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 : 1162099 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 : ['2723399'] with mtr_portfolio_ids : ['22049547'] and first list_photo_ids : [] new path : /proc/1162099/ Inside batchDatouExec : verbose : 0 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : mask_detect, crop_condition, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, velours_tree, send_mail_cod, split_time_score over limit max, limiting to limit_max 40 list_input_json : [] origin We have 1 , BFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 20 ; length of list_pids : 20 ; length of list_args : 20 time to download the photos : 2.926326274871826 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : 0 number of steps : 10 step1:mask_detect Tue Apr 15 21:00:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 7035 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-15 21:00:38.294859: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-15 21:00:38.335090: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-15 21:00:38.337461: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f4f5c000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-15 21:00:38.337521: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-15 21:00:38.341381: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-15 21:00:38.617326: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x379c2390 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-15 21:00:38.617413: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-15 21:00:38.618993: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-15 21:00:38.619948: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-15 21:00:38.627635: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-15 21:00:38.640142: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-15 21:00:38.642071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-15 21:00:38.650181: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-15 21:00:38.653982: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-15 21:00:38.673091: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-15 21:00:38.674743: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-15 21:00:38.674853: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-15 21:00:38.675757: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-15 21:00:38.675780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-15 21:00:38.675793: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-15 21:00:38.678241: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6470 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-15 21:00:39.107103: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-15 21:00:39.107197: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-15 21:00:39.107224: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-15 21:00:39.107247: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-15 21:00:39.107265: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-15 21:00:39.107283: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-15 21:00:39.107301: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-15 21:00:39.107321: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-15 21:00:39.108939: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-15 21:00:39.111725: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-15 21:00:39.111786: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-15 21:00:39.111806: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-15 21:00:39.111822: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-15 21:00:39.111839: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-15 21:00:39.111856: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-15 21:00:39.111873: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-15 21:00:39.111889: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-15 21:00:39.117120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-15 21:00:39.117172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-15 21:00:39.117190: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-15 21:00:39.117206: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-15 21:00:39.119134: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6470 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-15 21:00:50.751760: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-15 21:00:51.091010: 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 : 20 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 : 44 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 : 22 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 : 17 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 6.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 15 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 16.00000 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 : 28 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 : 52 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 : 59 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 15.00000 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 : 13 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 5.00000 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 : 29 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 18.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 23 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 1.00000 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 : 35 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 : 48 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 64 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 52 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 : 39 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 : 67 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 : 58 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 : 68 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 1.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 21 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 5.00000 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 : 36 Detection mask done ! Trying to reset tf kernel 1163111 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1746 tf kernel not reseted sub process len(results) : 20 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 20 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 : 7035 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 : 0.04744601249694824 nb_pixel_total : 14653 time to create 1 rle with old method : 0.0221254825592041 length of segment : 140 time for calcul the mask position with numpy : 0.00970315933227539 nb_pixel_total : 6132 time to create 1 rle with old method : 0.00942850112915039 length of segment : 79 time for calcul the mask position with numpy : 0.004692792892456055 nb_pixel_total : 21364 time to create 1 rle with old method : 0.02755594253540039 length of segment : 218 time for calcul the mask position with numpy : 0.06478476524353027 nb_pixel_total : 36864 time to create 1 rle with old method : 0.05777597427368164 length of segment : 255 time for calcul the mask position with numpy : 0.03214383125305176 nb_pixel_total : 12319 time to create 1 rle with old method : 0.019730567932128906 length of segment : 160 time for calcul the mask position with numpy : 0.008957862854003906 nb_pixel_total : 8954 time to create 1 rle with old method : 0.01572394371032715 length of segment : 105 time for calcul the mask position with numpy : 0.006089925765991211 nb_pixel_total : 20726 time to create 1 rle with old method : 0.02687692642211914 length of segment : 288 time for calcul the mask position with numpy : 0.0064547061920166016 nb_pixel_total : 24478 time to create 1 rle with old method : 0.03330111503601074 length of segment : 163 time for calcul the mask position with numpy : 0.054514408111572266 nb_pixel_total : 25478 time to create 1 rle with old method : 0.0346677303314209 length of segment : 181 time for calcul the mask position with numpy : 0.006979465484619141 nb_pixel_total : 26085 time to create 1 rle with old method : 0.03983116149902344 length of segment : 243 time for calcul the mask position with numpy : 0.031255245208740234 nb_pixel_total : 24563 time to create 1 rle with old method : 0.033841609954833984 length of segment : 221 time for calcul the mask position with numpy : 0.003422260284423828 nb_pixel_total : 4869 time to create 1 rle with old method : 0.005677223205566406 length of segment : 177 time for calcul the mask position with numpy : 0.0015196800231933594 nb_pixel_total : 14873 time to create 1 rle with old method : 0.01783275604248047 length of segment : 203 time for calcul the mask position with numpy : 0.001352548599243164 nb_pixel_total : 31678 time to create 1 rle with old method : 0.03968095779418945 length of segment : 213 time for calcul the mask position with numpy : 0.013943672180175781 nb_pixel_total : 10685 time to create 1 rle with old method : 0.01691460609436035 length of segment : 116 time for calcul the mask position with numpy : 0.0012826919555664062 nb_pixel_total : 9771 time to create 1 rle with old method : 0.014892816543579102 length of segment : 111 time for calcul the mask position with numpy : 0.08503508567810059 nb_pixel_total : 42814 time to create 1 rle with old method : 0.0748288631439209 length of segment : 336 time for calcul the mask position with numpy : 0.020737886428833008 nb_pixel_total : 86464 time to create 1 rle with old method : 0.10344338417053223 length of segment : 378 time for calcul the mask position with numpy : 0.09586548805236816 nb_pixel_total : 41944 time to create 1 rle with old method : 0.056244850158691406 length of segment : 301 time for calcul the mask position with numpy : 0.5453603267669678 nb_pixel_total : 457750 time to create 1 rle with new method : 0.04550290107727051 length of segment : 1249 time for calcul the mask position with numpy : 0.02452850341796875 nb_pixel_total : 34326 time to create 1 rle with old method : 0.046076297760009766 length of segment : 205 time for calcul the mask position with numpy : 0.08336019515991211 nb_pixel_total : 94599 time to create 1 rle with old method : 0.12329220771789551 length of segment : 515 time for calcul the mask position with numpy : 0.08663129806518555 nb_pixel_total : 91277 time to create 1 rle with old method : 0.14612293243408203 length of segment : 341 time for calcul the mask position with numpy : 0.004667520523071289 nb_pixel_total : 89423 time to create 1 rle with old method : 0.11354708671569824 length of segment : 415 time for calcul the mask position with numpy : 0.03631162643432617 nb_pixel_total : 41849 time to create 1 rle with old method : 0.053044795989990234 length of segment : 292 time for calcul the mask position with numpy : 0.05363965034484863 nb_pixel_total : 36334 time to create 1 rle with old method : 0.047811031341552734 length of segment : 248 time for calcul the mask position with numpy : 0.16901922225952148 nb_pixel_total : 195088 time to create 1 rle with new method : 0.009957075119018555 length of segment : 708 time for calcul the mask position with numpy : 0.4297823905944824 nb_pixel_total : 533620 time to create 1 rle with new method : 0.12739872932434082 length of segment : 1100 time for calcul the mask position with numpy : 0.06926441192626953 nb_pixel_total : 38222 time to create 1 rle with old method : 0.048438072204589844 length of segment : 306 time for calcul the mask position with numpy : 0.03203439712524414 nb_pixel_total : 27932 time to create 1 rle with old method : 0.03800320625305176 length of segment : 292 time for calcul the mask position with numpy : 0.008269309997558594 nb_pixel_total : 16507 time to create 1 rle with old method : 0.022433042526245117 length of segment : 192 time for calcul the mask position with numpy : 0.009017229080200195 nb_pixel_total : 18027 time to create 1 rle with old method : 0.024707794189453125 length of segment : 139 time for calcul the mask position with numpy : 0.015375137329101562 nb_pixel_total : 10161 time to create 1 rle with old method : 0.01791238784790039 length of segment : 101 time for calcul the mask position with numpy : 0.007935762405395508 nb_pixel_total : 9040 time to create 1 rle with old method : 0.0233309268951416 length of segment : 128 time for calcul the mask position with numpy : 0.19576597213745117 nb_pixel_total : 272039 time to create 1 rle with new method : 0.014017581939697266 length of segment : 537 time for calcul the mask position with numpy : 0.048941612243652344 nb_pixel_total : 79947 time to create 1 rle with old method : 0.10005354881286621 length of segment : 425 time for calcul the mask position with numpy : 0.018386125564575195 nb_pixel_total : 50245 time to create 1 rle with old method : 0.06283688545227051 length of segment : 239 time for calcul the mask position with numpy : 0.020125389099121094 nb_pixel_total : 85928 time to create 1 rle with old method : 0.09982728958129883 length of segment : 428 time for calcul the mask position with numpy : 0.017377376556396484 nb_pixel_total : 41228 time to create 1 rle with old method : 0.049884796142578125 length of segment : 293 time for calcul the mask position with numpy : 0.3537716865539551 nb_pixel_total : 834815 time to create 1 rle with new method : 0.3603193759918213 length of segment : 1150 time for calcul the mask position with numpy : 0.004697322845458984 nb_pixel_total : 18430 time to create 1 rle with old method : 0.03379988670349121 length of segment : 116 time for calcul the mask position with numpy : 0.011769771575927734 nb_pixel_total : 40791 time to create 1 rle with old method : 0.05129432678222656 length of segment : 373 time for calcul the mask position with numpy : 0.0002646446228027344 nb_pixel_total : 11949 time to create 1 rle with old method : 0.014703512191772461 length of segment : 206 time for calcul the mask position with numpy : 0.02383875846862793 nb_pixel_total : 59602 time to create 1 rle with old method : 0.08260869979858398 length of segment : 374 time for calcul the mask position with numpy : 0.004779815673828125 nb_pixel_total : 12387 time to create 1 rle with old method : 0.021747112274169922 length of segment : 184 time for calcul the mask position with numpy : 0.004253864288330078 nb_pixel_total : 11773 time to create 1 rle with old method : 0.016597509384155273 length of segment : 127 time for calcul the mask position with numpy : 0.009926319122314453 nb_pixel_total : 28143 time to create 1 rle with old method : 0.050421714782714844 length of segment : 180 time for calcul the mask position with numpy : 0.0007987022399902344 nb_pixel_total : 51714 time to create 1 rle with old method : 0.06360840797424316 length of segment : 430 time for calcul the mask position with numpy : 0.0004870891571044922 nb_pixel_total : 24450 time to create 1 rle with old method : 0.03190445899963379 length of segment : 187 time for calcul the mask position with numpy : 0.00019478797912597656 nb_pixel_total : 9724 time to create 1 rle with old method : 0.01177358627319336 length of segment : 123 time for calcul the mask position with numpy : 0.0006320476531982422 nb_pixel_total : 36645 time to create 1 rle with old method : 0.044054269790649414 length of segment : 218 time for calcul the mask position with numpy : 0.0022077560424804688 nb_pixel_total : 54048 time to create 1 rle with old method : 0.06767988204956055 length of segment : 313 time for calcul the mask position with numpy : 0.000507354736328125 nb_pixel_total : 24814 time to create 1 rle with old method : 0.029773235321044922 length of segment : 168 time for calcul the mask position with numpy : 0.0017638206481933594 nb_pixel_total : 95451 time to create 1 rle with old method : 0.11404275894165039 length of segment : 633 time for calcul the mask position with numpy : 0.00021195411682128906 nb_pixel_total : 11694 time to create 1 rle with old method : 0.014130115509033203 length of segment : 109 time for calcul the mask position with numpy : 0.00026726722717285156 nb_pixel_total : 8163 time to create 1 rle with old method : 0.010829687118530273 length of segment : 137 time for calcul the mask position with numpy : 0.006946563720703125 nb_pixel_total : 464179 time to create 1 rle with new method : 0.027600765228271484 length of segment : 758 time for calcul the mask position with numpy : 0.0009238719940185547 nb_pixel_total : 20662 time to create 1 rle with old method : 0.04562807083129883 length of segment : 169 time for calcul the mask position with numpy : 0.0002410411834716797 nb_pixel_total : 9918 time to create 1 rle with old method : 0.014439821243286133 length of segment : 140 time for calcul the mask position with numpy : 0.0019447803497314453 nb_pixel_total : 36424 time to create 1 rle with old method : 0.04370903968811035 length of segment : 178 time for calcul the mask position with numpy : 0.05508828163146973 nb_pixel_total : 45348 time to create 1 rle with old method : 0.05845212936401367 length of segment : 436 time for calcul the mask position with numpy : 0.038558244705200195 nb_pixel_total : 71302 time to create 1 rle with old method : 0.09045124053955078 length of segment : 493 time for calcul the mask position with numpy : 0.1389763355255127 nb_pixel_total : 195223 time to create 1 rle with new method : 0.012321710586547852 length of segment : 270 time for calcul the mask position with numpy : 0.054561614990234375 nb_pixel_total : 66224 time to create 1 rle with old method : 0.12613487243652344 length of segment : 317 time for calcul the mask position with numpy : 0.02245497703552246 nb_pixel_total : 26131 time to create 1 rle with old method : 0.03476214408874512 length of segment : 207 time for calcul the mask position with numpy : 0.011824369430541992 nb_pixel_total : 12639 time to create 1 rle with old method : 0.020731210708618164 length of segment : 112 time for calcul the mask position with numpy : 0.03009319305419922 nb_pixel_total : 80415 time to create 1 rle with old method : 0.11993408203125 length of segment : 297 time for calcul the mask position with numpy : 0.02640056610107422 nb_pixel_total : 28522 time to create 1 rle with old method : 0.03661775588989258 length of segment : 222 time for calcul the mask position with numpy : 0.030494213104248047 nb_pixel_total : 49445 time to create 1 rle with old method : 0.05785965919494629 length of segment : 295 time for calcul the mask position with numpy : 0.025416851043701172 nb_pixel_total : 23993 time to create 1 rle with old method : 0.0323946475982666 length of segment : 169 time for calcul the mask position with numpy : 0.007569074630737305 nb_pixel_total : 12099 time to create 1 rle with old method : 0.01636791229248047 length of segment : 87 time for calcul the mask position with numpy : 0.10736989974975586 nb_pixel_total : 205269 time to create 1 rle with new method : 0.00893092155456543 length of segment : 599 time for calcul the mask position with numpy : 0.014880180358886719 nb_pixel_total : 28657 time to create 1 rle with old method : 0.04799079895019531 length of segment : 128 time for calcul the mask position with numpy : 0.005535602569580078 nb_pixel_total : 70458 time to create 1 rle with old method : 0.10567283630371094 length of segment : 299 time for calcul the mask position with numpy : 0.022307634353637695 nb_pixel_total : 21107 time to create 1 rle with old method : 0.02896285057067871 length of segment : 190 time for calcul the mask position with numpy : 0.020676612854003906 nb_pixel_total : 20243 time to create 1 rle with old method : 0.030122995376586914 length of segment : 178 time for calcul the mask position with numpy : 0.007308244705200195 nb_pixel_total : 5296 time to create 1 rle with old method : 0.010012626647949219 length of segment : 89 time for calcul the mask position with numpy : 0.0028145313262939453 nb_pixel_total : 40916 time to create 1 rle with old method : 0.05347633361816406 length of segment : 294 time for calcul the mask position with numpy : 0.052736520767211914 nb_pixel_total : 55931 time to create 1 rle with old method : 0.06662988662719727 length of segment : 510 time for calcul the mask position with numpy : 0.011294841766357422 nb_pixel_total : 20737 time to create 1 rle with old method : 0.024156808853149414 length of segment : 206 time for calcul the mask position with numpy : 0.018084049224853516 nb_pixel_total : 27994 time to create 1 rle with old method : 0.03196573257446289 length of segment : 263 time for calcul the mask position with numpy : 0.09108257293701172 nb_pixel_total : 114156 time to create 1 rle with old method : 0.13371038436889648 length of segment : 566 time for calcul the mask position with numpy : 0.035341739654541016 nb_pixel_total : 10707 time to create 1 rle with old method : 0.015755176544189453 length of segment : 126 time for calcul the mask position with numpy : 0.001477956771850586 nb_pixel_total : 14740 time to create 1 rle with old method : 0.018030881881713867 length of segment : 145 time for calcul the mask position with numpy : 0.0034780502319335938 nb_pixel_total : 11595 time to create 1 rle with old method : 0.014094114303588867 length of segment : 167 time for calcul the mask position with numpy : 0.010552644729614258 nb_pixel_total : 9268 time to create 1 rle with old method : 0.016264677047729492 length of segment : 96 time for calcul the mask position with numpy : 0.0021812915802001953 nb_pixel_total : 22973 time to create 1 rle with old method : 0.027495861053466797 length of segment : 172 time for calcul the mask position with numpy : 0.12515568733215332 nb_pixel_total : 57215 time to create 1 rle with old method : 0.07223105430603027 length of segment : 324 time for calcul the mask position with numpy : 0.11376023292541504 nb_pixel_total : 64429 time to create 1 rle with old method : 0.11007571220397949 length of segment : 340 time for calcul the mask position with numpy : 0.011574268341064453 nb_pixel_total : 18310 time to create 1 rle with old method : 0.02765798568725586 length of segment : 213 time for calcul the mask position with numpy : 0.01026606559753418 nb_pixel_total : 15318 time to create 1 rle with old method : 0.023230314254760742 length of segment : 162 time for calcul the mask position with numpy : 0.02384209632873535 nb_pixel_total : 22567 time to create 1 rle with old method : 0.0324549674987793 length of segment : 169 time for calcul the mask position with numpy : 0.03946852684020996 nb_pixel_total : 23262 time to create 1 rle with old method : 0.030874967575073242 length of segment : 305 time for calcul the mask position with numpy : 0.0010187625885009766 nb_pixel_total : 9772 time to create 1 rle with old method : 0.011810541152954102 length of segment : 139 time for calcul the mask position with numpy : 0.40743398666381836 nb_pixel_total : 440075 time to create 1 rle with new method : 0.04327511787414551 length of segment : 762 time for calcul the mask position with numpy : 0.019962310791015625 nb_pixel_total : 47103 time to create 1 rle with old method : 0.0588226318359375 length of segment : 285 time for calcul the mask position with numpy : 0.02474498748779297 nb_pixel_total : 34145 time to create 1 rle with old method : 0.04627084732055664 length of segment : 291 time for calcul the mask position with numpy : 0.0174558162689209 nb_pixel_total : 30374 time to create 1 rle with old method : 0.04001283645629883 length of segment : 301 time for calcul the mask position with numpy : 0.04436993598937988 nb_pixel_total : 79954 time to create 1 rle with old method : 0.11109066009521484 length of segment : 395 time for calcul the mask position with numpy : 0.045351505279541016 nb_pixel_total : 175626 time to create 1 rle with new method : 0.01841282844543457 length of segment : 500 time for calcul the mask position with numpy : 0.0014195442199707031 nb_pixel_total : 27174 time to create 1 rle with old method : 0.03168463706970215 length of segment : 210 time for calcul the mask position with numpy : 0.007132530212402344 nb_pixel_total : 11218 time to create 1 rle with old method : 0.014961957931518555 length of segment : 274 time for calcul the mask position with numpy : 0.008327007293701172 nb_pixel_total : 10877 time to create 1 rle with old method : 0.01778554916381836 length of segment : 142 time for calcul the mask position with numpy : 0.013803482055664062 nb_pixel_total : 15859 time to create 1 rle with old method : 0.023941755294799805 length of segment : 169 time for calcul the mask position with numpy : 0.017385244369506836 nb_pixel_total : 91806 time to create 1 rle with old method : 0.11211133003234863 length of segment : 265 time for calcul the mask position with numpy : 0.00433659553527832 nb_pixel_total : 4527 time to create 1 rle with old method : 0.0068509578704833984 length of segment : 67 time for calcul the mask position with numpy : 0.0009057521820068359 nb_pixel_total : 15741 time to create 1 rle with old method : 0.019507169723510742 length of segment : 101 time for calcul the mask position with numpy : 0.0028760433197021484 nb_pixel_total : 5440 time to create 1 rle with old method : 0.0074977874755859375 length of segment : 93 time for calcul the mask position with numpy : 0.019365787506103516 nb_pixel_total : 27743 time to create 1 rle with old method : 0.042131662368774414 length of segment : 223 time for calcul the mask position with numpy : 0.001210927963256836 nb_pixel_total : 20791 time to create 1 rle with old method : 0.024672746658325195 length of segment : 186 time for calcul the mask position with numpy : 0.0003972053527832031 nb_pixel_total : 6528 time to create 1 rle with old method : 0.007961273193359375 length of segment : 116 time for calcul the mask position with numpy : 0.0010025501251220703 nb_pixel_total : 14124 time to create 1 rle with old method : 0.017126083374023438 length of segment : 141 time for calcul the mask position with numpy : 