022554397583007812 nb_pixel_total : 6471 time to create 1 rle with old method : 0.0076313018798828125 length of segment : 140 time for calcul the mask position with numpy : 0.0005788803100585938 nb_pixel_total : 9858 time to create 1 rle with old method : 0.01251530647277832 length of segment : 97 time spent for convertir_results : 37.376689195632935 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 691 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 93622 save missing photos in datou_result : time spend for datou_step_exec : 219.2759349346161 time spend to save output : 14.40048098564148 total time spend for step 1 : 233.67641592025757 step2:crop_condition Tue Apr 1 02:24:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 11 ! batch 1 Loaded 691 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 543 About to insert : list_path_to_insert length 543 new photo from crops ! About to upload 543 photos upload in portfolio : 3736932 init cache_photo without model_param we have 543 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743467124_2412904 we have uploaded 543 photos in the portfolio 3736932 time of upload the photos Elapsed time : 222.18873000144958 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 70 About to insert : list_path_to_insert length 70 new photo from crops ! About to upload 70 photos upload in portfolio : 3736932 init cache_photo without model_param we have 70 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743467361_2412904 we have uploaded 70 photos in the portfolio 3736932 time of upload the photos Elapsed time : 20.527290105819702 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 : 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/1743467384_2412904 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.9898831844329834 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 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/1743467395_2412904 we have uploaded 33 photos in the portfolio 3736932 time of upload the photos Elapsed time : 10.31273627281189 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 13 About to insert : list_path_to_insert length 13 new photo from crops ! About to upload 13 photos upload in portfolio : 3736932 init cache_photo without model_param we have 13 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743467408_2412904 we have uploaded 13 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.422945261001587 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1743467419_2412904 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.11513352394104 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349157421, 1349157390, 1349012795, 1349012792, 1349012787, 1349012783, 1349012779, 1349012747, 1349012680, 1349012676, 1349012671] Looping around the photos to save general results len do output : 664 /1349177060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177065Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177069Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177079Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177081Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177084Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177085Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177088Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177089Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177093Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177094Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177096Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177098Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177100Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177110Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177115Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177117Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177119Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177124Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177126Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177129Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177131Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177143Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177148Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177153Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177155Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177160Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177162Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177165Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177166Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177168Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177169Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177172Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177173Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177175Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177177Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177180Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177181Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177184Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177185Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177187Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177189Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177191Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177192Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177194Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177197Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177199Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177200Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177205Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177207Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177212Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177213Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177217Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177219Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177220Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177221Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177223Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177224Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177225Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177227Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177228Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177229Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177231Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177232Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177233Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177235Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177236Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177237Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177239Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177240Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177241Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177243Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177247Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177248Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177250Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177251Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177252Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177254Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177255Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177256Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177258Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177259Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177260Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177262Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177263Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177265Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177266Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177267Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177274Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177278Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177279Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177280Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177286Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177299Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177306Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177307Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177311Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177314Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177315Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177318Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177322Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177323Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177324Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177326Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177327Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177328Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177330Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177331Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177333Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177336Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177338Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177340Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177342Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177344Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177346Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177349Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177350Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177352Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177354Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177356Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177357Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177360Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177362Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177364Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177365Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177369Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177370Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177374Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177376Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177380Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177381Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177385Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177390Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177392Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177396Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177397Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177398Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177401Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177408Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177413Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177414Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177417Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177418Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177419Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177421Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177422Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177423Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177425Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177426Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177428Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177430Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177431Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177433Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177434Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177435Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177437Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177438Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177439Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177441Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177442Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177444Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177445Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177446Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177448Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177449Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177450Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177452Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177453Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177454Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177456Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177457Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177458Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177459Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177461Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177462Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177463Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177465Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177488Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177489Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177492Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177495Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177517Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177532Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177547Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177571Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177574Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177587Didn't