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 : 3422679 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 : ['2736603'] with mtr_portfolio_ids : ['22184729'] and first list_photo_ids : [] new path : /proc/3422679/ 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 : 2.327171802520752 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 Wed Apr 9 23:00:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec 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 : 10151 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-09 23:00:35.194429: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-09 23:00:35.223227: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-09 23:00:35.225527: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fb2e4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-09 23:00:35.225593: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-09 23:00:35.229674: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-09 23:00:35.372603: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x24cab9b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-09 23:00:35.372664: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-09 23:00:35.373897: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 23:00:35.374370: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 23:00:35.377667: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 23:00:35.380712: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 23:00:35.381251: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 23:00:35.384500: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 23:00:35.385631: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 23:00:35.390243: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 23:00:35.391787: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 23:00:35.391878: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 23:00:35.392616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 23:00:35.392632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 23:00:35.392641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 23:00:35.393933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9256 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. 2025-04-09 23:00:35.740465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 23:00:35.740558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 23:00:35.740586: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 23:00:35.740611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 23:00:35.740635: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 23:00:35.740658: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 23:00:35.740682: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 23:00:35.740706: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 23:00:35.742709: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 23:00:35.744045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-09 23:00:35.744075: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-09 23:00:35.744090: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 23:00:35.744104: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-09 23:00:35.744117: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-09 23:00:35.744131: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-09 23:00:35.744145: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-09 23:00:35.744159: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-09 23:00:35.745411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-09 23:00:35.745438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-09 23:00:35.745447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-09 23:00:35.745455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-09 23:00:35.746740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9256 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-09 23:00:48.857969: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-09 23:00:49.062066: 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 : 81 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 68 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 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 : 69 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 : 87 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 : 70 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 : 42 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 : 70 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 67 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 77 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 61 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 : 66 Detection mask done ! Trying to reset tf kernel 3423730 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1527 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 : 10593 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 : 0.007032155990600586 nb_pixel_total : 124888 time to create 1 rle with old method : 0.15046334266662598 length of segment : 323 time for calcul the mask position with numpy : 0.0027399063110351562 nb_pixel_total : 124764 time to create 1 rle with old method : 0.14400291442871094 length of segment : 375 time for calcul the mask position with numpy : 0.001636505126953125 nb_pixel_total : 40640 time to create 1 rle with old method : 0.04934048652648926 length of segment : 248 time for calcul the mask position with numpy : 0.0001583099365234375 nb_pixel_total : 5325 time to create 1 rle with old method : 0.006506204605102539 length of segment : 100 time for calcul the mask position with numpy : 0.0008361339569091797 nb_pixel_total : 32681 time to create 1 rle with old method : 0.03810882568359375 length of segment : 217 time for calcul the mask position with numpy : 0.0009663105010986328 nb_pixel_total : 41180 time to create 1 rle with old method : 0.05798673629760742 length of segment : 266 time for calcul the mask position with numpy : 0.0005869865417480469 nb_pixel_total : 24287 time to create 1 rle with old method : 0.04078483581542969 length of segment : 185 time for calcul the mask position with numpy : 0.0026123523712158203 nb_pixel_total : 69288 time to create 1 rle with old method : 0.0848531723022461 length of segment : 436 time for calcul the mask position with numpy : 0.00019121170043945312 nb_pixel_total : 7502 time to create 1 rle with old method : 0.008853673934936523 length of segment : 122 time for calcul the mask position with numpy : 0.0010943412780761719 nb_pixel_total : 46649 time to create 1 rle with old method : 0.05364704132080078 length of segment : 223 time for calcul the mask position with numpy : 0.000385284423828125 nb_pixel_total : 13684 time to create 1 rle with old method : 0.40230 time to create 1 rle with old method : 0.047591209411621094 length of segment : 353 time for calcul the mask position with numpy : 0.0007617473602294922 nb_pixel_total : 30941 time to create 1 rle with old method : 0.036450862884521484 length of segment : 176 time for calcul the mask position with numpy : 0.0010921955108642578 nb_pixel_total : 48693 time to create 1 rle with old method : 0.0632474422454834 length of segment : 231 time for calcul the mask position with numpy : 0.0006082057952880859 nb_pixel_total : 21512 time to create 1 rle with old method : 0.02581310272216797 length of segment : 162 time for calcul the mask position with numpy : 0.0004248619079589844 nb_pixel_total : 12287 time to create 1 rle with old method : 0.014888286590576172 length of segment : 188 time for calcul the mask position with numpy : 0.0014111995697021484 nb_pixel_total : 24498 time to create 1 rle with old method : 0.028502464294433594 length of segment : 206 time for calcul the mask position with numpy : 0.00024819374084472656 nb_pixel_total : 7933 time to create 1 rle with old method : 0.009886503219604492 length of segment : 89 time for calcul the mask position with numpy : 0.010094881057739258 nb_pixel_total : 179736 time to create 1 rle with new method : 0.011811017990112305 length of segment : 459 time for calcul the mask position with numpy : 0.0009455680847167969 nb_pixel_total : 15193 time to create 1 rle with old method : 0.02010345458984375 length of segment : 187 time for calcul the mask position with numpy : 0.0005748271942138672 nb_pixel_total : 27000 time to create 1 rle with old method : 0.033458709716796875 length of segment : 244 time for calcul the mask position with numpy : 0.0010998249053955078 nb_pixel_total : 16151 time to create 1 rle with old method : 0.020093202590942383 length of segment : 209 time for calcul the mask position with numpy : 0.0006263256072998047 nb_pixel_total : 17355 time to create 1 rle with old method : 0.020645618438720703 length of segment : 167 time for calcul the mask position with numpy : 0.0015358924865722656 nb_pixel_total : 25878 time to create 1 rle with old method : 0.030330896377563477 length of segment : 181 time for calcul the mask position with numpy : 0.0014333724975585938 nb_pixel_total : 62534 time to create 1 rle with old method : 0.0826258659362793 length of segment : 222 time for calcul the mask position with numpy : 0.0030558109283447266 nb_pixel_total : 54936 time to create 1 rle with old method : 0.0704505443572998 length of segment : 303 time for calcul the mask position with numpy : 0.0007750988006591797 nb_pixel_total : 13042 time to create 1 rle with old method : 0.015338897705078125 length of segment : 137 time for calcul the mask position with numpy : 0.001171112060546875 nb_pixel_total : 24245 time to create 1 rle with old method : 0.0309755802154541 length of segment : 189 time for calcul the mask position with numpy : 0.0005300045013427734 nb_pixel_total : 18337 time to create 1 rle with old method : 0.02271294593811035 length of segment : 157 time for calcul the mask position with numpy : 0.0014948844909667969 nb_pixel_total : 50430 time to create 1 rle with old method : 0.06135416030883789 length of segment : 500 time for calcul the mask position with numpy : 0.0006814002990722656 nb_pixel_total : 22794 time to create 1 rle with old method : 0.02792048454284668 length of segment : 220 time for calcul the mask position with numpy : 0.0010514259338378906 nb_pixel_total : 16603 time to create 1 rle with old method : 0.02068781852722168 length of segment : 184 time for calcul the mask position with numpy : 0.0035364627838134766 nb_pixel_total : 51831 time to create 1 rle with old method : 0.06239008903503418 length of segment : 231 time for calcul the mask position with numpy : 0.0034062862396240234 nb_pixel_total : 68277 time to create 1 rle with old method : 0.08157968521118164 length of segment : 338 time for calcul the mask position with numpy : 0.0032291412353515625 nb_pixel_total : 70451 time to create 1 rle with old method : 0.08781909942626953 length of segment : 429 time for calcul the mask position with numpy : 0.00048542022705078125 nb_pixel_total : 13945 time to create 1 rle with old method : 0.01695871353149414 length of segment : 104 time for calcul the mask position with numpy : 0.0005564689636230469 nb_pixel_total : 9786 time to create 1 rle with old method : 0.011994361877441406 length of segment : 125 time for calcul the mask position with numpy : 0.0017819404602050781 nb_pixel_total : 37790 time to create 1 rle with old method : 0.04393339157104492 length of segment : 173 time for calcul the mask position with numpy : 0.0002753734588623047 nb_pixel_total : 10995 time to create 1 rle with old method : 0.013509988784790039 length of segment : 136 time for calcul the mask position with numpy : 0.0007848739624023438 nb_pixel_total : 17821 time to create 1 rle with old method : 0.020540475845336914 length of segment : 178 time for calcul the mask position with numpy : 0.007124423980712891 nb_pixel_total : 156736 time to create 1 rle with new method : 0.010443925857543945 length of segment : 439 time for calcul the mask position with numpy : 0.0003960132598876953 nb_pixel_total : 13898 time to create 1 rle with old method : 0.017051219940185547 length of segment : 122 time for calcul the mask position with numpy : 0.002263784408569336 nb_pixel_total : 30724 time to create 1 rle with old method : 0.0361790657043457 length of segment : 313 time for calcul the mask position with numpy : 0.0005643367767333984 nb_pixel_total : 14912 time to create 1 rle with old method : 0.01725029945373535 length of segment : 255 time for calcul the mask position with numpy : 0.0002560615539550781 nb_pixel_total : 2376 time to create 1 rle with old method : 0.002887248992919922 length of segment : 66 time for calcul the mask position with numpy : 0.0002491474151611328 nb_pixel_total : 10229 time to create 1 rle with old method : 0.01698470115661621 length of segment : 103 time for calcul the mask position with numpy : 0.001550912857055664 nb_pixel_total : 32410 time to create 1 rle with old method : 0.04239034652709961 length of segment : 322 time for calcul the mask position with numpy : 0.0009608268737792969 nb_pixel_total : 15768 time to create 1 rle with old method : 0.019440174102783203 length of segment : 161 time for calcul the mask position with numpy : 0.006317615509033203 nb_pixel_total : 113449 time to create 1 rle with old method : 0.13319730758666992 length of segment : 465 time for calcul the mask position with numpy : 0.0010247230529785156 nb_pixel_total : 44815 time to create 1 rle with old method : 0.05192899703979492 length of segment : 255 time for calcul the mask position with numpy : 0.00045680999755859375 nb_pixel_total : 12323 time to create 1 rle with old method : 0.014723062515258789 length of segment : 198 time for calcul the mask position with numpy : 0.0013532638549804688 nb_pixel_total : 35066 time to create 1 rle with old method : 0.045621633529663086 length of segment : 456 time for calcul the mask position with numpy : 0.0012993812561035156 nb_pixel_total : 48460 time to create 1 rle with old method : 0.058860063552856445 length of segment : 275 time for calcul the mask position with numpy : 0.0007541179656982422 nb_pixel_total : 34120 time to create 1 rle with old method : 0.04100966453552246 length of segment : 271 time for calcul the mask position with numpy : 0.0006215572357177734 nb_pixel_total : 20001 time to create 1 rle with old method : 0.024296045303344727 length of segment : 202 time for calcul the mask position with numpy : 0.005616903305053711 nb_pixel_total : 112117 time to create 1 rle with old method : 0.130476713180542 length of segment : 420 time for calcul the mask position with numpy : 0.0010504722595214844 nb_pixel_total : 50955 time to create 1 rle with old method : 0.06357192993164062 length of segment : 231 time for calcul the mask position with numpy : 0.00034117698669433594 nb_pixel_total : 13204 time to create 1 rle with old method : 0.016045093536376953 length of segment : 114 time for calcul the mask position with numpy : 0.0007932186126708984 nb_pixel_total : 12120 time to create 1 rle with old method : 0.014557838439941406 length of segment : 176 time for calcul the mask position with numpy : 0.00019359588623046875 nb_pixel_total : 7126 time to create 1 rle with old method : 0.008359670639038086 length of segment : 152 time for calcul the mask position with numpy : 0.00028967857360839844 nb_pixel_total : 11552 time to create 1 rle with old method : 0.013757467269897461 length of segment : 191 time for calcul the mask position with numpy : 0.0005102157592773438 nb_pixel_total : 28154 time to create 1 rle with old method : 0.03305673599243164 length of segment : 224 time for calcul the mask position with numpy : 0.0005791187286376953 nb_pixel_total : 30024 time to create 1 rle with old method : 0.0349423885345459 length of segment : 129 time for calcul the mask position with numpy : 0.0015041828155517578 nb_pixel_total : 69139 time to create 1 rle with old method : 0.07932519912719727 length of segment : 572 time for calcul the mask position with numpy : 0.0012958049774169922 nb_pixel_total : 54753 time to create 1 rle with old method : 0.08173775672912598 length of segment : 395 time for calcul the mask position with numpy : 0.002603769302368164 nb_pixel_total : 120171 time to create 1 rle with old method : 0.14456462860107422 length of segment : 646 time for calcul the mask position with numpy : 0.00023627281188964844 nb_pixel_total : 8587 time to create 1 rle with old method : 0.010327816009521484 length of segment : 92 time for calcul the mask position with numpy : 0.00023651123046875 nb_pixel_total : 7260 time to create 1 rle with old method : 0.008737564086914062 length of segment : 75 time for calcul the mask position with numpy : 0.0029630661010742188 nb_pixel_total : 135263 time to create 1 rle with old method : 0.15918493270874023 length of segment : 643 time for calcul the mask position with numpy : 0.0007836818695068359 nb_pixel_total : 26263 time to create 1 rle with old method : 0.05051994323730469 length of segment : 176 time for calcul the mask position with numpy : 0.0019176006317138672 nb_pixel_total : 99843 time to create 1 rle with old method : 0.11522436141967773 length of segment : 304 time for calcul the mask position with numpy : 0.00045990943908691406 nb_pixel_total : 16978 time to create 1 rle with old method : 0.020859241485595703 length of segment : 186 time for calcul the mask position with numpy : 0.0005648136138916016 nb_pixel_total : 25286 time to create 1 rle with old method : 0.030800580978393555 length of segment : 179 time for calcul the mask position with numpy : 0.0011451244354248047 nb_pixel_total : 44105 time to create 1 rle with old method : 0.05216193199157715 length of segment : 310 time for calcul the mask position with numpy : 0.0009486675262451172 nb_pixel_total : 45194 time to create 1 rle with old method : 0.05353951454162598 length of segment : 302 time for calcul the mask position with numpy : 0.0037689208984375 nb_pixel_total : 191713 time to create 1 rle with new method : 0.015572786331176758 length of segment : 620 time for calcul the mask position with numpy : 0.0005428791046142578 nb_pixel_total : 29884 time to create 1 rle with old method : 0.035360097885131836 length of segment : 170 time for calcul the mask position with numpy : 0.0023279190063476562 nb_pixel_total : 103878 time to create 1 rle with old method : 0.11955952644348145 length of segment : 330 time for calcul the mask position with numpy : 0.0009183883666992188 nb_pixel_total : 30662 time to create 1 rle with old method : 0.037392377853393555 length of segment : 283 time for calcul the mask position with numpy : 0.00029730796813964844 nb_pixel_total : 7000 time to create 1 rle with old method : 0.008793115615844727 length of segment : 123 time for calcul the mask position with numpy : 0.0012848377227783203 nb_pixel_total : 51340 time to create 1 rle with old method : 0.06624603271484375 length of segment : 694 time for calcul the mask position with numpy : 0.00040721893310546875 nb_pixel_total : 14263 time to create 1 rle with old method : 0.01736760139465332 length of segment : 153 time for calcul the mask position with numpy : 0.0003113746643066406 nb_pixel_total : 13363 time to create 1 rle with old method : 0.0159914493560791 length of segment : 129 time for calcul the mask position with numpy : 0.0010900497436523438 nb_pixel_total : 44551 time to create 1 rle with old method : 0.052346229553222656 length of segment : 289 time for calcul the mask position with numpy : 0.0076181888580322266 nb_pixel_total : 181848 time to create 1 rle with new method : 0.011025667190551758 length of segment : 330 time for calcul the mask position with numpy : 0.0015227794647216797 nb_pixel_total : 15964 time to create 1 rle with old method : 0.02789926528930664 length of segment : 191 time for calcul the mask position with numpy : 0.009839773178100586 nb_pixel_total : 112909 time to create 1 rle with old method : 0.13567376136779785 length of segment : 535 time for calcul the mask position with numpy : 0.004971981048583984 nb_pixel_total : 66449 time to create 1 rle with old method : 0.07954978942871094 length of segment : 301 time for calcul the mask position with numpy : 0.0007336139678955078 nb_pixel_total : 10762 time to create 1 rle with old method : 0.01253962516784668 length of segment : 202 time for calcul the mask position with numpy : 0.001810312271118164 nb_pixel_total : 44604 time to create 1 rle with old method : 0.053459882736206055 length of segment : 246 time for calcul the mask position with numpy : 0.0030641555786132812 nb_pixel_total : 58481 time to create 1 rle with old method : 0.06716156005859375 length of segment : 415 time for calcul the mask position with numpy : 0.0002071857452392578 nb_pixel_total : 3947 time to create 1 rle with old method : 0.004665851593017578 length of segment : 77 time for calcul the mask position with numpy : 0.0011997222900390625 nb_pixel_total : 19998 time to create 1 rle with old method : 0.028179168701171875 length of segment : 145 time for calcul the mask position with numpy : 0.0014243125915527344 nb_pixel_total : 22199 time to create 1 rle with old method : 0.0253753662109375 length of segment : 231 time for calcul the mask position with numpy : 0.0007989406585693359 nb_pixel_total : 16491 time to create 1 rle with old method : 0.019664764404296875 length of segment : 115 time for calcul the mask position with numpy : 0.0014600753784179688 nb_pixel_total : 20744 time to create 1 rle with old method : 0.02506399154663086 length of segment : 208 time for calcul the mask position with numpy : 0.0034165382385253906 nb_pixel_total : 52505 time to create 1 rle with old method : 0.0641789436340332 length of segment : 328 time for calcul the mask position with numpy : 0.002966642379760742 nb_pixel_total : 32163 time to create 1 rle with old method : 0.041993141174316406 length of segment : 355 time for calcul the mask position with numpy : 0.008056879043579102 nb_pixel_total : 85677 time to create 1 rle with old method : 0.10059833526611328 length of segment : 483 time for calcul the mask position with numpy : 0.0018737316131591797 nb_pixel_total : 25791 time to create 1 rle with old method : 0.030951499938964844 length of segment : 189 time for calcul the mask position with numpy : 0.001917123794555664 nb_pixel_total : 35125 time to create 1 rle with old method : 0.041516780853271484 length of segment : 188 time for calcul the mask position with numpy : 0.0006387233734130859 