0.0016644001007080078 nb_pixel_total : 31050 time to create 1 rle with old method : 0.036524057388305664 length of segment : 314 time for calcul the mask position with numpy : 0.002101421356201172 nb_pixel_total : 45503 time to create 1 rle with old method : 0.05391216278076172 length of segment : 222 time for calcul the mask position with numpy : 0.0031714439392089844 nb_pixel_total : 59756 time to create 1 rle with old method : 0.06977534294128418 length of segment : 274 time for calcul the mask position with numpy : 0.0008516311645507812 nb_pixel_total : 11051 time to create 1 rle with old method : 0.01372981071472168 length of segment : 129 time for calcul the mask position with numpy : 0.003119230270385742 nb_pixel_total : 61423 time to create 1 rle with old method : 0.0715031623840332 length of segment : 326 time for calcul the mask position with numpy : 0.0004737377166748047 nb_pixel_total : 7491 time to create 1 rle with old method : 0.009116172790527344 length of segment : 119 time for calcul the mask position with numpy : 0.00379180908203125 nb_pixel_total : 54265 time to create 1 rle with old method : 0.06429529190063477 length of segment : 295 time for calcul the mask position with numpy : 0.000484466552734375 nb_pixel_total : 4858 time to create 1 rle with old method : 0.005928993225097656 length of segment : 101 time for calcul the mask position with numpy : 0.001756429672241211 nb_pixel_total : 40231 time to create 1 rle with old method : 0.047605276107788086 length of segment : 315 time for calcul the mask position with numpy : 0.0005738735198974609 nb_pixel_total : 9837 time to create 1 rle with old method : 0.01197195053100586 length of segment : 107 time for calcul the mask position with numpy : 0.001961231231689453 nb_pixel_total : 29310 time to create 1 rle with old method : 0.04939627647399902 length of segment : 350 time for calcul the mask position with numpy : 0.0006716251373291016 nb_pixel_total : 12898 time to create 1 rle with old method : 0.015467643737792969 length of segment : 200 time for calcul the mask position with numpy : 0.0003666877746582031 nb_pixel_total : 4793 time to create 1 rle with old method : 0.0060689449310302734 length of segment : 72 time for calcul the mask position with numpy : 0.002105236053466797 nb_pixel_total : 53898 time to create 1 rle with old method : 0.06397867202758789 length of segment : 500 time for calcul the mask position with numpy : 0.008479833602905273 nb_pixel_total : 113860 time to create 1 rle with old method : 0.13505148887634277 length of segment : 497 time for calcul the mask position with numpy : 0.00039696693420410156 nb_pixel_total : 4396 time to create 1 rle with old method : 0.005522727966308594 length of segment : 73 time for calcul the mask position with numpy : 0.006337642669677734 nb_pixel_total : 140134 time to create 1 rle with old method : 0.16481709480285645 length of segment : 306 time for calcul the mask position with numpy : 0.0015611648559570312 nb_pixel_total : 27589 time to create 1 rle with old method : 0.033998727798461914 length of segment : 193 time for calcul the mask position with numpy : 0.0011250972747802734 nb_pixel_total : 23295 time to create 1 rle with old method : 0.027986526489257812 length of segment : 189 time for calcul the mask position with numpy : 0.001462697982788086 nb_pixel_total : 19311 time to create 1 rle with old method : 0.0235903263092041 length of segment : 130 time for calcul the mask position with numpy : 0.0015959739685058594 nb_pixel_total : 22991 time to create 1 rle with old method : 0.03220343589782715 length of segment : 315 time for calcul the mask position with numpy : 0.0010824203491210938 nb_pixel_total : 15292 time to create 1 rle with old method : 0.018663883209228516 length of segment : 143 time for calcul the mask position with numpy : 0.0009355545043945312 nb_pixel_total : 12448 time to create 1 rle with old method : 0.015557289123535156 length of segment : 144 time for calcul the mask position with numpy : 0.00045037269592285156 nb_pixel_total : 3734 time to create 1 rle with old method : 0.004853487014770508 length of segment : 74 time for calcul the mask position with numpy : 0.0006566047668457031 nb_pixel_total : 9354 time to create 1 rle with old method : 0.01197052001953125 length of segment : 132 time for calcul the mask position with numpy : 0.0005881786346435547 nb_pixel_total : 15591 time to create 1 rle with old method : 0.019315242767333984 length of segment : 148 time for calcul the mask position with numpy : 0.0020678043365478516 nb_pixel_total : 54683 time to create 1 rle with old method : 0.06572175025939941 length of segment : 347 time for calcul the mask position with numpy : 0.0010640621185302734 nb_pixel_total : 25974 time to create 1 rle with old method : 0.041990041732788086 length of segment : 340 time for calcul the mask position with numpy : 0.0020308494567871094 nb_pixel_total : 46932 time to create 1 rle with old method : 0.054790496826171875 length of segment : 507 time for calcul the mask position with numpy : 0.00027370452880859375 nb_pixel_total : 5447 time to create 1 rle with old method : 0.006813526153564453 length of segment : 70 time for calcul the mask position with numpy : 0.0007090568542480469 nb_pixel_total : 18902 time to create 1 rle with old method : 0.02258014678955078 length of segment : 138 time for calcul the mask position with numpy : 0.0010399818420410156 nb_pixel_total : 29570 time to create 1 rle with old method : 0.03508949279785156 length of segment : 181 time for calcul the mask position with numpy : 0.0020148754119873047 nb_pixel_total : 47404 time to create 1 rle with old method : 0.05629992485046387 length of segment : 408 time for calcul the mask position with numpy : 0.00035953521728515625 nb_pixel_total : 6229 time to create 1 rle with old method : 0.00763702392578125 length of segment : 116 time for calcul the mask position with numpy : 0.0022449493408203125 nb_pixel_total : 44060 time to create 1 rle with old method : 0.07170224189758301 length of segment : 451 time for calcul the mask position with numpy : 0.0007448196411132812 nb_pixel_total : 20156 time to create 1 rle with old method : 0.024325132369995117 length of segment : 143 time for calcul the mask position with numpy : 0.0010025501251220703 nb_pixel_total : 26317 time to create 1 rle with old method : 0.0433039665222168 length of segment : 224 time for calcul the mask position with numpy : 0.0009844303131103516 nb_pixel_total : 21524 time to create 1 rle with old method : 0.025613069534301758 length of segment : 198 time for calcul the mask position with numpy : 0.0003299713134765625 nb_pixel_total : 6657 time to create 1 rle with old method : 0.010046005249023438 length of segment : 80 time for calcul the mask position with numpy : 0.0004992485046386719 nb_pixel_total : 12606 time to create 1 rle with old method : 0.01539158821105957 length of segment : 114 time for calcul the mask position with numpy : 0.0007519721984863281 nb_pixel_total : 19343 time to create 1 rle with old method : 0.022954463958740234 length of segment : 181 time for calcul the mask position with numpy : 0.0009214878082275391 nb_pixel_total : 27249 time to create 1 rle with old method : 0.031760215759277344 length of segment : 373 time for calcul the mask position with numpy : 0.00030517578125 nb_pixel_total : 15858 time to create 1 rle with old method : 0.01906728744506836 length of segment : 203 time for calcul the mask position with numpy : 0.002457141876220703 nb_pixel_total : 57558 time to create 1 rle with old method : 0.06720662117004395 length of segment : 373 time for calcul the mask position with numpy : 0.0008378028869628906 nb_pixel_total : 21303 time to create 1 rle with old method : 0.0250704288482666 length of segment : 271 time for calcul the mask position with numpy : 0.0003509521484375 nb_pixel_total : 10017 time to create 1 rle with old method : 0.012269258499145508 length of segment : 116 time for calcul the mask position with numpy : 0.0004589557647705078 nb_pixel_total : 13691 time to create 1 rle with old method : 0.016670703887939453 length of segment : 129 time for calcul the mask position with numpy : 0.0015439987182617188 nb_pixel_total : 61745 time to create 1 rle with old method : 0.07236552238464355 length of segment : 347 time for calcul the mask position with numpy : 0.0006499290466308594 nb_pixel_total : 17621 time to create 1 rle with old method : 0.021675586700439453 length of segment : 118 time for calcul the mask position with numpy : 0.0016927719116210938 nb_pixel_total : 49278 time to create 1 rle with old method : 0.05890202522277832 length of segment : 233 time for calcul the mask position with numpy : 0.008840560913085938 nb_pixel_total : 245149 time to create 1 rle with new method : 0.1025397777557373 length of segment : 689 time for calcul the mask position with numpy : 0.0009965896606445312 nb_pixel_total : 25597 time to create 1 rle with old method : 0.03057241439819336 length of segment : 222 time for calcul the mask position with numpy : 0.00530695915222168 nb_pixel_total : 135665 time to create 1 rle with old method : 0.16252875328063965 length of segment : 921 time for calcul the mask position with numpy : 0.0008418560028076172 nb_pixel_total : 21523 time to create 1 rle with old method : 0.02553105354309082 length of segment : 249 time for calcul the mask position with numpy : 0.005454063415527344 nb_pixel_total : 193093 time to create 1 rle with new method : 0.011898994445800781 length of segment : 546 time for calcul the mask position with numpy : 0.0005393028259277344 nb_pixel_total : 19783 time to create 1 rle with old method : 0.03367328643798828 length of segment : 208 time for calcul the mask position with numpy : 0.0001671314239501953 nb_pixel_total : 8248 time to create 1 rle with old method : 0.010208368301391602 length of segment : 71 time for calcul the mask position with numpy : 0.0004482269287109375 nb_pixel_total : 11613 time to create 1 rle with old method : 0.015493154525756836 length of segment : 125 time for calcul the mask position with numpy : 0.0002841949462890625 nb_pixel_total : 16347 time to create 1 rle with old method : 0.020062685012817383 length of segment : 166 time for calcul the mask position with numpy : 0.0006732940673828125 nb_pixel_total : 28487 time to create 1 rle with old method : 0.03439736366271973 length of segment : 191 time for calcul the mask position with numpy : 0.0019402503967285156 nb_pixel_total : 54028 time to create 1 rle with old method : 0.06351613998413086 length of segment : 339 time for calcul the mask position with numpy : 0.0009441375732421875 nb_pixel_total : 15995 time to create 1 rle with old method : 0.019614458084106445 length of segment : 235 time for calcul the mask position with numpy : 0.0007793903350830078 nb_pixel_total : 14444 time to create 1 rle with old method : 0.01746225357055664 length of segment : 193 time for calcul the mask position with numpy : 0.0024411678314208984 nb_pixel_total : 61097 time to create 1 rle with old method : 0.09572839736938477 length of segment : 268 time for calcul the mask position with numpy : 0.0015625953674316406 nb_pixel_total : 35263 time to create 1 rle with old method : 0.04207563400268555 length of segment : 281 time for calcul the mask position with numpy : 0.008047819137573242 nb_pixel_total : 241027 time to create 1 rle with new method : 0.010971546173095703 length of segment : 470 time for calcul the mask position with numpy : 0.0002727508544921875 nb_pixel_total : 4425 time to create 1 rle with old method : 0.005652904510498047 length of segment : 52 time for calcul the mask position with numpy : 0.0035834312438964844 nb_pixel_total : 149325 time to create 1 rle with old method : 0.17517662048339844 length of segment : 425 time for calcul the mask position with numpy : 0.005106449127197266 nb_pixel_total : 144510 time to create 1 rle with old method : 0.16784286499023438 length of segment : 444 time for calcul the mask position with numpy : 0.0017979145050048828 nb_pixel_total : 35436 time to create 1 rle with old method : 0.043154001235961914 length of segment : 223 time for calcul the mask position with numpy : 0.004206657409667969 nb_pixel_total : 118611 time to create 1 rle with old method : 0.13968849182128906 length of segment : 449 time for calcul the mask position with numpy : 0.0016438961029052734 nb_pixel_total : 60066 time to create 1 rle with old method : 0.07146406173706055 length of segment : 210 time for calcul the mask position with numpy : 0.001312255859375 nb_pixel_total : 52888 time to create 1 rle with old method : 0.06357860565185547 length of segment : 200 time for calcul the mask position with numpy : 0.0003762245178222656 nb_pixel_total : 11323 time to create 1 rle with old method : 0.014043569564819336 length of segment : 106 time for calcul the mask position with numpy : 0.0007593631744384766 nb_pixel_total : 11128 time to create 1 rle with old method : 0.01367330551147461 length of segment : 185 time for calcul the mask position with numpy : 0.0030045509338378906 nb_pixel_total : 91725 time to create 1 rle with old method : 0.1290302276611328 length of segment : 429 time for calcul the mask position with numpy : 0.009591817855834961 nb_pixel_total : 316289 time to create 1 rle with new method : 0.018213987350463867 length of segment : 764 time for calcul the mask position with numpy : 0.0037174224853515625 nb_pixel_total : 94578 time to create 1 rle with old method : 0.11154031753540039 length of segment : 566 time for calcul the mask position with numpy : 0.002948284149169922 nb_pixel_total : 96327 time to create 1 rle with old method : 0.11135601997375488 length of segment : 407 time for calcul the mask position with numpy : 0.003247499465942383 nb_pixel_total : 97393 time to create 1 rle with old method : 0.11607694625854492 length of segment : 376 time for calcul the mask position with numpy : 0.0020847320556640625 nb_pixel_total : 51698 time to create 1 rle with old method : 0.06170153617858887 length of segment : 306 time for calcul the mask position with numpy : 0.013418197631835938 nb_pixel_total : 441231 time to create 1 rle with new method : 0.02480912208557129 length of segment : 1192 time for calcul the mask position with numpy : 0.0004742145538330078 nb_pixel_total : 14679 time to create 1 rle with old method : 0.017782926559448242 length of segment : 155 time for calcul the mask position with numpy : 0.0005652904510498047 nb_pixel_total : 29916 time to create 1 rle with old method : 0.03629946708679199 length of segment : 403 time for calcul the mask position with numpy : 0.0014309883117675781 nb_pixel_total : 28177 time to create 1 rle with old method : 0.03423810005187988 length of segment : 177 time for calcul the mask position with numpy : 0.0012137889862060547 nb_pixel_total : 24864 time to create 1 rle with old method : 0.030098915100097656 length of segment : 180 time for calcul the mask position with numpy : 0.001275777816772461 nb_pixel_total : 20134 time to create 1 rle with old method : 0.024185657501220703 length of segment : 219 time for calcul the mask position with numpy : 0.0005552768707275391 nb_pixel_total : 7386 time to create 1 rle with old method : 0.009079456329345703 length of segment : 138 time for calcul the mask position with numpy : 0.0010111331939697266 nb_pixel_total : 19016 time to create 1 rle with old method : 0.02327752113342285 length of segment : 184 time for calcul the mask position with numpy : 0.0009062290191650391 nb_pixel_total : 17340 time to create 1 rle with old method : 0.021085023880004883 length of segment : 156 time for calcul the mask position with numpy : 0.0011000633239746094 nb_pixel_total : 22319 time to create 1 rle with old method : 0.030338764190673828 length of segment : 215 time for calcul the mask position with numpy : 0.0005655288696289062 nb_pixel_total : 7029 time to create 1 rle with old method : 0.009265661239624023 length of segment : 114 time for calcul the mask position with numpy : 0.0004374980926513672 nb_pixel_total : 8599 time to create 1 rle with old method : 0.010499954223632812 length of segment : 140 time for calcul the mask position with numpy : 0.0042836666107177734 nb_pixel_total : 40761 time to create 1 rle with old method : 0.04816389083862305 length of segment : 262 time for calcul the mask position with numpy : 0.0026602745056152344 nb_pixel_total : 29691 time to create 1 rle with old method : 0.03678560256958008 length of segment : 219 time for calcul the mask position with numpy : 0.0008425712585449219 nb_pixel_total : 16323 time to create 1 rle with old method : 0.019809961318969727 length of segment : 159 time for calcul the mask position with numpy : 0.0003371238708496094 nb_pixel_total : 4794 time to create 1 rle with old method : 0.00624394416809082 length of segment : 78 time for calcul the mask position with numpy : 0.0009992122650146484 nb_pixel_total : 13437 time to create 1 rle with old method : 0.01789999008178711 length of segment : 195 time for calcul the mask position with numpy : 0.0005245208740234375 nb_pixel_total : 5066 time to create 1 rle with old method : 0.006951332092285156 length of segment : 117 time for calcul the mask position with numpy : 0.0026209354400634766 nb_pixel_total : 35164 time to create 1 rle with old method : 0.04227805137634277 length of segment : 355 time for calcul the mask position with numpy : 0.0009567737579345703 nb_pixel_total : 8868 time to create 1 rle with old method : 0.010879039764404297 length of segment : 166 time for calcul the mask position with numpy : 0.0005273818969726562 nb_pixel_total : 10464 time to create 1 rle with old method : 0.012894153594970703 length of segment : 131 time for calcul the mask position with numpy : 0.0010688304901123047 nb_pixel_total : 24295 time to create 1 rle with old method : 0.029206037521362305 length of segment : 224 time for calcul the mask position with numpy : 0.0006890296936035156 nb_pixel_total : 18503 time to create 1 rle with old method : 0.022145748138427734 length of segment : 140 time for calcul the mask position with numpy : 0.0019333362579345703 nb_pixel_total : 43788 time to create 1 rle with old method : 0.05142045021057129 length of segment : 397 time for calcul the mask position with numpy : 0.0007805824279785156 nb_pixel_total : 12665 time to create 1 rle with old method : 0.015027046203613281 length of segment : 288 time for calcul the mask position with numpy : 0.00034117698669433594 nb_pixel_total : 16478 time to create 1 rle with old method : 0.020090579986572266 length of segment : 140 time for calcul the mask position with numpy : 0.0026657581329345703 nb_pixel_total : 40079 time to create 1 rle with old method : 0.048058509826660156 length of segment : 241 time for calcul the mask position with numpy : 0.0010762214660644531 nb_pixel_total : 24608 time to create 1 rle with old method : 0.02941298484802246 length of segment : 211 time for calcul the mask position with numpy : 0.0005633831024169922 nb_pixel_total : 7997 time to create 1 rle with old method : 0.009844779968261719 length of segment : 140 time for calcul the mask position with numpy : 0.0007681846618652344 nb_pixel_total : 15386 time to create 1 rle with old method : 0.018584012985229492 length of segment : 187 time for calcul the mask position with numpy : 0.0006260871887207031 nb_pixel_total : 3840 time to create 1 rle with old method : 0.00469970703125 length of segment : 180 time for calcul the mask position with numpy : 0.0008387565612792969 nb_pixel_total : 19632 time to create 1 rle with old method : 0.02338266372680664 length of segment : 226 time for calcul the mask position with numpy : 0.0007839202880859375 nb_pixel_total : 19739 time to create 1 rle with old method : 0.023896217346191406 length of segment : 201 time for calcul the mask position with numpy : 0.0019702911376953125 nb_pixel_total : 38979 time to create 1 rle with old method : 0.04668402671813965 length of segment : 282 time for calcul the mask position with numpy : 0.00093841552734375 nb_pixel_total : 26734 time to create 1 rle with old method : 0.032080888748168945 length of segment : 208 time for calcul the mask position with numpy : 0.0010654926300048828 nb_pixel_total : 16784 time to create 1 rle with old method : 0.02003765106201172 length of segment : 225 time for calcul the mask position with numpy : 0.00067138671875 nb_pixel_total : 11738 time to create 1 rle with old method : 0.014075517654418945 length of segment : 131 time for calcul the mask position with numpy : 0.0010864734649658203 nb_pixel_total : 17455 time to create 1 rle with old method : 0.020751237869262695 length of segment : 235 time for calcul the mask position with numpy : 0.0004515647888183594 nb_pixel_total : 7018 time to create 1 rle with old method : 0.008434057235717773 length of segment : 119 time for calcul the mask position with numpy : 0.0026657581329345703 nb_pixel_total : 36815 time to create 1 rle with old method : 0.04399514198303223 length of segment : 322 time for calcul the mask position with numpy : 0.0019125938415527344 nb_pixel_total : 32045 time to create 1 rle with old method : 0.0384063720703125 length of segment : 200 time for calcul the mask position with numpy : 0.0018353462219238281 nb_pixel_total : 23132 time to create 1 rle with old method : 0.028067350387573242 length of segment : 208 time for calcul the mask position with numpy : 0.0007009506225585938 nb_pixel_total : 16257 time to create 1 rle with old method : 0.019505023956298828 length of segment : 215 time for calcul the mask position with numpy : 0.0009930133819580078 nb_pixel_total : 20514 time to create 1 rle with old method : 0.024534225463867188 length of segment : 174 time for calcul the mask position with numpy : 0.001373291015625 nb_pixel_total : 14268 time to create 1 rle with old method : 0.017711400985717773 length of segment : 216 time for calcul the mask position with numpy : 0.0003261566162109375 nb_pixel_total : 5290 time to create 1 rle with old method : 0.0064313411712646484 length of segment : 72 time for calcul the mask position with numpy : 0.0008153915405273438 nb_pixel_total : 15775 time to create 1 rle with old method : 0.018961429595947266 length of segment : 170 time for calcul the mask position with numpy : 0.00046372413635253906 nb_pixel_total : 6638 time to create 1 rle with old method : 0.00832986831665039 length of segment : 91 time for calcul the mask position with numpy : 0.00039005279541015625 nb_pixel_total : 6296 time to create 1 rle with old method : 0.007969856262207031 length of segment : 71 time for calcul the mask position with numpy : 0.0002701282501220703 nb_pixel_total : 4642 time to create 1 rle with old method : 0.0057926177978515625 length of segment : 71 time for calcul the mask position with numpy : 0.003949165344238281 nb_pixel_total : 88032 time to create 1 rle with old method : 0.10934066772460938 length of segment : 398 time for calcul the mask position with numpy : 0.0004813671112060547 nb_pixel_total : 5701 time to create 1 rle with old method : 0.007027149200439453 length of segment : 96 time for calcul the mask position with numpy : 0.000759124755859375 nb_pixel_total : 12362 time to create 1 rle with old method : 0.01500248908996582 length of segment : 151 time for calcul the mask position with numpy : 0.000579833984375 nb_pixel_total : 7516 time to create 1 rle with old method : 0.012799263000488281 length of segment : 98 time for calcul the mask position with numpy : 0.001459360122680664 nb_pixel_total : 14829 time to create 1 rle with old method : 0.022153377532958984 length of segment : 135 time for calcul the mask position with numpy : 0.00046253204345703125 nb_pixel_total : 6277 time to create 1 rle with old method : 0.007798194885253906 length of segment : 84 time for calcul the mask position with numpy : 0.0027532577514648438 nb_pixel_total : 44560 time to create 1 rle with old method : 0.052895307540893555 length of segment : 331 time for calcul the mask position with numpy : 0.00043201446533203125 nb_pixel_total : 4503 time to create 1 rle with old method : 0.005510091781616211 length of segment : 116 time for calcul the mask position with numpy : 0.0003752708435058594 nb_pixel_total : 6122 time to create 1 rle with old method : 0.007622241973876953 length of segment : 69 time for calcul the mask position with numpy : 0.0022454261779785156 nb_pixel_total : 36913 time to create 1 rle with old method : 0.04518532752990723 length of segment : 307 time for calcul the mask position with numpy : 0.0016894340515136719 nb_pixel_total : 39043 time to create 1 rle with old method : 0.04634904861450195 length of segment : 258 time for calcul the mask position with numpy : 0.0008890628814697266 nb_pixel_total : 19495 time to create 1 rle with old method : 0.023355484008789062 length of segment : 179 time for calcul the mask position with numpy : 0.0021219253540039062 nb_pixel_total : 26458 time to create 1 rle with old method : 0.03174161911010742 length of segment : 268 time for calcul the mask position with numpy : 0.0013844966888427734 nb_pixel_total : 21883 time to create 1 rle with old method : 0.026020288467407227 length of segment : 228 time for calcul the mask position with numpy : 0.0007829666137695312 nb_pixel_total : 10315 time to create 1 rle with old method : 0.012388467788696289 length of segment : 189 time for calcul the mask position with numpy : 0.0030486583709716797 nb_pixel_total : 77798 time to create 1 rle with old method : 0.09194111824035645 length of segment : 524 time for calcul the mask position with numpy : 0.0007700920104980469 nb_pixel_total : 13768 time to create 1 rle with old method : 0.017388343811035156 length of segment : 126 time for calcul the mask position with numpy : 0.0022521018981933594 nb_pixel_total : 23750 time to create 1 