retrieve data 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retrieve data . /1349177809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177891Didn't retrieve data 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177906Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177907Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177908Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177909Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177910Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177911Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177912Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177914Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177915Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177916Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177918Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177919Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1349177935Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177936Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177937Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177938Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177939Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177940Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177942Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177943Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177944Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177945Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177946Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177947Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177948Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177949Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177950Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177951Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177952Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177953Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177954Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177955Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177956Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177957Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177958Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177959Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177960Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177961Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177986Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177992Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177996Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177997Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349177999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178001Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178003Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178005Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178006Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178007Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178018Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178019Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178024Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178025Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178026Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178027Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178032Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349178034Didn'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, '2711237') ('3318', '21930836', '1349157421', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349157390', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012795', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012792', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012787', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012783', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012779', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012747', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012680', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012676', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012671', None, None, None, None, None, '2711237') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 2003 time used for this insertion : 0.12022590637207031 save_final save missing photos in datou_result : time spend for datou_step_exec : 349.9867444038391 time spend to save output : 0.16370201110839844 total time spend for step 2 : 350.1504464149475 step3:rle_unique_nms_with_priority Tue Apr 1 02:30:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 691 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 16 nb_hashtags : 3 time to prepare the origin masks : 8.1440269947052 time for calcul the mask position with numpy : 0.23766803741455078 nb_pixel_total : 5960130 time to create 1 rle with new method : 0.3176279067993164 time for calcul the mask position with numpy : 0.03238725662231445 nb_pixel_total : 23218 time to create 1 rle with old method : 0.025685787200927734 time for calcul the mask position with numpy : 0.02425360679626465 nb_pixel_total : 210412 time to create 1 rle with new method : 0.3755955696105957 time for calcul the mask position with numpy : 0.0228269100189209 nb_pixel_total : 102424 time to create 1 rle with old method : 0.11282491683959961 time for calcul the mask position with numpy : 0.021817684173583984 nb_pixel_total : 5317 time to create 1 rle with old method : 0.005957126617431641 time for calcul the mask position with numpy : 0.020894289016723633 nb_pixel_total : 43664 time to create 1 rle with old method : 0.04815340042114258 time for calcul the mask position with numpy : 0.02077627182006836 nb_pixel_total : 6740 time to create 1 rle with old method : 0.0075261592864990234 time for calcul the mask position with numpy : 0.021418094635009766 nb_pixel_total : 90062 time to create 1 rle with old method : 0.09958934783935547 time for calcul the mask position with numpy : 0.02133655548095703 nb_pixel_total : 104280 time to create 1 rle with old method : 0.11478900909423828 time for calcul the mask position with numpy : 0.021825790405273438 nb_pixel_total : 50408 time to create 1 rle with old method : 0.05607128143310547 time for calcul the mask position with numpy : 0.022237300872802734 nb_pixel_total : 191587 time to create 1 rle with new method : 0.5323975086212158 time for calcul the mask position with numpy : 0.02756047248840332 nb_pixel_total : 71199 time to create 1 rle with old method : 0.12753605842590332 time for calcul the mask position with numpy : 0.03961801528930664 nb_pixel_total : 38185 time to create 1 rle with old method : 0.05390048027038574 time for calcul the mask position with numpy : 0.022353649139404297 nb_pixel_total : 95842 time to create 1 rle with old method : 0.10962176322937012 time for calcul the mask position with numpy : 0.02161717414855957 nb_pixel_total : 30531 time to create 1 rle with old method : 0.03396129608154297 time for calcul the mask position with numpy : 0.02528238296508789 nb_pixel_total : 13897 time to create 1 rle with old method : 0.017108678817749023 time for calcul the mask position with numpy : 0.02119612693786621 nb_pixel_total : 12344 time to create 1 rle with old method : 0.013726949691772461 create new chi : 2.760753870010376 time to delete rle : 0.01979970932006836 batch 1 Loaded 33 chid ids of type : 3594 +++++++++++++++++++++Number RLEs to save : 13575 TO DO : save crop sub photo not yet done ! save time : 1.9802203178405762 nb_obj : 59 nb_hashtags : 5 time to prepare the origin masks : 5.186855792999268 time for calcul the mask position with numpy : 0.3110501766204834 nb_pixel_total : 4324539 time to create 1 rle with new method : 0.7755777835845947 time for calcul the mask position with numpy : 0.02989053726196289 nb_pixel_total : 48013 time to create 1 rle with old method : 0.05581784248352051 time for calcul the mask position with numpy : 0.029997587203979492 nb_pixel_total : 20888 time to create 1 rle with old method : 0.023622751235961914 time for calcul the mask position with numpy : 0.03058028221130371 nb_pixel_total : 24791 time to create 1 rle with old method : 0.032364606857299805 time for calcul the mask position with numpy : 0.030623674392700195 nb_pixel_total : 33317 time to create 1 rle with old method : 0.039638519287109375 time for calcul the mask position with numpy : 0.031023025512695312 nb_pixel_total : 6711 time to create 1 rle with old method : 0.008222579956054688 time for calcul the mask position with numpy : 0.032607316970825195 nb_pixel_total : 150208 time to create 1 rle with new method : 0.6098794937133789 time for calcul the mask position with numpy : 0.029263734817504883 nb_pixel_total : 16472 time to create 1 rle with old method : 0.01867365837097168 time for calcul the mask position with numpy : 0.02929234504699707 nb_pixel_total : 18628 time to create 1 rle with old method : 0.021133899688720703 time for calcul the mask position with numpy : 0.04282712936401367 nb_pixel_total : 842515 time to create 1 rle with new method : 0.848339319229126 time for calcul the mask position with numpy : 0.030353784561157227 nb_pixel_total : 30721 time to create 1 rle with old method : 0.05209469795227051 time for calcul the mask position with numpy : 0.032137155532836914 nb_pixel_total : 5561 time to create 1 rle with old method : 0.00637364387512207 time for calcul the mask position with numpy : 0.036519765853881836 nb_pixel_total : 5138 time to create 1 rle with old method : 0.0073282718658447266 time for calcul the mask position with numpy : 0.03229379653930664 nb_pixel_total : 4846 time to create 1 rle with old method : 0.00545048713684082 time for calcul the mask position with numpy : 0.032156944274902344 nb_pixel_total : 94428 time to create 1 rle with old method : 0.12078380584716797 time for calcul the mask position with numpy : 0.02938985824584961 nb_pixel_total : 15583 time to create 1 rle with old method : 0.017399072647094727 time for calcul the mask position with numpy : 0.031225919723510742 nb_pixel_total : 125144 time to create 1 rle with old method : 0.14689064025878906 time for calcul the mask position with numpy : 0.02940201759338379 nb_pixel_total : 21060 time to create 1 rle with old method : 0.023429155349731445 time for calcul the mask position with numpy : 0.029526472091674805 nb_pixel_total : 8364 time to create 1 rle with old method : 0.01086282730102539 time for calcul the mask position with numpy : 0.029697895050048828 nb_pixel_total : 51521 time to create 1 rle with old method : 0.05678153038024902 time for calcul the mask position with numpy : 0.02921891212463379 nb_pixel_total : 10528 time to create 1 rle with old method : 0.01167750358581543 time for calcul the mask position with numpy : 0.030148983001708984 nb_pixel_total : 94075 time to create 1 rle with old method : 0.10445952415466309 time for calcul the mask position with numpy : 0.03870987892150879 nb_pixel_total : 102800 time to create 1 rle with old method : 0.1200261116027832 time for calcul the mask position with numpy : 0.02986454963684082 nb_pixel_total : 94979 time to create 1 rle with old method : 0.10610079765319824 time for calcul the mask position with numpy : 0.03349566459655762 nb_pixel_total : 4743 time to create 1 rle with old method : 0.005525112152099609 time for calcul the mask position with numpy : 0.029439687728881836 nb_pixel_total : 30014 time to create 1 rle with old method : 0.03832864761352539 time for calcul the mask position with numpy : 0.03541707992553711 nb_pixel_total : 58195 time to create 1 rle with old method : 0.06486034393310547 time for calcul the mask position with numpy : 0.05036759376525879 nb_pixel_total : 10420 time to create 1 rle with old method : 0.016800403594970703 time for calcul the mask position with numpy : 0.03812694549560547 nb_pixel_total : 31490 time to create 1 rle with old method : 0.057085275650024414 time for calcul the mask position with numpy : 0.03859686851501465 nb_pixel_total : 33581 time to create 1 rle with old method : 0.062499046325683594 time for calcul the mask position with numpy : 0.03554415702819824 nb_pixel_total : 12504 time to create 1 rle with old method : 0.014095783233642578 time for calcul the mask position with numpy : 0.029287099838256836 nb_pixel_total : 19617 time to create 1 rle with old method : 0.02204298973083496 time for calcul the mask position with numpy : 0.02966761589050293 nb_pixel_total : 35348 time to create 1 rle with old method : 0.03973650932312012 time for calcul the mask position with numpy : 0.029756784439086914 nb_pixel_total : 38107 time to create 1 rle with old method : 0.0426790714263916 time for calcul the mask position with numpy : 0.02963709831237793 nb_pixel_total : 17571 time to create 1 rle with old method : 0.0198824405670166 time for calcul the mask position with numpy : 0.029747962951660156 nb_pixel_total : 62439 time to create 1 rle with old method : 0.06965446472167969 time for calcul the mask position with numpy : 0.029407978057861328 nb_pixel_total : 33537 time to create 1 rle with old method : 0.03781914710998535 time for calcul the mask position with numpy : 0.029367446899414062 nb_pixel_total : 18081 time to create 1 rle with old method : 0.02015542984008789 time for calcul the mask position with numpy : 0.029300689697265625 nb_pixel_total : 16031 time to create 1 rle with old method : 0.018075227737426758 time for calcul the mask position with numpy : 0.029593229293823242 nb_pixel_total : 28970 time to create 1 rle with old method : 0.03457045555114746 