nb_pixel_total : 13492 time to create 1 rle with old method : 0.016107797622680664 length of segment : 181 time for calcul the mask position with numpy : 0.0025687217712402344 nb_pixel_total : 26280 time to create 1 rle with old method : 0.031876564025878906 length of segment : 274 time for calcul the mask position with numpy : 0.0007822513580322266 nb_pixel_total : 9040 time to create 1 rle with old method : 0.011180400848388672 length of segment : 164 time for calcul the mask position with numpy : 0.004534006118774414 nb_pixel_total : 71140 time to create 1 rle with old method : 0.08419394493103027 length of segment : 286 time for calcul the mask position with numpy : 0.0002887248992919922 nb_pixel_total : 3578 time to create 1 rle with old method : 0.004591703414916992 length of segment : 72 time for calcul the mask position with numpy : 0.0026879310607910156 nb_pixel_total : 39692 time to create 1 rle with old method : 0.04792642593383789 length of segment : 263 time for calcul the mask position with numpy : 0.003829479217529297 nb_pixel_total : 53333 time to create 1 rle with old method : 0.06555533409118652 length of segment : 229 time for calcul the mask position with numpy : 0.0011670589447021484 nb_pixel_total : 18106 time to create 1 rle with old method : 0.024276256561279297 length of segment : 98 time for calcul the mask position with numpy : 0.0012979507446289062 nb_pixel_total : 12021 time to create 1 rle with old method : 0.014604330062866211 length of segment : 218 time for calcul the mask position with numpy : 0.0016851425170898438 nb_pixel_total : 12285 time to create 1 rle with old method : 0.014940261840820312 length of segment : 138 time for calcul the mask position with numpy : 0.0018770694732666016 nb_pixel_total : 32408 time to create 1 rle with old method : 0.03908348083496094 length of segment : 217 time for calcul the mask position with numpy : 0.0012764930725097656 nb_pixel_total : 19507 time to create 1 rle with old method : 0.027115345001220703 length of segment : 179 time for calcul the mask position with numpy : 0.00038814544677734375 nb_pixel_total : 5689 time to create 1 rle with old method : 0.006867408752441406 length of segment : 88 time for calcul the mask position with numpy : 0.0011167526245117188 nb_pixel_total : 23712 time to create 1 rle with old method : 0.028516292572021484 length of segment : 190 time for calcul the mask position with numpy : 0.001638174057006836 nb_pixel_total : 31712 time to create 1 rle with old method : 0.03685927391052246 length of segment : 151 time for calcul the mask position with numpy : 0.0019381046295166016 nb_pixel_total : 33541 time to create 1 rle with old method : 0.0411229133605957 length of segment : 223 time for calcul the mask position with numpy : 0.0015680789947509766 nb_pixel_total : 27016 time to create 1 rle with old method : 0.032835960388183594 length of segment : 388 time for calcul the mask position with numpy : 0.001436471939086914 nb_pixel_total : 22012 time to create 1 rle with old method : 0.02839374542236328 length of segment : 189 time for calcul the mask position with numpy : 0.002532958984375 nb_pixel_total : 34202 time to create 1 rle with old method : 0.04156374931335449 length of segment : 289 time for calcul the mask position with numpy : 0.0007195472717285156 nb_pixel_total : 10375 time to create 1 rle with old method : 0.016858339309692383 length of segment : 186 time for calcul the mask position with numpy : 0.003226041793823242 nb_pixel_total : 32359 time to create 1 rle with old method : 0.05211925506591797 length of segment : 231 time for calcul the mask position with numpy : 0.005321025848388672 nb_pixel_total : 107991 time to create 1 rle with old method : 0.12726163864135742 length of segment : 316 time for calcul the mask position with numpy : 0.00017595291137695312 nb_pixel_total : 5842 time to create 1 rle with old method : 0.007642269134521484 length of segment : 48 time for calcul the mask position with numpy : 0.0021109580993652344 nb_pixel_total : 30380 time to create 1 rle with old method : 0.036951541900634766 length of segment : 236 time for calcul the mask position with numpy : 0.002332925796508789 nb_pixel_total : 31470 time to create 1 rle with old method : 0.03684234619140625 length of segment : 300 time for calcul the mask position with numpy : 0.0019114017486572266 nb_pixel_total : 23590 time to create 1 rle with old method : 0.028388261795043945 length of segment : 240 time for calcul the mask position with numpy : 0.0008199214935302734 nb_pixel_total : 14711 time to create 1 rle with old method : 0.017343759536743164 length of segment : 127 time for calcul the mask position with numpy : 0.0012652873992919922 nb_pixel_total : 19530 time to create 1 rle with old method : 0.02367258071899414 length of segment : 154 time for calcul the mask position with numpy : 0.0009059906005859375 nb_pixel_total : 14813 time to create 1 rle with old method : 0.0177762508392334 length of segment : 140 time for calcul the mask position with numpy : 0.0225069522857666 nb_pixel_total : 317112 time to create 1 rle with new method : 0.020339488983154297 length of segment : 735 time for calcul the mask position with numpy : 0.00113677978515625 nb_pixel_total : 16676 time to create 1 rle with old method : 0.020061731338500977 length of segment : 275 time for calcul the mask position with numpy : 0.0017304420471191406 nb_pixel_total : 26363 time to create 1 rle with old method : 0.032248497009277344 length of segment : 232 time for calcul the mask position with numpy : 0.002941608428955078 nb_pixel_total : 43973 time to create 1 rle with old method : 0.05089282989501953 length of segment : 452 time for calcul the mask position with numpy : 0.0010569095611572266 nb_pixel_total : 16353 time to create 1 rle with old method : 0.019756793975830078 length of segment : 182 time for calcul the mask position with numpy : 0.0036444664001464844 nb_pixel_total : 68031 time to create 1 rle with old method : 0.08238554000854492 length of segment : 310 time for calcul the mask position with numpy : 0.006791353225708008 nb_pixel_total : 126961 time to create 1 rle with old method : 0.14738202095031738 length of segment : 454 time for calcul the mask position with numpy : 0.003690004348754883 nb_pixel_total : 60118 time to create 1 rle with old method : 0.07355141639709473 length of segment : 222 time for calcul the mask position with numpy : 0.0002429485321044922 nb_pixel_total : 3583 time to create 1 rle with old method : 0.004517078399658203 length of segment : 63 time for calcul the mask position with numpy : 0.003166675567626953 nb_pixel_total : 58002 time to create 1 rle with old method : 0.07035374641418457 length of segment : 272 time for calcul the mask position with numpy : 0.0012257099151611328 nb_pixel_total : 20138 time to create 1 rle with old method : 0.02472686767578125 length of segment : 153 time for calcul the mask position with numpy : 0.0012500286102294922 nb_pixel_total : 15196 time to create 1 rle with old method : 0.0179440975189209 length of segment : 213 time for calcul the mask position with numpy : 0.0015435218811035156 nb_pixel_total : 20916 time to create 1 rle with old method : 0.0341339111328125 length of segment : 224 time for calcul the mask position with numpy : 0.003122568130493164 nb_pixel_total : 26542 time to create 1 rle with old method : 0.03304028511047363 length of segment : 185 time for calcul the mask position with numpy : 0.004800081253051758 nb_pixel_total : 108757 time to create 1 rle with old method : 0.13139605522155762 length of segment : 489 time for calcul the mask position with numpy : 0.0005159378051757812 nb_pixel_total : 7927 time to create 1 rle with old method : 0.009553194046020508 length of segment : 91 time for calcul the mask position with numpy : 0.0030107498168945312 nb_pixel_total : 42731 time to create 1 rle with old method : 0.05947685241699219 length of segment : 270 time for calcul the mask position with numpy : 0.0010480880737304688 nb_pixel_total : 13361 time to create 1 rle with old method : 0.022722721099853516 length of segment : 173 time for calcul the mask position with numpy : 0.005234956741333008 nb_pixel_total : 99603 time to create 1 rle with old method : 0.13756918907165527 length of segment : 386 time for calcul the mask position with numpy : 0.02514481544494629 nb_pixel_total : 337903 time to create 1 rle with new method : 0.04446887969970703 length of segment : 842 time for calcul the mask position with numpy : 0.002978086471557617 nb_pixel_total : 46333 time to create 1 rle with old method : 0.06438875198364258 length of segment : 195 time for calcul the mask position with numpy : 0.005666255950927734 nb_pixel_total : 109367 time to create 1 rle with old method : 0.1284627914428711 length of segment : 285 time for calcul the mask position with numpy : 0.0034635066986083984 nb_pixel_total : 92612 time to create 1 rle with old method : 0.11002564430236816 length of segment : 354 time for calcul the mask position with numpy : 0.0011010169982910156 nb_pixel_total : 16354 time to create 1 rle with old method : 0.019533395767211914 length of segment : 197 time for calcul the mask position with numpy : 0.0062389373779296875 nb_pixel_total : 138904 time to create 1 rle with old method : 0.1634535789489746 length of segment : 370 time for calcul the mask position with numpy : 0.0016388893127441406 nb_pixel_total : 36768 time to create 1 rle with old method : 0.0440518856048584 length of segment : 241 time for calcul the mask position with numpy : 0.0017964839935302734 nb_pixel_total : 38833 time to create 1 rle with old method : 0.046975135803222656 length of segment : 181 time for calcul the mask position with numpy : 0.0009338855743408203 nb_pixel_total : 18263 time to create 1 rle with old method : 0.02244734764099121 length of segment : 91 time for calcul the mask position with numpy : 0.0021152496337890625 nb_pixel_total : 44098 time to create 1 rle with old method : 0.05270862579345703 length of segment : 266 time for calcul the mask position with numpy : 0.0008306503295898438 nb_pixel_total : 14467 time to create 1 rle with old method : 0.017743825912475586 length of segment : 113 time for calcul the mask position with numpy : 0.0003306865692138672 nb_pixel_total : 3774 time to create 1 rle with old method : 0.004519224166870117 length of segment : 50 time for calcul the mask position with numpy : 0.002412557601928711 nb_pixel_total : 133404 time to create 1 rle with old method : 0.15787744522094727 length of segment : 365 time for calcul the mask position with numpy : 0.002377748489379883 nb_pixel_total : 42068 time to create 1 rle with old method : 0.0500795841217041 length of segment : 242 time for calcul the mask position with numpy : 0.0003955364227294922 nb_pixel_total : 15634 time to create 1 rle with old method : 0.01947307586669922 length of segment : 148 time for calcul the mask position with numpy : 0.0018169879913330078 nb_pixel_total : 29390 time to create 1 rle with old method : 0.035363197326660156 length of segment : 285 time for calcul the mask position with numpy : 0.00047397613525390625 nb_pixel_total : 9968 time to create 1 rle with old method : 0.012210845947265625 length of segment : 165 time for calcul the mask position with numpy : 0.0009739398956298828 nb_pixel_total : 24752 time to create 1 rle with old method : 0.03032207489013672 length of segment : 145 time for calcul the mask position with numpy : 0.002377033233642578 nb_pixel_total : 22019 time to create 1 rle with old method : 0.02655029296875 length of segment : 205 time for calcul the mask position with numpy : 0.0009982585906982422 nb_pixel_total : 15481 time to create 1 rle with old method : 0.018707275390625 length of segment : 160 time for calcul the mask position with numpy : 0.005229473114013672 nb_pixel_total : 61382 time to create 1 rle with old method : 0.07918953895568848 length of segment : 337 time for calcul the mask position with numpy : 0.0014729499816894531 nb_pixel_total : 16045 time to create 1 rle with old method : 0.029604673385620117 length of segment : 124 time for calcul the mask position with numpy : 0.002705097198486328 nb_pixel_total : 45786 time to create 1 rle with old method : 0.06569838523864746 length of segment : 223 time for calcul the mask position with numpy : 0.0006928443908691406 nb_pixel_total : 9870 time to create 1 rle with old method : 0.012408733367919922 length of segment : 161 time for calcul the mask position with numpy : 0.0023162364959716797 nb_pixel_total : 35433 time to create 1 rle with old method : 0.0457913875579834 length of segment : 276 time for calcul the mask position with numpy : 0.0012371540069580078 nb_pixel_total : 22436 time to create 1 rle with old method : 0.02652883529663086 length of segment : 159 time for calcul the mask position with numpy : 0.003864288330078125 nb_pixel_total : 89740 time to create 1 rle with old method : 0.10355401039123535 length of segment : 393 time for calcul the mask position with numpy : 0.0014209747314453125 nb_pixel_total : 27950 time to create 1 rle with old method : 0.03208518028259277 length of segment : 213 time for calcul the mask position with numpy : 0.004667043685913086 nb_pixel_total : 136279 time to create 1 rle with old method : 0.15741658210754395 length of segment : 421 time for calcul the mask position with numpy : 0.0002586841583251953 nb_pixel_total : 14231 time to create 1 rle with old method : 0.015651702880859375 length of segment : 129 time for calcul the mask position with numpy : 0.0022444725036621094 nb_pixel_total : 64750 time to create 1 rle with old method : 0.07441878318786621 length of segment : 323 time for calcul the mask position with numpy : 0.00017571449279785156 nb_pixel_total : 7324 time to create 1 rle with old method : 0.00839686393737793 length of segment : 124 time for calcul the mask position with numpy : 0.00064849853515625 nb_pixel_total : 12323 time to create 1 rle with old method : 0.013817310333251953 length of segment : 273 time for calcul the mask position with numpy : 0.0012707710266113281 nb_pixel_total : 32624 time to create 1 rle with old method : 0.036089181900024414 length of segment : 305 time for calcul the mask position with numpy : 0.003696918487548828 nb_pixel_total : 89917 time to create 1 rle with old method : 0.1023859977722168 length of segment : 333 time for calcul the mask position with numpy : 0.0029671192169189453 nb_pixel_total : 61197 time to create 1 rle with old method : 0.07105469703674316 length of segment : 299 time for calcul the mask position with numpy : 0.0007481575012207031 nb_pixel_total : 12493 time to create 1 rle with old method : 0.014569997787475586 length of segment : 182 time for calcul the mask position with numpy : 0.0013544559478759766 nb_pixel_total : 35178 time to create 1 rle with old method : 0.04193687438964844 length of segment : 173 time for calcul the mask position with numpy : 0.0007143020629882812 nb_pixel_total : 18866 time to create 1 rle with old method : 0.022027969360351562 length of segment : 225 time for calcul the mask position with numpy : 0.0022950172424316406 nb_pixel_total : 43815 time to create 1 rle with old method : 0.0517425537109375 length of segment : 308 time for calcul the mask position with numpy : 0.0002396106719970703 nb_pixel_total : 6496 time to create 1 rle with old method : 0.007884979248046875 length of segment : 142 time for calcul the mask position with numpy : 0.00040721893310546875 nb_pixel_total : 9669 time to create 1 rle with old method : 0.011642694473266602 length of segment : 115 time for calcul the mask position with numpy : 0.004656553268432617 nb_pixel_total : 156099 time to create 1 rle with new method : 0.006369352340698242 length of segment : 296 time for calcul the mask position with numpy : 0.0010313987731933594 nb_pixel_total : 26160 time to create 1 rle with old method : 0.031537771224975586 length of segment : 207 time for calcul the mask position with numpy : 0.00019168853759765625 nb_pixel_total : 9239 time to create 1 rle with old method : 0.010765790939331055 length of segment : 124 time for calcul the mask position with numpy : 0.002620697021484375 nb_pixel_total : 22650 time to create 1 rle with old method : 0.027849674224853516 length of segment : 552 time for calcul the mask position with numpy : 0.0004334449768066406 nb_pixel_total : 9816 time to create 1 rle with old method : 0.0120086669921875 length of segment : 107 time for calcul the mask position with numpy : 0.006519317626953125 nb_pixel_total : 124018 time to create 1 rle with old method : 0.13988804817199707 length of segment : 427 time for calcul the mask position with numpy : 0.0043697357177734375 nb_pixel_total : 99839 time to create 1 rle with old method : 0.1159517765045166 length of segment : 187 time for calcul the mask position with numpy : 0.001893758773803711 nb_pixel_total : 31439 time to create 1 rle with old method : 0.03626298904418945 length of segment : 253 time for calcul the mask position with numpy : 0.0024504661560058594 nb_pixel_total : 43463 time to create 1 rle with old method : 0.0506129264831543 length of segment : 181 time for calcul the mask position with numpy : 0.0006351470947265625 nb_pixel_total : 7835 time to create 1 rle with old method : 0.011065006256103516 length of segment : 136 time for calcul the mask position with numpy : 0.0014564990997314453 nb_pixel_total : 24358 time to create 1 rle with old method : 0.02874755859375 length of segment : 223 time for calcul the mask position with numpy : 0.004593610763549805 nb_pixel_total : 79866 time to create 1 rle with old method : 0.09110236167907715 length of segment : 676 time for calcul the mask position with numpy : 0.002286195755004883 nb_pixel_total : 42680 time to create 1 rle with old method : 0.04955935478210449 length of segment : 178 time for calcul the mask position with numpy : 0.0005054473876953125 nb_pixel_total : 5677 time to create 1 rle with old method : 0.0068819522857666016 length of segment : 119 time for calcul the mask position with numpy : 0.0014050006866455078 nb_pixel_total : 30802 time to create 1 rle with old method : 0.03596782684326172 length of segment : 250 time for calcul the mask position with numpy : 0.00376129150390625 nb_pixel_total : 97124 time to create 1 rle with old method : 0.10987043380737305 length of segment : 361 time for calcul the mask position with numpy : 0.0023317337036132812 nb_pixel_total : 46174 time to create 1 rle with old method : 0.05251955986022949 length of segment : 274 time for calcul the mask position with numpy : 0.003658771514892578 nb_pixel_total : 88885 time to create 1 rle with old method : 0.1033632755279541 length of segment : 388 time for calcul the mask position with numpy : 0.00435185432434082 nb_pixel_total : 84093 time to create 1 rle with old method : 0.0977168083190918 length of segment : 454 time for calcul the mask position with numpy : 0.0006487369537353516 nb_pixel_total : 13772 time to create 1 rle with old method : 0.01644611358642578 length of segment : 109 time for calcul the mask position with numpy : 0.007389068603515625 nb_pixel_total : 121227 time to create 1 rle with old method : 0.1457655429840088 length of segment : 553 time for calcul the mask position with numpy : 0.0030210018157958984 nb_pixel_total : 63110 time to create 1 rle with old method : 0.07577729225158691 length of segment : 339 time for calcul the mask position with numpy : 0.00044274330139160156 nb_pixel_total : 5488 time to create 1 rle with old method : 0.006905555725097656 length of segment : 99 time for calcul the mask position with numpy : 0.0005688667297363281 nb_pixel_total : 8147 time to create 1 rle with old method : 0.009934186935424805 length of segment : 123 time for calcul the mask position with numpy : 0.0021142959594726562 nb_pixel_total : 29684 time to create 1 rle with old method : 0.050173282623291016 length of segment : 318 time for calcul the mask position with numpy : 0.002153635025024414 nb_pixel_total : 43472 time to create 1 rle with old method : 0.05218219757080078 length of segment : 231 time for calcul the mask position with numpy : 0.005279064178466797 nb_pixel_total : 71392 time to create 1 rle with old method : 0.0871436595916748 length of segment : 409 time for calcul the mask position with numpy : 0.002056598663330078 nb_pixel_total : 57051 time to create 1 rle with old method : 0.06670260429382324 length of segment : 396 time for calcul the mask position with numpy : 0.05382108688354492 nb_pixel_total : 830986 time to create 1 rle with new method : 0.061272621154785156 length of segment : 1570 time for calcul the mask position with numpy : 0.002093076705932617 nb_pixel_total : 26339 time to create 1 rle with old method : 0.04094052314758301 length of segment : 173 time for calcul the mask position with numpy : 0.0004780292510986328 nb_pixel_total : 15823 time to create 1 rle with old method : 0.026696443557739258 length of segment : 207 time for calcul the mask position with numpy : 0.007057905197143555 nb_pixel_total : 161496 time to create 1 rle with new method : 0.00937795639038086 length of segment : 736 time for calcul the mask position with numpy : 0.0006966590881347656 nb_pixel_total : 17762 time to create 1 rle with old method : 0.021451234817504883 length of segment : 105 time for calcul the mask position with numpy : 0.00393986701965332 nb_pixel_total : 90620 time to create 1 rle with old method : 0.11123371124267578 length of segment : 162 time for calcul the mask position with numpy : 0.0031800270080566406 nb_pixel_total : 49419 time to create 1 rle with old method : 0.06508588790893555 length of segment : 271 time for calcul the mask position with numpy : 0.0021827220916748047 nb_pixel_total : 46817 time to create 1 rle with old method : 0.055188655853271484 length of segment : 225 time for calcul the mask position with numpy : 0.001863718032836914 nb_pixel_total : 32610 time to create 1 rle with old method : 0.038521766662597656 length of segment : 263 time for calcul the mask position with numpy : 0.0019259452819824219 nb_pixel_total : 36134 time to create 1 rle with old method : 0.04296541213989258 length of segment : 144 time for calcul the mask position with numpy : 0.0012717247009277344 nb_pixel_total : 20792 time to create 1 rle with old method : 0.036195993423461914 length of segment : 123 time for calcul the mask position with numpy : 0.0055828094482421875 nb_pixel_total : 73035 time to create 1 rle with old method : 0.0850677490234375 length of segment : 470 time for calcul the mask position with numpy : 0.002538442611694336 nb_pixel_total : 44001 time to create 1 rle with old method : 0.052156925201416016 length of segment : 196 time for calcul the mask position with numpy : 0.0024025440216064453 nb_pixel_total : 47138 time to create 1 rle with old method : 0.056131601333618164 length of segment : 272 time for calcul the mask position with numpy : 0.00639033317565918 nb_pixel_total : 104241 time to create 1 rle with old method : 0.13138866424560547 length of segment : 469 time for calcul the mask position with numpy : 0.002834320068359375 nb_pixel_total : 59936 time to create 1 rle with old method : 0.06948113441467285 length of segment : 318 time for calcul the mask position with numpy : 0.003022909164428711 nb_pixel_total : 71711 time to create 1 rle with old method : 0.08013272285461426 length of segment : 497 time for calcul the mask position with numpy : 0.0015981197357177734 nb_pixel_total : 26831 time to create 1 rle with old method : 0.031594038009643555 length of segment : 179 time for calcul the mask position with numpy : 0.0017426013946533203 nb_pixel_total : 37385 time to create 1 rle with old method : 0.044539690017700195 length of segment : 255 time for calcul the mask position with numpy : 0.0010318756103515625 nb_pixel_total : 19824 time to create 1 rle with old method : 0.023822784423828125 length of segment : 127 time for calcul the mask position with numpy : 0.0022177696228027344 nb_pixel_total : 49935 time to create 1 rle with old method : 0.05885720252990723 length of segment : 287 time for calcul the mask position with numpy : 0.0017211437225341797 nb_pixel_total : 34387 time to create 1 rle with old method : 0.041615962982177734 length of segment : 185 time for calcul the mask position with numpy : 0.00041747093200683594 nb_pixel_total : 13861 time to create 1 rle with old method : 0.019024133682250977 length of segment : 177 time for calcul the mask position with numpy : 0.0007536411285400391 nb_pixel_total : 12872 time to create 1 rle with old method : 0.015822649002075195 length of segment : 148 time for calcul the mask position with numpy : 0.002960681915283203 nb_pixel_total : 65027 time to create 1 rle with old method : 0.0764613151550293 length of segment : 312 time for calcul the mask position with numpy : 0.0007023811340332031 nb_pixel_total : 13709 time to create 1 rle with old method : 0.015876054763793945 length of segment : 142 time for calcul the mask position with numpy : 0.0004668235778808594 nb_pixel_total : 6416 time to create 1 rle with old method : 0.007968425750732422 length of segment : 113 time for calcul the mask position with numpy : 0.00044465065002441406 nb_pixel_total : 10051 time to create 1 rle with old method : 0.012156009674072266 length of segment : 116 time for calcul the mask position with numpy : 0.0021200180053710938 nb_pixel_total : 77233 time to create 1 rle with old method : 0.08976936340332031 length of segment : 474 time for calcul the mask position with numpy : 0.0013706684112548828 nb_pixel_total : 20753 time to create 1 rle with old method : 0.02462458610534668 length of segment : 206 time for calcul the mask position with numpy : 0.0030112266540527344 nb_pixel_total : 109463 time to create 1 rle with old method : 0.12632989883422852 length of segment : 491 time for calcul the mask position with numpy : 0.0009832382202148438 nb_pixel_total : 13644 time to create 1 rle with old method : 0.016071319580078125 length of segment : 206 time for calcul the mask position with numpy : 0.005345582962036133 nb_pixel_total : 108139 time to create 1 rle with old method : 0.12394261360168457 length of segment : 555 time for calcul the mask position with numpy : 0.0006060600280761719 nb_pixel_total : 13205 time to create 1 rle with old method : 0.016028165817260742 length of segment : 61 time for calcul the mask position with numpy : 0.0016956329345703125 nb_pixel_total : 39868 time to create 1 rle with old method : 0.04645967483520508 length of segment : 233 time for calcul the mask position with numpy : 0.0009403228759765625 nb_pixel_total : 13068 time to create 1 rle with old method : 0.015449762344360352 length of segment : 152 time for calcul the mask position with numpy : 0.002801179885864258 nb_pixel_total : 68223 time to create 1 rle with old method : 0.08052611351013184 length of segment : 553 time for calcul the mask position with numpy : 0.011105060577392578 nb_pixel_total : 150017 time to create 1 rle with new method : 0.015937328338623047 length of segment : 686 time for calcul the mask position with numpy : 0.0016922950744628906 nb_pixel_total : 23608 time to create 1 rle with old method : 0.03262662887573242 length of segment : 148 time for calcul the mask position with numpy : 0.0010943412780761719 nb_pixel_total : 12409 time to create 1 rle with old method : 0.015587806701660156 length of segment : 361 time for calcul the mask position with numpy : 0.007652759552001953 nb_pixel_total : 130749 time to create 1 rle with old method : 0.15243864059448242 length of segment : 440 time for calcul the mask position with numpy : 0.009166479110717773 nb_pixel_total : 129056 time to create 1 rle with old method : 0.16387701034545898 length of segment : 364 time for calcul the mask position with numpy : 0.0024900436401367188 nb_pixel_total : 36956 time to create 1 rle with old method : 0.04412579536437988 length of segment : 241 time for calcul the mask position with numpy : 0.0405731201171875 nb_pixel_total : 668256 time to create 1 rle with new method : 0.15305733680725098 length of segment : 862 time for calcul the mask position with numpy : 0.0047719478607177734 nb_pixel_total : 46024 time to create 1 rle with old method : 0.05489969253540039 length of segment : 304 time for calcul the mask position with numpy : 0.014932870864868164 nb_pixel_total : 235903 time to create 1 rle with new method : 0.01877307891845703 length of segment : 641 time for calcul the mask position with numpy : 0.0008924007415771484 nb_pixel_total : 15612 time to create 1 rle with old method : 0.018535852432250977 length of segment : 143 time for calcul the mask position with numpy : 0.0043599605560302734 nb_pixel_total : 93226 time to create 1 rle with old method : 0.10896658897399902 length of segment : 308 time for calcul the mask position with numpy : 0.001146554946899414 nb_pixel_total : 16302 time to create 1 rle with old method : 0.019218921661376953 length of segment : 246 time for calcul the mask position with numpy : 0.0003573894500732422 nb_pixel_total : 10646 time to create 1 rle with old method : 0.013217926025390625 length of segment : 94 time for calcul the mask position with numpy : 0.0024976730346679688 nb_pixel_total : 38234 time to create 1 rle with old method : 0.045809268951416016 length of segment : 430 time for calcul the mask position with numpy : 0.0019495487213134766 nb_pixel_total : 23419 time to create 1 rle with old method : 0.04409670829772949 length of segment : 309 time for calcul the mask position with numpy : 0.0001819133758544922 nb_pixel_total : 4996 time to create 1 rle with old method : 0.00623774528503418 length of segment : 76 time for calcul the mask position with numpy : 0.00423431396484375 nb_pixel_total : 61120 time to create 1 rle with old method : 0.0717003345489502 length of segment : 286 time for calcul the mask position with numpy : 0.011686325073242188 nb_pixel_total : 247561 time to create 1 rle with new method : 0.014935731887817383 length of segment : 352 time for calcul the mask position with numpy : 0.0026311874389648438 nb_pixel_total : 41148 time to create 1 rle with old method : 0.04937267303466797 length of segment : 205 time for calcul the mask position with numpy : 0.001276254653930664 nb_pixel_total : 36535 time to create 1 rle with old method : 0.0451807975769043 length of segment : 220 time for calcul the mask position with numpy : 0.001990795135498047 nb_pixel_total : 38742 time to create 1 rle with old method : 0.04628610610961914 length of segment : 231 time for calcul the mask position with numpy : 0.0012073516845703125 nb_pixel_total : 26157 time to create 1 rle with old method : 0.030486583709716797 length of segment : 243 time for calcul the mask position with numpy : 0.0025403499603271484 nb_pixel_total : 73360 time to create 1 rle with old method : 0.0859525203704834 length of segment : 342 time for calcul the mask position with numpy : 0.0044667720794677734 nb_pixel_total : 98385 time to create 1 rle with old method : 0.1110990047454834 length of segment : 409 time for calcul the mask position with numpy : 0.00034308433532714844 nb_pixel_total : 7748 time to create 1 rle with old method : 0.009278059005737305 length of segment : 108 time for calcul the mask position with numpy : 0.0006868839263916016 nb_pixel_total : 20096 time to create 1 rle with old method : 0.023598670959472656 length of segment : 181 time for calcul the mask position with numpy : 0.0009312629699707031 nb_pixel_total : 19897 time to create 1 rle with old method : 0.02396249771118164 length of segment : 152 time for calcul the mask position with numpy : 0.002849578857421875 nb_pixel_total : 63894 time to create 1 rle with old method : 0.07443380355834961 length of segment : 372 time for calcul the mask position with numpy : 0.0016438961029052734 nb_pixel_total : 38469 time to create 1 rle with old method : 0.04454994201660156 length of segment : 388 time for calcul the mask position with numpy : 0.0006077289581298828 nb_pixel_total : 18735 time to create 1 rle with old method : 0.021646738052368164 length of segment : 276 time for calcul the mask position with numpy : 0.002033710479736328 nb_pixel_total : 40372 time to create 1 rle with old method : 0.0472874641418457 length of segment : 244 time for calcul the mask position with numpy : 0.0015308856964111328 nb_pixel_total : 34257 time to create 1 rle with old method : 0.04146242141723633 length of segment : 196 time for calcul the mask position with numpy : 0.0005216598510742188 nb_pixel_total : 15163 time to create 1 rle with old method : 0.01876688003540039 length of segment : 89 time for calcul the mask position with numpy : 0.002605438232421875 nb_pixel_total : 59859 time to create 1 rle with old method : 0.07077407836914062 length of segment : 409 time for calcul the mask position with numpy : 0.00029921531677246094 nb_pixel_total : 12834 time to create 1 rle with old method : 0.01539921760559082 length of segment : 122 time for calcul the mask position with numpy : 0.0016567707061767578 nb_pixel_total : 39328 time to create 1 rle with old method : 0.04691600799560547 length of segment : 276 time for calcul the mask position with numpy : 0.00041937828063964844 nb_pixel_total : 5149 time to create 1 rle with old method : 0.0062541961669921875 length of segment : 87 time for calcul the mask position with numpy : 0.0015866756439208984 nb_pixel_total : 20651 time to create 1 rle with old method : 0.023820161819458008 length of segment : 267 time for calcul the mask position with numpy : 0.0017237663269042969 nb_pixel_total : 30750 time to create 1 rle with old method : 0.03572249412536621 length of segment : 285 time for calcul the mask position with numpy : 0.002658843994140625 nb_pixel_total : 54107 time to create 1 rle with old method : 0.06344914436340332 length of segment : 324 time for calcul the mask position with numpy : 0.0043909549713134766 nb_pixel_total : 96608 time to create 1 rle with old method : 0.10996103286743164 length of segment : 320 time for calcul the mask position with numpy : 0.0012176036834716797 nb_pixel_total : 27401 time to create 1 rle with old method : 0.03156685829162598 length of segment : 142 time for calcul the mask position with numpy : 0.003782987594604492 nb_pixel_total : 87280 time to create 1 rle with old method : 0.10106873512268066 length of segment : 292 time for calcul the mask position with numpy : 0.00017309188842773438 nb_pixel_total : 6982 time to create 1 rle with old method : 0.008702516555786133 length of segment : 92 time for calcul the mask position with numpy : 0.001062631607055664 nb_pixel_total : 16294 time to create 1 rle with old method : 0.019304275512695312 length of segment : 178 time for calcul the mask position with numpy : 0.0006909370422363281 nb_pixel_total : 16710 time to create 1 rle with old method : 0.020100831985473633 length of segment : 127 time for calcul the mask position with numpy : 0.0010061264038085938 nb_pixel_total : 16766 time to create 1 rle with old method : 0.02007746696472168 length of segment : 204 time for calcul the mask position with numpy : 0.0013713836669921875 nb_pixel_total : 27899 time to create 1 rle with old method : 0.0321507453918457 length of segment : 215 time for calcul the mask position with numpy : 0.0005266666412353516 nb_pixel_total : 14342 time to create 1 rle with old method : 0.016124963760375977 length of segment : 259 time for calcul the mask position with numpy : 0.0010385513305664062 nb_pixel_total : 21853 time to create 1 rle with old method : 0.02564859390258789 length of segment : 155 time for calcul the mask position with numpy : 0.00033855438232421875 nb_pixel_total : 8961 time to create 1 rle with old method : 0.010639667510986328 length of segment : 131 time for calcul the mask position with numpy : 0.0014042854309082031 nb_pixel_total : 23483 time to create 1 rle with old method : 0.02778458595275879 length of segment : 259 time for calcul the mask position with numpy : 0.0014612674713134766 nb_pixel_total : 25327 time to create 1 rle with old method : 0.030643463134765625 length of segment : 136 time for calcul the mask position with numpy : 0.0015387535095214844 nb_pixel_total : 27837 time to create 1 rle with old method : 0.03337669372558594 length of segment : 176 time for calcul the mask position with numpy : 0.0023164749145507812 nb_pixel_total : 51344 time to create 1 rle with old method : 0.060468435287475586 length of segment : 248 time for calcul the mask position with numpy : 0.00451207160949707 nb_pixel_total : 91811 time to create 1 rle with old method : 0.10563325881958008 length of segment : 339 time for calcul the mask position with numpy : 0.0012538433074951172 nb_pixel_total : 14993 time to create 1 rle with old method : 0.0182344913482666 length of segment : 154 time for calcul the mask position with numpy : 0.0021593570709228516 nb_pixel_total : 95345 time to create 1 rle with old method : 0.12627959251403809 length of segment : 317 time for calcul the mask position with numpy : 0.0003421306610107422 nb_pixel_total : 4936 time to create 1 rle with old method : 0.006202220916748047 length of segment : 75 time for calcul the mask position with numpy : 0.0008320808410644531 nb_pixel_total : 13560 time to create 1 rle with old method : 0.016592979431152344 length of segment : 192 time for calcul the mask position with numpy : 0.0005636215209960938 nb_pixel_total : 7421 time to create 1 rle with old method : 0.009159088134765625 length of segment : 101 time for calcul the mask position with numpy : 0.0009407997131347656 nb_pixel_total : 14500 time to create 1 rle with old method : 0.020175933837890625 length of segment : 143 time for calcul the mask position with numpy : 0.0013074874877929688 nb_pixel_total : 26963 time to create 1 rle with old method : 0.032341718673706055 length of segment : 219 time for calcul the mask position with numpy : 0.006291389465332031 nb_pixel_total : 93577 time to create 1 rle with old method : 0.11601710319519043 length of segment : 473 time for calcul the mask position with numpy : 0.0016222000122070312 nb_pixel_total : 33210 time to create 1 rle with old method : 0.039879560470581055 length of segment : 172 time for calcul the mask position with numpy : 0.00031828880310058594 nb_pixel_total : 4439 time to create 1 rle with old method : 0.005751609802246094 length of segment : 65 time for calcul the mask position with numpy : 0.0006191730499267578 nb_pixel_total : 8927 time to create 1 rle with old method : 0.012431859970092773 length of segment : 55 time for calcul the mask position with numpy : 0.0008826255798339844 nb_pixel_total : 13699 time to create 1 rle with old method : 0.01922607421875 length of segment : 140 time for calcul the mask position with numpy : 0.0008189678192138672 nb_pixel_total : 22869 time to create 1 rle with old method : 0.027324438095092773 length of segment : 226 time for calcul the mask position with numpy : 0.001256704330444336 nb_pixel_total : 18961 time to create 1 rle with old method : 0.02299785614013672 length of segment : 204 time for calcul the mask position with numpy : 0.005249738693237305 nb_pixel_total : 81799 time to create 1 rle with old method : 0.10564637184143066 length of segment : 447 time for calcul the mask position with numpy : 0.0020644664764404297 nb_pixel_total : 39792 time to create 1 rle with old method : 0.04767870903015137 length of segment : 227 time for calcul the mask position with numpy : 0.0008952617645263672 nb_pixel_total : 15034 time to create 1 rle with old method : 0.018382549285888672 length of segment : 137 time for calcul the mask position with numpy : 0.018467187881469727 nb_pixel_total : 316520 time to create 1 rle with new method : 0.22492265701293945 length of segment : 863 time for calcul the mask position with numpy : 0.0006461143493652344 nb_pixel_total : 6854 time to create 1 rle with old method : 0.008444786071777344 length of segment : 121 time for calcul the mask position with numpy : 0.0039522647857666016 nb_pixel_total : 46459 time to create 1 rle with old method : 0.056697845458984375 length of segment : 290 time for calcul the mask position with numpy : 0.0009377002716064453 nb_pixel_total : 14708 time to create 1 rle with old method : 0.018111705780029297 length of segment : 166 time for calcul the mask position with numpy : 0.005336284637451172 nb_pixel_total : 117082 time to create 1 rle with old method : 0.1627514362335205 length of segment : 374 time for calcul the mask position with numpy : 0.0013387203216552734 nb_pixel_total : 43866 time to create 1 rle with old method : 0.05325436592102051 length of segment : 275 time for calcul the mask position with numpy : 0.0011174678802490234 nb_pixel_total : 17431 time to create 1 rle with old method : 0.021364450454711914 length of segment : 148 time for calcul the mask position with numpy : 0.0004000663757324219 nb_pixel_total : 20135 time to create 1 rle with old method : 0.024968862533569336 length of segment : 193 time for calcul the mask position with numpy : 0.0030918121337890625 nb_pixel_total : 56079 time to create 1 rle with old method : 0.06663036346435547 length of segment : 256 time for calcul the mask position with numpy : 0.00135040283203125 nb_pixel_total : 25039 time to create 1 rle with old method : 0.03074026107788086 length of segment : 140 time for calcul the mask position with numpy : 0.0024335384368896484 nb_pixel_total : 42629 time to create 1 rle with old method : 0.051599740982055664 length of segment : 350 time for calcul the mask position with numpy : 0.0011091232299804688 nb_pixel_total : 16217 time to create 1 rle with old method : 0.024546384811401367 length of segment : 179 time for calcul the mask position with numpy : 0.0009713172912597656 nb_pixel_total : 15138 time to create 1 rle with old method : 0.018054485321044922 length of segment : 158 time for calcul the mask position with numpy : 0.0006732940673828125 nb_pixel_total : 7010 time to create 1 rle with old method : 0.008728265762329102 length of segment : 128 time for calcul the mask position with numpy : 0.0012056827545166016 nb_pixel_total : 17115 time to create 1 rle with old method : 0.02094101905822754 length of segment : 217 time for calcul the mask position with numpy : 0.001753091812133789 nb_pixel_total : 41548 time to create 1 rle with old method : 0.04968452453613281 length of segment : 273 time for calcul the mask position with numpy : 0.004243135452270508 nb_pixel_total : 93203 time to create 1 rle with old method : 0.11002159118652344 length of segment : 348 time for calcul the mask position with numpy : 0.0006515979766845703 nb_pixel_total : 21026 time to create 1 rle with old method : 0.024749040603637695 length of segment : 156 time for calcul the mask position with numpy : 0.001535654067993164 nb_pixel_total : 34160 time to create 1 rle with old method : 0.04137778282165527 length of segment : 202 time for calcul the mask position with numpy : 0.0010476112365722656 nb_pixel_total : 15938 time to create 1 rle with old method : 0.019053936004638672 length of segment : 197 time for calcul the mask position with numpy : 0.0007617473602294922 nb_pixel_total : 15136 time to create 1 rle with old method : 0.01842784881591797 length of segment : 125 time for calcul the mask position with numpy : 0.0015306472778320312 nb_pixel_total : 22047 time to create 1 rle with old method : 0.026784420013427734 length of segment : 189 time for calcul the mask position with numpy : 0.0014507770538330078 nb_pixel_total : 34271 time to create 1 rle with old method : 0.039926767349243164 length of segment : 267 time for calcul the mask position with numpy : 0.00045871734619140625 nb_pixel_total : 8622 time to create 1 rle with old method : 0.0105743408203125 length of segment : 243 time for calcul the mask position with numpy : 0.0033457279205322266 nb_pixel_total : 69695 time to create 1 rle with old method : 0.0819084644317627 length of segment : 301 time for calcul the mask position with numpy : 0.001031637191772461 nb_pixel_total : 16759 time to create 1 rle with old method : 0.020429611206054688 length of segment : 214 time for calcul the mask position with numpy : 0.002099275588989258 nb_pixel_total : 42722 time to create 1 rle with old method : 0.051403045654296875 length of segment : 382 time spent for convertir_results : 32.56744050979614 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 797 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 5453 save missing photos in datou_result : time spend for datou_step_exec : 164.358252286911 time spend to save output : 1.5014433860778809 total time spend for step 1 : 165.8596956729889 step2:crop_condition Wed Apr 9 23:03:18 