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 : 2118874 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 : ['2711139'] with mtr_portfolio_ids : ['21929822'] and first list_photo_ids : [] new path : /proc/2118874/ 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 , BFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 11 ; length of list_pids : 11 ; length of list_args : 11 time to download the photos : 2.405928134918213 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 1 01:20:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-01 01:20:36.069959: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-01 01:20:36.103250: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-01 01:20:36.106070: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fe638000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-01 01:20:36.106140: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-01 01:20:36.114499: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-01 01:20:36.361761: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x40491c40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-01 01:20:36.361832: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-01 01:20:36.363878: 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 2025-04-01 01:20:36.366463: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 01:20:36.404875: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 01:20:36.427633: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 01:20:36.432342: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 01:20:36.469895: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 01:20:36.475811: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 01:20:36.551089: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 01:20:36.553040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 01:20:36.553669: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 01:20:36.555536: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 01:20:36.555559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 01:20:36.555570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 01:20:36.557757: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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. 2025-04-01 01:20:37.111841: 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 2025-04-01 01:20:37.111978: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 01:20:37.111996: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 01:20:37.112011: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 01:20:37.112029: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 01:20:37.112047: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 01:20:37.112065: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 01:20:37.112082: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 01:20:37.113375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 01:20:37.114702: 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 2025-04-01 01:20:37.114746: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-01 01:20:37.114762: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 01:20:37.114776: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-01 01:20:37.114789: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-01 01:20:37.114802: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-01 01:20:37.114816: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-01 01:20:37.114830: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-01 01:20:37.116248: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-01 01:20:37.116295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-01 01:20:37.116310: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-01 01:20:37.116323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-01 01:20:37.118195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 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 : [] 2025-04-01 01:20:50.284648: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-01 01:20:50.669599: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23 /data/models_weight/learn_RUBBIA_REFUS_AMIENS_23/mask_model.h5 size_local : 256009536 size in s3 : 256009536 create time local : 2021-08-09 09:43:22 create time in s3 : 2021-08-06 18:54:04 mask_model.h5 already exist and didn't need to update list_images length : 11 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 89 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 12 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 : 73 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 37 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 : 98 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 : 76 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 : 24 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: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 Detection mask done ! Trying to reset tf kernel 2119586 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 13 tf kernel not reseted sub process len(results) : 11 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 11 len(list_Values) 0 process is alive process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10814 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.08572196960449219 nb_pixel_total : 30996 time to create 1 rle with old method : 0.04082846641540527 length of segment : 247 time for calcul the mask position with numpy : 0.016498327255249023 nb_pixel_total : 13455 time to create 1 rle with old method : 0.02165842056274414 length of segment : 162 time for calcul the mask position with numpy : 0.04248642921447754 nb_pixel_total : 17308 time to create 1 rle with old method : 0.02271866798400879 length of segment : 161 time for calcul the mask position with numpy : 0.0036025047302246094 nb_pixel_total : 11473 time to create 1 rle with old method : 0.0142822265625 length of segment : 143 time for calcul the mask position with numpy : 0.012855291366577148 nb_pixel_total : 15220 time to create 1 rle with old method : 0.023456573486328125 length of segment : 185 time for calcul the mask position with numpy : 0.001184701919555664 nb_pixel_total : 7354 time to create 1 rle with old method : 0.008740425109863281 length of segment : 82 time for calcul the mask position with numpy : 0.003559589385986328 nb_pixel_total : 4188 time to create 1 rle with old method : 0.005526065826416016 length of segment : 73 time for calcul the mask position with numpy : 0.01295781135559082 nb_pixel_total : 22129 time to create 1 rle with old method : 0.0321049690246582 length of segment : 227 time for calcul the mask position with numpy : 0.0314633846282959 nb_pixel_total : 18169 time to create 1 rle with old method : 0.024869918823242188 length of segment : 258 time for calcul the mask position with numpy : 0.06983494758605957 nb_pixel_total : 16701 time to create 1 rle with old method : 0.023576021194458008 length of segment : 202 time for calcul the mask position with numpy : 0.012828350067138672 nb_pixel_total : 9340 time to create 1 rle with old method : 0.012423515319824219 length of segment : 113 time for calcul the mask position with numpy : 0.059838056564331055 nb_pixel_total : 38693 time to create 1 rle with old method : 0.0531773567199707 length of segment : 268 time for calcul the mask position with numpy : 0.054260969161987305 nb_pixel_total : 19602 time to create 1 rle with old method : 0.02742171287536621 length of segment : 177 time for calcul the mask position with numpy : 0.2093517780303955 nb_pixel_total : 92445 time to create 1 rle with old method : 0.11198925971984863 length of segment : 370 time for calcul the mask position with numpy : 0.056931257247924805 nb_pixel_total : 26338 time to create 1 rle with old method : 0.0336613655090332 length of segment : 197 time for calcul the mask position with numpy : 0.027385711669921875 nb_pixel_total : 42090 time to create 1 rle with old method : 0.05525398254394531 length of segment : 291 time for calcul the mask position with numpy : 0.03795504570007324 nb_pixel_total : 12317 time to create 1 rle with old method : 0.014188528060913086 length of segment : 136 time for calcul the mask position with numpy : 0.10576701164245605 nb_pixel_total : 55964 time to create 1 rle with old method : 0.07526659965515137 length of segment : 432 time for calcul the mask position with numpy : 0.009401559829711914 nb_pixel_total : 21965 time to create 1 rle with old method : 0.027461767196655273 length of segment : 196 time for calcul the mask position with numpy : 0.05561494827270508 nb_pixel_total : 97771 time to create 1 rle with old method : 0.11246418952941895 length of segment : 514 time for calcul the mask position with numpy : 0.0684058666229248 nb_pixel_total : 28905 time to create 1 rle with old method : 0.03637218475341797 length of segment : 261 time for calcul the mask position with numpy : 0.0258636474609375 nb_pixel_total : 22794 time to create 1 rle with old method : 0.03291440010070801 length of segment : 275 time for calcul the mask position with numpy : 0.06501007080078125 nb_pixel_total : 14527 time to create 1 rle with old method : 0.018947839736938477 length of segment : 192 time for calcul the mask position with numpy : 0.014434576034545898 nb_pixel_total : 29825 time to create 1 rle with old method : 0.03472256660461426 length of segment : 217 time for calcul the mask position with numpy : 0.0015425682067871094 nb_pixel_total : 6157 time to create 1 rle with old method : 0.010887384414672852 length of segment : 70 time for calcul the mask position with numpy : 0.025462865829467773 nb_pixel_total : 10928 time to create 1 rle with old method : 0.029192686080932617 length of segment : 157 time for calcul the mask position with numpy : 0.048377275466918945 nb_pixel_total : 105743 time to create 1 rle with old method : 0.14804863929748535 length of segment : 419 time for calcul the mask position with numpy : 0.005854368209838867 nb_pixel_total : 6256 time to create 1 rle with old method : 0.01030874252319336 length of segment : 91 time for calcul the mask position with numpy : 0.16274333000183105 nb_pixel_total : 80324 time to create 1 rle with old method : 0.1060638427734375 length of segment : 417 time for calcul the mask position with numpy : 0.06591081619262695 nb_pixel_total : 17629 time to create 1 rle with old method : 0.031713247299194336 length of segment : 188 time for calcul the mask position with numpy : 0.035237789154052734 nb_pixel_total : 85220 time to create 1 rle with old method : 0.11023354530334473 length of segment : 615 time for calcul the mask position with numpy : 0.032991647720336914 nb_pixel_total : 18141 time to create 1 rle with old method : 0.025352954864501953 length of segment : 185 time for calcul the mask position with numpy : 0.024112939834594727 nb_pixel_total : 49291 time to create 1 rle with old method : 0.06202530860900879 length of segment : 462 time for calcul the mask position with numpy : 0.06569504737854004 nb_pixel_total : 44810 time to create 1 rle with old method : 0.0625309944152832 length of segment : 184 time for calcul the mask position with numpy : 0.005620002746582031 nb_pixel_total : 11335 time to create 1 rle with old method : 0.020900487899780273 length of segment : 193 time for calcul the mask position with numpy : 0.008365869522094727 nb_pixel_total : 14235 time to create 1 rle with old method : 0.022255659103393555 length of segment : 231 time for calcul the mask position with numpy : 0.021182537078857422 nb_pixel_total : 12348 time to create 1 rle with old method : 0.020360231399536133 length of segment : 208 time for calcul the mask position with numpy : 0.009249687194824219 nb_pixel_total : 12211 time to create 1 rle with old method : 0.020684242248535156 length of segment : 181 time for calcul the mask position with numpy : 0.06755328178405762 nb_pixel_total : 69659 time to create 1 rle with old method : 0.10020828247070312 length of segment : 463 time for calcul the mask position with numpy : 0.2870156764984131 nb_pixel_total : 301041 time to create 1 rle with new method : 0.029620885848999023 length of segment : 828 time for calcul the mask position with numpy : 0.06430745124816895 nb_pixel_total : 28928 time to create 1 rle with old method : 0.03917837142944336 length of segment : 342 time for calcul the mask position with numpy : 0.006248950958251953 nb_pixel_total : 54382 time to create 1 rle with old method : 0.06297183036804199 length of segment : 383 time for calcul the mask position with numpy : 0.004734992980957031 nb_pixel_total : 28368 time to create 1 rle with old method : 0.03341031074523926 length of segment : 90 time for calcul the mask position with numpy : 0.046738386154174805 nb_pixel_total : 28577 time to create 1 rle with old method : 0.03923678398132324 length of segment : 183 time for calcul the mask position with numpy : 0.002465486526489258 nb_pixel_total : 14936 time to create 1 rle with old method : 0.017595291137695312 length of segment : 177 time for calcul the mask position with numpy : 0.00042438507080078125 nb_pixel_total : 12607 time to create 1 rle with old method : 0.014880657196044922 length of segment : 199 time for calcul the mask position with numpy : 0.0018987655639648438 nb_pixel_total : 8888 time to create 1 rle with old method : 0.010389328002929688 length of segment : 126 time for calcul the mask position with numpy : 0.03388619422912598 nb_pixel_total : 29921 time to create 1 rle with old method : 0.03811001777648926 length of segment : 229 time for calcul the mask position with numpy : 0.011628866195678711 nb_pixel_total : 20647 time to create 1 rle with old method : 0.03221845626831055 length of segment : 200 time for calcul the mask position with numpy : 0.008270740509033203 nb_pixel_total : 29297 time to create 1 rle with old method : 0.03719973564147949 length of segment : 142 time for calcul the mask position with numpy : 0.0036916732788085938 nb_pixel_total : 22496 time to create 1 rle with old method : 0.027189016342163086 length of segment : 57 time for calcul the mask position with numpy : 0.010013818740844727 nb_pixel_total : 26684 time to create 1 rle with old method : 0.0349276065826416 length of segment : 223 time for calcul the mask position with numpy : 0.0864870548248291 nb_pixel_total : 523553 time to create 1 rle with new method : 0.01831817626953125 length of segment : 732 time for calcul the mask position with numpy : 0.0007853507995605469 nb_pixel_total : 3584 time to create 1 rle with old method : 0.0042459964752197266 length of segment : 90 time for calcul the mask position with numpy : 0.007515430450439453 nb_pixel_total : 14281 time to create 1 rle with old method : 0.018444299697875977 length of segment : 166 time for calcul the mask position with numpy : 0.03459024429321289 nb_pixel_total : 42810 time to create 1 rle with old method : 0.06815147399902344 length of segment : 294 time for calcul the mask position with numpy : 0.015090465545654297 nb_pixel_total : 17875 time to create 1 rle with old method : 0.02977609634399414 length of segment : 186 time for calcul the mask position with numpy : 0.007884025573730469 nb_pixel_total : 8917 time to create 1 rle with old method : 0.013051033020019531 length of segment : 127 time for calcul the mask position with numpy : 0.019287824630737305 nb_pixel_total : 22540 time to create 1 rle with old method : 0.028072595596313477 length of segment : 173 time for calcul the mask position with numpy : 0.06694936752319336 nb_pixel_total : 117155 time to create 1 rle with old method : 0.1357862949371338 length of segment : 409 time for calcul the mask position with numpy : 0.014596700668334961 nb_pixel_total : 26269 time to create 1 rle with old method : 0.0376737117767334 length of segment : 217 time for calcul the mask position with numpy : 0.014175891876220703 nb_pixel_total : 21762 time to create 1 rle with old method : 0.03423452377319336 length of segment : 256 time for calcul the mask position with numpy : 0.024373531341552734 nb_pixel_total : 30286 time to create 1 rle with old method : 0.04310035705566406 length of segment : 274 time for calcul the mask position with numpy : 0.005903959274291992 nb_pixel_total : 14014 time to create 1 rle with old method : 0.01604604721069336 length of segment : 204 time for calcul the mask position with numpy : 0.014402151107788086 nb_pixel_total : 13139 time to create 1 rle with old method : 0.019411087036132812 length of segment : 138 time for calcul the mask position with numpy : 0.006543636322021484 nb_pixel_total : 10543 time to create 1 rle with old method : 0.018248796463012695 length of segment : 124 time for calcul the mask position with numpy : 0.010584592819213867 nb_pixel_total : 15877 time to create 1 rle with old method : 0.03777027130126953 length of segment : 166 time for calcul the mask position with numpy : 0.05845832824707031 nb_pixel_total : 99400 time to create 1 rle with old method : 0.12632131576538086 length of segment : 528 time for calcul the mask position with numpy : 0.013981103897094727 nb_pixel_total : 17076 time to create 1 rle with old method : 0.02366471290588379 length of segment : 146 time for calcul the mask position with numpy : 0.009344100952148438 nb_pixel_total : 10406 time to create 1 rle with old method : 0.017415523529052734 length of segment : 173 time for calcul the mask position with numpy : 0.006999015808105469 nb_pixel_total : 7840 time to create 1 rle with old method : 0.009078502655029297 length of segment : 119 time for calcul the mask position with numpy : 0.006897926330566406 nb_pixel_total : 11266 time to create 1 rle with old method : 0.013069868087768555 length of segment : 148 time for calcul the mask position with numpy : 0.017958641052246094 nb_pixel_total : 14473 time to create 1 rle with old method : 0.027003049850463867 length of segment : 153 time for calcul the mask position with numpy : 0.006880283355712891 nb_pixel_total : 6376 time to create 1 rle with old method : 0.010659217834472656 length of segment : 95 time for calcul the mask position with numpy : 0.004398345947265625 nb_pixel_total : 3042 time to create 1 rle with old method : 0.0038237571716308594 length of segment : 55 time for calcul the mask position with numpy : 0.015880823135375977 nb_pixel_total : 10688 time to create 1 rle with old method : 0.016971588134765625 length of segment : 186 time for calcul the mask position with numpy : 0.01103663444519043 nb_pixel_total : 13657 time to create 1 rle with old method : 0.020292043685913086 length of segment : 152 time for calcul the mask position with numpy : 0.009409189224243164 nb_pixel_total : 10391 time to create 1 rle with old method : 0.017356157302856445 length of segment : 196 time for calcul the mask position with numpy : 0.0206911563873291 nb_pixel_total : 27980 time to create 1 rle with old method : 0.03533935546875 length of segment : 171 time for calcul the mask position with numpy : 0.015674829483032227 nb_pixel_total : 29863 time to create 1 rle with old method : 0.05005073547363281 length of segment : 293 time for calcul the mask position with numpy : 0.01897406578063965 nb_pixel_total : 18314 time to create 1 rle with old method : 0.025839567184448242 length of segment : 159 time for calcul the mask position with numpy : 0.034356117248535156 nb_pixel_total : 60721 time to create 1 rle with old method : 0.07261252403259277 length of segment : 393 time for calcul the mask position with numpy : 0.005791187286376953 nb_pixel_total : 7999 time to create 1 rle with old method : 0.010978221893310547 length of segment : 131 time for calcul the mask position with numpy : 0.000965118408203125 nb_pixel_total : 9031 time to create 1 rle with old method : 0.013050079345703125 length of segment : 137 time for calcul the mask position with numpy : 0.023675203323364258 nb_pixel_total : 32299 time to create 1 rle with old method : 0.03987431526184082 length of segment : 295 time for calcul the mask position with numpy : 0.10782313346862793 nb_pixel_total : 344542 time to create 1 rle with new method : 0.06761050224304199 length of segment : 856 time for calcul the mask position with numpy : 0.00307464599609375 nb_pixel_total : 22134 time to create 1 rle with old method : 0.035597801208496094 length of segment : 256 time for calcul the mask position with numpy : 0.0008921623229980469 nb_pixel_total : 16546 time to create 1 rle with old method : 0.0227658748626709 length of segment : 106 time for calcul the mask position with numpy : 0.0006093978881835938 nb_pixel_total : 11122 time to create 1 rle with old method : 0.013058662414550781 length of segment : 137 time for calcul the mask position with numpy : 0.0009067058563232422 nb_pixel_total : 9010 time to create 1 rle with old method : 0.010718822479248047 length of segment : 134 time for calcul the mask position with numpy : 0.0024170875549316406 nb_pixel_total : 16980 time to create 1 rle with old method : 0.019716501235961914 length of segment : 244 time for calcul the mask position with numpy : 0.0020401477813720703 nb_pixel_total : 12334 time to create 1 rle with old method : 0.018643617630004883 length of segment : 182 time for calcul the mask position with numpy : 0.015561819076538086 nb_pixel_total : 21878 time to create 1 rle with old method : 0.0269167423248291 length of segment : 184 time for calcul the mask position with numpy : 0.008372068405151367 nb_pixel_total : 23397 time to create 1 rle with old method : 0.029680967330932617 length of segment : 290 time for calcul the mask position with numpy : 0.0431978702545166 nb_pixel_total : 71992 time to create 1 rle with old method : 0.08674240112304688 length of segment : 373 time for calcul the mask position with numpy : 0.08291411399841309 nb_pixel_total : 137047 time to create 1 rle with old method : 0.16684484481811523 length of segment : 662 time for calcul the mask position with numpy : 0.039269208908081055 nb_pixel_total : 69556 time to create 1 rle with old method : 0.08124494552612305 length of segment : 275 time for calcul the mask position with numpy : 0.018173694610595703 nb_pixel_total : 34155 time to create 1 rle with old method : 0.04386019706726074 length of segment : 334 time for calcul the mask position with numpy : 0.03975701332092285 nb_pixel_total : 32455 time to create 1 rle with old method : 0.039626359939575195 length of segment : 217 time for calcul the mask position with numpy : 0.016149282455444336 nb_pixel_total : 16149 time to create 1 rle with old method : 0.029169559478759766 length of segment : 139 time for calcul the mask position with numpy : 0.008815288543701172 nb_pixel_total : 17670 time to create 1 rle with old method : 0.023212909698486328 length of segment : 210 time for calcul the mask position with numpy : 0.03479266166687012 nb_pixel_total : 48404 time to create 1 rle with old method : 0.05736851692199707 length of segment : 275 time for calcul the mask position with numpy : 0.017937660217285156 nb_pixel_total : 37379 time to create 1 rle with old method : 0.046842336654663086 length of segment : 251 time for calcul the mask position with numpy : 0.008444070816040039 nb_pixel_total : 18608 time to create 1 rle with old method : 0.026484012603759766 length of segment : 317 time for calcul the mask position with numpy : 0.03397655487060547 nb_pixel_total : 16919 time to create 1 rle with old method : 0.024693727493286133 length of segment : 182 time for calcul the mask position with numpy : 0.01551508903503418 nb_pixel_total : 34717 time to create 1 rle with old method : 0.04576539993286133 length of segment : 