time for calcul the mask position with numpy : 0.029451847076416016 nb_pixel_total : 41044 time to create 1 rle with old method : 0.04587531089782715 time for calcul the mask position with numpy : 0.029768943786621094 nb_pixel_total : 18541 time to create 1 rle with old method : 0.020833730697631836 time for calcul the mask position with numpy : 0.029701709747314453 nb_pixel_total : 25022 time to create 1 rle with old method : 0.028852462768554688 time for calcul the mask position with numpy : 0.02988290786743164 nb_pixel_total : 29965 time to create 1 rle with old method : 0.03376889228820801 time for calcul the mask position with numpy : 0.02921295166015625 nb_pixel_total : 5234 time to create 1 rle with old method : 0.005964517593383789 time for calcul the mask position with numpy : 0.029604673385620117 nb_pixel_total : 26946 time to create 1 rle with old method : 0.030846834182739258 time for calcul the mask position with numpy : 0.029207468032836914 nb_pixel_total : 7850 time to create 1 rle with old method : 0.008821964263916016 time for calcul the mask position with numpy : 0.029569625854492188 nb_pixel_total : 10961 time to create 1 rle with old method : 0.012233495712280273 time for calcul the mask position with numpy : 0.02920985221862793 nb_pixel_total : 13601 time to create 1 rle with old method : 0.015157699584960938 time for calcul the mask position with numpy : 0.030089139938354492 nb_pixel_total : 6289 time to create 1 rle with old method : 0.010014533996582031 time for calcul the mask position with numpy : 0.03535056114196777 nb_pixel_total : 11548 time to create 1 rle with old method : 0.01455235481262207 time for calcul the mask position with numpy : 0.0306246280670166 nb_pixel_total : 21580 time to create 1 rle with old method : 0.026972055435180664 time for calcul the mask position with numpy : 0.03564643859863281 nb_pixel_total : 17738 time to create 1 rle with old method : 0.035303354263305664 time for calcul the mask position with numpy : 0.0378873348236084 nb_pixel_total : 9499 time to create 1 rle with old method : 0.014196634292602539 time for calcul the mask position with numpy : 0.03052544593811035 nb_pixel_total : 22349 time to create 1 rle with old method : 0.024851560592651367 time for calcul the mask position with numpy : 0.029948711395263672 nb_pixel_total : 63847 time to create 1 rle with old method : 0.07189464569091797 time for calcul the mask position with numpy : 0.031341552734375 nb_pixel_total : 39310 time to create 1 rle with old method : 0.04469132423400879 time for calcul the mask position with numpy : 0.029546022415161133 nb_pixel_total : 33855 time to create 1 rle with old method : 0.03947257995605469 time for calcul the mask position with numpy : 0.029334068298339844 nb_pixel_total : 29792 time to create 1 rle with old method : 0.03322172164916992 time for calcul the mask position with numpy : 0.029226303100585938 nb_pixel_total : 13791 time to create 1 rle with old method : 0.015718460083007812 create new chi : 6.594917297363281 time to delete rle : 0.0044939517974853516 batch 1 Loaded 120 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29778 TO DO : save crop sub photo not yet done ! save time : 4.197964668273926 nb_obj : 86 nb_hashtags : 3 time to prepare the origin masks : 6.985379934310913 time for calcul the mask position with numpy : 0.7150228023529053 nb_pixel_total : 5341971 time to create 1 rle with new method : 0.4639112949371338 time for calcul the mask position with numpy : 0.0334622859954834 nb_pixel_total : 15515 time to create 1 rle with old method : 0.01913619041442871 time for calcul the mask position with numpy : 0.031821250915527344 nb_pixel_total : 43538 time to create 1 rle with old method : 0.05290699005126953 time for calcul the mask position with numpy : 0.030783414840698242 nb_pixel_total : 8953 time to create 1 rle with old method : 0.01285862922668457 time for calcul the mask position with numpy : 0.029581546783447266 nb_pixel_total : 23020 time to create 1 rle with old method : 0.026894569396972656 time for calcul the mask position with numpy : 0.029311656951904297 nb_pixel_total : 6394 time to create 1 rle with old method : 0.0072596073150634766 time for calcul the mask position with numpy : 0.029611825942993164 nb_pixel_total : 25301 time to create 1 rle with old method : 0.028193235397338867 time for calcul the mask position with numpy : 0.030155658721923828 nb_pixel_total : 49394 time to create 1 rle with old method : 0.05501890182495117 time for calcul the mask position with numpy : 0.02914714813232422 nb_pixel_total : 23189 time to create 1 rle with old method : 0.03206276893615723 time for calcul the mask position with numpy : 0.02946305274963379 nb_pixel_total : 75017 time to create 1 rle with old method : 0.08440065383911133 time for calcul the mask position with numpy : 0.03128194808959961 nb_pixel_total : 15052 time to create 1 rle with old method : 0.01683497428894043 time for calcul the mask position with numpy : 0.031168222427368164 nb_pixel_total : 62353 time to create 1 rle with old method : 0.07446098327636719 time for calcul the mask position with numpy : 0.02911829948425293 nb_pixel_total : 5007 time to create 1 rle with old method : 0.005708217620849609 time for calcul the mask position with numpy : 0.02903580665588379 nb_pixel_total : 79552 time to create 1 rle with old method : 0.0872185230255127 time for calcul the mask position with numpy : 0.02896428108215332 nb_pixel_total : 11329 time to create 1 rle with old method : 0.012387514114379883 time for calcul the mask position with numpy : 0.02794623374938965 nb_pixel_total : 26157 time to create 1 rle with old method : 0.029012203216552734 time for calcul the mask position with numpy : 0.029238224029541016 nb_pixel_total : 8523 time to create 1 rle with old method : 0.009455442428588867 time for calcul the mask position with numpy : 0.02919745445251465 nb_pixel_total : 25084 time to create 1 rle with old method : 0.028075218200683594 time for calcul the mask position with numpy : 0.029096603393554688 nb_pixel_total : 1477 time to create 1 rle with old method : 0.0018162727355957031 time for calcul the mask position with numpy : 0.029080867767333984 nb_pixel_total : 9064 time to create 1 rle with old method : 0.010263919830322266 time for calcul the mask position with numpy : 0.029222488403320312 nb_pixel_total : 15856 time to create 1 rle with old method : 0.01767587661743164 time for calcul the mask position with numpy : 0.029053926467895508 nb_pixel_total : 313 time to create 1 rle with old method : 0.0004909038543701172 time for calcul the mask position with numpy : 0.029470205307006836 nb_pixel_total : 21584 time to create 1 rle with old method : 0.026709556579589844 time for calcul the mask position with numpy : 0.031575918197631836 nb_pixel_total : 28575 time to create 1 rle with old method : 0.03225541114807129 time for calcul the mask position with numpy : 0.03153276443481445 nb_pixel_total : 13376 time to create 1 rle with old method : 0.015363454818725586 time for calcul the mask position with numpy : 0.03284478187561035 nb_pixel_total : 21965 time to create 1 rle with old method : 0.027768373489379883 time for calcul the mask position with numpy : 0.03350257873535156 nb_pixel_total : 10007 time to create 1 rle with old method : 0.013476133346557617 time for calcul the mask position with numpy : 0.029994964599609375 nb_pixel_total : 14511 time to create 1 rle with old method : 0.016991615295410156 time for calcul the mask position with numpy : 0.031819820404052734 nb_pixel_total : 4268 time to create 1 rle with old method : 0.004856586456298828 time for calcul the mask position with numpy : 0.029338598251342773 nb_pixel_total : 34396 time to create 1 rle with old method : 0.038407087326049805 time for calcul the mask position with numpy : 0.02911853790283203 nb_pixel_total : 15887 time to create 1 rle with old method : 0.017670392990112305 time for calcul the mask position with numpy : 0.028999805450439453 nb_pixel_total : 74264 time to create 1 rle with old method : 0.08242201805114746 time for calcul the mask position with numpy : 0.03420138359069824 nb_pixel_total : 11934 time to create 1 rle with old method : 0.01418447494506836 time for calcul the mask position with numpy : 0.029819965362548828 nb_pixel_total : 12256 time to create 1 rle with old method : 0.013590335845947266 time for calcul the mask position with numpy : 0.029422998428344727 nb_pixel_total : 79 time to create 1 rle with old method : 0.0003330707550048828 time for calcul the mask position with numpy : 0.029731273651123047 nb_pixel_total : 13260 time to create 1 rle with old method : 0.015390634536743164 time for calcul the mask position with numpy : 0.03183603286743164 nb_pixel_total : 28846 time to create 1 rle with old method : 0.0319523811340332 time for calcul the mask position with numpy : 0.030150651931762695 nb_pixel_total : 23159 time to create 1 rle with old method : 0.025953292846679688 time for calcul the mask position with numpy : 0.029619932174682617 nb_pixel_total : 8857 time to create 1 rle with old method : 0.009883642196655273 time for calcul the mask position with numpy : 0.029070377349853516 nb_pixel_total : 10097 time to create 1 rle with old method : 0.011252164840698242 time for calcul the mask position with numpy : 0.03151583671569824 nb_pixel_total : 176 time to create 1 rle with old method : 0.00039196014404296875 time for calcul the mask position with numpy : 0.032886505126953125 nb_pixel_total : 33575 time to create 1 rle with old method : 0.03853487968444824 time for calcul the mask position with numpy : 0.032959699630737305 nb_pixel_total : 1287 time to create 1 rle with old method : 0.0018620491027832031 time for calcul the mask position with numpy : 0.03336596488952637 nb_pixel_total : 40322 time to create 1 rle with old method : 0.04482388496398926 time for calcul the mask position with numpy : 0.029334545135498047 nb_pixel_total : 8283 time to create 1 rle with old method : 0.009362220764160156 time for calcul the mask position with numpy : 0.03021550178527832 nb_pixel_total : 85241 time to create 1 rle with old method : 0.09386610984802246 time for calcul the mask position with numpy : 0.0292510986328125 nb_pixel_total : 104 time to create 1 rle with old method : 0.0004482269287109375 time for calcul the mask position with numpy : 0.030056238174438477 nb_pixel_total : 18720 time to create 1 rle with old method : 0.02095818519592285 time for calcul the mask position with numpy : 0.03021860122680664 nb_pixel_total : 55727 time to create 1 rle with old method : 0.06179356575012207 time for calcul the mask position with numpy : 0.0298919677734375 nb_pixel_total : 20875 time to create 1 rle with old method : 0.023262739181518555 time for calcul the mask position with numpy : 0.02946186065673828 nb_pixel_total : 13233 time to create 1 rle with old method : 0.014859437942504883 time for calcul the mask position with numpy : 0.029614686965942383 nb_pixel_total : 27590 time to create 1 rle with old method : 0.030691146850585938 time for calcul the mask position with numpy : 0.0294342041015625 nb_pixel_total : 16034 time to create 1 rle with old method : 0.01779651641845703 time for calcul the mask position with numpy : 0.03057122230529785 nb_pixel_total : 16082 time to create 1 rle with old method : 0.019593000411987305 time for calcul the mask position with numpy : 0.029488563537597656 nb_pixel_total : 18708 time to create 1 rle with old method : 0.02093362808227539 time for calcul the mask position with numpy : 0.029416322708129883 nb_pixel_total : 19650 time to create 1 rle with old method : 0.021846532821655273 time for calcul the mask position with numpy : 0.029486656188964844 nb_pixel_total : 11225 time to create 1 rle with old method : 0.012731313705444336 time for calcul the mask position with numpy : 0.02950453758239746 nb_pixel_total : 27092 time to create 1 rle with old method : 0.03055548667907715 time for calcul the mask position with numpy : 0.029376506805419922 nb_pixel_total : 13030 time to create 1 rle with old method : 0.014561653137207031 time for calcul the mask position with numpy : 0.029534101486206055 nb_pixel_total : 133 time to create 1 rle with old method : 0.00042629241943359375 time for calcul the mask position with numpy : 0.03205370903015137 nb_pixel_total : 733 time to create 1 rle with old method : 0.0011479854583740234 time for calcul the mask position with numpy : 0.029477834701538086 nb_pixel_total : 31713 time to create 1 rle with old method : 0.035643577575683594 time for calcul the mask position with numpy : 0.02990579605102539 nb_pixel_total : 13916 time to create 1 rle with old method : 0.01582169532775879 time for calcul the mask position with numpy : 0.029353857040405273 nb_pixel_total : 18084 time to create 1 rle with old method : 0.0201570987701416 time for calcul the mask position with numpy : 0.029258251190185547 nb_pixel_total : 10362 time to create 1 rle with old method : 0.011700630187988281 time for calcul the mask position with numpy : 0.02964949607849121 nb_pixel_total : 25859 time to create 1 rle with old method : 0.028865814208984375 time for calcul the mask position with numpy : 0.029449939727783203 nb_pixel_total : 111 time to create 1 rle with old method : 0.0004181861877441406 time for calcul the mask position with numpy : 0.029362916946411133 nb_pixel_total : 4096 time to create 1 rle with old method : 0.004701137542724609 time for calcul the mask position with numpy : 0.030481576919555664 nb_pixel_total : 8994 time to create 1 rle with old method : 0.01013803482055664 time for calcul the mask position with numpy : 0.029579877853393555 nb_pixel_total : 