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 797 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 Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 567 About to insert : list_path_to_insert length 567 new photo from crops ! About to upload 567 photos upload in portfolio : 3736932 init cache_photo without model_param we have 567 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744232702_3422679 we have uploaded 567 photos in the portfolio 3736932 time of upload the photos Elapsed time : 194.85816979408264 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 92 About to insert : list_path_to_insert length 92 new photo from crops ! About to upload 92 photos upload in portfolio : 3736932 init cache_photo without model_param we have 92 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744232919_3422679 we have uploaded 92 photos in the portfolio 3736932 time of upload the photos Elapsed time : 25.96475076675415 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 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 99 About to insert : list_path_to_insert length 99 new photo from crops ! About to upload 99 photos upload in portfolio : 3736932 init cache_photo without model_param we have 99 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744232978_3422679 we have uploaded 99 photos in the portfolio 3736932 time of upload the photos Elapsed time : 48.370529890060425 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 ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 18 About to insert : list_path_to_insert length 18 new photo from crops ! About to upload 18 photos upload in portfolio : 3736932 init cache_photo without model_param we have 18 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744233038_3422679 we have uploaded 18 photos in the portfolio 3736932 time of upload the photos Elapsed time : 5.842401027679443 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! we have both polygon and rles Next one ! Next one ! map_result returned by crop_photo_return_map_crop : length : 10 About to insert : list_path_to_insert length 10 new photo from crops ! About to upload 10 photos upload in portfolio : 3736932 init cache_photo without model_param we have 10 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1744233051_3422679 we have uploaded 10 photos in the portfolio 3736932 time of upload the photos Elapsed time : 7.236623048782349 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1350682783, 1350682763, 1350682756, 1350682732, 1350682723, 1350682720, 1350682715, 1350682711, 1350682677, 1350682673, 1350682671] Looping around the photos to save general results len do output : 786 /1350917978Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917982Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917987Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917988Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917998Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350917999Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918000Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918002Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918004Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918005Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918008Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918009Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918014Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918016Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918017Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918020Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918021Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918022Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918023Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918028Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918029Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918030Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918033Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918034Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918035Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918039Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918040Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918041Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918042Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918045Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918046Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918047Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918050Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918051Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918052Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918055Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918056Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918057Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918060Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918061Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918062Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918063Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918066Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918067Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918068Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918071Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918072Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918073Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918075Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918077Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918078Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918080Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918082Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918083Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918085Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918087Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918088Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918089Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918090Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918092Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918093Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918094Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918096Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918097Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918098Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918100Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918101Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918102Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918103Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918105Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918106Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918107Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918111Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918112Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918116Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918117Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918118Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918122Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918123Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918126Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918128Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918131Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918132Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918133Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918137Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918138Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918139Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918143Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918144Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918145Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918149Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918150Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918151Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918152Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918156Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918157Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918158Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918161Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918163Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918164Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918165Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918169Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918170Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918171Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918175Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918176Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918177Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918179Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918182Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918183Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918184Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918188Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918189Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918190Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918193Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918195Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918196Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918197Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918201Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918202Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918203Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918204Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918209Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918211Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918215Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918216Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918217Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918221Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918222Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918225Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918227Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918230Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918231Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918232Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918236Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918237Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918238Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918242Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918243Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918244Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918245Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918249Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918250Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918251Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918255Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918256Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918257Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918259Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918262Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918263Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918264Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918268Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918269Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918270Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918273Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918275Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918276Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918278Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918281Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918282Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918284Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918287Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918288Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918289Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918291Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918294Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918295Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918297Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918300Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918301Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918302Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918304Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918307Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918308Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918310Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918313Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918314Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918316Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918319Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1350918345Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918347Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918348Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918351Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918353Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918355Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918358Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918359Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918361Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918363Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918366Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918367Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918368Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918371Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918372Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918373Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918375Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918377Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918378Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918379Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918382Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918383Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918384Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918386Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918388Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918389Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918390Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918393Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918394Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918395Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918397Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918399Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918400Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918402Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918404Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918405Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918406Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918409Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918410Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918411Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918414Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918415Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918416Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918419Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918420Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918421Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918422Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918425Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918426Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918427Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918430Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1350918454Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918455Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918456Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918460Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918461Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918462Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918466Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918467Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918468Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918471Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918474Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918475Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918479Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918482Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918483Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918484Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918487Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918490Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918491Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918494Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918497Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918498Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918499Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918503Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918505Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918506Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918511Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918512Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918513Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918516Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918520Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918521Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918524Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918527Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918528Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918531Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918534Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918535Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918538Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918541Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918542Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918543Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918546Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918549Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918551Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918553Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918556Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918557Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918563Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918564Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918565Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1350918595Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918596Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918597Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918598Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918599Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918600Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918601Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918602Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918603Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918604Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918605Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918606Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918607Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918608Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918609Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918610Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918611Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918612Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918613Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918614Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918615Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918616Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918617Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918618Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918623Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918624Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918626Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918627Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918632Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918635Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918636Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918641Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918642Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918643Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918648Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918649Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918650Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918655Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918656Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918657Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918663Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918664Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918667Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918670Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918671Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918674Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918677Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918678Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918680Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918683Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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retrieve data . /1350918712Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918714Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918715Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918718Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918720Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918721Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918724Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918726Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918727Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918730Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918732Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918733Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918737Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918739Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918741Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918744Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918746Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918748Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918751Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918752Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918754Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918757Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918758Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918762Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918763Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918764Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918767Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918769Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918771Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918775Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918776Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918777Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918781Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918782Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918783Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918787Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918788Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918791Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918793Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918794Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918797Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918799Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918800Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918803Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918883Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918895Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918896Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918897Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350918901Didn't retrieve data 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retrieve data . /1350920693Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350920697Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350920698Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350920699Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350920703Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350920704Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1350920705Didn'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, '2736603') ('3318', '22184729', '1350682783', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682763', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682756', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682732', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682723', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682720', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682715', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682711', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682677', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682673', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682671', None, None, None, None, None, '2736603') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 2369 time used for this insertion : 0.7512240409851074 save_final save missing photos in datou_result : time spend for datou_step_exec : 460.3321256637573 time spend to save output : 0.7744858264923096 total time spend for step 2 : 461.10661149024963 step3:rle_unique_nms_with_priority Wed Apr 9 23:10:59 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 797 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 48 nb_hashtags : 5 time to prepare the origin masks : 8.12218427658081 time for calcul the mask position with numpy : 0.34189629554748535 nb_pixel_total : 5711685 time to create 1 rle with new method : 0.5963106155395508 time for calcul the mask position with numpy : 0.03194546699523926 nb_pixel_total : 25524 time to create 1 rle with old method : 0.03435206413269043 time for calcul the mask position with numpy : 0.031415700912475586 nb_pixel_total : 12933 time to create 1 rle with old method : 0.017919301986694336 time for calcul the mask position with numpy : 0.03418087959289551 nb_pixel_total : 10053 time to create 1 rle with old method : 0.011824846267700195 time for calcul the mask position with numpy : 0.03251218795776367 nb_pixel_total : 7073 time to create 1 rle with old method : 0.012384414672851562 time for calcul the mask position with numpy : 0.03488659858703613 nb_pixel_total : 44006 time to create 1 rle with old method : 0.06636261940002441 time for calcul the mask position with numpy : 0.03272724151611328 nb_pixel_total : 27207 time to create 1 rle with old method : 0.038762807846069336 time for calcul the mask position with numpy : 0.03019547462463379 nb_pixel_total : 7074 time to create 1 rle with old method : 0.008398056030273438 time for calcul the mask position with numpy : 0.030324220657348633 nb_pixel_total : 7201 time to create 1 rle with old method : 0.012186765670776367 time for calcul the mask position with numpy : 0.03369283676147461 nb_pixel_total : 13684 time to create 1 rle with old method : 0.02334451675415039 time for calcul the mask position with numpy : 0.03159499168395996 nb_pixel_total : 52584 time to create 1 rle with old method : 0.06134676933288574 time for calcul the mask position with numpy : 0.030130863189697266 nb_pixel_total : 18133 time to create 1 rle with old method : 0.021280288696289062 time for calcul the mask position with numpy : 0.03054952621459961 nb_pixel_total : 30942 time to create 1 rle with old method : 0.03643202781677246 time for calcul the mask position with numpy : 0.030182838439941406 nb_pixel_total : 41182 time to create 1 rle with old method : 0.04852604866027832 time for calcul the mask position with numpy : 0.030258655548095703 nb_pixel_total : 32034 time to create 1 rle with old method : 0.04417991638183594 time for calcul the mask position with numpy : 0.03251004219055176 nb_pixel_total : 32684 time to create 1 rle with old method : 0.03832721710205078 time for calcul the mask position with numpy : 0.02933478355407715 nb_pixel_total : 34080 time to create 1 rle with old method : 0.03996753692626953 time for calcul the mask position with numpy : 0.02993941307067871 nb_pixel_total : 12780 time to create 1 rle with old method : 0.015279054641723633 time for calcul the mask position with numpy : 0.029634952545166016 nb_pixel_total : 48695 time to create 1 rle with old method : 0.05691933631896973 time for calcul the mask position with numpy : 0.030462265014648438 nb_pixel_total : 124901 time to create 1 rle with old method : 0.1471104621887207 time for calcul the mask position with numpy : 0.030445337295532227 nb_pixel_total : 20590 time to create 1 rle with old method : 0.026687145233154297 time for calcul the mask position with numpy : 0.029667139053344727 nb_pixel_total : 51107 time to create 1 rle with old method : 0.061186790466308594 time for calcul the mask position with numpy : 0.02974724769592285 nb_pixel_total : 21504 time to create 1 rle with old method : 0.025888919830322266 time for calcul the mask position with numpy : 0.029506444931030273 nb_pixel_total : 357 time to create 1 rle with old method : 0.0006937980651855469 time for calcul the mask position with numpy : 0.029709339141845703 nb_pixel_total : 24291 time to create 1 rle with old method : 0.02838897705078125 time for calcul the mask position with numpy : 0.02986001968383789 nb_pixel_total : 16495 time to create 1 rle with old method : 0.020817995071411133 time for calcul the mask position with numpy : 0.0308380126953125 nb_pixel_total : 11564 time to create 1 rle with old method : 0.013685941696166992 time for calcul the mask position with numpy : 0.029989957809448242 nb_pixel_total : 33438 time to create 1 rle with old method : 0.0389862060546875 time for calcul the mask position with numpy : 0.0297849178314209 nb_pixel_total : 35495 time to create 1 rle with old method : 0.04269146919250488 time for calcul the mask position with numpy : 0.031589508056640625 nb_pixel_total : 288 time to create 1 rle with old method : 0.0008656978607177734 time for calcul the mask position with numpy : 0.03154635429382324 nb_pixel_total : 68866 time to create 1 rle with old method : 0.08033871650695801 time for calcul the mask position with numpy : 0.03192329406738281 nb_pixel_total : 7501 time to create 1 rle with old method : 0.009995460510253906 time for calcul the mask position with numpy : 0.030817270278930664 nb_pixel_total : 46649 time to create 1 rle with old method : 0.0554959774017334 time for calcul the mask position with numpy : 0.030552148818969727 nb_pixel_total : 7865 time to create 1 rle with old method : 0.009332895278930664 time for calcul the mask position with numpy : 0.0306241512298584 nb_pixel_total : 27082 time to create 1 rle with old method : 0.034896135330200195 time for calcul the mask position with numpy : 0.030895709991455078 nb_pixel_total : 6680 time to create 1 rle with old method : 0.008198738098144531 time for calcul the mask position with numpy : 0.03275871276855469 nb_pixel_total : 40754 time to create 1 rle with old method : 0.048612117767333984 time for calcul the mask position with numpy : 0.03226637840270996 nb_pixel_total : 5326 time to create 1 rle with old method : 0.006429195404052734 time for calcul the mask position with numpy : 0.03126668930053711 nb_pixel_total : 14191 time to create 1 rle with old method : 0.01704692840576172 time for calcul the mask position with numpy : 0.03087306022644043 nb_pixel_total : 6496 time to create 1 rle with old method : 0.007722616195678711 time for calcul the mask position with numpy : 0.03095865249633789 nb_pixel_total : 40638 time to create 1 rle with old method : 0.047734737396240234 time for calcul the mask position with numpy : 0.032620906829833984 nb_pixel_total : 22359 time to create 1 rle with old method : 0.03779101371765137 time for calcul the mask position with numpy : 0.03352475166320801 nb_pixel_total : 124746 time to create 1 rle with old method : 0.15489697456359863 time for calcul the mask position with numpy : 0.029306411743164062 nb_pixel_total : 3208 time to create 1 rle with old method : 0.0038797855377197266 time for calcul the mask position with numpy : 0.029801130294799805 nb_pixel_total : 40213 time to create 1 rle with old method : 0.0514369010925293 time for calcul the mask position with numpy : 0.02959156036376953 nb_pixel_total : 28822 time to create 1 rle with old method : 0.03358340263366699 time for calcul the mask position with numpy : 0.02963113784790039 nb_pixel_total : 13263 time to create 1 rle with old method : 0.015485525131225586 time for calcul the mask position with numpy : 0.02932453155517578 nb_pixel_total : 35997 time to create 1 rle with old method : 0.04191088676452637 create new chi : 4.162039279937744 time to delete rle : 0.028304338455200195 batch 1 Loaded 98 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 750 TO DO : save crop sub photo not yet done ! save time : 0.34295177459716797 nb_obj : 46 nb_hashtags : 4 time to prepare the origin masks : 7.982611179351807 time for calcul the mask position with numpy : 0.5322895050048828 nb_pixel_total : 5467168 time to create 1 rle with new method : 1.0526456832885742 time for calcul the mask position with numpy : 0.02952122688293457 nb_pixel_total : 7933 time to create 1 rle with old method : 0.010875701904296875 time for calcul the mask position with numpy : 0.02845621109008789 nb_pixel_total : 27001 time to create 1 rle with old method : 0.0319981575012207 time for calcul the mask position with numpy : 0.029494524002075195 nb_pixel_total : 13894 time to create 1 rle with old method : 0.016501188278198242 time for calcul the mask position with numpy : 0.02957630157470703 nb_pixel_total : 44816 time to create 1 rle with old method : 0.0521543025970459 time for calcul the mask position with numpy : 0.029691696166992188 nb_pixel_total : 19962 time to create 1 rle with old method : 0.026169776916503906 time for calcul the mask position with numpy : 0.03524422645568848 nb_pixel_total : 10994 time to create 1 rle with old method : 0.012866497039794922 time for calcul the mask position with numpy : 0.027966022491455078 nb_pixel_total : 34122 time to create 1 rle with old method : 0.03978395462036133 time for calcul the mask position with numpy : 0.029322147369384766 nb_pixel_total : 10228 time to create 1 rle with old method : 0.012891769409179688 time for calcul the mask position with numpy : 0.035047292709350586 nb_pixel_total : 13202 time to create 1 rle with old method : 0.01568126678466797 time for calcul the mask position with numpy : 0.029064655303955078 nb_pixel_total : 50356 time to create 1 rle with old method : 0.07017254829406738 time for calcul the mask position with numpy : 0.03375649452209473 nb_pixel_total : 48464 time to create 1 rle with old method : 0.058862924575805664 time for calcul the mask position with numpy : 0.02882695198059082 nb_pixel_total : 7128 time to create 1 rle with old method : 0.008275032043457031 time for calcul the mask position with numpy : 0.029021024703979492 nb_pixel_total : 22793 time to create 1 rle with old method : 0.025818586349487305 time for calcul the mask position with numpy : 0.02825450897216797 nb_pixel_total : 18336 time to create 1 rle with old method : 0.020829200744628906 time for calcul the mask position with numpy : 0.02902364730834961 nb_pixel_total : 31174 time to create 1 rle with old method : 0.036353349685668945 time for calcul the mask position with numpy : 0.029010534286499023 nb_pixel_total : 62518 time to create 1 rle with old method : 0.07102680206298828 time for calcul the mask position with numpy : 0.02907419204711914 nb_pixel_total : 12299 time to create 1 rle with old method : 0.014370203018188477 time for calcul the mask position with numpy : 0.02924346923828125 nb_pixel_total : 24246 time to create 1 rle with old method : 0.028499841690063477 time for calcul the mask position with numpy : 0.029262304306030273 nb_pixel_total : 17820 time to create 1 rle with old method : 0.020990371704101562 time for calcul the mask position with numpy : 0.029403209686279297 nb_pixel_total : 15769 time to create 1 rle with old method : 0.018431425094604492 time for calcul the mask position with numpy : 0.029325485229492188 nb_pixel_total : 25881 time to create 1 rle with old method : 0.03217673301696777 time for calcul the mask position with numpy : 0.029031038284301758 nb_pixel_total : 16149 time to create 1 rle with old method : 0.01886725425720215 time for calcul the mask position with numpy : 0.028991222381591797 nb_pixel_total : 54235 time to create 1 rle with old method : 0.06368660926818848 time for calcul the mask position with numpy : 0.029477596282958984 nb_pixel_total : 130 time to create 1 rle with old method : 0.00046706199645996094 time for calcul the mask position with numpy : 0.029843568801879883 nb_pixel_total : 30729 time to create 1 rle with old method : 0.03666496276855469 time for calcul the mask position with numpy : 0.031088590621948242 nb_pixel_total : 70453 time to create 1 rle with old method : 0.09070467948913574 time for calcul the mask position with numpy : 0.03109431266784668 nb_pixel_total : 51828 time to create 1 rle with old method : 0.059537649154663086 time for calcul the mask position with numpy : 0.028804540634155273 nb_pixel_total : 342 time to create 1 rle with old method : 0.0007257461547851562 time for calcul the mask position with numpy : 0.028873443603515625 nb_pixel_total : 12119 time to create 1 rle with old method : 0.014246940612792969 time for calcul the mask position with numpy : 0.0304110050201416 nb_pixel_total : 113547 time to create 1 rle with old method : 0.14734578132629395 time for calcul the mask position with numpy : 0.03772115707397461 nb_pixel_total : 156772 time to create 1 rle with new method : 1.807521104812622 time for calcul the mask position with numpy : 0.030455589294433594 nb_pixel_total : 32411 time to create 1 rle with old method : 0.03980112075805664 time for calcul the mask position with numpy : 0.030434370040893555 nb_pixel_total : 112120 time to create 1 rle with old method : 0.12756109237670898 time for calcul the mask position with numpy : 0.029305219650268555 nb_pixel_total : 68207 time to create 1 rle with old method : 0.07887053489685059 time for calcul the mask position with numpy : 0.03266549110412598 nb_pixel_total : 24496 time to create 1 rle with old method : 0.02871251106262207 time for calcul the mask position with numpy : 0.0298006534576416 nb_pixel_total : 37790 time to create 1 rle with old method : 0.044524192810058594 time for calcul the mask position with numpy : 0.03423595428466797 nb_pixel_total : 179737 time to create 1 rle with new method : 0.5482752323150635 time for calcul the mask position with numpy : 0.029082298278808594 nb_pixel_total : 15196 time to create 1 rle with old method : 0.017397165298461914 time for calcul the mask position with numpy : 0.030071735382080078 nb_pixel_total : 17357 time to create 1 rle with old method : 0.02124810218811035 time for calcul the mask position with numpy : 0.03559589385986328 nb_pixel_total : 13044 time to create 1 rle with old method : 0.01596999168395996 time for calcul the mask position with numpy : 0.02942180633544922 nb_pixel_total : 16605 time to create 1 rle with old method : 0.019385099411010742 time for calcul the mask position with numpy : 0.029328346252441406 nb_pixel_total : 2377 time to create 1 rle with old method : 0.0028502941131591797 time for calcul the mask position with numpy : 0.029520273208618164 nb_pixel_total : 14765 time to create 1 rle with old method : 0.017559528350830078 time for calcul the mask position with numpy : 0.029099464416503906 nb_pixel_total : 9785 time to create 1 rle with old method : 0.012451410293579102 time for calcul the mask position with numpy : 0.029611825942993164 nb_pixel_total : 13942 time to create 1 rle with old method : 0.016492366790771484 create new chi : 6.925025701522827 time to delete rle : 0.005183696746826172 batch 1 Loaded 91 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 99 TO DO : save crop sub photo not yet done ! save time : 0.23781824111938477 nb_obj : 38 nb_hashtags : 6 time to prepare the origin masks : 7.942101716995239 time for calcul the mask position with numpy : 0.6655905246734619 nb_pixel_total : 5244010 time to create 1 rle with new method : 0.5300235748291016 time for calcul the mask position with numpy : 0.027123212814331055 nb_pixel_total : 99830 time to create 1 rle with old method : 0.11160540580749512 time for calcul the mask position with numpy : 0.027196645736694336 nb_pixel_total : 16486 time to create 1 rle with old method : 0.01807999610900879 time for calcul the mask position with numpy : 0.02784442901611328 nb_pixel_total : 181882 time to create 1 rle with new method : 1.2647995948791504 time for calcul the mask position with numpy : 0.02878856658935547 nb_pixel_total : 22201 time to create 1 rle with old method : 0.0261843204498291 time for calcul the mask position with numpy : 0.0293424129486084 nb_pixel_total : 135271 time to create 1 rle with old method : 0.15676379203796387 time for calcul the mask position with numpy : 0.02936720848083496 nb_pixel_total : 58479 time to create 1 rle with old method : 0.06807589530944824 time for calcul the mask position with numpy : 0.029410600662231445 nb_pixel_total : 30657 time to create 1 rle with old method : 0.03558707237243652 time for calcul the mask position with numpy : 0.02860546112060547 nb_pixel_total : 54772 time to create 1 rle with old method : 0.06373238563537598 time for calcul the mask position with numpy : 0.030112743377685547 nb_pixel_total : 11551 time to create 1 rle with old method : 0.01348257064819336 time for calcul the mask position with numpy : 0.02943873405456543 nb_pixel_total : 7261 time to create 1 rle with old method : 0.008441686630249023 time for calcul the mask position with numpy : 0.029245376586914062 nb_pixel_total : 52486 time to create 1 rle with old method : 0.06757068634033203 time for calcul the mask position with numpy : 0.02916407585144043 nb_pixel_total : 8587 time to create 1 rle with old method : 0.010123014450073242 time for calcul the mask position with numpy : 0.02963852882385254 nb_pixel_total : 112907 time to create 1 rle with old method : 0.12863397598266602 time for calcul the mask position with numpy : 0.028510570526123047 nb_pixel_total : 16978 time to create 1 rle with old method : 0.019017934799194336 time for calcul the mask position with numpy : 0.02736520767211914 nb_pixel_total : 30026 time to create 1 rle with old method : 0.032776594161987305 time for calcul the mask position with numpy : 0.02715611457824707 nb_pixel_total : 26263 time to create 1 rle with old method : 0.02832174301147461 time for calcul the mask position with numpy : 0.035608768463134766 nb_pixel_total : 20724 time to create 1 rle with old method : 0.0265960693359375 time for calcul the mask position with numpy : 0.03381848335266113 nb_pixel_total : 69137 time to create 1 rle with old method : 0.09275054931640625 time for calcul the mask position with numpy : 0.029148340225219727 nb_pixel_total : 120174 time to create 1 rle with old method : 0.14283132553100586 time for calcul the mask position with numpy : 0.028057336807250977 nb_pixel_total : 12421 time to create 1 rle with old method : 0.014451265335083008 time for calcul the mask position with numpy : 0.02866363525390625 nb_pixel_total : 44110 time to create 1 rle with old method : 0.0511167049407959 time for calcul the mask position