164 time for calcul the mask position with numpy : 0.022171974182128906 nb_pixel_total : 11591 time to create 1 rle with old method : 0.017212390899658203 length of segment : 170 time for calcul the mask position with numpy : 0.011983871459960938 nb_pixel_total : 13756 time to create 1 rle with old method : 0.019602298736572266 length of segment : 145 time for calcul the mask position with numpy : 0.0731515884399414 nb_pixel_total : 105367 time to create 1 rle with old method : 0.13343381881713867 length of segment : 634 time for calcul the mask position with numpy : 0.3242819309234619 nb_pixel_total : 365235 time to create 1 rle with new method : 0.05646181106567383 length of segment : 754 time for calcul the mask position with numpy : 0.0013697147369384766 nb_pixel_total : 14231 time to create 1 rle with old method : 0.0167233943939209 length of segment : 134 time for calcul the mask position with numpy : 0.0038056373596191406 nb_pixel_total : 9120 time to create 1 rle with old method : 0.013002157211303711 length of segment : 117 time for calcul the mask position with numpy : 0.005942344665527344 nb_pixel_total : 10867 time to create 1 rle with old method : 0.014803171157836914 length of segment : 137 time for calcul the mask position with numpy : 0.003116607666015625 nb_pixel_total : 13039 time to create 1 rle with old method : 0.017786741256713867 length of segment : 135 time for calcul the mask position with numpy : 0.02977132797241211 nb_pixel_total : 47012 time to create 1 rle with old method : 0.08188819885253906 length of segment : 209 time for calcul the mask position with numpy : 0.005021572113037109 nb_pixel_total : 4271 time to create 1 rle with old method : 0.006447553634643555 length of segment : 72 time for calcul the mask position with numpy : 0.014618873596191406 nb_pixel_total : 22317 time to create 1 rle with old method : 0.0307314395904541 length of segment : 139 time for calcul the mask position with numpy : 0.006582975387573242 nb_pixel_total : 16074 time to create 1 rle with old method : 0.01914381980895996 length of segment : 191 time for calcul the mask position with numpy : 0.0032567977905273438 nb_pixel_total : 14373 time to create 1 rle with old method : 0.016989946365356445 length of segment : 120 time for calcul the mask position with numpy : 0.013140201568603516 nb_pixel_total : 20062 time to create 1 rle with old method : 0.023554325103759766 length of segment : 188 time for calcul the mask position with numpy : 0.049094200134277344 nb_pixel_total : 377616 time to create 1 rle with new method : 0.06405162811279297 length of segment : 757 time for calcul the mask position with numpy : 0.010537147521972656 nb_pixel_total : 17376 time to create 1 rle with old method : 0.02583765983581543 length of segment : 169 time for calcul the mask position with numpy : 0.0037903785705566406 nb_pixel_total : 13944 time to create 1 rle with old method : 0.017688274383544922 length of segment : 138 time for calcul the mask position with numpy : 0.0209808349609375 nb_pixel_total : 9134 time to create 1 rle with old method : 0.01576709747314453 length of segment : 102 time for calcul the mask position with numpy : 0.03575611114501953 nb_pixel_total : 107072 time to create 1 rle with old method : 0.14441728591918945 length of segment : 440 time for calcul the mask position with numpy : 0.016816139221191406 nb_pixel_total : 11464 time to create 1 rle with old method : 0.018457651138305664 length of segment : 202 time for calcul the mask position with numpy : 0.06595635414123535 nb_pixel_total : 56760 time to create 1 rle with old method : 0.06773710250854492 length of segment : 430 time for calcul the mask position with numpy : 0.004866123199462891 nb_pixel_total : 9621 time to create 1 rle with old method : 0.012889862060546875 length of segment : 80 time for calcul the mask position with numpy : 0.011313915252685547 nb_pixel_total : 11308 time to create 1 rle with old method : 0.018274307250976562 length of segment : 122 time for calcul the mask position with numpy : 0.02153754234313965 nb_pixel_total : 21284 time to create 1 rle with old method : 0.0267789363861084 length of segment : 314 time for calcul the mask position with numpy : 0.005437374114990234 nb_pixel_total : 5862 time to create 1 rle with old method : 0.008711576461791992 length of segment : 81 time for calcul the mask position with numpy : 0.005274057388305664 nb_pixel_total : 8738 time to create 1 rle with old method : 0.01225423812866211 length of segment : 114 time for calcul the mask position with numpy : 0.0026769638061523438 nb_pixel_total : 3684 time to create 1 rle with old method : 0.005588531494140625 length of segment : 75 time for calcul the mask position with numpy : 0.0258333683013916 nb_pixel_total : 18129 time to create 1 rle with old method : 0.028923749923706055 length of segment : 172 time for calcul the mask position with numpy : 0.020104408264160156 nb_pixel_total : 20060 time to create 1 rle with old method : 0.031000852584838867 length of segment : 260 time for calcul the mask position with numpy : 0.02637648582458496 nb_pixel_total : 24586 time to create 1 rle with old method : 0.030510902404785156 length of segment : 150 time for calcul the mask position with numpy : 0.013278722763061523 nb_pixel_total : 34723 time to create 1 rle with old method : 0.04163360595703125 length of segment : 306 time for calcul the mask position with numpy : 0.010451793670654297 nb_pixel_total : 11340 time to create 1 rle with old method : 0.01609945297241211 length of segment : 225 time for calcul the mask position with numpy : 0.005241870880126953 nb_pixel_total : 13066 time to create 1 rle with old method : 0.019290685653686523 length of segment : 130 time for calcul the mask position with numpy : 0.017748355865478516 nb_pixel_total : 14667 time to create 1 rle with old method : 0.0236358642578125 length of segment : 116 time for calcul the mask position with numpy : 0.05928635597229004 nb_pixel_total : 44020 time to create 1 rle with old method : 0.05344963073730469 length of segment : 319 time for calcul the mask position with numpy : 0.029377222061157227 nb_pixel_total : 65509 time to create 1 rle with old method : 0.07900285720825195 length of segment : 406 time for calcul the mask position with numpy : 0.0279848575592041 nb_pixel_total : 35339 time to create 1 rle with old method : 0.04500389099121094 length of segment : 125 time for calcul the mask position with numpy : 0.05989384651184082 nb_pixel_total : 133800 time to create 1 rle with old method : 0.1580348014831543 length of segment : 685 time for calcul the mask position with numpy : 0.04584383964538574 nb_pixel_total : 713691 time to create 1 rle with new method : 0.25536513328552246 length of segment : 1249 time for calcul the mask position with numpy : 0.0005545616149902344 nb_pixel_total : 10419 time to create 1 rle with old method : 0.012545585632324219 length of segment : 127 time for calcul the mask position with numpy : 0.00030684471130371094 nb_pixel_total : 5286 time to create 1 rle with old method : 0.006207704544067383 length of segment : 100 time for calcul the mask position with numpy : 0.0011620521545410156 nb_pixel_total : 25000 time to create 1 rle with old method : 0.02855515480041504 length of segment : 172 time for calcul the mask position with numpy : 0.0012722015380859375 nb_pixel_total : 30726 time to create 1 rle with old method : 0.0357365608215332 length of segment : 316 time for calcul the mask position with numpy : 0.00047135353088378906 nb_pixel_total : 10116 time to create 1 rle with old method : 0.012359142303466797 length of segment : 108 time for calcul the mask position with numpy : 0.007791280746459961 nb_pixel_total : 257659 time to create 1 rle with new method : 0.009883642196655273 length of segment : 642 time for calcul the mask position with numpy : 0.0009913444519042969 nb_pixel_total : 23441 time to create 1 rle with old method : 0.03108954429626465 length of segment : 163 time for calcul the mask position with numpy : 0.0015079975128173828 nb_pixel_total : 64196 time to create 1 rle with old method : 0.0773618221282959 length of segment : 453 time for calcul the mask position with numpy : 0.0063114166259765625 nb_pixel_total : 204198 time to create 1 rle with new method : 0.0081939697265625 length of segment : 551 time for calcul the mask position with numpy : 0.0021331310272216797 nb_pixel_total : 57385 time to create 1 rle with old method : 0.07856416702270508 length of segment : 201 time for calcul the mask position with numpy : 0.008067846298217773 nb_pixel_total : 259139 time to create 1 rle with new method : 0.016142845153808594 length of segment : 1226 time for calcul the mask position with numpy : 0.0012416839599609375 nb_pixel_total : 23471 time to create 1 rle with old method : 0.027408599853515625 length of segment : 250 time for calcul the mask position with numpy : 0.0030188560485839844 nb_pixel_total : 92877 time to create 1 rle with old method : 0.10740232467651367 length of segment : 372 time for calcul the mask position with numpy : 0.0017883777618408203 nb_pixel_total : 39436 time to create 1 rle with old method : 0.048645973205566406 length of segment : 190 time for calcul the mask position with numpy : 0.0018658638000488281 nb_pixel_total : 30092 time to create 1 rle with old method : 0.039423465728759766 length of segment : 251 time for calcul the mask position with numpy : 0.0025472640991210938 nb_pixel_total : 27494 time to create 1 rle with old method : 0.031952857971191406 length of segment : 331 time for calcul the mask position with numpy : 0.0010848045349121094 nb_pixel_total : 12582 time to create 1 rle with old method : 0.01529693603515625 length of segment : 147 time for calcul the mask position with numpy : 0.002374887466430664 nb_pixel_total : 25653 time to create 1 rle with old method : 0.03000640869140625 length of segment : 200 time for calcul the mask position with numpy : 0.001806497573852539 nb_pixel_total : 25552 time to create 1 rle with old method : 0.032152652740478516 length of segment : 196 time for calcul the mask position with numpy : 0.0007190704345703125 nb_pixel_total : 10755 time to create 1 rle with old method : 0.012884140014648438 length of segment : 131 time for calcul the mask position with numpy : 0.002247333526611328 nb_pixel_total : 30261 time to create 1 rle with old method : 0.035488128662109375 length of segment : 210 time for calcul the mask position with numpy : 0.00119781494140625 nb_pixel_total : 16745 time to create 1 rle with old method : 0.01999044418334961 length of segment : 219 time for calcul the mask position with numpy : 0.0018210411071777344 nb_pixel_total : 19079 time to create 1 rle with old method : 0.022377729415893555 length of segment : 198 time for calcul the mask position with numpy : 0.002047300338745117 nb_pixel_total : 25742 time to create 1 rle with old method : 0.031606197357177734 length of segment : 197 time for calcul the mask position with numpy : 0.0013570785522460938 nb_pixel_total : 17681 time to create 1 rle with old method : 0.02037644386291504 length of segment : 196 time for calcul the mask position with numpy : 0.0008006095886230469 nb_pixel_total : 14803 time to create 1 rle with old method : 0.0173337459564209 length of segment : 147 time for calcul the mask position with numpy : 0.00042748451232910156 nb_pixel_total : 12674 time to create 1 rle with old method : 0.01499176025390625 length of segment : 150 time for calcul the mask position with numpy : 0.0031769275665283203 nb_pixel_total : 29984 time to create 1 rle with old method : 0.03546428680419922 length of segment : 309 time for calcul the mask position with numpy : 0.002419710159301758 nb_pixel_total : 27089 time to create 1 rle with old method : 0.03277325630187988 length of segment : 423 time for calcul the mask position with numpy : 0.009001731872558594 nb_pixel_total : 134367 time to create 1 rle with old method : 0.15198802947998047 length of segment : 732 time for calcul the mask position with numpy : 0.001138925552368164 nb_pixel_total : 14821 time to create 1 rle with old method : 0.017027616500854492 length of segment : 155 time for calcul the mask position with numpy : 0.008586883544921875 nb_pixel_total : 117288 time to create 1 rle with old method : 0.1351621150970459 length of segment : 376 time for calcul the mask position with numpy : 0.000720977783203125 nb_pixel_total : 7890 time to create 1 rle with old method : 0.009443044662475586 length of segment : 105 time for calcul the mask position with numpy : 0.0014801025390625 nb_pixel_total : 15061 time to create 1 rle with old method : 0.01783609390258789 length of segment : 209 time for calcul the mask position with numpy : 0.005270242691040039 nb_pixel_total : 59887 time to create 1 rle with old method : 0.06780195236206055 length of segment : 324 time for calcul the mask position with numpy : 0.0020525455474853516 nb_pixel_total : 18096 time to create 1 rle with old method : 0.022249937057495117 length of segment : 181 time for calcul the mask position with numpy : 0.001020669937133789 nb_pixel_total : 11158 time to create 1 rle with old method : 0.013486385345458984 length of segment : 115 time for calcul the mask position with numpy : 0.0002548694610595703 nb_pixel_total : 3972 time to create 1 rle with old method : 0.005070686340332031 length of segment : 57 time for calcul the mask position with numpy : 0.001875162124633789 nb_pixel_total : 21411 time to create 1 rle with old method : 0.024967670440673828 length of segment : 221 time for calcul the mask position with numpy : 0.0008862018585205078 nb_pixel_total : 11595 time to create 1 rle with old method : 0.014251708984375 length of segment : 123 time for calcul the mask position with numpy : 0.0015702247619628906 nb_pixel_total : 16166 time to create 1 rle with old method : 0.018835783004760742 length of segment : 171 time for calcul the mask position with numpy : 0.0009183883666992188 nb_pixel_total : 10838 time to create 1 rle with old method : 0.012846231460571289 length of segment : 180 time for calcul the mask position with numpy : 0.0009248256683349609 nb_pixel_total : 14201 time to create 1 rle with old method : 0.016757965087890625 length of segment : 122 time for calcul the mask position with numpy : 0.002680063247680664 nb_pixel_total : 21104 time to create 1 rle with old method : 0.024610519409179688 length of segment : 209 time for calcul the mask position with numpy : 0.0010960102081298828 nb_pixel_total : 15474 time to create 1 rle with old method : 0.01856708526611328 length of segment : 140 time for calcul the mask position with numpy : 0.0013556480407714844 nb_pixel_total : 18497 time to create 1 rle with old method : 0.0215914249420166 length of segment : 230 time for calcul the mask position with numpy : 0.0007433891296386719 nb_pixel_total : 11385 time to create 1 rle with old method : 0.013255119323730469 length of segment : 137 time for calcul the mask position with numpy : 0.0028564929962158203 nb_pixel_total : 67703 time to create 1 rle with old method : 0.08446455001831055 length of segment : 328 time for calcul the mask position with numpy : 0.003966808319091797 nb_pixel_total : 42388 time to create 1 rle with old method : 0.04774808883666992 length of segment : 300 time for calcul the mask position with numpy : 0.0008091926574707031 nb_pixel_total : 9864 time to create 1 rle with old method : 0.011867523193359375 length of segment : 97 time for calcul the mask position with numpy : 0.0016884803771972656 nb_pixel_total : 29184 time to create 1 rle with old method : 0.03560638427734375 length of segment : 230 time for calcul the mask position with numpy : 0.0034499168395996094 nb_pixel_total : 48578 time to create 1 rle with old method : 0.056685447692871094 length of segment : 209 time for calcul the mask position with numpy : 0.0004100799560546875 nb_pixel_total : 4201 time to create 1 rle with old method : 0.0052471160888671875 length of segment : 92 time for calcul the mask position with numpy : 0.0009479522705078125 nb_pixel_total : 12780 time to create 1 rle with old method : 0.015120744705200195 length of segment : 231 time for calcul the mask position with numpy : 0.0007534027099609375 nb_pixel_total : 14037 time to create 1 rle with old method : 0.016489505767822266 length of segment : 157 time for calcul the mask position with numpy : 0.0009438991546630859 nb_pixel_total : 14056 time to create 1 rle with old method : 0.01648402214050293 length of segment : 175 time for calcul the mask position with numpy : 0.0025243759155273438 nb_pixel_total : 35973 time to create 1 rle with old method : 0.04166674613952637 length of segment : 207 time for calcul the mask position with numpy : 0.0013821125030517578 nb_pixel_total : 12742 time to create 1 rle with old method : 0.021368741989135742 length of segment : 308 time for calcul the mask position with numpy : 0.0004208087921142578 nb_pixel_total : 9686 time to create 1 rle with old method : 0.011260747909545898 length of segment : 125 time for calcul the mask position with numpy : 0.0014493465423583984 nb_pixel_total : 22317 time to create 1 rle with old method : 0.025635480880737305 length of segment : 177 time for calcul the mask position with numpy : 0.002546548843383789 nb_pixel_total : 32973 time to create 1 rle with old method : 0.039331912994384766 length of segment : 212 time for calcul the mask position with numpy : 0.0010728836059570312 nb_pixel_total : 18883 time to create 1 rle with old method : 0.02180647850036621 length of segment : 172 time for calcul the mask position with numpy : 0.0019452571868896484 nb_pixel_total : 30876 time to create 1 rle with old method : 0.047940969467163086 length of segment : 262 time for calcul the mask position with numpy : 0.003136157989501953 nb_pixel_total : 40660 time to create 1 rle with old method : 0.04711484909057617 length of segment : 252 time for calcul the mask position with numpy : 0.0017046928405761719 nb_pixel_total : 17332 time to create 1 rle with old method : 0.020674943923950195 length of segment : 214 time for calcul the mask position with numpy : 0.002046823501586914 nb_pixel_total : 36565 time to create 1 rle with old method : 0.04385018348693848 length of segment : 147 time for calcul the mask position with numpy : 0.00034427642822265625 nb_pixel_total : 4196 time to create 1 rle with old method : 0.005133628845214844 length of segment : 68 time for calcul the mask position with numpy : 0.002916574478149414 nb_pixel_total : 33739 time to create 1 rle with old method : 0.0411069393157959 length of segment : 322 time for calcul the mask position with numpy : 0.012776374816894531 nb_pixel_total : 198120 time to create 1 rle with new method : 0.017293930053710938 length of segment : 591 time for calcul the mask position with numpy : 0.0015425682067871094 nb_pixel_total : 26019 time to create 1 rle with old method : 0.030928850173950195 length of segment : 213 time for calcul the mask position with numpy : 0.012544631958007812 nb_pixel_total : 199038 time to create 1 rle with new method : 0.012883901596069336 length of segment : 499 time for calcul the mask position with numpy : 0.0010726451873779297 nb_pixel_total : 13487 time to create 1 rle with old method : 0.015968799591064453 length of segment : 235 time for calcul the mask position with numpy : 0.0009372234344482422 nb_pixel_total : 14463 time to create 1 rle with old method : 0.017240285873413086 length of segment : 124 time for calcul the mask position with numpy : 0.0033311843872070312 nb_pixel_total : 34041 time to create 1 rle with old method : 0.04043722152709961 length of segment : 221 time for calcul the mask position with numpy : 0.00658416748046875 nb_pixel_total : 85208 time to create 1 rle with old method : 0.10235023498535156 length of segment : 784 time for calcul the mask position with numpy : 0.02541208267211914 nb_pixel_total : 547690 time to create 1 rle with new method : 0.42847633361816406 length of segment : 1163 time for calcul the mask position with numpy : 0.0005023479461669922 nb_pixel_total : 10835 time to create 1 rle with old method : 0.01267099380493164 length of segment : 143 time for calcul the mask position with numpy : 0.002978801727294922 nb_pixel_total : 55228 time to create 1 rle with old method : 0.08205938339233398 length of segment : 362 time for calcul the mask position with numpy : 0.0007443428039550781 nb_pixel_total : 9502 time to create 1 rle with old method : 0.011489629745483398 length of segment : 124 time for calcul the mask position with numpy : 0.0054874420166015625 nb_pixel_total : 135246 time to create 1 rle with old method : 0.15591907501220703 length of segment : 448 time for calcul the mask position with numpy : 0.0032701492309570312 nb_pixel_total : 28408 time to create 1 rle with old method : 0.03342175483703613 length of segment : 294 time for calcul the mask position with numpy : 0.007241964340209961 nb_pixel_total : 131003 time to create 1 rle with old method : 0.1489858627319336 length of segment : 488 time for calcul the mask position with numpy : 0.0032880306243896484 nb_pixel_total : 60114 time to create 1 rle with old method : 0.06984925270080566 length of segment : 271 time for calcul the mask position with numpy : 0.0018172264099121094 nb_pixel_total : 32038 time to create 1 rle with old method : 0.03748321533203125 length of segment : 199 time for calcul the mask position with numpy : 0.0023987293243408203 nb_pixel_total : 52482 time to create 1 rle with old method : 0.060373544692993164 length of segment : 226 time for calcul the mask position with numpy : 0.0011184215545654297 nb_pixel_total : 18780 time to create 1 rle with old method : 0.022075414657592773 length of segment : 187 time for calcul the mask position with numpy : 0.00030994415283203125 nb_pixel_total : 11645 time to create 1 rle with old method : 0.014406204223632812 length of segment : 128 time for calcul the mask position with numpy : 0.0003979206085205078 nb_pixel_total : 6807 time to create 1 rle with old method : 0.008270502090454102 length of segment : 75 time for calcul the mask position with numpy : 0.002443075180053711 nb_pixel_total : 75979 time to create 1 rle with old method : 0.08888840675354004 length of segment : 371 time for calcul the mask position with numpy : 0.0002613067626953125 nb_pixel_total : 6525 time to create 1 rle with old method : 0.008107662200927734 length of segment : 206 time for calcul the mask position with numpy : 0.002832651138305664 nb_pixel_total : 42815 time to create 1 rle with old method : 0.05099916458129883 length of segment : 366 time for calcul the mask position with numpy : 0.0012323856353759766 nb_pixel_total : 20939 time to create 1 rle with old method : 0.024492502212524414 length of segment : 169 time for calcul the mask position with numpy : 0.0006353855133056641 nb_pixel_total : 9866 time to create 1 rle with old method : 0.012295007705688477 length of segment : 109 time for calcul the mask position with numpy : 0.013657093048095703 nb_pixel_total : 531648 time to create 1 rle with new method : 0.05537819862365723 length of segment : 1298 time for calcul the mask position with numpy : 0.007877111434936523 nb_pixel_total : 140339 time to create 1 rle with old method : 0.15685749053955078 length of segment : 494 time for calcul the mask position with numpy : 0.0003409385681152344 nb_pixel_total : 11974 time to create 1 rle with old method : 0.013954401016235352 length of segment : 137 