48130 time to create 1 rle with old method : 0.05380368232727051 time for calcul the mask position with numpy : 0.029221057891845703 nb_pixel_total : 5578 time to create 1 rle with old method : 0.006311178207397461 time for calcul the mask position with numpy : 0.0291135311126709 nb_pixel_total : 16526 time to create 1 rle with old method : 0.01848888397216797 time for calcul the mask position with numpy : 0.029162883758544922 nb_pixel_total : 12988 time to create 1 rle with old method : 0.014530181884765625 time for calcul the mask position with numpy : 0.028725385665893555 nb_pixel_total : 414 time to create 1 rle with old method : 0.0005526542663574219 time for calcul the mask position with numpy : 0.028851985931396484 nb_pixel_total : 3773 time to create 1 rle with old method : 0.004244565963745117 time for calcul the mask position with numpy : 0.029070138931274414 nb_pixel_total : 27 time to create 1 rle with old method : 0.00013136863708496094 time for calcul the mask position with numpy : 0.029933691024780273 nb_pixel_total : 6410 time to create 1 rle with old method : 0.007254362106323242 time for calcul the mask position with numpy : 0.028935670852661133 nb_pixel_total : 22210 time to create 1 rle with old method : 0.028110265731811523 time for calcul the mask position with numpy : 0.03294110298156738 nb_pixel_total : 4172 time to create 1 rle with old method : 0.0073320865631103516 time for calcul the mask position with numpy : 0.03379225730895996 nb_pixel_total : 105668 time to create 1 rle with old method : 0.11786746978759766 time for calcul the mask position with numpy : 0.029505252838134766 nb_pixel_total : 9600 time to create 1 rle with old method : 0.010733604431152344 time for calcul the mask position with numpy : 0.029694080352783203 nb_pixel_total : 1554 time to create 1 rle with old method : 0.0017933845520019531 time for calcul the mask position with numpy : 0.029950380325317383 nb_pixel_total : 6039 time to create 1 rle with old method : 0.0068171024322509766 time for calcul the mask position with numpy : 0.035036563873291016 nb_pixel_total : 12013 time to create 1 rle with old method : 0.014135360717773438 time for calcul the mask position with numpy : 0.02889108657836914 nb_pixel_total : 10469 time to create 1 rle with old method : 0.011687994003295898 time for calcul the mask position with numpy : 0.03134655952453613 nb_pixel_total : 13808 time to create 1 rle with old method : 0.0159914493560791 time for calcul the mask position with numpy : 0.030045270919799805 nb_pixel_total : 10526 time to create 1 rle with old method : 0.011951684951782227 create new chi : 5.773676156997681 time to delete rle : 0.0048046112060546875 batch 1 Loaded 181 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 34446 TO DO : save crop sub photo not yet done ! save time : 2.407238006591797 nb_obj : 66 nb_hashtags : 5 time to prepare the origin masks : 5.403344631195068 time for calcul the mask position with numpy : 0.5401990413665771 nb_pixel_total : 5347984 time to create 1 rle with new method : 0.9435458183288574 time for calcul the mask position with numpy : 0.02994251251220703 nb_pixel_total : 17098 time to create 1 rle with old method : 0.01999521255493164 time for calcul the mask position with numpy : 0.029989957809448242 nb_pixel_total : 17851 time to create 1 rle with old method : 0.01998281478881836 time for calcul the mask position with numpy : 0.0300595760345459 nb_pixel_total : 8839 time to create 1 rle with old method : 0.010145187377929688 time for calcul the mask position with numpy : 0.02981400489807129 nb_pixel_total : 2603 time to create 1 rle with old method : 0.0032002925872802734 time for calcul the mask position with numpy : 0.03597140312194824 nb_pixel_total : 268631 time to create 1 rle with new method : 0.8581185340881348 time for calcul the mask position with numpy : 0.030104637145996094 nb_pixel_total : 31618 time to create 1 rle with old method : 0.03569912910461426 time for calcul the mask position with numpy : 0.029659748077392578 nb_pixel_total : 14257 time to create 1 rle with old method : 0.016071557998657227 time for calcul the mask position with numpy : 0.03002762794494629 nb_pixel_total : 4327 time to create 1 rle with old method : 0.005308389663696289 time for calcul the mask position with numpy : 0.02988910675048828 nb_pixel_total : 336 time to create 1 rle with old method : 0.00048041343688964844 time for calcul the mask position with numpy : 0.03002452850341797 nb_pixel_total : 3138 time to create 1 rle with old method : 0.0038199424743652344 time for calcul the mask position with numpy : 0.030778884887695312 nb_pixel_total : 16339 time to create 1 rle with old method : 0.02011418342590332 time for calcul the mask position with numpy : 0.030097007751464844 nb_pixel_total : 20281 time to create 1 rle with old method : 0.0231630802154541 time for calcul the mask position with numpy : 0.030130863189697266 nb_pixel_total : 19653 time to create 1 rle with old method : 0.022330045700073242 time for calcul the mask position with numpy : 0.030261754989624023 nb_pixel_total : 174 time to create 1 rle with old method : 0.0004322528839111328 time for calcul the mask position with numpy : 0.030338764190673828 nb_pixel_total : 19760 time to create 1 rle with old method : 0.022205114364624023 time for calcul the mask position with numpy : 0.03210258483886719 nb_pixel_total : 35111 time to create 1 rle with old method : 0.04278254508972168 time for calcul the mask position with numpy : 0.030295133590698242 nb_pixel_total : 19552 time to create 1 rle with old method : 0.021851301193237305 time for calcul the mask position with numpy : 0.030484914779663086 nb_pixel_total : 362 time to create 1 rle with old method : 0.0006308555603027344 time for calcul the mask position with numpy : 0.03223896026611328 nb_pixel_total : 5050 time to create 1 rle with old method : 0.005828857421875 time for calcul the mask position with numpy : 0.030201196670532227 nb_pixel_total : 15811 time to create 1 rle with old method : 0.018076181411743164 time for calcul the mask position with numpy : 0.031046152114868164 nb_pixel_total : 11107 time to create 1 rle with old method : 0.013316154479980469 time for calcul the mask position with numpy : 0.030596256256103516 nb_pixel_total : 39370 time to create 1 rle with old method : 0.04741191864013672 time for calcul the mask position with numpy : 0.030616044998168945 nb_pixel_total : 42381 time to create 1 rle with old method : 0.04731273651123047 time for calcul the mask position with numpy : 0.030382156372070312 nb_pixel_total : 13971 time to create 1 rle with old method : 0.015859127044677734 time for calcul the mask position with numpy : 0.030550003051757812 nb_pixel_total : 18521 time to create 1 rle with old method : 0.020819425582885742 time for calcul the mask position with numpy : 0.03053450584411621 nb_pixel_total : 38893 time to create 1 rle with old method : 0.043782949447631836 time for calcul the mask position with numpy : 0.03044581413269043 nb_pixel_total : 12039 time to create 1 rle with old method : 0.02711653709411621 time for calcul the mask position with numpy : 0.030439376831054688 nb_pixel_total : 20047 time to create 1 rle with old method : 0.02255082130432129 time for calcul the mask position with numpy : 0.0303647518157959 nb_pixel_total : 14939 time to create 1 rle with old method : 0.01681828498840332 time for calcul the mask position with numpy : 0.03035879135131836 nb_pixel_total : 24993 time to create 1 rle with old method : 0.027985572814941406 time for calcul the mask position with numpy : 0.031221866607666016 nb_pixel_total : 114691 time to create 1 rle with old method : 0.13440346717834473 time for calcul the mask position with numpy : 0.02961254119873047 nb_pixel_total : 12476 time to create 1 rle with old method : 0.014055013656616211 time for calcul the mask position with numpy : 0.03162574768066406 nb_pixel_total : 58814 time to create 1 rle with old method : 0.06593108177185059 time for calcul the mask position with numpy : 0.029213905334472656 nb_pixel_total : 35993 time to create 1 rle with old method : 0.03977632522583008 time for calcul the mask position with numpy : 0.029366254806518555 nb_pixel_total : 38207 time to create 1 rle with old method : 0.042443037033081055 time for calcul the mask position with numpy : 0.03350710868835449 nb_pixel_total : 2984 time to create 1 rle with old method : 0.0035207271575927734 time for calcul the mask position with numpy : 0.029828310012817383 nb_pixel_total : 26239 time to create 1 rle with old method : 0.032052040100097656 time for calcul the mask position with numpy : 0.031226396560668945 nb_pixel_total : 4894 time to create 1 rle with old method : 0.007455110549926758 time for calcul the mask position with numpy : 0.03247213363647461 nb_pixel_total : 66205 time to create 1 rle with old method : 0.09347009658813477 time for calcul the mask position with numpy : 0.029120445251464844 nb_pixel_total : 5993 time to create 1 rle with old method : 0.006864070892333984 time for calcul the mask position with numpy : 0.03542160987854004 nb_pixel_total : 67397 time to create 1 rle with old method : 0.07804536819458008 time for calcul the mask position with numpy : 0.03241729736328125 nb_pixel_total : 18150 time to create 1 rle with old method : 0.021460771560668945 time for calcul the mask position with numpy : 0.03001713752746582 nb_pixel_total : 56420 time to create 1 rle with old method : 0.06893420219421387 time for calcul the mask position with numpy : 0.02998495101928711 nb_pixel_total : 26100 time to create 1 rle with old method : 0.03263497352600098 time for calcul the mask position with numpy : 0.030225753784179688 nb_pixel_total : 29837 time to create 1 rle with old method : 0.03472757339477539 time for calcul the mask position with numpy : 0.030551433563232422 nb_pixel_total : 9695 time to create 1 rle with old method : 0.011981487274169922 time for calcul the mask position with numpy : 0.03040146827697754 nb_pixel_total : 4664 time to create 1 rle with old method : 0.00591588020324707 time for calcul the mask position with numpy : 0.030640840530395508 nb_pixel_total : 20740 time to create 1 rle with old method : 0.025434017181396484 time for calcul the mask position with numpy : 0.03563261032104492 nb_pixel_total : 29438 time to create 1 rle with old method : 0.03670096397399902 time for calcul the mask position with numpy : 0.03052997589111328 nb_pixel_total : 26436 time to create 1 rle with old method : 0.03275942802429199 time for calcul the mask position with numpy : 0.03060770034790039 nb_pixel_total : 34859 time to create 1 rle with old method : 0.04046916961669922 time for calcul the mask position with numpy : 0.030652284622192383 nb_pixel_total : 20568 time to create 1 rle with old method : 0.02599048614501953 time for calcul the mask position with numpy : 0.031768083572387695 nb_pixel_total : 6757 time to create 1 rle with old method : 0.007858037948608398 time for calcul the mask position with numpy : 0.032744646072387695 nb_pixel_total : 4364 time to create 1 rle with old method : 0.0049054622650146484 time for calcul the mask position with numpy : 0.029201507568359375 nb_pixel_total : 8100 time to create 1 rle with old method : 0.009133577346801758 time for calcul the mask position with numpy : 0.04097461700439453 nb_pixel_total : 14357 time to create 1 rle with old method : 0.016045331954956055 time for calcul the mask position with numpy : 0.03148937225341797 nb_pixel_total : 8342 time to create 1 rle with old method : 0.009437084197998047 time for calcul the mask position with numpy : 0.02940511703491211 nb_pixel_total : 18823 time to create 1 rle with old method : 0.024744272232055664 time for calcul the mask position with numpy : 0.039768218994140625 nb_pixel_total : 35137 time to create 1 rle with old method : 0.043241262435913086 time for calcul the mask position with numpy : 0.02967524528503418 nb_pixel_total : 8012 time to create 1 rle with old method : 0.009018659591674805 time for calcul the mask position with numpy : 0.031646728515625 nb_pixel_total : 3412 time to create 1 rle with old method : 0.003911733627319336 time for calcul the mask position with numpy : 0.029331445693969727 nb_pixel_total : 5208 time to create 1 rle with old method : 0.005979061126708984 time for calcul the mask position with numpy : 0.03982377052307129 nb_pixel_total : 9866 time to create 1 rle with old method : 0.011073112487792969 time for calcul the mask position with numpy : 0.02937483787536621 nb_pixel_total : 34476 time to create 1 rle with old method : 0.038657426834106445 time for calcul the mask position with numpy : 0.03134918212890625 nb_pixel_total : 10968 time to create 1 rle with old method : 0.012255430221557617 time for calcul the mask position with numpy : 0.029584169387817383 nb_pixel_total : 66582 time to create 1 rle with old method : 0.07437705993652344 create new chi : 6.18795919418335 time to delete rle : 0.008111953735351562 batch 1 Loaded 139 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29615 TO DO : save crop sub photo not yet done ! save time : 2.352411985397339 nb_obj : 29 nb_hashtags : 3 time to prepare the origin masks : 4.03754186630249 time for calcul the mask position with numpy : 2.2401516437530518 nb_pixel_total : 5846392 time to create 1 rle with new method : 0.6793942451477051 time for calcul the mask position with numpy : 0.02946329116821289 nb_pixel_total : 10101 time to create 1 rle with old method : 0.011394977569580078 time for calcul the mask position with numpy : 0.03131365776062012 nb_pixel_total : 51848 time to create 1 rle with old method : 0.05711054801940918 time for calcul