with numpy : 0.030022144317626953 nb_pixel_total : 191691 time to create 1 rle with new method : 1.5482556819915771 time for calcul the mask position with numpy : 0.028829574584960938 nb_pixel_total : 3947 time to create 1 rle with old method : 0.004678010940551758 time for calcul the mask position with numpy : 0.028835773468017578 nb_pixel_total : 51303 time to create 1 rle with old method : 0.06040692329406738 time for calcul the mask position with numpy : 0.02787637710571289 nb_pixel_total : 66443 time to create 1 rle with old method : 0.07398486137390137 time for calcul the mask position with numpy : 0.028882980346679688 nb_pixel_total : 10763 time to create 1 rle with old method : 0.012639522552490234 time for calcul the mask position with numpy : 0.02927255630493164 nb_pixel_total : 45185 time to create 1 rle with old method : 0.05264616012573242 time for calcul the mask position with numpy : 0.029094219207763672 nb_pixel_total : 28154 time to create 1 rle with old method : 0.03411412239074707 time for calcul the mask position with numpy : 0.03300666809082031 nb_pixel_total : 19997 time to create 1 rle with old method : 0.03295612335205078 time for calcul the mask position with numpy : 0.03131508827209473 nb_pixel_total : 29885 time to create 1 rle with old method : 0.03442573547363281 time for calcul the mask position with numpy : 0.027779817581176758 nb_pixel_total : 31907 time to create 1 rle with old method : 0.035727739334106445 time for calcul the mask position with numpy : 0.028583049774169922 nb_pixel_total : 25287 time to create 1 rle with old method : 0.02795124053955078 time for calcul the mask position with numpy : 0.027714967727661133 nb_pixel_total : 14263 time to create 1 rle with old method : 0.01615619659423828 time for calcul the mask position with numpy : 0.028697967529296875 nb_pixel_total : 103891 time to create 1 rle with old method : 0.11882472038269043 time for calcul the mask position with numpy : 0.028423309326171875 nb_pixel_total : 6997 time to create 1 rle with old method : 0.008053302764892578 time for calcul the mask position with numpy : 0.02803492546081543 nb_pixel_total : 44096 time to create 1 rle with old method : 0.04986166954040527 time for calcul the mask position with numpy : 0.02849292755126953 nb_pixel_total : 218 time to create 1 rle with old method : 0.00033664703369140625 create new chi : 6.88459849357605 time to delete rle : 0.0032892227172851562 batch 1 Loaded 75 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.17525172233581543 nb_obj : 48 nb_hashtags : 6 time to prepare the origin masks : 7.884241819381714 time for calcul the mask position with numpy : 0.45684170722961426 nb_pixel_total : 5331312 time to create 1 rle with new method : 1.349048137664795 time for calcul the mask position with numpy : 0.0272371768951416 nb_pixel_total : 14813 time to create 1 rle with old method : 0.016007661819458008 time for calcul the mask position with numpy : 0.027647733688354492 nb_pixel_total : 18107 time to create 1 rle with old method : 0.019693374633789062 time for calcul the mask position with numpy : 0.027222156524658203 nb_pixel_total : 13493 time to create 1 rle with old method : 0.015029668807983398 time for calcul the mask position with numpy : 0.028756141662597656 nb_pixel_total : 32168 time to create 1 rle with old method : 0.035864830017089844 time for calcul the mask position with numpy : 0.028269529342651367 nb_pixel_total : 107922 time to create 1 rle with old method : 0.11804580688476562 time for calcul the mask position with numpy : 0.02701401710510254 nb_pixel_total : 30380 time to create 1 rle with old method : 0.03350543975830078 time for calcul the mask position with numpy : 0.026943445205688477 nb_pixel_total : 19508 time to create 1 rle with old method : 0.021270036697387695 time for calcul the mask position with numpy : 0.02699589729309082 nb_pixel_total : 5842 time to create 1 rle with old method : 0.006315708160400391 time for calcul the mask position with numpy : 0.027043581008911133 nb_pixel_total : 16358 time to create 1 rle with old method : 0.018108844757080078 time for calcul the mask position with numpy : 0.026897430419921875 nb_pixel_total : 71131 time to create 1 rle with old method : 0.07638001441955566 time for calcul the mask position with numpy : 0.02756023406982422 nb_pixel_total : 10373 time to create 1 rle with old method : 0.012032032012939453 time for calcul the mask position with numpy : 0.02935194969177246 nb_pixel_total : 317108 time to create 1 rle with new method : 0.5261573791503906 time for calcul the mask position with numpy : 0.029266357421875 nb_pixel_total : 34203 time to create 1 rle with old method : 0.03932642936706543 time for calcul the mask position with numpy : 0.028583049774169922 nb_pixel_total : 22012 time to create 1 rle with old method : 0.0260467529296875 time for calcul the mask position with numpy : 0.0290374755859375 nb_pixel_total : 27026 time to create 1 rle with old method : 0.031177282333374023 time for calcul the mask position with numpy : 0.028301715850830078 nb_pixel_total : 20133 time to create 1 rle with old method : 0.023512601852416992 time for calcul the mask position with numpy : 0.028592824935913086 nb_pixel_total : 191 time to create 1 rle with old method : 0.0006074905395507812 time for calcul the mask position with numpy : 0.028329849243164062 nb_pixel_total : 125965 time to create 1 rle with old method : 0.15025019645690918 time for calcul the mask position with numpy : 0.029068470001220703 nb_pixel_total : 53343 time to create 1 rle with old method : 0.06095743179321289 time for calcul the mask position with numpy : 0.028217315673828125 nb_pixel_total : 23717 time to create 1 rle with old method : 0.02696371078491211 time for calcul the mask position with numpy : 0.027845144271850586 nb_pixel_total : 19529 time to create 1 rle with old method : 0.022327899932861328 time for calcul the mask position with numpy : 0.028568029403686523 nb_pixel_total : 35097 time to create 1 rle with old method : 0.04393625259399414 time for calcul the mask position with numpy : 0.029265642166137695 nb_pixel_total : 24235 time to create 1 rle with old method : 0.026924610137939453 time for calcul the mask position with numpy : 0.027939319610595703 nb_pixel_total : 33286 time to create 1 rle with old method : 0.036509037017822266 time for calcul the mask position with numpy : 0.027339458465576172 nb_pixel_total : 1229 time to create 1 rle with old method : 0.0016083717346191406 time for calcul the mask position with numpy : 0.028293848037719727 nb_pixel_total : 32427 time to create 1 rle with old method : 0.03564190864562988 time for calcul the mask position with numpy : 0.032896995544433594 nb_pixel_total : 39693 time to create 1 rle with old method : 0.04556679725646973 time for calcul the mask position with numpy : 0.02822566032409668 nb_pixel_total : 23577 time to create 1 rle with old method : 0.027554750442504883 time for calcul the mask position with numpy : 0.029146194458007812 nb_pixel_total : 25789 time to create 1 rle with old method : 0.03046393394470215 time for calcul the mask position with numpy : 0.02822136878967285 nb_pixel_total : 85704 time to create 1 rle with old method : 0.09648466110229492 time for calcul the mask position with numpy : 0.02785038948059082 nb_pixel_total : 4701 time to create 1 rle with old method : 0.005320310592651367 time for calcul the mask position with numpy : 0.026948928833007812 nb_pixel_total : 26208 time to create 1 rle with old method : 0.02861618995666504 time for calcul the mask position with numpy : 0.02700066566467285 nb_pixel_total : 16751 time to create 1 rle with old method : 0.018438100814819336 time for calcul the mask position with numpy : 0.026662588119506836 nb_pixel_total : 32863 time to create 1 rle with old method : 0.03651142120361328 time for calcul the mask position with numpy : 0.027380704879760742 nb_pixel_total : 12021 time to create 1 rle with old method : 0.013601064682006836 time for calcul the mask position with numpy : 0.027016401290893555 nb_pixel_total : 14704 time to create 1 rle with old method : 0.016594886779785156 time for calcul the mask position with numpy : 0.027057409286499023 nb_pixel_total : 43969 time to create 1 rle with old method : 0.050580739974975586 time for calcul the mask position with numpy : 0.028834104537963867 nb_pixel_total : 31474 time to create 1 rle with old method : 0.0661616325378418 time for calcul the mask position with numpy : 0.05676746368408203 nb_pixel_total : 3578 time to create 1 rle with old method : 0.0043642520904541016 time for calcul the mask position with numpy : 0.02973151206970215 nb_pixel_total : 58005 time to create 1 rle with old method : 0.06873726844787598 time for calcul the mask position with numpy : 0.02958393096923828 nb_pixel_total : 9038 time to create 1 rle with old method : 0.010595083236694336 time for calcul the mask position with numpy : 0.029363632202148438 nb_pixel_total : 12283 time to create 1 rle with old method : 0.014985084533691406 time for calcul the mask position with numpy : 0.029902219772338867 nb_pixel_total : 68037 time to create 1 rle with old method : 0.07871890068054199 time for calcul the mask position with numpy : 0.0290529727935791 nb_pixel_total : 5691 time to create 1 rle with old method : 0.006766080856323242 time for calcul the mask position with numpy : 0.031235456466674805 nb_pixel_total : 59947 time to create 1 rle with old method : 0.07326793670654297 time for calcul the mask position with numpy : 0.02943563461303711 nb_pixel_total : 31719 time to create 1 rle with old method : 0.03695321083068848 time for calcul the mask position with numpy : 0.028661012649536133 nb_pixel_total : 3580 time to create 1 rle with old method : 0.004034519195556641 create new chi : 5.415661096572876 time to delete rle : 0.0033919811248779297 batch 1 Loaded 99 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 1032 TO DO : save crop sub photo not yet done ! save time : 0.27103519439697266 nb_obj : 36 nb_hashtags : 5 time to prepare the origin masks : 8.077491283416748 time for calcul the mask position with numpy : 1.0655245780944824 nb_pixel_total : 5374937 time to create 1 rle with new method : 0.6670079231262207 time for calcul the mask position with numpy : 0.030002593994140625 nb_pixel_total : 108744 time to create 1 rle with old method : 0.12940001487731934 time for calcul the mask position with numpy : 0.03140139579772949 nb_pixel_total : 26546 time to create 1 rle with old method : 0.036609649658203125 time for calcul the mask position with numpy : 0.0290985107421875 nb_pixel_total : 45787 time to create 1 rle with old method : 0.05327916145324707 time for calcul the mask position with numpy : 0.02889418601989746 nb_pixel_total : 13364 time to create 1 rle with old method : 0.015468120574951172 time for calcul the mask position with numpy : 0.028799772262573242 nb_pixel_total : 24752 time to create 1 rle with old method : 0.028536081314086914 time for calcul the mask position with numpy : 0.02890300750732422 nb_pixel_total : 36770 time to create 1 rle with old method : 0.04452061653137207 time for calcul the mask position with numpy : 0.0289156436920166 nb_pixel_total : 16051 time to create 1 rle with old method : 0.019299030303955078 time for calcul the mask position with numpy : 0.028760433197021484 nb_pixel_total : 9936 time to create 1 rle with old method : 0.011750936508178711 time for calcul the mask position with numpy : 0.028496980667114258 nb_pixel_total : 14468 time to create 1 rle with old method : 0.016954898834228516 time for calcul the mask position with numpy : 0.028443098068237305 nb_pixel_total : 15483 time to create 1 rle with old method : 0.02541375160217285 time for calcul the mask position with numpy : 0.03307390213012695 nb_pixel_total : 29390 time to create 1 rle with old method : 0.040079593658447266 time for calcul the mask position with numpy : 0.028242826461791992 nb_pixel_total : 22479 time to create 1 rle with old method : 0.02620983123779297 time for calcul the mask position with numpy : 0.028226375579833984 nb_pixel_total : 16359 time to create 1 rle with old method : 0.018993616104125977 time for calcul the mask position with numpy : 0.02861642837524414 nb_pixel_total : 34574 time to create 1 rle with old method : 0.039968252182006836 time for calcul the mask position with numpy : 0.029187679290771484 nb_pixel_total : 44086 time to create 1 rle with old method : 0.05205035209655762 time for calcul the mask position with numpy : 0.02871990203857422 nb_pixel_total : 38828 time to create 1 rle with old method : 0.044759511947631836 time for calcul the mask position with numpy : 0.031038522720336914 nb_pixel_total : 337905 time to create 1 rle with new method : 0.7301383018493652 time for calcul the mask position with numpy : 0.028800249099731445 nb_pixel_total : 46341 time to create 1 rle with old method : 0.05313754081726074 time for calcul the mask position with numpy : 0.02904486656188965 nb_pixel_total : 109375 time to create 1 rle with old method : 0.1262073516845703 time for calcul the mask position with numpy : 0.02973461151123047 nb_pixel_total : 99615 time to create 1 rle with old method : 0.13029193878173828 time for calcul the mask position with numpy : 0.033185720443725586 nb_pixel_total : 9871 time to create 1 rle with old method : 0.014252424240112305 time for calcul the mask position with numpy : 0.029169559478759766 nb_pixel_total : 61382 time to create 1 rle with old method : 0.07278013229370117 time for calcul the mask position with numpy : 0.029280424118041992 nb_pixel_total : 92608 time to create 1 rle with old method : 0.10895681381225586 time for calcul the mask position with numpy : 0.029963970184326172 nb_pixel_total : 42072 time to create 1 rle with old method : 0.04894590377807617 time for calcul the mask position with numpy : 0.028847932815551758 nb_pixel_total : 20918 time to create 1 rle with old method : 0.024516820907592773 time for calcul the mask position with numpy : 0.02866053581237793 nb_pixel_total : 15281 time to create 1 rle with old method : 0.017973661422729492 time for calcul the mask position with numpy : 0.028859376907348633 nb_pixel_total : 22017 time to create 1 rle with old method : 0.025475263595581055 time for calcul the mask position with numpy : 0.029947519302368164 nb_pixel_total : 89720 time to create 1 rle with old method : 0.12369346618652344 time for calcul the mask position with numpy : 0.028204917907714844 nb_pixel_total : 3803 time to create 1 rle with old method : 0.004741191864013672 time for calcul the mask position with numpy : 0.02895188331604004 nb_pixel_total : 138887 time to create 1 rle with old method : 0.18260431289672852 time for calcul the mask position with numpy : 0.029212236404418945 nb_pixel_total : 42729 time to create 1 rle with old method : 0.04970860481262207 time for calcul the mask position with numpy : 0.028916597366333008 nb_pixel_total : 15196 time to create 1 rle with old method : 0.018209457397460938 time for calcul the mask position with numpy : 0.02876758575439453 nb_pixel_total : 7928 time to create 1 rle with old method : 0.009522438049316406 time for calcul the mask position with numpy : 0.02870345115661621 nb_pixel_total : 3774 time to create 1 rle with old method : 0.004542827606201172 time for calcul the mask position with numpy : 0.028951406478881836 nb_pixel_total : 18264 time to create 1 rle with old method : 0.02210235595703125 create new chi : 5.221113920211792 time to delete rle : 0.005726814270019531 batch 1 Loaded 71 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.18523883819580078 nb_obj : 19 nb_hashtags : 5 time to prepare the origin masks : 8.086881160736084 time for calcul the mask position with numpy : 0.32780981063842773 nb_pixel_total : 6277072 time to create 1 rle with new method : 0.7103073596954346 time for calcul the mask position with numpy : 0.029401779174804688 nb_pixel_total : 156182 time to create 1 rle with new method : 0.40781545639038086 time for calcul the mask position with numpy : 0.02908039093017578 nb_pixel_total : 12492 time to create 1 rle with old method : 0.014873504638671875 time for calcul the mask position with numpy : 0.029006242752075195 nb_pixel_total : 35150 time to create 1 rle with old method : 0.041188955307006836 time for calcul the mask position with numpy : 0.02888178825378418 nb_pixel_total : 26162 time to create 1 rle with old method : 0.030390501022338867 time for calcul the mask position with numpy : 0.02875542640686035 nb_pixel_total : 9668 time to create 1 rle with old method : 0.011518478393554688 time for calcul the mask position with numpy : 0.029187440872192383 nb_pixel_total : 64746 time to create 1 rle with old method : 0.0753316879272461 time for calcul the mask position with numpy : 0.028960466384887695 nb_pixel_total : 9815 time to create 1 rle with old method : 0.011490345001220703 time for calcul the mask position with numpy : 0.029011011123657227 nb_pixel_total : 43815 time to create 1 rle with old method : 0.05138421058654785 time for calcul the mask position with numpy : 0.028916597366333008 nb_pixel_total : 6496 time to create 1 rle with old method : 0.0076258182525634766 time for calcul the mask position with numpy : 0.028622865676879883 nb_pixel_total : 32563 time to create 1 rle with old method : 0.037415266036987305 time for calcul the mask position with numpy : 0.029051542282104492 nb_pixel_total : 61194 time to create 1 rle with old method : 0.07240843772888184 time for calcul the mask position with numpy : 0.029344797134399414 nb_pixel_total : 18863 time to create 1 rle with old method : 0.02204751968383789 time for calcul the mask position with numpy : 0.030192136764526367 nb_pixel_total : 136301 time to create 1 rle with old method : 0.16271328926086426 time for calcul the mask position with numpy : 0.02850937843322754 nb_pixel_total : 28034 time to create 1 rle with old method : 0.032740116119384766 time for calcul the mask position with numpy : 0.027816295623779297 nb_pixel_total : 12325 time to create 1 rle with old method : 0.013661861419677734 time for calcul the mask position with numpy : 0.027209043502807617 nb_pixel_total : 1525 time to create 1 rle with old method : 0.0017921924591064453 time for calcul the mask position with numpy : 0.02759552001953125 nb_pixel_total : 89888 time to create 1 rle with old method : 0.09921455383300781 time for calcul the mask position with numpy : 0.027484416961669922 nb_pixel_total : 27949 time to create 1 rle with old method : 0.030836105346679688 create new chi : 2.7697572708129883 time to delete rle : 0.0015552043914794922 batch 1 Loaded 41 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.1226341724395752 nb_obj : 29 nb_hashtags : 4 time to prepare the origin masks : 8.345426559448242 time for calcul the mask position with numpy : 0.3312203884124756 nb_pixel_total : 4824989 time to create 1 rle with new method : 0.49257588386535645 time for calcul the mask position with numpy : 0.029035091400146484 nb_pixel_total : 99839 time to create 1 rle with old method : 0.11342382431030273 time for calcul the mask position with numpy : 0.034342288970947266 nb_pixel_total : 786049 time to create 1 rle with new method : 0.5110213756561279 time for calcul the mask position with numpy : 0.030663013458251953 nb_pixel_total : 121224 time to create 1 rle with old method : 0.14014697074890137 time for calcul the mask position with numpy : 0.029097318649291992 nb_pixel_total : 63086 time to create 1 rle with old method : 0.07216119766235352 time for calcul the mask position with numpy : 0.02899336814880371 nb_pixel_total : 26344 time to create 1 rle with old method : 0.03136944770812988 time for calcul the mask position with numpy : 0.029119253158569336 nb_pixel_total : 46170 time to create 1 rle with old method : 0.053720951080322266 time for calcul the mask position with numpy : 0.028797388076782227 nb_pixel_total : 43458 time to create 1 rle with old method : 0.049558401107788086 time for calcul the mask position with numpy : 0.027546405792236328 nb_pixel_total : 31440 time to create 1 rle with old method : 0.036202192306518555 time for calcul the mask position with numpy : 0.028316497802734375 nb_pixel_total : 42675 time to create 1 rle with old method : 0.048978567123413086 time for calcul the mask position with numpy : 0.026865243911743164 nb_pixel_total : 17385 time to create 1 rle with old method : 0.019885778427124023 time for calcul the mask position with numpy : 0.028302669525146484 nb_pixel_total : 97125 time to create 1 rle with old method : 0.11063838005065918 time for calcul the mask position with numpy : 0.030240774154663086 nb_pixel_total : 88903 time to create 1 rle with old method : 0.10258674621582031 time for calcul the mask position with numpy : 0.029162168502807617 nb_pixel_total : 57051 time to create 1 rle with old method : 0.06604766845703125 time for calcul the mask position with numpy : 0.028903722763061523 nb_pixel_total : 30804 time to create 1 rle with old method : 0.037046194076538086 time for calcul the mask position with numpy : 0.0330657958984375 nb_pixel_total : 79851 time to create 1 rle with old method : 0.09623050689697266 time for calcul the mask position with numpy : 0.027971506118774414 nb_pixel_total : 14378 time to create 1 rle with old method : 0.017524003982543945 time for calcul the mask position with numpy : 0.02820897102355957 nb_pixel_total : 13771 time to create 1 rle with old method : 0.023141145706176758 time for calcul the mask position with numpy : 0.03352618217468262 nb_pixel_total : 124025 time to create 1 rle with old method : 0.1457676887512207 time for calcul the mask position with numpy : 0.029284954071044922 nb_pixel_total : 161498 time to create 1 rle with new method : 0.4481184482574463 time for calcul the mask position with numpy : 0.027504444122314453 nb_pixel_total : 29704 time to create 1 rle with old method : 0.03357052803039551 time for calcul the mask position with numpy : 0.028386354446411133 nb_pixel_total : 84092 time to create 1 rle with old method : 0.09484386444091797 time for calcul the mask position with numpy : 0.030359506607055664 nb_pixel_total : 71388 time to create 1 rle with old method : 0.07961583137512207 time for calcul the mask position with numpy : 0.026764869689941406 nb_pixel_total : 8147 time to create 1 rle with old method : 