time for calcul the mask position with numpy : 0.00024127960205078125 nb_pixel_total : 10506 time to create 1 rle with old method : 0.012522459030151367 length of segment : 112 time for calcul the mask position with numpy : 0.0013341903686523438 nb_pixel_total : 19260 time to create 1 rle with old method : 0.02388763427734375 length of segment : 161 time for calcul the mask position with numpy : 0.0003020763397216797 nb_pixel_total : 5354 time to create 1 rle with old method : 0.01238560676574707 length of segment : 73 time for calcul the mask position with numpy : 0.014954805374145508 nb_pixel_total : 225186 time to create 1 rle with new method : 0.01972675323486328 length of segment : 823 time for calcul the mask position with numpy : 0.0006823539733886719 nb_pixel_total : 9983 time to create 1 rle with old method : 0.01641106605529785 length of segment : 113 time for calcul the mask position with numpy : 0.017226457595825195 nb_pixel_total : 315556 time to create 1 rle with new method : 0.022510766983032227 length of segment : 599 time for calcul the mask position with numpy : 0.002145051956176758 nb_pixel_total : 68635 time to create 1 rle with old method : 0.08275318145751953 length of segment : 307 time for calcul the mask position with numpy : 0.002889871597290039 nb_pixel_total : 96644 time to create 1 rle with old method : 0.1107168197631836 length of segment : 352 time for calcul the mask position with numpy : 0.0006694793701171875 nb_pixel_total : 17189 time to create 1 rle with old method : 0.021578073501586914 length of segment : 230 time for calcul the mask position with numpy : 0.0059087276458740234 nb_pixel_total : 92097 time to create 1 rle with old method : 0.10810279846191406 length of segment : 894 time for calcul the mask position with numpy : 0.0012295246124267578 nb_pixel_total : 21084 time to create 1 rle with old method : 0.027086973190307617 length of segment : 266 time for calcul the mask position with numpy : 0.0004909038543701172 nb_pixel_total : 5289 time to create 1 rle with old method : 0.006477832794189453 length of segment : 88 time for calcul the mask position with numpy : 0.00039315223693847656 nb_pixel_total : 8955 time to create 1 rle with old method : 0.010963201522827148 length of segment : 198 time for calcul the mask position with numpy : 0.0004360675811767578 nb_pixel_total : 8784 time to create 1 rle with old method : 0.010823965072631836 length of segment : 102 time for calcul the mask position with numpy : 0.0042629241943359375 nb_pixel_total : 116814 time to create 1 rle with old method : 0.13406610488891602 length of segment : 443 time for calcul the mask position with numpy : 0.0007023811340332031 nb_pixel_total : 14267 time to create 1 rle with old method : 0.016725540161132812 length of segment : 202 time for calcul the mask position with numpy : 0.002535581588745117 nb_pixel_total : 87766 time to create 1 rle with old method : 0.10338521003723145 length of segment : 367 time for calcul the mask position with numpy : 0.003118276596069336 nb_pixel_total : 98753 time to create 1 rle with old method : 0.19819307327270508 length of segment : 415 time for calcul the mask position with numpy : 0.0011458396911621094 nb_pixel_total : 12749 time to create 1 rle with old method : 0.014851808547973633 length of segment : 214 time for calcul the mask position with numpy : 0.0006368160247802734 nb_pixel_total : 9218 time to create 1 rle with old method : 0.010848045349121094 length of segment : 124 time for calcul the mask position with numpy : 0.004483222961425781 nb_pixel_total : 109230 time to create 1 rle with old method : 0.1272292137145996 length of segment : 296 time for calcul the mask position with numpy : 0.0037801265716552734 nb_pixel_total : 79724 time to create 1 rle with old method : 0.0897209644317627 length of segment : 326 time for calcul the mask position with numpy : 0.0032014846801757812 nb_pixel_total : 46283 time to create 1 rle with old method : 0.052606821060180664 length of segment : 505 time for calcul the mask position with numpy : 0.01483607292175293 nb_pixel_total : 247292 time to create 1 rle with new method : 0.03340959548950195 length of segment : 903 time for calcul the mask position with numpy : 0.0014362335205078125 nb_pixel_total : 25881 time to create 1 rle with old method : 0.03060603141784668 length of segment : 227 time for calcul the mask position with numpy : 0.0024275779724121094 nb_pixel_total : 43481 time to create 1 rle with old method : 0.052445411682128906 length of segment : 217 time for calcul the mask position with numpy : 0.0016324520111083984 nb_pixel_total : 24215 time to create 1 rle with old method : 0.03224754333496094 length of segment : 212 time for calcul the mask position with numpy : 0.0011548995971679688 nb_pixel_total : 23974 time to create 1 rle with old method : 0.02836918830871582 length of segment : 147 time for calcul the mask position with numpy : 0.0017235279083251953 nb_pixel_total : 30714 time to create 1 rle with old method : 0.05180168151855469 length of segment : 156 time for calcul the mask position with numpy : 0.0014488697052001953 nb_pixel_total : 16487 time to create 1 rle with old method : 0.02406930923461914 length of segment : 160 time for calcul the mask position with numpy : 0.0012989044189453125 nb_pixel_total : 18455 time to create 1 rle with old method : 0.022906780242919922 length of segment : 176 time for calcul the mask position with numpy : 0.0033936500549316406 nb_pixel_total : 82923 time to create 1 rle with old method : 0.09588909149169922 length of segment : 595 time for calcul the mask position with numpy : 0.00083160400390625 nb_pixel_total : 16082 time to create 1 rle with old method : 0.018626928329467773 length of segment : 147 time for calcul the mask position with numpy : 0.0057713985443115234 nb_pixel_total : 83441 time to create 1 rle with old method : 0.1028749942779541 length of segment : 366 time for calcul the mask position with numpy : 0.0025196075439453125 nb_pixel_total : 65009 time to create 1 rle with old method : 0.07436275482177734 length of segment : 213 time for calcul the mask position with numpy : 0.0008587837219238281 nb_pixel_total : 16397 time to create 1 rle with old method : 0.020453453063964844 length of segment : 128 time for calcul the mask position with numpy : 0.005988359451293945 nb_pixel_total : 106825 time to create 1 rle with old method : 0.12180018424987793 length of segment : 509 time for calcul the mask position with numpy : 0.0008249282836914062 nb_pixel_total : 18061 time to create 1 rle with old method : 0.022310972213745117 length of segment : 106 time for calcul the mask position with numpy : 0.002096891403198242 nb_pixel_total : 42118 time to create 1 rle with old method : 0.04890847206115723 length of segment : 191 time for calcul the mask position with numpy : 0.003238677978515625 nb_pixel_total : 59435 time to create 1 rle with old method : 0.0681154727935791 length of segment : 190 time for calcul the mask position with numpy : 0.0015134811401367188 nb_pixel_total : 19468 time to create 1 rle with old method : 0.022815465927124023 length of segment : 210 time for calcul the mask position with numpy : 0.0003027915954589844 nb_pixel_total : 3437 time to create 1 rle with old method : 0.004207134246826172 length of segment : 71 time for calcul the mask position with numpy : 0.0019409656524658203 nb_pixel_total : 41594 time to create 1 rle with old method : 0.04747128486633301 length of segment : 239 time for calcul the mask position with numpy : 0.0012042522430419922 nb_pixel_total : 26092 time to create 1 rle with old method : 0.031164884567260742 length of segment : 140 time for calcul the mask position with numpy : 0.0005979537963867188 nb_pixel_total : 11918 time to create 1 rle with old method : 0.014927387237548828 length of segment : 125 time for calcul the mask position with numpy : 0.00046443939208984375 nb_pixel_total : 7792 time to create 1 rle with old method : 0.009588241577148438 length of segment : 109 time for calcul the mask position with numpy : 0.009314537048339844 nb_pixel_total : 75808 time to create 1 rle with old method : 0.08784365653991699 length of segment : 557 time for calcul the mask position with numpy : 0.002267599105834961 nb_pixel_total : 44164 time to create 1 rle with old method : 0.0500948429107666 length of segment : 350 time for calcul the mask position with numpy : 0.004364728927612305 nb_pixel_total : 88539 time to create 1 rle with old method : 0.10542035102844238 length of segment : 850 time for calcul the mask position with numpy : 0.001462697982788086 nb_pixel_total : 47824 time to create 1 rle with old method : 0.05483675003051758 length of segment : 342 time for calcul the mask position with numpy : 0.0008339881896972656 nb_pixel_total : 9863 time to create 1 rle with old method : 0.012063741683959961 length of segment : 132 time for calcul the mask position with numpy : 0.0025124549865722656 nb_pixel_total : 40429 time to create 1 rle with old method : 0.04739856719970703 length of segment : 214 time for calcul the mask position with numpy : 0.0014827251434326172 nb_pixel_total : 29277 time to create 1 rle with old method : 0.03416299819946289 length of segment : 206 time for calcul the mask position with numpy : 0.0003376007080078125 nb_pixel_total : 6927 time to create 1 rle with old method : 0.009960174560546875 length of segment : 108 time for calcul the mask position with numpy : 0.002269744873046875 nb_pixel_total : 34494 time to create 1 rle with old method : 0.040583133697509766 length of segment : 266 time for calcul the mask position with numpy : 0.001344919204711914 nb_pixel_total : 11996 time to create 1 rle with old method : 0.014277935028076172 length of segment : 123 time for calcul the mask position with numpy : 0.003439188003540039 nb_pixel_total : 34329 time to create 1 rle with old method : 0.039313316345214844 length of segment : 270 time for calcul the mask position with numpy : 0.006357908248901367 nb_pixel_total : 75479 time to create 1 rle with old method : 0.08501267433166504 length of segment : 460 time for calcul the mask position with numpy : 0.01016688346862793 nb_pixel_total : 127282 time to create 1 rle with old method : 0.14726805686950684 length of segment : 266 time for calcul the mask position with numpy : 0.004830598831176758 nb_pixel_total : 42901 time to create 1 rle with old method : 0.04873371124267578 length of segment : 433 time for calcul the mask position with numpy : 0.0044100284576416016 nb_pixel_total : 39549 time to create 1 rle with old method : 0.0469512939453125 length of segment : 345 time for calcul the mask position with numpy : 0.0014400482177734375 nb_pixel_total : 19258 time to create 1 rle with old method : 0.022544384002685547 length of segment : 261 time for calcul the mask position with numpy : 0.002398967742919922 nb_pixel_total : 20938 time to create 1 rle with old method : 0.02468562126159668 length of segment : 168 time for calcul the mask position with numpy : 0.004206418991088867 nb_pixel_total : 66183 time to create 1 rle with old method : 0.0752260684967041 length of segment : 393 time for calcul the mask position with numpy : 0.006109952926635742 nb_pixel_total : 67072 time to create 1 rle with old method : 0.07895469665527344 length of segment : 368 time for calcul the mask position with numpy : 0.00036263465881347656 nb_pixel_total : 3331 time to create 1 rle with old method : 0.004282712936401367 length of segment : 58 time for calcul the mask position with numpy : 0.010424137115478516 nb_pixel_total : 62406 time to create 1 rle with old method : 0.07149076461791992 length of segment : 542 time for calcul the mask position with numpy : 0.014225482940673828 nb_pixel_total : 158033 time to create 1 rle with new method : 0.022420644760131836 length of segment : 665 time for calcul the mask position with numpy : 0.00028252601623535156 nb_pixel_total : 3175 time to create 1 rle with old method : 0.003879070281982422 length of segment : 66 time for calcul the mask position with numpy : 0.003694295883178711 nb_pixel_total : 38196 time to create 1 rle with old method : 0.04469037055969238 length of segment : 222 time for calcul the mask position with numpy : 0.0011582374572753906 nb_pixel_total : 13158 time to create 1 rle with old method : 0.015917301177978516 length of segment : 114 time for calcul the mask position with numpy : 0.0018374919891357422 nb_pixel_total : 18937 time to create 1 rle with old method : 0.0223391056060791 length of segment : 417 time for calcul the mask position with numpy : 0.0029664039611816406 nb_pixel_total : 27320 time to create 1 rle with old method : 0.03271794319152832 length of segment : 155 time for calcul the mask position with numpy : 0.0023741722106933594 nb_pixel_total : 27569 time to create 1 rle with old method : 0.03172588348388672 length of segment : 220 time for calcul the mask position with numpy : 0.0022735595703125 nb_pixel_total : 23171 time to create 1 rle with old method : 0.027127981185913086 length of segment : 217 time for calcul the mask position with numpy : 0.001247406005859375 nb_pixel_total : 11171 time to create 1 rle with old method : 0.013210296630859375 length of segment : 232 time for calcul the mask position with numpy : 0.003518342971801758 nb_pixel_total : 48843 time to create 1 rle with old method : 0.05743074417114258 length of segment : 293 time for calcul the mask position with numpy : 0.003996610641479492 nb_pixel_total : 45293 time to create 1 rle with old method : 0.052442312240600586 length of segment : 294 time for calcul the mask position with numpy : 0.0013997554779052734 nb_pixel_total : 16422 time to create 1 rle with old method : 0.01925063133239746 length of segment : 188 time for calcul the mask position with numpy : 0.0005633831024169922 nb_pixel_total : 3856 time to create 1 rle with old method : 0.00510859489440918 length of segment : 81 time for calcul the mask position with numpy : 0.0006763935089111328 nb_pixel_total : 21333 time to create 1 rle with old method : 0.027569055557250977 length of segment : 126 time for calcul the mask position with numpy : 0.00480198860168457 nb_pixel_total : 82269 time to create 1 rle with old method : 0.09378981590270996 length of segment : 563 time for calcul the mask position with numpy : 0.0010063648223876953 nb_pixel_total : 5597 time to create 1 rle with old method : 0.0068700313568115234 length of segment : 112 time for calcul the mask position with numpy : 0.0003895759582519531 nb_pixel_total : 4967 time to create 1 rle with old method : 0.006338596343994141 length of segment : 88 time for calcul the mask position with numpy : 0.008623123168945312 nb_pixel_total : 95441 time to create 1 rle with old method : 0.11038684844970703 length of segment : 378 time for calcul the mask position with numpy : 0.0013103485107421875 nb_pixel_total : 12993 time to create 1 rle with old method : 0.015213727951049805 length of segment : 200 time for calcul the mask position with numpy : 0.003221750259399414 nb_pixel_total : 43656 time to create 1 rle with old method : 0.05035972595214844 length of segment : 231 time for calcul the mask position with numpy : 0.004817485809326172 nb_pixel_total : 53579 time to create 1 rle with old method : 0.06184840202331543 length of segment : 285 time for calcul the mask position with numpy : 0.0030193328857421875 nb_pixel_total : 24335 time to create 1 rle with old method : 0.029434919357299805 length of segment : 375 time for calcul the mask position with numpy : 0.0013074874877929688 nb_pixel_total : 13372 time to create 1 rle with old method : 0.015804290771484375 length of segment : 324 time for calcul the mask position with numpy : 0.0010585784912109375 nb_pixel_total : 17532 time to create 1 rle with old method : 0.021425247192382812 length of segment : 187 time for calcul the mask position with numpy : 0.0001583099365234375 nb_pixel_total : 1449 time to create 1 rle with old method : 0.0018546581268310547 length of segment : 47 time for calcul the mask position with numpy : 0.0009341239929199219 nb_pixel_total : 9519 time to create 1 rle with old method : 0.011242151260375977 length of segment : 165 time for calcul the mask position with numpy : 0.0006492137908935547 nb_pixel_total : 14055 time to create 1 rle with old method : 0.016898393630981445 length of segment : 208 time for calcul the mask position with numpy : 0.0035843849182128906 nb_pixel_total : 51107 time to create 1 rle with old method : 0.06004166603088379 length of segment : 483 time for calcul the mask position with numpy : 0.0008654594421386719 nb_pixel_total : 10125 time to create 1 rle with old method : 0.012326240539550781 length of segment : 117 time for calcul the mask position with numpy : 0.0005848407745361328 nb_pixel_total : 7772 time to create 1 rle with old method : 0.00906825065612793 length of segment : 241 time for calcul the mask position with numpy : 0.0039014816284179688 nb_pixel_total : 27966 time to create 1 rle with old method : 0.03257894515991211 length of segment : 368 time for calcul the mask position with numpy : 0.002051115036010742 nb_pixel_total : 24832 time to create 1 rle with old method : 0.029353857040405273 length of segment : 169 time for calcul the mask position with numpy : 0.0007045269012451172 nb_pixel_total : 8078 time to create 1 rle with old method : 0.009790420532226562 length of segment : 108 time for calcul the mask position with numpy : 0.0014069080352783203 nb_pixel_total : 13013 time to create 1 rle with old method : 0.016004085540771484 length of segment : 128 time for calcul the mask position with numpy : 0.0053594112396240234 nb_pixel_total : 39122 time to create 1 rle with old method : 0.04516315460205078 length of segment : 361 time for calcul the mask position with numpy : 0.005377531051635742 nb_pixel_total : 81772 time to create 1 rle with old method : 0.09397435188293457 length of segment : 365 time for calcul the mask position with numpy : 0.0021817684173583984 nb_pixel_total : 16831 time to create 1 rle with old method : 0.02017354965209961 length of segment : 211 time for calcul the mask position with numpy : 0.0026717185974121094 nb_pixel_total : 20532 time to create 1 rle with old method : 0.02380514144897461 length of segment : 230 time for calcul the mask position with numpy : 0.0016129016876220703 nb_pixel_total : 19531 time to create 1 rle with old method : 0.026903867721557617 length of segment : 169 time for calcul the mask position with numpy : 0.0037338733673095703 nb_pixel_total : 39713 time to create 1 rle with old method : 0.04616665840148926 length of segment : 237 time for calcul the mask position with numpy : 0.001871347427368164 nb_pixel_total : 30400 time to create 1 rle with old method : 0.0357508659362793 length of segment : 123 time for calcul the mask position with numpy : 0.0011053085327148438 nb_pixel_total : 9110 time to create 1 rle with old method : 0.010712146759033203 length of segment : 250 time for calcul the mask position with numpy : 0.0052716732025146484 nb_pixel_total : 60126 time to create 1 rle with old method : 0.07199907302856445 length of segment : 267 time for calcul the mask position with numpy : 0.0014607906341552734 nb_pixel_total : 13240 time to create 1 rle with old method : 0.015715837478637695 length of segment : 224 time for calcul the mask position with numpy : 0.0031731128692626953 nb_pixel_total : 42268 time to create 1 rle with old method : 0.04955244064331055 length of segment : 306 time for calcul the mask position with numpy : 0.00193023681640625 nb_pixel_total : 23459 time to create 1 rle with old method : 0.02720046043395996 length of segment : 219 time for calcul the mask position with numpy : 0.0008535385131835938 nb_pixel_total : 10737 time to create 1 rle with old method : 0.012505769729614258 length of segment : 173 time for calcul the mask position with numpy : 0.002003192901611328 nb_pixel_total : 28080 time to create 1 rle with old method : 0.03314042091369629 length of segment : 170 time for calcul the mask position with numpy : 0.0026788711547851562 nb_pixel_total : 34267 time to create 1 rle with old method : 0.03933858871459961 length of segment : 194 time for calcul the mask position with numpy : 0.006373405456542969 nb_pixel_total : 111132 time to create 1 rle with old method : 0.1369946002960205 length of segment : 325 time for calcul the mask position with numpy : 0.0014655590057373047 nb_pixel_total : 13857 time to create 1 rle with old method : 0.016263961791992188 length of segment : 177 time for calcul the mask position with numpy : 0.0025000572204589844 nb_pixel_total : 27389 time to create 1 rle with old method : 0.03145790100097656 length of segment : 327 time for calcul the mask position with numpy : 0.0034444332122802734 nb_pixel_total : 37032 time to create 1 rle with old method : 0.042897701263427734 length of segment : 278 time for calcul the mask position with numpy : 0.0053021907806396484 nb_pixel_total : 63542 time to create 1 rle with old method : 0.07251930236816406 length of segment : 407 time for calcul the mask position with numpy : 0.004890918731689453 nb_pixel_total : 60008 time to create 1 rle with old method : 0.09193277359008789 length of segment : 361 time for calcul the mask position with numpy : 0.000637054443359375 nb_pixel_total : 5983 time to create 1 rle with old method : 0.007218837738037109 length of segment : 112 time for calcul the mask position with numpy : 0.005076885223388672 nb_pixel_total : 40374 time to create 1 rle with old method : 0.04704928398132324 length of segment : 460 time for calcul the mask position with numpy : 0.0028562545776367188 nb_pixel_total : 24153 time to create 1 rle with old method : 0.029001712799072266 length of segment : 219 time for calcul the mask position with numpy : 0.004470109939575195 nb_pixel_total : 42748 time to create 1 rle with old method : 0.05017828941345215 length of segment : 237 time for calcul the mask position with numpy : 0.0011985301971435547 nb_pixel_total : 13120 time to create 1 rle with old method : 0.015732765197753906 length of segment : 134 time for calcul the mask position with numpy : 0.0005640983581542969 nb_pixel_total : 6868 time to create 1 rle with old method : 0.008318185806274414 length of segment : 99 time for calcul the mask position with numpy : 0.0037031173706054688 nb_pixel_total : 42897 time to create 1 rle with old method : 0.0519711971282959 length of segment : 308 time for calcul the mask position with numpy : 0.0016815662384033203 nb_pixel_total : 18645 time to create 1 rle with old method : 0.02196502685546875 length of segment : 233 time for calcul the mask position with numpy : 0.0015764236450195312 nb_pixel_total : 20509 time to create 1 rle with old method : 0.024255037307739258 length of segment : 152 time for calcul the mask position with numpy : 0.0034456253051757812 nb_pixel_total : 52755 time to create 1 rle with old method : 0.060949087142944336 length of segment : 321 time for calcul the mask position with numpy : 0.001445770263671875 nb_pixel_total : 20008 time to create 1 rle with old method : 0.0229339599609375 length of segment : 172 time for calcul the mask position with numpy : 0.0023889541625976562 nb_pixel_total : 24985 time to create 1 rle with old method : 0.02938055992126465 length of segment : 232 time for calcul the mask position with numpy : 0.0011603832244873047 nb_pixel_total : 19511 time to create 1 rle with old method : 0.023075103759765625 length of segment : 