the mask position with numpy : 0.029484272003173828 nb_pixel_total : 5777 time to create 1 rle with old method : 0.0067102909088134766 time for calcul the mask position with numpy : 0.0295865535736084 nb_pixel_total : 23353 time to create 1 rle with old method : 0.02580571174621582 time for calcul the mask position with numpy : 0.03254222869873047 nb_pixel_total : 80173 time to create 1 rle with old method : 0.09152340888977051 time for calcul the mask position with numpy : 0.02976202964782715 nb_pixel_total : 3811 time to create 1 rle with old method : 0.004877328872680664 time for calcul the mask position with numpy : 0.02976083755493164 nb_pixel_total : 9897 time to create 1 rle with old method : 0.011363983154296875 time for calcul the mask position with numpy : 0.03031301498413086 nb_pixel_total : 25585 time to create 1 rle with old method : 0.0288851261138916 time for calcul the mask position with numpy : 0.03110480308532715 nb_pixel_total : 67838 time to create 1 rle with old method : 0.0770728588104248 time for calcul the mask position with numpy : 0.030544042587280273 nb_pixel_total : 10917 time to create 1 rle with old method : 0.012621879577636719 time for calcul the mask position with numpy : 0.03096747398376465 nb_pixel_total : 61167 time to create 1 rle with old method : 0.07189321517944336 time for calcul the mask position with numpy : 0.03663206100463867 nb_pixel_total : 307165 time to create 1 rle with new method : 0.6223297119140625 time for calcul the mask position with numpy : 0.03269529342651367 nb_pixel_total : 175075 time to create 1 rle with new method : 0.40184521675109863 time for calcul the mask position with numpy : 0.02986311912536621 nb_pixel_total : 50380 time to create 1 rle with old method : 0.05575060844421387 time for calcul the mask position with numpy : 0.02935647964477539 nb_pixel_total : 10104 time to create 1 rle with old method : 0.011285066604614258 time for calcul the mask position with numpy : 0.02895951271057129 nb_pixel_total : 33694 time to create 1 rle with old method : 0.04636859893798828 time for calcul the mask position with numpy : 0.03005385398864746 nb_pixel_total : 39811 time to create 1 rle with old method : 0.04416990280151367 time for calcul the mask position with numpy : 0.02895212173461914 nb_pixel_total : 19159 time to create 1 rle with old method : 0.021511554718017578 time for calcul the mask position with numpy : 0.02909994125366211 nb_pixel_total : 64179 time to create 1 rle with old method : 0.07107877731323242 time for calcul the mask position with numpy : 0.02909088134765625 nb_pixel_total : 66176 time to create 1 rle with old method : 0.07322216033935547 time for calcul the mask position with numpy : 0.03297591209411621 nb_pixel_total : 12778 time to create 1 rle with old method : 0.021953582763671875 time for calcul the mask position with numpy : 0.0293734073638916 nb_pixel_total : 21737 time to create 1 rle with old method : 0.024411439895629883 time for calcul the mask position with numpy : 0.029115915298461914 nb_pixel_total : 1381 time to create 1 rle with old method : 0.0017082691192626953 time for calcul the mask position with numpy : 0.02904057502746582 nb_pixel_total : 22901 time to create 1 rle with old method : 0.025591135025024414 time for calcul the mask position with numpy : 0.02905869483947754 nb_pixel_total : 835 time to create 1 rle with old method : 0.0011658668518066406 time for calcul the mask position with numpy : 0.029000520706176758 nb_pixel_total : 7670 time to create 1 rle with old method : 0.008581399917602539 time for calcul the mask position with numpy : 0.02898406982421875 nb_pixel_total : 5828 time to create 1 rle with old method : 0.006554603576660156 time for calcul the mask position with numpy : 0.029146671295166016 nb_pixel_total : 6869 time to create 1 rle with old method : 0.007716178894042969 time for calcul the mask position with numpy : 0.02903270721435547 nb_pixel_total : 7639 time to create 1 rle with old method : 0.008549213409423828 create new chi : 5.740054130554199 time to delete rle : 0.003476381301879883 batch 1 Loaded 63 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 16585 TO DO : save crop sub photo not yet done ! save time : 1.108949899673462 nb_obj : 27 nb_hashtags : 2 time to prepare the origin masks : 4.864824295043945 time for calcul the mask position with numpy : 0.600006103515625 nb_pixel_total : 6081265 time to create 1 rle with new method : 0.5098941326141357 time for calcul the mask position with numpy : 0.02978825569152832 nb_pixel_total : 129793 time to create 1 rle with old method : 0.1386868953704834 time for calcul the mask position with numpy : 0.0284731388092041 nb_pixel_total : 2409 time to create 1 rle with old method : 0.003164529800415039 time for calcul the mask position with numpy : 0.030354022979736328 nb_pixel_total : 383002 time to create 1 rle with new method : 0.534125804901123 time for calcul the mask position with numpy : 0.029389619827270508 nb_pixel_total : 2647 time to create 1 rle with old method : 0.0030655860900878906 time for calcul the mask position with numpy : 0.029240131378173828 nb_pixel_total : 14531 time to create 1 rle with old method : 0.016162872314453125 time for calcul the mask position with numpy : 0.0281527042388916 nb_pixel_total : 17801 time to create 1 rle with old method : 0.018934965133666992 time for calcul the mask position with numpy : 0.027619600296020508 nb_pixel_total : 21554 time to create 1 rle with old method : 0.022825241088867188 time for calcul the mask position with numpy : 0.028795719146728516 nb_pixel_total : 21846 time to create 1 rle with old method : 0.024044275283813477 time for calcul the mask position with numpy : 0.02947998046875 nb_pixel_total : 76868 time to create 1 rle with old method : 0.08293366432189941 time for calcul the mask position with numpy : 0.028998851776123047 nb_pixel_total : 2034 time to create 1 rle with old method : 0.002318859100341797 time for calcul the mask position with numpy : 0.02887272834777832 nb_pixel_total : 1508 time to create 1 rle with old method : 0.002036571502685547 time for calcul the mask position with numpy : 0.029918193817138672 nb_pixel_total : 76572 time to create 1 rle with old method : 0.08498573303222656 time for calcul the mask position with numpy : 0.029645204544067383 nb_pixel_total : 42461 time to create 1 rle with old method : 0.04682040214538574 time for calcul the mask position with numpy : 0.029213905334472656 nb_pixel_total : 4938 time to create 1 rle with old method : 0.005495786666870117 time for calcul the mask position with numpy : 0.029408693313598633 nb_pixel_total : 40838 time to create 1 rle with old method : 0.04484200477600098 time for calcul the mask position with numpy : 0.028769731521606445 nb_pixel_total : 9656 time to create 1 rle with old method : 0.010646820068359375 time for calcul the mask position with numpy : 0.02880716323852539 nb_pixel_total : 11226 time to create 1 rle with old method : 0.01425933837890625 time for calcul the mask position with numpy : 0.02868485450744629 nb_pixel_total : 1228 time to create 1 rle with old method : 0.0016434192657470703 time for calcul the mask position with numpy : 0.028113365173339844 nb_pixel_total : 11240 time to create 1 rle with old method : 0.012635469436645508 time for calcul the mask position with numpy : 0.028788328170776367 nb_pixel_total : 13339 time to create 1 rle with old method : 0.014809131622314453 time for calcul the mask position with numpy : 0.0287172794342041 nb_pixel_total : 9926 time to create 1 rle with old method : 0.010620594024658203 time for calcul the mask position with numpy : 0.036570072174072266 nb_pixel_total : 30 time to create 1 rle with old method : 0.00015354156494140625 time for calcul the mask position with numpy : 0.029088497161865234 nb_pixel_total : 22745 time to create 1 rle with old method : 0.02485823631286621 time for calcul the mask position with numpy : 0.031430721282958984 nb_pixel_total : 21027 time to create 1 rle with old method : 0.023327350616455078 time for calcul the mask position with numpy : 0.02860283851623535 nb_pixel_total : 324 time to create 1 rle with old method : 0.0004684925079345703 time for calcul the mask position with numpy : 0.028498172760009766 nb_pixel_total : 16635 time to create 1 rle with old method : 0.01764082908630371 time for calcul the mask position with numpy : 0.0280609130859375 nb_pixel_total : 12797 time to create 1 rle with old method : 0.01391911506652832 create new chi : 3.1465847492218018 time to delete rle : 0.0028200149536132812 batch 1 Loaded 61 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++Number RLEs to save : 13937 TO DO : save crop sub photo not yet done ! save time : 0.8104557991027832 nb_obj : 21 nb_hashtags : 3 time to prepare the origin masks : 3.887355089187622 time for calcul the mask position with numpy : 0.3025376796722412 nb_pixel_total : 6214461 time to create 1 rle with new method : 1.5740482807159424 time for calcul the mask position with numpy : 0.029175758361816406 nb_pixel_total : 13151 time to create 1 rle with old method : 0.014560937881469727 time for calcul the mask position with numpy : 0.029558658599853516 nb_pixel_total : 22753 time to create 1 rle with old method : 0.02565908432006836 time for calcul the mask position with numpy : 0.02904224395751953 nb_pixel_total : 52307 time to create 1 rle with old method : 0.05759596824645996 time for calcul the mask position with numpy : 0.02909255027770996 nb_pixel_total : 16620 time to create 1 rle with old method : 0.03625226020812988 time for calcul the mask position with numpy : 0.029160499572753906 nb_pixel_total : 10701 time to create 1 rle with old method : 0.01208353042602539 time for calcul the mask position with numpy : 0.029210567474365234 nb_pixel_total : 15177 time to create 1 rle with old method : 0.017006397247314453 time for calcul the mask position with numpy : 0.02915787696838379 nb_pixel_total : 22511 time to create 1 rle with old method : 0.02507781982421875 time for calcul the mask position with numpy : 0.03034353256225586 nb_pixel_total : 23127 time to create 1 rle with old method : 0.026270389556884766 time for calcul the mask position with numpy : 0.029987812042236328 nb_pixel_total : 190 time to create 1 rle with old method : 0.0003876686096191406 time for calcul the mask position with numpy : 0.03021717071533203 nb_pixel_total : 33295 time to create 1 rle with old method : 0.037184715270996094 time for calcul the mask position with numpy : 0.029749631881713867 nb_pixel_total : 7433 time to create 1 rle with old method : 0.008368730545043945 time for calcul the mask position with numpy : 0.029807567596435547 nb_pixel_total : 36116 time to create 1 rle with old method : 0.0415043830871582 time for calcul the mask position with numpy : 0.038140296936035156 nb_pixel_total : 475696 time to create 1 rle with new method : 0.3653135299682617 time for calcul the mask position with numpy : 0.03140425682067871 nb_pixel_total : 19175 time to create 1 rle with old method : 0.021600723266601562 time for calcul the mask position with numpy : 0.029746294021606445 nb_pixel_total : 14525 time to create 1 rle with old method : 0.0164186954498291 time for calcul the mask position with numpy : 0.029587984085083008 nb_pixel_total : 17329 time to create 1 rle with old method : 0.019580364227294922 time for calcul the mask position with numpy : 0.03012561798095703 nb_pixel_total : 13111 time to create 1 rle with old method : 0.014928579330444336 time for calcul the mask position with numpy : 0.029454946517944336 nb_pixel_total : 4830 time to create 1 rle with old method : 0.005526542663574219 time for calcul the mask position with numpy : 0.02953624725341797 nb_pixel_total : 15019 time to create 1 rle with old method : 0.01668548583984375 time for calcul the mask position with numpy : 0.02952885627746582 nb_pixel_total : 11169 time to create 1 rle with old method : 0.012458086013793945 time for calcul the mask position with numpy : 0.029119491577148438 nb_pixel_total : 11544 time to create 1 rle with old method : 0.012984037399291992 create new chi : 3.358619213104248 time to delete rle : 0.0019774436950683594 batch 1 Loaded 43 chid ids of type : 3594 ++++++++++++++++++++++++Number RLEs to save : 11690 TO DO : save crop sub photo not yet done ! save time : 1.0949828624725342 nb_obj : 35 nb_hashtags : 4 time to prepare the origin masks : 4.627070188522339 time for calcul the mask position with numpy : 0.2861635684967041 nb_pixel_total : 5347772 time to create 1 rle with new method : 0.9015405178070068 time for calcul the mask position with numpy : 0.029875516891479492 nb_pixel_total : 70986 time to create 1 rle with old method : 0.08262419700622559 time for calcul the mask position with numpy : 0.029664278030395508 nb_pixel_total : 43813 time to create 1 rle with old method : 0.048509836196899414 time for calcul the mask position with numpy : 0.029271841049194336 nb_pixel_total : 97772 time to create 1 rle with old method : 0.10937833786010742 time for calcul the mask position with numpy : 0.03039860725402832 nb_pixel_total : 160457 time to create 1 rle with new method : 0.30718445777893066 time for calcul the mask position with numpy : 0.028619050979614258 nb_pixel_total : 632 time to create 1 rle with old method : 0.0012836456298828125 time for calcul the mask position with numpy : 0.03033447265625 nb_pixel_total : 13551 time to create 1 rle with old method : 0.015204668045043945 time for calcul the mask position with numpy : 0.028914213180541992 nb_pixel_total : 42258 time to create 1 rle with old method : 0.04701042175292969 time for calcul the mask position with numpy : 0.030317068099975586 