0.009224414825439453 time for calcul the mask position with numpy : 0.026800155639648438 nb_pixel_total : 5487 time to create 1 rle with old method : 0.006218433380126953 time for calcul the mask position with numpy : 0.026450157165527344 nb_pixel_total : 7833 time to create 1 rle with old method : 0.00868844985961914 time for calcul the mask position with numpy : 0.02658534049987793 nb_pixel_total : 24360 time to create 1 rle with old method : 0.027172327041625977 time for calcul the mask position with numpy : 0.02676701545715332 nb_pixel_total : 43481 time to create 1 rle with old method : 0.04850482940673828 time for calcul the mask position with numpy : 0.028345823287963867 nb_pixel_total : 5683 time to create 1 rle with old method : 0.00673222541809082 create new chi : 4.182456970214844 time to delete rle : 0.0032112598419189453 batch 1 Loaded 58 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.16704010963439941 nb_obj : 36 nb_hashtags : 6 time to prepare the origin masks : 7.80706524848938 time for calcul the mask position with numpy : 0.5971481800079346 nb_pixel_total : 5680834 time to create 1 rle with new method : 0.8413875102996826 time for calcul the mask position with numpy : 0.029794692993164062 nb_pixel_total : 90621 time to create 1 rle with old method : 0.10419201850891113 time for calcul the mask position with numpy : 0.028502702713012695 nb_pixel_total : 13164 time to create 1 rle with old method : 0.015439748764038086 time for calcul the mask position with numpy : 0.028344154357910156 nb_pixel_total : 8461 time to create 1 rle with old method : 0.010796070098876953 time for calcul the mask position with numpy : 0.029083967208862305 nb_pixel_total : 144 time to create 1 rle with old method : 0.0005323886871337891 time for calcul the mask position with numpy : 0.029762744903564453 nb_pixel_total : 103967 time to create 1 rle with old method : 0.12033247947692871 time for calcul the mask position with numpy : 0.029527664184570312 nb_pixel_total : 59948 time to create 1 rle with old method : 0.07110738754272461 time for calcul the mask position with numpy : 0.027719736099243164 nb_pixel_total : 26831 time to create 1 rle with old method : 0.03182649612426758 time for calcul the mask position with numpy : 0.0292966365814209 nb_pixel_total : 47137 time to create 1 rle with old method : 0.0563664436340332 time for calcul the mask position with numpy : 0.029453754425048828 nb_pixel_total : 36132 time to create 1 rle with old method : 0.04229283332824707 time for calcul the mask position with numpy : 0.028504610061645508 nb_pixel_total : 32609 time to create 1 rle with old method : 0.03744983673095703 time for calcul the mask position with numpy : 0.02790212631225586 nb_pixel_total : 44002 time to create 1 rle with old method : 0.05136442184448242 time for calcul the mask position with numpy : 0.02905750274658203 nb_pixel_total : 19821 time to create 1 rle with old method : 0.02320694923400879 time for calcul the mask position with numpy : 0.028558731079101562 nb_pixel_total : 65504 time to create 1 rle with old method : 0.0772240161895752 time for calcul the mask position with numpy : 0.02913951873779297 nb_pixel_total : 9959 time to create 1 rle with old method : 0.011789321899414062 time for calcul the mask position with numpy : 0.029090404510498047 nb_pixel_total : 26 time to create 1 rle with old method : 0.0001201629638671875 time for calcul the mask position with numpy : 0.02891063690185547 nb_pixel_total : 5685 time to create 1 rle with old method : 0.007200717926025391 time for calcul the mask position with numpy : 0.030174970626831055 nb_pixel_total : 34392 time to create 1 rle with old method : 0.0477755069732666 time for calcul the mask position with numpy : 0.028472185134887695 nb_pixel_total : 49411 time to create 1 rle with old method : 0.05577683448791504 time for calcul the mask position with numpy : 0.028940200805664062 nb_pixel_total : 150021 time to create 1 rle with new method : 0.6240575313568115 time for calcul the mask position with numpy : 0.02880239486694336 nb_pixel_total : 13711 time to create 1 rle with old method : 0.01549530029296875 time for calcul the mask position with numpy : 0.02854156494140625 nb_pixel_total : 65022 time to create 1 rle with old method : 0.07387924194335938 time for calcul the mask position with numpy : 0.028110742568969727 nb_pixel_total : 20751 time to create 1 rle with old method : 0.023597240447998047 time for calcul the mask position with numpy : 0.028650522232055664 nb_pixel_total : 13070 time to create 1 rle with old method : 0.015060901641845703 time for calcul the mask position with numpy : 0.028107643127441406 nb_pixel_total : 5159 time to create 1 rle with old method : 0.006226539611816406 time for calcul the mask position with numpy : 0.02712225914001465 nb_pixel_total : 73007 time to create 1 rle with old method : 0.08119416236877441 time for calcul the mask position with numpy : 0.027909517288208008 nb_pixel_total : 108144 time to create 1 rle with old method : 0.1200108528137207 time for calcul the mask position with numpy : 0.02815556526184082 nb_pixel_total : 71631 time to create 1 rle with old method : 0.0815439224243164 time for calcul the mask position with numpy : 0.02945256233215332 nb_pixel_total : 37373 time to create 1 rle with old method : 0.04358839988708496 time for calcul the mask position with numpy : 0.029264450073242188 nb_pixel_total : 13648 time to create 1 rle with old method : 0.016042709350585938 time for calcul the mask position with numpy : 0.028528690338134766 nb_pixel_total : 6416 time to create 1 rle with old method : 0.007345914840698242 time for calcul the mask position with numpy : 0.028177738189697266 nb_pixel_total : 49944 time to create 1 rle with old method : 0.05614352226257324 time for calcul the mask position with numpy : 0.028745651245117188 nb_pixel_total : 46820 time to create 1 rle with old method : 0.05343365669250488 time for calcul the mask position with numpy : 0.028351545333862305 nb_pixel_total : 12872 time to create 1 rle with old method : 0.014884710311889648 time for calcul the mask position with numpy : 0.02936553955078125 nb_pixel_total : 20796 time to create 1 rle with old method : 0.024519681930541992 time for calcul the mask position with numpy : 0.029326915740966797 nb_pixel_total : 13207 time to create 1 rle with old method : 0.015434503555297852 create new chi : 4.567411184310913 time to delete rle : 0.0033109188079833984 batch 1 Loaded 72 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 563 TO DO : save crop sub photo not yet done ! save time : 0.27456068992614746 nb_obj : 39 nb_hashtags : 5 time to prepare the origin masks : 7.85110330581665 time for calcul the mask position with numpy : 0.4929649829864502 nb_pixel_total : 4546943 time to create 1 rle with new method : 0.3915541172027588 time for calcul the mask position with numpy : 0.027780771255493164 nb_pixel_total : 12831 time to create 1 rle with old method : 0.014387369155883789 time for calcul the mask position with numpy : 0.026913881301879883 nb_pixel_total : 10643 time to create 1 rle with old method : 0.011805057525634766 time for calcul the mask position with numpy : 0.02716803550720215 nb_pixel_total : 26146 time to create 1 rle with old method : 0.02898383140563965 time for calcul the mask position with numpy : 0.031238555908203125 nb_pixel_total : 668269 time to create 1 rle with new method : 0.6806588172912598 time for calcul the mask position with numpy : 0.027794837951660156 nb_pixel_total : 23435 time to create 1 rle with old method : 0.026277780532836914 time for calcul the mask position with numpy : 0.027995586395263672 nb_pixel_total : 12408 time to create 1 rle with old method : 0.014384984970092773 time for calcul the mask position with numpy : 0.02819037437438965 nb_pixel_total : 19894 time to create 1 rle with old method : 0.02247309684753418 time for calcul the mask position with numpy : 0.027491331100463867 nb_pixel_total : 23610 time to create 1 rle with old method : 0.02642202377319336 time for calcul the mask position with numpy : 0.028440475463867188 nb_pixel_total : 4949 time to create 1 rle with old method : 0.00579380989074707 time for calcul the mask position with numpy : 0.027985572814941406 nb_pixel_total : 20097 time to create 1 rle with old method : 0.02301931381225586 time for calcul the mask position with numpy : 0.02820730209350586 nb_pixel_total : 93210 time to create 1 rle with old method : 0.10529899597167969 time for calcul the mask position with numpy : 0.027541160583496094 nb_pixel_total : 46019 time to create 1 rle with old method : 0.05097055435180664 time for calcul the mask position with numpy : 0.027634620666503906 nb_pixel_total : 40354 time to create 1 rle with old method : 0.05673503875732422 time for calcul the mask position with numpy : 0.035660505294799805 nb_pixel_total : 38742 time to create 1 rle with old method : 0.04377245903015137 time for calcul the mask position with numpy : 0.028061866760253906 nb_pixel_total : 36458 time to create 1 rle with old method : 0.04188942909240723 time for calcul the mask position with numpy : 0.029332637786865234 nb_pixel_total : 38227 time to create 1 rle with old method : 0.04504251480102539 time for calcul the mask position with numpy : 0.029584646224975586 nb_pixel_total : 41118 time to create 1 rle with old method : 0.04787874221801758 time for calcul the mask position with numpy : 0.02893996238708496 nb_pixel_total : 39328 time to create 1 rle with old method : 0.04484677314758301 time for calcul the mask position with numpy : 0.02866840362548828 nb_pixel_total : 72588 time to create 1 rle with old method : 0.08473777770996094 time for calcul the mask position with numpy : 0.028901100158691406 nb_pixel_total : 63893 time to create 1 rle with old method : 0.07304787635803223 time for calcul the mask position with numpy : 0.028211355209350586 nb_pixel_total : 7748 time to create 1 rle with old method : 0.009017229080200195 time for calcul the mask position with numpy : 0.029285430908203125 nb_pixel_total : 129062 time to create 1 rle with old method : 0.14887404441833496 time for calcul the mask position with numpy : 0.03293967247009277 nb_pixel_total : 5146 time to create 1 rle with old method : 0.008658170700073242 time for calcul the mask position with numpy : 0.03297543525695801 nb_pixel_total : 20653 time to create 1 rle with old method : 0.026364803314208984 time for calcul the mask position with numpy : 0.0287625789642334 nb_pixel_total : 34137 time to create 1 rle with old method : 0.03972196578979492 time for calcul the mask position with numpy : 0.03160905838012695 nb_pixel_total : 36961 time to create 1 rle with old method : 0.06141853332519531 time for calcul the mask position with numpy : 0.03043055534362793 nb_pixel_total : 15612 time to create 1 rle with old method : 0.01825857162475586 time for calcul the mask position with numpy : 0.028908252716064453 nb_pixel_total : 98384 time to create 1 rle with old method : 0.11184406280517578 time for calcul the mask position with numpy : 0.028328895568847656 nb_pixel_total : 38486 time to create 1 rle with old method : 0.04334425926208496 time for calcul the mask position with numpy : 0.02865290641784668 nb_pixel_total : 16301 time to create 1 rle with old method : 0.018898487091064453 time for calcul the mask position with numpy : 0.02943110466003418 nb_pixel_total : 235895 time to create 1 rle with new method : 0.6679360866546631 time for calcul the mask position with numpy : 0.028674840927124023 nb_pixel_total : 59861 time to create 1 rle with old method : 0.06823348999023438 time for calcul the mask position with numpy : 0.0287017822265625 nb_pixel_total : 18732 time to create 1 rle with old method : 0.0225675106048584 time for calcul the mask position with numpy : 0.0291593074798584 nb_pixel_total : 61116 time to create 1 rle with old method : 0.07120609283447266 time for calcul the mask position with numpy : 0.029902219772338867 nb_pixel_total : 130751 time to create 1 rle with old method : 0.1742994785308838 time for calcul the mask position with numpy : 0.03215765953063965 nb_pixel_total : 247549 time to create 1 rle with new method : 0.7562377452850342 time for calcul the mask position with numpy : 0.028095722198486328 nb_pixel_total : 14575 time to create 1 rle with old method : 0.01677870750427246 time for calcul the mask position with numpy : 0.027545452117919922 nb_pixel_total : 109 time to create 1 rle with old method : 0.0003571510314941406 create new chi : 5.832134962081909 time to delete rle : 0.003484487533569336 batch 1 Loaded 78 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 48 TO DO : save crop sub photo not yet done ! save time : 0.25557637214660645 nb_obj : 32 nb_hashtags : 4 time to prepare the origin masks : 7.75143837928772 time for calcul the mask position with numpy : 0.5980045795440674 nb_pixel_total : 6153732 time to create 1 rle with new method : 0.6533172130584717 time for calcul the mask position with numpy : 0.028506994247436523 nb_pixel_total : 4438 time to create 1 rle with old method : 0.0051136016845703125 time for calcul the mask position with numpy : 0.028392314910888672 nb_pixel_total : 27899 time to create 1 rle with old method : 0.032385826110839844 time for calcul the mask position with numpy : 0.027779817581176758 nb_pixel_total : 14501 time to create 1 rle with old method : 0.016823530197143555 time for calcul the mask position with numpy : 0.028406620025634766 nb_pixel_total : 25316 time to create 1 rle with old method : 0.029680728912353516 time for calcul the mask position with numpy : 0.028515338897705078 nb_pixel_total : 26965 time to create 1 rle with old method : 0.03126716613769531 time for calcul the mask position with numpy : 0.030248403549194336 nb_pixel_total : 13704 time to create 1 rle with old method : 0.015733718872070312 time for calcul the mask position with numpy : 0.02855992317199707 nb_pixel_total : 16300 time to create 1 rle with old method : 0.018951416015625 time for calcul the mask position with numpy : 0.029821395874023438 nb_pixel_total : 27833 time to create 1 rle with old method : 0.03194069862365723 time for calcul the mask position with numpy : 0.028473854064941406 nb_pixel_total : 12097 time to create 1 rle with old method : 0.014076948165893555 time for calcul the mask position with numpy : 0.028096675872802734 nb_pixel_total : 16769 time to create 1 rle with old method : 0.019289493560791016 time for calcul the mask position with numpy : 0.02841496467590332 nb_pixel_total : 91807 time to create 1 rle with old method : 0.11510992050170898 time for calcul the mask position with numpy : 0.03291606903076172 nb_pixel_total : 14977 time to create 1 rle with old method : 0.023128747940063477 time for calcul the mask position with numpy : 0.028024673461914062 nb_pixel_total : 22868 time to create 1 rle with old method : 0.026306629180908203 time for calcul the mask position with numpy : 0.02793407440185547 nb_pixel_total : 33207 time to create 1 rle with old method : 0.03762078285217285 time for calcul the mask position with numpy : 0.02816486358642578 nb_pixel_total : 7421 time to create 1 rle with old method : 0.008554697036743164 time for calcul the mask position with numpy : 0.027758359909057617 nb_pixel_total : 13555 time to create 1 rle with old method : 0.015895366668701172 time for calcul the mask position with numpy : 0.028597354888916016 nb_pixel_total : 54100 time to create 1 rle with old method : 0.061476945877075195 time for calcul the mask position with numpy : 0.02850484848022461 nb_pixel_total : 23477 time to create 1 rle with old method : 0.027254104614257812 time for calcul the mask position with numpy : 0.028072595596313477 nb_pixel_total : 16709 time to create 1 rle with old method : 0.019478321075439453 time for calcul the mask position with numpy : 0.028295278549194336 nb_pixel_total : 4939 time to create 1 rle with old method : 0.005891561508178711 time for calcul the mask position with numpy : 0.02838444709777832 nb_pixel_total : 93454 time to create 1 rle with old method : 0.11739253997802734 time for calcul the mask position with numpy : 0.02904534339904785 nb_pixel_total : 8961 time to create 1 rle with old method : 0.010245800018310547 time for calcul the mask position with numpy : 0.029181718826293945 nb_pixel_total : 96611 time to create 1 rle with old method : 0.13725852966308594 time for calcul the mask position with numpy : 0.030860424041748047 nb_pixel_total : 850 time to create 1 rle with old method : 0.002148866653442383 time for calcul the mask position with numpy : 0.03000497817993164 nb_pixel_total : 30751 time to create 1 rle with old method : 0.036302804946899414 time for calcul the mask position with numpy : 0.030467748641967773 nb_pixel_total : 87281 time to create 1 rle with old method : 0.11036896705627441 time for calcul the mask position with numpy : 0.030798673629760742 nb_pixel_total : 21852 time to create 1 rle with old method : 0.02811741828918457 time for calcul the mask position with numpy : 0.028863191604614258 nb_pixel_total : 51353 time to create 1 rle with old method : 0.05977916717529297 time for calcul the mask position with numpy : 0.028337717056274414 nb_pixel_total : 183 time to create 1 rle with old method : 0.00027823448181152344 time for calcul the mask position with numpy : 0.028221607208251953 nb_pixel_total : 27402 time to create 1 rle with old method : 0.03369784355163574 time for calcul the mask position with numpy : 0.028308391571044922 nb_pixel_total : 8928 time to create 1 rle with old method : 0.011061668395996094 create new chi : 3.313819646835327 time to delete rle : 0.0020096302032470703 batch 1 Loaded 63 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.13643550872802734 nb_obj : 33 nb_hashtags : 4 time to prepare the origin masks : 7.779915809631348 time for calcul the mask position with numpy : 0.7970089912414551 nb_pixel_total : 5733339 time to create 1 rle with new method : 0.5849201679229736 time for calcul the mask position with numpy : 0.029054641723632812 nb_pixel_total : 16758 time to create 1 rle with old method : 0.024909019470214844 time for calcul the mask position with numpy : 0.03285074234008789 nb_pixel_total : 34151 time to create 1 rle with old method : 0.04792976379394531 time for calcul the mask position with numpy : 0.029233455657958984 nb_pixel_total : 6813 time to create 1 rle with old method : 0.009692668914794922 time for calcul the mask position with numpy : 0.029061555862426758 nb_pixel_total : 16215 time to create 1 rle with old method : 0.01982426643371582 time for calcul the mask position with numpy : 0.03118157386779785 nb_pixel_total : 6853 time to create 1 rle with old method : 0.008035659790039062 time for calcul the mask position with numpy : 0.028966188430786133 nb_pixel_total : 17116 time to create 1 rle with old method : 0.019849061965942383 time for calcul the mask position with numpy : 0.030838727951049805 nb_pixel_total : 42627 time to create 1 rle with old method : 0.05019021034240723 time for calcul the mask position with numpy : 0.030678987503051758 nb_pixel_total : 25041 time to create 1 rle with old method : 0.03899836540222168 time for calcul the mask position with numpy : 0.032955169677734375 nb_pixel_total : 7001 time to create 1 rle with old method : 0.011542081832885742 time for calcul the mask position with numpy : 0.029029369354248047 nb_pixel_total : 15031 time to create 1 rle with old method : 0.017558574676513672 time for calcul the mask position with numpy : 0.02894306182861328 nb_pixel_total : 18961 time to create 1 rle with old method : 0.022129058837890625 time for calcul the mask position with numpy : 0.0288851261138916 nb_pixel_total : 69702 time to create 1 rle with old method : 0.0804440975189209 time for calcul the mask position with numpy : 0.02872467041015625 nb_pixel_total : 42718 time to create 1 rle with old method : 0.05134916305541992 time for calcul the mask position with numpy : 0.02917647361755371 nb_pixel_total : 56071 time to create 1 rle with old method : 0.08147192001342773 time for calcul the mask position with numpy : 0.02876591682434082 nb_pixel_total : 41547 time to create 1 rle with old method : 0.048201799392700195 time for calcul the mask position with numpy : 0.029612064361572266 nb_pixel_total : 93201 time to create 1 rle with old method : 0.13272738456726074 time for calcul the mask position with numpy : 0.028928518295288086 nb_pixel_total : 5468 time to create 1 rle with old method : 0.0065479278564453125 time for calcul the mask position with numpy : 0.028766155242919922 nb_pixel_total : 63 time to create 1 rle with old method : 0.00021791458129882812 time for calcul the mask position with numpy : 0.030107975006103516 nb_pixel_total : 46775 time to create 1 rle with old method : 0.057770729064941406 time for calcul the mask position with numpy : 0.028781890869140625 nb_pixel_total : 15946 time to create 1 rle with old method : 0.01865410804748535 time for calcul the mask position with numpy : 0.03059077262878418 nb_pixel_total : 15137 time to create 1 rle with old method : 0.020996570587158203 time for calcul the mask position with numpy : 0.027992725372314453 nb_pixel_total : 17432 time to create 1 rle with old method : 0.019838571548461914 time for calcul the mask position with numpy : 0.029177427291870117 nb_pixel_total : 117111 time to create 1 rle with old method : 0.13604211807250977 time for calcul the mask position with numpy : 0.030300378799438477 nb_pixel_total : 316524 time to create 1 rle with new method : 0.623018741607666 time for calcul the mask position with numpy : 0.02876591682434082 nb_pixel_total : 22048 time to create 1 rle with old method : 0.025461673736572266 time for calcul the mask position with numpy : 0.02848362922668457 nb_pixel_total : 15135 time to create 1 rle with old method : 0.0178220272064209 time for calcul the mask position with numpy : 0.029318571090698242 nb_pixel_total : 34272 time to create 1 rle with old method : 0.04008316993713379 time for calcul the mask position with numpy : 0.02921295166015625 nb_pixel_total : 81788 time to create 1 rle with old method : 0.09479475021362305 time for calcul the mask position with numpy : 0.028990507125854492 nb_pixel_total : 14710 time to create 1 rle with old method : 0.01731586456298828 time for calcul the mask position with numpy : 0.03211045265197754 nb_pixel_total : 43868 time to create 1 rle with old method : 0.05154895782470703 time for calcul the mask position with numpy : 0.029444217681884766 nb_pixel_total : 39792 time to create 1 rle with old method : 0.08057641983032227 time for calcul the mask position with numpy : 0.029483795166015625 nb_pixel_total : 21026 time to create 1 rle with old method : 0.024569272994995117 create new chi : 4.317669153213501 time to delete rle : 0.0027904510498046875 batch 1 Loaded 65 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 18 TO DO : save crop sub photo not yet done ! save time : 0.19717645645141602 map_output_result : {1350682783: (0.0, 'Should be the crop_list due to order', 0), 1350682763: (0.0, 'Should be the crop_list due to order', 0), 1350682756: (0.0, 'Should be the crop_list due to order', 0), 1350682732: (0.0, 'Should be the crop_list due to order', 0), 1350682723: (0.0, 'Should be the crop_list due to order', 0), 1350682720: (0.0, 'Should be the crop_list due to order', 0), 1350682715: (0.0, 'Should be the crop_list due to order', 0), 1350682711: (0.0, 'Should be the crop_list due to order', 0), 1350682677: (0.0, 'Should be the crop_list due to order', 0), 1350682673: (0.0, 'Should be the crop_list due to order', 0), 1350682671: (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 [1350682783, 1350682763, 1350682756, 1350682732, 1350682723, 1350682720, 1350682715, 1350682711, 1350682677, 1350682673, 1350682671] Looping around the photos to save general results len do output : 11 /1350682783.Didn't retrieve data . /1350682763.Didn't retrieve data . /1350682756.Didn't retrieve data . /1350682732.Didn't retrieve data . /1350682723.Didn't retrieve data . /1350682720.Didn't retrieve data . /1350682715.Didn't retrieve data . /1350682711.Didn't retrieve data . /1350682677.Didn't retrieve data . /1350682673.Didn't retrieve data . /1350682671.