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 : 2145890 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 : ['2606877'] with mtr_portfolio_ids : ['20744710'] and first list_photo_ids : [] new path : /proc/2145890/ 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 , BFBFBFBFBFBFBFBFBFBFBFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 12 ; length of list_pids : 12 ; length of list_args : 12 time to download the photos : 2.308248996734619 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 Sat Feb 22 04:30:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-02-22 04:30:33.848241: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-02-22 04:30:33.875059: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-02-22 04:30:33.877063: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5560000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-02-22 04:30:33.877096: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-02-22 04:30:33.881350: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-02-22 04:30:34.131188: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x790d3f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-02-22 04:30:34.131245: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-02-22 04:30:34.132566: 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 2025-02-22 04:30:34.133019: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-22 04:30:34.136296: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-22 04:30:34.141569: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-22 04:30:34.142001: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-22 04:30:34.170059: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-22 04:30:34.175262: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-22 04:30:34.193894: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-22 04:30:34.195841: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-22 04:30:34.196207: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-22 04:30:34.197261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-22 04:30:34.197282: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-22 04:30:34.197295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-22 04:30:34.199160: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-02-22 04:30:34.475637: 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 2025-02-22 04:30:34.475761: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-22 04:30:34.475807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-22 04:30:34.475830: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-22 04:30:34.475848: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-22 04:30:34.475866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-22 04:30:34.475884: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-22 04:30:34.475901: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-22 04:30:34.477738: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-22 04:30:34.479599: 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 2025-02-22 04:30:34.479670: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-02-22 04:30:34.479690: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-22 04:30:34.479708: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-02-22 04:30:34.479725: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-02-22 04:30:34.479742: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-02-22 04:30:34.479759: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-02-22 04:30:34.479776: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-02-22 04:30:34.481249: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-02-22 04:30:34.481279: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-02-22 04:30:34.481288: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-02-22 04:30:34.481296: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-02-22 04:30:34.482722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl2847 thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5275, 'learn_RUBBIA_REFUS_AMIENS_23', 16384, 25088, 'learn_RUBBIA_REFUS_AMIENS_23', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2021, 4, 23, 14, 19, 39), datetime.datetime(2021, 4, 23, 14, 19, 39)) {'thcl': {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'], 'list_hashtags_csv': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'svm_hashtag_type_desc': 5275, 'photo_desc_type': 5275, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME learn_RUBBIA_REFUS_AMIENS_23 NUM_CLASSES 9 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : learn_RUBBIA_REFUS_AMIENS_23 model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-02-22 04:30:44.422615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-02-22 04:30:44.925900: 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 : 12 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 40 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 55 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 76 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 14 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 : 46 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 : 18 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 100 NEW PHOTO Processing 1 images image shape: (2160, 3264, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 17) min: 0.00000 max: 3264.00000 nb d'objets trouves : 65 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 : 21 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 : 11 Detection mask done ! Trying to reset tf kernel 2146615 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 tf kernel not reseted sub process len(results) : 12 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 12 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10814 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl2847 Catched exception ! Connect or reconnect ! thcls : [{'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'}] thcl {'id': 2847, 'mtr_user_id': 31, 'name': 'learn_RUBBIA_REFUS_AMIENS_23', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'background,papier,carton,metal,pet_clair,autre,pehd,pet_fonce,environnement', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 3594, 'photo_desc_type': 5275, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 5275 ['background', 'papier', 'carton', 'metal', 'pet_clair', 'autre', 'pehd', 'pet_fonce', 'environnement'] time for calcul the mask position with numpy : 0.004797220230102539 nb_pixel_total : 173255 time to create 1 rle with new method : 0.013833284378051758 length of segment : 639 time for calcul the mask position with numpy : 0.0007078647613525391 nb_pixel_total : 29851 time to create 1 rle with old method : 0.034484148025512695 length of segment : 335 time for calcul the mask position with numpy : 0.0006411075592041016 nb_pixel_total : 36521 time to create 1 rle with old method : 0.040663957595825195 length of segment : 197 time for calcul the mask position with numpy : 0.0007443428039550781 nb_pixel_total : 35233 time to create 1 rle with old method : 0.040770769119262695 length of segment : 209 time for calcul the mask position with numpy : 0.001636505126953125 nb_pixel_total : 101626 time to create 1 rle with old method : 0.1141214370727539 length of segment : 572 time for calcul the mask position with numpy : 0.001547098159790039 nb_pixel_total : 104013 time to create 1 rle with old method : 0.11345195770263672 length of segment : 390 time for calcul the mask position with numpy : 0.0015892982482910156 nb_pixel_total : 90263 time to create 1 rle with old method : 0.09963679313659668 length of segment : 522 time for calcul the mask position with numpy : 0.0005557537078857422 nb_pixel_total : 36540 time to create 1 rle with old method : 0.03981208801269531 length of segment : 147 time for calcul the mask position with numpy : 0.0002636909484863281 nb_pixel_total : 12436 time to create 1 rle with old method : 0.01642632484436035 length of segment : 135 time for calcul the mask position with numpy : 0.0038099288940429688 nb_pixel_total : 194945 time to create 1 rle with new method : 0.01212763786315918 length of segment : 502 time for calcul the mask position with numpy : 0.0077359676361083984 nb_pixel_total : 237805 time to create 1 rle with new method : 0.015246868133544922 length of segment : 769 time for calcul the mask position with numpy : 0.0026760101318359375 nb_pixel_total : 162220 time to create 1 rle with new method : 0.00834512710571289 length of segment : 414 time for calcul the mask position with numpy : 0.0008268356323242188 nb_pixel_total : 56906 time to create 1 rle with old method : 0.0650014877319336 length of segment : 223 time for calcul the mask position with numpy : 0.0016880035400390625 nb_pixel_total : 100116 time to create 1 rle with old method : 0.11068129539489746 length of segment : 343 time for calcul the mask position with numpy : 0.002714395523071289 nb_pixel_total : 160757 time to create 1 rle with new method : 0.01063394546508789 length of segment : 673 time for calcul the mask position with numpy : 0.0007662773132324219 nb_pixel_total : 54805 time to create 1 rle with old method : 0.05951833724975586 length of segment : 302 time for calcul the mask position with numpy : 0.0003962516784667969 nb_pixel_total : 28765 time to create 1 rle with old method : 0.03160810470581055 length of segment : 197 time for calcul the mask position with numpy : 0.0004367828369140625 nb_pixel_total : 25915 time to create 1 rle with old method : 0.028312206268310547 length of segment : 211 time for calcul the mask position with numpy : 0.0009257793426513672 nb_pixel_total : 69690 time to create 1 rle with old method : 0.0748894214630127 length of segment : 222 time for calcul the mask position with numpy : 0.0009405612945556641 nb_pixel_total : 66732 time to create 1 rle with old method : 0.07636117935180664 length of segment : 294 time for calcul the mask position with numpy : 0.0009398460388183594 nb_pixel_total : 67687 time to create 1 rle with old method : 0.07462811470031738 length of segment : 298 time for calcul the mask position with numpy : 0.0005843639373779297 nb_pixel_total : 29956 time to create 1 rle with old method : 0.03361392021179199 length of segment : 206 time for calcul the mask position with numpy : 0.0013272762298583984 nb_pixel_total : 67712 time to create 1 rle with old method : 0.07982850074768066 length of segment : 378 time for calcul the mask position with numpy : 0.002991199493408203 nb_pixel_total : 125605 time to create 1 rle with old method : 0.14268946647644043 length of segment : 675 time for calcul the mask position with numpy : 0.0015614032745361328 nb_pixel_total : 81984 time to create 1 rle with old method : 0.09155464172363281 length of segment : 356 time for calcul the mask position with numpy : 0.0004477500915527344 nb_pixel_total : 20702 time to create 1 rle with old method : 0.02319622039794922 length of segment : 201 time for calcul the mask position with numpy : 0.0008845329284667969 nb_pixel_total : 36273 time to create 1 rle with old method : 0.044004201889038086 length of segment : 377 time for calcul the mask position with numpy : 0.002282381057739258 nb_pixel_total : 122069 time to create 1 rle with old method : 0.12998247146606445 length of segment : 636 time for calcul the mask position with numpy : 0.0002551078796386719 nb_pixel_total : 12841 time to create 1 rle with old method : 0.014583826065063477 length of segment : 120 time for calcul the mask position with numpy : 0.00012350082397460938 nb_pixel_total : 5047 time to create 1 rle with old method : 0.005753993988037109 length of segment : 82 time for calcul the mask position with numpy : 0.0005278587341308594 nb_pixel_total : 27968 time to create 1 rle with old method : 0.03121781349182129 length of segment : 269 time for calcul the mask position with numpy : 0.01560068130493164 nb_pixel_total : 314975 time to create 1 rle with new method : 0.05413532257080078 length of segment : 1809 time for calcul the mask position with numpy : 0.0012919902801513672 nb_pixel_total : 23216 time to create 1 rle with old method : 0.025482177734375 length of segment : 335 time for calcul the mask position with numpy : 0.01032114028930664 nb_pixel_total : 181033 time to create 1 rle with new method : 0.015975236892700195 length of segment : 514 time for calcul the mask position with numpy : 0.006959199905395508 nb_pixel_total : 101028 time to create 1 rle with old method : 0.11886715888977051 length of segment : 653 time for calcul the mask position with numpy : 0.0006244182586669922 nb_pixel_total : 6868 time to create 1 rle with old method : 0.008708715438842773 length of segment : 127 time for calcul the mask position with numpy : 0.8385918140411377 nb_pixel_total : 527750 time to create 1 rle with new method : 0.054428815841674805 length of segment : 1116 time for calcul the mask position with numpy : 0.0013420581817626953 nb_pixel_total : 30164 time to create 1 rle with old method : 0.03728318214416504 length of segment : 249 time for calcul the mask position with numpy : 0.0008127689361572266 nb_pixel_total : 14067 time to create 1 rle with old method : 0.016258955001831055 length of segment : 235 time for calcul the mask position with numpy : 0.0002129077911376953 nb_pixel_total : 5027 time to create 1 rle with old method : 0.006388425827026367 length of segment : 91 time for calcul the mask position with numpy : 0.0005450248718261719 nb_pixel_total : 10191 time to create 1 rle with old method : 0.011925220489501953 length of segment : 154 time for calcul the mask position with numpy : 0.001760721206665039 nb_pixel_total : 102837 time to create 1 rle with old method : 0.11787724494934082 length of segment : 511 time for calcul the mask position with numpy : 0.0032110214233398438 nb_pixel_total : 82826 time to create 1 rle with old method : 0.09497857093811035 length of segment : 417 time for calcul the mask position with numpy : 0.0004220008850097656 nb_pixel_total : 6829 time to create 1 rle with old method : 0.00821065902709961 length of segment : 91 time for calcul the mask position with numpy : 0.0002808570861816406 nb_pixel_total : 5059 time to create 1 rle with old method : 0.005711555480957031 length of segment : 96 time for calcul the mask position with numpy : 0.0001404285430908203 nb_pixel_total : 6437 time to create 1 rle with old method : 0.009308338165283203 length of segment : 89 time for calcul the mask position with numpy : 0.0017862319946289062 nb_pixel_total : 29522 time to create 1 rle with old method : 0.032533884048461914 length of segment : 236 time for calcul the mask position with numpy : 0.0008831024169921875 nb_pixel_total : 22757 time to create 1 rle with old method : 0.026567697525024414 length of segment : 365 time for calcul the mask position with numpy : 0.0006167888641357422 nb_pixel_total : 16944 time to create 1 rle with old method : 0.019861459732055664 length of segment : 131 time for calcul the mask position with numpy : 0.0010216236114501953 nb_pixel_total : 25732 time to create 1 rle with old method : 0.02922368049621582 length of segment : 252 time for calcul the mask position with numpy : 0.002562999725341797 nb_pixel_total : 58283 time to create 1 rle with old method : 0.06374859809875488 length of segment : 291 time for calcul the mask position with numpy : 0.002367258071899414 nb_pixel_total : 46400 time to create 1 rle with old method : 0.05289745330810547 length of segment : 306 time for calcul the mask position with numpy : 0.005658626556396484 nb_pixel_total : 184560 time to create 1 rle with new method : 0.008558034896850586 length of segment : 707 time for calcul the mask position with numpy : 0.0006301403045654297 nb_pixel_total : 10151 time to create 1 rle with old method : 0.014869451522827148 length of segment : 166 time for calcul the mask position with numpy : 0.00041961669921875 nb_pixel_total : 8083 time to create 1 rle with old method : 0.01340174674987793 length of segment : 101 time for calcul the mask position with numpy : 0.0003657341003417969 nb_pixel_total : 5888 time to create 1 rle with old method : 0.010043621063232422 length of segment : 137 time for calcul the mask position with numpy : 0.0010480880737304688 nb_pixel_total : 10887 time to create 1 rle with old method : 0.01973724365234375 length of segment : 127 time for calcul the mask position with numpy : 0.0022857189178466797 nb_pixel_total : 32896 time to create 1 rle with old method : 0.12459826469421387 length of segment : 214 time for calcul the mask position with numpy : 0.0019617080688476562 nb_pixel_total : 25089 time to create 1 rle with old method : 0.10777497291564941 length of segment : 196 time for calcul the mask position with numpy : 0.0017015933990478516 nb_pixel_total : 23378 time to create 1 rle with old method : 0.04686403274536133 length of segment : 249 time for calcul the mask position with numpy : 0.001997709274291992 nb_pixel_total : 14720 time to create 1 rle with old method : 0.035103559494018555 length of segment : 176 time for calcul the mask position with numpy : 0.003032207489013672 nb_pixel_total : 36627 time to create 1 rle with old method : 0.06682991981506348 length of segment : 194 time for calcul the mask position with numpy : 0.0016062259674072266 nb_pixel_total : 17361 time to create 1 rle with old method : 0.03261566162109375 length of segment : 166 time for calcul the mask position with numpy : 0.0016984939575195312 nb_pixel_total : 16950 time to create 1 rle with old method : 0.03172159194946289 length of segment : 215 time for calcul the mask position with numpy : 0.007640361785888672 nb_pixel_total : 137510 time to create 1 rle with old method : 0.2228834629058838 length of segment : 492 time for calcul the mask position with numpy : 0.0005393028259277344 nb_pixel_total : 5788 time to create 1 rle with old method : 0.007047414779663086 length of segment : 96 time for calcul the mask position with numpy : 0.0010290145874023438 nb_pixel_total : 11367 time to create 1 rle with old method : 0.015896320343017578 length of segment : 142 time for calcul the mask position with numpy : 0.0003387928009033203 nb_pixel_total : 4912 time to create 1 rle with old method : 0.00897836685180664 length of segment : 68 time for calcul the mask position with numpy : 0.012278556823730469 nb_pixel_total : 136576 time to create 1 rle with old method : 0.165513277053833 length of segment : 552 time for calcul the mask position with numpy : 0.0010843276977539062 nb_pixel_total : 12994 time to create 1 rle with old method : 0.017386674880981445 length of segment : 185 time for calcul the mask position with numpy : 0.0009644031524658203 nb_pixel_total : 14312 time to create 1 rle with old method : 0.017737865447998047 length of segment : 156 time for calcul the mask position with numpy : 0.0006322860717773438 nb_pixel_total : 10974 time to create 1 rle with old method : 0.012374639511108398 length of segment : 138 time for calcul the mask position with numpy : 0.002707958221435547 nb_pixel_total : 39886 time to create 1 rle with old method : 0.04387474060058594 length of segment : 232 time for calcul the mask position with numpy : 0.0003743171691894531 nb_pixel_total : 7984 time to create 1 rle with old method : 0.009477615356445312 length of segment : 101 time for calcul the mask position with numpy : 0.0005168914794921875 nb_pixel_total : 8073 time to create 1 rle with old method : 0.009809732437133789 length of segment : 89 time for calcul the mask position with numpy : 0.0024302005767822266 nb_pixel_total : 42799 time to create 1 rle with old method : 0.060028076171875 length of segment : 371 time for calcul the mask position with numpy : 0.0006852149963378906 nb_pixel_total : 9735 time to create 1 rle with old method : 0.01167917251586914 length of segment : 167 time for calcul the mask position with numpy : 0.0037572383880615234 nb_pixel_total : 68216 time to create 1 rle with old method : 0.07587170600891113 length of segment : 373 time for calcul the mask position with numpy : 0.0020084381103515625 nb_pixel_total : 20029 time to create 1 rle with old method : 0.024306535720825195 length of segment : 311 time for calcul the mask position with numpy : 0.0004725456237792969 nb_pixel_total : 7417 time to create 1 rle with old method : 0.00907278060913086 length of segment : 145 time for calcul the mask position with numpy : 0.0005471706390380859 nb_pixel_total : 6498 time to create 1 rle with old method : 0.008397102355957031 length of segment : 64 time for calcul the mask position with numpy : 0.0023975372314453125 nb_pixel_total : 27829 time to create 1 rle with old method : 0.03312873840332031 length of segment : 153 time for calcul the mask position with numpy : 0.0007269382476806641 nb_pixel_total : 10602 time to create 1 rle with old method : 0.013139486312866211 length of segment : 103 time for calcul the mask position with numpy : 0.00014162063598632812 nb_pixel_total : 5599 time to create 1 rle with old method : 0.0067598819732666016 length of segment : 118 time for calcul the mask position with numpy : 0.0002944469451904297 nb_pixel_total : 7237 time to create 1 rle with old method : 0.008904457092285156 length of segment : 102 time for calcul the mask position with numpy : 