214 time for calcul the mask position with numpy : 0.002453327178955078 nb_pixel_total : 29120 time to create 1 rle with old method : 0.033365488052368164 length of segment : 200 time for calcul the mask position with numpy : 0.0005702972412109375 nb_pixel_total : 7145 time to create 1 rle with old method : 0.008774280548095703 length of segment : 124 time for calcul the mask position with numpy : 0.0011029243469238281 nb_pixel_total : 17803 time to create 1 rle with old method : 0.021747350692749023 length of segment : 140 time for calcul the mask position with numpy : 0.0005970001220703125 nb_pixel_total : 6933 time to create 1 rle with old method : 0.008265972137451172 length of segment : 82 time for calcul the mask position with numpy : 0.004980325698852539 nb_pixel_total : 49939 time to create 1 rle with old method : 0.06002044677734375 length of segment : 255 time for calcul the mask position with numpy : 0.0017895698547363281 nb_pixel_total : 25187 time to create 1 rle with old method : 0.0325925350189209 length of segment : 198 time for calcul the mask position with numpy : 0.0011582374572753906 nb_pixel_total : 16283 time to create 1 rle with old method : 0.021062374114990234 length of segment : 130 time for calcul the mask position with numpy : 0.001356363296508789 nb_pixel_total : 12924 time to create 1 rle with old method : 0.01620626449584961 length of segment : 143 time for calcul the mask position with numpy : 0.0014188289642333984 nb_pixel_total : 15720 time to create 1 rle with old method : 0.019089221954345703 length of segment : 210 time for calcul the mask position with numpy : 0.0009870529174804688 nb_pixel_total : 12208 time to create 1 rle with old method : 0.01460123062133789 length of segment : 131 time for calcul the mask position with numpy : 0.0033037662506103516 nb_pixel_total : 20731 time to create 1 rle with old method : 0.02827286720275879 length of segment : 174 time for calcul the mask position with numpy : 0.0005593299865722656 nb_pixel_total : 6126 time to create 1 rle with old method : 0.007170200347900391 length of segment : 110 time for calcul the mask position with numpy : 0.0009417533874511719 nb_pixel_total : 9686 time to create 1 rle with old method : 0.011898994445800781 length of segment : 136 time for calcul the mask position with numpy : 0.0016317367553710938 nb_pixel_total : 12571 time to create 1 rle with old method : 0.015058755874633789 length of segment : 132 time for calcul the mask position with numpy : 0.0004887580871582031 nb_pixel_total : 5414 time to create 1 rle with old method : 0.006810426712036133 length of segment : 83 time for calcul the mask position with numpy : 0.00223541259765625 nb_pixel_total : 36993 time to create 1 rle with old method : 0.04689335823059082 length of segment : 266 time for calcul the mask position with numpy : 0.00040340423583984375 nb_pixel_total : 4445 time to create 1 rle with old method : 0.005583286285400391 length of segment : 70 time for calcul the mask position with numpy : 0.000978231430053711 nb_pixel_total : 16006 time to create 1 rle with old method : 0.022007465362548828 length of segment : 190 time for calcul the mask position with numpy : 0.0005385875701904297 nb_pixel_total : 4366 time to create 1 rle with old method : 0.005537986755371094 length of segment : 75 time for calcul the mask position with numpy : 0.0009598731994628906 nb_pixel_total : 14105 time to create 1 rle with old method : 0.016989707946777344 length of segment : 130 time for calcul the mask position with numpy : 0.0012772083282470703 nb_pixel_total : 10202 time to create 1 rle with old method : 0.011966943740844727 length of segment : 121 time for calcul the mask position with numpy : 0.0022530555725097656 nb_pixel_total : 30922 time to create 1 rle with old method : 0.036682844161987305 length of segment : 247 time for calcul the mask position with numpy : 0.0005488395690917969 nb_pixel_total : 5746 time to create 1 rle with old method : 0.007071018218994141 length of segment : 86 time for calcul the mask position with numpy : 0.002088308334350586 nb_pixel_total : 12819 time to create 1 rle with old method : 0.01574850082397461 length of segment : 193 time for calcul the mask position with numpy : 0.0028128623962402344 nb_pixel_total : 22508 time to create 1 rle with old method : 0.02647686004638672 length of segment : 291 time for calcul the mask position with numpy : 0.0018007755279541016 nb_pixel_total : 23788 time to create 1 rle with old method : 0.029485464096069336 length of segment : 179 time for calcul the mask position with numpy : 0.003266572952270508 nb_pixel_total : 49873 time to create 1 rle with old method : 0.057344913482666016 length of segment : 341 time for calcul the mask position with numpy : 0.0015823841094970703 nb_pixel_total : 18246 time to create 1 rle with old method : 0.021360158920288086 length of segment : 202 time for calcul the mask position with numpy : 0.00125885009765625 nb_pixel_total : 10578 time to create 1 rle with old method : 0.012659549713134766 length of segment : 142 time for calcul the mask position with numpy : 0.0018177032470703125 nb_pixel_total : 25589 time to create 1 rle with old method : 0.030463695526123047 length of segment : 218 time spent for convertir_results : 36.63895559310913 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 763 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 102773 save missing photos in datou_result : time spend for datou_step_exec : 206.54470586776733 time spend to save output : 18.259583711624146 total time spend for step 1 : 224.80428957939148 step2:crop_condition Tue Apr 1 01:24:16 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Loading chi in step crop with photo_hashtag_type : 3594 Loading chi in step crop for list_pids : 11 ! batch 1 Loaded 763 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 ! map_result returned by crop_photo_return_map_crop : length : 544 About to insert : list_path_to_insert length 544 new photo from crops ! About to upload 544 photos upload in portfolio : 3736932 init cache_photo without model_param we have 544 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743463516_2118874 we have uploaded 544 photos in the portfolio 3736932 time of upload the photos Elapsed time : 200.3528835773468 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 ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! 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 : 119 About to insert : list_path_to_insert length 119 new photo from crops ! About to upload 119 photos upload in portfolio : 3736932 init cache_photo without model_param we have 119 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743463737_2118874 we have uploaded 119 photos in the portfolio 3736932 time of upload the photos Elapsed time : 45.34626078605652 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 2 new photo from crops ! About to upload 2 photos upload in portfolio : 3736932 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743463784_2118874 we have uploaded 2 photos in the portfolio 3736932 time of upload the photos Elapsed time : 0.8687736988067627 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 ! map_result returned by crop_photo_return_map_crop : length : 42 About to insert : list_path_to_insert length 42 new photo from crops ! About to upload 42 photos upload in portfolio : 3736932 init cache_photo without model_param we have 42 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743463797_2118874 we have uploaded 42 photos in the portfolio 3736932 time of upload the photos Elapsed time : 23.91352677345276 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 ! map_result returned by crop_photo_return_map_crop : length : 11 About to insert : list_path_to_insert length 11 new photo from crops ! About to upload 11 photos upload in portfolio : 3736932 init cache_photo without model_param we have 11 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743463823_2118874 we have uploaded 11 photos in the portfolio 3736932 time of upload the photos Elapsed time : 4.590345621109009 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 9 About to insert : list_path_to_insert length 9 new photo from crops ! About to upload 9 photos upload in portfolio : 3736932 init cache_photo without model_param we have 9 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1743463830_2118874 we have uploaded 9 photos in the portfolio 3736932 time of upload the photos Elapsed time : 9.055230617523193 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 delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1349149899, 1349149894, 1349149873, 1349022602, 1349022596, 1349022592, 1349022529, 1349022521, 1349022516, 1349022513, 1349022510] Looping around the photos to save general results len do output : 727 /1349160257Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160258Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160259Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160260Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160261Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160262Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160263Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160264Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160266Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160267Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160268Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160274Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160278Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160279Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160281Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160286Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160290Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160293Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160298Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160303Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160305Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160307Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160309Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160311Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160312Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160315Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160317Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160320Didn't retrieve data 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data .Didn't retrieve data . /1349160763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160765Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160768Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160772Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160774Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160778Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160779Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160780Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160784Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160786Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160790Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160792Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160795Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160796Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349160798Didn't 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retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161271Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161272Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161274Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161277Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161279Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161281Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161283Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161285Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161292Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161296Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161401Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161403Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161407Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1349161408Didn'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, '2711139') ('3318', '21929822', '1349149899', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149894', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149873', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022602', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022596', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022592', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022529', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022521', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022516', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022513', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022510', None, None, None, None, None, '2711139') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 2192 time used for this insertion : 1.9563803672790527 save_final save missing photos in datou_result : time spend for datou_step_exec : 383.55865693092346 time spend to save output : 2.121351957321167 total time spend for step 2 : 385.68000888824463 step3:rle_unique_nms_with_priority Tue Apr 1 01:30:42 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 763 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 49 nb_hashtags : 4 time to prepare the origin masks : 4.861960411071777 time for calcul the mask position with numpy : 0.683154821395874 nb_pixel_total : 5323556 time to create 1 rle with new method : 2.2828893661499023 time for calcul the mask position with numpy : 0.03679776191711426 nb_pixel_total : 6256 time to create 1 rle with old method : 0.011485099792480469 time for calcul the mask position with numpy : 0.03731799125671387 nb_pixel_total : 18141 time to create 1 rle with old method : 0.0233919620513916 time for calcul the mask position with numpy : 0.029411792755126953 nb_pixel_total : 17308 time to create 1 rle with old method : 0.024407386779785156 time for calcul the mask position with numpy : 0.03036785125732422 nb_pixel_total : 69659 time to create 1 rle with old method : 0.08234167098999023 time for calcul the mask position with numpy : 0.032259225845336914 nb_pixel_total : 28905 time to create 1 rle with old method : 0.03277707099914551 time for calcul the mask position with numpy : 0.030077695846557617 nb_pixel_total : 38693 time to create 1 rle with old method : 0.04662466049194336 time for calcul the mask position with numpy : 0.03249835968017578 nb_pixel_total : 55964 time to create 1 rle with old method : 0.06363725662231445 time for calcul the mask position with numpy : 0.030742883682250977 nb_pixel_total : 29921 time to create 1 rle with old method : 0.03473353385925293 time for calcul the mask position with numpy : 0.030225515365600586 nb_pixel_total : 15220 time to create 1 rle with old method : 0.02122950553894043 time for calcul the mask position with numpy : 0.04207253456115723 nb_pixel_total : 290300 time to create 1 rle with new method : 0.6224920749664307 time for calcul the mask position with numpy : 0.029215574264526367 nb_pixel_total : 12348 time to create 1 rle with old method : 0.013829469680786133 time for calcul the mask position with numpy : 0.02936530113220215 nb_pixel_total : 18169 time to create 1 rle with old method : 0.020364046096801758 time for calcul the mask position with numpy : 0.030083417892456055 nb_pixel_total : 92445 time to create 1 rle with old method : 0.10846519470214844 time for calcul the mask position with numpy : 0.029696226119995117 nb_pixel_total : 10926 time to create 1 rle with old method : 0.012521982192993164 time for calcul the mask position with numpy : 0.02947258949279785 nb_pixel_total : 13679 time to create 1 rle with old method : 0.01573038101196289 time for calcul the mask position with numpy : 0.030080318450927734 nb_pixel_total : 44810 time to create 1 rle with old method : 0.05136466026306152 time for calcul the mask position with numpy : 0.029642343521118164 nb_pixel_total : 16701 time to create 1 rle with old method : 0.019573450088500977 time for calcul the mask position with numpy : 0.029412031173706055 nb_pixel_total : 12317 time to create 1 rle with old method : 0.014998197555541992 time for calcul the mask position with numpy : 0.030750036239624023 nb_pixel_total : 17629 time to create 1 rle with old method : 0.021544933319091797 time for calcul the mask position with numpy : 0.031473398208618164 nb_pixel_total : 26338 time to create 1 rle with old method : 0.0344700813293457 time for calcul the mask position with numpy : 0.03005671501159668 nb_pixel_total : 9340 time to create 1 rle with old method : 0.010521888732910156 time for calcul the mask position with numpy : 0.031281232833862305 nb_pixel_total : 22794 time to create 1 rle with old method : 0.035532474517822266 time for calcul the mask position with numpy : 0.029251813888549805 nb_pixel_total : 30996 time to create 1 rle with old method : 0.0355677604675293 time for calcul the mask position with numpy : 0.031016826629638672 nb_pixel_total : 28577 time to create 1 rle with old method : 0.03440976142883301 time for calcul the mask position with numpy : 0.030266523361206055 nb_pixel_total : 20647 time to create 1 rle with old method : 0.026066303253173828 time for calcul the mask position with numpy : 0.029607295989990234 nb_pixel_total : 80324 time to create 1 rle with old method : 0.09074950218200684 time for calcul the mask position with numpy : 0.029393672943115234 nb_pixel_total : 28928 time to create 1 rle with old method : 0.034012794494628906 time for calcul the mask position with numpy : 0.03241395950317383 nb_pixel_total : 19602 time to create 1 rle with old method : 0.03192257881164551 time for calcul the mask position with numpy : 0.03300333023071289 nb_pixel_total : 14527 time to create 1 rle with old method : 0.016625642776489258 time for calcul the mask position with numpy : 0.030431270599365234 nb_pixel_total : 85220 time to create 1 rle with old method : 0.0963294506072998 time for calcul the mask position with numpy : 0.029713869094848633 nb_pixel_total : 13455 time to create 1 rle with old method : 0.024239778518676758 time for calcul the mask position with numpy : 0.03329730033874512 nb_pixel_total : 21965 time to create 1 rle with old method : 0.02473592758178711 time for calcul the mask position with numpy : 0.029091596603393555 nb_pixel_total : 22129 time to create 1 rle with old method : 0.025588035583496094 time for calcul the mask position with numpy : 0.038455963134765625 nb_pixel_total : 97771 time to create 1 rle with old method : 0.1407604217529297 time for calcul the mask position with numpy : 0.03326153755187988 nb_pixel_total : 50320 time to create 1 rle with old method : 0.05656743049621582 time for calcul the mask position with numpy : 0.029293298721313477 nb_pixel_total : 49291 time to create 1 rle with old method : 0.05677676200866699 time for calcul the mask position with numpy : 0.029479503631591797 nb_pixel_total : 14830 time to create 1 rle with old method : 0.018578767776489258 time for calcul the mask position with numpy : 0.029533863067626953 nb_pixel_total : 12607 time to create 1 rle with old method : 0.014246463775634766 time for calcul the mask position with numpy : 0.02997756004333496 nb_pixel_total : 105743 time to create 1 rle with old method : 0.11786961555480957 time for calcul the mask position with numpy : 0.029529809951782227 nb_pixel_total : 8888 time to create 1 rle with old method : 0.010248184204101562 time for calcul the mask position with numpy : 0.02976202964782715 nb_pixel_total : 12211 time to create 1 rle with old method : 0.013886213302612305 time for calcul the mask position with numpy : 0.029706239700317383 nb_pixel_total : 11335 time to create 1 rle with old method : 0.012924432754516602 time for calcul the mask position with numpy : 0.029737234115600586 nb_pixel_total : 29825 time to create 1 rle with old method : 0.0336909294128418 time for calcul the mask position with numpy : 0.0298616886138916 nb_pixel_total : 42090 time to create 1 rle with old method : 0.04769110679626465 time for calcul the mask position with numpy : 0.030366897583007812 nb_pixel_total : 4188 time to create 1 rle with old method : 0.004993915557861328 time for calcul the mask position with numpy : 0.03023052215576172 nb_pixel_total : 11473 time to create 1 rle with old method : 0.013869524002075195 time for calcul the mask position with numpy : 0.0303647518157959 nb_pixel_total : 7354 time to create 1 rle with old method : 0.008430957794189453 time for calcul the mask position with numpy : 0.030046701431274414 nb_pixel_total : 28368 time to create 1 rle with old method : 0.03475475311279297 time for calcul the mask position with numpy : 0.030698537826538086 nb_pixel_total : 6157 time to create 1 rle with old method : 0.0075795650482177734 create new chi : 7.0038001537323 time to delete rle : 0.02854776382446289 batch 1 Loaded 99 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26210 TO DO : save crop sub photo not yet done ! save time : 4.557603120803833 nb_obj : 5 nb_hashtags : 3 time to prepare the origin masks : 1.6654465198516846 time for calcul the mask position with numpy : 0.256455659866333 nb_pixel_total : 6444626 time to create 1 rle with new method : 0.5948898792266846 time for calcul the mask position with numpy : 0.036023616790771484 nb_pixel_total : 3584 time to create 1 rle with old method : 0.004354953765869141 time for calcul the mask position with numpy : 0.04126334190368652 nb_pixel_total : 523553 time to create 1 rle with new method : 0.35726237297058105 time for calcul the mask position with numpy : 0.028449296951293945 nb_pixel_total : 26684 time to create 1 rle with old method : 0.04192471504211426 time for calcul the mask position with numpy : 0.02528071403503418 nb_pixel_total : 22496 time to create 1 rle with old method : 0.026160717010498047 time for calcul the mask position with numpy : 0.02358722686767578 nb_pixel_total : 29297 time to create 1 rle with old method : 0.032346248626708984 create new chi : 1.543842077255249 time to delete rle : 0.0007998943328857422 batch 1 Loaded 11 chid ids of type : 3594 +++++Number RLEs to save : 4648 TO DO : save crop sub photo not yet done ! save time : 0.48967623710632324 nb_obj : 40 nb_hashtags : 3 time to prepare the origin masks : 4.273083925247192 time for calcul the mask position with numpy : 0.5907919406890869 nb_pixel_total : 5872876 time to create 1 rle with new method : 0.729611873626709 time for calcul the mask position with numpy : 0.030381202697753906 nb_pixel_total : 6376 time to create 1 rle with old method : 0.0073740482330322266 time for calcul the mask position with numpy : 0.03159689903259277 nb_pixel_total : 10688 time to create 1 rle with old method : 0.012125730514526367 time for calcul the mask position with numpy : 0.02920818328857422 nb_pixel_total : 7841 time to create 1 rle with old method : 0.009274959564208984 time for calcul the mask position with numpy : 0.03046894073486328 nb_pixel_total : 10406 time to create 1 rle with old method : 0.01177835464477539 time for calcul the mask position with numpy : 0.029364824295043945 nb_pixel_total : 14281 time to create 1 rle with old method : 0.01614546775817871 time for calcul the mask position with numpy : 0.03252553939819336 nb_pixel_total : 8917 time to create 1 rle with old method : 0.01015019416809082 time for calcul the mask position with numpy : 0.03130745887756348 nb_pixel_total : 21762 time to create 1 rle with old method : 0.024811744689941406 time for calcul the mask position with numpy : 0.029576778411865234 nb_pixel_total : 14014 time to create 1 rle with old method : 0.01592087745666504 time for calcul the mask position with numpy : 0.03029608726501465 nb_pixel_total : 117155 time to create 1 rle with old method : 0.13605642318725586 time for calcul the mask position with numpy : 0.030506610870361328 nb_pixel_total : 11266 time to create 1 rle with old method : 0.0173492431640625 time for calcul the mask position with numpy : 0.03242921829223633 nb_pixel_total : 22540 time to create 1 rle with old method : 0.03823137283325195 time for calcul the mask position with numpy : 0.032855987548828125 nb_pixel_total : 10543 time to create 1 rle with old method : 0.011943578720092773 time for calcul the mask position with numpy : 0.031534671783447266 nb_pixel_total : 17076 time to create 1 rle with old method : 0.01972794532775879 time for calcul the mask position with numpy : 0.031449079513549805 nb_pixel_total : 30286 time to create 1 rle with old method : 0.03574419021606445 time for calcul the mask position with numpy : 0.029986143112182617 nb_pixel_total : 7840 time to create 1 rle with old method : 0.009078741073608398 time for calcul the mask position with numpy : 0.029958724975585938 nb_pixel_total : 13657 time to create 1 rle with old method : 0.015806198120117188 time for calcul the mask position with numpy : 0.030040740966796875 nb_pixel_total : 24051 time to create 1 rle with old method : 0.031253814697265625 time for calcul the mask position with numpy : 0.030103445053100586 nb_pixel_total : 3042 time to create 1 rle with old method : 0.0038115978240966797 time for calcul the mask position with numpy : 0.03029918670654297 nb_pixel_total : 26269 time to create 1 rle with old method : 0.04484438896179199 time for calcul the mask position with numpy : 0.02955460548400879 nb_pixel_total : 14473 time to create 1 rle with old method : 0.01627063751220703 time for calcul the mask position with numpy : 0.02947998046875 nb_pixel_total : 32299 time to create 1 rle with old method : 0.03834033012390137 time for calcul the mask position with numpy : 0.03015732765197754 nb_pixel_total : 99349 time to create 1 rle with old method : 0.1266176700592041 time for calcul the mask position with numpy : 0.03224778175354004 nb_pixel_total : 7999 time to create 1 rle with old method : 0.010036230087280273 time for calcul the mask position with numpy : 0.03196406364440918 nb_pixel_total : 12334 time to create 1 rle with old method : 0.014563560485839844 time for calcul the mask position with numpy : 0.030933141708374023 nb_pixel_total : 21878 time to create 1 rle with old method : 0.026822328567504883 time for calcul the mask position with numpy : 0.031025409698486328 nb_pixel_total : 7612 time to create 1 rle with old method : 0.009576082229614258 time for calcul the mask position with numpy : 0.031147480010986328 nb_pixel_total : 17875 time to create 1 rle with old method : 0.02105236053466797 time for calcul the mask position with numpy : 0.03266763687133789 nb_pixel_total : 42810 time to create 1 rle with old method : 0.04905056953430176 time for calcul the mask position with numpy : 0.029660463333129883 nb_pixel_total : 15877 time to create 1 rle with old method : 0.31485795974731445 time for calcul the mask position with numpy : 0.032537221908569336 nb_pixel_total : 13139 time to create 1 rle with old method : 0.015891551971435547 time for calcul the mask position with numpy : 0.032822370529174805 nb_pixel_total : 18314 time to create 1 rle with old method : 0.02198314666748047 time for calcul the mask position with numpy : 0.04285311698913574 nb_pixel_total : 317251 time to create 1 rle with new method : 0.802541971206665 time for calcul the mask position with numpy : 0.029901981353759766 nb_pixel_total : 11122 time to create 1 rle with old method : 0.012649059295654297 time for calcul the mask position with numpy : 0.02979302406311035 nb_pixel_total : 23397 time to create 1 rle with old method : 0.027070045471191406 time for calcul the mask position with numpy : 0.0301053524017334 nb_pixel_total : 22134 time to create 1 rle with old method : 0.024800539016723633 time for calcul the mask position with numpy : 0.029848814010620117 nb_pixel_total : 27980 time to create 1 rle with old method : 0.031649112701416016 time for calcul the mask position with numpy : 0.02942180633544922 nb_pixel_total : 6822 time to create 1 rle with old method : 0.007855653762817383 time for calcul the mask position with numpy : 0.030119657516479492 nb_pixel_total : 15577 time to create 1 rle with old method : 0.017876386642456055 time for calcul the mask position with numpy : 0.031194448471069336 nb_pixel_total : 60721 time to create 1 rle with old method : 0.06855607032775879 time for calcul the mask position with numpy : 0.030312061309814453 nb_pixel_total : 10391 time to create 1 rle with old method : 0.012284994125366211 create new chi : 4.76859188079834 time to delete rle : 0.003370523452758789 batch 1 Loaded 81 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18871 TO DO : save crop sub photo not yet done ! save time : 1.4900479316711426 nb_obj : 53 nb_hashtags : 3 time to prepare the origin masks : 5.087374687194824 time for calcul the mask position with numpy : 0.30002927780151367 nb_pixel_total : 5150537 time to create 1 rle with new method : 0.7355422973632812 time for calcul the mask position with numpy : 0.029755592346191406 nb_pixel_total : 9120 time to create 1 rle with old method : 0.010587930679321289 time for calcul the mask position with numpy : 0.029590845108032227 nb_pixel_total : 13284 time to create 1 rle with old method : 0.015240907669067383 time for calcul the mask position with numpy : 0.029593944549560547 nb_pixel_total : 13039 time to create 1 rle with old method : 0.01482844352722168 time for calcul the mask position with numpy : 0.03002476692199707 nb_pixel_total : 34723 time to create 1 rle with old method : 0.039186716079711914 time for calcul the mask position with numpy : 0.030070066452026367 nb_pixel_total : 34717 time to create 1 rle with old method : 0.040363311767578125 time for calcul the mask position with numpy : 0.031005859375 nb_pixel_total : 21284 time to create 1 rle with old method : 0.02599644660949707 time for calcul the mask position with numpy : 0.030614614486694336 nb_pixel_total : 34155 time to create 1 rle with old method : 0.03863859176635742 time for calcul the mask position with numpy : 0.029754161834716797 nb_pixel_total : 47012 time to create 1 rle with old method : 0.05291247367858887 time for calcul the mask position with numpy : 0.02919459342956543 nb_pixel_total : 71992 time to create 1 rle with old method : 0.08064985275268555 time for calcul the mask position with numpy : 0.02930736541748047 nb_pixel_total : 13066 time to create 1 rle with old method : 0.015057563781738281 time for calcul the mask position with numpy : 0.03252840042114258 nb_pixel_total : 10867 time to create 1 rle with old method : 0.012958765029907227 time for calcul the mask position with numpy : 0.030298471450805664 nb_pixel_total : 4271 time to create 1 rle with old method : 0.004959583282470703 time for calcul the mask position with numpy : 0.031147003173828125 nb_pixel_total : 18129 time to create 1 rle with old method : 0.02150130271911621 time for calcul the mask position with numpy : 0.03186464309692383 nb_pixel_total : 122361 time to create 1 rle with old method : 0.16257238388061523 time for calcul the mask position with numpy : 0.031830787658691406 nb_pixel_total : 20060 time to create 1 rle with old method : 0.02302694320678711 time for calcul the mask position with numpy : 0.029223203659057617 nb_pixel_total : 32455 time to create 1 rle with old method : 0.03729248046875 time for calcul the mask position with numpy : 0.029832124710083008 nb_pixel_total : 11340 time to create 1 rle with old method : 0.013182401657104492 time for calcul the mask position with numpy : 0.030503273010253906 nb_pixel_total : 48404 time to create 1 rle with old method : 0.05545854568481445 time for calcul the mask position with numpy : 0.030638933181762695 nb_pixel_total : 17376 time to create 1 rle with old method : 0.027636289596557617 time for calcul the mask position with numpy : 0.03503727912902832 nb_pixel_total : 11591 time to create 1 rle with old method : 0.013792037963867188 time for calcul the mask position with numpy : 0.03170371055603027 nb_pixel_total : 105362 time to create 1 rle with old method : 0.12044548988342285 time for calcul the mask position with numpy : 0.029827356338500977 nb_pixel_total : 11464 time to create 1 rle with old method : 0.01778864860534668 time for calcul the mask position with numpy : 0.035848379135131836 nb_pixel_total : 13756 time to create 1 rle with old method : 0.021389245986938477 time for calcul the mask position with numpy : 0.03620409965515137 nb_pixel_total : 17559 time to create 1 rle with old method : 0.02844071388244629 time for calcul the mask position with numpy : 0.03468799591064453 nb_pixel_total : 365235 time to create 1 rle with new method : 0.4686610698699951 time for calcul the mask position with numpy : 0.047701358795166016 nb_pixel_total : 5862 time to create 1 rle with old method : 0.009171724319458008 time for calcul the mask position with numpy : 0.04443836212158203 nb_pixel_total : 65509 time to create 1 rle with old method : 0.12590265274047852 time for calcul the mask position with numpy : 0.03699088096618652 nb_pixel_total : 16074 time to create 1 rle with old method : 0.018241167068481445 time for calcul the mask position with numpy : 0.031815528869628906 nb_pixel_total : 56760 time to create 1 rle with old method : 0.07948088645935059 time for calcul the mask position with numpy : 0.03453850746154785 nb_pixel_total : 8775 time to create 1 rle with old method : 0.010708332061767578 time for calcul the mask position with numpy : 0.030310869216918945 nb_pixel_total : 56 time to create 1 rle with old method : 0.00022840499877929688 time for calcul the mask position with numpy : 0.030440330505371094 nb_pixel_total : 20062 time to create 1 rle with old method : 0.022868871688842773 time for calcul the mask position with numpy : 0.033800601959228516 nb_pixel_total : 9134 time to create 1 rle with old method : 0.011072397232055664 time for calcul the mask position with numpy : 0.03209877014160156 nb_pixel_total : 16919 time to create 1 rle with old method : 0.019873380661010742 time for calcul the mask position with numpy : 0.036405324935913086 nb_pixel_total : 14667 time to create 1 rle with old method : 0.02628636360168457 time for calcul the mask position with numpy : 0.03733181953430176 nb_pixel_total : 24586 time to create 1 rle with old method : 0.03022170066833496 time for calcul the mask position with numpy : 0.030382633209228516 nb_pixel_total : 44020 time to create 1 rle with old method : 0.051053524017333984 time for calcul the mask position with numpy : 0.030300617218017578 nb_pixel_total : 534 time to create 1 rle with old method : 0.0008857250213623047 time for calcul the mask position with numpy : 0.03121638298034668 nb_pixel_total : 10903 time to create 1 rle with old method : 0.012743711471557617 time for calcul the mask position with numpy : 0.030159711837768555 nb_pixel_total : 9621 time to create 1 rle with old method : 0.011638641357421875 time for calcul the mask position with numpy : 0.03136467933654785 nb_pixel_total : 85 time to create 1 rle with old method : 0.00025463104248046875 time for calcul the mask position with numpy : 0.032160043716430664 nb_pixel_total : 14462 time to create 1 rle with old method : 0.01771092414855957 time for calcul the mask position with numpy : 0.03543448448181152 nb_pixel_total : 137047 time to create 1 rle with old method : 0.1884620189666748 time for calcul the mask position with numpy : 0.03817415237426758 nb_pixel_total : 107072 time to create 1 rle with old method : 0.16887140274047852 time for calcul the mask position with numpy : 0.030431747436523438 nb_pixel_total : 17670 time to create 1 rle with old method : 0.02117919921875 time for calcul the mask position with numpy : 0.03034210205078125 nb_pixel_total : 14231 time to create 1 rle with old method : 0.016286849975585938 time for calcul the mask position with numpy : 0.02980828285217285 nb_pixel_total : 69556 time to create 1 rle with old method : 0.08925867080688477 time for calcul the mask position with numpy : 0.029309749603271484 nb_pixel_total : 37379 time to create 1 rle with old method : 0.04235529899597168 time for calcul the mask position with numpy : 0.02982020378112793 nb_pixel_total : 18608 time to create 1 rle with old method : 0.02818894386291504 time for calcul the mask position with numpy : 0.06060481071472168 nb_pixel_total : 16149 time to create 1 rle with old method : 0.0204622745513916 time for calcul the mask position with numpy : 0.032341957092285156 nb_pixel_total : 35339 time to create 1 rle with old method : 0.043184757232666016 time for calcul the mask position with numpy : 0.030466318130493164 nb_pixel_total : 22317 time to create 1 rle with old method : 0.025609970092773438 time for calcul the mask position with numpy : 0.031560659408569336 nb_pixel_total : 3684 time to create 1 rle with old method : 0.0046613216400146484 create new chi : 5.3083319664001465 time to delete rle : 0.004399538040161133 batch 1 Loaded 113 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 26722 TO DO : save crop sub photo not yet done ! save time : 2.636488676071167 nb_obj : 16 nb_hashtags : 3 time to prepare the origin masks : 4.687697649002075 time for calcul the mask position with numpy : 0.286388635635376 nb_pixel_total : 5206652 time to create 1 rle with new method : 0.7014586925506592 time for calcul the mask position with numpy : 0.029435157775878906 nb_pixel_total : 57385 time to create 1 rle with old method : 0.0651710033416748 time for calcul the mask position with numpy : 0.03391122817993164 nb_pixel_total : 713691 time to create 1 rle with new method : 1.0175788402557373 time for calcul the mask position with numpy : 0.029622554779052734 nb_pixel_total : 30726 time to create 1 rle with old method : 0.03848838806152344 time for calcul the mask position with numpy : 0.029252290725708008 nb_pixel_total : 10419 time to create 1 rle with old method : 0.011944770812988281 time for calcul the mask position with numpy : 0.03095722198486328 nb_pixel_total : 254979 time to create 1 rle with new method : 0.4241943359375 time for calcul the mask position with numpy : 0.029520511627197266 nb_pixel_total : 23471 time to create 1 rle with old method : 0.02736973762512207 time for calcul the mask position with numpy : 0.03151750564575195 nb_pixel_total : 5286 time to create 1 rle with old method : 0.0061266422271728516 time for calcul the mask position with numpy : 0.030190706253051758 nb_pixel_total : 258275 time to create 1 rle with new method : 2.1627140045166016 time for calcul the mask position with numpy : 0.029324054718017578 nb_pixel_total : 30092 time to create 1 rle with old method : 0.03460240364074707 time for calcul the mask position with numpy : 0.029288291931152344 nb_pixel_total : 64196 time to create 1 rle with old method : 0.07217931747436523 time for calcul the mask position with numpy : 0.0300750732421875 nb_pixel_total : 92877 time to create 1 rle with old method : 0.11750054359436035 time for calcul the mask position with numpy : 0.03340506553649902 nb_pixel_total : 39436 time to create 1 rle with old method : 0.058449745178222656 time for calcul the mask position with numpy : 0.03031444549560547 nb_pixel_total : 204198 time to create 1 rle with new method : 0.6214656829833984 time for calcul the mask position with numpy : 0.029198169708251953 nb_pixel_total : 25000 time to create 1 rle with old method : 0.028453350067138672 time for calcul the mask position with numpy : 0.029154062271118164 nb_pixel_total : 23441 time to create 1 rle with old method : 0.02673959732055664 time for calcul the mask position with numpy : 0.03045177459716797 nb_pixel_total : 10116 time to create 1 rle with old method : 0.012844085693359375 create new chi : 6.331223964691162 time to delete rle : 0.0048520565032958984 batch 1 Loaded 33 chid ids of type : 3594 ++++++++++++++++++++++Number RLEs to save : 14772 TO DO : save crop sub photo not yet done ! save time : 3.3271868228912354 nb_obj : 52 nb_hashtags : 5 time to prepare the origin masks : 3.9606757164001465 time for calcul the mask position with numpy : 0.342360258102417 nb_pixel_total : 5729834 time to create 1 rle with new method : 0.6307697296142578 time for calcul the mask position with numpy : 0.028984785079956055 nb_pixel_total : 9864 time to create 1 rle with old method : 0.011098384857177734 time for calcul the mask position with numpy : 0.02875685691833496 nb_pixel_total : 15061 time to create 1 rle with old method : 0.017049551010131836 time for calcul the mask position with numpy : 0.028932809829711914 nb_pixel_total : 25552 time to create 1 rle with old method : 0.02868342399597168 time for calcul the mask position with numpy : 0.0290372371673584 nb_pixel_total : 27494 time to create 1 rle with old method : 0.03249692916870117 time for calcul the mask position with numpy : 0.0343317985534668 nb_pixel_total : 22317 time to create 1 rle with old method : 0.03297734260559082 time for calcul the mask position with numpy : 0.03302717208862305 nb_pixel_total : 12582 time to create 1 rle with old method : 0.02032303810119629 time for calcul the mask position with numpy : 0.02897787094116211 nb_pixel_total : 14821 time to create 1 rle with old method : 0.016763687133789062 time for calcul the mask position with numpy : 0.029018878936767578 nb_pixel_total : 12444 time to create 1 rle with old method : 0.013967037200927734 time for calcul the mask position with numpy : 0.029154539108276367 nb_pixel_total : 16745 time to create 1 rle with old method : 0.018709659576416016 time for calcul the mask position with numpy : 0.029701709747314453 nb_pixel_total : 42388 time to create 1 rle with old method : 0.04750823974609375 time for calcul the mask position with numpy : 0.029234647750854492 nb_pixel_total : 48578 time to create 1 rle with old method : 0.05400705337524414 time for calcul the mask position with numpy : 0.028212785720825195 nb_pixel_total : 4196 time to create 1 rle with old method : 0.004569053649902344 time for calcul the mask position with numpy : 0.02779841423034668 nb_pixel_total : 14037 time to create 1 rle with old method : 0.023452281951904297 time for calcul the mask position with numpy : 0.031827688217163086 nb_pixel_total : 10755 time to create 1 rle with old method : 0.02084636688232422 time for calcul the mask position with numpy : 0.04196047782897949 nb_pixel_total : 16166 time to create 1 rle with old method : 0.018253564834594727 time for calcul the mask position with numpy : 0.029451370239257812 nb_pixel_total : 25653 time to create 1 rle with old method : 0.02886486053466797 time for calcul the mask position with numpy : 0.029184341430664062 nb_pixel_total : 17681 time to create 1 rle with old method : 0.019927978515625 time for calcul the mask position with numpy : 0.029612302780151367 nb_pixel_total : 59887 time to create 1 rle with old method : 0.06724238395690918 time for calcul the mask position with numpy : 0.0292050838470459 nb_pixel_total : 11385 time to create 1 rle with old method : 0.012840509414672852 time for calcul the mask position with numpy : 0.02919793128967285 nb_pixel_total : 21104 time to create 1 rle with old method : 0.023964881896972656 time for calcul the mask position with numpy : 0.02903580665588379 nb_pixel_total : 7890 time to create 1 rle with old method : 0.008895635604858398 time for calcul the mask position with numpy : 0.028944969177246094 nb_pixel_total : 4201 time to create 1 rle with old method : 0.00491023063659668 time for calcul the mask position with numpy : 0.02893376350402832 nb_pixel_total : 18497 time to create 1 rle with old method : 0.02086782455444336 time for calcul the mask position with numpy : 0.029651403427124023 nb_pixel_total : 40660 time to create 1 rle with old method : 0.04540681838989258 time for calcul the mask position with numpy : 0.029218435287475586 nb_pixel_total : 21411 time to create 1 rle with old method : 0.024120092391967773 time for calcul the mask position with numpy : 0.03487277030944824 nb_pixel_total : 14803 time to create 1 rle with old method : 0.016918659210205078 time for calcul the mask position with numpy : 0.0298764705657959 nb_pixel_total : 134367 time to create 1 rle with old method : 0.14956402778625488 time for calcul the mask position with numpy : 0.028844356536865234 nb_pixel_total : 30261 time to create 1 rle with old method : 0.03420114517211914 time for calcul the mask position with numpy : 0.028939008712768555 nb_pixel_total : 35810 time to create 1 rle with old method : 0.039743661880493164 time for calcul the mask position with numpy : 0.029314279556274414 nb_pixel_total : 25742 time to create 1 rle with old method : 0.02874302864074707 time for calcul the mask position with numpy : 0.029495716094970703 nb_pixel_total : 14056 time to create 1 rle with old method : 0.01799321174621582 time for calcul the mask position with numpy : 0.02893686294555664 nb_pixel_total : 29984 time to create 1 rle with old method : 0.03384137153625488 time for calcul the mask position with numpy : 0.02901172637939453 nb_pixel_total : 11595 time to create 1 rle with old method : 0.013175010681152344 time for calcul the mask position with numpy : 0.028667926788330078 nb_pixel_total : 19079 time to create 1 rle with old method : 0.021417617797851562 time for calcul the mask position with numpy : 0.028995037078857422 nb_pixel_total : 10838 time to create 1 rle with old method : 0.012163162231445312 time for calcul the mask position with numpy : 0.02882671356201172 nb_pixel_total : 3624 time to create 1 rle with old method : 0.004097461700439453 time for calcul the mask position with numpy : 0.029181480407714844 nb_pixel_total : 67703 time to create 1 rle with old method : 0.07595705986022949 time for calcul the mask position with numpy : 0.02902531623840332 nb_pixel_total : 28772 time to create 1 rle with old method : 0.03214454650878906 time for calcul the mask position with numpy : 0.02894735336303711 nb_pixel_total : 12742 time to create 1 rle with old method : 0.01434469223022461 time for calcul the mask position with numpy : 0.028763532638549805 nb_pixel_total : 18883 time to create 1 rle with old method : 0.02119898796081543 time for calcul the mask position with numpy : 0.028700590133666992 nb_pixel_total : 27089 time to create 1 rle with old method : 0.030303001403808594 time for calcul the mask position with numpy : 0.02989649772644043 nb_pixel_total : 30876 time to create 1 rle with old method : 0.03495478630065918 time for calcul the mask position with numpy : 0.028801441192626953 nb_pixel_total : 32973 time to create 1 rle with old method : 0.03683662414550781 time for calcul the mask position with numpy : 0.02878284454345703 nb_pixel_total : 11158 time to create 1 rle with old method : 0.012594223022460938 time for calcul the mask position with numpy : 0.028753280639648438 nb_pixel_total : 17332 time to create 1 rle with old method : 0.019543170928955078 time for calcul the