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, '2736603') ('3318', '22184729', '1350682783', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682763', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682756', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682732', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682723', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682720', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682715', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682711', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682677', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682673', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682671', None, None, None, None, None, '2736603') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.017957448959350586 save_final save missing photos in datou_result : time spend for datou_step_exec : 145.82529401779175 time spend to save output : 0.01843714714050293 total time spend for step 3 : 145.84373116493225 step4:ventilate_hashtags_in_portfolio Wed Apr 9 23:13:25 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 : 22184729 get user id for portfolio 22184729 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`=22184729 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','pet_fonce','pehd','metal','autre','mal_croppe','pet_clair','background','carton','flou','environnement')) 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`=22184729 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','pet_fonce','pehd','metal','autre','mal_croppe','pet_clair','background','carton','flou','environnement')) 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`=22184729 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','pet_fonce','pehd','metal','autre','mal_croppe','pet_clair','background','carton','flou','environnement')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/22187737,22187738,22187739,22187740,22187741,22187742,22187743,22187744,22187745,22187746,22187747?tags=papier,pet_fonce,pehd,metal,autre,mal_croppe,pet_clair,background,carton,flou,environnement Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1350682783, 1350682763, 1350682756, 1350682732, 1350682723, 1350682720, 1350682715, 1350682711, 1350682677, 1350682673, 1350682671] Looping around the photos to save general results len do output : 1 /22184729. 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, '2736603') ('3318', '22184729', '1350682783', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682763', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682756', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682732', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682723', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682720', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682715', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682711', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682677', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682673', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682671', None, None, None, None, None, '2736603') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.016669750213623047 save_final save missing photos in datou_result : time spend for datou_step_exec : 3.6640501022338867 time spend to save output : 0.01695537567138672 total time spend for step 4 : 3.6810054779052734 step5:final Wed Apr 9 23:13:28 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 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 : {1350682783: ('0.2218701903636033',), 1350682763: ('0.2218701903636033',), 1350682756: ('0.2218701903636033',), 1350682732: ('0.2218701903636033',), 1350682723: ('0.2218701903636033',), 1350682720: ('0.2218701903636033',), 1350682715: ('0.2218701903636033',), 1350682711: ('0.2218701903636033',), 1350682677: ('0.2218701903636033',), 1350682673: ('0.2218701903636033',), 1350682671: ('0.2218701903636033',)} new output for save of step final : {1350682783: ('0.2218701903636033',), 1350682763: ('0.2218701903636033',), 1350682756: ('0.2218701903636033',), 1350682732: ('0.2218701903636033',), 1350682723: ('0.2218701903636033',), 1350682720: ('0.2218701903636033',), 1350682715: ('0.2218701903636033',), 1350682711: ('0.2218701903636033',), 1350682677: ('0.2218701903636033',), 1350682673: ('0.2218701903636033',), 1350682671: ('0.2218701903636033',)} [1350682783, 1350682763, 1350682756, 1350682732, 1350682723, 1350682720, 1350682715, 1350682711, 1350682677, 1350682673, 1350682671] Looping around the photos to save general results len do output : 11 /1350682783.Didn't retrieve data . /1350682763.Didn't retrieve data . /1350682756.Didn't retrieve data . /1350682732.Didn't retrieve data . /1350682723.Didn't retrieve data . /1350682720.Didn't retrieve data . /1350682715.Didn't retrieve data . /1350682711.Didn't retrieve data . /1350682677.Didn't retrieve data . /1350682673.Didn't retrieve data . /1350682671.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, '2736603') ('3318', '22184729', '1350682783', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682763', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682756', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682732', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682723', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682720', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682715', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682711', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682677', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682673', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682671', None, None, None, None, None, '2736603') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 33 time used for this insertion : 0.015663623809814453 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.1199185848236084 time spend to save output : 0.01620173454284668 total time spend for step 5 : 0.13612031936645508 step6:blur_detection Wed Apr 9 23:13:29 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 toutes les photos sont déjà traitées, on saute les calculs 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 : 11 time used for this insertion : 0.011717796325683594 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 11 time used for this insertion : 0.010943412780761719 save missing photos in datou_result : time spend for datou_step_exec : 0.027526140213012695 time spend to save output : 0.02805924415588379 total time spend for step 6 : 0.055585384368896484 step7:brightness Wed Apr 9 23:13:29 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 toutes les photos sont déjà traitées, on saute les calculs 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 : 11 time used for this insertion : 0.011869430541992188 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 11 time used for this insertion : 0.01194906234741211 save missing photos in datou_result : time spend for datou_step_exec : 0.03381752967834473 time spend to save output : 0.029343128204345703 total time spend for step 7 : 0.06316065788269043 step8:velours_tree Wed Apr 9 23:13:29 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 : 0.15121817588806152 time spend to save output : 3.457069396972656e-05 total time spend for step 8 : 0.15125274658203125 step9:send_mail_cod Wed Apr 9 23:13:29 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 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_P22184729_09-04-2025_23_13_29.pdf 22187737 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 .imagette221877371744233209 22187738 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 .imagette221877381744233210 22187739 imagette221877391744233211 22187740 imagette221877401744233211 22187741 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 .imagette221877411744233211 22187742 imagette221877421744233212 22187743 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 .imagette221877431744233212 22187744 imagette221877441744233214 22187745 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 .imagette221877451744233214 22187746 imagette221877461744233215 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=22184729 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/22187737,22187738,22187739,22187740,22187741,22187742,22187743,22187744,22187745,22187746,22187747?tags=papier,pet_fonce,pehd,metal,autre,mal_croppe,pet_clair,background,carton,flou,environnement args[1350682783] : ((1350682783, -7.074410273787239, 492609224), (1350682783, -0.042342848826188925, 2107752395), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682763] : ((1350682763, -5.4960188305663005, 492609224), (1350682763, -0.019213704194043838, 2107752395), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682756] : ((1350682756, -3.703724910983021, 492609224), (1350682756, -0.0481292084411889, 2107752395), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682732] : ((1350682732, -7.15907927564055, 492609224), (1350682732, -0.057633892907794894, 2107752395), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682723] : ((1350682723, -4.358681461112972, 492609224), (1350682723, -0.042476463793426116, 2107752395), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682720] : ((1350682720, -3.9486874574222193, 492609224), (1350682720, 0.05154869214436544, 2107752395), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682715] : ((1350682715, -6.127723215322038, 492609224), (1350682715, -0.2243804290170739, 496442774), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682711] : ((1350682711, -6.546930996767882, 492609224), (1350682711, -0.23741852299956814, 496442774), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682677] : ((1350682677, -4.167078762988099, 492609224), (1350682677, -0.1782390407293428, 496442774), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682673] : ((1350682673, -5.605520769984973, 492609224), (1350682673, 0.08103424609450412, 2107752395), '0.2218701903636033') We are sending mail with results at report@fotonower.com args[1350682671] : ((1350682671, -7.328593711695289, 492609224), (1350682671, 0.04667602691853358, 2107752395), '0.2218701903636033') We are sending mail with results at report@fotonower.com refus_total : 0.2218701903636033 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=22184729 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1350682673,1350682720,1350682732,1350682763,1350682671,1350682677,1350682711,1350682715,1350682723,1350682756,1350682783) Found this number of photos: 11 begin to download photo : 1350682673 begin to download photo : 1350682763 begin to download photo : 1350682711 begin to download photo : 1350682756 download finish for photo 1350682763 begin to download photo : 1350682671 download finish for photo 1350682756 begin to download photo : 1350682783 download finish for photo 1350682711 begin to download photo : 1350682715 download finish for photo 1350682673 begin to download photo : 1350682720 download finish for photo 1350682783 download finish for photo 1350682671 begin to download photo : 1350682677 download finish for photo 1350682715 begin to download photo : 1350682723 download finish for photo 1350682720 begin to download photo : 1350682732 download finish for photo 1350682677 download finish for photo 1350682723 download finish for photo 1350682732 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22184729_09-04-2025_23_13_29.pdf results_Auto_P22184729_09-04-2025_23_13_29.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22184729_09-04-2025_23_13_29.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','22184729','results_Auto_P22184729_09-04-2025_23_13_29.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22184729_09-04-2025_23_13_29.pdf','pdf','','0.75','0.2218701903636033') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/22184729

https://www.fotonower.com/image?json=false&list_photos_id=1350682783
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682763
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682756
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682732
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682723
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682720
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682715
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682711
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682677
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682673
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1350682671
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 22.19%
Veuillez trouver les photos des contaminants.

exemples de contaminants: papier: https://www.fotonower.com/view/22187737?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/22187738?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/22187741?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/22187743?limit=200
exemples de contaminants: carton: https://www.fotonower.com/view/22187745?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22184729_09-04-2025_23_13_29.pdf.

Lien vers velours :https://www.fotonower.com/velours/22187737,22187738,22187739,22187740,22187741,22187742,22187743,22187744,22187745,22187746,22187747?tags=papier,pet_fonce,pehd,metal,autre,mal_croppe,pet_clair,background,carton,flou,environnement.


L'équipe Fotonower 202 b'' Server: nginx Date: Wed, 09 Apr 2025 21:13:42 GMT Content-Length: 0 Connection: close X-Message-Id: KU8qR7K3Sf6rIQtEhArDHw 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 [1350682783, 1350682763, 1350682756, 1350682732, 1350682723, 1350682720, 1350682715, 1350682711, 1350682677, 1350682673, 1350682671] 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, '2736603') ('3318', '22184729', '1350682783', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682763', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682756', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682732', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682723', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682720', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682715', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682711', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682677', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682673', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682671', None, None, None, None, None, '2736603') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.018430709838867188 save_final save missing photos in datou_result : time spend for datou_step_exec : 13.517150163650513 time spend to save output : 0.01874375343322754 total time spend for step 9 : 13.53589391708374 step10:split_time_score Wed Apr 9 23:13:42 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 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'}] (('07', 11),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 09042025 22184729 Nombre de photos uploadées : 11 / 23040 (0%) 09042025 22184729 Nombre de photos taguées (types de déchets): 0 / 11 (0%) 09042025 22184729 Nombre de photos taguées (volume) : 0 / 11 (0%) elapsed_time : load_data_split_time_score 1.1920928955078125e-06 elapsed_time : order_list_meta_photo_and_scores 5.0067901611328125e-06 ??????????? elapsed_time : fill_and_build_computed_from_old_data 0.000553131103515625 elapsed_time : insert_dashboard_record_day_entry 0.028918743133544922 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 : 0.2218701903636033 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22184729_09-04-2025_23_13_29.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22184729 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`=22184729 AND mptpi.`type`=3594 To do Qualite : 0.04609045052441947 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161067_09-04-2025_12_22_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161067 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161067 AND mptpi.`type`=3726 To do Qualite : 0.1886047378056162 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161073_09-04-2025_12_52_49.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161073 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161073 AND mptpi.`type`=3594 To do Qualite : 0.22379874656749282 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161075_09-04-2025_12_27_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161075 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161075 AND mptpi.`type`=3594 To do Qualite : 0.0944780203179495 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22161654_09-04-2025_13_05_02.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22161654 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22161654 AND mptpi.`type`=3726 To do Qualite : 0.23583165253948815 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163334_09-04-2025_14_48_45.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22163334 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22163334 AND mptpi.`type`=3594 To do Qualite : 0.16103178757035222 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22163336_09-04-2025_14_36_47.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22163336 order by id desc limit 1 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=22163336 AND mptpi.`type`=3594 To do Qualite : 0.2097409504444585 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22172049_09-04-2025_18_16_26.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22172049 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`=22172049 AND mptpi.`type`=3594 To do Qualite : 0.18248340045598005 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22175831_09-04-2025_19_40_53.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22175831 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`=22175831 AND mptpi.`type`=3594 To do Qualite : 0.0746421277879351 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22175833_09-04-2025_19_38_55.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22175833 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`=22175833 AND mptpi.`type`=3726 To do find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22184771 order by id desc limit 1 Qualite : 0.23301532588668714 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22184790_09-04-2025_22_55_42.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22184790 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`=22184790 AND mptpi.`type`=3726 To do Qualite : 0.23131665257731168 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22184803_09-04-2025_23_00_25.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22184803 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`=22184803 AND mptpi.`type`=3594 To do Qualite : 0.15197172956759866 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P22184812_09-04-2025_22_49_12.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 22184812 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`=22184812 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'09042025': {'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 [1350682783, 1350682763, 1350682756, 1350682732, 1350682723, 1350682720, 1350682715, 1350682711, 1350682677, 1350682673, 1350682671] Looping around the photos to save general results len do output : 1 /22184729Didn'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, '2736603') ('3318', '22184729', '1350682783', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682763', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682756', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682732', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682723', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682720', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682715', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682711', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682677', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682673', None, None, None, None, None, '2736603') ('3318', None, None, None, None, None, None, None, '2736603') ('3318', '22184729', '1350682671', None, None, None, None, None, '2736603') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.018929243087768555 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.0346648693084717 time spend to save output : 0.01920485496520996 total time spend for step 10 : 2.0538697242736816 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 375.68user 123.91system 13:19.42elapsed 62%CPU (0avgtext+0avgdata 8144440maxresident)k 2255016inputs+408296outputs (61143major+32871005minor)pagefaults 0swaps