0.00012135505676269531 nb_pixel_total : 4784 time to create 1 rle with old method : 0.005707979202270508 length of segment : 90 time for calcul the mask position with numpy : 0.0036008358001708984 nb_pixel_total : 63629 time to create 1 rle with old method : 0.07591843605041504 length of segment : 282 time for calcul the mask position with numpy : 0.0015137195587158203 nb_pixel_total : 24554 time to create 1 rle with old method : 0.028715848922729492 length of segment : 184 time for calcul the mask position with numpy : 0.0003688335418701172 nb_pixel_total : 5861 time to create 1 rle with old method : 0.007098674774169922 length of segment : 94 time for calcul the mask position with numpy : 0.00026106834411621094 nb_pixel_total : 8576 time to create 1 rle with old method : 0.01032710075378418 length of segment : 83 time for calcul the mask position with numpy : 0.00016164779663085938 nb_pixel_total : 4098 time to create 1 rle with old method : 0.005316495895385742 length of segment : 70 time for calcul the mask position with numpy : 0.008472204208374023 nb_pixel_total : 164813 time to create 1 rle with new method : 0.011806249618530273 length of segment : 536 time for calcul the mask position with numpy : 0.0002448558807373047 nb_pixel_total : 5969 time to create 1 rle with old method : 0.007564544677734375 length of segment : 102 time for calcul the mask position with numpy : 0.0012497901916503906 nb_pixel_total : 20588 time to create 1 rle with old method : 0.024775028228759766 length of segment : 178 time for calcul the mask position with numpy : 0.0009992122650146484 nb_pixel_total : 16092 time to create 1 rle with old method : 0.018999338150024414 length of segment : 128 time for calcul the mask position with numpy : 0.0001876354217529297 nb_pixel_total : 5655 time to create 1 rle with old method : 0.0067768096923828125 length of segment : 101 time for calcul the mask position with numpy : 0.0015091896057128906 nb_pixel_total : 35543 time to create 1 rle with old method : 0.04299449920654297 length of segment : 214 time for calcul the mask position with numpy : 0.017077207565307617 nb_pixel_total : 463670 time to create 1 rle with new method : 0.04177117347717285 length of segment : 874 time for calcul the mask position with numpy : 0.017905235290527344 nb_pixel_total : 461628 time to create 1 rle with new method : 0.033832550048828125 length of segment : 867 time for calcul the mask position with numpy : 0.0014426708221435547 nb_pixel_total : 31192 time to create 1 rle with old method : 0.04071784019470215 length of segment : 175 time for calcul the mask position with numpy : 0.00022673606872558594 nb_pixel_total : 3782 time to create 1 rle with old method : 0.004770040512084961 length of segment : 59 time for calcul the mask position with numpy : 0.0014145374298095703 nb_pixel_total : 28141 time to create 1 rle with old method : 0.03280186653137207 length of segment : 232 time for calcul the mask position with numpy : 0.0017113685607910156 nb_pixel_total : 44756 time to create 1 rle with old method : 0.05134868621826172 length of segment : 460 time for calcul the mask position with numpy : 0.01854729652404785 nb_pixel_total : 541593 time to create 1 rle with new method : 0.6189742088317871 length of segment : 849 time for calcul the mask position with numpy : 0.0027709007263183594 nb_pixel_total : 79883 time to create 1 rle with old method : 0.09166097640991211 length of segment : 429 time for calcul the mask position with numpy : 0.001077413558959961 nb_pixel_total : 19034 time to create 1 rle with old method : 0.021353483200073242 length of segment : 229 time for calcul the mask position with numpy : 0.0015025138854980469 nb_pixel_total : 17549 time to create 1 rle with old method : 0.021499156951904297 length of segment : 222 time for calcul the mask position with numpy : 0.0012240409851074219 nb_pixel_total : 15038 time to create 1 rle with old method : 0.017586946487426758 length of segment : 121 time for calcul the mask position with numpy : 0.015796184539794922 nb_pixel_total : 236351 time to create 1 rle with new method : 0.01888132095336914 length of segment : 710 time for calcul the mask position with numpy : 0.0020542144775390625 nb_pixel_total : 26735 time to create 1 rle with old method : 0.0294802188873291 length of segment : 284 time for calcul the mask position with numpy : 0.0025489330291748047 nb_pixel_total : 42892 time to create 1 rle with old method : 0.04848599433898926 length of segment : 277 time for calcul the mask position with numpy : 0.0006775856018066406 nb_pixel_total : 22551 time to create 1 rle with old method : 0.025274991989135742 length of segment : 172 time for calcul the mask position with numpy : 0.0013802051544189453 nb_pixel_total : 22712 time to create 1 rle with old method : 0.02526235580444336 length of segment : 268 time for calcul the mask position with numpy : 0.0009236335754394531 nb_pixel_total : 9219 time to create 1 rle with old method : 0.010243415832519531 length of segment : 173 time for calcul the mask position with numpy : 0.0016875267028808594 nb_pixel_total : 32740 time to create 1 rle with old method : 0.036043405532836914 length of segment : 283 time for calcul the mask position with numpy : 0.0010454654693603516 nb_pixel_total : 25499 time to create 1 rle with old method : 0.029142379760742188 length of segment : 176 time for calcul the mask position with numpy : 0.005075931549072266 nb_pixel_total : 100963 time to create 1 rle with old method : 0.11765384674072266 length of segment : 462 time for calcul the mask position with numpy : 0.0015113353729248047 nb_pixel_total : 22888 time to create 1 rle with old method : 0.026239633560180664 length of segment : 263 time for calcul the mask position with numpy : 0.001939535140991211 nb_pixel_total : 26304 time to create 1 rle with old method : 0.029065847396850586 length of segment : 219 time for calcul the mask position with numpy : 0.0018229484558105469 nb_pixel_total : 26586 time to create 1 rle with old method : 0.02909541130065918 length of segment : 252 time for calcul the mask position with numpy : 0.0017116069793701172 nb_pixel_total : 23610 time to create 1 rle with old method : 0.025464534759521484 length of segment : 160 time for calcul the mask position with numpy : 0.001338958740234375 nb_pixel_total : 14259 time to create 1 rle with old method : 0.015833377838134766 length of segment : 173 time for calcul the mask position with numpy : 0.0011749267578125 nb_pixel_total : 13334 time to create 1 rle with old method : 0.01541447639465332 length of segment : 179 time for calcul the mask position with numpy : 0.0016078948974609375 nb_pixel_total : 30991 time to create 1 rle with old method : 0.03522300720214844 length of segment : 300 time for calcul the mask position with numpy : 0.003412961959838867 nb_pixel_total : 52993 time to create 1 rle with old method : 0.06195688247680664 length of segment : 362 time for calcul the mask position with numpy : 0.0005013942718505859 nb_pixel_total : 5266 time to create 1 rle with old method : 0.006500720977783203 length of segment : 116 time for calcul the mask position with numpy : 0.004123687744140625 nb_pixel_total : 41432 time to create 1 rle with old method : 0.04828596115112305 length of segment : 271 time for calcul the mask position with numpy : 0.0021696090698242188 nb_pixel_total : 32730 time to create 1 rle with old method : 0.03858542442321777 length of segment : 162 time for calcul the mask position with numpy : 0.0015511512756347656 nb_pixel_total : 6858 time to create 1 rle with old method : 0.008596658706665039 length of segment : 251 time for calcul the mask position with numpy : 0.0011386871337890625 nb_pixel_total : 15341 time to create 1 rle with old method : 0.018161773681640625 length of segment : 127 time for calcul the mask position with numpy : 0.0013358592987060547 nb_pixel_total : 12098 time to create 1 rle with old method : 0.014923334121704102 length of segment : 210 time for calcul the mask position with numpy : 0.002441883087158203 nb_pixel_total : 30484 time to create 1 rle with old method : 0.03661847114562988 length of segment : 318 time for calcul the mask position with numpy : 0.0013442039489746094 nb_pixel_total : 15345 time to create 1 rle with old method : 0.01966118812561035 length of segment : 160 time for calcul the mask position with numpy : 0.00039196014404296875 nb_pixel_total : 3145 time to create 1 rle with old method : 0.003822803497314453 length of segment : 105 time for calcul the mask position with numpy : 0.002955913543701172 nb_pixel_total : 24759 time to create 1 rle with old method : 0.02892446517944336 length of segment : 334 time for calcul the mask position with numpy : 0.002134561538696289 nb_pixel_total : 18022 time to create 1 rle with old method : 0.022485733032226562 length of segment : 212 time for calcul the mask position with numpy : 0.0016853809356689453 nb_pixel_total : 17157 time to create 1 rle with old method : 0.01983189582824707 length of segment : 197 time for calcul the mask position with numpy : 0.0008921623229980469 nb_pixel_total : 10885 time to create 1 rle with old method : 0.012848138809204102 length of segment : 154 time for calcul the mask position with numpy : 0.0004627704620361328 nb_pixel_total : 6467 time to create 1 rle with old method : 0.0076732635498046875 length of segment : 94 time for calcul the mask position with numpy : 0.0012288093566894531 nb_pixel_total : 18882 time to create 1 rle with old method : 0.022095203399658203 length of segment : 367 time for calcul the mask position with numpy : 0.003217458724975586 nb_pixel_total : 38074 time to create 1 rle with old method : 0.04358530044555664 length of segment : 232 time for calcul the mask position with numpy : 0.0074405670166015625 nb_pixel_total : 72063 time to create 1 rle with old method : 0.0812690258026123 length of segment : 320 time for calcul the mask position with numpy : 0.0007212162017822266 nb_pixel_total : 11681 time to create 1 rle with old method : 0.014017105102539062 length of segment : 111 time for calcul the mask position with numpy : 0.001285552978515625 nb_pixel_total : 20545 time to create 1 rle with old method : 0.024174213409423828 length of segment : 161 time for calcul the mask position with numpy : 0.001979827880859375 nb_pixel_total : 28562 time to create 1 rle with old method : 0.03341317176818848 length of segment : 301 time for calcul the mask position with numpy : 0.003780841827392578 nb_pixel_total : 54938 time to create 1 rle with old method : 0.06411290168762207 length of segment : 392 time for calcul the mask position with numpy : 0.0013453960418701172 nb_pixel_total : 18093 time to create 1 rle with old method : 0.020948410034179688 length of segment : 190 time for calcul the mask position with numpy : 0.0011260509490966797 nb_pixel_total : 20178 time to create 1 rle with old method : 0.023468494415283203 length of segment : 230 time for calcul the mask position with numpy : 0.007586956024169922 nb_pixel_total : 53886 time to create 1 rle with old method : 0.06182670593261719 length of segment : 399 time for calcul the mask position with numpy : 0.0008008480072021484 nb_pixel_total : 4781 time to create 1 rle with old method : 0.005867719650268555 length of segment : 126 time for calcul the mask position with numpy : 0.0009768009185791016 nb_pixel_total : 15156 time to create 1 rle with old method : 0.017746448516845703 length of segment : 117 time for calcul the mask position with numpy : 0.003709554672241211 nb_pixel_total : 43402 time to create 1 rle with old method : 0.0515904426574707 length of segment : 256 time for calcul the mask position with numpy : 0.0014219284057617188 nb_pixel_total : 14592 time to create 1 rle with old method : 0.01839447021484375 length of segment : 143 time for calcul the mask position with numpy : 0.0031485557556152344 nb_pixel_total : 26749 time to create 1 rle with old method : 0.03456234931945801 length of segment : 243 time for calcul the mask position with numpy : 0.0016832351684570312 nb_pixel_total : 15236 time to create 1 rle with old method : 0.01874518394470215 length of segment : 176 time for calcul the mask position with numpy : 0.004106044769287109 nb_pixel_total : 44649 time to create 1 rle with old method : 0.061551809310913086 length of segment : 377 time for calcul the mask position with numpy : 0.003991603851318359 nb_pixel_total : 42133 time to create 1 rle with old method : 0.06372642517089844 length of segment : 320 time for calcul the mask position with numpy : 0.0008013248443603516 nb_pixel_total : 3717 time to create 1 rle with old method : 0.004913806915283203 length of segment : 199 time for calcul the mask position with numpy : 0.0019812583923339844 nb_pixel_total : 22843 time to create 1 rle with old method : 0.02855086326599121 length of segment : 431 time for calcul the mask position with numpy : 0.002040863037109375 nb_pixel_total : 14660 time to create 1 rle with old method : 0.018922805786132812 length of segment : 300 time for calcul the mask position with numpy : 0.0014896392822265625 nb_pixel_total : 14373 time to create 1 rle with old method : 0.017098426818847656 length of segment : 230 time for calcul the mask position with numpy : 0.0012769699096679688 nb_pixel_total : 31691 time to create 1 rle with old method : 0.053475141525268555 length of segment : 202 time for calcul the mask position with numpy : 0.0003323554992675781 nb_pixel_total : 8763 time to create 1 rle with old method : 0.010416030883789062 length of segment : 117 time for calcul the mask position with numpy : 0.0005087852478027344 nb_pixel_total : 17498 time to create 1 rle with old method : 0.020801544189453125 length of segment : 184 time for calcul the mask position with numpy : 0.026730060577392578 nb_pixel_total : 597967 time to create 1 rle with new method : 0.19351983070373535 length of segment : 946 time for calcul the mask position with numpy : 0.0006763935089111328 nb_pixel_total : 32519 time to create 1 rle with old method : 0.03920459747314453 length of segment : 270 time for calcul the mask position with numpy : 0.000985860824584961 nb_pixel_total : 53105 time to create 1 rle with old method : 0.06016898155212402 length of segment : 517 time for calcul the mask position with numpy : 0.0007207393646240234 nb_pixel_total : 46980 time to create 1 rle with old method : 0.05753612518310547 length of segment : 258 time for calcul the mask position with numpy : 0.0008308887481689453 nb_pixel_total : 36695 time to create 1 rle with old method : 0.04204988479614258 length of segment : 217 time for calcul the mask position with numpy : 0.00039315223693847656 nb_pixel_total : 11491 time to create 1 rle with old method : 0.013887166976928711 length of segment : 189 time for calcul the mask position with numpy : 0.0016436576843261719 nb_pixel_total : 37024 time to create 1 rle with old method : 0.04353189468383789 length of segment : 322 time for calcul the mask position with numpy : 0.0004761219024658203 nb_pixel_total : 7213 time to create 1 rle with old method : 0.008941173553466797 length of segment : 112 time for calcul the mask position with numpy : 0.0003008842468261719 nb_pixel_total : 4292 time to create 1 rle with old method : 0.005328178405761719 length of segment : 88 time for calcul the mask position with numpy : 0.0003223419189453125 nb_pixel_total : 6209 time to create 1 rle with old method : 0.007981538772583008 length of segment : 58 time for calcul the mask position with numpy : 0.0004012584686279297 nb_pixel_total : 12390 time to create 1 rle with old method : 0.015106439590454102 length of segment : 132 time for calcul the mask position with numpy : 0.0013256072998046875 nb_pixel_total : 29765 time to create 1 rle with old method : 0.037156105041503906 length of segment : 206 time for calcul the mask position with numpy : 0.0016481876373291016 nb_pixel_total : 34291 time to create 1 rle with old method : 0.04128432273864746 length of segment : 224 time for calcul the mask position with numpy : 0.00023221969604492188 nb_pixel_total : 3547 time to create 1 rle with old method : 0.00458073616027832 length of segment : 74 time for calcul the mask position with numpy : 0.0009694099426269531 nb_pixel_total : 22087 time to create 1 rle with old method : 0.026478052139282227 length of segment : 215 time for calcul the mask position with numpy : 0.00035309791564941406 nb_pixel_total : 6515 time to create 1 rle with old method : 0.007883310317993164 length of segment : 102 time for calcul the mask position with numpy : 0.002259492874145508 nb_pixel_total : 43496 time to create 1 rle with old method : 0.051688194274902344 length of segment : 542 time for calcul the mask position with numpy : 0.003156900405883789 nb_pixel_total : 80200 time to create 1 rle with old method : 0.09679174423217773 length of segment : 329 time for calcul the mask position with numpy : 0.0008087158203125 nb_pixel_total : 18226 time to create 1 rle with old method : 0.02350902557373047 length of segment : 138 time for calcul the mask position with numpy : 0.00048160552978515625 nb_pixel_total : 6261 time to create 1 rle with old method : 0.00816202163696289 length of segment : 111 time for calcul the mask position with numpy : 0.0008070468902587891 nb_pixel_total : 16104 time to create 1 rle with old method : 0.018802642822265625 length of segment : 197 time for calcul the mask position with numpy : 0.004148244857788086 nb_pixel_total : 76169 time to create 1 rle with old method : 0.08932828903198242 length of segment : 698 time for calcul the mask position with numpy : 0.0031499862670898438 nb_pixel_total : 63233 time to create 1 rle with old method : 0.07316303253173828 length of segment : 415 time for calcul the mask position with numpy : 0.0003974437713623047 nb_pixel_total : 14315 time to create 1 rle with old method : 0.017191410064697266 length of segment : 259 time for calcul the mask position with numpy : 0.000579833984375 nb_pixel_total : 8865 time to create 1 rle with old method : 0.011146068572998047 length of segment : 147 time for calcul the mask position with numpy : 0.0009555816650390625 nb_pixel_total : 22302 time to create 1 rle with old method : 0.031070470809936523 length of segment : 141 time for calcul the mask position with numpy : 0.0007491111755371094 nb_pixel_total : 13536 time to create 1 rle with old method : 0.016158580780029297 length of segment : 193 time for calcul the mask position with numpy : 0.0008823871612548828 nb_pixel_total : 30742 time to create 1 rle with old method : 0.03580951690673828 length of segment : 198 time for calcul the mask position with numpy : 0.0002837181091308594 nb_pixel_total : 4791 time to create 1 rle with old method : 0.005772829055786133 length of segment : 83 time for calcul the mask position with numpy : 0.0005168914794921875 nb_pixel_total : 14718 time to create 1 rle with old method : 0.017258167266845703 length of segment : 174 time for calcul the mask position with numpy : 0.001573324203491211 nb_pixel_total : 29974 time to create 1 rle with old method : 0.03460693359375 length of segment : 287 time for calcul the mask position with numpy : 0.00031375885009765625 nb_pixel_total : 8034 time to create 1 rle with old method : 0.010194778442382812 length of segment : 104 time for calcul the mask position with numpy : 0.00022840499877929688 nb_pixel_total : 7501 time to create 1 rle with old method : 0.009101629257202148 length of segment : 116 time for calcul the mask position with numpy : 0.0005297660827636719 nb_pixel_total : 13282 time to create 1 rle with old method : 0.016217947006225586 length of segment : 159 time for calcul the mask position with numpy : 0.001897573471069336 nb_pixel_total : 58243 time to create 1 rle with old method : 0.06857705116271973 length of segment : 469 time for calcul the mask position with numpy : 0.0008351802825927734 nb_pixel_total : 37322 time to create 1 rle with old method : 0.046042442321777344 length of segment : 177 time for calcul the mask position with numpy : 0.00032639503479003906 nb_pixel_total : 6738 time to create 1 rle with old method : 0.008373737335205078 length of segment : 114 time for calcul the mask position with numpy : 0.0026242733001708984 nb_pixel_total : 101021 time to create 1 rle with old method : 0.12112689018249512 length of segment : 386 time for calcul the mask position with numpy : 0.0009770393371582031 nb_pixel_total : 29789 time to create 1 rle with old method : 0.03525829315185547 length of segment : 160 time for calcul the mask position with numpy : 0.0008900165557861328 nb_pixel_total : 43802 time to create 1 rle with old method : 0.050910234451293945 length of segment : 379 time for calcul the mask position with numpy : 0.000942230224609375 nb_pixel_total : 28148 time to create 1 rle with old method : 0.03383612632751465 length of segment : 231 time for calcul the mask position with numpy : 0.0005855560302734375 nb_pixel_total : 21329 time to create 1 rle with old method : 0.025109291076660156 length of segment : 137 time for calcul the mask position with numpy : 0.00045299530029296875 nb_pixel_total : 19137 time to create 1 rle with old method : 0.024246692657470703 length of segment : 