rle with old method : 0.039292097091674805 length of segment : 286 time for calcul the mask position with numpy : 0.0008056163787841797 nb_pixel_total : 11463 time to create 1 rle with old method : 0.013826847076416016 length of segment : 161 time for calcul the mask position with numpy : 0.0009009838104248047 nb_pixel_total : 13749 time to create 1 rle with old method : 0.01701664924621582 length of segment : 225 time for calcul the mask position with numpy : 0.00926971435546875 nb_pixel_total : 250109 time to create 1 rle with new method : 0.013714075088500977 length of segment : 623 time for calcul the mask position with numpy : 0.00035262107849121094 nb_pixel_total : 6323 time to create 1 rle with old method : 0.007906436920166016 length of segment : 75 time for calcul the mask position with numpy : 0.0023539066314697266 nb_pixel_total : 38207 time to create 1 rle with old method : 0.04468703269958496 length of segment : 288 time for calcul the mask position with numpy : 0.008562803268432617 nb_pixel_total : 235668 time to create 1 rle with new method : 0.010436296463012695 length of segment : 634 time for calcul the mask position with numpy : 0.004355192184448242 nb_pixel_total : 80316 time to create 1 rle with old method : 0.09477853775024414 length of segment : 357 time for calcul the mask position with numpy : 0.0006573200225830078 nb_pixel_total : 12013 time to create 1 rle with old method : 0.020076990127563477 length of segment : 146 time for calcul the mask position with numpy : 0.0005347728729248047 nb_pixel_total : 29625 time to create 1 rle with old method : 0.03579115867614746 length of segment : 209 time for calcul the mask position with numpy : 0.0004830360412597656 nb_pixel_total : 6331 time to create 1 rle with old method : 0.007990837097167969 length of segment : 335 time for calcul the mask position with numpy : 0.0006170272827148438 nb_pixel_total : 16890 time to create 1 rle with old method : 0.020869970321655273 length of segment : 155 time for calcul the mask position with numpy : 0.0005211830139160156 nb_pixel_total : 5633 time to create 1 rle with old method : 0.0070912837982177734 length of segment : 96 time for calcul the mask position with numpy : 0.0009136199951171875 nb_pixel_total : 18061 time to create 1 rle with old method : 0.021509885787963867 length of segment : 198 time for calcul the mask position with numpy : 0.009256124496459961 nb_pixel_total : 200087 time to create 1 rle with new method : 0.016208648681640625 length of segment : 942 time for calcul the mask position with numpy : 0.005389690399169922 nb_pixel_total : 108073 time to create 1 rle with old method : 0.1524972915649414 length of segment : 437 time for calcul the mask position with numpy : 0.000885009765625 nb_pixel_total : 12703 time to create 1 rle with old method : 0.015221118927001953 length of segment : 162 time for calcul the mask position with numpy : 0.0010802745819091797 nb_pixel_total : 28834 time to create 1 rle with old method : 0.036557674407958984 length of segment : 219 time for calcul the mask position with numpy : 0.0008766651153564453 nb_pixel_total : 23010 time to create 1 rle with old method : 0.027753829956054688 length of segment : 258 time for calcul the mask position with numpy : 0.0013391971588134766 nb_pixel_total : 25164 time to create 1 rle with old method : 0.03087449073791504 length of segment : 201 time for calcul the mask position with numpy : 0.001493692398071289 nb_pixel_total : 24456 time to create 1 rle with old method : 0.02940964698791504 length of segment : 163 time for calcul the mask position with numpy : 0.0006947517395019531 nb_pixel_total : 11837 time to create 1 rle with old method : 0.014413833618164062 length of segment : 105 time for calcul the mask position with numpy : 0.0007948875427246094 nb_pixel_total : 16855 time to create 1 rle with old method : 0.023224353790283203 length of segment : 200 time for calcul the mask position with numpy : 0.0004949569702148438 nb_pixel_total : 8551 time to create 1 rle with old method : 0.01033473014831543 length of segment : 148 time for calcul the mask position with numpy : 0.0006580352783203125 nb_pixel_total : 15292 time to create 1 rle with old method : 0.018803119659423828 length of segment : 207 time for calcul the mask position with numpy : 0.0032858848571777344 nb_pixel_total : 75126 time to create 1 rle with old method : 0.09177041053771973 length of segment : 388 time for calcul the mask position with numpy : 0.001211404800415039 nb_pixel_total : 22060 time to create 1 rle with old method : 0.026822566986083984 length of segment : 267 time for calcul the mask position with numpy : 0.0015976428985595703 nb_pixel_total : 26765 time to create 1 rle with old method : 0.03187084197998047 length of segment : 205 time for calcul the mask position with numpy : 0.0018091201782226562 nb_pixel_total : 48021 time to create 1 rle with old method : 0.05633687973022461 length of segment : 301 time for calcul the mask position with numpy : 0.0025653839111328125 nb_pixel_total : 63058 time to create 1 rle with old method : 0.0743107795715332 length of segment : 307 time for calcul the mask position with numpy : 0.0004889965057373047 nb_pixel_total : 9436 time to create 1 rle with old method : 0.01144099235534668 length of segment : 114 time for calcul the mask position with numpy : 0.00047850608825683594 nb_pixel_total : 8920 time to create 1 rle with old method : 0.011104345321655273 length of segment : 83 time for calcul the mask position with numpy : 0.0007810592651367188 nb_pixel_total : 19708 time to create 1 rle with old method : 0.024088382720947266 length of segment : 147 time for calcul the mask position with numpy : 0.0012252330780029297 nb_pixel_total : 31907 time to create 1 rle with old method : 0.03818106651306152 length of segment : 218 time for calcul the mask position with numpy : 0.001001119613647461 nb_pixel_total : 24118 time to create 1 rle with old method : 0.03237152099609375 length of segment : 264 time for calcul the mask position with numpy : 0.0002186298370361328 nb_pixel_total : 3667 time to create 1 rle with old method : 0.004588127136230469 length of segment : 66 time for calcul the mask position with numpy : 0.002237081527709961 nb_pixel_total : 50554 time to create 1 rle with old method : 0.05973076820373535 length of segment : 317 time for calcul the mask position with numpy : 0.0013408660888671875 nb_pixel_total : 34993 time to create 1 rle with old method : 0.04181337356567383 length of segment : 233 time for calcul the mask position with numpy : 0.002740621566772461 nb_pixel_total : 69908 time to create 1 rle with old method : 0.08293271064758301 length of segment : 328 time for calcul the mask position with numpy : 0.0004057884216308594 nb_pixel_total : 7631 time to create 1 rle with old method : 0.009813308715820312 length of segment : 118 time for calcul the mask position with numpy : 0.0014696121215820312 nb_pixel_total : 50752 time to create 1 rle with old method : 0.06086373329162598 length of segment : 169 time for calcul the mask position with numpy : 0.0006000995635986328 nb_pixel_total : 17351 time to create 1 rle with old method : 0.021285295486450195 length of segment : 118 time for calcul the mask position with numpy : 0.0005800724029541016 nb_pixel_total : 16313 time to create 1 rle with old method : 0.01963019371032715 length of segment : 131 time for calcul the mask position with numpy : 0.0005209445953369141 nb_pixel_total : 12729 time to create 1 rle with old method : 0.015176057815551758 length of segment : 248 time for calcul the mask position with numpy : 0.009169816970825195 nb_pixel_total : 236070 time to create 1 rle with new method : 0.020122766494750977 length of segment : 772 time for calcul the mask position with numpy : 0.0004191398620605469 nb_pixel_total : 14904 time to create 1 rle with old method : 0.018285512924194336 length of segment : 160 time for calcul the mask position with numpy : 0.0012192726135253906 nb_pixel_total : 36388 time to create 1 rle with old method : 0.04293560981750488 length of segment : 214 time for calcul the mask position with numpy : 0.003189563751220703 nb_pixel_total : 91557 time to create 1 rle with old method : 0.1297459602355957 length of segment : 343 time for calcul the mask position with numpy : 0.0002377033233642578 nb_pixel_total : 8061 time to create 1 rle with old method : 0.011219024658203125 length of segment : 87 time for calcul the mask position with numpy : 0.001249551773071289 nb_pixel_total : 32124 time to create 1 rle with old method : 0.03800606727600098 length of segment : 247 time for calcul the mask position with numpy : 0.0017058849334716797 nb_pixel_total : 33374 time to create 1 rle with old method : 0.03938174247741699 length of segment : 194 time for calcul the mask position with numpy : 0.00041937828063964844 nb_pixel_total : 7595 time to create 1 rle with old method : 0.009151458740234375 length of segment : 93 time for calcul the mask position with numpy : 0.0011878013610839844 nb_pixel_total : 21689 time to create 1 rle with old method : 0.02566361427307129 length of segment : 198 time for calcul the mask position with numpy : 0.002004861831665039 nb_pixel_total : 36213 time to create 1 rle with old method : 0.04322934150695801 length of segment : 319 time for calcul the mask position with numpy : 0.0005974769592285156 nb_pixel_total : 10555 time to create 1 rle with old method : 0.012884855270385742 length of segment : 100 time for calcul the mask position with numpy : 0.0013082027435302734 nb_pixel_total : 13893 time to create 1 rle with old method : 0.016606807708740234 length of segment : 187 time for calcul the mask position with numpy : 0.0024204254150390625 nb_pixel_total : 47860 time to create 1 rle with old method : 0.05634784698486328 length of segment : 206 time for calcul the mask position with numpy : 0.0004329681396484375 nb_pixel_total : 3856 time to create 1 rle with old method : 0.004758119583129883 length of segment : 108 time for calcul the mask position with numpy : 0.0030879974365234375 nb_pixel_total : 59698 time to create 1 rle with old method : 0.06954717636108398 length of segment : 370 time for calcul the mask position with numpy : 0.0008385181427001953 nb_pixel_total : 15804 time to create 1 rle with old method : 0.019249916076660156 length of segment : 140 time for calcul the mask position with numpy : 0.003045797348022461 nb_pixel_total : 45083 time to create 1 rle with old method : 0.05315423011779785 length of segment : 307 time for calcul the mask position with numpy : 0.004412651062011719 nb_pixel_total : 85987 time to create 1 rle with old method : 0.09987831115722656 length of segment : 488 time for calcul the mask position with numpy : 0.0014529228210449219 nb_pixel_total : 18908 time to create 1 rle with old method : 0.022788047790527344 length of segment : 186 time for calcul the mask position with numpy : 0.0007584095001220703 nb_pixel_total : 3913 time to create 1 rle with old method : 0.005256175994873047 length of segment : 110 time for calcul the mask position with numpy : 0.0011835098266601562 nb_pixel_total : 19512 time to create 1 rle with old method : 0.024045705795288086 length of segment : 204 time for calcul the mask position with numpy : 0.0005877017974853516 nb_pixel_total : 12734 time to create 1 rle with old method : 0.018767356872558594 length of segment : 128 time for calcul the mask position with numpy : 0.004018068313598633 nb_pixel_total : 67991 time to create 1 rle with old method : 0.07933950424194336 length of segment : 352 time for calcul the mask position with numpy : 0.0016579627990722656 nb_pixel_total : 25260 time to create 1 rle with old method : 0.04217863082885742 length of segment : 290 time for calcul the mask position with numpy : 0.0014607906341552734 nb_pixel_total : 24412 time to create 1 rle with old method : 0.03085470199584961 length of segment : 171 time for calcul the mask position with numpy : 0.0008535385131835938 nb_pixel_total : 11085 time to create 1 rle with old method : 0.018997669219970703 length of segment : 119 time for calcul the mask position with numpy : 0.00249481201171875 nb_pixel_total : 30111 time to create 1 rle with old method : 0.03622245788574219 length of segment : 233 time for calcul the mask position with numpy : 0.0014986991882324219 nb_pixel_total : 27721 time to create 1 rle with old method : 0.03346133232116699 length of segment : 153 time for calcul the mask position with numpy : 0.002392292022705078 nb_pixel_total : 31317 time to create 1 rle with old method : 0.03842759132385254 length of segment : 394 time for calcul the mask position with numpy : 0.0010726451873779297 nb_pixel_total : 14597 time to create 1 rle with old method : 0.01943826675415039 length of segment : 215 time for calcul the mask position with numpy : 0.0026092529296875 nb_pixel_total : 58458 time to create 1 rle with old method : 0.0698084831237793 length of segment : 273 time for calcul the mask position with numpy : 0.003950834274291992 nb_pixel_total : 58334 time to create 1 rle with old method : 0.06839561462402344 length of segment : 487 time for calcul the mask position with numpy : 0.001657247543334961 nb_pixel_total : 32987 time to create 1 rle with old method : 0.039472103118896484 length of segment : 208 time for calcul the mask position with numpy : 0.001398324966430664 nb_pixel_total : 22504 time to create 1 rle with old method : 0.027588605880737305 length of segment : 236 time for calcul the mask position with numpy : 0.003900289535522461 nb_pixel_total : 69665 time to create 1 rle with old method : 0.08223247528076172 length of segment : 306 time for calcul the mask position with numpy : 0.0031049251556396484 nb_pixel_total : 54415 time to create 1 rle with old method : 0.06449103355407715 length of segment : 349 time for calcul the mask position with numpy : 0.0014536380767822266 nb_pixel_total : 30238 time to create 1 rle with old method : 0.036077260971069336 length of segment : 248 time for calcul the mask position with numpy : 0.0009443759918212891 nb_pixel_total : 14245 time to create 1 rle with old method : 0.017420053482055664 length of segment : 222 time for calcul the mask position with numpy : 0.00029921531677246094 nb_pixel_total : 11740 time to create 1 rle with old method : 0.014541864395141602 length of segment : 102 time for calcul the mask position with numpy : 0.001111745834350586 nb_pixel_total : 18912 time to create 1 rle with old method : 0.02284693717956543 length of segment : 255 time for calcul the mask position with numpy : 0.0025835037231445312 nb_pixel_total : 44104 time to create 1 rle with old method : 0.05275917053222656 length of segment : 269 time for calcul the mask position with numpy : 0.0020585060119628906 nb_pixel_total : 22355 time to create 1 rle with old method : 0.02652454376220703 length of segment : 315 time for calcul the mask position with numpy : 0.006231546401977539 nb_pixel_total : 141183 time to create 1 rle with old method : 0.16624140739440918 length of segment : 682 time for calcul the mask position with numpy : 0.003858804702758789 nb_pixel_total : 78629 time to create 1 rle with old method : 0.09208440780639648 length of segment : 438 time for calcul the mask position with numpy : 0.005205392837524414 nb_pixel_total : 130817 time to create 1 rle with old method : 0.15488386154174805 length of segment : 450 time for calcul the mask position with numpy : 0.004624128341674805 nb_pixel_total : 95437 time to create 1 rle with old method : 0.11135005950927734 length of segment : 351 time for calcul the mask position with numpy : 0.0017604827880859375 nb_pixel_total : 34736 time to create 1 rle with old method : 0.05400443077087402 length of segment : 181 time for calcul the mask position with numpy : 0.0021889209747314453 nb_pixel_total : 38184 time to create 1 rle with old method : 0.045140743255615234 length of segment : 315 time for calcul the mask position with numpy : 0.0026514530181884766 nb_pixel_total : 55467 time to create 1 rle with old method : 0.06564044952392578 length of segment : 303 time for calcul the mask position with numpy : 0.0013587474822998047 nb_pixel_total : 26706 time to create 1 rle with old method : 0.03155255317687988 length of segment : 250 time for calcul the mask position with numpy : 0.0005040168762207031 nb_pixel_total : 14703 time to create 1 rle with old method : 0.017853498458862305 length of segment : 141 time for calcul the mask position with numpy : 0.0014760494232177734 nb_pixel_total : 33131 time to create 1 rle with old method : 0.039049625396728516 length of segment : 237 time for calcul the mask position with numpy : 0.0008156299591064453 nb_pixel_total : 21205 time to create 1 rle with old method : 0.024721622467041016 length of segment : 206 time for calcul the mask position with numpy : 0.0033621788024902344 nb_pixel_total : 94944 time to create 1 rle with old method : 0.13587164878845215 length of segment : 226 time for calcul the mask position with numpy : 0.0028383731842041016 nb_pixel_total : 51693 time to create 1 rle with old method : 0.06011795997619629 length of segment : 432 time for calcul the mask position with numpy : 0.0013499259948730469 nb_pixel_total : 25854 time to create 1 rle with old method : 0.03197455406188965 length of segment : 163 time for calcul the mask position with numpy : 0.004678964614868164 nb_pixel_total : 128318 time to create 1 rle with old method : 0.14812564849853516 length of segment : 530 time for calcul the mask position with numpy : 0.0006451606750488281 nb_pixel_total : 17998 time to create 1 rle with old method : 0.021390914916992188 length of segment : 289 time for calcul the mask position with numpy : 0.0008656978607177734 nb_pixel_total : 14272 time to create 1 rle with old method : 0.01768803596496582 length of segment : 105 time for calcul the mask position with numpy : 0.0025739669799804688 nb_pixel_total : 44742 time to create 1 rle with old method : 0.053910017013549805 length of segment : 305 time for calcul the mask position with numpy : 0.0006756782531738281 nb_pixel_total : 14220 time to create 1 rle with old method : 0.017426013946533203 length of segment : 134 time for calcul the mask position with numpy : 0.00046944618225097656 nb_pixel_total : 3070 time to create 1 rle with old method : 0.003988742828369141 length of segment : 102 time for calcul the mask position with numpy : 0.0007724761962890625 nb_pixel_total : 18433 time to create 1 rle with old method : 0.02206707000732422 length of segment : 172 time for calcul the mask position with numpy : 0.0032083988189697266 nb_pixel_total : 55957 time to create 1 rle with old method : 0.06559300422668457 length of segment : 387 time for calcul the mask position with numpy : 0.0010821819305419922 nb_pixel_total : 26247 time to create 1 rle with old method : 0.032366275787353516 length of segment : 193 time for calcul the mask position with numpy : 0.0014290809631347656 nb_pixel_total : 31820 time to create 1 rle with old method : 0.03835487365722656 length of segment : 211 time for calcul the mask position with numpy : 0.0038785934448242188 nb_pixel_total : 67684 time to create 1 rle with old method : 0.0817863941192627 length of segment : 671 time for calcul the mask position with numpy : 0.00033164024353027344 nb_pixel_total : 6049 time to create 1 rle with old method : 0.00750732421875 length of segment : 108 time for calcul the mask position with numpy : 0.000904083251953125 nb_pixel_total : 11587 time to create 1 rle with old method : 0.014567136764526367 length of segment : 143 time for calcul the mask position with numpy : 0.0005588531494140625 nb_pixel_total : 9161 time to create 1 rle with old method : 0.011166095733642578 length of segment : 141 time for calcul the mask position with numpy : 0.0025298595428466797 nb_pixel_total : 42648 time to create 1 rle with old method : 0.05058574676513672 length of segment : 454 time for calcul the mask position with numpy : 0.002002716064453125 nb_pixel_total : 31238 time to create 1 rle with old method : 0.03970527648925781 length of segment : 192 time for calcul the mask position with numpy : 0.0010907649993896484 nb_pixel_total : 13965 time to create 1 rle with old method : 0.01951456069946289 length of segment : 146 time for calcul the mask position with numpy : 0.0033206939697265625 nb_pixel_total : 34144 time to create 1 rle with old method : 0.052338361740112305 length of segment : 306 time for calcul the mask position with numpy : 0.0017385482788085938 nb_pixel_total : 23325 time to create 1 rle with old method : 0.02834033966064453 length of segment : 267 time for calcul the mask position with numpy : 0.002157926559448242 nb_pixel_total : 12638 time to create 1 rle with old method : 0.015465497970581055 length of segment : 215 time for calcul the mask position with numpy : 0.006788015365600586 nb_pixel_total : 122782 time to create 1 rle with old method : 0.15813136100769043 length of segment : 536 time for calcul the mask position with numpy : 0.0014567375183105469 nb_pixel_total : 14593 time to create 1 rle with old method : 0.0197446346282959 length of segment : 139 time for calcul the mask position with numpy : 0.002557516098022461 nb_pixel_total : 35708 time to create 1 rle with old method : 0.042253732681274414 length of segment : 365 time for calcul the mask position with numpy : 0.00152587890625 nb_pixel_total : 23306 time to create 1 rle with old method : 0.027555227279663086 length of segment : 265 time for calcul the mask position with numpy : 0.0016317367553710938 nb_pixel_total : 25134 time to create 1 rle with old method : 0.030748367309570312 length of segment : 136 time for calcul the mask position with numpy : 0.0010678768157958984 nb_pixel_total : 8587 time to create 1 rle with old method : 0.010545015335083008 length of segment : 237 time for calcul the mask position with numpy : 0.001466989517211914 nb_pixel_total : 26258 time to create 1 rle with old method : 0.03146719932556152 length of segment : 211 time for calcul the mask position with numpy : 0.0016078948974609375 nb_pixel_total : 27056 time to create 1 rle with old method : 0.03228354454040527 length of segment : 177 time for calcul the mask position with numpy : 0.002163410186767578 nb_pixel_total : 43145 time to create 1 rle with old method : 0.05263113975524902 length of segment : 151 time for calcul the mask position with numpy : 0.010910987854003906 nb_pixel_total : 182691 time to create 1 rle with new method : 0.013824462890625 length of segment : 560 time for calcul the mask position with numpy : 0.0013427734375 nb_pixel_total : 15792 time to create 1 rle with old method : 0.02078986167907715 length of segment : 156 time for calcul the mask position with numpy : 0.0003619194030761719 nb_pixel_total : 5563 time to create 1 rle with old method : 0.00702667236328125 length of segment : 58 time for calcul the mask position with numpy : 0.0003998279571533203 nb_pixel_total : 5492 time to create 1 rle with old method : 0.006859302520751953 length of segment : 82 time for calcul the mask position with numpy : 0.005495548248291016 nb_pixel_total : 108651 time to create 1 rle with old method : 0.12855863571166992 length of segment : 452 time for calcul the mask position with numpy : 0.00418543815612793 nb_pixel_total : 72665 time to create 1 rle with old method : 0.0846104621887207 length of segment : 393 time for calcul the mask position with numpy : 0.0007877349853515625 nb_pixel_total : 11084 time to create 1 rle with old method : 0.013792991638183594 length of segment : 96 time for calcul the mask position with numpy : 0.0052394866943359375 nb_pixel_total : 80322 time to create 1 rle with old method : 0.09505796432495117 length of segment : 462 time for calcul the mask position with numpy : 0.0001785755157470703 nb_pixel_total : 7182 time to create 1 rle with old method : 0.008827686309814453 length of segment : 94 time for calcul the mask position with numpy : 0.0010945796966552734 nb_pixel_total : 16761 time to create 1 rle with old method : 0.020499467849731445 length of segment : 156 time for calcul the mask position with numpy : 0.0004892349243164062 nb_pixel_total : 6817 time to create 1 rle with old method : 0.008383512496948242 length of segment : 86 time for calcul the mask position with numpy : 0.0005064010620117188 nb_pixel_total : 8597 time to create 1 rle with old method : 0.010617256164550781 length of segment : 108 time for calcul the mask position with numpy : 0.0005266666412353516 nb_pixel_total : 18683 time to create 1 rle with old method : 0.022955894470214844 length of segment : 121 time for calcul the mask position with numpy : 0.0015926361083984375 nb_pixel_total : 49097 time to create 1 rle with old method : 0.0577082633972168 length of segment : 299 time for calcul the mask position with numpy : 0.0004956722259521484 nb_pixel_total : 12806 time to create 1 rle with old method : 0.01559591293334961 length of segment : 113 time for calcul the mask position with numpy : 0.0006806850433349609 nb_pixel_total : 24500 time to create 1 rle with old method : 0.029700756072998047 length of segment : 136 time for calcul the mask position with numpy : 0.0012145042419433594 nb_pixel_total : 11854 time to create 1 rle with old method : 0.02534961700439453 length of segment : 131 time for calcul the mask position with numpy : 0.0006124973297119141 nb_pixel_total : 11180 time to create 1 rle with old method : 0.013657331466674805 length of segment : 139 time for calcul the mask position with numpy : 0.001300811767578125 nb_pixel_total : 42475 time to create 1 rle with old method : 0.06313705444335938 length of segment : 278 time for calcul the mask position with numpy : 0.0006825923919677734 nb_pixel_total : 16236 time to create 1 rle with old method : 0.019557952880859375 length of segment : 189 