nb_pixel_total : 226614 time to create 1 rle with new method : 0.3802485466003418 time for calcul the mask position with numpy : 0.029413223266601562 nb_pixel_total : 11446 time to create 1 rle with old method : 0.012868165969848633 time for calcul the mask position with numpy : 0.02970123291015625 nb_pixel_total : 9134 time to create 1 rle with old method : 0.010344266891479492 time for calcul the mask position with numpy : 0.029053926467895508 nb_pixel_total : 15332 time to create 1 rle with old method : 0.016945838928222656 time for calcul the mask position with numpy : 0.029649019241333008 nb_pixel_total : 108909 time to create 1 rle with old method : 0.12221479415893555 time for calcul the mask position with numpy : 0.03005075454711914 nb_pixel_total : 66989 time to create 1 rle with old method : 0.07547283172607422 time for calcul the mask position with numpy : 0.031853437423706055 nb_pixel_total : 48723 time to create 1 rle with old method : 0.05388522148132324 time for calcul the mask position with numpy : 0.029086589813232422 nb_pixel_total : 6054 time to create 1 rle with old method : 0.0068073272705078125 time for calcul the mask position with numpy : 0.02906036376953125 nb_pixel_total : 19297 time to create 1 rle with old method : 0.02162766456604004 time for calcul the mask position with numpy : 0.033072471618652344 nb_pixel_total : 29474 time to create 1 rle with old method : 0.04051351547241211 time for calcul the mask position with numpy : 0.029264450073242188 nb_pixel_total : 20284 time to create 1 rle with old method : 0.022487163543701172 time for calcul the mask position with numpy : 0.02895951271057129 nb_pixel_total : 15994 time to create 1 rle with old method : 0.017890453338623047 time for calcul the mask position with numpy : 0.02918720245361328 nb_pixel_total : 74952 time to create 1 rle with old method : 0.08391404151916504 time for calcul the mask position with numpy : 0.029704809188842773 nb_pixel_total : 112629 time to create 1 rle with old method : 0.13087248802185059 time for calcul the mask position with numpy : 0.03232169151306152 nb_pixel_total : 49300 time to create 1 rle with old method : 0.05826854705810547 time for calcul the mask position with numpy : 0.03008127212524414 nb_pixel_total : 147801 time to create 1 rle with old method : 0.17186260223388672 time for calcul the mask position with numpy : 0.029529094696044922 nb_pixel_total : 21375 time to create 1 rle with old method : 0.023983478546142578 time for calcul the mask position with numpy : 0.03430461883544922 nb_pixel_total : 6903 time to create 1 rle with old method : 0.008558273315429688 time for calcul the mask position with numpy : 0.02999424934387207 nb_pixel_total : 12050 time to create 1 rle with old method : 0.013958454132080078 time for calcul the mask position with numpy : 0.03450894355773926 nb_pixel_total : 15950 time to create 1 rle with old method : 0.01791214942932129 time for calcul the mask position with numpy : 0.029383420944213867 nb_pixel_total : 22522 time to create 1 rle with old method : 0.02623271942138672 time for calcul the mask position with numpy : 0.03026866912841797 nb_pixel_total : 125581 time to create 1 rle with old method : 0.1388084888458252 time for calcul the mask position with numpy : 0.029469013214111328 nb_pixel_total : 14896 time to create 1 rle with old method : 0.01666855812072754 time for calcul the mask position with numpy : 0.029425621032714844 nb_pixel_total : 11498 time to create 1 rle with old method : 0.013201475143432617 time for calcul the mask position with numpy : 0.03006601333618164 nb_pixel_total : 31486 time to create 1 rle with old method : 0.03550529479980469 time for calcul the mask position with numpy : 0.031236886978149414 nb_pixel_total : 20944 time to create 1 rle with old method : 0.024396896362304688 time for calcul the mask position with numpy : 0.031449317932128906 nb_pixel_total : 11218 time to create 1 rle with old method : 0.013571500778198242 time for calcul the mask position with numpy : 0.029648780822753906 nb_pixel_total : 15644 time to create 1 rle with old method : 0.01755237579345703 create new chi : 4.51986837387085 time to delete rle : 0.0035178661346435547 batch 1 Loaded 75 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21255 TO DO : save crop sub photo not yet done ! save time : 2.0620810985565186 nb_obj : 17 nb_hashtags : 4 time to prepare the origin masks : 8.599098682403564 time for calcul the mask position with numpy : 0.30504274368286133 nb_pixel_total : 6056564 time to create 1 rle with new method : 0.5357491970062256 time for calcul the mask position with numpy : 0.034380197525024414 nb_pixel_total : 92078 time to create 1 rle with old method : 0.10653090476989746 time for calcul the mask position with numpy : 0.041892051696777344 nb_pixel_total : 6677 time to create 1 rle with old method : 0.007904767990112305 time for calcul the mask position with numpy : 0.02184009552001953 nb_pixel_total : 8364 time to create 1 rle with old method : 0.009378194808959961 time for calcul the mask position with numpy : 0.02120208740234375 nb_pixel_total : 33063 time to create 1 rle with old method : 0.0398867130279541 time for calcul the mask position with numpy : 0.022695302963256836 nb_pixel_total : 10728 time to create 1 rle with old method : 0.017766475677490234 time for calcul the mask position with numpy : 0.022881031036376953 nb_pixel_total : 6393 time to create 1 rle with old method : 0.00895071029663086 time for calcul the mask position with numpy : 0.02311849594116211 nb_pixel_total : 271378 time to create 1 rle with new method : 0.514880895614624 time for calcul the mask position with numpy : 0.021659374237060547 nb_pixel_total : 25277 time to create 1 rle with old method : 0.028171062469482422 time for calcul the mask position with numpy : 0.02202320098876953 nb_pixel_total : 45953 time to create 1 rle with old method : 0.05126953125 time for calcul the mask position with numpy : 0.022641658782958984 nb_pixel_total : 58106 time to create 1 rle with old method : 0.08597350120544434 time for calcul the mask position with numpy : 0.021917104721069336 nb_pixel_total : 45630 time to create 1 rle with old method : 0.05053377151489258 time for calcul the mask position with numpy : 0.023702621459960938 nb_pixel_total : 309560 time to create 1 rle with new method : 0.6144590377807617 time for calcul the mask position with numpy : 0.02719855308532715 nb_pixel_total : 17637 time to create 1 rle with old method : 0.019731998443603516 time for calcul the mask position with numpy : 0.025995969772338867 nb_pixel_total : 18553 time to create 1 rle with old method : 0.020839452743530273 time for calcul the mask position with numpy : 0.026418447494506836 nb_pixel_total : 11459 time to create 1 rle with old method : 0.012886524200439453 time for calcul the mask position with numpy : 0.026212692260742188 nb_pixel_total : 18261 time to create 1 rle with old method : 0.020274877548217773 time for calcul the mask position with numpy : 0.027209758758544922 nb_pixel_total : 14559 time to create 1 rle with old method : 0.01620173454284668 create new chi : 2.9868640899658203 time to delete rle : 0.0023941993713378906 batch 1 Loaded 35 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 9523 TO DO : save crop sub photo not yet done ! save time : 0.8156938552856445 nb_obj : 25 nb_hashtags : 3 time to prepare the origin masks : 3.9459192752838135 time for calcul the mask position with numpy : 0.2971038818359375 nb_pixel_total : 6153423 time to create 1 rle with new method : 0.8966515064239502 time for calcul the mask position with numpy : 0.0335996150970459 nb_pixel_total : 13328 time to create 1 rle with old method : 0.022026777267456055 time for calcul the mask position with numpy : 0.03303337097167969 nb_pixel_total : 25231 time to create 1 rle with old method : 0.040611982345581055 time for calcul the mask position with numpy : 0.030172109603881836 nb_pixel_total : 31621 time to create 1 rle with old method : 0.03565239906311035 time for calcul the mask position with numpy : 0.029712915420532227 nb_pixel_total : 894 time to create 1 rle with old method : 0.0011262893676757812 time for calcul the mask position with numpy : 0.02957606315612793 nb_pixel_total : 9556 time to create 1 rle with old method : 0.011005401611328125 time for calcul the mask position with numpy : 0.029913663864135742 nb_pixel_total : 14638 time to create 1 rle with old method : 0.016600608825683594 time for calcul the mask position with numpy : 0.029503822326660156 nb_pixel_total : 18547 time to create 1 rle with old method : 0.020666837692260742 time for calcul the mask position with numpy : 0.029493093490600586 nb_pixel_total : 16227 time to create 1 rle with old method : 0.018395185470581055 time for calcul the mask position with numpy : 0.02966451644897461 nb_pixel_total : 14344 time to create 1 rle with old method : 0.01607537269592285 time for calcul the mask position with numpy : 0.02954244613647461 nb_pixel_total : 16212 time to create 1 rle with old method : 0.01838994026184082 time for calcul the mask position with numpy : 0.029221057891845703 nb_pixel_total : 17169 time to create 1 rle with old method : 0.019235849380493164 time for calcul the mask position with numpy : 0.029562950134277344 nb_pixel_total : 24610 time to create 1 rle with old method : 0.027543306350708008 time for calcul the mask position with numpy : 0.03133344650268555 nb_pixel_total : 218388 time to create 1 rle with new method : 0.7127490043640137 time for calcul the mask position with numpy : 0.02902054786682129 nb_pixel_total : 12773 time to create 1 rle with old method : 0.014252424240112305 time for calcul the mask position with numpy : 0.02890324592590332 nb_pixel_total : 20905 time to create 1 rle with old method : 0.023350238800048828 time for calcul the mask position with numpy : 0.02906179428100586 nb_pixel_total : 47211 time to create 1 rle with old method : 0.05267071723937988 time for calcul the mask position with numpy : 0.03130340576171875 nb_pixel_total : 218817 time to create 1 rle with new method : 0.45578551292419434 time for calcul the mask position with numpy : 0.030986785888671875 nb_pixel_total : 14810 time to create 1 rle with old method : 0.01969742774963379 time for calcul the mask position with numpy : 0.03147745132446289 nb_pixel_total : 53036 time to create 1 rle with old method : 0.0676414966583252 time for calcul the mask position with numpy : 0.031162500381469727 nb_pixel_total : 11375 time to create 1 rle with old method : 0.015404224395751953 time for calcul the mask position with numpy : 0.031220197677612305 nb_pixel_total : 14419 time to create 1 rle with old method : 0.021860122680664062 time for calcul the mask position with numpy : 0.028956890106201172 nb_pixel_total : 16402 time to create 1 rle with old method : 0.019386768341064453 time for calcul the mask position with numpy : 0.031020164489746094 nb_pixel_total : 24331 time to create 1 rle with old method : 0.029603004455566406 time for calcul the mask position with numpy : 0.030276060104370117 nb_pixel_total : 26384 time to create 1 rle with old method : 0.03223729133605957 time for calcul the mask position with numpy : 0.030142545700073242 nb_pixel_total : 15589 time to create 1 rle with old method : 0.019453763961791992 create new chi : 3.7712836265563965 time to delete rle : 0.0029871463775634766 batch 1 Loaded 53 chid ids of type : 3594 +++++++++++++++++++++++++++++++Number RLEs to save : 16393 TO DO : save crop sub photo not yet done ! save time : 1.1991968154907227 nb_obj : 15 nb_hashtags : 2 time to prepare the origin masks : 3.7995517253875732 time for calcul the mask position with numpy : 0.9415159225463867 nb_pixel_total : 6420342 time to create 1 rle with new method : 0.4726557731628418 time for calcul the mask position with numpy : 0.02973031997680664 nb_pixel_total : 5819 time to create 1 rle with old method : 0.006644010543823242 time for calcul the mask position with numpy : 0.03376960754394531 nb_pixel_total : 262776 time to create 1 rle with new method : 0.4835052490234375 time for calcul the mask position with numpy : 0.03065323829650879 nb_pixel_total : 18572 time to create 1 rle with old method : 0.021128177642822266 time for calcul the mask position with numpy : 0.03025341033935547 nb_pixel_total : 34945 time to create 1 rle with old method : 0.03892207145690918 time for calcul the mask position with numpy : 0.03009939193725586 nb_pixel_total : 1673 time to create 1 rle with old method : 0.0020210742950439453 time for calcul the mask position with numpy : 0.029989957809448242 nb_pixel_total : 9858 time to create 1 rle with old method : 0.01141357421875 time for calcul the mask position with numpy : 0.030342817306518555 nb_pixel_total : 11006 time to create 1 rle with old method : 0.012440204620361328 time for calcul the mask position with numpy : 0.030142784118652344 nb_pixel_total : 6471 time to create 1 rle with old method : 0.00739288330078125 time for calcul the mask position with numpy : 0.030219316482543945 nb_pixel_total : 6741 time to create 1 rle with old method : 0.007815361022949219 time for calcul the mask position with numpy : 0.030488252639770508 nb_pixel_total : 51573 time to create 1 rle with old method : 0.06341099739074707 time for calcul the mask position with numpy : 0.03418755531311035 nb_pixel_total : 27647 time to create 1 rle with old method : 0.03531646728515625 time for calcul the mask position with numpy : 0.04246664047241211 nb_pixel_total : 13100 time to create 1 rle with old method : 0.018157958984375 time for calcul the mask position with numpy : 0.035181522369384766 nb_pixel_total : 75449 time to create 1 rle with old method : 0.08910012245178223 time for calcul the mask position with numpy : 0.03161358833312988 nb_pixel_total : 95148 time to create 1 rle with old method : 0.11039876937866211 time for calcul the mask position with numpy : 0.030765771865844727 nb_pixel_total : 9120 time to create 1 rle with old method : 0.010358095169067383 create new chi : 2.895955801010132 time to delete rle : 0.0033485889434814453 batch 1 Loaded 31 chid ids of type : 3594 ++++++++++++++++++++Number RLEs to save : 9194 TO DO : save crop sub photo not yet done ! save time : 0.7195150852203369 map_output_result : {1349157421: (0.0, 'Should be the crop_list due to order', 0), 1349157390: (0.0, 'Should be the crop_list due to order', 0), 1349012795: (0.0, 'Should be the crop_list due to order', 0), 1349012792: (0.0, 'Should be the crop_list due to order', 0), 1349012787: (0.0, 'Should be the crop_list due to order', 0), 1349012783: (0.0, 'Should be the crop_list due to order', 0), 1349012779: (0.0, 'Should be the crop_list due to order', 0), 1349012747: (0.0, 'Should be the crop_list due to order', 0), 1349012680: (0.0, 'Should be the crop_list due to order', 0), 1349012676: (0.0, 'Should be the crop_list due to order', 0), 1349012671: (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 [1349157421, 1349157390, 1349012795, 1349012792, 1349012787, 1349012783, 1349012779, 1349012747, 1349012680, 1349012676, 1349012671] Looping around the photos to save general results len do output : 11 /1349157421.Didn't retrieve data . /1349157390.Didn't retrieve data . /1349012795.Didn't retrieve data . /1349012792.Didn't retrieve data . /1349012787.Didn't retrieve data . /1349012783.Didn't retrieve data . /1349012779.Didn't retrieve data . /1349012747.Didn't retrieve data . /1349012680.Didn't retrieve data . /1349012676.Didn't retrieve data . /1349012671.