178 time for calcul the mask position with numpy : 0.0002295970916748047 nb_pixel_total : 15359 time to create 1 rle with old method : 0.018361330032348633 length of segment : 133 time for calcul the mask position with numpy : 0.00021505355834960938 nb_pixel_total : 10216 time to create 1 rle with old method : 0.011404752731323242 length of segment : 94 time for calcul the mask position with numpy : 0.0009300708770751953 nb_pixel_total : 15677 time to create 1 rle with old method : 0.018194198608398438 length of segment : 133 time for calcul the mask position with numpy : 0.0011246204376220703 nb_pixel_total : 18789 time to create 1 rle with old method : 0.021956443786621094 length of segment : 247 time for calcul the mask position with numpy : 0.0008490085601806641 nb_pixel_total : 10005 time to create 1 rle with old method : 0.011696577072143555 length of segment : 171 time for calcul the mask position with numpy : 0.002237558364868164 nb_pixel_total : 39093 time to create 1 rle with old method : 0.043993473052978516 length of segment : 276 time for calcul the mask position with numpy : 0.0004429817199707031 nb_pixel_total : 6597 time to create 1 rle with old method : 0.008049964904785156 length of segment : 80 time for calcul the mask position with numpy : 0.000576019287109375 nb_pixel_total : 11607 time to create 1 rle with old method : 0.014370203018188477 length of segment : 84 time for calcul the mask position with numpy : 0.007300853729248047 nb_pixel_total : 106293 time to create 1 rle with old method : 0.1247861385345459 length of segment : 423 time for calcul the mask position with numpy : 0.004535675048828125 nb_pixel_total : 53105 time to create 1 rle with old method : 0.06232166290283203 length of segment : 399 time for calcul the mask position with numpy : 0.001079559326171875 nb_pixel_total : 12028 time to create 1 rle with old method : 0.013778209686279297 length of segment : 163 time for calcul the mask position with numpy : 0.0010745525360107422 nb_pixel_total : 13504 time to create 1 rle with old method : 0.01605844497680664 length of segment : 144 time for calcul the mask position with numpy : 0.000926971435546875 nb_pixel_total : 11865 time to create 1 rle with old method : 0.014024972915649414 length of segment : 151 time for calcul the mask position with numpy : 0.0025582313537597656 nb_pixel_total : 40408 time to create 1 rle with old method : 0.04676103591918945 length of segment : 237 time for calcul the mask position with numpy : 0.00706791877746582 nb_pixel_total : 92037 time to create 1 rle with old method : 0.10503911972045898 length of segment : 429 time for calcul the mask position with numpy : 0.001522064208984375 nb_pixel_total : 22118 time to create 1 rle with old method : 0.024864912033081055 length of segment : 200 time for calcul the mask position with numpy : 0.001367330551147461 nb_pixel_total : 18159 time to create 1 rle with old method : 0.019887208938598633 length of segment : 142 time for calcul the mask position with numpy : 0.0010783672332763672 nb_pixel_total : 11325 time to create 1 rle with old method : 0.013354301452636719 length of segment : 130 time for calcul the mask position with numpy : 0.0005838871002197266 nb_pixel_total : 8627 time to create 1 rle with old method : 0.010205268859863281 length of segment : 115 time for calcul the mask position with numpy : 0.003292560577392578 nb_pixel_total : 38950 time to create 1 rle with old method : 0.04764151573181152 length of segment : 295 time for calcul the mask position with numpy : 0.0017490386962890625 nb_pixel_total : 16665 time to create 1 rle with old method : 0.01980876922607422 length of segment : 269 time for calcul the mask position with numpy : 0.0003991127014160156 nb_pixel_total : 4519 time to create 1 rle with old method : 0.005638599395751953 length of segment : 69 time for calcul the mask position with numpy : 0.0012958049774169922 nb_pixel_total : 17243 time to create 1 rle with old method : 0.021539688110351562 length of segment : 182 time for calcul the mask position with numpy : 0.000591278076171875 nb_pixel_total : 5958 time to create 1 rle with old method : 0.007863759994506836 length of segment : 87 time for calcul the mask position with numpy : 0.004374504089355469 nb_pixel_total : 64240 time to create 1 rle with old method : 0.0736088752746582 length of segment : 276 time for calcul the mask position with numpy : 0.0006644725799560547 nb_pixel_total : 8636 time to create 1 rle with old method : 0.010622739791870117 length of segment : 125 time for calcul the mask position with numpy : 0.0014510154724121094 nb_pixel_total : 17319 time to create 1 rle with old method : 0.02081894874572754 length of segment : 181 time for calcul the mask position with numpy : 0.00046825408935546875 nb_pixel_total : 6899 time to create 1 rle with old method : 0.008272171020507812 length of segment : 91 time for calcul the mask position with numpy : 0.0014328956604003906 nb_pixel_total : 20877 time to create 1 rle with old method : 0.0252535343170166 length of segment : 228 time for calcul the mask position with numpy : 0.0026121139526367188 nb_pixel_total : 32171 time to create 1 rle with old method : 0.03708362579345703 length of segment : 273 time for calcul the mask position with numpy : 0.00027489662170410156 nb_pixel_total : 5106 time to create 1 rle with old method : 0.006353139877319336 length of segment : 98 time for calcul the mask position with numpy : 0.004513740539550781 nb_pixel_total : 50380 time to create 1 rle with old method : 0.05779123306274414 length of segment : 325 time for calcul the mask position with numpy : 0.0017380714416503906 nb_pixel_total : 20170 time to create 1 rle with old method : 0.025612592697143555 length of segment : 164 time for calcul the mask position with numpy : 0.0003123283386230469 nb_pixel_total : 9982 time to create 1 rle with old method : 0.01711440086364746 length of segment : 139 time for calcul the mask position with numpy : 0.009122610092163086 nb_pixel_total : 118427 time to create 1 rle with old method : 0.13801932334899902 length of segment : 838 time for calcul the mask position with numpy : 0.0012183189392089844 nb_pixel_total : 25300 time to create 1 rle with old method : 0.02943587303161621 length of segment : 251 time for calcul the mask position with numpy : 0.001506805419921875 nb_pixel_total : 30072 time to create 1 rle with old method : 0.034316301345825195 length of segment : 260 time for calcul the mask position with numpy : 0.0007226467132568359 nb_pixel_total : 9898 time to create 1 rle with old method : 0.011451005935668945 length of segment : 164 time for calcul the mask position with numpy : 0.004572629928588867 nb_pixel_total : 67355 time to create 1 rle with old method : 0.0801692008972168 length of segment : 257 time for calcul the mask position with numpy : 0.0007481575012207031 nb_pixel_total : 11485 time to create 1 rle with old method : 0.013309240341186523 length of segment : 112 time for calcul the mask position with numpy : 0.0021538734436035156 nb_pixel_total : 40354 time to create 1 rle with old method : 0.04839038848876953 length of segment : 222 time for calcul the mask position with numpy : 0.0008254051208496094 nb_pixel_total : 5925 time to create 1 rle with old method : 0.007024049758911133 length of segment : 164 time for calcul the mask position with numpy : 0.0022971630096435547 nb_pixel_total : 27977 time to create 1 rle with old method : 0.03796029090881348 length of segment : 323 time for calcul the mask position with numpy : 0.0014801025390625 nb_pixel_total : 14518 time to create 1 rle with old method : 0.017459869384765625 length of segment : 112 time for calcul the mask position with numpy : 0.0019423961639404297 nb_pixel_total : 23345 time to create 1 rle with old method : 0.028787612915039062 length of segment : 183 time for calcul the mask position with numpy : 0.0036592483520507812 nb_pixel_total : 50055 time to create 1 rle with old method : 0.05984354019165039 length of segment : 327 time for calcul the mask position with numpy : 0.0011210441589355469 nb_pixel_total : 15690 time to create 1 rle with old method : 0.019622802734375 length of segment : 141 time for calcul the mask position with numpy : 0.00328826904296875 nb_pixel_total : 41716 time to create 1 rle with old method : 0.06502723693847656 length of segment : 305 time for calcul the mask position with numpy : 0.0019969940185546875 nb_pixel_total : 33959 time to create 1 rle with old method : 0.04129791259765625 length of segment : 180 time for calcul the mask position with numpy : 0.004861116409301758 nb_pixel_total : 69342 time to create 1 rle with old method : 0.08475708961486816 length of segment : 460 time for calcul the mask position with numpy : 0.0024225711822509766 nb_pixel_total : 29768 time to create 1 rle with old method : 0.03692483901977539 length of segment : 287 time for calcul the mask position with numpy : 0.0006887912750244141 nb_pixel_total : 8753 time to create 1 rle with old method : 0.011130809783935547 length of segment : 170 time for calcul the mask position with numpy : 0.0028138160705566406 nb_pixel_total : 42894 time to create 1 rle with old method : 0.0516507625579834 length of segment : 215 time for calcul the mask position with numpy : 0.002275705337524414 nb_pixel_total : 22642 time to create 1 rle with old method : 0.028668642044067383 length of segment : 265 time for calcul the mask position with numpy : 0.0020399093627929688 nb_pixel_total : 77851 time to create 1 rle with old method : 0.09577107429504395 length of segment : 324 time for calcul the mask position with numpy : 0.0017502307891845703 nb_pixel_total : 27574 time to create 1 rle with old method : 0.03265643119812012 length of segment : 190 time for calcul the mask position with numpy : 0.000988006591796875 nb_pixel_total : 14085 time to create 1 rle with old method : 0.017015457153320312 length of segment : 141 time for calcul the mask position with numpy : 0.0026450157165527344 nb_pixel_total : 38509 time to create 1 rle with old method : 0.04632902145385742 length of segment : 237 time for calcul the mask position with numpy : 0.0010807514190673828 nb_pixel_total : 20725 time to create 1 rle with old method : 0.02470874786376953 length of segment : 166 time for calcul the mask position with numpy : 0.0029168128967285156 nb_pixel_total : 59427 time to create 1 rle with old method : 0.06677412986755371 length of segment : 688 time for calcul the mask position with numpy : 0.0010383129119873047 nb_pixel_total : 15494 time to create 1 rle with old method : 0.018981456756591797 length of segment : 162 time for calcul the mask position with numpy : 0.00048661231994628906 nb_pixel_total : 9742 time to create 1 rle with old method : 0.012685060501098633 length of segment : 95 time for calcul the mask position with numpy : 0.0015451908111572266 nb_pixel_total : 17745 time to create 1 rle with old method : 0.02133965492248535 length of segment : 313 time for calcul the mask position with numpy : 0.0007069110870361328 nb_pixel_total : 11627 time to create 1 rle with old method : 0.01441049575805664 length of segment : 133 time for calcul the mask position with numpy : 0.0011429786682128906 nb_pixel_total : 20839 time to create 1 rle with old method : 0.02519083023071289 length of segment : 163 time for calcul the mask position with numpy : 0.0005047321319580078 nb_pixel_total : 6518 time to create 1 rle with old method : 0.008177518844604492 length of segment : 105 time for calcul the mask position with numpy : 0.0037767887115478516 nb_pixel_total : 57720 time to create 1 rle with old method : 0.06639218330383301 length of segment : 397 time for calcul the mask position with numpy : 0.0016655921936035156 nb_pixel_total : 24515 time to create 1 rle with old method : 0.029926538467407227 length of segment : 209 time for calcul the mask position with numpy : 0.0028934478759765625 nb_pixel_total : 28615 time to create 1 rle with old method : 0.03376460075378418 length of segment : 270 time for calcul the mask position with numpy : 0.0007376670837402344 nb_pixel_total : 10376 time to create 1 rle with old method : 0.011877775192260742 length of segment : 105 time for calcul the mask position with numpy : 0.0018839836120605469 nb_pixel_total : 27266 time to create 1 rle with old method : 0.03083658218383789 length of segment : 189 time for calcul the mask position with numpy : 0.005241870880126953 nb_pixel_total : 57598 time to create 1 rle with old method : 0.06649613380432129 length of segment : 455 time for calcul the mask position with numpy : 0.0029544830322265625 nb_pixel_total : 48113 time to create 1 rle with old method : 0.0544283390045166 length of segment : 340 time for calcul the mask position with numpy : 0.0018229484558105469 nb_pixel_total : 24395 time to create 1 rle with old method : 0.028874635696411133 length of segment : 175 time for calcul the mask position with numpy : 0.0013878345489501953 nb_pixel_total : 15950 time to create 1 rle with old method : 0.019103527069091797 length of segment : 175 time for calcul the mask position with numpy : 0.0017960071563720703 nb_pixel_total : 16173 time to create 1 rle with old method : 0.020112991333007812 length of segment : 167 time for calcul the mask position with numpy : 0.003730297088623047 nb_pixel_total : 43357 time to create 1 rle with old method : 0.04969525337219238 length of segment : 350 time for calcul the mask position with numpy : 0.0024335384368896484 nb_pixel_total : 20255 time to create 1 rle with old method : 0.025022506713867188 length of segment : 245 time for calcul the mask position with numpy : 0.0013573169708251953 nb_pixel_total : 19653 time to create 1 rle with old method : 0.02428293228149414 length of segment : 170 time for calcul the mask position with numpy : 0.0007793903350830078 nb_pixel_total : 11178 time to create 1 rle with old method : 0.013674736022949219 length of segment : 94 time for calcul the mask position with numpy : 0.0008068084716796875 nb_pixel_total : 9471 time to create 1 rle with old method : 0.011815071105957031 length of segment : 104 time for calcul the mask position with numpy : 0.001226663589477539 nb_pixel_total : 23440 time to create 1 rle with old method : 0.027479171752929688 length of segment : 159 time for calcul the mask position with numpy : 0.004403829574584961 nb_pixel_total : 47782 time to create 1 rle with old method : 0.05792069435119629 length of segment : 205 time for calcul the mask position with numpy : 0.001726388931274414 nb_pixel_total : 23417 time to create 1 rle with old method : 0.028450965881347656 length of segment : 253 time for calcul the mask position with numpy : 0.003108978271484375 nb_pixel_total : 42621 time to create 1 rle with old method : 0.051427602767944336 length of segment : 307 time for calcul the mask position with numpy : 0.00136566162109375 nb_pixel_total : 19611 time to create 1 rle with old method : 0.023935556411743164 length of segment : 98 time for calcul the mask position with numpy : 0.002446889877319336 nb_pixel_total : 30833 time to create 1 rle with old method : 0.03747415542602539 length of segment : 270 time for calcul the mask position with numpy : 0.0010704994201660156 nb_pixel_total : 16349 time to create 1 rle with old method : 0.019854307174682617 length of segment : 192 time for calcul the mask position with numpy : 0.0019600391387939453 nb_pixel_total : 24602 time to create 1 rle with old method : 0.02994561195373535 length of segment : 226 time for calcul the mask position with numpy : 0.0010385513305664062 nb_pixel_total : 18008 time to create 1 rle with old method : 0.021695375442504883 length of segment : 148 time for calcul the mask position with numpy : 0.0017397403717041016 nb_pixel_total : 23518 time to create 1 rle with old method : 0.029024839401245117 length of segment : 233 time for calcul the mask position with numpy : 0.0007085800170898438 nb_pixel_total : 12015 time to create 1 rle with old method : 0.01492452621459961 length of segment : 139 time for calcul the mask position with numpy : 0.0077054500579833984 nb_pixel_total : 52362 time to create 1 rle with old method : 0.06323456764221191 length of segment : 516 time for calcul the mask position with numpy : 0.0010175704956054688 nb_pixel_total : 15321 time to create 1 rle with old method : 0.018147945404052734 length of segment : 143 time for calcul the mask position with numpy : 0.0013091564178466797 nb_pixel_total : 18450 time to create 1 rle with old method : 0.02179741859436035 length of segment : 143 time for calcul the mask position with numpy : 0.0029785633087158203 nb_pixel_total : 40765 time to create 1 rle with old method : 0.053255319595336914 length of segment : 237 time for calcul the mask position with numpy : 0.0026302337646484375 nb_pixel_total : 21914 time to create 1 rle with old method : 0.026866436004638672 length of segment : 542 time for calcul the mask position with numpy : 0.0008122920989990234 nb_pixel_total : 11983 time to create 1 rle with old method : 0.014276981353759766 length of segment : 150 time for calcul the mask position with numpy : 0.0011327266693115234 nb_pixel_total : 14160 time to create 1 rle with old method : 0.016785383224487305 length of segment : 134 time for calcul the mask position with numpy : 0.00202178955078125 nb_pixel_total : 16910 time to create 1 rle with old method : 0.020053863525390625 length of segment : 188 time for calcul the mask position with numpy : 0.0015902519226074219 nb_pixel_total : 19653 time to create 1 rle with old method : 0.02466416358947754 length of segment : 147 time for calcul the mask position with numpy : 0.00030994415283203125 nb_pixel_total : 3899 time to create 1 rle with old method : 0.004927396774291992 length of segment : 86 time for calcul the mask position with numpy : 0.002082347869873047 nb_pixel_total : 26838 time to create 1 rle with old method : 0.034475088119506836 length of segment : 270 time for calcul the mask position with numpy : 0.0021173954010009766 nb_pixel_total : 25102 time to create 1 rle with old method : 0.030520200729370117 length of segment : 253 time for calcul the mask position with numpy : 0.0030579566955566406 nb_pixel_total : 32540 time to create 1 rle with old method : 0.03659176826477051 length of segment : 286 time for calcul the mask position with numpy : 0.0016841888427734375 nb_pixel_total : 21513 time to create 1 rle with old method : 0.024978160858154297 length of segment : 210 time for calcul the mask position with numpy : 0.0010204315185546875 nb_pixel_total : 11350 time to create 1 rle with old method : 0.013233661651611328 length of segment : 146 time for calcul the mask position with numpy : 0.0003933906555175781 nb_pixel_total : 4944 time to create 1 rle with old method : 0.00571131706237793 length of segment : 79 time for calcul the mask position with numpy : 0.0006425380706787109 nb_pixel_total : 8480 time to create 1 rle with old method : 0.01002645492553711 length of segment : 94 time for calcul the mask position with numpy : 0.0055277347564697266 nb_pixel_total : 99300 time to create 1 rle with old method : 0.11454272270202637 length of segment : 436 time for calcul the mask position with numpy : 0.014089107513427734 nb_pixel_total : 191825 time to create 1 rle with new method : 0.02017807960510254 length of segment : 509 time for calcul the mask position with numpy : 0.0006592273712158203 nb_pixel_total : 8599 time to create 1 rle with old method : 0.010099411010742188 length of segment : 160 time for calcul the mask position with numpy : 0.0019326210021972656 nb_pixel_total : 26092 time to create 1 rle with old method : 0.029495716094970703 length of segment : 186 time for calcul the mask position with numpy : 0.0011508464813232422 nb_pixel_total : 16607 time to create 1 rle with old method : 0.018403053283691406 length of segment : 155 time for calcul the mask position with numpy : 0.002075672149658203 nb_pixel_total : 34696 time to create 1 rle with old method : 0.03864431381225586 length of segment : 210 time for calcul the mask position with numpy : 0.0013604164123535156 nb_pixel_total : 12837 time to create 1 rle with old method : 0.014458179473876953 length of segment : 208 time for calcul the mask position with numpy : 0.0015416145324707031 nb_pixel_total : 25061 time to create 1 rle with old method : 0.029941797256469727 length of segment : 190 time for calcul the mask position with numpy : 0.0011675357818603516 nb_pixel_total : 21548 time to create 1 rle with old method : 0.024758577346801758 length of segment : 246 time for calcul the mask position with numpy : 0.0008287429809570312 nb_pixel_total : 7844 time to create 1 rle with old method : 0.009362220764160156 length of segment : 246 time for calcul the mask position with numpy : 0.0006411075592041016 nb_pixel_total : 9076 time to create 1 rle with old method : 0.011269330978393555 length of segment : 102 time for calcul the mask position with numpy : 0.0009083747863769531 nb_pixel_total : 10328 time to create 1 rle with old method : 0.012599945068359375 length of segment : 113 time for calcul the mask position with numpy : 0.0010097026824951172 nb_pixel_total : 13239 time to create 1 rle with old method : 0.016222715377807617 length of segment : 115 time for calcul the mask position with numpy : 0.001332998275756836 nb_pixel_total : 13489 time to create 1 rle with old method : 0.016435623168945312 length of segment : 233 time for calcul the mask position with numpy : 0.0008833408355712891 nb_pixel_total : 24790 time to create 1 rle with old method : 0.02835559844970703 length of segment : 230 time for calcul the mask position with numpy : 0.0015368461608886719 nb_pixel_total : 31169 time to create 1 rle with old method : 0.036473989486694336 length of segment : 256 time for calcul the mask position with numpy : 0.0002703666687011719 nb_pixel_total : 4556 time to create 1 rle with old method : 0.005676984786987305 length of segment : 103 time for calcul the mask position with numpy : 0.001081228256225586 nb_pixel_total : 14364 time to create 1 rle with old method : 0.01710796356201172 length of segment : 179 time for calcul the mask position with numpy : 0.00048089027404785156 nb_pixel_total : 16861 time to create 1 rle with old method : 0.02004528045654297 length of segment : 152 time for calcul the mask position with numpy : 0.004148721694946289 nb_pixel_total : 59288 time to create 1 rle with old method : 0.07044291496276855 length of segment : 487 time for calcul the mask position with numpy : 0.005877494812011719 nb_pixel_total : 40716 time to create 1 rle with old method : 0.04850006103515625 length of segment : 246 time for calcul the mask position with numpy : 0.0010387897491455078 nb_pixel_total : 17685 time to create 1 rle with old method : 0.02054309844970703 length of segment : 195 time for calcul the mask position with numpy : 0.0009429454803466797 nb_pixel_total : 14573 time to create 1 rle with old method : 0.01761770248413086 length of segment : 255 time for calcul the mask position with numpy : 0.0013587474822998047 nb_pixel_total : 26478 time to create 1 rle with old method : 0.02959442138671875 length of segment : 163 time for calcul the mask position with numpy : 0.001268148422241211 nb_pixel_total : 23821 time to create 1 rle with old method : 0.02797842025756836 length of segment : 157 time for calcul the mask position with numpy : 0.004858970642089844 nb_pixel_total : 85106 time to create 1 rle with old method : 0.09942150115966797 length of segment : 350 time for calcul the mask position with numpy : 0.0009591579437255859 nb_pixel_total : 12809 time to create 1 rle with old method : 0.015508413314819336 length of segment : 147 time for calcul the mask position with numpy : 0.0005388259887695312 nb_pixel_total : 8199 time to create 1 rle with old method : 0.010013818740844727 length of segment : 119 time for calcul the mask position with numpy : 0.002546548843383789 nb_pixel_total : 35425 time to create 1 rle with old method : 0.04062986373901367 length of segment : 270 time for calcul the mask position with numpy : 0.002507925033569336 nb_pixel_total : 58712 time to create 1 rle with old method : 0.06598663330078125 length of segment : 258 time for calcul the mask position with numpy : 0.0009367465972900391 nb_pixel_total : 21856 time to create 1 rle with old method : 0.026337862014770508 length of segment : 117 time for calcul the mask position with numpy : 0.0008931159973144531 nb_pixel_total : 14440 time to create 1 rle with old method : 0.017251014709472656 length of segment : 149 time for calcul the mask position with numpy : 0.0008549690246582031 nb_pixel_total : 15950 time to create 1 rle with old method : 0.018898725509643555 length of segment : 145 time for calcul the mask position with numpy : 0.0009856224060058594 nb_pixel_total : 21036 time to create 1 rle with old method : 0.024183273315429688 length of segment : 290 time for calcul the mask position with numpy : 0.0022552013397216797 nb_pixel_total : 32402 time to create 1 rle with old method : 0.03720498085021973 length of segment : 220 time for calcul the mask position with numpy : 0.0025899410247802734 nb_pixel_total : 40074 time to create 1 rle with old method : 0.04541945457458496 length of segment : 316 time for calcul the mask position with numpy : 0.0008761882781982422 nb_pixel_total : 11564 time to create 1 rle with old method : 0.014116525650024414 length of segment : 102 time for calcul the mask position with numpy : 0.0004837512969970703 nb_pixel_total : 6159 time to create 1 rle with old method : 0.007742166519165039 length of segment : 96 time for calcul the mask position with numpy : 0.0007712841033935547 nb_pixel_total : 12138 time to create 1 rle with old method : 0.014693737030029297 length of segment : 142 time for calcul the mask position with numpy : 0.0012164115905761719 nb_pixel_total : 14375 time to create 1 rle with old method : 0.024760007858276367 length of segment : 157 time for calcul the mask position with numpy : 0.0005242824554443359 nb_pixel_total : 7724 time to create 1 rle with old method : 0.010359764099121094 length of segment : 120 time for calcul the mask position with numpy : 0.0008885860443115234 nb_pixel_total : 15011 time to create 1 rle with old method : 0.017913341522216797 length of segment : 172 time for calcul the mask position with numpy : 0.0011768341064453125 nb_pixel_total : 27804 time to create 1 rle with old method : 0.034444570541381836 length of segment : 122 time for calcul the mask position with numpy : 0.0004220008850097656 nb_pixel_total : 6795 time to create 1 rle with old method : 0.008555173873901367 length of segment : 105 time for calcul the mask position with numpy : 0.0007777214050292969 nb_pixel_total : 15693 time to create 1 rle with old method : 0.01888275146484375 length of segment : 123 time for calcul the mask position with numpy : 0.0007214546203613281 nb_pixel_total : 13767 time to create 1 rle with old method : 0.016809940338134766 length of segment : 136 time for calcul the mask position with numpy : 0.0009515285491943359 nb_pixel_total : 14449 time to create 1 rle with old method : 0.01722884178161621 length of segment : 165 time for calcul the mask position with numpy : 0.000946044921875 nb_pixel_total : 20309 time to create 1 rle with old method : 0.025133609771728516 length of segment : 117 time for calcul the mask position with numpy : 0.0004124641418457031 nb_pixel_total : 7400 time to create 1 rle with old method : 0.009428024291992188 length of segment : 118 time for calcul the mask position with numpy : 0.0005443096160888672 nb_pixel_total : 7457 time to create 1 rle with old method : 0.009033441543579102 length of segment : 94 time for calcul the mask position with numpy : 0.0005066394805908203 nb_pixel_total : 8716 time to create 1 rle with old method : 0.01121377944946289 length of segment : 112 time for calcul the mask position with numpy : 0.002490997314453125 nb_pixel_total : 52508 time to create 1 rle with old method : 0.06418514251708984 length of segment : 409 time for calcul the mask position with numpy : 0.000446319580078125 nb_pixel_total : 8892 time to create 1 rle with old method : 0.011145353317260742 length of segment : 82 time for calcul the mask position with numpy : 0.0005106925964355469 nb_pixel_total : 9625 time to create 1 rle with old method : 0.012253522872924805 length of segment : 94 time for calcul the mask position with numpy : 0.0010476112365722656 nb_pixel_total : 17494 time to create 1 rle with old method : 0.02230525016784668 length of segment : 178 time for calcul the mask position with numpy : 0.0004107952117919922 nb_pixel_total : 6025 time to create 1 rle with old method : 0.007410764694213867 length of segment : 106 time for calcul the mask position with numpy : 0.0006425380706787109 nb_pixel_total : 21287 time to create 1 rle with old method : 0.026870250701904297 length of segment : 140 time for calcul the mask position with numpy : 0.0006630420684814453 nb_pixel_total : 13836 time to create 1 rle with old method : 0.017145872116088867 length of segment : 151 time for calcul the mask position with numpy : 0.0006837844848632812 nb_pixel_total : 7533 time to create 1 rle with old method : 0.009364604949951172 length of segment : 372 time for calcul the mask position with numpy : 0.004191875457763672 nb_pixel_total : 190456 time to create 1 rle with new method : 0.008562088012695312 length of segment : 539 time for calcul the mask position with numpy : 0.004373073577880859 nb_pixel_total : 203296 time to create 1 rle with new method : 0.005825042724609375 length of segment : 395 time for calcul the mask position with numpy : 0.00040268898010253906 nb_pixel_total : 11036 time to create 1 rle with old method : 0.013681173324584961 length of segment : 169 time for calcul the mask position with numpy : 0.0031888484954833984 nb_pixel_total : 133375 time to create 1 rle with old method : 0.15729546546936035 length of segment : 398 time for calcul the mask position with numpy : 0.0023992061614990234 nb_pixel_total : 86843 time to create 1 rle with old method : 0.10279178619384766 length of segment : 253 time for calcul the mask position with numpy : 0.0006198883056640625 nb_pixel_total : 24210 time to create 1 rle with old method : 0.029853343963623047 length of segment : 118 time for calcul the mask position with numpy : 0.005148410797119141 nb_pixel_total : 259743 time to create 1 rle with new method : 0.007406711578369141 length of segment : 617 time for calcul the mask position with numpy : 0.0006914138793945312 nb_pixel_total : 22992 time to create 1 rle with old method : 0.028892040252685547 length of segment : 129 time for calcul the mask position with numpy : 0.00046253204345703125 nb_pixel_total : 13164 time to create 1 rle with old method : 0.01560831069946289 length of segment : 145 time for calcul the mask position with numpy : 0.0002777576446533203 nb_pixel_total : 13153 time to create 1 rle with old method : 0.018249034881591797 length of segment : 168 time for calcul the mask position with numpy : 0.00038361549377441406 nb_pixel_total : 20796 time to create 1 rle with old method : 0.024904489517211914 length of segment : 134 time for calcul the mask position with numpy : 0.0008473396301269531 nb_pixel_total : 32216 time to create 1 rle with old method : 0.03864479064941406 length of segment : 224 time for calcul the mask position with numpy : 0.0014786720275878906 nb_pixel_total : 48862 time to create 1 rle with old method : 0.058589935302734375 length of segment : 552 time for calcul the mask position with numpy : 0.00037550926208496094 nb_pixel_total : 10301 time to create 1 rle with old method : 0.012991189956665039 length of segment : 102 time for calcul the mask position with numpy : 0.0029761791229248047 nb_pixel_total : 135950 time to create 1 rle with old method : 0.15674233436584473 length of segment : 346 time for calcul the mask position with numpy : 0.0012776851654052734 nb_pixel_total : 80874 time to create 1 rle with old method : 0.09558486938476562 length of segment : 230 time for calcul the mask position with numpy : 0.0010025501251220703 nb_pixel_total : 61664 time to create 1 rle with old method : 0.07384085655212402 length of segment : 187 time for calcul the mask position with numpy : 0.0018639564514160156 nb_pixel_total : 95293 time to create 1 rle with old method : 0.1184549331665039 length of segment : 404 time for calcul the mask position with numpy : 0.00037932395935058594 nb_pixel_total : 13841 time to create 1 rle with old method : 0.016847610473632812 length of segment : 140 time spent for convertir_results : 29.1215877532959 Inside saveOutput : final : False verbose : 0 eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 394 chid ids of type : 3594 Number RLEs to save : 96728 save missing photos in datou_result : time spend for datou_step_exec : 137.42030930519104 time spend to save output : 10.136249542236328 total time spend for step 1 : 147.55655884742737 step2:crop_condition Sat Feb 22 04:32:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! 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 : 12 ! batch 1 Loaded 394 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : papier param for this class : {'min_score': 0.7} filtre for class : papier hashtag_id of this class : 492668766 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 292 About to insert : list_path_to_insert length 292 new photo from crops ! About to upload 292 photos upload in portfolio : 3736932 init cache_photo without model_param we have 292 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740195227_2145890 we have uploaded 292 photos in the portfolio 3736932 time of upload the photos Elapsed time : 108.37254309654236 we have finished the crop for the class : papier begin to crop the class : carton param for this class : {'min_score': 0.7} filtre for class : carton hashtag_id of this class : 492774966 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 54 About to insert : list_path_to_insert length 54 new photo from crops ! About to upload 54 photos upload in portfolio : 3736932 init cache_photo without model_param we have 54 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740195353_2145890 we have uploaded 54 photos in the portfolio 3736932 time of upload the photos Elapsed time : 15.722596168518066 we have finished the crop for the class : carton begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 8 About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos upload in portfolio : 3736932 init cache_photo without model_param we have 8 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740195372_2145890 we have uploaded 8 photos in the portfolio 3736932 time of upload the photos Elapsed time : 2.8877243995666504 we have finished the crop for the class : metal begin to crop the class : pet_clair param for this class : {'min_score': 0.7} filtre for class : pet_clair hashtag_id of this class : 2107755846 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 23 About to insert : list_path_to_insert length 23 new photo from crops ! About to upload 23 photos upload in portfolio : 3736932 init cache_photo without model_param we have 23 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740195387_2145890 we have uploaded 23 photos in the portfolio 3736932 time of upload the photos Elapsed time : 8.870940923690796 we have finished the crop for the class : pet_clair begin to crop the class : autre param for this class : {'min_score': 0.7} filtre for class : autre hashtag_id of this class : 494826614 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 9 About to insert : list_path_to_insert length 9 new photo from crops ! About to upload 9 photos upload in portfolio : 3736932 init cache_photo without model_param we have 9 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740195401_2145890 we have uploaded 9 photos in the portfolio 3736932 time of upload the photos Elapsed time : 3.326803207397461 we have finished the crop for the class : autre begin to crop the class : pehd param for this class : {'min_score': 0.7} filtre for class : pehd hashtag_id of this class : 628944319 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 3 new photo from crops ! About to upload 3 photos upload in portfolio : 3736932 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740195407_2145890 we have uploaded 3 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.304062843322754 we have finished the crop for the class : pehd begin to crop the class : pet_fonce param for this class : {'min_score': 0.7} filtre for class : pet_fonce hashtag_id of this class : 2107755900 we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! we have both polygon and rles Next one ! map_result returned by crop_photo_return_map_crop : length : 5 About to insert : list_path_to_insert length 5 new photo from crops ! About to upload 5 photos upload in portfolio : 3736932 init cache_photo without model_param we have 5 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1740195410_2145890 we have uploaded 5 photos in the portfolio 3736932 time of upload the photos Elapsed time : 1.8580539226531982 we have finished the crop for the class : pet_fonce delete rles from all chi we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles we have 0 chi objets contains the rles Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1338913054, 1338913046, 1338912999, 1338912994, 1338912953, 1338765990, 1338765987, 1338765911, 1338765907, 1338765897, 1338765458, 1338765330] Looping around the photos to save general results len do output : 394 /1338982805Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982806Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982807Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982809Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982810Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982811Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982812Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982813Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982814Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982815Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982816Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982817Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982818Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982819Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982820Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982821Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982822Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982823Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982824Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982825Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982826Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982827Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982828Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982829Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982830Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982831Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982832Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982833Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982834Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982835Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982836Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982837Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982838Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982839Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982840Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982841Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982842Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982843Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982844Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982845Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982846Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982847Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982848Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982849Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982850Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982851Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982852Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982853Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982854Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982855Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982856Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982857Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982858Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982859Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982860Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982862Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982863Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982864Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982865Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982866Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982867Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982868Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982869Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982870Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982871Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982872Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982873Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982874Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982875Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982876Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982877Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982878Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982879Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982880Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982881Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982882Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982883Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982884Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982885Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982886Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982887Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982888Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982889Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982890Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982891Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982892Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1338982893Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . 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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, '2606877') ('3318', '20744710', '1338913054', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338913046', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912999', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912994', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912953', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765990', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765987', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765911', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765907', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765897', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765458', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765330', None, None, None, None, None, '2606877') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1194 time used for this insertion : 1.268963098526001 save_final save missing photos in datou_result : time spend for datou_step_exec : 233.79587721824646 time spend to save output : 1.3095571994781494 total time spend for step 2 : 235.1054344177246 step3:rle_unique_nms_with_priority Sat Feb 22 04:36:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 394 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 24 nb_hashtags : 4 time to prepare the origin masks : 4.866016387939453 time for calcul the mask position with numpy : 0.2346179485321045 nb_pixel_total : 5071555 time to create 1 rle with new method : 0.3078303337097168 time for calcul the mask position with numpy : 0.022599458694458008 nb_pixel_total : 125605 time to create 1 rle with old method : 0.14640259742736816 time for calcul the mask position with numpy : 0.022673368453979492 nb_pixel_total : 67712 time to create 1 rle with old method : 0.08660483360290527 time for calcul the mask position with numpy : 0.023728370666503906 nb_pixel_total : 4234 time to create 1 rle with old method : 0.00503849983215332 time for calcul the mask position with numpy : 0.023108482360839844 nb_pixel_total : 3978 time to create 1 rle with old method : 0.004912137985229492 time for calcul the mask position with numpy : 0.036347150802612305 nb_pixel_total : 66732 time to create 1 rle with old method : 0.07673072814941406 time for calcul the mask position with numpy : 0.035047292709350586 nb_pixel_total : 69690 time to create 1 rle with old method : 0.07802534103393555 time for calcul the mask position with numpy : 0.03384876251220703 nb_pixel_total : 25915 time to create 1 rle with old method : 0.029622316360473633 time for calcul the mask position with numpy : 0.03576493263244629 nb_pixel_total : 28765 time to create 1 rle with old method : 0.03503751754760742 time for calcul the mask position with numpy : 0.034484148025512695 nb_pixel_total : 54733 time to create 1 rle with old method : 0.06368613243103027 time for calcul the mask position with numpy : 0.022420406341552734 nb_pixel_total : 160757 time to create 1 rle with new method : 0.2909224033355713 time for calcul the mask position with numpy : 0.027884960174560547 nb_pixel_total : 100116 time to create 1 rle with old method : 0.11042952537536621 time for calcul the mask position with numpy : 0.023286104202270508 nb_pixel_total : 56906 time to create 1 rle with old method : 0.06307005882263184 time for calcul the mask position with numpy : 0.023957014083862305 nb_pixel_total : 162220 time to create 1 rle with new method : 0.3224453926086426 time for calcul the mask position with numpy : 0.02485513687133789 nb_pixel_total : 237364 time to create 1 rle with new method : 0.2827951908111572 time for calcul the mask position with numpy : 0.02196645736694336 nb_pixel_total : 194324 time to create 1 rle with new method : 0.31696319580078125 time for calcul the mask position with numpy : 0.02058124542236328 nb_pixel_total : 12436 time to create 1 rle with old method : 0.015583276748657227 time for calcul the mask position with numpy : 0.021234512329101562 nb_pixel_total : 36540 time to create 1 rle with old method : 0.041890621185302734 time for calcul the mask position with numpy : 0.022003650665283203 nb_pixel_total : 90263 time to create 1 rle with old method : 0.10867714881896973 time for calcul the mask position with numpy : 0.020939111709594727 nb_pixel_total : 104013 time to create 1 rle with old method : 0.11469912528991699 time for calcul the mask position with numpy : 0.025087356567382812 nb_pixel_total : 101522 time to create 1 rle with old method : 0.11444568634033203 time for calcul the mask position with numpy : 0.024983882904052734 nb_pixel_total : 35233 time to create 1 rle with old method : 0.04020071029663086 time for calcul the mask position with numpy : 0.024763822555541992 nb_pixel_total : 36521 time to create 1 rle with old method : 0.04065060615539551 time for calcul the mask position with numpy : 0.020750045776367188 nb_pixel_total : 29851 time to create 1 rle with old method : 0.0351259708404541 time for calcul the mask position with numpy : 0.02180767059326172 nb_pixel_total : 173255 time to create 1 rle with new method : 0.28345656394958496 create new chi : 4.0338873863220215 time to delete rle : 0.01786327362060547 batch 1 Loaded 49 chid ids of type : 3594 ++++++++++++++++++++++++++++++++Number RLEs to save : 19246 TO DO : save crop sub photo not yet done ! save time : 11.8581383228302 nb_obj : 32 nb_hashtags : 4 time to prepare the origin masks : 4.9106879234313965 time for calcul the mask position with numpy : 0.6930229663848877 nb_pixel_total : 4968808 time to create 1 rle with new method : 0.8451743125915527 time for calcul the mask position with numpy : 0.029427528381347656 nb_pixel_total : 7625 time to create 1 rle with old method : 0.009517192840576172 time for calcul the mask position with numpy : 0.029660940170288086 nb_pixel_total : 23216 time to create 1 rle with old method : 0.02709197998046875 time for calcul the mask position with numpy : 0.030084609985351562 nb_pixel_total : 58283 time to create 1 rle with old method : 0.07005572319030762 time for calcul the mask position with numpy : 0.030363082885742188 nb_pixel_total : 81984 time to create 1 rle with old method : 0.1063086986541748 time for calcul the mask position with numpy : 0.02973794937133789 nb_pixel_total : 122069 time to create 1 rle with old method : 0.14239788055419922 time for calcul the mask position with numpy : 0.03226351737976074 nb_pixel_total : 314975 time to create 1 rle with new method : 0.28237223625183105 time for calcul the mask position with numpy : 0.028509855270385742 nb_pixel_total : 1369 time to create 1 rle with old method : 0.09521603584289551 time for calcul the mask position with numpy : 0.03388261795043945 nb_pixel_total : 181033 time to create 1 rle with new method : 0.816063404083252 time for calcul the mask position with numpy : 0.03333592414855957 nb_pixel_total : 23017 time to create 1 rle with old method : 0.0265805721282959 time for calcul the mask position with numpy : 0.02933025360107422 nb_pixel_total : 46400 time to create 1 rle with old method : 0.05406928062438965 time for calcul the mask position with numpy : 0.028651952743530273 nb_pixel_total : 1952 time to create 1 rle with old method : 0.0022478103637695312 time for calcul the mask position with numpy : 0.029147624969482422 nb_pixel_total : 101028 time to create 1 rle with old method : 0.11561346054077148 time for calcul the mask position with numpy : 0.028948545455932617 nb_pixel_total : 36273 time to create 1 rle with old method : 0.041692256927490234 time for calcul the mask position with numpy : 0.032806396484375 nb_pixel_total : 527750 time to create 1 rle with new method : 0.5625174045562744 time for calcul the mask position with numpy : 0.029285430908203125 nb_pixel_total : 102621 time to create 1 rle with old method : 0.1249990463256836 time for calcul the mask position with numpy : 0.032434940338134766 nb_pixel_total : 184560 time to create 1 rle with new method : 0.7884206771850586 time for calcul the mask position with numpy : 0.029746294021606445 nb_pixel_total : 27968 time to create 1 rle with old method : 0.031858205795288086 time for calcul the mask position with numpy : 0.029132366180419922 nb_pixel_total : 25732 time to create 1 rle with old method : 0.029481887817382812 time for calcul the mask position with numpy : 0.029283523559570312 nb_pixel_total : 5888 time to create 1 rle with old method : 0.006697893142700195 time for calcul the mask position with numpy : 0.029474496841430664 nb_pixel_total : 14067 time to create 1 rle with old method : 0.016932964324951172 time for calcul the mask position with numpy : 0.02890944480895996 nb_pixel_total : 8083 time to create 1 rle with old method : 0.009491920471191406 time for calcul the mask position with numpy : 0.029235124588012695 nb_pixel_total : 20702 time to create 1 rle with old method : 0.024098634719848633 time for calcul the mask position with numpy : 0.029181957244873047 nb_pixel_total : 10151 time to create 1 rle with old method : 0.01178884506225586 time for calcul the mask position with numpy : 0.02901482582092285 nb_pixel_total : 16944 time to create 1 rle with old method : 0.020102977752685547 time for calcul the mask position with numpy : 0.028942108154296875 nb_pixel_total : 5059 time to create 1 rle with old method : 0.0058917999267578125 time for calcul the mask position with numpy : 0.028992414474487305 nb_pixel_total : 82826 time to create 1 rle with old method : 0.09620118141174316 time for calcul the mask position with numpy : 0.029248714447021484 nb_pixel_total : 6868 time to create 1 rle with old method : 0.007996559143066406 time for calcul the mask position with numpy : 0.029077768325805664 nb_pixel_total : 8101 time to create 1 rle with old method : 0.00963449478149414 time for calcul the mask position with numpy : 0.029213905334472656 nb_pixel_total : 12841 time to create 1 rle with old method : 0.014626741409301758 time for calcul the mask position with numpy : 0.02904033660888672 nb_pixel_total : 5027 time to create 1 rle with old method : 0.00579380989074707 time for calcul the mask position with numpy : 0.028854846954345703 nb_pixel_total : 6829 time to create 1 rle with old method : 0.008200645446777344 time for calcul the mask position with numpy : 0.029150962829589844 nb_pixel_total : 10191 time to create 1 rle with old method : 0.016132831573486328 create new chi : 6.205771207809448 time to delete rle : 0.005307435989379883 batch 1 Loaded 65 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 23579 TO DO : save crop sub photo not yet done ! save time : 1.9414589405059814 nb_obj : 49 nb_hashtags : 6 time to prepare the origin masks : 4.76476788520813 time for calcul the mask position with numpy : 0.19575023651123047 nb_pixel_total : 4734989 time to create 1 rle with new method : 0.5237874984741211 time for calcul the mask position with numpy : 0.029837846755981445 nb_pixel_total : 10887 time to create 1 rle with old method : 0.01260685920715332 time for calcul the mask position with numpy : 0.03379321098327637 nb_pixel_total : 23378 time to create 1 rle with old method : 0.033812522888183594 time for calcul the mask position with numpy : 0.029566287994384766 nb_pixel_total : 17361 time to create 1 rle with old method : 0.019436359405517578 time for calcul the mask position with numpy : 0.0292203426361084 nb_pixel_total : 29336 time to create 1 rle with old method : 0.033762216567993164 time for calcul the mask position with numpy : 0.028789520263671875 nb_pixel_total : 36627 time to create 1 rle with old method : 0.040926456451416016 time for calcul the mask position with numpy : 0.035776615142822266 nb_pixel_total : 461628 time to create 1 rle with new method : 0.40622878074645996 time for calcul the mask position with numpy : 0.03286266326904297 nb_pixel_total : 91672 time to create 1 rle with old method : 0.1046907901763916 time for calcul the mask position with numpy : 0.030984163284301758 nb_pixel_total : 164813 time to create 1 rle with new method : 0.752518892288208 time for calcul the mask position with numpy : 0.02916860580444336 nb_pixel_total : 4784 time to create 1 rle with old method : 0.005605459213256836 time for calcul the mask position with numpy : 0.029305219650268555 nb_pixel_total : 5969 time to create 1 rle with old method : 0.007146358489990234 time for calcul the mask position with numpy : 0.029788494110107422 nb_pixel_total : 136576 time to create 1 rle with old method : 0.16301989555358887 time for calcul the mask position with numpy : 0.032872676849365234 nb_pixel_total : 7417 time to create 1 rle with old method : 0.012491703033447266 time for calcul the mask position with numpy : 0.030354022979736328 nb_pixel_total : 5488 time to create 1 rle with old method : 0.0063304901123046875 time for calcul the mask position with numpy : 0.029286861419677734 nb_pixel_total : 137510 time to create 1 rle with old method : 0.15607500076293945 time for calcul the mask position with numpy : 0.034763336181640625 nb_pixel_total : 463670 time to create 1 rle with new method : 0.41057920455932617 time for calcul the mask position with numpy : 0.028088092803955078 nb_pixel_total : 35543 time to create 1 rle with old method : 0.03951597213745117 time for calcul the mask position with numpy : 0.0280454158782959 nb_pixel_total : 24554 time to create 1 rle with old method : 0.028030872344970703 time for calcul the mask position with numpy : 0.0281064510345459 nb_pixel_total : 9735 time to create 1 rle with old method : 0.01058340072631836 time for calcul the mask position with numpy : 0.028324604034423828 nb_pixel_total : 14720 time to create 1 rle with old method : 0.0162508487701416 time for calcul the mask position with numpy : 0.028191804885864258 nb_pixel_total : 68216 time to create 1 rle with old method : 0.07976531982421875 time for calcul the mask position with numpy : 0.02836012840270996 nb_pixel_total : 31192 time to create 1 rle with old method : 0.03550362586975098 time for calcul the mask position with numpy : 0.028523921966552734 nb_pixel_total : 10602 time to create 1 rle with old method : 0.012835502624511719 time for calcul the mask position with numpy : 0.029192209243774414 nb_pixel_total : 5788 time to create 1 rle with old method : 0.006468772888183594 time for calcul the mask position with numpy : 0.02920818328857422 nb_pixel_total : 27829 time to create 1 rle with old method : 0.03273963928222656 time for calcul the mask position with numpy : 0.0293428897857666 nb_pixel_total : 5861 time to create 1 rle with old method : 0.00693511962890625 time for calcul the mask position with numpy : 0.029203414916992188 nb_pixel_total : 79883 time to create 1 rle with old method : 0.09080266952514648 time for calcul the mask position with numpy : 0.029569387435913086 nb_pixel_total : 28141 time to create 1 rle with old method : 0.03232932090759277 time for calcul the mask position with numpy : 0.033368587493896484 nb_pixel_total : 7237 time to create 1 rle with old method : 0.00870370864868164 time for calcul the mask position with numpy : 0.028828859329223633 nb_pixel_total : 11367 time to create 1 rle with old method : 0.012958049774169922 time for calcul the mask position with numpy : 0.02925729751586914 nb_pixel_total : 42799 time to create 1 rle with old method : 0.049286603927612305 time for calcul the mask position with numpy : 0.02909541130065918 nb_pixel_total : 63629 time to create 1 rle with old method : 0.07307720184326172 time for calcul the mask position with numpy : 0.029392719268798828 nb_pixel_total : 20443 time to create 1 rle with old method : 0.023526430130004883 time for calcul the mask position with numpy : 0.02822279930114746 nb_pixel_total : 19696 time to create 1 rle with old method : 0.022569656372070312 time for calcul the mask position with numpy : 0.029348134994506836 nb_pixel_total : 10974 time to create 1 rle with old method : 0.012942075729370117 time for calcul the mask position with numpy : 0.030118227005004883 nb_pixel_total : 25089 time to create 1 rle with old method : 0.028399229049682617 time for calcul the mask position with numpy : 0.02777886390686035 nb_pixel_total : 14312 time to create 1 rle with old method : 0.015527725219726562 time for calcul the mask position with numpy : 0.027765750885009766 nb_pixel_total : 12994 time to create 1 rle with old method : 0.01389622688293457 time for calcul the mask position with numpy : 0.027418136596679688 nb_pixel_total : 2565 time to create 1 rle with old method : 0.0028295516967773438 time for calcul the mask position with numpy : 0.028740644454956055 nb_pixel_total : 39886 time to create 1 rle with old method : 0.0447390079498291 time for calcul the mask position with numpy : 0.028831005096435547 nb_pixel_total : 4063 time to create 1 rle with old method : 0.004700183868408203 time for calcul the mask position with numpy : 0.028919219970703125 nb_pixel_total : 8073 time to create 1 rle with old method : 0.00915217399597168 time for calcul the mask position with numpy : 0.028734683990478516 nb_pixel_total : 4052 time to create 1 rle with old method : 0.004665374755859375 time for calcul the mask position with numpy : 0.0305483341217041 nb_pixel_total : 32896 time to create 1 rle with old method : 0.03920936584472656 time for calcul the mask position with numpy : 0.029195547103881836 nb_pixel_total : 16950 time to create 1 rle with old method : 0.019569873809814453 time for calcul the mask position with numpy : 0.02947402000427246 nb_pixel_total : 16092 time to create 1 rle with old method : 0.017939090728759766 time for calcul the mask position with numpy : 0.030638694763183594 nb_pixel_total : 7984 time to create 1 rle with old method : 0.010263442993164062 time for calcul the mask position with numpy : 0.02883458137512207 nb_pixel_total : 4912 time to create 1 rle with old method : 0.005812406539916992 time for calcul the mask position with numpy : 0.0285494327545166 nb_pixel_total : 6498 time to create 1 rle with old method : 0.007306575775146484 time for calcul the mask position with numpy : 0.02832794189453125 nb_pixel_total : 3560 time to create 1 rle with old method : 0.004099607467651367 create new chi : 5.262786388397217 time to delete rle : 0.003671407699584961 batch 1 Loaded 99 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 24595 TO DO : save crop sub photo not yet done ! save time : 2.624826192855835 nb_obj : 55 nb_hashtags : 4 time to prepare the origin masks : 4.217381477355957 time for calcul the mask position with numpy : 0.3635678291320801 nb_pixel_total : 5516497 time to create 1 rle with new method : 0.6111173629760742 time for calcul the mask position with numpy : 0.027147769927978516 nb_pixel_total : 11681 time to create 1 rle with old method : 0.013825416564941406 time for calcul the mask position with numpy : 0.029256105422973633 nb_pixel_total : 17157 time to create 1 rle with old method : 0.019252300262451172 time for calcul the mask position with numpy : 0.029668092727661133 nb_pixel_total : 41432 time to create 1 rle with old method : 0.0458827018737793 time for calcul the mask position with numpy : 0.029158830642700195 nb_pixel_total : 12098 time to create 1 rle with old method : 0.013919830322265625 time for calcul the mask position with numpy : 0.029151201248168945 nb_pixel_total : 17549 time to create 1 rle with old method : 0.019971132278442383 time for calcul the mask position with numpy : 0.02905440330505371 nb_pixel_total : 3717 time to create 1 rle with old method : 0.004401206970214844 time for calcul the mask position with numpy : 0.029132604598999023 nb_pixel_total : 2912 time to create 1 rle with old method : 0.0034034252166748047 time for calcul the mask position with numpy : 0.028162717819213867 nb_pixel_total : 32740 time to create 1 rle with old method : 0.03542804718017578 time for calcul the mask position with numpy : 0.027812957763671875 nb_pixel_total : 4781 time to create 1 rle with old method : 0.005553722381591797 time for calcul the mask position with numpy : 0.028977155685424805 nb_pixel_total : 18882 time to create 1 rle with old method : 0.026871204376220703 time for calcul the mask position with numpy : 0.032059669494628906 nb_pixel_total : 29357 time to create 1 rle with old method : 0.033341407775878906 time for calcul the mask position with numpy : 0.028507471084594727 nb_pixel_total : 54938 time to create 1 rle with old method : 0.06262922286987305 time for calcul the mask position with numpy : 0.028821945190429688 nb_pixel_total : 20545 time to create 1 rle with old method : 0.022968053817749023 time for calcul the mask position with numpy : 0.028267383575439453 nb_pixel_total : 26586 time to create 1 rle with old method : 0.02982473373413086 time for calcul the mask position with numpy : 0.02891373634338379 nb_pixel_total : 19034 time to create 1 rle with old method : 0.02216172218322754 time for calcul the mask position with numpy : 0.02913975715637207 