mask position with numpy : 0.029269695281982422 nb_pixel_total : 117288 time to create 1 rle with old method : 0.15642333030700684 time for calcul the mask position with numpy : 0.02840113639831543 nb_pixel_total : 6946 time to create 1 rle with old method : 0.007725715637207031 time for calcul the mask position with numpy : 0.028013229370117188 nb_pixel_total : 36565 time to create 1 rle with old method : 0.04006791114807129 time for calcul the mask position with numpy : 0.02962660789489746 nb_pixel_total : 12780 time to create 1 rle with old method : 0.01428079605102539 time for calcul the mask position with numpy : 0.02873992919921875 nb_pixel_total : 15474 time to create 1 rle with old method : 0.0173032283782959 time for calcul the mask position with numpy : 0.028722763061523438 nb_pixel_total : 18096 time to create 1 rle with old method : 0.020281076431274414 time for calcul the mask position with numpy : 0.028877735137939453 nb_pixel_total : 14201 time to create 1 rle with old method : 0.01603841781616211 create new chi : 4.089749336242676 time to delete rle : 0.0036177635192871094 batch 1 Loaded 107 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23580 TO DO : save crop sub photo not yet done ! save time : 2.8586204051971436 nb_obj : 33 nb_hashtags : 5 time to prepare the origin masks : 4.762919664382935 time for calcul the mask position with numpy : 0.4850006103515625 nb_pixel_total : 4815283 time to create 1 rle with new method : 0.6638591289520264 time for calcul the mask position with numpy : 0.02923750877380371 nb_pixel_total : 6807 time to create 1 rle with old method : 0.007693290710449219 time for calcul the mask position with numpy : 0.029100418090820312 nb_pixel_total : 20939 time to create 1 rle with old method : 0.0239105224609375 time for calcul the mask position with numpy : 0.029928922653198242 nb_pixel_total : 131003 time to create 1 rle with old method : 0.15850210189819336 time for calcul the mask position with numpy : 0.029781818389892578 nb_pixel_total : 140339 time to create 1 rle with old method : 0.15790581703186035 time for calcul the mask position with numpy : 0.030359268188476562 nb_pixel_total : 225186 time to create 1 rle with new method : 0.4516620635986328 time for calcul the mask position with numpy : 0.02897930145263672 nb_pixel_total : 42815 time to create 1 rle with old method : 0.048133134841918945 time for calcul the mask position with numpy : 0.029285907745361328 nb_pixel_total : 52482 time to create 1 rle with old method : 0.05860710144042969 time for calcul the mask position with numpy : 0.031427860260009766 nb_pixel_total : 74963 time to create 1 rle with old method : 0.08646464347839355 time for calcul the mask position with numpy : 0.029764413833618164 nb_pixel_total : 85208 time to create 1 rle with old method : 0.09682059288024902 time for calcul the mask position with numpy : 0.02936577796936035 nb_pixel_total : 60114 time to create 1 rle with old method : 0.06790900230407715 time for calcul the mask position with numpy : 0.03396010398864746 nb_pixel_total : 199038 time to create 1 rle with new method : 0.644817590713501 time for calcul the mask position with numpy : 0.02936244010925293 nb_pixel_total : 8230 time to create 1 rle with old method : 0.010192632675170898 time for calcul the mask position with numpy : 0.03325295448303223 nb_pixel_total : 514909 time to create 1 rle with new method : 0.4741189479827881 time for calcul the mask position with numpy : 0.029083967208862305 nb_pixel_total : 6142 time to create 1 rle with old method : 0.007643938064575195 time for calcul the mask position with numpy : 0.029419422149658203 nb_pixel_total : 14463 time to create 1 rle with old method : 0.01638936996459961 time for calcul the mask position with numpy : 0.029973268508911133 nb_pixel_total : 18780 time to create 1 rle with old method : 0.027252197265625 time for calcul the mask position with numpy : 0.03303933143615723 nb_pixel_total : 28408 time to create 1 rle with old method : 0.04006075859069824 time for calcul the mask position with numpy : 0.029216289520263672 nb_pixel_total : 33739 time to create 1 rle with old method : 0.038092851638793945 time for calcul the mask position with numpy : 0.029146909713745117 nb_pixel_total : 26019 time to create 1 rle with old method : 0.035143136978149414 time for calcul the mask position with numpy : 0.029435396194458008 nb_pixel_total : 34041 time to create 1 rle with old method : 0.03834676742553711 time for calcul the mask position with numpy : 0.02942657470703125 nb_pixel_total : 55228 time to create 1 rle with old method : 0.06188225746154785 time for calcul the mask position with numpy : 0.029340744018554688 nb_pixel_total : 32038 time to create 1 rle with old method : 0.03567981719970703 time for calcul the mask position with numpy : 0.03053736686706543 nb_pixel_total : 19260 time to create 1 rle with old method : 0.022390365600585938 time for calcul the mask position with numpy : 0.034293174743652344 nb_pixel_total : 9866 time to create 1 rle with old method : 0.010877132415771484 time for calcul the mask position with numpy : 0.02958965301513672 nb_pixel_total : 198120 time to create 1 rle with new method : 0.49614429473876953 time for calcul the mask position with numpy : 0.029200077056884766 nb_pixel_total : 10835 time to create 1 rle with old method : 0.012208700180053711 time for calcul the mask position with numpy : 0.02910637855529785 nb_pixel_total : 903 time to create 1 rle with old method : 0.0011641979217529297 time for calcul the mask position with numpy : 0.02895355224609375 nb_pixel_total : 11645 time to create 1 rle with old method : 0.013135671615600586 time for calcul the mask position with numpy : 0.029658794403076172 nb_pixel_total : 135111 time to create 1 rle with old method : 0.15195107460021973 time for calcul the mask position with numpy : 0.029296875 nb_pixel_total : 13487 time to create 1 rle with old method : 0.015210151672363281 time for calcul the mask position with numpy : 0.02922224998474121 nb_pixel_total : 9983 time to create 1 rle with old method : 0.011149406433105469 time for calcul the mask position with numpy : 0.029164552688598633 nb_pixel_total : 9502 time to create 1 rle with old method : 0.011055469512939453 time for calcul the mask position with numpy : 0.030969858169555664 nb_pixel_total : 5354 time to create 1 rle with old method : 0.0061109066009521484 create new chi : 5.612426519393921 time to delete rle : 0.0037298202514648438 batch 1 Loaded 68 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 22463 TO DO : save crop sub photo not yet done ! save time : 6.451408624649048 nb_obj : 35 nb_hashtags : 5 time to prepare the origin masks : 9.59916877746582 time for calcul the mask position with numpy : 0.3857414722442627 nb_pixel_total : 4736472 time to create 1 rle with new method : 0.45406413078308105 time for calcul the mask position with numpy : 0.032506465911865234 nb_pixel_total : 352985 time to create 1 rle with new method : 0.5609691143035889 time for calcul the mask position with numpy : 0.029056072235107422 nb_pixel_total : 84738 time to create 1 rle with old method : 0.09406065940856934 time for calcul the mask position with numpy : 0.029012680053710938 nb_pixel_total : 6176 time to create 1 rle with old method : 0.007318258285522461 time for calcul the mask position with numpy : 0.028987646102905273 nb_pixel_total : 5319 time to create 1 rle with old method : 0.006636142730712891 time for calcul the mask position with numpy : 0.029064416885375977 nb_pixel_total : 21084 time to create 1 rle with old method : 0.02363896369934082 time for calcul the mask position with numpy : 0.028986215591430664 nb_pixel_total : 823 time to create 1 rle with old method : 0.0010097026824951172 time for calcul the mask position with numpy : 0.029232263565063477 nb_pixel_total : 4756 time to create 1 rle with old method : 0.005373239517211914 time for calcul the mask position with numpy : 0.02951836585998535 nb_pixel_total : 110403 time to create 1 rle with old method : 0.12369108200073242 time for calcul the mask position with numpy : 0.028903722763061523 nb_pixel_total : 13620 time to create 1 rle with old method : 0.01588892936706543 time for calcul the mask position with numpy : 0.028895854949951172 nb_pixel_total : 7158 time to create 1 rle with old method : 0.008196830749511719 time for calcul the mask position with numpy : 0.029098033905029297 nb_pixel_total : 1772 time to create 1 rle with old method : 0.0022094249725341797 time for calcul the mask position with numpy : 0.02910327911376953 nb_pixel_total : 14262 time to create 1 rle with old method : 0.015965938568115234 time for calcul the mask position with numpy : 0.028967857360839844 nb_pixel_total : 456 time to create 1 rle with old method : 0.0006060600280761719 time for calcul the mask position with numpy : 0.029856443405151367 nb_pixel_total : 111030 time to create 1 rle with old method : 0.12616324424743652 time for calcul the mask position with numpy : 0.033005475997924805 nb_pixel_total : 56748 time to create 1 rle with old method : 0.13379192352294922 time for calcul the mask position with numpy : 0.061209678649902344 nb_pixel_total : 20391 time to create 1 rle with old method : 0.037958621978759766 time for calcul the mask position with numpy : 0.03428196907043457 nb_pixel_total : 38 time to create 1 rle with old method : 0.00021123886108398438 time for calcul the mask position with numpy : 0.03288102149963379 nb_pixel_total : 78346 time to create 1 rle with old method : 0.1434173583984375 time for calcul the mask position with numpy : 0.030225753784179688 nb_pixel_total : 2146 time to create 1 rle with old method : 0.0028700828552246094 time for calcul the mask position with numpy : 0.0358736515045166 nb_pixel_total : 288148 time to create 1 rle with new method : 0.827251672744751 time for calcul the mask position with numpy : 0.029872655868530273 nb_pixel_total : 16768 time to create 1 rle with old method : 0.025150775909423828 time for calcul the mask position with numpy : 0.035677433013916016 nb_pixel_total : 19530 time to create 1 rle with old method : 0.03493666648864746 time for calcul the mask position with numpy : 0.031697750091552734 nb_pixel_total : 290624 time to create 1 rle with new method : 0.7078185081481934 time for calcul the mask position with numpy : 0.03164196014404297 nb_pixel_total : 87335 time to create 1 rle with old method : 0.10029292106628418 time for calcul the mask position with numpy : 0.0341181755065918 nb_pixel_total : 414123 time to create 1 rle with new method : 0.6133263111114502 time for calcul the mask position with numpy : 0.02915358543395996 nb_pixel_total : 22888 time to create 1 rle with old method : 0.02576303482055664 time for calcul the mask position with numpy : 0.02929401397705078 nb_pixel_total : 1364 time to create 1 rle with old method : 0.0015785694122314453 time for calcul the mask position with numpy : 0.029023170471191406 nb_pixel_total : 11212 time to create 1 rle with old method : 0.013494253158569336 time for calcul the mask position with numpy : 0.030310630798339844 nb_pixel_total : 91447 time to create 1 rle with old method : 0.13152527809143066 time for calcul the mask position with numpy : 0.028283119201660156 nb_pixel_total : 8031 time to create 1 rle with old method : 0.009263277053833008 time for calcul the mask position with numpy : 0.028249025344848633 nb_pixel_total : 103882 time to create 1 rle with old method : 0.11220431327819824 time for calcul the mask position with numpy : 0.028076648712158203 nb_pixel_total : 51336 time to create 1 rle with old method : 0.055731773376464844 time for calcul the mask position with numpy : 0.028873920440673828 nb_pixel_total : 3635 time to create 1 rle with old method : 0.00452113151550293 time for calcul the mask position with numpy : 0.02892756462097168 nb_pixel_total : 5641 time to create 1 rle with old method : 0.006390571594238281 time for calcul the mask position with numpy : 0.029393434524536133 nb_pixel_total : 5553 time to create 1 rle with old method : 0.006535053253173828 create new chi : 6.048349380493164 time to delete rle : 0.00413823127746582 batch 1 Loaded 85 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24440 TO DO : save crop sub photo not yet done ! save time : 4.573526620864868 nb_obj : 37 nb_hashtags : 3 time to prepare the origin masks : 4.195433139801025 time for calcul the mask position with numpy : 0.38734889030456543 nb_pixel_total : 5521005 time to create 1 rle with new method : 0.32875943183898926 time for calcul the mask position with numpy : 0.03000664710998535 nb_pixel_total : 30714 time to create 1 rle with old method : 0.034806013107299805 time for calcul the mask position with numpy : 0.02904057502746582 nb_pixel_total : 26092 time to create 1 rle with old method : 0.030889034271240234 time for calcul the mask position with numpy : 0.02947711944580078 nb_pixel_total : 109230 time to create 1 rle with old method : 0.12360644340515137 time for calcul the mask position with numpy : 0.02903604507446289 nb_pixel_total : 16397 time to create 1 rle with old method : 0.018378496170043945 time for calcul the mask position with numpy : 0.02871084213256836 nb_pixel_total : 88539 time to create 1 rle with old method : 0.1060781478881836 time for calcul the mask position with numpy : 0.02906942367553711 nb_pixel_total : 83441 time to create 1 rle with old method : 0.09288167953491211 time for calcul the mask position with numpy : 0.029124736785888672 nb_pixel_total : 18455 time to create 1 rle with old method : 0.02107977867126465 time for calcul the mask position with numpy : 0.03197026252746582 nb_pixel_total : 9863 time to create 1 rle with old method : 0.011115789413452148 time for calcul the mask position with numpy : 0.03189206123352051 nb_pixel_total : 106825 time to create 1 rle with old method : 0.12294864654541016 time for calcul the mask position with numpy : 0.031107425689697266 nb_pixel_total : 12749 time to create 1 rle with old method : 0.014500141143798828 time for calcul the mask position with numpy : 0.02985358238220215 nb_pixel_total : 59435 time to create 1 rle with old method : 0.06644392013549805 time for calcul the mask position with numpy : 0.02882695198059082 nb_pixel_total : 29277 time to create 1 rle with old method : 0.03359675407409668 time for calcul the mask position with numpy : 0.029316186904907227 nb_pixel_total : 16487 time to create 1 rle with old method : 0.019496917724609375 time for calcul the mask position with numpy : 0.028893232345581055 nb_pixel_total : 34494 time to create 1 rle with old method : 0.03833961486816406 time for calcul the mask position with numpy : 0.02892279624938965 nb_pixel_total : 42118 time to create 1 rle with old method : 0.04732775688171387 time for calcul the mask position with numpy : 0.02918100357055664 nb_pixel_total : 48626 time to create 1 rle with old method : 0.05492568016052246 time for calcul the mask position with numpy : 0.028840303421020508 nb_pixel_total : 40429 time to create 1 rle with old method : 0.04500412940979004 time for calcul the mask position with numpy : 0.028749465942382812 nb_pixel_total : 46283 time to create 1 rle with old method : 0.05191493034362793 time for calcul the mask position with numpy : 0.029200315475463867 nb_pixel_total : 11918 time to create 1 rle with old method : 0.015586137771606445 time for calcul the mask position with numpy : 0.03284716606140137 nb_pixel_total : 41594 time to create 1 rle with old method : 0.05559849739074707 time for calcul the mask position with numpy : 0.029603958129882812 nb_pixel_total : 188049 time to create 1 rle with new method : 0.6317617893218994 time for calcul the mask position with numpy : 0.029360532760620117 nb_pixel_total : 79724 time to create 1 rle with old method : 0.08907198905944824 time for calcul the mask position with numpy : 0.029274463653564453 nb_pixel_total : 7792 time to create 1 rle with old method : 0.008880853652954102 time for calcul the mask position with numpy : 0.029766082763671875 nb_pixel_total : 43481 time to create 1 rle with old method : 0.04866743087768555 time for calcul the mask position with numpy : 0.028883934020996094 nb_pixel_total : 24215 time to create 1 rle with old method : 0.027159690856933594 time for calcul the mask position with numpy : 0.029145479202270508 nb_pixel_total : 75621 time to create 1 rle with old method : 0.08463144302368164 time for calcul the mask position with numpy : 0.028931856155395508 nb_pixel_total : 3437 time to create 1 rle with old method : 0.003962278366088867 time for calcul the mask position with numpy : 0.028914213180541992 nb_pixel_total : 9218 time to create 1 rle with old method : 0.010337591171264648 time for calcul the mask position with numpy : 0.028876543045043945 nb_pixel_total : 25881 time to create 1 rle with old method : 0.02887105941772461 time for calcul the mask position with numpy : 0.029112577438354492 nb_pixel_total : 19468 time to create 1 rle with old method : 0.021822452545166016 time for calcul the mask position with numpy : 0.028829097747802734 nb_pixel_total : 5166 time to create 1 rle with old method : 0.006101369857788086 time for calcul the mask position with numpy : 0.029587984085083008 nb_pixel_total : 18061 time to create 1 rle with old method : 0.02019190788269043 time for calcul the mask position with numpy : 0.028966903686523438 nb_pixel_total : 44164 time to create 1 rle with old method : 0.049286842346191406 time for calcul the mask position with numpy : 0.02938103675842285 nb_pixel_total : 16082 time to create 1 rle with old method : 0.0180511474609375 time for calcul the mask position with numpy : 0.029386281967163086 nb_pixel_total : 65009 time to create 1 rle with old method : 0.07225584983825684 time for calcul the mask position with numpy : 0.02897167205810547 nb_pixel_total : 6927 time to create 1 rle with old method : 0.007916688919067383 time for calcul the mask position with numpy : 0.030947208404541016 nb_pixel_total : 23974 time to create 1 rle with old method : 0.03941035270690918 create new chi : 4.040471315383911 time to delete rle : 0.0040569305419921875 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 21133 TO DO : save crop sub photo not yet done ! save time : 3.7071776390075684 nb_obj : 58 nb_hashtags : 4 time to prepare the origin masks : 4.927861213684082 time for calcul the mask position with numpy : 0.21103882789611816 nb_pixel_total : 5135128 time to create 1 rle with new method : 0.3560667037963867 time for calcul the mask position with numpy : 0.030209064483642578 nb_pixel_total : 10125 time to create 1 rle with old method : 0.011430025100708008 time for calcul the mask position with numpy : 0.028768539428710938 nb_pixel_total : 13013 time to create 1 rle with old method : 0.014711141586303711 time for calcul the mask position with numpy : 0.02893686294555664 nb_pixel_total : 39549 time to create 1 rle with old method : 0.04406452178955078 time for calcul the mask position with numpy : 0.028801918029785156 nb_pixel_total : 7772 time to create 1 rle with old method : 0.00870966911315918 time for calcul the mask position with numpy : 0.028761625289916992 nb_pixel_total : 39713 time to create 1 rle with old method : 0.044857025146484375 time for calcul the mask position with numpy : 0.032248497009277344 nb_pixel_total : 3856 time to create 1 rle with old method : 0.0054318904876708984 time for calcul the mask position with numpy : 0.029099464416503906 nb_pixel_total : 16831 time to create 1 rle with old method : 0.018748998641967773 time for calcul the mask position with numpy : 0.02869391441345215 nb_pixel_total : 14055 time to create 1 rle with old method : 0.015768766403198242 time for calcul the mask position with numpy : 0.028882980346679688 nb_pixel_total : 51106 time to create 1 rle with old method : 0.05689811706542969 time for calcul the mask position with numpy : 0.028788089752197266 nb_pixel_total : 13372 time to create 1 rle with old method : 0.015697002410888672 time for calcul the mask position with numpy : 0.028789758682250977 nb_pixel_total : 27569 time to create 1 rle with old method : 0.03123641014099121 time for calcul the mask position with numpy : 0.029755115509033203 nb_pixel_total : 11171 time to create 1 rle with old method : 0.01284337043762207 time for calcul the mask position with numpy : 0.03527569770812988 nb_pixel_total : 45293 time to create 1 rle with old method : 0.06688594818115234 time for calcul the mask position with numpy : 0.028937339782714844 nb_pixel_total : 12993 time to create 1 rle with old method : 0.014636516571044922 time for calcul the mask position with numpy : 0.028739452362060547 nb_pixel_total : 62406 time to create 1 rle with old method : 0.069305419921875 time for calcul the mask position with numpy : 0.02887558937072754 nb_pixel_total : 42901 time to create 1 rle with old method : 0.049996137619018555 time for calcul the mask position with numpy : 0.030855894088745117 nb_pixel_total : 66183 time to create 1 rle with old method : 0.07994604110717773 time for calcul the mask position with numpy : 0.030052900314331055 nb_pixel_total : 67072 time to create 1 rle with old method : 0.07530879974365234 time for calcul the mask position with numpy : 0.028911590576171875 nb_pixel_total : 8078 time to create 1 rle with old method : 0.010390043258666992 time for calcul the mask position with numpy : 0.029257535934448242 nb_pixel_total : 34329 time to create 1 rle with old method : 0.038285255432128906 time for calcul the mask position with numpy : 0.029012441635131836 nb_pixel_total : 48843 time to create 1 rle with old method : 0.05585193634033203 time for calcul the mask position with numpy : 0.02921319007873535 nb_pixel_total : 13240 time to create 1 rle with old method : 0.014910459518432617 time for calcul the mask position with numpy : 0.02965092658996582 nb_pixel_total : 158033 time to create 1 rle with new method : 0.4831717014312744 time for calcul the mask position with numpy : 0.029122114181518555 nb_pixel_total : 82269 time to create 1 rle with old method : 0.09373831748962402 time for calcul the mask position with numpy : 0.030095338821411133 nb_pixel_total : 13158 time to create 1 rle with old method : 0.01542806625366211 time for calcul the mask position with numpy : 0.029388427734375 nb_pixel_total : 43656 time to create 1 rle with old method : 0.04860234260559082 time for calcul the mask position with numpy : 0.029363393783569336 nb_pixel_total : 18937 time to create 1 rle with old method : 0.022405385971069336 time for calcul the mask position with numpy : 0.03065633773803711 nb_pixel_total : 27966 time to create 1 rle with old method : 0.03123927116394043 time for calcul the mask position with numpy : 0.02897191047668457 nb_pixel_total : 9110 time to create 1 rle with old method : 0.010387420654296875 time for calcul the mask position with numpy : 0.02958536148071289 nb_pixel_total : 23171 time to create 1 rle with old method : 0.025880813598632812 time for calcul the mask position with numpy : 0.029587984085083008 nb_pixel_total : 39122 time to create 1 rle with old method : 0.045060157775878906 time for calcul the mask position with numpy : 0.0319063663482666 nb_pixel_total : 75479 time to create 1 rle with old method : 0.08650898933410645 time for calcul the mask position with numpy : 0.030312538146972656 nb_pixel_total : 24335 time to create 1 rle with old method : 0.027850866317749023 time for calcul the mask position with numpy : 0.028838396072387695 