time for calcul the mask position with numpy : 0.0003447532653808594 nb_pixel_total : 11744 time to create 1 rle with old method : 0.014429330825805664 length of segment : 117 time for calcul the mask position with numpy : 0.0032515525817871094 nb_pixel_total : 109133 time to create 1 rle with old method : 0.13158321380615234 length of segment : 526 time for calcul the mask position with numpy : 0.0005970001220703125 nb_pixel_total : 14320 time to create 1 rle with old method : 0.02147078514099121 length of segment : 149 time for calcul the mask position with numpy : 0.0002505779266357422 nb_pixel_total : 6183 time to create 1 rle with old method : 0.008440494537353516 length of segment : 55 time for calcul the mask position with numpy : 0.000453948974609375 nb_pixel_total : 8417 time to create 1 rle with old method : 0.010482311248779297 length of segment : 80 time for calcul the mask position with numpy : 0.0005881786346435547 nb_pixel_total : 16501 time to create 1 rle with old method : 0.020181894302368164 length of segment : 161 time for calcul the mask position with numpy : 0.0008642673492431641 nb_pixel_total : 24948 time to create 1 rle with old method : 0.031093597412109375 length of segment : 152 time for calcul the mask position with numpy : 0.0025064945220947266 nb_pixel_total : 89119 time to create 1 rle with old method : 0.12928223609924316 length of segment : 689 time for calcul the mask position with numpy : 0.0007295608520507812 nb_pixel_total : 16293 time to create 1 rle with old method : 0.019737958908081055 length of segment : 128 time for calcul the mask position with numpy : 0.008497238159179688 nb_pixel_total : 181674 time to create 1 rle with new method : 0.009915351867675781 length of segment : 832 time for calcul the mask position with numpy : 0.001337289810180664 nb_pixel_total : 23735 time to create 1 rle with old method : 0.027795791625976562 length of segment : 236 time for calcul the mask position with numpy : 0.00040340423583984375 nb_pixel_total : 20436 time to create 1 rle with old method : 0.024843692779541016 length of segment : 218 time for calcul the mask position with numpy : 0.007004261016845703 nb_pixel_total : 173689 time to create 1 rle with new method : 0.0075151920318603516 length of segment : 485 time for calcul the mask position with numpy : 0.0016345977783203125 nb_pixel_total : 48166 time to create 1 rle with old method : 0.05830693244934082 length of segment : 196 time for calcul the mask position with numpy : 0.001997232437133789 nb_pixel_total : 44507 time to create 1 rle with old method : 0.06282901763916016 length of segment : 322 time for calcul the mask position with numpy : 0.0005462169647216797 nb_pixel_total : 10841 time to create 1 rle with old method : 0.012900590896606445 length of segment : 119 time for calcul the mask position with numpy : 0.0009119510650634766 nb_pixel_total : 17161 time to create 1 rle with old method : 0.023598909378051758 length of segment : 177 time for calcul the mask position with numpy : 0.0015761852264404297 nb_pixel_total : 6327 time to create 1 rle with old method : 0.00789785385131836 length of segment : 307 time for calcul the mask position with numpy : 0.0016973018646240234 nb_pixel_total : 40763 time to create 1 rle with old method : 0.048818111419677734 length of segment : 240 time for calcul the mask position with numpy : 0.0007925033569335938 nb_pixel_total : 16922 time to create 1 rle with old method : 0.023663997650146484 length of segment : 194 time for calcul the mask position with numpy : 0.000415802001953125 nb_pixel_total : 11572 time to create 1 rle with old method : 0.015965700149536133 length of segment : 100 time for calcul the mask position with numpy : 0.003705263137817383 nb_pixel_total : 80823 time to create 1 rle with old method : 0.09743595123291016 length of segment : 286 time for calcul the mask position with numpy : 0.0007302761077880859 nb_pixel_total : 20894 time to create 1 rle with old method : 0.024968624114990234 length of segment : 162 time for calcul the mask position with numpy : 0.0010640621185302734 nb_pixel_total : 28699 time to create 1 rle with old method : 0.03473854064941406 length of segment : 171 time for calcul the mask position with numpy : 0.0007059574127197266 nb_pixel_total : 17776 time to create 1 rle with old method : 0.0213472843170166 length of segment : 161 time for calcul the mask position with numpy : 0.0017576217651367188 nb_pixel_total : 54812 time to create 1 rle with old method : 0.0648505687713623 length of segment : 291 time for calcul the mask position with numpy : 0.0027501583099365234 nb_pixel_total : 29557 time to create 1 rle with old method : 0.03583788871765137 length of segment : 393 time spent for convertir_results : 39.05472111701965 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 436 chid ids of type : 3594 Number RLEs to save : 111091 save missing photos in datou_result : time spend for datou_step_exec : 220.84361267089844 time spend to save output : 6.887594938278198 total time spend for step 1 : 227.73120760917664 step2:crop_condition Tue Apr 15 21:04:22 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 20 ! batch 1 Loaded 436 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 ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 330 About to insert : list_path_to_insert length 330 new photo from crops ! About to upload 330 photos upload in portfolio : 3736932 init cache_photo without model_param we have 330 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744743926_1162099 we have uploaded 330 photos in the portfolio 3736932 time of upload the photos Elapsed time : 79.33431601524353 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 ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 53 About to insert : list_path_to_insert length 53 new photo from crops ! About to upload 53 photos upload in portfolio : 3736932 init cache_photo without model_param we have 53 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744744022_1162099 we have uploaded 53 photos in the portfolio 3736932 time of upload the photos Elapsed time : 12.373624324798584 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1744744039_1162099 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.9728255271911621 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 36 About to insert : list_path_to_insert length 36 new photo from crops ! About to upload 36 photos upload in portfolio : 3736932 init cache_photo without model_param we have 36 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744744058_1162099 we have uploaded 36 photos in the portfolio 3736932 time of upload the photos Elapsed time : 10.335412740707397 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 ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 10 About to insert : list_path_to_insert length 10 new photo from crops ! About to upload 10 photos upload in portfolio : 3736932 init cache_photo without model_param we have 10 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744744074_1162099 we have uploaded 10 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.030421733856201 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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/1744744079_1162099 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.1206519603729248 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744744083_1162099 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.8048098087310791 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349984441, 1349984424, 1349984411, 1349984408, 1349984388, 1349984384, 1349984379, 1349984367, 1349984353, 1349979483, 1349979434, 1349979333, 1349979328, 1349979286, 1349979258, 1349979228, 1349979145, 1349979139, 1349979135, 1349978774] Looping around the photos to save general results len do output : 436 /1350004464Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004465Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004469Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004470Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004472Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004473Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004476Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004477Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004478Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004480Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004481Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004493Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004496Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004500Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004501Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004502Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004504Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004507Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004508Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004509Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004510Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004514Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004515Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004518Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004519Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004522Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004523Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004525Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004526Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004529Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004530Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004539Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004540Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004545Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004548Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004550Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004552Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004554Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004555Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004558Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004559Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004560Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004561Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004562Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004566Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004567Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004568Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004569Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004570Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004572Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004573Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004575Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004576Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004577Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004578Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004579Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004580Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004581Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004582Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004583Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004584Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004585Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004586Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004587Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004588Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004589Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004590Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004591Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004593Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004594Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004595Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004619Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004621Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004622Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004628Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004629Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004630Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004631Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004633Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004634Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004637Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004638Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004640Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004644Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004645Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004646Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004647Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004651Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004652Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004653Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004654Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004658Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004659Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004660Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004661Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004665Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004666Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004668Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004669Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004672Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004673Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004675Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004676Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004679Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004681Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004682Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004684Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004685Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004687Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004690Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004691Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004692Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004694Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004695Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004696Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004700Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004701Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004702Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004705Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004706Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004707Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004708Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004709Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004710Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004711Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004713Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004716Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004717Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004722Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004723Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004725Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004728Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004729Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004731Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004735Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004736Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004738Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004740Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004742Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004743Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004745Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004747Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004749Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004750Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004753Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350004755Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1350005098Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005099Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005100Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005102Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005104Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005108Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005109Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005113Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005114Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005117Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005118Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005119Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005120Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005121Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005139Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005140Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005141Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005142Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005146Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005147Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005173Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005178Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350005180Didn'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, '2723399') ('3318', '22049547', '1349984441', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984424', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984411', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984408', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984388', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984384', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984379', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984367', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984353', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979483', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979434', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979333', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979328', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979286', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979258', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979228', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979145', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979139', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979135', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349978774', None, None, None, None, None, '2723399') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1328 time used for this insertion : 0.06187629699707031 save_final save missing photos in datou_result : time spend for datou_step_exec : 221.87888193130493 time spend to save output : 0.0713798999786377 total time spend for step 2 : 221.95026183128357 step3:rle_unique_nms_with_priority Tue Apr 15 21:08:04 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 436 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 25 nb_hashtags : 5 time to prepare the origin masks : 9.470896482467651 time for calcul the mask position with numpy : 0.5427889823913574 nb_pixel_total : 5872580 time to create 1 rle with new method : 0.9388670921325684 time for calcul the mask position with numpy : 0.03701281547546387 nb_pixel_total : 41849 time to create 1 rle with old method : 0.04837322235107422 time for calcul the mask position with numpy : 0.03871870040893555 nb_pixel_total : 4302 time to create 1 rle with old method : 0.005637168884277344 time for calcul the mask position with numpy : 0.04282355308532715 nb_pixel_total : 91277 time to create 1 rle with old method : 0.10935330390930176 time for calcul the mask position with numpy : 0.041617393493652344 nb_pixel_total : 94599 time to create 1 rle with old method : 0.14084100723266602 time for calcul the mask position with numpy : 0.046278953552246094 nb_pixel_total : 34326 time to create 1 rle with old method : 0.04847574234008789 time for calcul the mask position with numpy : 0.047395944595336914 nb_pixel_total : 449386 time to create 1 rle with new method : 0.5068869590759277 time for calcul the mask position with numpy : 0.03839397430419922 nb_pixel_total : 41944 time to create 1 rle with old method : 0.055191755294799805 time for calcul the mask position with numpy : 0.041524410247802734 nb_pixel_total : 86464 time to create 1 rle with old method : 0.1068422794342041 time for calcul the mask position with numpy : 0.03525829315185547 nb_pixel_total : 42813 time to create 1 rle with old method : 0.05003619194030762 time for calcul the mask position with numpy : 0.0440974235534668 nb_pixel_total : 9761 time to create 1 rle with old method : 0.012250661849975586 time for calcul the mask position with numpy : 0.038060665130615234 nb_pixel_total : 10685 time to create 1 rle with old method : 0.013293743133544922 time for calcul the mask position with numpy : 0.03920865058898926 nb_pixel_total : 31678 time to create 1 rle with old method : 0.03894329071044922 time for calcul the mask position with numpy : 0.040158987045288086 nb_pixel_total : 12091 time to create 1 rle with old method : 0.01409006118774414 time for calcul the mask position with numpy : 0.03579974174499512 nb_pixel_total : 4869 time to create 1 rle with old method : 0.005712270736694336 time for calcul the mask position with numpy : 0.04157829284667969 nb_pixel_total : 24563 time to create 1 rle with old method : 0.03012704849243164 time for calcul the mask position with numpy : 0.03492927551269531 nb_pixel_total : 26085 time to create 1 rle with old method : 0.030260562896728516 time for calcul the mask position with numpy : 0.038497209548950195 nb_pixel_total : 25478 time to create 1 rle with old method : 0.029280900955200195 time for calcul the mask position with numpy : 0.03557753562927246 nb_pixel_total : 24478 time to create 1 rle with old method : 0.0281522274017334 time for calcul the mask position with numpy : 0.040796518325805664 nb_pixel_total : 20726 time to create 1 rle with old method : 0.02377176284790039 time for calcul the mask position with numpy : 0.03764081001281738 nb_pixel_total : 8954 time to create 1 rle with old method : 0.010559797286987305 time for calcul the mask position with numpy : 0.037309885025024414 nb_pixel_total : 12319 time to create 1 rle with old method : 0.01447916030883789 time for calcul the mask position with numpy : 0.03651595115661621 nb_pixel_total : 36864 time to create 1 rle with old method : 0.04289698600769043 time for calcul the mask position with numpy : 0.03531599044799805 nb_pixel_total : 21364 time to create 1 rle with old method : 0.025029420852661133 time for calcul the mask position with numpy : 0.03573870658874512 nb_pixel_total : 6132 time to create 1 rle with old method : 0.007207632064819336 time for calcul the mask position with numpy : 0.0346531867980957 nb_pixel_total : 14653 time to create 1 rle with old method : 0.018805742263793945 create new chi : 3.9337079524993896 time to delete rle : 0.027004241943359375 batch 1 Loaded 51 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++Number RLEs to save : 15426 TO DO : save crop sub photo not yet done ! save time : 0.9564130306243896 nb_obj : 14 nb_hashtags : 3 time to prepare the origin masks : 7.412668228149414 time for calcul the mask position with numpy : 0.43834781646728516 nb_pixel_total : 5684485 time to create 1 rle with new method : 0.889843225479126 time for calcul the mask position with numpy : 0.04167795181274414 nb_pixel_total : 41228 time to create 1 rle with old method : 0.04995870590209961 time for calcul the mask position with numpy : 0.026027917861938477 nb_pixel_total : 37365 time to create 1 rle with old method : 0.04513978958129883 time for calcul the mask position with numpy : 0.02480292320251465 nb_pixel_total : 50245 time to create 1 rle with old method : 0.061037540435791016 time for calcul the mask position with numpy : 0.026494503021240234 nb_pixel_total : 79947 time to create 1 rle with old method : 0.09658980369567871 time for calcul the mask position with numpy : 0.02811431884765625 nb_pixel_total : 272039 time to create 1 rle with new method : 0.5564842224121094 time for calcul the mask position with numpy : 0.04136180877685547 nb_pixel_total : 9040 time to create 1 rle with old method : 0.013734817504882812 time for calcul the mask position with numpy : 0.03858804702758789 nb_pixel_total : 10161 time to create 1 rle with old method : 0.012455940246582031 time for calcul the mask position with numpy : 0.03909897804260254 nb_pixel_total : 18027 time to create 1 rle with old method : 0.02168583869934082 time for calcul the mask position with numpy : 0.03892183303833008 nb_pixel_total : 16507 time to create 1 rle with old method : 0.020503759384155273 time for calcul the mask position with numpy : 0.03678774833679199 nb_pixel_total : 27932 time to create 1 rle with old method : 0.03345799446105957 time for calcul the mask position with numpy : 0.03330492973327637 nb_pixel_total : 38222 time to create 1 rle with old method : 0.04643893241882324 time for calcul the mask position with numpy : 0.02896404266357422 nb_pixel_total : 533620 time to create 1 rle with new method : 1.203080415725708 time for calcul the mask position with numpy : 0.02429342269897461 nb_pixel_total : 195088 time to create 1 rle with new method : 0.6845133304595947 time