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, '2711237') ('3318', '21930836', '1349157421', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349157390', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012795', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012792', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012787', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012783', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012779', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012747', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012680', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012676', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012671', None, None, None, None, None, '2711237') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.014536142349243164 save_final save missing photos in datou_result : time spend for datou_step_exec : 130.10718250274658 time spend to save output : 0.023751497268676758 total time spend for step 3 : 130.13093400001526 step4:ventilate_hashtags_in_portfolio Tue Apr 1 02:32:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 21930836 get user id for portfolio 21930836 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`=21930836 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('mal_croppe','pehd','background','papier','flou','carton','pet_fonce','pet_clair','metal','environnement','autre')) AND mptpi.`min_score`=0.5 To do To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21930836 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('mal_croppe','pehd','background','papier','flou','carton','pet_fonce','pet_clair','metal','environnement','autre')) AND mptpi.`min_score`=0.5 To do Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") Catched exception ! Connect or reconnect ! (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ')\n and cspi.crop_hashtag_id = chi.id' at line 3") To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21930836 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('mal_croppe','pehd','background','papier','flou','carton','pet_fonce','pet_clair','metal','environnement','autre')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21931353,21931354,21931355,21931356,21931357,21931358,21931359,21931360,21931361,21931362,21931363?tags=mal_croppe,pehd,background,papier,flou,carton,pet_fonce,pet_clair,metal,environnement,autre Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349157421, 1349157390, 1349012795, 1349012792, 1349012787, 1349012783, 1349012779, 1349012747, 1349012680, 1349012676, 1349012671] Looping around the photos to save general results len do output : 1 /21930836. 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, '2711237') ('3318', '21930836', '1349157421', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349157390', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012795', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012792', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012787', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012783', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012779', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012747', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012680', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012676', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012671', None, None, None, None, None, '2711237') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.01912379264831543 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.9153892993927002 time spend to save output : 0.019434452056884766 total time spend for step 4 : 1.934823751449585 step5:final Tue Apr 1 02:32:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : {1349157421: ('0.18642559428022043',), 1349157390: ('0.18642559428022043',), 1349012795: ('0.18642559428022043',), 1349012792: ('0.18642559428022043',), 1349012787: ('0.18642559428022043',), 1349012783: ('0.18642559428022043',), 1349012779: ('0.18642559428022043',), 1349012747: ('0.18642559428022043',), 1349012680: ('0.18642559428022043',), 1349012676: ('0.18642559428022043',), 1349012671: ('0.18642559428022043',)} new output for save of step final : {1349157421: ('0.18642559428022043',), 1349157390: ('0.18642559428022043',), 1349012795: ('0.18642559428022043',), 1349012792: ('0.18642559428022043',), 1349012787: ('0.18642559428022043',), 1349012783: ('0.18642559428022043',), 1349012779: ('0.18642559428022043',), 1349012747: ('0.18642559428022043',), 1349012680: ('0.18642559428022043',), 1349012676: ('0.18642559428022043',), 1349012671: ('0.18642559428022043',)} [1349157421, 1349157390, 1349012795, 1349012792, 1349012787, 1349012783, 1349012779, 1349012747, 1349012680, 1349012676, 1349012671] Looping around the photos to save general results len do output : 11 /1349157421.Didn't retrieve data . /1349157390.Didn't retrieve data . /1349012795.Didn't retrieve data . /1349012792.Didn't retrieve data . /1349012787.Didn't retrieve data . /1349012783.Didn't retrieve data . /1349012779.Didn't retrieve data . /1349012747.Didn't retrieve data . /1349012680.Didn't retrieve data . /1349012676.Didn't retrieve data . /1349012671.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, '2711237') ('3318', '21930836', '1349157421', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349157390', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012795', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012792', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012787', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012783', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012779', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012747', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012680', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012676', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012671', None, None, None, None, None, '2711237') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.012989282608032227 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.12675833702087402 time spend to save output : 0.013865947723388672 total time spend for step 5 : 0.1406242847442627 step6:blur_detection Tue Apr 1 02:32:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c.jpg resize: (2160, 3264) 1349157421 -5.126019504808712 treat image : temp/1743466834_2412904_1349157390_93864601456a8aee05173040e2312fdc.jpg resize: (2160, 3264) 1349157390 -4.021760297849459 treat image : temp/1743466834_2412904_1349012795_9614adf6a5fa7f5d29444ba78c6d8e34.jpg resize: (2160, 3264) 1349012795 -5.029884370180184 treat image : temp/1743466834_2412904_1349012792_5a52ce5f2e85f2d1fdb68f1d4c9f2ff6.jpg resize: (2160, 3264) 1349012792 -5.378815904029964 treat image : temp/1743466834_2412904_1349012787_5fadcab5f42431cde5caeb2f47d1bd04.jpg resize: (2160, 3264) 1349012787 -4.3001941220243225 treat image : temp/1743466834_2412904_1349012783_b8913d0bfd97a5d91efe2f98787288fb.jpg resize: (2160, 3264) 1349012783 -1.9189986320711054 treat image : temp/1743466834_2412904_1349012779_d85fa12dd96b992f88a2973faad6abaa.jpg resize: (2160, 3264) 1349012779 -2.3259210234762726 treat image : temp/1743466834_2412904_1349012747_b26b37ef3f86e3bd642246f21ded1cc5.jpg resize: (2160, 3264) 1349012747 -0.7442065133178921 treat image : temp/1743466834_2412904_1349012680_254762dbefdcebaa6be11d1bb51a03b8.jpg resize: (2160, 3264) 1349012680 -2.5040626135330446 treat image : temp/1743466834_2412904_1349012676_540749ad8db537e55378f4e25ce69871.jpg resize: (2160, 3264) 1349012676 -5.0119020261258855 treat image : temp/1743466834_2412904_1349012671_6c769d892f0ca707a96a3b91c1a546b0.jpg resize: (2160, 3264) 1349012671 -1.950123087416525 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146342_0.png resize: (294, 214) 1349177060 -2.8106677788856507 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146343_0.png resize: (496, 319) 1349177062 -3.188904387557492 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146353_0.png resize: (302, 120) 1349177065 -3.139231873790635 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146347_0.png resize: (442, 430) 1349177067 -3.1908923243121663 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146345_0.png resize: (301, 257) 1349177069 -3.061588150172902 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146340_0.png resize: (207, 258) 1349177071 -3.5743012440224717 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146346_0.png resize: (459, 340) 1349177073 -4.574964792181756 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146339_0.png resize: (168, 177) 1349177075 -4.129046885866254 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146348_0.png resize: (115, 95) 1349177077 -3.7926753612587962 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146338_0.png resize: (181, 76) 1349177079 2.0575615806320044 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146350_0.png resize: (93, 73) 1349177081 -5.322514253984697 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146341_0.png resize: (335, 497) 1349177084 -4.673068401468221 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146344_0.png resize: (567, 554) 1349177085 -1.296619827752757 treat image : temp/1743466834_2412904_1349157421_93896c44318beab7f178f363859ce21c_rle_crop_3742146349_0.png resize: (191, 368) 1349177086 -2.5659678706758595 treat image : temp/1743466834_2412904_1349157390_93864601456a8aee05173040e2312fdc_rle_crop_3742146394_0.png resize: (384, 166) 1349177088 -2.0429857053893032 treat image : temp/1743466834_2412904_1349157390_93864601456a8aee05173040e2312fdc_rle_crop_3742146408_0.png resize: (219, 115) 1349177089 -3.49319619897549 treat image : temp/1743466834_2412904_1349157390_93864601456a8aee05173040e2312fdc_rle_crop_3742146400_0.png resize: (131, 96) 1349177090 -2.790427495787285 treat image : temp/1743466834_2412904_1349157390_93864601456a8aee05173040e2312fdc_rle_crop_3742146372_0.png resize: (1161, 1015) 1349177092 -1.905095099449684 treat image : temp/1743466834_2412904_1349157390_93864601456a8aee05173040e2312fdc_rle_crop_3742146396_0.png resize: (143, 266) 1349177093 -1.7880192443314995 treat image : temp/1743466834_2412904_1349157390_93864601456a8aee05173040e2312fdc_rle_crop_3742146401_0.png resize: (200, 492) 1349177094 -2.361149724512215 treat image : 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Connect or reconnect ! In save_photo_hashtag_id_thcl_score : (1205, 'Lock wait timeout exceeded; try restarting transaction') [('1533', '1349157421', '492609224', '-5.126019504808712'), ('1533', '1349157390', '492609224', '-4.021760297849459'), ('1533', '1349012795', '492609224', '-5.029884370180184'), ('1533', '1349012792', '492609224', '-5.378815904029964'), ('1533', '1349012787', '492609224', '-4.3001941220243225'), ('1533', '1349012783', '492688767', '-1.9189986320711054'), ('1533', '1349012779', '492609224', '-2.3259210234762726'), ('1533', '1349012747', '492688767', '-0.7442065133178921'), ('1533', '1349012680', '492609224', '-2.5040626135330446'), ('1533', '1349012676', '492609224', '-5.0119020261258855'), ('1533', '1349012671', '492688767', '-1.950123087416525'), ('1533', '1349177060', '492609224', '-2.8106677788856507'), ('1533', '1349177062', '492609224', '-3.188904387557492'), ('1533', '1349177065', '492609224', '-3.139231873790635'), ('1533', '1349177067', '492609224', '-3.1908923243121663'), 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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 ! 