nb_pixel_total : 20532 time to create 1 rle with old method : 0.023173809051513672 time for calcul the mask position with numpy : 0.029310941696166992 nb_pixel_total : 81772 time to create 1 rle with old method : 0.09089946746826172 time for calcul the mask position with numpy : 0.029531478881835938 nb_pixel_total : 24832 time to create 1 rle with old method : 0.027573347091674805 time for calcul the mask position with numpy : 0.028992652893066406 nb_pixel_total : 38196 time to create 1 rle with old method : 0.0424351692199707 time for calcul the mask position with numpy : 0.028910160064697266 nb_pixel_total : 9519 time to create 1 rle with old method : 0.012824296951293945 time for calcul the mask position with numpy : 0.030349016189575195 nb_pixel_total : 95441 time to create 1 rle with old method : 0.10718774795532227 time for calcul the mask position with numpy : 0.028831005096435547 nb_pixel_total : 19258 time to create 1 rle with old method : 0.022759675979614258 time for calcul the mask position with numpy : 0.029056072235107422 nb_pixel_total : 20938 time to create 1 rle with old method : 0.023438453674316406 time for calcul the mask position with numpy : 0.028859615325927734 nb_pixel_total : 36269 time to create 1 rle with old method : 0.041060686111450195 time for calcul the mask position with numpy : 0.030512571334838867 nb_pixel_total : 60126 time to create 1 rle with old method : 0.07058525085449219 time for calcul the mask position with numpy : 0.028764963150024414 nb_pixel_total : 5597 time to create 1 rle with old method : 0.006373882293701172 time for calcul the mask position with numpy : 0.02886795997619629 nb_pixel_total : 21314 time to create 1 rle with old method : 0.02454209327697754 time for calcul the mask position with numpy : 0.0338132381439209 nb_pixel_total : 212 time to create 1 rle with old method : 0.0005068778991699219 time for calcul the mask position with numpy : 0.028883934020996094 nb_pixel_total : 27320 time to create 1 rle with old method : 0.030701160430908203 time for calcul the mask position with numpy : 0.028954029083251953 nb_pixel_total : 53579 time to create 1 rle with old method : 0.06001925468444824 time for calcul the mask position with numpy : 0.02929854393005371 nb_pixel_total : 127282 time to create 1 rle with old method : 0.14407920837402344 time for calcul the mask position with numpy : 0.031801462173461914 nb_pixel_total : 17134 time to create 1 rle with old method : 0.019876718521118164 time for calcul the mask position with numpy : 0.02937602996826172 nb_pixel_total : 16422 time to create 1 rle with old method : 0.0184633731842041 time for calcul the mask position with numpy : 0.029051542282104492 nb_pixel_total : 19531 time to create 1 rle with old method : 0.021816730499267578 time for calcul the mask position with numpy : 0.032660722732543945 nb_pixel_total : 3331 time to create 1 rle with old method : 0.0047588348388671875 time for calcul the mask position with numpy : 0.029382705688476562 nb_pixel_total : 11996 time to create 1 rle with old method : 0.013465642929077148 time for calcul the mask position with numpy : 0.029711008071899414 nb_pixel_total : 1345 time to create 1 rle with old method : 0.0015804767608642578 time for calcul the mask position with numpy : 0.029943466186523438 nb_pixel_total : 30400 time to create 1 rle with old method : 0.04260540008544922 time for calcul the mask position with numpy : 0.028939008712768555 nb_pixel_total : 4885 time to create 1 rle with old method : 0.005491018295288086 time for calcul the mask position with numpy : 0.02884197235107422 nb_pixel_total : 3175 time to create 1 rle with old method : 0.0035982131958007812 create new chi : 4.853870630264282 time to delete rle : 0.004643440246582031 batch 1 Loaded 119 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 31082 TO DO : save crop sub photo not yet done ! save time : 5.002406120300293 nb_obj : 55 nb_hashtags : 3 time to prepare the origin masks : 4.285730361938477 time for calcul the mask position with numpy : 0.773658037185669 nb_pixel_total : 5734704 time to create 1 rle with new method : 0.6099622249603271 time for calcul the mask position with numpy : 0.03878307342529297 nb_pixel_total : 6126 time to create 1 rle with old method : 0.008618593215942383 time for calcul the mask position with numpy : 0.029674530029296875 nb_pixel_total : 12208 time to create 1 rle with old method : 0.01584792137145996 time for calcul the mask position with numpy : 0.029696226119995117 nb_pixel_total : 12819 time to create 1 rle with old method : 0.019941091537475586 time for calcul the mask position with numpy : 0.03192424774169922 nb_pixel_total : 37032 time to create 1 rle with old method : 0.05433464050292969 time for calcul the mask position with numpy : 0.030353307723999023 nb_pixel_total : 27389 time to create 1 rle with old method : 0.031749725341796875 time for calcul the mask position with numpy : 0.029347896575927734 nb_pixel_total : 60008 time to create 1 rle with old method : 0.0699610710144043 time for calcul the mask position with numpy : 0.029396533966064453 nb_pixel_total : 20731 time to create 1 rle with old method : 0.023361682891845703 time for calcul the mask position with numpy : 0.028983354568481445 nb_pixel_total : 13857 time to create 1 rle with old method : 0.01756596565246582 time for calcul the mask position with numpy : 0.02893352508544922 nb_pixel_total : 25187 time to create 1 rle with old method : 0.0279085636138916 time for calcul the mask position with numpy : 0.029035091400146484 nb_pixel_total : 12924 time to create 1 rle with old method : 0.014510869979858398 time for calcul the mask position with numpy : 0.028818845748901367 nb_pixel_total : 19511 time to create 1 rle with old method : 0.021825790405273438 time for calcul the mask position with numpy : 0.02908921241760254 nb_pixel_total : 18645 time to create 1 rle with old method : 0.020873546600341797 time for calcul the mask position with numpy : 0.029068946838378906 nb_pixel_total : 24153 time to create 1 rle with old method : 0.027746915817260742 time for calcul the mask position with numpy : 0.029253482818603516 nb_pixel_total : 10202 time to create 1 rle with old method : 0.012135744094848633 time for calcul the mask position with numpy : 0.029013633728027344 nb_pixel_total : 10737 time to create 1 rle with old method : 0.012683868408203125 time for calcul the mask position with numpy : 0.029416561126708984 nb_pixel_total : 6933 time to create 1 rle with old method : 0.00813913345336914 time for calcul the mask position with numpy : 0.02964329719543457 nb_pixel_total : 42897 time to create 1 rle with old method : 0.04954123497009277 time for calcul the mask position with numpy : 0.029238462448120117 nb_pixel_total : 23788 time to create 1 rle with old method : 0.027028799057006836 time for calcul the mask position with numpy : 0.02913045883178711 nb_pixel_total : 23459 time to create 1 rle with old method : 0.026833772659301758 time for calcul the mask position with numpy : 0.029267072677612305 nb_pixel_total : 49873 time to create 1 rle with old method : 0.05529904365539551 time for calcul the mask position with numpy : 0.028931140899658203 nb_pixel_total : 15270 time to create 1 rle with old method : 0.017367124557495117 time for calcul the mask position with numpy : 0.02901434898376465 nb_pixel_total : 52755 time to create 1 rle with old method : 0.061347007751464844 time for calcul the mask position with numpy : 0.02889704704284668 nb_pixel_total : 42748 time to create 1 rle with old method : 0.047506093978881836 time for calcul the mask position with numpy : 0.02897953987121582 nb_pixel_total : 24985 time to create 1 rle with old method : 0.02841782569885254 time for calcul the mask position with numpy : 0.028902292251586914 nb_pixel_total : 25589 time to create 1 rle with old method : 0.028800487518310547 time for calcul the mask position with numpy : 0.029668569564819336 nb_pixel_total : 28080 time to create 1 rle with old method : 0.03217053413391113 time for calcul the mask position with numpy : 0.029496431350708008 nb_pixel_total : 49939 time to create 1 rle with old method : 0.057199716567993164 time for calcul the mask position with numpy : 0.04060554504394531 nb_pixel_total : 12158 time to create 1 rle with old method : 0.013887405395507812 time for calcul the mask position with numpy : 0.028821945190429688 nb_pixel_total : 20509 time to create 1 rle with old method : 0.022998809814453125 time for calcul the mask position with numpy : 0.029076337814331055 nb_pixel_total : 20008 time to create 1 rle with old method : 0.022343158721923828 time for calcul the mask position with numpy : 0.029105663299560547 nb_pixel_total : 30715 time to create 1 rle with old method : 0.03546476364135742 time for calcul the mask position with numpy : 0.02958989143371582 nb_pixel_total : 34267 time to create 1 rle with old method : 0.03853750228881836 time for calcul the mask position with numpy : 0.029215335845947266 nb_pixel_total : 17803 time to create 1 rle with old method : 0.019832134246826172 time for calcul the mask position with numpy : 0.029238224029541016 nb_pixel_total : 13120 time to create 1 rle with old method : 0.014773130416870117 time for calcul the mask position with numpy : 0.029433012008666992 nb_pixel_total : 4366 time to create 1 rle with old method : 0.00653076171875 time for calcul the mask position with numpy : 0.03043961524963379 nb_pixel_total : 29120 time to create 1 rle with old method : 0.03243589401245117 time for calcul the mask position with numpy : 0.029263734817504883 nb_pixel_total : 36993 time to create 1 rle with old method : 0.04176521301269531 time for calcul the mask position with numpy : 0.029488086700439453 nb_pixel_total : 7145 time to create 1 rle with old method : 0.008133411407470703 time for calcul the mask position with numpy : 0.030643939971923828 nb_pixel_total : 111132 time to create 1 rle with old method : 0.12587189674377441 time for calcul the mask position with numpy : 0.02986907958984375 nb_pixel_total : 12571 time to create 1 rle with old method : 0.0144195556640625 time for calcul the mask position with numpy : 0.029291391372680664 nb_pixel_total : 42268 time to create 1 rle with old method : 0.048367977142333984 time for calcul the mask position with numpy : 0.029205799102783203 nb_pixel_total : 4445 time to create 1 rle with old method : 0.005274772644042969 time for calcul the mask position with numpy : 0.03030705451965332 nb_pixel_total : 15956 time to create 1 rle with old method : 0.020122289657592773 time for calcul the mask position with numpy : 0.030555248260498047 nb_pixel_total : 450 time to create 1 rle with old method : 0.0007195472717285156 time for calcul the mask position with numpy : 0.029199838638305664 nb_pixel_total : 6868 time to create 1 rle with old method : 0.007931947708129883 time for calcul the mask position with numpy : 0.029527664184570312 nb_pixel_total : 22508 time to create 1 rle with old method : 0.026088953018188477 time for calcul the mask position with numpy : 0.029062747955322266 nb_pixel_total : 5746 time to create 1 rle with old method : 0.006782054901123047 time for calcul the mask position with numpy : 0.02920055389404297 nb_pixel_total : 5983 time to create 1 rle with old method : 0.006980419158935547 time for calcul the mask position with numpy : 0.030445575714111328 nb_pixel_total : 63542 time to create 1 rle with old method : 0.09444713592529297 time for calcul the mask position with numpy : 0.029087066650390625 nb_pixel_total : 5414 time to create 1 rle with old method : 0.006238698959350586 time for calcul the mask position with numpy : 0.029088973999023438 nb_pixel_total : 15720 time to create 1 rle with old method : 0.01774883270263672 time for calcul the mask position with numpy : 0.029876708984375 nb_pixel_total : 40374 time to create 1 rle with old method : 0.045124053955078125 time for calcul the mask position with numpy : 0.03169536590576172 nb_pixel_total : 18246 time to create 1 rle with old method : 0.020574331283569336 time for calcul the mask position with numpy : 0.029039382934570312 nb_pixel_total : 9686 time to create 1 rle with old method : 0.011142730712890625 time for calcul the mask position with numpy : 0.029663562774658203 nb_pixel_total : 10578 time to create 1 rle with old method : 0.012771368026733398 create new chi : 4.61731219291687 time to delete rle : 0.003860950469970703 batch 1 Loaded 117 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23785 TO DO : save crop sub photo not yet done ! save time : 5.332261323928833 map_output_result : {1349149899: (0.0, 'Should be the crop_list due to order', 0), 1349149894: (0.0, 'Should be the crop_list due to order', 0), 1349149873: (0.0, 'Should be the crop_list due to order', 0), 1349022602: (0.0, 'Should be the crop_list due to order', 0), 1349022596: (0.0, 'Should be the crop_list due to order', 0), 1349022592: (0.0, 'Should be the crop_list due to order', 0), 1349022529: (0.0, 'Should be the crop_list due to order', 0), 1349022521: (0.0, 'Should be the crop_list due to order', 0), 1349022516: (0.0, 'Should be the crop_list due to order', 0), 1349022513: (0.0, 'Should be the crop_list due to order', 0), 1349022510: (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 [1349149899, 1349149894, 1349149873, 1349022602, 1349022596, 1349022592, 1349022529, 1349022521, 1349022516, 1349022513, 1349022510] Looping around the photos to save general results len do output : 11 /1349149899.Didn't retrieve data . /1349149894.Didn't retrieve data . /1349149873.Didn't retrieve data . /1349022602.Didn't retrieve data . /1349022596.Didn't retrieve data . /1349022592.Didn't retrieve data . /1349022529.Didn't retrieve data . /1349022521.Didn't retrieve data . /1349022516.Didn't retrieve data . /1349022513.Didn't retrieve data . /1349022510.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, '2711139') ('3318', '21929822', '1349149899', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149894', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149873', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022602', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022596', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022592', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022529', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022521', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022516', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022513', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022510', None, None, None, None, None, '2711139') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.09287691116333008 save_final save missing photos in datou_result : time spend for datou_step_exec : 152.5829689502716 time spend to save output : 0.0936424732208252 total time spend for step 3 : 152.67661142349243 step4:ventilate_hashtags_in_portfolio Tue Apr 1 01:33:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 21929822 get user id for portfolio 21929822 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929822 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pehd','flou','papier','metal','pet_clair','background','autre','environnement','mal_croppe','pet_fonce')) 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`=21929822 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pehd','flou','papier','metal','pet_clair','background','autre','environnement','mal_croppe','pet_fonce')) 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`=21929822 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','pehd','flou','papier','metal','pet_clair','background','autre','environnement','mal_croppe','pet_fonce')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/21930179,21930180,21930181,21930182,21930183,21930184,21930185,21930186,21930187,21930188,21930189?tags=carton,pehd,flou,papier,metal,pet_clair,background,autre,environnement,mal_croppe,pet_fonce Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1349149899, 1349149894, 1349149873, 1349022602, 1349022596, 1349022592, 1349022529, 1349022521, 1349022516, 1349022513, 1349022510] Looping around the photos to save general results len do output : 1 /21929822. 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, '2711139') ('3318', '21929822', '1349149899', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149894', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149873', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022602', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022596', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022592', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022529', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022521', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022516', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022513', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022510', None, None, None, None, None, '2711139') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.01723003387451172 save_final save missing photos in datou_result : time spend for datou_step_exec : 1.95497465133667 time spend to save output : 0.01751542091369629 total time spend for step 4 : 1.9724900722503662 step5:final Tue Apr 1 01:33:16 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 : {1349149899: ('0.2305784432354592',), 1349149894: ('0.2305784432354592',), 1349149873: ('0.2305784432354592',), 1349022602: ('0.2305784432354592',), 1349022596: ('0.2305784432354592',), 1349022592: ('0.2305784432354592',), 1349022529: ('0.2305784432354592',), 1349022521: ('0.2305784432354592',), 1349022516: ('0.2305784432354592',), 1349022513: ('0.2305784432354592',), 1349022510: ('0.2305784432354592',)} new output for save of step final : {1349149899: ('0.2305784432354592',), 1349149894: ('0.2305784432354592',), 1349149873: ('0.2305784432354592',), 1349022602: ('0.2305784432354592',), 1349022596: ('0.2305784432354592',), 1349022592: ('0.2305784432354592',), 1349022529: ('0.2305784432354592',), 1349022521: ('0.2305784432354592',), 1349022516: ('0.2305784432354592',), 1349022513: ('0.2305784432354592',), 1349022510: ('0.2305784432354592',)} [1349149899, 1349149894, 1349149873, 1349022602, 1349022596, 1349022592, 1349022529, 1349022521, 1349022516, 1349022513, 1349022510] Looping around the photos to save general results len do output : 11 /1349149899.Didn't retrieve data . /1349149894.Didn't retrieve data . /1349149873.Didn't retrieve data . /1349022602.Didn't retrieve data . /1349022596.Didn't retrieve data . /1349022592.Didn't retrieve data . /1349022529.Didn't retrieve data . /1349022521.Didn't retrieve data . /1349022516.Didn't retrieve data . /1349022513.Didn't retrieve data . /1349022510.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, '2711139') ('3318', '21929822', '1349149899', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149894', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149873', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022602', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022596', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022592', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022529', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022521', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022516', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022513', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022510', None, None, None, None, None, '2711139') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.016772747039794922 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.11956357955932617 time spend to save output : 0.017341136932373047 total time spend for step 5 : 0.13690471649169922 step6:blur_detection Tue Apr 1 01:33:16 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/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc.jpg resize: (2160, 3264) 1349149899 -4.781035277568166 treat image : temp/1743463229_2118874_1349149894_b8c9555c67685665b5e672483d4c232d.jpg resize: (2160, 3264) 1349149894 0.10329349212571454 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10.jpg resize: (2160, 3264) 1349149873 -3.778652140509909 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010.jpg resize: (2160, 3264) 1349022602 -3.9777691145821428 treat image : temp/1743463229_2118874_1349022596_b894685a562865d5970a8942ec846a9a.jpg resize: (2160, 3264) 1349022596 -2.6957747199973467 treat image : temp/1743463229_2118874_1349022592_a1406c3fc266c03e51e2054d4ab56cd2.jpg resize: (2160, 3264) 1349022592 -4.120994932840084 treat image : temp/1743463229_2118874_1349022529_4d2bb473c09b75cdd41e3082ebd485d3.jpg resize: (2160, 3264) 1349022529 -2.9226322230742876 treat image : temp/1743463229_2118874_1349022521_5cfeaafe69392c40c8e8c0278df43e89.jpg resize: (2160, 3264) 1349022521 -4.534028291131058 treat image : temp/1743463229_2118874_1349022516_3f850dfe3d18d70e14ea1b284261dbb7.jpg resize: (2160, 3264) 1349022516 -3.1809672901393484 treat image : temp/1743463229_2118874_1349022513_91bb0ce289c80d5f0b98fa7f7dd6d645.jpg resize: (2160, 3264) 1349022513 -4.426074610590849 treat image : temp/1743463229_2118874_1349022510_bbffe2280505c5e6b214aeb1c46a19bd.jpg resize: (2160, 3264) 1349022510 -4.09405627004192 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043042_0.png resize: (214, 161) 1349160257 -2.9911186191728754 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043037_0.png resize: (119, 155) 1349160258 -2.741391165762465 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043045_0.png resize: (70, 114) 1349160259 -1.2567054761396874 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043021_0.png resize: (226, 201) 1349160260 -2.7178406589876003 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043026_0.png resize: (80, 108) 1349160261 0.5479476395535912 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043055_0.png resize: (195, 117) 1349160262 -2.8629034789801913 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043052_0.png resize: (185, 144) 1349160263 -1.5517113103823057 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043068_0.png resize: (181, 259) 1349160264 -3.1185374910867507 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043027_0.png resize: (73, 78) 1349160266 0.17164629535236045 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043047_0.png resize: (452, 381) 1349160267 -2.2035038889769964 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043034_0.png resize: (367, 441) 1349160268 -1.177503837889203 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043066_0.png resize: (170, 110) 1349160270 -2.650262470437309 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043061_0.png resize: (239, 199) 1349160271 -3.435473982232777 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043039_0.png resize: (196, 170) 1349160272 -1.9596519416094387 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043022_0.png resize: (162, 141) 1349160274 -2.4587270595992274 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043040_0.png resize: (487, 407) 1349160275 -3.2656807024526113 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043056_0.png resize: (230, 121) 1349160276 -1.8904073429560655 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043033_0.png resize: (177, 136) 1349160278 -2.4199468038212384 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043049_0.png resize: (399, 476) 1349160279 -1.1559023391451584 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043062_0.png resize: (365, 215) 1349160281 -2.3252764585663996 