nb_pixel_total : 14592 time to create 1 rle with old method : 0.01765751838684082 time for calcul the mask position with numpy : 0.028148889541625977 nb_pixel_total : 15341 time to create 1 rle with old method : 0.017302989959716797 time for calcul the mask position with numpy : 0.028479814529418945 nb_pixel_total : 32730 time to create 1 rle with old method : 0.03691601753234863 time for calcul the mask position with numpy : 0.0281980037689209 nb_pixel_total : 14373 time to create 1 rle with old method : 0.016183853149414062 time for calcul the mask position with numpy : 0.028348207473754883 nb_pixel_total : 22843 time to create 1 rle with old method : 0.026215791702270508 time for calcul the mask position with numpy : 0.029020071029663086 nb_pixel_total : 72063 time to create 1 rle with old method : 0.08371114730834961 time for calcul the mask position with numpy : 0.028703689575195312 nb_pixel_total : 43402 time to create 1 rle with old method : 0.0485529899597168 time for calcul the mask position with numpy : 0.029082775115966797 nb_pixel_total : 6858 time to create 1 rle with old method : 0.008017778396606445 time for calcul the mask position with numpy : 0.029125452041625977 nb_pixel_total : 53886 time to create 1 rle with old method : 0.06114649772644043 time for calcul the mask position with numpy : 0.0295562744140625 nb_pixel_total : 14259 time to create 1 rle with old method : 0.01663947105407715 time for calcul the mask position with numpy : 0.028485536575317383 nb_pixel_total : 15236 time to create 1 rle with old method : 0.017178058624267578 time for calcul the mask position with numpy : 0.02807307243347168 nb_pixel_total : 18022 time to create 1 rle with old method : 0.01984691619873047 time for calcul the mask position with numpy : 0.027328014373779297 nb_pixel_total : 26735 time to create 1 rle with old method : 0.028604745864868164 time for calcul the mask position with numpy : 0.026703834533691406 nb_pixel_total : 30991 time to create 1 rle with old method : 0.035703420639038086 time for calcul the mask position with numpy : 0.02868056297302246 nb_pixel_total : 236351 time to create 1 rle with new method : 0.5576303005218506 time for calcul the mask position with numpy : 0.03289461135864258 nb_pixel_total : 26749 time to create 1 rle with old method : 0.04456663131713867 time for calcul the mask position with numpy : 0.029174089431762695 nb_pixel_total : 22712 time to create 1 rle with old method : 0.02536487579345703 time for calcul the mask position with numpy : 0.028957366943359375 nb_pixel_total : 100963 time to create 1 rle with old method : 0.11416959762573242 time for calcul the mask position with numpy : 0.02898716926574707 nb_pixel_total : 4441 time to create 1 rle with old method : 0.005190610885620117 time for calcul the mask position with numpy : 0.030224084854125977 nb_pixel_total : 10448 time to create 1 rle with old method : 0.011928558349609375 time for calcul the mask position with numpy : 0.028723478317260742 nb_pixel_total : 26304 time to create 1 rle with old method : 0.03002643585205078 time for calcul the mask position with numpy : 0.028890609741210938 nb_pixel_total : 14660 time to create 1 rle with old method : 0.01639866828918457 time for calcul the mask position with numpy : 0.029021739959716797 nb_pixel_total : 38074 time to create 1 rle with old method : 0.04328417778015137 time for calcul the mask position with numpy : 0.028913259506225586 nb_pixel_total : 52993 time to create 1 rle with old method : 0.06125593185424805 time for calcul the mask position with numpy : 0.028862476348876953 nb_pixel_total : 23610 time to create 1 rle with old method : 0.026492595672607422 time for calcul the mask position with numpy : 0.028514623641967773 nb_pixel_total : 18093 time to create 1 rle with old method : 0.02039337158203125 time for calcul the mask position with numpy : 0.028436899185180664 nb_pixel_total : 1680 time to create 1 rle with old method : 0.0020873546600341797 time for calcul the mask position with numpy : 0.027524709701538086 nb_pixel_total : 9219 time to create 1 rle with old method : 0.010516166687011719 time for calcul the mask position with numpy : 0.027786970138549805 nb_pixel_total : 24514 time to create 1 rle with old method : 0.02834010124206543 time for calcul the mask position with numpy : 0.02896714210510254 nb_pixel_total : 43777 time to create 1 rle with old method : 0.050241708755493164 time for calcul the mask position with numpy : 0.028899669647216797 nb_pixel_total : 6467 time to create 1 rle with old method : 0.007456541061401367 time for calcul the mask position with numpy : 0.028615474700927734 nb_pixel_total : 22888 time to create 1 rle with old method : 0.02592611312866211 time for calcul the mask position with numpy : 0.027111053466796875 nb_pixel_total : 42892 time to create 1 rle with old method : 0.045591115951538086 time for calcul the mask position with numpy : 0.02725052833557129 nb_pixel_total : 15345 time to create 1 rle with old method : 0.016742467880249023 time for calcul the mask position with numpy : 0.02706289291381836 nb_pixel_total : 5266 time to create 1 rle with old method : 0.005772829055786133 time for calcul the mask position with numpy : 0.027164697647094727 nb_pixel_total : 22551 time to create 1 rle with old method : 1.1409273147583008 time for calcul the mask position with numpy : 0.030487060546875 nb_pixel_total : 13334 time to create 1 rle with old method : 0.014691352844238281 time for calcul the mask position with numpy : 0.026825666427612305 nb_pixel_total : 15038 time to create 1 rle with old method : 0.015824317932128906 time for calcul the mask position with numpy : 0.02666616439819336 nb_pixel_total : 25478 time to create 1 rle with old method : 0.02758336067199707 time for calcul the mask position with numpy : 0.02794933319091797 nb_pixel_total : 15156 time to create 1 rle with old method : 0.016958951950073242 create new chi : 5.765830755233765 time to delete rle : 0.0039784908294677734 batch 1 Loaded 112 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 27754 TO DO : save crop sub photo not yet done ! save time : 1.902735948562622 nb_obj : 9 nb_hashtags : 2 time to prepare the origin masks : 2.8898892402648926 time for calcul the mask position with numpy : 0.6599206924438477 nb_pixel_total : 6300952 time to create 1 rle with new method : 0.767956018447876 time for calcul the mask position with numpy : 0.020358562469482422 nb_pixel_total : 11491 time to create 1 rle with old method : 0.012816667556762695 time for calcul the mask position with numpy : 0.020216703414916992 nb_pixel_total : 36695 time to create 1 rle with old method : 0.04075956344604492 time for calcul the mask position with numpy : 0.0193173885345459 nb_pixel_total : 265 time to create 1 rle with old method : 0.00043964385986328125 time for calcul the mask position with numpy : 0.019603490829467773 nb_pixel_total : 16335 time to create 1 rle with old method : 0.017987489700317383 time for calcul the mask position with numpy : 0.01999521255493164 nb_pixel_total : 28583 time to create 1 rle with old method : 0.03183245658874512 time for calcul the mask position with numpy : 0.02642989158630371 nb_pixel_total : 597967 time to create 1 rle with new method : 0.5777847766876221 time for calcul the mask position with numpy : 0.025313615798950195 nb_pixel_total : 17498 time to create 1 rle with old method : 0.020009517669677734 time for calcul the mask position with numpy : 0.02112436294555664 nb_pixel_total : 8763 time to create 1 rle with old method : 0.009839296340942383 time for calcul the mask position with numpy : 0.022930145263671875 nb_pixel_total : 31691 time to create 1 rle with old method : 0.04144763946533203 create new chi : 2.4279561042785645 time to delete rle : 0.0020308494567871094 batch 1 Loaded 19 chid ids of type : 3594 +++++++++++++Number RLEs to save : 6949 TO DO : save crop sub photo not yet done ! save time : 0.6441841125488281 nb_obj : 27 nb_hashtags : 4 time to prepare the origin masks : 3.475771903991699 time for calcul the mask position with numpy : 0.39231204986572266 nb_pixel_total : 6428440 time to create 1 rle with new method : 0.8130340576171875 time for calcul the mask position with numpy : 0.02776479721069336 nb_pixel_total : 6209 time to create 1 rle with old method : 0.006901264190673828 time for calcul the mask position with numpy : 0.0277554988861084 nb_pixel_total : 3547 time to create 1 rle with old method : 0.0040128231048583984 time for calcul the mask position with numpy : 0.02847146987915039 nb_pixel_total : 43496 time to create 1 rle with old method : 0.04883098602294922 time for calcul the mask position with numpy : 0.0279693603515625 nb_pixel_total : 7501 time to create 1 rle with old method : 0.008746862411499023 time for calcul the mask position with numpy : 0.027994632720947266 nb_pixel_total : 37024 time to create 1 rle with old method : 0.041348934173583984 time for calcul the mask position with numpy : 0.02788996696472168 nb_pixel_total : 6261 time to create 1 rle with old method : 0.007210254669189453 time for calcul the mask position with numpy : 0.028531551361083984 nb_pixel_total : 4292 time to create 1 rle with old method : 0.0050487518310546875 time for calcul the mask position with numpy : 0.0286407470703125 nb_pixel_total : 12390 time to create 1 rle with old method : 0.014500141143798828 time for calcul the mask position with numpy : 0.028710365295410156 nb_pixel_total : 22087 time to create 1 rle with old method : 0.025583267211914062 time for calcul the mask position with numpy : 0.02782917022705078 nb_pixel_total : 8865 time to create 1 rle with old method : 0.010083675384521484 time for calcul the mask position with numpy : 0.027777671813964844 nb_pixel_total : 80200 time to create 1 rle with old method : 0.08926033973693848 time for calcul the mask position with numpy : 0.02890777587890625 nb_pixel_total : 13536 time to create 1 rle with old method : 0.015311002731323242 time for calcul the mask position with numpy : 0.02873992919921875 nb_pixel_total : 34291 time to create 1 rle with old method : 0.03876805305480957 time for calcul the mask position with numpy : 0.0287477970123291 nb_pixel_total : 7213 time to create 1 rle with old method : 0.00843667984008789 time for calcul the mask position with numpy : 0.02907252311706543 nb_pixel_total : 76169 time to create 1 rle with old method : 0.08785223960876465 time for calcul the mask position with numpy : 0.028222084045410156 nb_pixel_total : 18226 time to create 1 rle with old method : 0.020378589630126953 time for calcul the mask position with numpy : 0.027988195419311523 nb_pixel_total : 6515 time to create 1 rle with old method : 0.007361173629760742 time for calcul the mask position with numpy : 0.027997493743896484 nb_pixel_total : 14718 time to create 1 rle with old method : 0.016161680221557617 time for calcul the mask position with numpy : 0.027352094650268555 nb_pixel_total : 29765 time to create 1 rle with old method : 0.032566070556640625 time for calcul the mask position with numpy : 0.027916908264160156 nb_pixel_total : 63233 time to create 1 rle with old method : 0.06893157958984375 time for calcul the mask position with numpy : 0.027928829193115234 nb_pixel_total : 4791 time to create 1 rle with old method : 0.005292654037475586 time for calcul the mask position with numpy : 0.027899503707885742 nb_pixel_total : 29974 time to create 1 rle with old method : 0.03448915481567383 time for calcul the mask position with numpy : 0.03257346153259277 nb_pixel_total : 14315 time to create 1 rle with old method : 0.017255783081054688 time for calcul the mask position with numpy : 0.028418540954589844 nb_pixel_total : 16104 time to create 1 rle with old method : 0.017817020416259766 time for calcul the mask position with numpy : 0.029254674911499023 nb_pixel_total : 30742 time to create 1 rle with old method : 0.03484296798706055 time for calcul the mask position with numpy : 0.028055429458618164 nb_pixel_total : 8034 time to create 1 rle with old method : 0.009150028228759766 time for calcul the mask position with numpy : 0.028850078582763672 nb_pixel_total : 22302 time to create 1 rle with old method : 0.024712562561035156 create new chi : 2.7086963653564453 time to delete rle : 0.002035856246948242 batch 1 Loaded 55 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++Number RLEs to save : 13490 TO DO : save crop sub photo not yet done ! save time : 1.8537039756774902 nb_obj : 12 nb_hashtags : 2 time to prepare the origin masks : 4.6491241455078125 time for calcul the mask position with numpy : 0.5499985218048096 nb_pixel_total : 6665854 time to create 1 rle with new method : 0.7175040245056152 time for calcul the mask position with numpy : 0.021853923797607422 nb_pixel_total : 10216 time to create 1 rle with old method : 0.012064456939697266 time for calcul the mask position with numpy : 0.021476030349731445 nb_pixel_total : 15359 time to create 1 rle with old method : 0.017195701599121094 time for calcul the mask position with numpy : 0.020414113998413086 nb_pixel_total : 19137 time to create 1 rle with old method : 0.02128291130065918 time for calcul the mask position with numpy : 0.022510290145874023 nb_pixel_total : 21329 time to create 1 rle with old method : 0.02428889274597168 time for calcul the mask position with numpy : 0.022185802459716797 nb_pixel_total : 28148 time to create 1 rle with old method : 0.03197336196899414 time for calcul the mask position with numpy : 0.023077011108398438 nb_pixel_total : 43802 time to create 1 rle with old method : 0.049633026123046875 time for calcul the mask position with numpy : 0.021399497985839844 nb_pixel_total : 29789 time to create 1 rle with old method : 0.03376150131225586 time for calcul the mask position with numpy : 0.021714210510253906 nb_pixel_total : 101021 time to create 1 rle with old method : 0.11228799819946289 time for calcul the mask position with numpy : 0.020528316497802734 nb_pixel_total : 6738 time to create 1 rle with old method : 0.007291078567504883 time for calcul the mask position with numpy : 0.02038407325744629 nb_pixel_total : 37322 time to create 1 rle with old method : 0.04024243354797363 time for calcul the mask position with numpy : 0.02182745933532715 nb_pixel_total : 58243 time to create 1 rle with old method : 0.06175065040588379 time for calcul the mask position with numpy : 0.020382165908813477 nb_pixel_total : 13282 time to create 1 rle with old method : 0.015102863311767578 create new chi : 1.9846522808074951 time to delete rle : 0.0010335445404052734 batch 1 Loaded 25 chid ids of type : 3594 ++++++++++++Number RLEs to save : 7388 TO DO : save crop sub photo not yet done ! save time : 0.6540007591247559 nb_obj : 63 nb_hashtags : 5 time to prepare the origin masks : 4.347051382064819 time for calcul the mask position with numpy : 0.22011709213256836 nb_pixel_total : 5316780 time to create 1 rle with new method : 0.6669819355010986 time for calcul the mask position with numpy : 0.027945518493652344 nb_pixel_total : 14085 time to create 1 rle with old method : 0.015284061431884766 time for calcul the mask position with numpy : 0.028573989868164062 nb_pixel_total : 20170 time to create 1 rle with old method : 0.024082660675048828 time for calcul the mask position with numpy : 0.029041767120361328 nb_pixel_total : 8627 time to create 1 rle with old method : 0.009941816329956055 time for calcul the mask position with numpy : 0.028008222579956055 nb_pixel_total : 64240 time to create 1 rle with old method : 0.06840658187866211 time for calcul the mask position with numpy : 0.027615785598754883 nb_pixel_total : 17745 time to create 1 rle with old method : 0.021238327026367188 time for calcul the mask position with numpy : 0.02807140350341797 nb_pixel_total : 23345 time to create 1 rle with old method : 0.0265042781829834 time for calcul the mask position with numpy : 0.0272672176361084 nb_pixel_total : 49640 time to create 1 rle with old method : 0.05484294891357422 time for calcul the mask position with numpy : 0.028568506240844727 nb_pixel_total : 20877 time to create 1 rle with old method : 0.024618864059448242 time for calcul the mask position with numpy : 0.029355287551879883 nb_pixel_total : 8753 time to create 1 rle with old method : 0.009849309921264648 time for calcul the mask position with numpy : 0.027810096740722656 nb_pixel_total : 17243 time to create 1 rle with old method : 0.018685340881347656 time for calcul the mask position with numpy : 0.026785850524902344 nb_pixel_total : 4519 time to create 1 rle with old method : 0.00497746467590332 time for calcul the mask position with numpy : 0.027761459350585938 nb_pixel_total : 12028 time to create 1 rle with old method : 0.013068199157714844 time for calcul the mask position with numpy : 0.028207063674926758 nb_pixel_total : 5925 time to create 1 rle with old method : 0.006415843963623047 time for calcul the mask position with numpy : 0.028138399124145508 nb_pixel_total : 11627 time to create 1 rle with old method : 0.013154983520507812 time for calcul the mask position with numpy : 0.028633832931518555 nb_pixel_total : 41716 time to create 1 rle with old method : 0.04811406135559082 time for calcul the mask position with numpy : 0.0283815860748291 nb_pixel_total : 33959 time to create 1 rle with old method : 0.03861808776855469 time for calcul the mask position with numpy : 0.028993606567382812 nb_pixel_total : 22642 time to create 1 rle with old method : 0.025458574295043945 time for calcul the mask position with numpy : 0.03045344352722168 nb_pixel_total : 9898 time to create 1 rle with old method : 0.01111745834350586 time for calcul the mask position with numpy : 0.02902817726135254 nb_pixel_total : 106293 time to create 1 rle with old method : 0.12505459785461426 time for calcul the mask position with numpy : 0.029670000076293945 nb_pixel_total : 9982 time to create 1 rle with old method : 0.01195383071899414 time for calcul the mask position with numpy : 0.029258251190185547 nb_pixel_total : 18159 time to create 1 rle with old method : 0.02079486846923828 time for calcul the mask position with numpy : 0.029429197311401367 nb_pixel_total : 53105 time to create 1 rle with old method : 0.06098532676696777 time for calcul the mask position with numpy : 0.029277324676513672 nb_pixel_total : 22118 time to create 1 rle with old method : 0.025217294692993164 time for calcul the mask position with numpy : 0.028929948806762695 nb_pixel_total : 1290 time to create 1 rle with old method : 0.0017507076263427734 time for calcul the mask position with numpy : 0.0291445255279541 nb_pixel_total : 118427 time to create 1 rle with old method : 0.13671469688415527 time for calcul the mask position with numpy : 0.028829097747802734 nb_pixel_total : 32171 time to create 1 rle with old method : 0.036476850509643555 time for calcul the mask position with numpy : 0.02870917320251465 nb_pixel_total : 9742 time to create 1 rle with old method : 0.011148452758789062 time for calcul the mask position with numpy : 0.02850794792175293 nb_pixel_total : 20839 time to create 1 rle with old method : 0.02317523956298828 time for calcul the mask position with numpy : 0.028514385223388672 nb_pixel_total : 40354 time to create 1 rle with old method : 0.045540809631347656 time for calcul the mask position with numpy : 0.02805781364440918 nb_pixel_total : 17319 time to create 1 rle with old method : 0.019580602645874023 time for calcul the mask position with numpy : 0.027385234832763672 nb_pixel_total : 20725 time to create 1 rle with old method : 0.023514509201049805 time for calcul the mask position with numpy : 0.027910709381103516 nb_pixel_total : 11485 time to create 1 rle with old method : 0.013059139251708984 time for calcul the mask position with numpy : 0.028176307678222656 nb_pixel_total : 50380 time to create 1 rle with old method : 0.0729062557220459 time for calcul the mask position with numpy : 0.0328373908996582 nb_pixel_total : 5958 time to create 1 rle with old method : 0.0073163509368896484 time for calcul the mask position with numpy : 0.027873754501342773 nb_pixel_total : 38950 time to create 1 rle with old method : 0.04352140426635742 time for calcul the mask position with numpy : 0.028259992599487305 nb_pixel_total : 69342 time to create 1 rle with old method : 0.07661557197570801 time for calcul the mask position with numpy : 0.027175188064575195 nb_pixel_total : 40408 time to create 1 rle with old method : 0.04404306411743164 time for calcul the mask position with numpy : 0.027996540069580078 nb_pixel_total : 29768 time to create 1 rle with old method : 0.03389787673950195 time for calcul the mask position with numpy : 0.029424190521240234 nb_pixel_total : 27792 time to create 1 rle with old method : 0.032148122787475586 time for calcul the mask position with numpy : 0.03082752227783203 nb_pixel_total : 92037 time to create 1 rle with old method : 0.10355019569396973 time for calcul the mask position with numpy : 0.0286715030670166 nb_pixel_total : 30048 time to create 1 rle with old method : 0.03387594223022461 time for calcul the mask position with numpy : 0.02871847152709961 nb_pixel_total : 59427 time to create 1 rle with old method : 0.06926512718200684 