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save_photo_hashtag_id_thcl_score : 675 time used for this insertion : 0.057404518127441406 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 675 time used for this insertion : 0.13460874557495117 save missing photos in datou_result : time spend for datou_step_exec : 14.414375305175781 time spend to save output : 0.20318150520324707 total time spend for step 7 : 14.617556810379028 step8:velours_tree Tue Apr 1 02:34:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 1.3472421169281006 time spend to save output : 6.175041198730469e-05 total time spend for step 8 : 1.347303867340088 step9:send_mail_cod Tue Apr 1 02:34:33 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_P21930836_01-04-2025_02_34_33.pdf 21931353 imagette219313531743467673 21931354 imagette219313541743467673 21931355 imagette219313551743467673 21931356 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 .imagette219313561743467673 21931357 imagette219313571743467675 21931358 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 .imagette219313581743467675 21931359 change filename to text .change filename to text .change filename to text .imagette219313591743467676 21931360 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 .imagette219313601743467676 21931361 change filename to text .change filename to text .imagette219313611743467678 21931363 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 .imagette219313631743467678 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21930836 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21931353,21931354,21931355,21931356,21931357,21931358,21931359,21931360,21931361,21931362,21931363?tags=mal_croppe,pehd,background,papier,flou,carton,pet_fonce,pet_clair,metal,environnement,autre args[1349157421] : ((1349157421, -5.126019504808712, 492609224), (1349157421, -0.09526916586634533, 496442774), '0.18642559428022043') no score found for photo 1349157421 We are sending mail with results at report@fotonower.com args[1349157390] : ((1349157390, -4.021760297849459, 492609224), (1349157390, -0.1537078771454772, 496442774), '0.18642559428022043') no score found for photo 1349157390 We are sending mail with results at report@fotonower.com args[1349012795] : ((1349012795, -5.029884370180184, 492609224), (1349012795, -0.2163402920072806, 496442774), '0.18642559428022043') We are sending mail with results at report@fotonower.com args[1349012792] : ((1349012792, -5.378815904029964, 492609224), (1349012792, -0.022552909096522005, 2107752395), '0.18642559428022043') We are sending mail with results at report@fotonower.com args[1349012787] : ((1349012787, -4.3001941220243225, 492609224), (1349012787, -0.0958029848520551, 496442774), '0.18642559428022043') We are sending mail with results at report@fotonower.com args[1349012783] : ((1349012783, -1.9189986320711054, 492688767), (1349012783, -0.4372140040998721, 496442774), '0.18642559428022043') We are sending mail with results at report@fotonower.com args[1349012779] : ((1349012779, -2.3259210234762726, 492609224), (1349012779, 0.03512083894071973, 2107752395), '0.18642559428022043') We are sending mail with results at report@fotonower.com args[1349012747] : ((1349012747, -0.7442065133178921, 492688767), (1349012747, -0.3166914872637221, 496442774), '0.18642559428022043') We are sending mail with results at report@fotonower.com args[1349012680] : ((1349012680, -2.5040626135330446, 492609224), (1349012680, 0.06875962037089023, 2107752395), '0.18642559428022043') We are sending mail with results at report@fotonower.com args[1349012676] : ((1349012676, -5.0119020261258855, 492609224), (1349012676, -0.09267666489494163, 496442774), '0.18642559428022043') We are sending mail with results at report@fotonower.com args[1349012671] : ((1349012671, -1.950123087416525, 492688767), (1349012671, -0.14035996483111834, 496442774), '0.18642559428022043') We are sending mail with results at report@fotonower.com refus_total : 0.18642559428022043 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=21930836 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349012792,1349012795,1349012676,1349012671,1349012680,1349012747,1349012779,1349012783,1349012787,1349157390,1349157421) Found this number of photos: 11 begin to download photo : 1349012792 begin to download photo : 1349012671 begin to download photo : 1349012779 begin to download photo : 1349157390 download finish for photo 1349012671 begin to download photo : 1349012680 download finish for photo 1349157390 begin to download photo : 1349157421 download finish for photo 1349012779 begin to download photo : 1349012783 download finish for photo 1349012792 begin to download photo : 1349012795 download finish for photo 1349012680 begin to download photo : 1349012747 download finish for photo 1349012783 begin to download photo : 1349012787 download finish for photo 1349157421 download finish for photo 1349012795 begin to download photo : 1349012676 download finish for photo 1349012747 download finish for photo 1349012787 download finish for photo 1349012676 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930836_01-04-2025_02_34_33.pdf results_Auto_P21930836_01-04-2025_02_34_33.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930836_01-04-2025_02_34_33.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','21930836','results_Auto_P21930836_01-04-2025_02_34_33.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930836_01-04-2025_02_34_33.pdf','pdf','','0.85','0.18642559428022043') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21930836

https://www.fotonower.com/image?json=false&list_photos_id=1349012795
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012792
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012787
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012783
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012779
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012747
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012680
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012676
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349012671
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 18.64%
Veuillez trouver les photos des contaminants.

exemples de contaminants: papier: https://www.fotonower.com/view/21931356?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/21931358?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/21931359?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21931360?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21931361?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21931363?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930836_01-04-2025_02_34_33.pdf.

Lien vers velours :https://www.fotonower.com/velours/21931353,21931354,21931355,21931356,21931357,21931358,21931359,21931360,21931361,21931362,21931363?tags=mal_croppe,pehd,background,papier,flou,carton,pet_fonce,pet_clair,metal,environnement,autre.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 01 Apr 2025 00:34:43 GMT Content-Length: 0 Connection: close X-Message-Id: oGUa63TcRlO6VcaVC6biUw 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 [1349157421, 1349157390, 1349012795, 1349012792, 1349012787, 1349012783, 1349012779, 1349012747, 1349012680, 1349012676, 1349012671] 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, '2711237') ('3318', '21930836', '1349157421', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349157390', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012795', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012792', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012787', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012783', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012779', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012747', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012680', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012676', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012671', None, None, None, None, None, '2711237') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.01417994499206543 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.33751392364502 time spend to save output : 0.014728784561157227 total time spend for step 9 : 10.352242708206177 step10:split_time_score Tue Apr 1 02:34:43 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'}] (('14', 11),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 31032025 21930836 Nombre de photos uploadées : 11 / 23040 (0%) 31032025 21930836 Nombre de photos taguées (types de déchets): 0 / 11 (0%) 31032025 21930836 Nombre de photos taguées (volume) : 0 / 11 (0%) elapsed_time : load_data_split_time_score 1.430511474609375e-06 elapsed_time : order_list_meta_photo_and_scores 5.0067901611328125e-06 ??????????? elapsed_time : fill_and_build_computed_from_old_data 0.00042939186096191406 elapsed_time : insert_dashboard_record_day_entry 0.023349523544311523 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 : 0.14630428184005087 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925661_31-03-2025_22_59_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925661 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`=21925661 AND mptpi.`type`=3594 To do Qualite : 0.14145216719895026 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925662_31-03-2025_22_51_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925662 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`=21925662 AND mptpi.`type`=3594 To do Qualite : 0.0939584877250109 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21905169_31-03-2025_11_54_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21905169 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`=21905169 AND mptpi.`type`=3726 To do Qualite : 0.24895207748060239 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929800_01-04-2025_02_11_03.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929800 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`=21929800 AND mptpi.`type`=3594 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929818 order by id desc limit 1 Qualite : 0.083542372459225 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930826_01-04-2025_02_21_48.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930826 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`=21930826 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930834 order by id desc limit 1 Qualite : 0.18642559428022043 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21930836_01-04-2025_02_34_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21930836 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`=21930836 AND mptpi.`type`=3594 To do Qualite : 0.2305784432354592 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929822_01-04-2025_01_34_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929822 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`=21929822 AND mptpi.`type`=3594 To do Qualite : 0.06655992376993317 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929825_01-04-2025_01_30_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929825 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`=21929825 AND mptpi.`type`=3726 To do Qualite : 0.22924124322132222 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21926965_31-03-2025_23_29_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21926965 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`=21926965 AND mptpi.`type`=3594 To do Qualite : 0.2189041275706411 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925669_31-03-2025_22_45_27.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925669 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`=21925669 AND mptpi.`type`=3594 To do Qualite : 0.18545522031874095 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925670_31-03-2025_22_36_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925670 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`=21925670 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'31032025': {'nb_upload': 11, '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 [1349157421, 1349157390, 1349012795, 1349012792, 1349012787, 1349012783, 1349012779, 1349012747, 1349012680, 1349012676, 1349012671] Looping around the photos to save general results len do output : 1 /21930836Didn'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, '2711237') ('3318', '21930836', '1349157421', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349157390', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012795', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012792', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012787', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012783', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012779', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012747', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012680', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012676', None, None, None, None, None, '2711237') ('3318', None, None, None, None, None, None, None, '2711237') ('3318', '21930836', '1349012671', None, None, None, None, None, '2711237') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.10004091262817383 save_final save missing photos in datou_result : time spend for datou_step_exec : 4.558379411697388 time spend to save output : 0.10031342506408691 total time spend for step 10 : 4.658692836761475 caffe_path_current : /home/admin/workarea/git/Velours/python/mtr/datou/detect_blur_image.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice variance = laplacian[10:(x-10),10:(y-10)].var() /home/admin/.local/lib/python3.8/site-packages/numpy/core/_methods.py:222: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide(arrmean, div, out=arrmean, casting='unsafe', /home/admin/.local/lib/python3.8/site-packages/numpy/core/_methods.py:254: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 11 set_done_treatment 304.10user 157.83system 14:23.42elapsed 53%CPU (0avgtext+0avgdata 5476428maxresident)k 12502248inputs+217632outputs (372125major+25874674minor)pagefaults 0swaps