for calcul the mask position with numpy : 0.025623083114624023 nb_pixel_total : 36334 time to create 1 rle with old method : 0.05243229866027832 create new chi : 4.8138587474823 time to delete rle : 0.0023391246795654297 batch 1 Loaded 29 chid ids of type : 3594 ++++++++++++++++Number RLEs to save : 12040 TO DO : save crop sub photo not yet done ! save time : 0.7888152599334717 nb_obj : 9 nb_hashtags : 4 time to prepare the origin masks : 3.856466770172119 time for calcul the mask position with numpy : 0.5437333583831787 nb_pixel_total : 5980636 time to create 1 rle with new method : 0.8893067836761475 time for calcul the mask position with numpy : 0.022808074951171875 nb_pixel_total : 51714 time to create 1 rle with old method : 0.06763601303100586 time for calcul the mask position with numpy : 0.029419660568237305 nb_pixel_total : 28143 time to create 1 rle with old method : 0.04694652557373047 time for calcul the mask position with numpy : 0.024811744689941406 nb_pixel_total : 11773 time to create 1 rle with old method : 0.01585865020751953 time for calcul the mask position with numpy : 0.024691104888916016 nb_pixel_total : 12387 time to create 1 rle with old method : 0.014936447143554688 time for calcul the mask position with numpy : 0.025252342224121094 nb_pixel_total : 59602 time to create 1 rle with old method : 0.07159233093261719 time for calcul the mask position with numpy : 0.02457737922668457 nb_pixel_total : 11949 time to create 1 rle with old method : 0.014750480651855469 time for calcul the mask position with numpy : 0.022947311401367188 nb_pixel_total : 40791 time to create 1 rle with old method : 0.04735684394836426 time for calcul the mask position with numpy : 0.02346348762512207 nb_pixel_total : 18430 time to create 1 rle with old method : 0.02150702476501465 time for calcul the mask position with numpy : 0.03239941596984863 nb_pixel_total : 834815 time to create 1 rle with new method : 0.42690587043762207 create new chi : 2.45764422416687 time to delete rle : 0.0013661384582519531 batch 1 Loaded 19 chid ids of type : 3594 ++++++++++++++Number RLEs to save : 8440 TO DO : save crop sub photo not yet done ! save time : 0.5632896423339844 nb_obj : 12 nb_hashtags : 4 time to prepare the origin masks : 5.240614891052246 time for calcul the mask position with numpy : 0.390411376953125 nb_pixel_total : 6256507 time to create 1 rle with new method : 0.7160999774932861 time for calcul the mask position with numpy : 0.022479534149169922 nb_pixel_total : 36424 time to create 1 rle with old method : 0.04198646545410156 time for calcul the mask position with numpy : 0.021926164627075195 nb_pixel_total : 9918 time to create 1 rle with old method : 0.011507272720336914 time for calcul the mask position with numpy : 0.024485349655151367 nb_pixel_total : 20662 time to create 1 rle with old method : 0.034589529037475586 time for calcul the mask position with numpy : 0.02756333351135254 nb_pixel_total : 464179 time to create 1 rle with new method : 0.7836484909057617 time for calcul the mask position with numpy : 0.024933815002441406 nb_pixel_total : 8163 time to create 1 rle with old method : 0.010016441345214844 time for calcul the mask position with numpy : 0.026839017868041992 nb_pixel_total : 11694 time to create 1 rle with old method : 0.015157461166381836 time for calcul the mask position with numpy : 0.022913694381713867 nb_pixel_total : 93012 time to create 1 rle with old method : 0.11228728294372559 time for calcul the mask position with numpy : 0.02367258071899414 nb_pixel_total : 24814 time to create 1 rle with old method : 0.029210329055786133 time for calcul the mask position with numpy : 0.022886276245117188 nb_pixel_total : 54048 time to create 1 rle with old method : 0.06318306922912598 time for calcul the mask position with numpy : 0.023181438446044922 nb_pixel_total : 36645 time to create 1 rle with old method : 0.04384875297546387 time for calcul the mask position with numpy : 0.031873226165771484 nb_pixel_total : 9724 time to create 1 rle with old method : 0.011868000030517578 time for calcul the mask position with numpy : 0.025654077529907227 nb_pixel_total : 24450 time to create 1 rle with old method : 0.04088902473449707 create new chi : 2.6726770401000977 time to delete rle : 0.0031023025512695312 batch 1 Loaded 25 chid ids of type : 3594 ++++++++++++Number RLEs to save : 8293 TO DO : save crop sub photo not yet done ! save time : 0.5543267726898193 nb_obj : 17 nb_hashtags : 3 time to prepare the origin masks : 7.199570178985596 time for calcul the mask position with numpy : 0.40843915939331055 nb_pixel_total : 6089399 time to create 1 rle with new method : 0.8016033172607422 time for calcul the mask position with numpy : 0.025162458419799805 nb_pixel_total : 5296 time to create 1 rle with old method : 0.006181478500366211 time for calcul the mask position with numpy : 0.02405261993408203 nb_pixel_total : 20243 time to create 1 rle with old method : 0.02343463897705078 time for calcul the mask position with numpy : 0.023638010025024414 nb_pixel_total : 21107 time to create 1 rle with old method : 0.024739980697631836 time for calcul the mask position with numpy : 0.02428150177001953 nb_pixel_total : 70458 time to create 1 rle with old method : 0.12076067924499512 time for calcul the mask position with numpy : 0.03975653648376465 nb_pixel_total : 28657 time to create 1 rle with old method : 0.035915374755859375 time for calcul the mask position with numpy : 0.03817391395568848 nb_pixel_total : 205269 time to create 1 rle with new method : 0.5394206047058105 time for calcul the mask position with numpy : 0.03427600860595703 nb_pixel_total : 12099 time to create 1 rle with old method : 0.014057397842407227 time for calcul the mask position with numpy : 0.03524637222290039 nb_pixel_total : 23993 time to create 1 rle with old method : 0.029399633407592773 time for calcul the mask position with numpy : 0.026500940322875977 nb_pixel_total : 47915 time to create 1 rle with old method : 0.056131839752197266 time for calcul the mask position with numpy : 0.022745132446289062 nb_pixel_total : 28522 time to create 1 rle with old method : 0.03276944160461426 time for calcul the mask position with numpy : 0.02260279655456543 nb_pixel_total : 80415 time to create 1 rle with old method : 0.10093832015991211 time for calcul the mask position with numpy : 0.024146556854248047 nb_pixel_total : 12639 time to create 1 rle with old method : 0.019685983657836914 time for calcul the mask position with numpy : 0.027490615844726562 nb_pixel_total : 26131 time to create 1 rle with old method : 0.03338623046875 time for calcul the mask position with numpy : 0.02469944953918457 nb_pixel_total : 66224 time to create 1 rle with old method : 0.07681846618652344 time for calcul the mask position with numpy : 0.02455615997314453 nb_pixel_total : 195223 time to create 1 rle with new method : 0.46278977394104004 time for calcul the mask position with numpy : 0.028154373168945312 nb_pixel_total : 71302 time to create 1 rle with old method : 0.08674335479736328 time for calcul the mask position with numpy : 0.03913426399230957 nb_pixel_total : 45348 time to create 1 rle with old method : 0.05762171745300293 create new chi : 3.515155076980591 time to delete rle : 0.0026543140411376953 batch 1 Loaded 35 chid ids of type : 3594 +++++++++++++++++++++Number RLEs to save : 10858 TO DO : save crop sub photo not yet done ! save time : 0.6987199783325195 nb_obj : 32 nb_hashtags : 4 time to prepare the origin masks : 4.432819604873657 time for calcul the mask position with numpy : 0.5186450481414795 nb_pixel_total : 5493429 time to create 1 rle with new method : 0.9304490089416504 time for calcul the mask position with numpy : 0.02950763702392578 nb_pixel_total : 20149 time to create 1 rle with old method : 0.0233914852142334 time for calcul the mask position with numpy : 0.029692411422729492 nb_pixel_total : 55931 time to create 1 rle with old method : 0.06423330307006836 time for calcul the mask position with numpy : 0.029923200607299805 nb_pixel_total : 27994 time to create 1 rle with old method : 0.03297591209411621 time for calcul the mask position with numpy : 0.029444456100463867 nb_pixel_total : 15859 time to create 1 rle with old method : 0.018691062927246094 time for calcul the mask position with numpy : 0.029277563095092773 nb_pixel_total : 27743 time to create 1 rle with old method : 0.03251457214355469 time for calcul the mask position with numpy : 0.030884265899658203 nb_pixel_total : 18310 time to create 1 rle with old method : 0.02993011474609375 time for calcul the mask position with numpy : 0.033400774002075195 nb_pixel_total : 15318 time to create 1 rle with old method : 0.018427133560180664 time for calcul the mask position with numpy : 0.02992558479309082 nb_pixel_total : 11595 time to create 1 rle with old method : 0.01948857307434082 time for calcul the mask position with numpy : 0.0332639217376709 nb_pixel_total : 14740 time to create 1 rle with old method : 0.022688627243041992 time for calcul the mask position with numpy : 0.029885053634643555 nb_pixel_total : 10877 time to create 1 rle with old method : 0.012848377227783203 time for calcul the mask position with numpy : 0.03093719482421875 nb_pixel_total : 114156 time to create 1 rle with old method : 0.13405299186706543 time for calcul the mask position with numpy : 0.03364300727844238 nb_pixel_total : 79954 time to create 1 rle with old method : 0.10160040855407715 time for calcul the mask position with numpy : 0.029364347457885742 nb_pixel_total : 11218 time to create 1 rle with old method : 0.013365983963012695 time for calcul the mask position with numpy : 0.02907705307006836 nb_pixel_total : 5440 time to create 1 rle with old method : 0.00646209716796875 time for calcul the mask position with numpy : 0.029720306396484375 nb_pixel_total : 22567 time to create 1 rle with old method : 0.026508331298828125 time for calcul the mask position with numpy : 0.0292360782623291 nb_pixel_total : 4527 time to create 1 rle with old method : 0.00532078742980957 time for calcul the mask position with numpy : 0.029119491577148438 nb_pixel_total : 9268 time to create 1 rle with old method : 0.01081538200378418 time for calcul the mask position with numpy : 0.029183626174926758 nb_pixel_total : 91806 time to create 1 rle with old method : 0.10522294044494629 time for calcul the mask position with numpy : 0.033513784408569336 nb_pixel_total : 440075 time to create 1 rle with new method : 0.628713846206665 time for calcul the mask position with numpy : 0.030818939208984375 nb_pixel_total : 34145 time to create 1 rle with old method : 0.04224801063537598 time for calcul the mask position with numpy : 0.029881954193115234 nb_pixel_total : 175473 time to create 1 rle with new method : 0.6788656711578369 time for calcul the mask position with numpy : 0.029619216918945312 nb_pixel_total : 57215 time to create 1 rle with old method : 0.06622314453125 time for calcul the mask position with numpy : 0.03007793426513672 nb_pixel_total : 64429 time to create 1 rle with old method : 0.07552218437194824 time for calcul the mask position with numpy : 0.03151965141296387 nb_pixel_total : 10707 time to create 1 rle with old method : 0.012600183486938477 time for calcul the mask position with numpy : 0.029550552368164062 nb_pixel_total : 30374 time to create 1 rle with old method : 0.03722572326660156 time for calcul the mask position with numpy : 0.029735326766967773 nb_pixel_total : 23262 time to create 1 rle with old method : 0.035099029541015625 time for calcul the mask position with numpy : 0.03332042694091797 nb_pixel_total : 47103 time to create 1 rle with old method : 0.06551861763000488 time for calcul the mask position with numpy : 0.029175519943237305 nb_pixel_total : 22973 time to create 1 rle with old method : 0.026635408401489258 time for calcul the mask position with numpy : 0.029784679412841797 nb_pixel_total : 9772 time to create 1 rle with old method : 0.01148223876953125 time for calcul the mask position with numpy : 0.030706405639648438 nb_pixel_total : 40916 time to create 1 rle with old method : 0.051835060119628906 time for calcul the mask position with numpy : 0.02949690818786621 nb_pixel_total : 27174 time to create 1 rle with old method : 0.031754493713378906 time for calcul the mask position with numpy : 0.029479503631591797 nb_pixel_total : 15741 time to create 1 rle with old method : 0.018393993377685547 create new chi : 4.978914022445679 time to delete rle : 0.005073070526123047 batch 1 Loaded 65 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++Number RLEs to save : 18638 TO DO : save crop sub photo not yet done ! save time : 1.1530430316925049 nb_obj : 28 nb_hashtags : 5 time to prepare the origin masks : 3.813699722290039 time for calcul the mask position with numpy : 0.8119747638702393 nb_pixel_total : 6204706 time to create 1 rle with new method : 1.3604450225830078 time for calcul the mask position with numpy : 0.029241323471069336 nb_pixel_total : 7491 time to create 1 rle with old method : 0.008806705474853516 time for calcul the mask position with numpy : 0.02872490882873535 nb_pixel_total : 14124 time to create 1 rle with old method : 0.01637578010559082 time for calcul the mask position with numpy : 0.029534578323364258 nb_pixel_total : 113860 time to create 1 rle with old method : 0.1315627098083496 time for calcul the mask position with numpy : 0.029789209365844727 nb_pixel_total : 3734 time to create 1 rle with old method : 0.0044744014739990234 time for calcul the mask position with numpy : 0.029804706573486328 nb_pixel_total : 4793 time to create 1 rle with old method : 0.005755424499511719 time for calcul the mask position with numpy : 0.029900789260864258 nb_pixel_total : 4858 time to create 1 rle with old method : 0.005647182464599609 time for calcul the mask position with numpy : 0.029599905014038086 nb_pixel_total : 31050 time to create 1 rle with old method : 0.03626513481140137 time for calcul the mask position with numpy : 0.029332876205444336 nb_pixel_total : 27589 time to create 1 rle with old method : 0.03230643272399902 time for calcul the mask position with numpy : 0.031325340270996094 nb_pixel_total : 45503 time to create 1 rle with old method : 0.05505228042602539 time for calcul the mask position with numpy : 0.0336308479309082 nb_pixel_total : 11051 time to create 1 rle with old method : 0.014614105224609375 time for calcul the mask position with numpy : 0.03118157386779785 nb_pixel_total : 21585 time to create 1 rle with old method : 0.025392770767211914 time for calcul the mask position with numpy : 0.037993431091308594 nb_pixel_total : 7644 time to create 1 rle with old method : 0.008923530578613281 time for calcul the mask position with numpy : 0.029289960861206055 nb_pixel_total : 40231 time to create 1 rle with old method : 0.06391358375549316 time for calcul the mask position with numpy : 0.03288626670837402 nb_pixel_total : 15292 time to create 1 rle with old method : 0.017882347106933594 time for calcul the mask position with numpy : 0.030516862869262695 nb_pixel_total : 59756 time to create 1 rle with old method : 0.07185125350952148 time for calcul the mask position with numpy : 0.03335452079772949 nb_pixel_total : 9837 time to create 1 rle with old method : 0.016876935958862305 time for calcul the mask position with numpy : 0.03388381004333496 nb_pixel_total : 61423 time to create 1 rle with old method : 0.09005045890808105 time for calcul the mask position with numpy : 0.03350973129272461 nb_pixel_total : 20791 time to create 1 rle with old method : 0.03421378135681152 time for calcul the mask position with numpy : 0.03003692626953125 nb_pixel_total : 54265 time to create 1 rle with old method : 0.06259346008300781 time for calcul the mask position with numpy : 0.029180049896240234 nb_pixel_total : 51750 time to create 1 rle with old method : 0.060063838958740234 time for calcul the mask position with numpy : 0.02941131591796875 nb_pixel_total : 12898 time to create 1 rle with old method : 0.014841318130493164 time for calcul the mask position with numpy : 0.029245853424072266 nb_pixel_total : 23295 time to create 1 rle with old method : 0.027382612228393555 time for calcul the mask position with numpy : 0.029116392135620117 nb_pixel_total : 9354 time to create 1 rle with old method : 0.01086568832397461 time for calcul the mask position with numpy : 0.029605865478515625 nb_pixel_total : 4396 time to create 1 rle with old method : 0.005198240280151367 time for calcul the mask position with numpy : 0.02936863899230957 nb_pixel_total : 140134 time to create 1 rle with old method : 0.15933895111083984 time for calcul the mask position with numpy : 0.028954505920410156 nb_pixel_total : 22991 time to create 1 rle with old method : 0.028748750686645508 time for calcul the mask position with numpy : 0.02909374237060547 nb_pixel_total : 6528 time to create 1 rle with old method : 0.007590055465698242 time for calcul the mask position with numpy : 0.029003381729125977 nb_pixel_total : 19311 time to create 1 rle with old method : 0.022606611251831055 create new chi : 4.110668659210205 time to delete rle : 0.004002809524536133 batch 1 Loaded 57 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++Number RLEs to save : 13679 TO DO : save crop sub photo not yet done ! save time : 0.8871579170227051 nb_obj : 5 nb_hashtags : 1 time to prepare the origin masks : 3.532745361328125 time for calcul the mask position with numpy : 0.620065450668335 nb_pixel_total : 6901613 time to create 1 rle with new method : 0.6863253116607666 time for calcul the mask position with numpy : 0.025943756103515625 nb_pixel_total : 5447 time to create 1 rle with old method : 0.0063593387603759766 time for calcul the mask position with numpy : 0.022099018096923828 nb_pixel_total : 46932 time to create 1 rle with old method : 0.05379652976989746 time for calcul the mask position with numpy : 0.02457714080810547 nb_pixel_total : 25974 time to create 1 rle with old method : 0.030599117279052734 time for calcul the mask position with numpy : 0.025693655014038086 nb_pixel_total : 54683 time to create 1 rle with old method : 0.06621956825256348 time for calcul the mask position with numpy : 0.03742027282714844 nb_pixel_total : 15591 time to create 1 rle with old method : 0.018176555633544922 create new chi : 1.652311086654663 time to delete rle : 0.0009913444519042969 batch 1 Loaded 11 chid ids of type : 3594 ++++++++++Number RLEs to save : 4984 TO DO : save crop sub photo not yet done ! save time : 0.35254812240600586 nb_obj : 16 nb_hashtags : 2 time to prepare the origin masks : 6.2079386711120605 time for calcul the mask position with numpy : 0.8811995983123779 nb_pixel_total : 6685327 time to create 1 rle with new method : 1.0848798751831055 time for calcul the mask position with numpy : 0.03617715835571289 nb_pixel_total : 10017 time to create 1 rle with old method : 0.011641263961791992 time for calcul the mask position with numpy : 0.03982043266296387 nb_pixel_total : 21303 time to create 1 rle with old method : 0.024624347686767578 time for calcul the mask position with numpy : 0.03655600547790527 nb_pixel_total : 57558 time to create 1 rle with old method : 0.0728144645690918 time for calcul the mask position with numpy : 0.04649496078491211 nb_pixel_total : 854 time to create 1 rle with old method : 0.00122833251953125 time for calcul the mask position with numpy : 0.037856340408325195 nb_pixel_total : 22413 time to create 1 rle with old method : 0.027218341827392578 time for calcul the mask position with numpy : 0.032851457595825195 nb_pixel_total : 19343 time to create 1 rle with old method : 0.028230905532836914 time for calcul the mask position with numpy : 0.03433632850646973 nb_pixel_total : 12606 time to create 1 rle with old method : 0.014853477478027344 time for calcul the mask position with numpy : 0.034459829330444336 nb_pixel_total : 6657 time to create 1 rle with old method : 0.008714437484741211 time for calcul the mask position with numpy : 0.0305483341217041 nb_pixel_total : 21524 time to create 1 rle with old method : 0.027409076690673828 time for calcul the mask position with numpy : 0.03881120681762695 nb_pixel_total : 26317 time to create 1 rle with old method : 0.03038787841796875 time for calcul the mask position with numpy : 0.03703951835632324 nb_pixel_total : 20156 time to create 1 rle with old method : 0.02358841896057129 time for calcul the mask position with numpy : 0.036614179611206055 nb_pixel_total : 44060 time to create 1 rle with old method : 0.05183005332946777 time for calcul the mask position with numpy : 0.03669929504394531 nb_pixel_total : 6229 time to create 1 rle with old method : 0.007370471954345703 time for calcul the mask position with numpy : 0.0354766845703125 nb_pixel_total : 47404 time to create 1 rle with old method : 0.0552215576171875 time for calcul the mask position with numpy : 0.04358363151550293 nb_pixel_total : 29570 time to create 1 rle with old method : 0.03498220443725586 time for calcul the mask position with numpy : 0.03736472129821777 nb_pixel_total : 18902 time to create 1 rle with old method : 0.021939754486083984 create new chi : 3.0477259159088135 time to delete rle : 0.0016522407531738281 batch 1 Loaded 33 chid ids of type : 3594 +++++++++++++++++++++++++Number RLEs to save : 9054 TO DO : save crop sub photo not yet done ! save time : 0.6387076377868652 nb_obj : 14 nb_hashtags : 2 time to prepare the origin masks : 6.603986024856567 time for calcul the mask position with numpy : 0.7069129943847656 nb_pixel_total : 6214810 time to create 1 rle with new method : 0.6666784286499023 time for calcul the mask position with numpy : 0.037619590759277344 nb_pixel_total : 28487 time to create 1 rle with old method : 0.039223670959472656 time for calcul the mask position with numpy : 0.03786444664001465 nb_pixel_total : 3937 time to create 1 rle with old method : 0.004628658294677734 time for calcul the mask position with numpy : 0.0339970588684082 nb_pixel_total : 11613 time to create 1 rle with old method : 0.013575315475463867 time for calcul the mask position with numpy : 0.03618812561035156 nb_pixel_total : 8248 time to create 1 rle with old method : 0.009609222412109375 time for calcul the mask position with numpy : 0.03566169738769531 nb_pixel_total : 19783 time to create 1 rle with old method : 0.02311396598815918 time for calcul the mask position with numpy : 0.02787470817565918 nb_pixel_total : 193093 time to create 1 rle with new method : 0.8055868148803711 time for calcul the mask position with numpy : 0.026187658309936523 nb_pixel_total : 21523 time to create 1 rle with old method : 0.028866052627563477 time for calcul the mask position with numpy : 0.030283451080322266 nb_pixel_total : 135665 time to create 1 rle with old method : 0.15854358673095703 time for calcul the mask position with numpy : 0.02349686622619629 nb_pixel_total : 25597 time to create 1 rle with old method : 0.03451347351074219 time for calcul the mask position with numpy : 0.03875875473022461 nb_pixel_total : 245149 time to create 1 rle with new method : 0.3364982604980469 time for calcul the mask position with numpy : 0.03905344009399414 nb_pixel_total : 49278 time to create 1 rle with old method : 0.05681324005126953 time for calcul the mask position with numpy : 0.03880047798156738 nb_pixel_total : 17621 time to create 1 rle with old method : 0.02483510971069336 time for calcul the mask position with numpy : 0.03726339340209961 nb_pixel_total : 61745 time to create 1 rle with old method : 0.08554220199584961 time for calcul the mask position with numpy : 0.04401135444641113 nb_pixel_total : 13691 time to create 1 rle with old method : 0.02118086814880371 create new chi : 3.5987956523895264 time to delete rle : 0.00261688232421875 batch 1 Loaded 29 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++Number RLEs to save : 10424 TO DO : save crop sub photo not yet done ! save time : 0.7221202850341797 nb_obj : 24 nb_hashtags : 4 time to prepare the origin masks : 9.65732192993164 time for calcul the mask position with numpy : 0.35489463806152344 nb_pixel_total : 4838234 time to create 1 rle with new method : 0.7824902534484863 time for calcul the mask position with numpy : 0.03625822067260742 nb_pixel_total : 2500 time to create 1 rle with old method : 0.003100872039794922 time for calcul the mask position with numpy : 0.03554964065551758 nb_pixel_total : 13361 time to create 1 rle with old method : 0.030402183532714844 time for calcul the mask position with numpy : 0.049875497817993164 nb_pixel_total : 441161 time to create 1 rle with new method : 0.46389126777648926 time for calcul the mask position with numpy : 0.026038169860839844 nb_pixel_total : 51698 time to create 1 rle with old method : 0.059923410415649414 time for calcul the mask position with numpy : 0.02698206901550293 nb_pixel_total : 97393 time to create 1 rle with old method : 0.11156487464904785 time for calcul the mask position with numpy : 0.022913217544555664 nb_pixel_total : 96327 time to create 1 rle with old method : 0.12074398994445801 time for calcul the mask position with numpy : 0.040348052978515625 nb_pixel_total : 94578 time to create 1 rle with old method : 0.12000727653503418 time for calcul the mask position with numpy : 0.037065744400024414 nb_pixel_total : 313697 time to create 1 rle with new method : 0.45693206787109375 time for calcul the mask position with numpy : 0.04015660285949707 nb_pixel_total : 91725 time to create 1 rle with old method : 0.10523772239685059 time for calcul the mask position with numpy : 0.03541088104248047 nb_pixel_total : 11128 time to create 1 rle with old method : 0.012962102890014648 time for calcul the mask position with numpy : 0.035360097885131836 nb_pixel_total : 11323 time to create 1 rle with old method : 