time for calcul the mask position with numpy : 0.02922224998474121 nb_pixel_total : 38509 time to create 1 rle with old method : 0.05632328987121582 time for calcul the mask position with numpy : 0.031290531158447266 nb_pixel_total : 14518 time to create 1 rle with old method : 0.01700115203857422 time for calcul the mask position with numpy : 0.029858827590942383 nb_pixel_total : 13504 time to create 1 rle with old method : 0.0227048397064209 time for calcul the mask position with numpy : 0.03362250328063965 nb_pixel_total : 16665 time to create 1 rle with old method : 0.024886131286621094 time for calcul the mask position with numpy : 0.033098459243774414 nb_pixel_total : 27574 time to create 1 rle with old method : 0.0487673282623291 time for calcul the mask position with numpy : 0.03800034523010254 nb_pixel_total : 15690 time to create 1 rle with old method : 0.023868799209594727 time for calcul the mask position with numpy : 0.033405303955078125 nb_pixel_total : 15677 time to create 1 rle with old method : 0.026839494705200195 time for calcul the mask position with numpy : 0.03860807418823242 nb_pixel_total : 18789 time to create 1 rle with old method : 0.03033447265625 time for calcul the mask position with numpy : 0.03556180000305176 nb_pixel_total : 11865 time to create 1 rle with old method : 0.018617630004882812 time for calcul the mask position with numpy : 0.034589290618896484 nb_pixel_total : 6597 time to create 1 rle with old method : 0.00796961784362793 time for calcul the mask position with numpy : 0.029445409774780273 nb_pixel_total : 42894 time to create 1 rle with old method : 0.04889273643493652 time for calcul the mask position with numpy : 0.03058910369873047 nb_pixel_total : 39093 time to create 1 rle with old method : 0.0487062931060791 time for calcul the mask position with numpy : 0.029363393783569336 nb_pixel_total : 25218 time to create 1 rle with old method : 0.030019044876098633 time for calcul the mask position with numpy : 0.029294967651367188 nb_pixel_total : 2388 time to create 1 rle with old method : 0.0028204917907714844 time for calcul the mask position with numpy : 0.029178857803344727 nb_pixel_total : 6899 time to create 1 rle with old method : 0.008031368255615234 time for calcul the mask position with numpy : 0.0290985107421875 nb_pixel_total : 10005 time to create 1 rle with old method : 0.011485815048217773 time for calcul the mask position with numpy : 0.03134465217590332 nb_pixel_total : 8636 time to create 1 rle with old method : 0.010169506072998047 time for calcul the mask position with numpy : 0.029414892196655273 nb_pixel_total : 67355 time to create 1 rle with old method : 0.07893681526184082 time for calcul the mask position with numpy : 0.03058624267578125 nb_pixel_total : 15494 time to create 1 rle with old method : 0.017755508422851562 time for calcul the mask position with numpy : 0.029484272003173828 nb_pixel_total : 11325 time to create 1 rle with old method : 0.013772964477539062 time for calcul the mask position with numpy : 0.034152984619140625 nb_pixel_total : 11607 time to create 1 rle with old method : 0.019479751586914062 create new chi : 4.867756605148315 time to delete rle : 0.006740093231201172 batch 1 Loaded 127 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 29759 TO DO : save crop sub photo not yet done ! save time : 2.260150671005249 nb_obj : 67 nb_hashtags : 6 time to prepare the origin masks : 4.3506340980529785 time for calcul the mask position with numpy : 0.3807845115661621 nb_pixel_total : 5322657 time to create 1 rle with new method : 0.7990083694458008 time for calcul the mask position with numpy : 0.029143571853637695 nb_pixel_total : 27266 time to create 1 rle with old method : 0.03146100044250488 time for calcul the mask position with numpy : 0.03058600425720215 nb_pixel_total : 13489 time to create 1 rle with old method : 0.02064204216003418 time for calcul the mask position with numpy : 0.03508591651916504 nb_pixel_total : 7844 time to create 1 rle with old method : 0.013249397277832031 time for calcul the mask position with numpy : 0.03293299674987793 nb_pixel_total : 28615 time to create 1 rle with old method : 0.03676915168762207 time for calcul the mask position with numpy : 0.03079533576965332 nb_pixel_total : 16607 time to create 1 rle with old method : 0.019819259643554688 time for calcul the mask position with numpy : 0.029030799865722656 nb_pixel_total : 32540 time to create 1 rle with old method : 0.03718280792236328 time for calcul the mask position with numpy : 0.030032634735107422 nb_pixel_total : 21914 time to create 1 rle with old method : 0.025113821029663086 time for calcul the mask position with numpy : 0.02904820442199707 nb_pixel_total : 11350 time to create 1 rle with old method : 0.013373374938964844 time for calcul the mask position with numpy : 0.03221249580383301 nb_pixel_total : 15321 time to create 1 rle with old method : 0.0184938907623291 time for calcul the mask position with numpy : 0.03044891357421875 nb_pixel_total : 16910 time to create 1 rle with old method : 0.020824670791625977 time for calcul the mask position with numpy : 0.028905391693115234 nb_pixel_total : 43357 time to create 1 rle with old method : 0.05049753189086914 time for calcul the mask position with numpy : 0.028668642044067383 nb_pixel_total : 13239 time to create 1 rle with old method : 0.015342235565185547 time for calcul the mask position with numpy : 0.02885127067565918 nb_pixel_total : 21470 time to create 1 rle with old method : 0.02437138557434082 time for calcul the mask position with numpy : 0.02884960174560547 nb_pixel_total : 16349 time to create 1 rle with old method : 0.018637895584106445 time for calcul the mask position with numpy : 0.029008150100708008 nb_pixel_total : 30833 time to create 1 rle with old method : 0.03585648536682129 time for calcul the mask position with numpy : 0.028893470764160156 nb_pixel_total : 48113 time to create 1 rle with old method : 0.05649065971374512 time for calcul the mask position with numpy : 0.029018163681030273 nb_pixel_total : 23417 time to create 1 rle with old method : 0.027661561965942383 time for calcul the mask position with numpy : 0.03031444549560547 nb_pixel_total : 59288 time to create 1 rle with old method : 0.06936430931091309 time for calcul the mask position with numpy : 0.03902697563171387 nb_pixel_total : 34696 time to create 1 rle with old method : 0.04944109916687012 time for calcul the mask position with numpy : 0.03119492530822754 nb_pixel_total : 24602 time to create 1 rle with old method : 0.03133988380432129 time for calcul the mask position with numpy : 0.028905630111694336 nb_pixel_total : 14160 time to create 1 rle with old method : 0.016225337982177734 time for calcul the mask position with numpy : 0.03129148483276367 nb_pixel_total : 23518 time to create 1 rle with old method : 0.028818607330322266 time for calcul the mask position with numpy : 0.03093743324279785 nb_pixel_total : 14364 time to create 1 rle with old method : 0.02234363555908203 time for calcul the mask position with numpy : 0.02938675880432129 nb_pixel_total : 26838 time to create 1 rle with old method : 0.031507015228271484 time for calcul the mask position with numpy : 0.029307126998901367 nb_pixel_total : 11983 time to create 1 rle with old method : 0.017683029174804688 time for calcul the mask position with numpy : 0.03531479835510254 nb_pixel_total : 26092 time to create 1 rle with old method : 0.03133749961853027 time for calcul the mask position with numpy : 0.029300928115844727 nb_pixel_total : 25102 time to create 1 rle with old method : 0.030547380447387695 time for calcul the mask position with numpy : 0.028694629669189453 nb_pixel_total : 40196 time to create 1 rle with old method : 0.04851722717285156 time for calcul the mask position with numpy : 0.0283358097076416 nb_pixel_total : 96700 time to create 1 rle with old method : 0.11307835578918457 time for calcul the mask position with numpy : 0.02903580665588379 nb_pixel_total : 10376 time to create 1 rle with old method : 0.011934757232666016 time for calcul the mask position with numpy : 0.029407978057861328 nb_pixel_total : 16173 time to create 1 rle with old method : 0.030327796936035156 time for calcul the mask position with numpy : 0.0329592227935791 nb_pixel_total : 57598 time to create 1 rle with old method : 0.06688904762268066 time for calcul the mask position with numpy : 0.029026269912719727 nb_pixel_total : 20255 time to create 1 rle with old method : 0.023054838180541992 time for calcul the mask position with numpy : 0.027905702590942383 nb_pixel_total : 12837 time to create 1 rle with old method : 0.01422572135925293 time for calcul the mask position with numpy : 0.028786897659301758 nb_pixel_total : 10328 time to create 1 rle with old method : 0.011734724044799805 time for calcul the mask position with numpy : 0.02878737449645996 nb_pixel_total : 52362 time to create 1 rle with old method : 0.059340476989746094 time for calcul the mask position with numpy : 0.029787778854370117 nb_pixel_total : 15950 time to create 1 rle with old method : 0.018368005752563477 time for calcul the mask position with numpy : 0.028205156326293945 nb_pixel_total : 8480 time to create 1 rle with old method : 0.009925365447998047 time for calcul the mask position with numpy : 0.03130388259887695 nb_pixel_total : 18450 time to create 1 rle with old method : 0.03482484817504883 time for calcul the mask position with numpy : 0.03382611274719238 nb_pixel_total : 173281 time to create 1 rle with new method : 0.3992757797241211 time for calcul the mask position with numpy : 0.02924513816833496 nb_pixel_total : 23440 time to create 1 rle with old method : 0.026727676391601562 time for calcul the mask position with numpy : 0.0281524658203125 nb_pixel_total : 25061 time to create 1 rle with old method : 0.028790712356567383 time for calcul the mask position with numpy : 0.030924320220947266 nb_pixel_total : 17685 time to create 1 rle with old method : 0.021023273468017578 time for calcul the mask position with numpy : 0.02913808822631836 nb_pixel_total : 40765 time to create 1 rle with old method : 0.050515174865722656 time for calcul the mask position with numpy : 0.02923583984375 nb_pixel_total : 19653 time to create 1 rle with old method : 0.023102760314941406 time for calcul the mask position with numpy : 0.03328871726989746 nb_pixel_total : 57720 time to create 1 rle with old method : 0.07277560234069824 time for calcul the mask position with numpy : 0.03195452690124512 nb_pixel_total : 6518 time to create 1 rle with old method : 0.00795602798461914 time for calcul the mask position with numpy : 0.02995896339416504 nb_pixel_total : 31169 time to create 1 rle with old method : 0.03781867027282715 time for calcul the mask position with numpy : 0.029853343963623047 nb_pixel_total : 24790 time to create 1 rle with old method : 0.030802011489868164 time for calcul the mask position with numpy : 0.029931306838989258 nb_pixel_total : 24395 time to create 1 rle with old method : 0.029442310333251953 time for calcul the mask position with numpy : 0.04437971115112305 nb_pixel_total : 8599 time to create 1 rle with old method : 0.014176607131958008 time for calcul the mask position with numpy : 0.029193401336669922 nb_pixel_total : 12015 time to create 1 rle with old method : 0.014230728149414062 time for calcul the mask position with numpy : 0.0291595458984375 nb_pixel_total : 2110 time to create 1 rle with old method : 0.00260162353515625 time for calcul the mask position with numpy : 0.03136181831359863 nb_pixel_total : 19653 time to create 1 rle with old method : 0.02247309684753418 time for calcul the mask position with numpy : 0.0290067195892334 nb_pixel_total : 4944 time to create 1 rle with old method : 0.005835294723510742 time for calcul the mask position with numpy : 0.029091835021972656 nb_pixel_total : 42621 time to create 1 rle with old method : 0.051175832748413086 time for calcul the mask position with numpy : 0.030765295028686523 nb_pixel_total : 4556 time to create 1 rle with old method : 0.005235910415649414 time for calcul the mask position with numpy : 0.02866339683532715 nb_pixel_total : 3899 time to create 1 rle with old method : 0.004513740539550781 time for calcul the mask position with numpy : 0.03304624557495117 nb_pixel_total : 47782 time to create 1 rle with old method : 0.05573916435241699 time for calcul the mask position with numpy : 0.029322147369384766 nb_pixel_total : 18008 time to create 1 rle with old method : 0.022609233856201172 time for calcul the mask position with numpy : 0.02907085418701172 nb_pixel_total : 21513 time to create 1 rle with old method : 0.025202274322509766 time for calcul the mask position with numpy : 0.028512954711914062 nb_pixel_total : 9471 time to create 1 rle with old method : 0.01072549819946289 time for calcul the mask position with numpy : 0.030872583389282227 nb_pixel_total : 24515 time to create 1 rle with old method : 0.029336929321289062 time for calcul the mask position with numpy : 0.02928018569946289 nb_pixel_total : 14573 time to create 1 rle with old method : 0.017688274383544922 time for calcul the mask position with numpy : 0.030592918395996094 nb_pixel_total : 9076 time to create 1 rle with old method : 0.011422157287597656 time for calcul the mask position with numpy : 0.031816959381103516 nb_pixel_total : 19611 time to create 1 rle with old method : 0.02356266975402832 time for calcul the mask position with numpy : 0.030245304107666016 nb_pixel_total : 11178 time to create 1 rle with old method : 0.014734983444213867 create new chi : 5.57387900352478 time to delete rle : 0.005091667175292969 batch 1 Loaded 135 chid ids of type : 3594 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 30896 TO DO : save crop sub photo not yet done ! save time : 2.0920069217681885 nb_obj : 33 nb_hashtags : 4 time to prepare the origin masks : 3.7361202239990234 time for calcul the mask position with numpy : 0.510023832321167 nb_pixel_total : 6371234 time to create 1 rle with new method : 0.7028911113739014 time for calcul the mask position with numpy : 0.028533458709716797 nb_pixel_total : 17494 time to create 1 rle with old method : 0.02009415626525879 time for calcul the mask position with numpy : 0.02876424789428711 nb_pixel_total : 6025 time to create 1 rle with old method : 0.006993293762207031 time for calcul the mask position with numpy : 0.028839826583862305 nb_pixel_total : 12809 time to create 1 rle with old method : 0.014414072036743164 time for calcul the mask position with numpy : 0.028893709182739258 nb_pixel_total : 14375 time to create 1 rle with old method : 0.016275882720947266 time for calcul the mask position with numpy : 0.029529571533203125 nb_pixel_total : 21036 time to create 1 rle with old method : 0.025147438049316406 time for calcul the mask position with numpy : 0.02981877326965332 nb_pixel_total : 23821 time to create 1 rle with old method : 0.026920795440673828 time for calcul the mask position with numpy : 0.02727985382080078 nb_pixel_total : 6795 time to create 1 rle with old method : 0.007971763610839844 time for calcul the mask position with numpy : 0.032276153564453125 nb_pixel_total : 8199 time to create 1 rle with old method : 0.010003328323364258 time for calcul the mask position with numpy : 0.03055572509765625 nb_pixel_total : 35425 time to create 1 rle with old method : 0.042633771896362305 time for calcul the mask position with numpy : 0.02921462059020996 nb_pixel_total : 9625 time to create 1 rle with old method : 0.011242866516113281 time for calcul the mask position with numpy : 0.028967857360839844 nb_pixel_total : 26478 time to create 1 rle with old method : 0.030742883682250977 time for calcul the mask position with numpy : 0.02926921844482422 nb_pixel_total : 12138 time to create 1 rle with old method : 0.014083147048950195 time for calcul the mask position with numpy : 0.02933812141418457 nb_pixel_total : 15693 time to create 1 rle with old method : 0.023588180541992188 time for calcul the mask position with numpy : 0.031473636627197266 nb_pixel_total : 13767 time to create 1 rle with old method : 0.01616525650024414 time for calcul the mask position with numpy : 0.029405832290649414 nb_pixel_total : 58712 time to create 1 rle with old method : 0.06547784805297852 time for calcul the mask position with numpy : 0.029033660888671875 nb_pixel_total : 85106 time to create 1 rle with old method : 0.09970974922180176 time for calcul the mask position with numpy : 0.028973817825317383 nb_pixel_total : 32402 time to create 1 rle with old method : 0.03631305694580078 time for calcul the mask position with numpy : 0.028530359268188477 nb_pixel_total : 14440 time to create 1 rle with old method : 0.016892433166503906 time for calcul the mask position with numpy : 0.028830289840698242 nb_pixel_total : 14449 time to create 1 rle with old method : 0.016251802444458008 time for calcul the mask position with numpy : 0.028524160385131836 nb_pixel_total : 6159 time to create 1 rle with old method : 0.006957054138183594 time for calcul the mask position with numpy : 0.028194904327392578 nb_pixel_total : 20649 time to create 1 rle with old method : 0.023637771606445312 time for calcul the mask position with numpy : 0.02846813201904297 nb_pixel_total : 40074 time to create 1 rle with old method : 0.045149803161621094 time for calcul the mask position with numpy : 0.02860093116760254 nb_pixel_total : 52508 time to create 1 rle with old method : 0.058637142181396484 time for calcul the mask position with numpy : 0.028762102127075195 nb_pixel_total : 15950 time to create 1 rle with old method : 0.017962217330932617 time for calcul the mask position with numpy : 0.029569387435913086 nb_pixel_total : 11564 time to create 1 rle with old method : 0.01942729949951172 time for calcul the mask position with numpy : 0.03301858901977539 nb_pixel_total : 15011 time to create 1 rle with old method : 0.023471593856811523 time for calcul the mask position with numpy : 0.0275113582611084 nb_pixel_total : 7724 time to create 1 rle with old method : 0.008519411087036133 time for calcul the mask position with numpy : 0.02696371078491211 nb_pixel_total : 8716 time to create 1 rle with old method : 0.00989222526550293 time for calcul the mask position with numpy : 0.027140140533447266 nb_pixel_total : 7457 time to create 1 rle with old method : 0.00826573371887207 time for calcul the mask position with numpy : 0.028506755828857422 nb_pixel_total : 27804 time to create 1 rle with old method : 0.03162956237792969 time for calcul the mask position with numpy : 0.027751684188842773 nb_pixel_total : 20309 time to create 1 rle with old method : 0.0231478214263916 time for calcul the mask position with numpy : 0.028481721878051758 nb_pixel_total : 7400 time to create 1 rle with old method : 0.00860285758972168 time for calcul the mask position with numpy : 0.0290529727935791 nb_pixel_total : 8892 time to create 1 rle with old method : 0.010070562362670898 create new chi : 3.002220869064331 time to delete rle : 0.002183675765991211 batch 1 Loaded 67 chid ids of type : 3594 +++++++++++++++++++++++++++++++++++++++Number RLEs to save : 13016 TO DO : save crop sub photo not yet done ! save time : 1.3812763690948486 nb_obj : 14 nb_hashtags : 2 time to prepare the origin masks : 3.7023675441741943 time for calcul the mask position with numpy : 0.41126489639282227 nb_pixel_total : 6028520 time to create 1 rle with new method : 0.5205962657928467 time for calcul the mask position with numpy : 0.0334315299987793 nb_pixel_total : 20796 time to create 1 rle with old method : 0.022993803024291992 time for calcul the mask position with numpy : 0.03245139122009277 nb_pixel_total : 13153 time to create 1 rle with old method : 0.01472020149230957 time for calcul the mask position with numpy : 0.03536486625671387 nb_pixel_total : 13164 time to create 1 rle with old method : 0.017958641052246094 time for calcul the mask position with numpy : 0.03675580024719238 nb_pixel_total : 22992 time to create 1 rle with old method : 0.02619481086730957 time for calcul the mask position with numpy : 0.02084660530090332 nb_pixel_total : 259743 time to create 1 rle with new method : 0.6295948028564453 time for calcul the mask position with numpy : 0.022672176361083984 nb_pixel_total : 24210 time to create 1 rle with old method : 0.02772378921508789 time for calcul the mask position with numpy : 0.022489070892333984 nb_pixel_total : 86843 time to create 1 rle with old method : 0.09841585159301758 time for calcul the mask position with numpy : 0.021602869033813477 nb_pixel_total : 133375 time to create 1 rle with old method : 0.14961624145507812 time for calcul the mask position with numpy : 0.021892786026000977 nb_pixel_total : 11036 time to create 1 rle with old method : 0.01253652572631836 time for calcul the mask position with numpy : 0.0222930908203125 nb_pixel_total : 203296 time to create 1 rle with new method : 0.36637139320373535 time for calcul the mask position with numpy : 0.02240443229675293 nb_pixel_total : 190456 time to create 1 rle with new method : 0.5922625064849854 time for calcul the mask position with numpy : 0.0230255126953125 nb_pixel_total : 7533 time to create 