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043051_0.png resize: (390, 368) 1349160282 -3.643378467425084 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043063_0.png resize: (86, 438) 1349160283 -2.522780443924799 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043059_0.png resize: (409, 236) 1349160285 -3.9270670375970953 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043024_0.png resize: (134, 116) 1349160286 -2.5192650441418993 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043038_0.png resize: (426, 312) 1349160287 -3.0767861436517494 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043041_0.png resize: (217, 280) 1349160289 -3.3864967188335178 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043025_0.png resize: (185, 143) 1349160290 -3.187345499282789 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043058_0.png resize: (150, 102) 1349160292 -1.235803659325269 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043036_0.png resize: (283, 310) 1349160293 -2.596996469882179 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043067_0.png resize: (126, 110) 1349160296 -2.0480476527513143 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043032_0.png resize: (268, 212) 1349160297 -3.8566772355234895 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043065_0.png resize: (151, 166) 1349160298 -3.2049439254732195 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043046_0.png resize: (136, 127) 1349160300 -2.331730312038731 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043053_0.png resize: (453, 160) 1349160301 -3.52877508780673 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043030_0.png resize: (185, 126) 1349160303 -3.6402416011812098 treat image : temp/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc_rle_crop_3742043029_0.png resize: (248, 115) 1349160304 -2.916099883163272 treat image : temp/1743463229_2118874_1349149894_b8c9555c67685665b5e672483d4c232d_rle_crop_3742043070_0.png resize: (142, 281) 1349160305 0.548727873654664 treat image : temp/1743463229_2118874_1349149894_b8c9555c67685665b5e672483d4c232d_rle_crop_3742043074_0.png resize: (90, 50) 1349160307 -1.9498298541845878 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043106_0.png resize: (582, 765) 1349160308 -2.4917212291575757 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043080_0.png resize: (393, 415) 1349160309 -1.754478898130946 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043090_0.png resize: (173, 93) 1349160311 -2.7007717249552585 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043100_0.png resize: (253, 238) 1349160312 -2.7934728345556863 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043093_0.png resize: (153, 141) 1349160313 -1.9233924516778793 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043091_0.png resize: (119, 83) 1349160315 0.2145406330410731 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043099_0.png resize: (169, 241) 1349160316 -1.2247785071239834 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043105_0.png resize: (257, 259) 1349160317 -2.5134161343360035 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043112_0.png resize: (181, 115) 1349160319 -1.395808786629791 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043109_0.png resize: (137, 115) 1349160320 -2.8588751307993365 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043076_0.png resize: (294, 269) 1349160321 -1.6276325637933537 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043107_0.png resize: (254, 116) 1349160323 -1.3780495694654944 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043114_0.png resize: (232, 282) 1349160324 -2.1738008183426087 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043104_0.png resize: (137, 84) 1349160325 -1.1327261264047603 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043096_0.png resize: (196, 150) 1349160327 -2.2256761280249027 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043084_0.png resize: (202, 83) 1349160328 -3.2102150718658584 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043088_0.png resize: (512, 368) 1349160329 -3.2469818275053286 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043082_0.png resize: (200, 145) 1349160331 -4.087144304392715 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043102_0.png resize: (307, 344) 1349160332 -2.1106174938412314 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043108_0.png resize: (100, 211) 1349160334 -1.4013235601295453 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043085_0.png resize: (137, 139) 1349160335 -3.424776648041723 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043087_0.png resize: (160, 151) 1349160336 -1.4581941809772885 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043078_0.png resize: (127, 109) 1349160338 -2.7431406216151224 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043079_0.png resize: (173, 208) 1349160339 -1.8425260984120213 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043095_0.png resize: (55, 81) 1349160340 -2.229120708253954 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043075_0.png resize: (166, 125) 1349160342 -0.5231443995038021 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043092_0.png resize: (146, 101) 1349160343 -3.5599803457345804 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043097_0.png resize: (150, 126) 1349160344 -2.1381441438104725 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043081_0.png resize: (151, 222) 1349160346 -2.4116430307194214 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043077_0.png resize: (184, 170) 1349160347 -1.0714809600343198 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043098_0.png resize: (195, 83) 1349160349 -1.9340054720631534 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043089_0.png resize: (134, 185) 1349160350 -1.4965037782482349 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043113_0.png resize: (178, 202) 1349160351 -1.2407905193816018 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043083_0.png resize: (262, 197) 1349160353 -1.9697160770631605 treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10_rle_crop_3742043086_0.png resize: (124, 144) 1349160354 -2.657125765787251 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_rle_crop_3741315308_0.png resize: (72, 69) 1349160355 0.9619315933485548 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_bib_crop_3741323429_0.jpg resize: (137, 159) 1349160357 2.993753299305377 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_rle_crop_3741315314_0.png resize: (138, 160) 1349160358 -1.769941201509835 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_rle_crop_3741315302_0.png resize: (748, 694) 1349160359 -2.666104986583452 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_bib_crop_3741323422_0.jpg resize: (71, 68) 1349160361 4.341737681006445 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_bib_crop_3741323435_0.jpg resize: (747, 693) 1349160362 -0.8123377148900642 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_rle_crop_3741315309_0.png resize: (122, 248) 1349160364 -1.9438346201117116 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_bib_crop_3741323459_0.jpg resize: (121, 247) 1349160365 20.0 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_rle_crop_3741315316_0.png resize: (369, 364) 1349160366 -2.1290824549050673 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_bib_crop_3741323451_0.jpg resize: (368, 363) 1349160368 4.238715345122502 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_rle_crop_3741315315_0.png resize: (124, 160) 1349160369 -2.180752982933512 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_bib_crop_3741323412_0.jpg resize: (123, 159) 1349160375 2.4924570457234725 treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_rle_crop_3741315304_0.png resize: (117, 132) 1349160377 -1.996737339620555 treat image : 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insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 738 time used for this insertion : 0.14229035377502441 save missing photos in datou_result : time spend for datou_step_exec : 55.80872869491577 time spend to save output : 0.21404743194580078 total time spend for step 6 : 56.02277612686157 step7:brightness Tue Apr 1 01:34:12 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/1743463229_2118874_1349149899_6e9e217a7cf568b98f2da6b0fd81eecc.jpg treat image : temp/1743463229_2118874_1349149894_b8c9555c67685665b5e672483d4c232d.jpg treat image : temp/1743463229_2118874_1349149873_a29aefabb7097284d7db74606ac81d10.jpg treat image : temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010.jpg treat image : temp/1743463229_2118874_1349022596_b894685a562865d5970a8942ec846a9a.jpg treat image : temp/1743463229_2118874_1349022592_a1406c3fc266c03e51e2054d4ab56cd2.jpg treat image : temp/1743463229_2118874_1349022529_4d2bb473c09b75cdd41e3082ebd485d3.jpg treat image : temp/1743463229_2118874_1349022521_5cfeaafe69392c40c8e8c0278df43e89.jpg treat image : temp/1743463229_2118874_1349022516_3f850dfe3d18d70e14ea1b284261dbb7.jpg treat image : temp/1743463229_2118874_1349022513_91bb0ce289c80d5f0b98fa7f7dd6d645.jpg treat image : 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temp/1743463229_2118874_1349022602_cbe73778748791f4e41832926fbe4010_bib_crop_3741323442_0.jpg treat image : temp/1743463229_2118874_1349022592_a1406c3fc266c03e51e2054d4ab56cd2_rle_crop_3741315399_0.png treat image : temp/1743463229_2118874_1349022592_a1406c3fc266c03e51e2054d4ab56cd2_bib_crop_3741323892_0.jpg treat image : temp/1743463229_2118874_1349022521_5cfeaafe69392c40c8e8c0278df43e89_bib_crop_3741324625_0.jpg treat image : temp/1743463229_2118874_1349022510_bbffe2280505c5e6b214aeb1c46a19bd_rle_crop_3741315575_0.png treat image : temp/1743463229_2118874_1349022510_bbffe2280505c5e6b214aeb1c46a19bd_bib_crop_3741325329_0.jpg treat image : temp/1743463229_2118874_1349022510_bbffe2280505c5e6b214aeb1c46a19bd_rle_crop_3741315608_0.png treat image : temp/1743463229_2118874_1349022510_bbffe2280505c5e6b214aeb1c46a19bd_bib_crop_3741325325_0.jpg 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 : 738 time used for this insertion : 0.06103825569152832 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 738 time used for this insertion : 0.15257716178894043 save missing photos in datou_result : time spend for datou_step_exec : 15.471285343170166 time spend to save output : 0.22258949279785156 total time spend for step 7 : 15.693874835968018 step8:velours_tree Tue Apr 1 01:34:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure can't find the photo_desc_type Inside saveOutput : final : False verbose : 0 ouput is None No outpout to save, returning out of save general time spend for datou_step_exec : 0.24916791915893555 time spend to save output : 6.175041198730469e-05 total time spend for step 8 : 0.24922966957092285 step9:send_mail_cod Tue Apr 1 01:34:28 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_P21929822_01-04-2025_01_34_28.pdf 21930179 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 .imagette219301791743464068 21930180 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 .imagette219301801743464070 21930181 imagette219301811743464071 21930182 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 .imagette219301821743464071 21930183 change filename to text .change filename to text .imagette219301831743464083 21930184 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 .imagette219301841743464084 21930185 imagette219301851743464085 21930186 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 .imagette219301861743464085 21930188 imagette219301881743464086 21930189 imagette219301891743464086 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=21929822 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/21930179,21930180,21930181,21930182,21930183,21930184,21930185,21930186,21930187,21930188,21930189?tags=carton,pehd,flou,papier,metal,pet_clair,background,autre,environnement,mal_croppe,pet_fonce args[1349149899] : ((1349149899, -4.781035277568166, 492609224), (1349149899, -0.22363682220312392, 496442774), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349149894] : ((1349149894, 0.10329349212571454, 492688767), (1349149894, 0.3541774499308828, 2107752395), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349149873] : ((1349149873, -3.778652140509909, 492609224), (1349149873, -0.055489947523573765, 2107752395), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349022602] : ((1349022602, -3.9777691145821428, 492609224), (1349022602, -0.24337545772103575, 496442774), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349022596] : ((1349022596, -2.6957747199973467, 492609224), (1349022596, 0.13557167384467728, 2107752395), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349022592] : ((1349022592, -4.120994932840084, 492609224), (1349022592, -0.46405375905376817, 496442774), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349022529] : ((1349022529, -2.9226322230742876, 492609224), (1349022529, -0.1887224286114881, 496442774), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349022521] : ((1349022521, -4.534028291131058, 492609224), (1349022521, -0.08154569589192706, 496442774), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349022516] : ((1349022516, -3.1809672901393484, 492609224), (1349022516, -0.19580164639841757, 496442774), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349022513] : ((1349022513, -4.426074610590849, 492609224), (1349022513, -0.018531553712417305, 2107752395), '0.2305784432354592') We are sending mail with results at report@fotonower.com args[1349022510] : ((1349022510, -4.09405627004192, 492609224), (1349022510, -0.15710767907431253, 496442774), '0.2305784432354592') We are sending mail with results at report@fotonower.com refus_total : 0.2305784432354592 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=21929822 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1349022510,1349022516,1349022521,1349022529,1349022513,1349149873,1349149899,1349022592,1349022596,1349022602,1349149894) Found this number of photos: 11 begin to download photo : 1349022510 begin to download photo : 1349022529 begin to download photo : 1349149899 begin to download photo : 1349022602 download finish for photo 1349022529 begin to download photo : 1349022513 download finish for photo 1349149899 begin to download photo : 1349022592 download finish for photo 1349022602 begin to download photo : 1349149894 download finish for photo 1349022510 begin to download photo : 1349022516 download finish for photo 1349149894 download finish for photo 1349022513 begin to download photo : 1349149873 download finish for photo 1349022592 begin to download photo : 1349022596 download finish for photo 1349022516 begin to download photo : 1349022521 download finish for photo 1349149873 download finish for photo 1349022596 download finish for photo 1349022521 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929822_01-04-2025_01_34_28.pdf results_Auto_P21929822_01-04-2025_01_34_28.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929822_01-04-2025_01_34_28.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','21929822','results_Auto_P21929822_01-04-2025_01_34_28.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929822_01-04-2025_01_34_28.pdf','pdf','','0.9','0.2305784432354592') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/21929822

https://www.fotonower.com/image?json=false&list_photos_id=1349149899
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349149894
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349149873
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349022602
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349022596
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349022592
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349022529
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349022521
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349022516
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349022513
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1349022510
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 23.06%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/21930179?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/21930180?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/21930182?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/21930183?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/21930184?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/21930186?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929822_01-04-2025_01_34_28.pdf.

Lien vers velours :https://www.fotonower.com/velours/21930179,21930180,21930181,21930182,21930183,21930184,21930185,21930186,21930187,21930188,21930189?tags=carton,pehd,flou,papier,metal,pet_clair,background,autre,environnement,mal_croppe,pet_fonce.


L'équipe Fotonower 202 b'' Server: nginx Date: Mon, 31 Mar 2025 23:34:49 GMT Content-Length: 0 Connection: close X-Message-Id: jUHVaiE7SAGAg1aU-3h8CQ 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 [1349149899, 1349149894, 1349149873, 1349022602, 1349022596, 1349022592, 1349022529, 1349022521, 1349022516, 1349022513, 1349022510] 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, '2711139') ('3318', '21929822', '1349149899', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149894', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149873', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022602', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022596', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022592', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022529', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022521', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022516', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022513', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022510', None, None, None, None, None, '2711139') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.10673356056213379 save_final save missing photos in datou_result : time spend for datou_step_exec : 21.064953804016113 time spend to save output : 0.10702848434448242 total time spend for step 9 : 21.171982288360596 step10:split_time_score Tue Apr 1 01:34:50 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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'}] (('15', 11),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 31032025 21929822 Nombre de photos uploadées : 11 / 23040 (0%) 31032025 21929822 Nombre de photos taguées (types de déchets): 0 / 11 (0%) 31032025 21929822 Nombre de photos taguées (volume) : 0 / 11 (0%) elapsed_time : load_data_split_time_score 2.384185791015625e-06 elapsed_time : order_list_meta_photo_and_scores 1.0728836059570312e-05 ??????????? elapsed_time : fill_and_build_computed_from_old_data 0.0006101131439208984 elapsed_time : insert_dashboard_record_day_entry 0.03265976905822754 We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps Qualite : 0.14630428184005087 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925661_31-03-2025_22_59_57.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925661 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21925661 AND mptpi.`type`=3594 To do Qualite : 0.14145216719895026 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925662_31-03-2025_22_51_38.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925662 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21925662 AND mptpi.`type`=3594 To do Qualite : 0.0939584877250109 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21905169_31-03-2025_11_54_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21905169 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21905169 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929800 order by id desc limit 1 find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929818 order by id desc limit 1 Qualite : 0.10137259221816262 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21911519_31-03-2025_15_32_52.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21911519 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`=21911519 AND mptpi.`type`=3726 To do Qualite : 0.17051974471791034 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21913880_31-03-2025_16_28_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21913880 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`=21913880 AND mptpi.`type`=3594 To do Qualite : 0.2305784432354592 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929822_01-04-2025_01_34_28.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929822 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929822 AND mptpi.`type`=3594 To do Qualite : 0.06655992376993317 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21929825_01-04-2025_01_30_31.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21929825 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21929825 AND mptpi.`type`=3726 To do Qualite : 0.22924124322132222 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21926965_31-03-2025_23_29_21.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21926965 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21926965 AND mptpi.`type`=3594 To do Qualite : 0.2189041275706411 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925669_31-03-2025_22_45_27.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925669 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21925669 AND mptpi.`type`=3594 To do Qualite : 0.18545522031874095 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P21925670_31-03-2025_22_36_34.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 21925670 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=21925670 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'31032025': {'nb_upload': 11, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} Inside saveOutput : final : True verbose : 0 saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1349149899, 1349149894, 1349149873, 1349022602, 1349022596, 1349022592, 1349022529, 1349022521, 1349022516, 1349022513, 1349022510] Looping around the photos to save general results len do output : 1 /21929822Didn'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, '2711139') ('3318', '21929822', '1349149899', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149894', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349149873', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022602', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022596', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022592', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022529', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022521', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022516', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022513', None, None, None, None, None, '2711139') ('3318', None, None, None, None, None, None, None, '2711139') ('3318', '21929822', '1349022510', None, None, None, None, None, '2711139') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.01680779457092285 save_final save missing photos in datou_result : time spend for datou_step_exec : 3.5182065963745117 time spend to save output : 0.01702260971069336 total time spend for step 10 : 3.535229206085205 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 11 set_done_treatment 346.54user 149.75system 14:28.68elapsed 57%CPU (0avgtext+0avgdata 6832060maxresident)k 7279904inputs+254664outputs (246797major+28695380minor)pagefaults 0swaps