0.012885332107543945 time for calcul the mask position with numpy : 0.03586721420288086 nb_pixel_total : 52888 time to create 1 rle with old method : 0.05946636199951172 time for calcul the mask position with numpy : 0.03468823432922363 nb_pixel_total : 60066 time to create 1 rle with old method : 0.07156109809875488 time for calcul the mask position with numpy : 0.0361025333404541 nb_pixel_total : 118611 time to create 1 rle with old method : 0.14409208297729492 time for calcul the mask position with numpy : 0.03415346145629883 nb_pixel_total : 35436 time to create 1 rle with old method : 0.04751706123352051 time for calcul the mask position with numpy : 0.023333072662353516 nb_pixel_total : 144510 time to create 1 rle with old method : 0.1659257411956787 time for calcul the mask position with numpy : 0.02206730842590332 nb_pixel_total : 149325 time to create 1 rle with old method : 0.17395758628845215 time for calcul the mask position with numpy : 0.021481990814208984 nb_pixel_total : 4425 time to create 1 rle with old method : 0.005143880844116211 time for calcul the mask position with numpy : 0.023043394088745117 nb_pixel_total : 241027 time to create 1 rle with new method : 0.552509069442749 time for calcul the mask position with numpy : 0.02188253402709961 nb_pixel_total : 35263 time to create 1 rle with old method : 0.040308475494384766 time for calcul the mask position with numpy : 0.02302861213684082 nb_pixel_total : 61097 time to create 1 rle with old method : 0.06988787651062012 time for calcul the mask position with numpy : 0.02268362045288086 nb_pixel_total : 14444 time to create 1 rle with old method : 0.01680612564086914 time for calcul the mask position with numpy : 0.02198052406311035 nb_pixel_total : 15995 time to create 1 rle with old method : 0.020576000213623047 time for calcul the mask position with numpy : 0.023755788803100586 nb_pixel_total : 54028 time to create 1 rle with old method : 0.06212282180786133 create new chi : 4.904616832733154 time to delete rle : 0.0033922195434570312 batch 1 Loaded 49 chid ids of type : 3594 +++++++++++++++++++++++++++++++Number RLEs to save : 18652 TO DO : save crop sub photo not yet done ! save time : 1.2830910682678223 nb_obj : 32 nb_hashtags : 2 time to prepare the origin masks : 3.73700213432312 time for calcul the mask position with numpy : 0.9718363285064697 nb_pixel_total : 6427343 time to create 1 rle with new method : 0.7682421207427979 time for calcul the mask position with numpy : 0.030045032501220703 nb_pixel_total : 24295 time to create 1 rle with old method : 0.02871537208557129 time for calcul the mask position with numpy : 0.029979705810546875 nb_pixel_total : 18503 time to create 1 rle with old method : 0.02177572250366211 time for calcul the mask position with numpy : 0.02985382080078125 nb_pixel_total : 5066 time to create 1 rle with old method : 0.0061419010162353516 time for calcul the mask position with numpy : 0.030951499938964844 nb_pixel_total : 8190 time to create 1 rle with old method : 0.010006189346313477 time for calcul the mask position with numpy : 0.030003070831298828 nb_pixel_total : 7029 time to create 1 rle with old method : 0.008734464645385742 time for calcul the mask position with numpy : 0.0302584171295166 nb_pixel_total : 3840 time to create 1 rle with old method : 0.004834651947021484 time for calcul the mask position with numpy : 0.0325922966003418 nb_pixel_total : 26734 time to create 1 rle with old method : 0.03290271759033203 time for calcul the mask position with numpy : 0.03006911277770996 nb_pixel_total : 7629 time to create 1 rle with old method : 0.009516000747680664 time for calcul the mask position with numpy : 0.030431270599365234 nb_pixel_total : 29691 time to create 1 rle with old method : 0.03528094291687012 time for calcul the mask position with numpy : 0.03022003173828125 nb_pixel_total : 28177 time to create 1 rle with old method : 0.03408217430114746 time for calcul the mask position with numpy : 0.030063152313232422 nb_pixel_total : 15386 time to create 1 rle with old method : 0.018255949020385742 time for calcul the mask position with numpy : 0.02996206283569336 nb_pixel_total : 35164 time to create 1 rle with old method : 0.04180288314819336 time for calcul the mask position with numpy : 0.03214240074157715 nb_pixel_total : 24864 time to create 1 rle with old method : 0.03815007209777832 time for calcul the mask position with numpy : 0.029976367950439453 nb_pixel_total : 7997 time to create 1 rle with old method : 0.009450674057006836 time for calcul the mask position with numpy : 0.02943277359008789 nb_pixel_total : 38979 time to create 1 rle with old method : 0.05241513252258301 time for calcul the mask position with numpy : 0.03069925308227539 nb_pixel_total : 24608 time to create 1 rle with old method : 0.02931046485900879 time for calcul the mask position with numpy : 0.02972269058227539 nb_pixel_total : 43788 time to create 1 rle with old method : 0.05203604698181152 time for calcul the mask position with numpy : 0.030738353729248047 nb_pixel_total : 40761 time to create 1 rle with old method : 0.047766923904418945 time for calcul the mask position with numpy : 0.030699491500854492 nb_pixel_total : 19739 time to create 1 rle with old method : 0.024968862533569336 time for calcul the mask position with numpy : 0.031871795654296875 nb_pixel_total : 40079 time to create 1 rle with old method : 0.04732680320739746 time for calcul the mask position with numpy : 0.03217816352844238 nb_pixel_total : 16323 time to create 1 rle with old method : 0.031145572662353516 time for calcul the mask position with numpy : 0.03335738182067871 nb_pixel_total : 19632 time to create 1 rle with old method : 0.02597784996032715 time for calcul the mask position with numpy : 0.02995610237121582 nb_pixel_total : 7386 time to create 1 rle with old method : 0.008710861206054688 time for calcul the mask position with numpy : 0.03397560119628906 nb_pixel_total : 19016 time to create 1 rle with old method : 0.03101181983947754 time for calcul the mask position with numpy : 0.03327631950378418 nb_pixel_total : 20134 time to create 1 rle with old method : 0.02364373207092285 time for calcul the mask position with numpy : 0.029761314392089844 nb_pixel_total : 10464 time to create 1 rle with old method : 0.01228022575378418 time for calcul the mask position with numpy : 0.03313636779785156 nb_pixel_total : 12665 time to create 1 rle with old method : 0.020823001861572266 time for calcul the mask position with numpy : 0.03328132629394531 nb_pixel_total : 13437 time to create 1 rle with old method : 0.01576685905456543 time for calcul the mask position with numpy : 0.029406070709228516 nb_pixel_total : 4794 time to create 1 rle with old method : 0.0056591033935546875 time for calcul the mask position with numpy : 0.030398130416870117 nb_pixel_total : 8868 time to create 1 rle with old method : 0.010440826416015625 time for calcul the mask position with numpy : 0.030210256576538086 nb_pixel_total : 22319 time to create 1 rle with old method : 0.026341915130615234 time for calcul the mask position with numpy : 0.03092813491821289 nb_pixel_total : 17340 time to create 1 rle with old method : 0.0201876163482666 create new chi : 3.5575716495513916 time to delete rle : 0.002947568893432617 batch 1 Loaded 65 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 14608 TO DO : save crop sub photo not yet done ! save time : 0.9333798885345459 nb_obj : 28 nb_hashtags : 4 time to prepare the origin masks : 3.612640380859375 time for calcul the mask position with numpy : 0.4185459613800049 nb_pixel_total : 6504643 time to create 1 rle with new method : 0.7580351829528809 time for calcul the mask position with numpy : 0.032242536544799805 nb_pixel_total : 32045 time to create 1 rle with old method : 0.037676095962524414 time for calcul the mask position with numpy : 0.029868602752685547 nb_pixel_total : 20514 time to create 1 rle with old method : 0.023829936981201172 time for calcul the mask position with numpy : 0.034694671630859375 nb_pixel_total : 6638 time to create 1 rle with old method : 0.008480548858642578 time for calcul the mask position with numpy : 0.030032634735107422 nb_pixel_total : 43919 time to create 1 rle with old method : 0.053678035736083984 time for calcul the mask position with numpy : 0.02971935272216797 nb_pixel_total : 39043 time to create 1 rle with old method : 0.045639991760253906 time for calcul the mask position with numpy : 0.029263734817504883 nb_pixel_total : 23132 time to create 1 rle with old method : 0.027385234832763672 time for calcul the mask position with numpy : 0.029073715209960938 nb_pixel_total : 6296 time to create 1 rle with old method : 0.007395029067993164 time for calcul the mask position with numpy : 0.02886795997619629 nb_pixel_total : 16257 time to create 1 rle with old method : 0.01897573471069336 time for calcul the mask position with numpy : 0.029095172882080078 nb_pixel_total : 36913 time to create 1 rle with old method : 0.04295039176940918 time for calcul the mask position with numpy : 0.029390811920166016 nb_pixel_total : 7018 time to create 1 rle with old method : 0.008365154266357422 time for calcul the mask position with numpy : 0.0297396183013916 nb_pixel_total : 26458 time to create 1 rle with old method : 0.030893564224243164 time for calcul the mask position with numpy : 0.029346704483032227 nb_pixel_total : 6122 time to create 1 rle with old method : 0.007140636444091797 time for calcul the mask position with numpy : 0.029180049896240234 nb_pixel_total : 14268 time to create 1 rle with old method : 0.019002676010131836 time for calcul the mask position with numpy : 0.029300212860107422 nb_pixel_total : 6277 time to create 1 rle with old method : 0.007681369781494141 time for calcul the mask position with numpy : 0.031119108200073242 nb_pixel_total : 14589 time to create 1 rle with old method : 0.017378568649291992 time for calcul the mask position with numpy : 0.030893802642822266 nb_pixel_total : 16784 time to create 1 rle with old method : 0.020524024963378906 time for calcul the mask position with numpy : 0.029376506805419922 nb_pixel_total : 4642 time to create 1 rle with old method : 0.005495786666870117 time for calcul the mask position with numpy : 0.02915191650390625 nb_pixel_total : 19495 time to create 1 rle with old method : 0.022789955139160156 time for calcul the mask position with numpy : 0.029231548309326172 nb_pixel_total : 15775 time to create 1 rle with old method : 0.018541812896728516 time for calcul the mask position with numpy : 0.0296175479888916 nb_pixel_total : 88032 time to create 1 rle with old method : 0.10272383689880371 time for calcul the mask position with numpy : 0.02922368049621582 nb_pixel_total : 12362 time to create 1 rle with old method : 0.014386653900146484 time for calcul the mask position with numpy : 0.029489994049072266 nb_pixel_total : 36815 time to create 1 rle with old method : 0.04303383827209473 time for calcul the mask position with numpy : 0.02941274642944336 nb_pixel_total : 5701 time to create 1 rle with old method : 0.006757259368896484 time for calcul the mask position with numpy : 0.029370784759521484 nb_pixel_total : 7516 time to create 1 rle with old method : 0.008720159530639648 time for calcul the mask position with numpy : 0.02942800521850586 nb_pixel_total : 11738 time to create 1 rle with old method : 0.013649702072143555 time for calcul the mask position with numpy : 0.02943587303161621 nb_pixel_total : 17455 time to create 1 rle with old method : 0.02046799659729004 time for calcul the mask position with numpy : 0.029225587844848633 nb_pixel_total : 4503 time to create 1 rle with old method : 0.005290985107421875 time for calcul the mask position with numpy : 0.03008294105529785 nb_pixel_total : 5290 time to create 1 rle with old method : 0.009123086929321289 create new chi : 2.7017176151275635 time to delete rle : 0.0026366710662841797 batch 1 Loaded 57 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++Number RLEs to save : 12100 TO DO : save crop sub photo not yet done ! save time : 0.7831630706787109 nb_obj : 29 nb_hashtags : 3 time to prepare the origin masks : 4.107541084289551 time for calcul the mask position with numpy : 0.7480616569519043 nb_pixel_total : 5745898 time to create 1 rle with new method : 0.5596156120300293 time for calcul the mask position with numpy : 0.03999519348144531 nb_pixel_total : 6323 time to create 1 rle with old method : 0.007406949996948242 time for calcul the mask position with numpy : 0.029760360717773438 nb_pixel_total : 77798 time to create 1 rle with old method : 0.10997891426086426 time for calcul the mask position with numpy : 0.032767295837402344 nb_pixel_total : 28834 time to create 1 rle with old method : 0.036623239517211914 time for calcul the mask position with numpy : 0.0295407772064209 nb_pixel_total : 7614 time to create 1 rle with old method : 0.011843681335449219 time for calcul the mask position with numpy : 0.03836655616760254 nb_pixel_total : 10315 time to create 1 rle with old method : 0.01676654815673828 time for calcul the mask position with numpy : 0.0363616943359375 nb_pixel_total : 11463 time to create 1 rle with old method : 0.019145965576171875 time for calcul the mask position with numpy : 0.0394132137298584 nb_pixel_total : 23750 time to create 1 rle with old method : 0.03801321983337402 time for calcul the mask position with numpy : 0.0385439395904541 nb_pixel_total : 235668 time to create 1 rle with new method : 0.7521677017211914 time for calcul the mask position with numpy : 0.03170132637023926 nb_pixel_total : 80316 time to create 1 rle with old method : 0.09451842308044434 time for calcul the mask position with numpy : 0.029206275939941406 nb_pixel_total : 16590 time to create 1 rle with old method : 0.01921534538269043 time for calcul the mask position with numpy : 0.029410362243652344 nb_pixel_total : 18061 time to create 1 rle with old method : 0.020766496658325195 time for calcul the mask position with numpy : 0.029552936553955078 nb_pixel_total : 21883 time to create 1 rle with old method : 0.02697157859802246 time for calcul the mask position with numpy : 0.02936863899230957 nb_pixel_total : 24809 time to create 1 rle with old method : 0.02969646453857422 time for calcul the mask position with numpy : 0.029769420623779297 nb_pixel_total : 38207 time to create 1 rle with old method : 0.044439077377319336 time for calcul the mask position with numpy : 0.029299497604370117 nb_pixel_total : 12703 time to create 1 rle with old method : 0.015019655227661133 time for calcul the mask position with numpy : 0.03158831596374512 nb_pixel_total : 250109 time to create 1 rle with new method : 0.5213797092437744 time for calcul the mask position with numpy : 0.030518054962158203 nb_pixel_total : 197644 time to create 1 rle with new method : 0.9753005504608154 time for calcul the mask position with numpy : 0.03808116912841797 nb_pixel_total : 2323 time to create 1 rle with old method : 0.004491567611694336 time for calcul the mask position with numpy : 0.07996869087219238 nb_pixel_total : 4392 time to create 1 rle with old method : 0.015443563461303711 time for calcul the mask position with numpy : 0.05203080177307129 nb_pixel_total : 8551 time to create 1 rle with old method : 0.03606438636779785 time for calcul the mask position with numpy : 0.0382845401763916 nb_pixel_total : 16855 time to create 1 rle with old method : 0.02004384994506836 time for calcul the mask position with numpy : 0.03116321563720703 nb_pixel_total : 18602 time to create 1 rle with old method : 0.034516096115112305 time for calcul the mask position with numpy : 0.03842282295227051 nb_pixel_total : 13749 time to create 1 rle with old method : 0.026163101196289062 time for calcul the mask position with numpy : 0.038933753967285156 nb_pixel_total : 108073 time to create 1 rle with old method : 0.19079899787902832 time for calcul the mask position with numpy : 0.043129682540893555 nb_pixel_total : 24456 time to create 1 rle with old method : 0.04188895225524902 time for calcul the mask position with numpy : 0.03882622718811035 nb_pixel_total : 5633 time to create 1 rle with old method : 0.008962631225585938 time for calcul the mask position with numpy : 0.041179656982421875 nb_pixel_total : 13768 time to create 1 rle with old method : 0.023182392120361328 time for calcul the mask position with numpy : 0.04246640205383301 nb_pixel_total : 14016 time to create 1 rle with old method : 0.02154374122619629 time for calcul the mask position with numpy : 0.05656290054321289 nb_pixel_total : 11837 time to create 1 rle with old method : 0.02000284194946289 create new chi : 5.731277227401733 time to delete rle : 0.004094839096069336 batch 1 Loaded 59 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 17078 TO DO : save crop sub photo not yet done ! save time : 1.1894543170928955 nb_obj : 25 nb_hashtags : 5 time to prepare the origin masks : 10.500667810440063 time for calcul the mask position with numpy : 0.8823652267456055 nb_pixel_total : 6043403 time to create 1 rle with new method : 1.2184138298034668 time for calcul the mask position with numpy : 0.04253530502319336 nb_pixel_total : 32124 time to create 1 rle with old method : 0.0374600887298584 time for calcul the mask position with numpy : 0.046679019927978516 nb_pixel_total : 8061 time to create 1 rle with old method : 0.00949406623840332 time for calcul the mask position with numpy : 0.045589447021484375 nb_pixel_total : 91557 time to create 1 rle with old method : 0.1351308822631836 time for calcul the mask position with numpy : 0.04052162170410156 nb_pixel_total : 36388 time to create 1 rle with old method : 0.042214393615722656 time for calcul the mask position with numpy : 0.04616975784301758 nb_pixel_total : 9949 time to create 1 rle with old method : 0.01226663589477539 time for calcul the mask position with numpy : 0.049442291259765625 nb_pixel_total : 236070 time to create 1 rle with new method : 0.6613101959228516 time for calcul the mask position with numpy : 0.047914743423461914 nb_pixel_total : 12400 time to create 1 rle with old method : 0.014664173126220703 time for calcul the mask position with numpy : 0.04182124137878418 nb_pixel_total : 16313 time to create 1 rle with old method : 0.021582841873168945 time for calcul the mask position with numpy : 0.040857791900634766 nb_pixel_total : 17351 time to create 1 rle with old method : 0.020589590072631836 time for calcul the mask position with numpy : 0.043080806732177734 nb_pixel_total : 50752 time to create 1 rle with old method : 0.0628664493560791 time for calcul the mask position with numpy : 0.03394055366516113 nb_pixel_total : 7631 time to create 1 rle with old method : 0.01290130615234375 time for calcul the mask position with numpy : 0.03738975524902344 nb_pixel_total : 69908 time to create 1 rle with old method : 0.10916328430175781 time for calcul the mask position with numpy : 0.044126272201538086 nb_pixel_total : 34993 time to create 1 rle with old method : 0.042536020278930664 time for calcul the mask position with numpy : 0.04488348960876465 nb_pixel_total : 50554 time to create 1 rle with old method : 0.09028339385986328 time for calcul the mask position with numpy : 0.05263113975524902 nb_pixel_total : 3667 time to create 1 rle with old method : 0.0062716007232666016 time for calcul the mask position with numpy : 0.05247044563293457 nb_pixel_total : 24118 time to create 1 rle with old method : 0.04003000259399414 time for calcul the mask position with numpy : 0.05862140655517578 nb_pixel_total : 31907 time to create 1 rle with old method : 0.04295825958251953 time for calcul the mask position with numpy : 0.03986978530883789 nb_pixel_total : 19708 time to create 1 rle with old method : 0.02284383773803711 time for calcul the mask position with numpy : 0.04346346855163574 nb_pixel_total : 8920 time to create 1 rle with old method : 0.010804891586303711 time for calcul the mask position with numpy : 0.04280424118041992 nb_pixel_total : 9436 time to create 1 rle with old method : 0.011056184768676758 time for calcul the mask position with numpy : 0.046985626220703125 nb_pixel_total : 63058 time to create 1 rle with old method : 0.09837579727172852 time for calcul the mask position with numpy : 0.04630136489868164 nb_pixel_total : 48021 time to create 1 rle with old method : 0.05620837211608887 time for calcul the mask position with numpy : 0.042851924896240234 nb_pixel_total : 26765 time to create 1 rle with old method : 0.03195333480834961 time for calcul the mask position with numpy : 0.04063010215759277 nb_pixel_total : 22060 time to create 1 rle with old method : 0.02835702896118164 time for calcul the mask position with numpy : 0.04812765121459961 nb_pixel_total : 75126 time to create 1 rle with old method : 0.09084177017211914 create new chi : 5.01514744758606 time to delete rle : 0.2073383331298828 batch 1 Loaded 51 chid ids of type : 3594 ++++++++++++++++++++++++++++Number RLEs to save : 13620 TO DO : save crop sub photo not yet done ! save time : 0.9572281837463379 nb_obj : 36 nb_hashtags : 5 time to prepare the origin masks : 4.593301057815552 time for calcul the mask position with numpy : 0.40196847915649414 nb_pixel_total : 5943149 time to create 1 rle with new method : 1.1992974281311035 time for calcul the mask position with numpy : 0.029253482818603516 nb_pixel_total : 3913 time to create 1 rle with old method : 0.004662036895751953 time for calcul the mask position with numpy : 0.029830455780029297 nb_pixel_total : 15804 time to create 1 rle with old method : 0.018275976181030273 time for calcul the mask position with numpy : 0.029772520065307617 nb_pixel_total : 7595 time to create 1 rle with old method : 0.008939743041992188 time for calcul the mask position with numpy : 0.029736757278442383 nb_pixel_total : 3856 time to create 1 rle with old method : 0.004669189453125 time for calcul the mask position with numpy : 0.029331684112548828 nb_pixel_total : 13893 time to create 1 rle with old method : 0.01645183563232422 time for calcul the mask position with numpy : 0.030112266540527344 nb_pixel_total : 32987 time to create 1 rle with old method : 0.0440824031829834 time for calcul the mask position with numpy : 0.029650449752807617 nb_pixel_total : 10555 time to create 1 rle with old method : 0.012210845947265625 time for calcul the mask position with numpy : 0.02987837791442871 nb_pixel_total : 58334 time to create 1 rle with old method : 0.07038211822509766 time for calcul the mask position with numpy : 0.029782533645629883 nb_pixel_total : 54381 time to create 1 rle with old method : 0.06748080253601074 time for calcul the mask position with numpy : 0.03012561798095703 nb_pixel_total : 18908 time to create 1 rle with old method : 0.022029638290405273 time for calcul the mask position with numpy : 0.030630826950073242 nb_pixel_total : 69665 time to create 1 rle with old method : 0.0810093879699707 time for calcul the mask position with numpy : 0.030046701431274414 nb_pixel_total : 21689 time to create 1 rle with old method : 0.026374101638793945 time for calcul the mask position with numpy : 0.03194427490234375 nb_pixel_total : 59698 time to create 1 rle with old method : 0.07299399375915527 time for calcul the mask position with numpy : 0.030495643615722656 nb_pixel_total : 44104 time to create 1 rle with old method : 0.06196165084838867 time for calcul the mask position with numpy : 0.0351262092590332 nb_pixel_total : 85987 time to create 1 rle with old method : 0.10491323471069336 time for calcul the mask position with numpy : 0.0318143367767334 nb_pixel_total : 19512 time to create 1 rle with old method : 0.023122549057006836 time for calcul the mask position with numpy : 0.032578229904174805 nb_pixel_total : 11085 time to create 1 rle with old method : 0.023775577545166016 time for calcul the mask position with numpy : 0.0375823974609375 nb_pixel_total : 33374 time to create 1 rle with old method : 0.05930495262145996 time for calcul the mask position with numpy : 0.034265756607055664 nb_pixel_total : 14245 time to create 1 rle with old method : 0.01961350440979004 time for calcul the mask position with numpy : 0.029685497283935547 nb_pixel_total : 47860 time to create 1 rle with old method : 0.05824160575866699 time for calcul the mask position with numpy : 0.02988910675048828 nb_pixel_total : 30111 time to create 1 rle with old method : 0.03529071807861328 time for calcul the mask position with numpy : 0.03473615646362305 nb_pixel_total : 22504 time to create 1 rle with old method : 0.035227298736572266 time for calcul the mask position with numpy : 0.038055419921875 nb_pixel_total : 14597 time to create 1 rle with old method : 0.024672508239746094 time for calcul the mask position with numpy : 0.030864953994750977 nb_pixel_total : 22355 time to create 1 rle with old method : 0.026319026947021484 time for calcul the mask position with numpy : 0.029480934143066406 nb_pixel_total : 18912 time to create 1 rle with old method : 0.023513078689575195 time for calcul the mask position with numpy : 0.0294342041015625 nb_pixel_total : 25260 time to create 1 rle with old method : 0.029769420623779297 time for calcul the mask position with numpy : 0.029284238815307617 nb_pixel_total : 31317 time to create 1 rle with old method : 0.03750443458557129 time for calcul the mask position with numpy : 0.02985525131225586 nb_pixel_total : 45083 time to create 1 rle with old method : 0.0651247501373291 time for calcul the mask position with numpy : 0.03392529487609863 nb_pixel_total : 30238 time to create 1 rle with old method : 0.03951382637023926 time for calcul the mask position with numpy : 0.03080272674560547 nb_pixel_total : 24412 time to create 1 rle with old method : 0.028957366943359375 time for calcul the mask position with numpy : 0.033373355865478516 nb_pixel_total : 36213 time to create 1 rle with old method : 0.043144941329956055 time for calcul the mask position with numpy : 0.0319066047668457 nb_pixel_total : 67991 time to create 1 rle with old method : 0.10358524322509766 time for calcul the mask position with numpy : 0.029436826705932617 nb_pixel_total : 27721 time to create 1 rle with old method : 0.034780263900756836 time for calcul the mask position with numpy : 0.03495597839355469 nb_pixel_total : 12734 time to create 1 rle with old method : 0.020273923873901367 time for calcul the mask position with numpy : 0.0343022346496582 nb_pixel_total : 58458 time to create 1 rle with old method : 0.07081365585327148 time for calcul the mask position with numpy : 0.030068397521972656 nb_pixel_total : 11740 time to create 1 rle with old method : 0.014171361923217773 create new chi : 4.211452484130859 time to delete rle : 0.005978822708129883 batch 1 Loaded 73 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 19221 TO DO : save crop sub photo not yet done ! save time : 1.2625524997711182 nb_obj : 29 nb_hashtags : 4 time to prepare the origin masks : 4.649321794509888 time for calcul the mask position with numpy : 0.5674412250518799 nb_pixel_total : 5715506 time to create 1 rle with new method : 0.9983198642730713 time for calcul the mask position with numpy : 0.03239583969116211 nb_pixel_total : 38184 time to create 1 rle with old method : 0.05896353721618652 time for calcul the mask position with numpy : 0.030757427215576172 nb_pixel_total : 3070 time to create 1 rle with old method : 0.0037512779235839844 time for calcul the mask position with numpy : 0.031075239181518555 nb_pixel_total : 31820 time to create 1 rle with old method : 0.03771328926086426 time for calcul the mask position with numpy : 0.029799699783325195 nb_pixel_total : 18299 time to create 1 rle with old method : 0.021288633346557617 time for calcul the mask position with numpy : 0.030125856399536133 nb_pixel_total : 14272 time to create 1 rle with old method : 0.017143964767456055 time for calcul the mask position with numpy : 0.031813621520996094 nb_pixel_total : 67684 time to create 1 rle with old method : 0.0803840160369873 time for calcul the mask position with numpy : 0.032152414321899414 nb_pixel_total : 33131 time to create 1 rle with old method : 0.03901338577270508 time for calcul the mask position with numpy : 0.03550291061401367 nb_pixel_total : 6049 time to create 1 rle with old method : 0.010955572128295898 time for calcul the mask position with numpy : 0.03887462615966797 nb_pixel_total : 44742 time to create 1 rle with old method : 0.07290363311767578 time for calcul the mask position with numpy : 0.030436277389526367 nb_pixel_total : 26706 time to create 1 rle with old method : 0.031206369400024414 time for calcul the mask position with numpy : 0.03044915199279785 nb_pixel_total : 55957 time to create 1 rle with old method : 0.0655829906463623 time for calcul the mask position with numpy : 0.029673099517822266 nb_pixel_total : 11587 time to create 1 rle with old method : 0.013939380645751953 time for calcul the mask position with numpy : 0.03407716751098633 nb_pixel_total : 141183 time to create 1 rle with old method : 0.1766200065612793 time for calcul the mask position with numpy : 0.03362751007080078 nb_pixel_total : 9161 time to create 1 rle with old method : 0.011314153671264648 time for calcul the mask position with numpy : 0.030628442764282227 nb_pixel_total : 25854 time to create 1 rle with old method : 0.030657529830932617 time for calcul the mask position with numpy : 0.030615568161010742 nb_pixel_total : 21205 time to create 1 rle with old method : 0.025078773498535156 time for calcul the mask position with numpy : 0.03076791763305664 nb_pixel_total : 78629 time to create 1 rle with old method : 0.09988760948181152 time for calcul the mask position with numpy : 0.031841278076171875 nb_pixel_total : 17971 time to create 1 rle with old method : 0.021535158157348633 time for calcul the mask position with numpy : 0.030298709869384766 nb_pixel_total : 51693 time to create 1 rle with old method : 0.06826376914978027 time for calcul the mask position with numpy : 0.03478693962097168 nb_pixel_total : 42648 time to create 1 rle with old method : 0.0533449649810791 time for calcul the mask position with numpy : 0.032911062240600586 nb_pixel_total : 14703 time to create 1 rle with old method : 0.017372608184814453 time for calcul the mask position with numpy : 0.03353619575500488 nb_pixel_total : 128318 time to create 1 rle with old method : 0.1583724021911621 time for calcul the mask position with numpy : 0.03033161163330078 nb_pixel_total : 34736 time to create 1 rle with old method : 0.04057574272155762 time for calcul the mask position with numpy : 0.030520915985107422 nb_pixel_total : 94944 time to create 1 rle with old method : 0.10936236381530762 time for calcul the mask position with numpy : 0.03813290596008301 nb_pixel_total : 55467 time to create 1 rle with old method : 0.06437468528747559 time for calcul the mask position with numpy : 0.03139615058898926 nb_pixel_total : 130817 time to create 1 rle with old method : 0.1553645133972168 time for calcul the mask position with numpy : 0.03035879135131836 nb_pixel_total : 95437 time to create 1 rle with old method : 0.11212730407714844 time for calcul the mask position with numpy : 0.029987812042236328 nb_pixel_total : 26247 time to create 1 rle with old method : 0.03156542778015137 time for calcul the mask position with numpy : 0.03313279151916504 nb_pixel_total : 14220 time to create 1 rle with old method : 0.017229557037353516 create new chi : 4.1997082233428955 time to delete rle : 0.005553483963012695 batch 1 Loaded 59 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18769 TO DO : save crop sub photo not yet done ! save time : 1.1752021312713623 nb_obj : 27 nb_hashtags : 3 time to prepare the origin masks : 4.1700356006622314 time for calcul the mask position with numpy : 0.9598791599273682 nb_pixel_total : 6070773 time to create 1 rle with new method : 1.2358729839324951 time for calcul the mask position with numpy : 0.03237795829772949 nb_pixel_total : 34144 time to create 1 rle with old method : 0.04248046875 time for calcul the mask position with numpy : 0.029572486877441406 nb_pixel_total : 8587 time to create 1 rle with old method : 0.011569738388061523 time for calcul the mask position with numpy : 0.03429460525512695 nb_pixel_total : 26258 time to create 1 rle with old method : 0.03176116943359375 time for calcul the mask position with numpy : 0.032659053802490234 nb_pixel_total : 11084 time to create 1 rle with old method : 0.015488386154174805 time for calcul the mask position with numpy : 0.030806303024291992 nb_pixel_total : 5492 time to create 1 rle with old method : 0.006439924240112305 time for calcul the mask position with numpy : 0.031097412109375 nb_pixel_total : 12638 time to create 1 rle with old method : 0.018526554107666016 time for calcul the mask position with numpy : 0.033254384994506836 nb_pixel_total : 122782 time to create 1 rle with old method : 0.14747905731201172 time for calcul the mask position with numpy : 0.03211164474487305 nb_pixel_total : 43145 time to create 1 rle with old method : 0.08582615852355957 time for calcul the mask position with numpy : 0.04715251922607422 nb_pixel_total : 72665 time to create 1 rle with old method : 0.17692112922668457 time for calcul the mask position with numpy : 0.040749549865722656 nb_pixel_total : 27056 time to create 1 rle with old method : 0.042169809341430664 time for calcul the mask position with numpy : 0.0312652587890625 nb_pixel_total : 25134 time to create 1 rle with old method : 0.0301516056060791 time for calcul the mask position with numpy : 0.03239703178405762 nb_pixel_total : 108651 time to create 1 rle with old method : 0.13309693336486816 time for calcul the mask position with numpy : 0.03318524360656738 nb_pixel_total : 8597 time to create 1 rle with old method : 0.010346412658691406 time for calcul the mask position with numpy : 0.03294014930725098 nb_pixel_total : 182691 time to create 1 rle with new method : 0.5147037506103516 time for calcul the mask position with numpy : 0.03365468978881836 nb_pixel_total : 16761 time to create 1 rle with old method : 0.028542041778564453 time for calcul the mask position with numpy : 0.032508134841918945 nb_pixel_total : 35708 time to create 1 rle with old method : 0.04741072654724121 time for calcul the mask position with numpy : 0.03140616416931152 nb_pixel_total : 15971 time to create 1 rle with old method : 0.021631956100463867 time for calcul the mask position with numpy : 0.03147244453430176 nb_pixel_total : 7182 time to create 1 rle with old method : 0.010964155197143555 time for calcul the mask position with numpy : 0.033652544021606445 nb_pixel_total : 31238 time to create 1 rle with old method : 0.04978632926940918 time for calcul the mask position with numpy : 0.03264284133911133 nb_pixel_total : 13965 time to create 1 rle with old method : 0.018408775329589844 time for calcul the mask position with numpy : 0.03152751922607422 nb_pixel_total : 5563 time to create 1 rle with old method : 0.007283210754394531 time for calcul the mask position with numpy : 0.03188514709472656 nb_pixel_total : 80322 time to create 1 rle with old method : 0.12518858909606934 time for calcul the mask position with numpy : 0.033091068267822266 nb_pixel_total : 14593 time to create 1 rle with old method : 0.016985177993774414 time for calcul the mask position with numpy : 0.029901504516601562 nb_pixel_total : 15792 time to create 1 rle with old method : 0.018575429916381836 time for calcul the mask position with numpy : 0.03002619743347168 nb_pixel_total : 23306 time to create 1 rle with old method : 0.02702808380126953 time for calcul the mask position with numpy : 0.031125545501708984 nb_pixel_total : 23325 time to create 1 rle with old method : 0.03291726112365723 time for calcul the mask position with numpy : 0.03343677520751953 nb_pixel_total : 6817 time to create 1 rle with old method : 0.011380672454833984 create new chi : 4.85315728187561 time to delete rle : 0.004790306091308594 batch 1 Loaded 55 chid ids of type : 3594 +++++++++++++++++++++++++++++++++Number RLEs to save : 14427 TO DO : save crop sub photo not yet done ! save time : 0.9234230518341064 nb_obj : 15 nb_hashtags : 3 time to prepare the origin masks : 7.645314693450928 time for calcul the mask position with numpy : 0.9573032855987549 nb_pixel_total : 6601727 time to create 1 rle with new method : 0.8124475479125977 time for calcul the mask position with numpy : 0.050858497619628906 nb_pixel_total : 89119 time to create 1 rle with old method : 0.10994696617126465 time for calcul the mask position with numpy : 0.05078768730163574 nb_pixel_total : 24948 time to create 1 rle with old method : 0.03261399269104004 time for calcul the mask position with numpy : 0.046375274658203125 nb_pixel_total : 16501 time to create 1 rle with old method : 0.020175695419311523 time for calcul the mask position with numpy : 0.04575848579406738 nb_pixel_total : 8417 time to create 1 rle with old method : 0.009975671768188477 time for calcul the mask position with numpy : 0.04643440246582031 nb_pixel_total : 6183 time to create 1 rle with old method : 0.10159945487976074 time for calcul the mask position with numpy : 0.04853987693786621 nb_pixel_total : 14320 time to create 1 rle with old method : 0.01709914207458496 time for calcul the mask position with numpy : 0.04487180709838867 nb_pixel_total : 109133 time to create 1 rle with old method : 0.13615655899047852 time for calcul the mask position with numpy : 0.058725595474243164 nb_pixel_total : 11744 time to create 1 rle with old method : 0.016039133071899414 time for calcul the mask position with numpy : 0.053247690200805664 nb_pixel_total : 16236 time to create 1 rle with old method : 0.019011974334716797 time for calcul the mask position with numpy : 0.04970240592956543 nb_pixel_total : 42475 time to create 1 rle with old method : 0.054070472717285156 time for calcul the mask position with numpy : 0.038170814514160156 nb_pixel_total : 11180 time to create 1 rle with old method : 0.01311182975769043 time for calcul the mask position with numpy : 0.04043698310852051 nb_pixel_total : 11854 time to create 1 rle with old method : 0.01425027847290039 time for calcul the mask position with numpy : 0.037751197814941406 nb_pixel_total : 24500 time to create 1 rle with old method : 0.03886985778808594 time for calcul the mask position with numpy : 0.03246283531188965 nb_pixel_total : 12806 time to create 1 rle with old method : 0.018234968185424805 time for calcul the mask position with numpy : 0.026034832000732422 nb_pixel_total : 49097 time to create 1 rle with old method : 0.060410499572753906 create new chi : 3.1522462368011475 time to delete rle : 0.002579927444458008 batch 1 Loaded 31 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 8588 TO DO : save crop sub photo not yet done ! save time : 0.5625360012054443 nb_obj : 19 nb_hashtags : 2 time to prepare the origin masks : 10.214311361312866 time for calcul the mask position with numpy : 0.40215182304382324 nb_pixel_total : 6207839 time to create 1 rle with new method : 0.6120054721832275 time for calcul the mask position with numpy : 0.039107322692871094 nb_pixel_total : 27311 time to create 1 rle with old method : 0.032468318939208984 time for calcul the mask position with numpy : 0.03978705406188965 nb_pixel_total : 54812 time to create 1 rle with old method : 0.0633089542388916 time for calcul the mask position with numpy : 0.03622317314147949 nb_pixel_total : 17776 time to create 1 rle with old method : 0.021451473236083984 time for calcul the mask position with numpy : 0.041756391525268555 nb_pixel_total : 28699 time to create 1 rle with old method : 0.04228043556213379 time for calcul the mask position with numpy : 0.032981157302856445 nb_pixel_total : 20894 time to create 1 rle with old method : 0.0242767333984375 time for calcul the mask position with numpy : 0.03321027755737305 nb_pixel_total : 80823 time to create 1 rle with old method : 0.09291815757751465 time for calcul the mask position with numpy : 0.03857994079589844 nb_pixel_total : 11572 time to create 1 rle with old method : 0.013624191284179688 time for calcul the mask position with numpy : 0.03615140914916992 nb_pixel_total : 16922 time to create 1 rle with old method : 0.019703149795532227 time for calcul the mask position with numpy : 0.0341801643371582 nb_pixel_total : 40763 time to create 1 rle with old method : 0.04848980903625488 time for calcul the mask position with numpy : 0.032024383544921875 nb_pixel_total : 6327 time to create 1 rle with old method : 0.00757908821105957 time for calcul the mask position with numpy : 0.028751373291015625 nb_pixel_total : 17161 time to create 1 rle with old method : 0.02002120018005371 time for calcul the mask position with numpy : 0.028426647186279297 nb_pixel_total : 10841 time to create 1 rle with old method : 0.012609004974365234 time for calcul the mask position with numpy : 0.02857494354248047 nb_pixel_total : 44507 time to create 1 rle with old method : 0.05189037322998047 time for calcul the mask position with numpy : 0.028594017028808594 nb_pixel_total : 48166 time to create 1 rle with old method : 0.05558276176452637 time for calcul the mask position with numpy : 0.028377771377563477 nb_pixel_total : 173689 time to create 1 rle with new method : 0.8145995140075684 time for calcul the mask position with numpy : 0.03994417190551758 nb_pixel_total : 20436 time to create 1 rle with old method : 0.027095556259155273 time for calcul the mask position with numpy : 0.039539337158203125 nb_pixel_total : 23735 time to create 1 rle with old method : 0.027852535247802734 time for calcul the mask position with numpy : 0.037915706634521484 nb_pixel_total : 181674 time to create 1 rle with new method : 0.4200558662414551 time for calcul the mask position with numpy : 0.03136014938354492 nb_pixel_total : 16293 time to create 1 rle with old method : 0.019090890884399414 create new chi : 3.5915913581848145 time to delete rle : 0.0020487308502197266 batch 1 Loaded 39 chid ids of type : 3594 ++++++++++++++++++++++++++++Number RLEs to save : 12082 TO DO : save crop sub photo not yet done ! save time : 0.7550456523895264 map_output_result : {1349984441: (0.0, 'Should be the crop_list due to order', 0), 1349984424: (0.0, 'Should be the crop_list due to order', 0), 1349984411: (0.0, 'Should be the crop_list due to order', 0), 1349984408: (0.0, 'Should be the crop_list due to order', 0), 1349984388: (0.0, 'Should be the crop_list due to order', 0), 1349984384: (0.0, 'Should be the crop_list due to order', 0), 1349984379: (0.0, 'Should be the crop_list due to order', 0), 1349984367: (0.0, 'Should be the crop_list due to order', 0), 1349984353: (0.0, 'Should be the crop_list due to order', 0), 1349979483: (0.0, 'Should be the crop_list due to order', 0), 1349979434: (0.0, 'Should be the crop_list due to order', 0), 1349979333: (0.0, 'Should be the crop_list due to order', 0), 1349979328: (0.0, 'Should be the crop_list due to order', 0), 1349979286: (0.0, 'Should be the crop_list due to order', 0), 1349979258: (0.0, 'Should be the crop_list due to order', 0), 1349979228: (0.0, 'Should be the crop_list due to order', 0), 1349979145: (0.0, 'Should be the crop_list due to order', 0), 1349979139: (0.0, 'Should be the crop_list due to order', 0), 1349979135: (0.0, 'Should be the crop_list due to order', 0), 1349978774: (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 [1349984441, 1349984424, 1349984411, 1349984408, 1349984388, 1349984384, 1349984379, 1349984367, 1349984353, 1349979483, 1349979434, 1349979333, 1349979328, 1349979286, 1349979258, 1349979228, 1349979145, 1349979139, 1349979135, 1349978774] Looping around the photos to save general results len do output : 20 /1349984441.Didn't retrieve data . /1349984424.Didn't retrieve data . /1349984411.Didn't retrieve data . /1349984408.Didn't retrieve data . /1349984388.Didn't retrieve data . /1349984384.Didn't retrieve data . /1349984379.Didn't retrieve data . /1349984367.Didn't retrieve data . /1349984353.Didn't retrieve data . /1349979483.Didn't retrieve data . /1349979434.Didn't retrieve data . /1349979333.Didn't retrieve data . /1349979328.Didn't retrieve data . /1349979286.Didn't retrieve data . /1349979258.Didn't retrieve data . /1349979228.Didn't retrieve data . /1349979145.Didn't retrieve data . /1349979139.Didn't retrieve data . /1349979135.Didn't retrieve data . /1349978774.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, '2723399') ('3318', '22049547', '1349984441', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984424', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984411', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984408', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984388', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984384', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984379', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984367', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984353', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979483', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979434', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979333', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979328', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979286', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979258', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979228', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979145', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979139', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979135', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349978774', None, None, None, None, None, '2723399') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 60 time used for this insertion : 0.01525115966796875 save_final save missing photos in datou_result : time spend for datou_step_exec : 217.39269351959229 time spend to save output : 0.02153635025024414 total time spend for step 3 : 217.41422986984253 step4:ventilate_hashtags_in_portfolio Tue Apr 15 21:11:41 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 : 22049547 get user id for portfolio 22049547 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`=22049547 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','pet_clair','environnement','carton','background','autre','metal','flou','pehd','pet_fonce','mal_croppe')) 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`=22049547 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','pet_clair','environnement','carton','background','autre','metal','flou','pehd','pet_fonce','mal_croppe')) 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`=22049547 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','pet_clair','environnement','carton','background','autre','metal','flou','pehd','pet_fonce','mal_croppe')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22050160,22050161,22050162,22050163,22050164,22050165,22050166,22050167,22050168,22050169,22050170?tags=papier,pet_clair,environnement,carton,background,autre,metal,flou,pehd,pet_fonce,mal_croppe Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349984441, 1349984424, 1349984411, 1349984408, 1349984388, 1349984384, 1349984379, 1349984367, 1349984353, 1349979483, 1349979434, 1349979333, 1349979328, 1349979286, 1349979258, 1349979228, 1349979145, 1349979139, 1349979135, 1349978774] Looping around the photos to save general results len do output : 1 /22049547. 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, '2723399') ('3318', '22049547', '1349984441', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984424', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984411', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984408', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984388', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984384', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984379', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984367', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984353', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979483', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979434', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979333', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979328', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979286', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979258', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979228', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979145', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979139', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979135', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349978774', None, None, None, None, None, '2723399') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.016787290573120117 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.6930952072143555 time spend to save output : 0.017518043518066406 total time spend for step 4 : 1.7106132507324219 step5:final Tue Apr 15 21:11:43 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 ! 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 : {1349984441: ('0.1384548114674111',), 1349984424: ('0.1384548114674111',), 1349984411: ('0.1384548114674111',), 1349984408: ('0.1384548114674111',), 1349984388: ('0.1384548114674111',), 1349984384: ('0.1384548114674111',), 1349984379: ('0.1384548114674111',), 1349984367: ('0.1384548114674111',), 1349984353: ('0.1384548114674111',), 1349979483: ('0.1384548114674111',), 1349979434: ('0.1384548114674111',), 1349979333: ('0.1384548114674111',), 1349979328: ('0.1384548114674111',), 1349979286: ('0.1384548114674111',), 1349979258: ('0.1384548114674111',), 1349979228: ('0.1384548114674111',), 1349979145: ('0.1384548114674111',), 1349979139: ('0.1384548114674111',), 1349979135: ('0.1384548114674111',), 1349978774: ('0.1384548114674111',)} new output for save of step final : {1349984441: ('0.1384548114674111',), 1349984424: ('0.1384548114674111',), 1349984411: ('0.1384548114674111',), 1349984408: ('0.1384548114674111',), 1349984388: ('0.1384548114674111',), 1349984384: ('0.1384548114674111',), 1349984379: ('0.1384548114674111',), 1349984367: ('0.1384548114674111',), 1349984353: ('0.1384548114674111',), 1349979483: ('0.1384548114674111',), 1349979434: ('0.1384548114674111',), 1349979333: ('0.1384548114674111',), 1349979328: ('0.1384548114674111',), 1349979286: ('0.1384548114674111',), 1349979258: ('0.1384548114674111',), 1349979228: ('0.1384548114674111',), 1349979145: ('0.1384548114674111',), 1349979139: ('0.1384548114674111',), 1349979135: ('0.1384548114674111',), 1349978774: ('0.1384548114674111',)} [1349984441, 1349984424, 1349984411, 1349984408, 1349984388, 1349984384, 1349984379, 1349984367, 1349984353, 1349979483, 1349979434, 1349979333, 1349979328, 1349979286, 1349979258, 1349979228, 1349979145, 1349979139, 1349979135, 1349978774] Looping around the photos to save general results len do output : 20 /1349984441.Didn't retrieve data . /1349984424.Didn't retrieve data . /1349984411.Didn't retrieve data . /1349984408.Didn't retrieve data . /1349984388.Didn't retrieve data . /1349984384.Didn't retrieve data . /1349984379.Didn't retrieve data . /1349984367.Didn't retrieve data . /1349984353.Didn't retrieve data . /1349979483.Didn't retrieve data . /1349979434.Didn't retrieve data . /1349979333.Didn't retrieve data . /1349979328.Didn't retrieve data . /1349979286.Didn't retrieve data . /1349979258.Didn't retrieve data . /1349979228.Didn't retrieve data . /1349979145.Didn't retrieve data . /1349979139.Didn't retrieve data . /1349979135.Didn't retrieve data . /1349978774.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, '2723399') ('3318', '22049547', '1349984441', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984424', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984411', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984408', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984388', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984384', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984379', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984367', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984353', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979483', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979434', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979333', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979328', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979286', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979258', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979228', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979145', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979139', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979135', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349978774', None, None, None, None, None, '2723399') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 60 time used for this insertion : 0.017526865005493164 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.13142776489257812 time spend to save output : 0.018824100494384766 total time spend for step 5 : 0.1502518653869629 step6:blur_detection Tue Apr 15 21:11:43 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 ! 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/1744743631_1162099_1349984441_a9861c48d430d77f7be1da2369fe50d9.jpg resize: (2160, 3264) 1349984441 -2.300668496648032 treat image : temp/1744743631_1162099_1349984424_5bd7c61667f99dccc9cae02cc798698c.jpg resize: (2160, 3264) 1349984424 -0.3083754089011918 treat image : temp/1744743631_1162099_1349984411_9c7e8ce40ff5bed9e30f8afb214d3187.jpg resize: (2160, 3264) 1349984411 -0.40000972215517105 treat image : temp/1744743631_1162099_1349984408_e7c9a2b3388d0665a8e94990f2b18357.jpg resize: (2160, 3264) 1349984408 -1.6340576780302205 treat image : temp/1744743631_1162099_1349984388_838fb13879f9b1d2cb5c30f6051ce316.jpg resize: (2160, 3264) 1349984388 -0.6616610830417063 treat image : temp/1744743631_1162099_1349984384_6e3175437750a3f390bc7dc02b2adce2.jpg resize: (2160, 3264) 1349984384 -4.561014331643432 treat image : temp/1744743631_1162099_1349984379_7a7c49aa659ced4c0f4e3af292ec5f97.jpg resize: (2160, 3264) 1349984379 -4.653602208652279 treat image : temp/1744743631_1162099_1349984367_66c00db67908e97dbb90dca642747f20.jpg resize: (2160, 3264) 1349984367 -0.4091036149163924 treat image : temp/1744743631_1162099_1349984353_ebbb93c8ae56702aac6690fc197e28d9.jpg resize: (2160, 3264) 1349984353 -1.4786172959928001 treat image : temp/1744743631_1162099_1349979483_75ad77d200c99f7515810e4e96c9dcd9.jpg resize: (2160, 3264) 1349979483 -2.1456095609127517 treat image : temp/1744743631_1162099_1349979434_4ba3b760abf9e649243196fa738e241f.jpg resize: (2160, 3264) 1349979434 -0.5794783024064504 treat image : temp/1744743631_1162099_1349979333_95446dad71f791b92cb34b72570ea7d1.jpg resize: (2160, 3264) 1349979333 -4.313246385483905 treat image : temp/1744743631_1162099_1349979328_2e3c4c6dd0a7e6e502914131cb43fb8a.jpg resize: (2160, 3264) 1349979328 -5.905618981657044 treat image : temp/1744743631_1162099_1349979286_6e67f8e8fabedbce1f900554bab3de02.jpg resize: (2160, 3264) 1349979286 -3.825088641172106 treat image : temp/1744743631_1162099_1349979258_95bd1e7936bcdaa75aad5d41ff7d033d.jpg resize: (2160, 3264) 1349979258 -3.5009337720936564 treat image : temp/1744743631_1162099_1349979228_c4088d7d6187715d6a2a0e5820da467c.jpg resize: (2160, 3264) 1349979228 -2.8383413547661105 treat image : temp/1744743631_1162099_1349979145_be7043b8ec35deb488ba7eb2f0c17c52.jpg resize: (2160, 3264) 1349979145 -4.1242760807706045 treat image : temp/1744743631_1162099_1349979139_6b410b7545ba68165a20f8242152d595.jpg resize: (2160, 3264) 1349979139 -3.8400259961701595 treat image : temp/1744743631_1162099_1349979135_a386a0537a9ce536858f4fccdbb0273f.jpg resize: (2160, 3264) 1349979135 2.179197866623937 treat image : temp/1744743631_1162099_1349978774_95f4b3150a26e017a5ab75191925eea2.jpg resize: (2160, 3264) 1349978774 -1.0542542347771697 treat image : 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missing photos in datou_result : time spend for datou_step_exec : 86.41352844238281 time spend to save output : 0.13756203651428223 total time spend for step 6 : 86.5510904788971 step7:brightness Tue Apr 15 21:13:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1744743631_1162099_1349984441_a9861c48d430d77f7be1da2369fe50d9.jpg treat image : temp/1744743631_1162099_1349984424_5bd7c61667f99dccc9cae02cc798698c.jpg treat image : temp/1744743631_1162099_1349984411_9c7e8ce40ff5bed9e30f8afb214d3187.jpg treat image : temp/1744743631_1162099_1349984408_e7c9a2b3388d0665a8e94990f2b18357.jpg treat image : temp/1744743631_1162099_1349984388_838fb13879f9b1d2cb5c30f6051ce316.jpg treat image : temp/1744743631_1162099_1349984384_6e3175437750a3f390bc7dc02b2adce2.jpg treat image : temp/1744743631_1162099_1349984379_7a7c49aa659ced4c0f4e3af292ec5f97.jpg treat image : temp/1744743631_1162099_1349984367_66c00db67908e97dbb90dca642747f20.jpg treat image : temp/1744743631_1162099_1349984353_ebbb93c8ae56702aac6690fc197e28d9.jpg treat image : temp/1744743631_1162099_1349979483_75ad77d200c99f7515810e4e96c9dcd9.jpg treat image : 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temp/1744743631_1162099_1349984424_5bd7c61667f99dccc9cae02cc798698c_rle_crop_3747319071_0.png treat image : temp/1744743631_1162099_1349984411_9c7e8ce40ff5bed9e30f8afb214d3187_rle_crop_3747319075_0.png treat image : temp/1744743631_1162099_1349984408_e7c9a2b3388d0665a8e94990f2b18357_rle_crop_3747319086_0.png treat image : temp/1744743631_1162099_1349984408_e7c9a2b3388d0665a8e94990f2b18357_rle_crop_3747319091_0.png treat image : temp/1744743631_1162099_1349984388_838fb13879f9b1d2cb5c30f6051ce316_rle_crop_3747319096_0.png treat image : temp/1744743631_1162099_1349984388_838fb13879f9b1d2cb5c30f6051ce316_rle_crop_3747319098_0.png treat image : temp/1744743631_1162099_1349984388_838fb13879f9b1d2cb5c30f6051ce316_rle_crop_3747319105_0.png treat image : temp/1744743631_1162099_1349984384_6e3175437750a3f390bc7dc02b2adce2_rle_crop_3747319133_0.png treat image : temp/1744743631_1162099_1349984384_6e3175437750a3f390bc7dc02b2adce2_rle_crop_3747319122_0.png treat image : temp/1744743631_1162099_1349984379_7a7c49aa659ced4c0f4e3af292ec5f97_rle_crop_3747319162_0.png treat image : temp/1744743631_1162099_1349979434_4ba3b760abf9e649243196fa738e241f_rle_crop_3747319221_0.png treat image : temp/1744743631_1162099_1349979434_4ba3b760abf9e649243196fa738e241f_rle_crop_3747319224_0.png treat image : temp/1744743631_1162099_1349979434_4ba3b760abf9e649243196fa738e241f_rle_crop_3747319227_0.png treat image : temp/1744743631_1162099_1349979434_4ba3b760abf9e649243196fa738e241f_rle_crop_3747319226_0.png treat image : temp/1744743631_1162099_1349979328_2e3c4c6dd0a7e6e502914131cb43fb8a_rle_crop_3747319287_0.png treat image : temp/1744743631_1162099_1349979286_6e67f8e8fabedbce1f900554bab3de02_rle_crop_3747319298_0.png treat image : temp/1744743631_1162099_1349979258_95bd1e7936bcdaa75aad5d41ff7d033d_rle_crop_3747319322_0.png treat image : temp/1744743631_1162099_1349979258_95bd1e7936bcdaa75aad5d41ff7d033d_rle_crop_3747319340_0.png treat image : 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temp/1744743631_1162099_1349979139_6b410b7545ba68165a20f8242152d595_rle_crop_3747319414_0.png treat image : temp/1744743631_1162099_1349979139_6b410b7545ba68165a20f8242152d595_rle_crop_3747319428_0.png treat image : temp/1744743631_1162099_1349979135_a386a0537a9ce536858f4fccdbb0273f_rle_crop_3747319443_0.png treat image : temp/1744743631_1162099_1349978774_95f4b3150a26e017a5ab75191925eea2_rle_crop_3747319455_0.png treat image : temp/1744743631_1162099_1349978774_95f4b3150a26e017a5ab75191925eea2_rle_crop_3747319464_0.png treat image : temp/1744743631_1162099_1349978774_95f4b3150a26e017a5ab75191925eea2_rle_crop_3747319468_0.png treat image : temp/1744743631_1162099_1349978774_95f4b3150a26e017a5ab75191925eea2_rle_crop_3747319461_0.png treat image : temp/1744743631_1162099_1349984411_9c7e8ce40ff5bed9e30f8afb214d3187_rle_crop_3747319077_0.png treat image : temp/1744743631_1162099_1349984379_7a7c49aa659ced4c0f4e3af292ec5f97_rle_crop_3747319155_0.png treat image : temp/1744743631_1162099_1349979434_4ba3b760abf9e649243196fa738e241f_rle_crop_3747319213_0.png treat image : temp/1744743631_1162099_1349979328_2e3c4c6dd0a7e6e502914131cb43fb8a_rle_crop_3747319263_0.png treat image : temp/1744743631_1162099_1349979258_95bd1e7936bcdaa75aad5d41ff7d033d_rle_crop_3747319336_0.png treat image : temp/1744743631_1162099_1349979258_95bd1e7936bcdaa75aad5d41ff7d033d_rle_crop_3747319323_0.png treat image : temp/1744743631_1162099_1349979258_95bd1e7936bcdaa75aad5d41ff7d033d_rle_crop_3747319342_0.png treat image : temp/1744743631_1162099_1349979258_95bd1e7936bcdaa75aad5d41ff7d033d_rle_crop_3747319343_0.png treat image : temp/1744743631_1162099_1349979228_c4088d7d6187715d6a2a0e5820da467c_rle_crop_3747319353_0.png treat image : temp/1744743631_1162099_1349979145_be7043b8ec35deb488ba7eb2f0c17c52_rle_crop_3747319393_0.png treat image : temp/1744743631_1162099_1349984379_7a7c49aa659ced4c0f4e3af292ec5f97_rle_crop_3747319157_0.png treat image : temp/1744743631_1162099_1349979228_c4088d7d6187715d6a2a0e5820da467c_rle_crop_3747319376_0.png treat image : temp/1744743631_1162099_1349984441_a9861c48d430d77f7be1da2369fe50d9_rle_crop_3747319054_0.png treat image : temp/1744743631_1162099_1349984384_6e3175437750a3f390bc7dc02b2adce2_rle_crop_3747319135_0.png Inside saveOutput : final : False verbose : 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 456 time used for this insertion : 0.03373265266418457 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 456 time used for this insertion : 0.08472347259521484 save missing photos in datou_result : time spend for datou_step_exec : 20.304935932159424 time spend to save output : 0.1249384880065918 total time spend for step 7 : 20.429874420166016 step8:velours_tree Tue Apr 15 21:13:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 2.887972593307495 time spend to save output : 0.14448189735412598 total time spend for step 8 : 3.032454490661621 step9:send_mail_cod Tue Apr 15 21:13:33 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_P22049547_15-04-2025_21_13_33.pdf 22050160 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 .imagette220501601744744413 22050161 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 .imagette220501611744744414 22050163 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 .imagette220501631744744416 22050164 imagette220501641744744417 22050165 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 .imagette220501651744744417 22050166 change filename to text .change filename to text .change filename to text .imagette220501661744744418 22050167 imagette220501671744744418 22050168 change filename to text .change filename to text .imagette220501681744744418 22050169 change filename to text .change filename to text .imagette220501691744744418 22050170 imagette220501701744744418 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22049547 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22050160,22050161,22050162,22050163,22050164,22050165,22050166,22050167,22050168,22050169,22050170?tags=papier,pet_clair,environnement,carton,background,autre,metal,flou,pehd,pet_fonce,mal_croppe args[1349984441] : ((1349984441, -2.300668496648032, 492609224), (1349984441, -0.12584218813720455, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349984424] : ((1349984424, -0.3083754089011918, 492688767), (1349984424, -0.1393085464633259, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349984411] : ((1349984411, -0.40000972215517105, 492688767), (1349984411, -0.36064481299992834, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349984408] : ((1349984408, -1.6340576780302205, 492688767), (1349984408, -0.16220270316258573, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349984388] : ((1349984388, -0.6616610830417063, 492688767), (1349984388, 0.292460917014064, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349984384] : ((1349984384, -4.561014331643432, 492609224), (1349984384, 0.138480145149085, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349984379] : ((1349984379, -4.653602208652279, 492609224), (1349984379, -0.04643810080782548, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349984367] : ((1349984367, -0.4091036149163924, 492688767), (1349984367, 0.23432686614419182, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349984353] : ((1349984353, -1.4786172959928001, 492688767), (1349984353, -0.22035061403016723, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979483] : ((1349979483, -2.1456095609127517, 492609224), (1349979483, 0.4283584959639978, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979434] : ((1349979434, -0.5794783024064504, 492688767), (1349979434, -0.2519655861952176, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979333] : ((1349979333, -4.313246385483905, 492609224), (1349979333, -0.04993149576172414, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979328] : ((1349979328, -5.905618981657044, 492609224), (1349979328, -0.007017059097484609, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979286] : ((1349979286, -3.825088641172106, 492609224), (1349979286, 0.16080591480849052, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979258] : ((1349979258, -3.5009337720936564, 492609224), (1349979258, -0.3816757608095337, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979228] : ((1349979228, -2.8383413547661105, 492609224), (1349979228, -0.024266608984526095, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979145] : ((1349979145, -4.1242760807706045, 492609224), (1349979145, 0.06445689003244745, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979139] : ((1349979139, -3.8400259961701595, 492609224), (1349979139, -0.10222658725059094, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349979135] : ((1349979135, 2.179197866623937, 492688767), (1349979135, -0.29319489709233326, 496442774), '0.1384548114674111') We are sending mail with results at report@fotonower.com args[1349978774] : ((1349978774, -1.0542542347771697, 492688767), (1349978774, 0.32074060668854093, 2107752395), '0.1384548114674111') We are sending mail with results at report@fotonower.com refus_total : 0.1384548114674111 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=22049547 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349979139,1349979228,1349984379,1349979258,1349979483,1349984441,1349984424,1349984411,1349984408,1349984388,1349984384,1349984367,1349984353,1349978774,1349979434,1349979333,1349979328,1349979286,1349979145,1349979135) Found this number of photos: 20 begin to download photo : 1349979139 begin to download photo : 1349984441 begin to download photo : 1349984384 begin to download photo : 1349979333 download finish for photo 1349984441 begin to download photo : 1349984424 download finish for photo 1349979333 begin to download photo : 1349979328 download finish for photo 1349979139 begin to download photo : 1349979228 download finish for photo 1349984384 begin to download photo : 1349984367 download finish for photo 1349984424 begin to download photo : 1349984411 download finish for photo 1349979328 begin to download photo : 1349979286 download finish for photo 1349984411 begin to download photo : 1349984408 download finish for photo 1349984367 begin to download photo : 1349984353 download finish for photo 1349979228 begin to download photo : 1349984379 download finish for photo 1349979286 begin to download photo : 1349979145 download finish for photo 1349984408 begin to download photo : 1349984388 download finish for photo 1349984353 begin to download photo : 1349978774 download finish for photo 1349984379 begin to download photo : 1349979258 download finish for photo 1349984388 download finish for photo 1349979145 begin to download photo : 1349979135 download finish for photo 1349978774 begin to download photo : 1349979434 download finish for photo 1349979258 begin to download photo : 1349979483 download finish for photo 1349979434 download finish for photo 1349979483 download finish for photo 1349979135 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22049547_15-04-2025_21_13_33.pdf results_Auto_P22049547_15-04-2025_21_13_33.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22049547_15-04-2025_21_13_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','22049547','results_Auto_P22049547_15-04-2025_21_13_33.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22049547_15-04-2025_21_13_33.pdf','pdf','','1.06','0.1384548114674111') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22049547

https://www.fotonower.com/image?json=false&list_photos_id=1349984441
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349984424
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349984411
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349984408
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349984388
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349984384
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349984379
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349984367
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349984353
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979483
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979434
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979333
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979328
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979286
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979258
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979228
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979145
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979139
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349979135
La photo est trop floue, merci de reprendre une photo.(avec le score = 2.179197866623937)
https://www.fotonower.com/image?json=false&list_photos_id=1349978774
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 13.85%
Veuillez trouver les photos des contaminants.

exemples de contaminants: papier: https://www.fotonower.com/view/22050160?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22050161?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22050163?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22050165?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/22050166?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/22050168?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22050169?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22049547_15-04-2025_21_13_33.pdf.

Lien vers velours :https://www.fotonower.com/velours/22050160,22050161,22050162,22050163,22050164,22050165,22050166,22050167,22050168,22050169,22050170?tags=papier,pet_clair,environnement,carton,background,autre,metal,flou,pehd,pet_fonce,mal_croppe.


L'équipe Fotonower 202 b'' Server: nginx Date: Tue, 15 Apr 2025 19:13:44 GMT Content-Length: 0 Connection: close X-Message-Id: Wlm_kXALTEe3tgkzRozBMQ 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 [1349984441, 1349984424, 1349984411, 1349984408, 1349984388, 1349984384, 1349984379, 1349984367, 1349984353, 1349979483, 1349979434, 1349979333, 1349979328, 1349979286, 1349979258, 1349979228, 1349979145, 1349979139, 1349979135, 1349978774] 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, '2723399') ('3318', '22049547', '1349984441', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984424', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984411', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984408', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984388', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984384', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984379', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984367', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984353', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979483', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979434', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979333', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979328', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979286', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979258', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979228', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979145', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979139', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979135', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349978774', None, None, None, None, None, '2723399') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.016366243362426758 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.80610728263855 time spend to save output : 0.016705751419067383 total time spend for step 9 : 10.822813034057617 step10:split_time_score Tue Apr 15 21:13:44 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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'}] (('19', 11), ('20', 9)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 15042025 22049547 Nombre de photos uploadées : 20 / 23040 (0%) 15042025 22049547 Nombre de photos taguées (types de déchets): 0 / 20 (0%) 15042025 22049547 Nombre de photos taguées (volume) : 0 / 20 (0%) elapsed_time : load_data_split_time_score 3.337860107421875e-06 elapsed_time : order_list_meta_photo_and_scores 9.298324584960938e-06 ???????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0007815361022949219 elapsed_time : insert_dashboard_record_day_entry 0.0223388671875 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 : 0.20541652095918328 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22027920_15-04-2025_12_49_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22027920 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`=22027920 AND mptpi.`type`=3594 To do Qualite : 0.10993276470020877 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22027923_15-04-2025_12_24_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22027923 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`=22027923 AND mptpi.`type`=3594 To do Qualite : 0.05652087531411031 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22028718_15-04-2025_13_11_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22028718 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`=22028718 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22049536 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22049537 order by id desc limit 1 Qualite : 0.04204030528478445 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22049539_15-04-2025_21_13_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22049539 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`=22049539 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22049544 order by id desc limit 1 Qualite : 0.1384548114674111 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22049547_15-04-2025_21_13_33.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22049547 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`=22049547 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'15042025': {'nb_upload': 20, '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 [1349984441, 1349984424, 1349984411, 1349984408, 1349984388, 1349984384, 1349984379, 1349984367, 1349984353, 1349979483, 1349979434, 1349979333, 1349979328, 1349979286, 1349979258, 1349979228, 1349979145, 1349979139, 1349979135, 1349978774] Looping around the photos to save general results len do output : 1 /22049547Didn'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, '2723399') ('3318', '22049547', '1349984441', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984424', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984411', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984408', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984388', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984384', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984379', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984367', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349984353', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979483', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979434', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979333', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979328', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979286', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979258', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979228', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979145', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979139', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349979135', None, None, None, None, None, '2723399') ('3318', None, None, None, None, None, None, None, '2723399') ('3318', '22049547', '1349978774', None, None, None, None, None, '2723399') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 21 time used for this insertion : 0.016959428787231445 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.946643829345703 time spend to save output : 0.017260313034057617 total time spend for step 10 : 2.9639041423797607 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 20 set_done_treatment 374.56user 265.48system 13:21.26elapsed 79%CPU (0avgtext+0avgdata 6673772maxresident)k 8452440inputs+227208outputs (284013major+34758492minor)pagefaults 0swaps