1 rle with old method : 0.01289510726928711 time for calcul the mask position with numpy : 0.022870302200317383 nb_pixel_total : 13836 time to create 1 rle with old method : 0.02350926399230957 time for calcul the mask position with numpy : 0.02351069450378418 nb_pixel_total : 21287 time to create 1 rle with old method : 0.02360057830810547 create new chi : 3.4125795364379883 time to delete rle : 0.0025489330291748047 batch 1 Loaded 29 chid ids of type : 3594 +++++++++++++++++++Number RLEs to save : 9616 TO DO : save crop sub photo not yet done ! save time : 0.64186692237854 nb_obj : 8 nb_hashtags : 2 time to prepare the origin masks : 2.7506232261657715 time for calcul the mask position with numpy : 0.563490629196167 nb_pixel_total : 6571239 time to create 1 rle with new method : 0.6267924308776855 time for calcul the mask position with numpy : 0.020508766174316406 nb_pixel_total : 13841 time to create 1 rle with old method : 0.01583242416381836 time for calcul the mask position with numpy : 0.02135467529296875 nb_pixel_total : 95293 time to create 1 rle with old method : 0.11217451095581055 time for calcul the mask position with numpy : 0.02402782440185547 nb_pixel_total : 61664 time to create 1 rle with old method : 0.07069206237792969 time for calcul the mask position with numpy : 0.02421259880065918 nb_pixel_total : 80874 time to create 1 rle with old method : 0.10155606269836426 time for calcul the mask position with numpy : 0.023003339767456055 nb_pixel_total : 135950 time to create 1 rle with old method : 0.15824437141418457 time for calcul the mask position with numpy : 0.022063255310058594 nb_pixel_total : 10301 time to create 1 rle with old method : 0.011780261993408203 time for calcul the mask position with numpy : 0.021884441375732422 nb_pixel_total : 48862 time to create 1 rle with old method : 0.058260202407836914 time for calcul the mask position with numpy : 0.024456501007080078 nb_pixel_total : 32216 time to create 1 rle with old method : 0.0389704704284668 create new chi : 1.9755449295043945 time to delete rle : 0.001018524169921875 batch 1 Loaded 17 chid ids of type : 3594 +++++++++Number RLEs to save : 6530 TO DO : save crop sub photo not yet done ! save time : 0.48826074600219727 map_output_result : {1338913054: (0.0, 'Should be the crop_list due to order', 0), 1338913046: (0.0, 'Should be the crop_list due to order', 0), 1338912999: (0.0, 'Should be the crop_list due to order', 0), 1338912994: (0.0, 'Should be the crop_list due to order', 0), 1338912953: (0.0, 'Should be the crop_list due to order', 0), 1338765990: (0.0, 'Should be the crop_list due to order', 0), 1338765987: (0.0, 'Should be the crop_list due to order', 0), 1338765911: (0.0, 'Should be the crop_list due to order', 0), 1338765907: (0.0, 'Should be the crop_list due to order', 0), 1338765897: (0.0, 'Should be the crop_list due to order', 0), 1338765458: (0.0, 'Should be the crop_list due to order', 0), 1338765330: (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 [1338913054, 1338913046, 1338912999, 1338912994, 1338912953, 1338765990, 1338765987, 1338765911, 1338765907, 1338765897, 1338765458, 1338765330] Looping around the photos to save general results len do output : 12 /1338913054.Didn't retrieve data . /1338913046.Didn't retrieve data . /1338912999.Didn't retrieve data . /1338912994.Didn't retrieve data . /1338912953.Didn't retrieve data . /1338765990.Didn't retrieve data . /1338765987.Didn't retrieve data . /1338765911.Didn't retrieve data . /1338765907.Didn't retrieve data . /1338765897.Didn't retrieve data . /1338765458.Didn't retrieve data . /1338765330.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, '2606877') ('3318', '20744710', '1338913054', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338913046', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912999', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912994', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912953', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765990', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765987', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765911', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765907', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765897', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765458', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765330', None, None, None, None, None, '2606877') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.017562389373779297 save_final save missing photos in datou_result : time spend for datou_step_exec : 128.26370406150818 time spend to save output : 0.018072843551635742 total time spend for step 3 : 128.28177690505981 step4:ventilate_hashtags_in_portfolio Sat Feb 22 04:39:02 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 : 20744710 get user id for portfolio 20744710 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`=20744710 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','autre','flou','papier','background','metal','pet_fonce','environnement','mal_croppe','pehd','pet_clair')) 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`=20744710 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','autre','flou','papier','background','metal','pet_fonce','environnement','mal_croppe','pehd','pet_clair')) 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`=20744710 AND mptpi.`type`=3594 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('carton','autre','flou','papier','background','metal','pet_fonce','environnement','mal_croppe','pehd','pet_clair')) AND mptpi.`min_score`=0.5 To do lien utilise dans velours : https://www.fotonower.com/velours/20746604,20746605,20746606,20746607,20746608,20746609,20746610,20746611,20746612,20746613,20746614?tags=carton,autre,flou,papier,background,metal,pet_fonce,environnement,mal_croppe,pehd,pet_clair Inside saveOutput : final : False verbose : 0 saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1338913054, 1338913046, 1338912999, 1338912994, 1338912953, 1338765990, 1338765987, 1338765911, 1338765907, 1338765897, 1338765458, 1338765330] Looping around the photos to save general results len do output : 1 /20744710. 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, '2606877') ('3318', '20744710', '1338913054', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338913046', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912999', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912994', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912953', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765990', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765987', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765911', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765907', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765897', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765458', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765330', None, None, None, None, None, '2606877') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.017413854598999023 save_final save missing photos in datou_result : time spend for datou_step_exec : 3.5396502017974854 time spend to save output : 0.017714738845825195 total time spend for step 4 : 3.5573649406433105 step5:final Sat Feb 22 04:39:05 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 : {1338913054: ('0.180908203125',), 1338913046: ('0.180908203125',), 1338912999: ('0.180908203125',), 1338912994: ('0.180908203125',), 1338912953: ('0.180908203125',), 1338765990: ('0.180908203125',), 1338765987: ('0.180908203125',), 1338765911: ('0.180908203125',), 1338765907: ('0.180908203125',), 1338765897: ('0.180908203125',), 1338765458: ('0.180908203125',), 1338765330: ('0.180908203125',)} new output for save of step final : {1338913054: ('0.180908203125',), 1338913046: ('0.180908203125',), 1338912999: ('0.180908203125',), 1338912994: ('0.180908203125',), 1338912953: ('0.180908203125',), 1338765990: ('0.180908203125',), 1338765987: ('0.180908203125',), 1338765911: ('0.180908203125',), 1338765907: ('0.180908203125',), 1338765897: ('0.180908203125',), 1338765458: ('0.180908203125',), 1338765330: ('0.180908203125',)} [1338913054, 1338913046, 1338912999, 1338912994, 1338912953, 1338765990, 1338765987, 1338765911, 1338765907, 1338765897, 1338765458, 1338765330] Looping around the photos to save general results len do output : 12 /1338913054.Didn't retrieve data . /1338913046.Didn't retrieve data . /1338912999.Didn't retrieve data . /1338912994.Didn't retrieve data . /1338912953.Didn't retrieve data . /1338765990.Didn't retrieve data . /1338765987.Didn't retrieve data . /1338765911.Didn't retrieve data . /1338765907.Didn't retrieve data . /1338765897.Didn't retrieve data . /1338765458.Didn't retrieve data . /1338765330.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, '2606877') ('3318', '20744710', '1338913054', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338913046', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912999', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912994', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912953', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765990', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765987', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765911', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765907', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765897', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765458', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765330', None, None, None, None, None, '2606877') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 36 time used for this insertion : 0.014213800430297852 save_final save missing photos in datou_result : time spend for datou_step_exec : 2.223689317703247 time spend to save output : 0.014844655990600586 total time spend for step 5 : 2.2385339736938477 step6:blur_detection Sat Feb 22 04:39:07 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8.jpg resize: (2160, 3264) 1338913054 -3.3308133976488654 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298.jpg resize: (2160, 3264) 1338913046 -5.251287219211992 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46.jpg resize: (2160, 3264) 1338912999 -3.8693647942848433 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023.jpg resize: (2160, 3264) 1338912994 -6.05281044255983 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495.jpg resize: (2160, 3264) 1338912953 -1.018765934457715 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f.jpg resize: (2160, 3264) 1338765990 -3.1536540856768864 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f.jpg resize: (2160, 3264) 1338765987 -2.225710243451797 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33.jpg resize: (2160, 3264) 1338765911 -5.85952920294326 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52.jpg resize: (2160, 3264) 1338765907 -5.494774286768804 treat image : temp/1740195028_2145890_1338765897_afc886add820ee618a13b564c609e233.jpg resize: (2160, 3264) 1338765897 -4.061415492927072 treat image : temp/1740195028_2145890_1338765458_9c9e03d9aa6f514c0a472655643f206d.jpg resize: (2160, 3264) 1338765458 -3.9956594588051333 treat image : temp/1740195028_2145890_1338765330_87a7426b76976f070e2675178e300b68.jpg resize: (2160, 3264) 1338765330 -4.921552155850853 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113827_0.png resize: (562, 291) 1338982805 -1.4700625685886992 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113828_0.png resize: (360, 408) 1338982806 -2.92519812135437 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113833_0.png resize: (643, 624) 1338982807 -2.2747856613407436 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113840_0.png resize: (202, 205) 1338982809 -2.2539093238450105 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113831_0.png resize: (128, 162) 1338982810 -3.512071629323922 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113835_0.png resize: (223, 343) 1338982811 -3.906903673256723 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113836_0.png resize: (339, 471) 1338982812 -2.444195723534527 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113845_0.png resize: (330, 476) 1338982813 -1.684898350376914 treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8_rle_crop_3683113829_0.png resize: (482, 298) 1338982814 -2.1249439213998267 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113878_0.png resize: (136, 58) 1338982815 -3.348281296345269 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113872_0.png resize: (246, 136) 1338982816 -3.589215311827251 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113869_0.png resize: (232, 260) 1338982817 -3.2708282116333107 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113864_0.png resize: (506, 332) 1338982818 -2.8778089182161297 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113855_0.png resize: (278, 130) 1338982819 -3.702673012610287 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113868_0.png resize: (89, 98) 1338982820 -3.7145477573868475 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113857_0.png resize: (589, 415) 1338982821 -3.5588691918828044 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113871_0.png resize: (127, 181) 1338982822 -4.075875748037051 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113849_0.png resize: (309, 283) 1338982823 -3.4849123390428276 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113874_0.png resize: (265, 286) 1338982824 -2.3371048806592603 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113850_0.png resize: (636, 313) 1338982825 -2.736489605641652 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113861_0.png resize: (230, 82) 1338982826 -2.7447204481856353 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113851_0.png resize: (120, 148) 1338982827 -3.3136039552372263 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113867_0.png resize: (96, 66) 1338982828 -2.9079840894885853 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113863_0.png resize: (130, 108) 1338982829 -2.5338535758035765 treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298_rle_crop_3683113852_0.png resize: (82, 99) 1338982830 -3.533947444117793 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113909_0.png resize: (263, 366) 1338982831 -3.246055501433462 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113902_0.png resize: (145, 87) 1338982832 -2.5025889359149893 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113892_0.png resize: (185, 136) 1338982833 -2.297396738063187 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113917_0.png resize: (128, 194) 1338982834 -3.1964614817370465 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113903_0.png resize: (79, 141) 1338982835 -3.1553077720234364 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113923_0.png resize: (56, 91) 1338982836 -1.613954225550261 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113921_0.png resize: (766, 765) 1338982837 -1.0285056786302038 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113907_0.png resize: (102, 99) 1338982838 -2.8228262439030547 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113910_0.png resize: (178, 234) 1338982839 -1.5067743913378664 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113895_0.png resize: (212, 271) 1338982840 -4.113881344257291 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113898_0.png resize: (256, 277) 1338982841 -3.86337089484792 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113879_0.png resize: (127, 143) 1338982842 0.42938651268918543 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113880_0.png resize: (186, 219) 1338982843 -4.498261274464642 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113924_0.png resize: (232, 188) 1338982844 -3.0630037109777652 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113881_0.png resize: (184, 193) 1338982845 -2.991807179318976 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113911_0.png resize: (94, 99) 1338982846 -3.7337841706161625 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113884_0.png resize: (194, 258) 1338982847 -2.5978661666286684 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113906_0.png resize: (116, 72) 1338982848 -2.0936885964709755 treat image : 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temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113905_0.png resize: (103, 141) 1338982855 -2.904737300656607 treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46_rle_crop_3683113894_0.png resize: (137, 104) 1338982856 -2.143195952811283 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113967_0.png resize: (299, 147) 1338982857 -3.268360973488464 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113978_0.png resize: (353, 204) 1338982858 -2.562566058388388 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113931_0.png resize: (561, 633) 1338982859 -3.8767629702797923 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113971_0.png resize: (321, 436) 1338982860 -2.8053388766113665 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113970_0.png resize: (203, 169) 1338982862 -3.4989634748496927 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113928_0.png resize: (228, 157) 1338982863 -3.3901181111497904 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113938_0.png resize: (160, 205) 1338982864 -1.7346562486170072 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113979_0.png resize: (254, 304) 1338982865 -2.8004641438025115 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113934_0.png resize: (172, 197) 1338982866 -2.8845590342006937 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113933_0.png resize: (277, 228) 1338982867 -4.085339556899137 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113939_0.png resize: (457, 307) 1338982868 -5.063489579737805 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113959_0.png resize: (197, 112) 1338982869 -3.9563418531462715 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113983_0.png resize: (230, 86) 1338982870 -4.679708523131409 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113972_0.png resize: (88, 92) 1338982871 -3.020765445942551 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113980_0.png resize: (114, 86) 1338982872 -2.3881998574665206 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113948_0.png resize: (116, 64) 1338982873 -2.1879301530458535 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113975_0.png resize: (143, 147) 1338982874 -3.9644143956306883 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113981_0.png resize: (231, 242) 1338982875 -3.4355927638483523 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113929_0.png resize: (222, 155) 1338982876 -1.7501184460440515 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113946_0.png resize: (240, 219) 1338982877 -3.4069394612185184 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113953_0.png resize: (203, 105) 1338982878 -3.052541507060016 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113951_0.png resize: (156, 159) 1338982879 -3.6411199752199583 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113977_0.png resize: (176, 184) 1338982880 -1.898727505473343 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113932_0.png resize: (255, 174) 1338982881 -4.003366414664942 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113974_0.png resize: (175, 426) 1338982882 -3.523199476911663 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113963_0.png resize: (227, 262) 1338982883 -4.76462614946892 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113935_0.png resize: (233, 125) 1338982884 -3.974742080372592 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113976_0.png resize: (242, 179) 1338982885 -3.426164958118156 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113941_0.png resize: (219, 179) 1338982886 -4.003922328518794 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113937_0.png resize: (281, 168) 1338982887 -4.597003853339997 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113956_0.png resize: (105, 43) 1338982888 -2.7043377970402367 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113961_0.png resize: (92, 86) 1338982889 -5.850883186992865 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113968_0.png resize: (333, 267) 1338982890 -2.6647805644245564 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113947_0.png resize: (339, 237) 1338982891 -4.603166299760939 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113930_0.png resize: (119, 201) 1338982892 -3.7049218215806397 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113957_0.png resize: (231, 242) 1338982893 -3.388890208919378 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113954_0.png resize: (246, 233) 1338982894 -3.674213609031684 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113945_0.png resize: (136, 172) 1338982895 -3.311953521059878 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113958_0.png resize: (202, 183) 1338982896 -3.4335639653018197 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113943_0.png resize: (159, 210) 1338982897 -4.161455490065613 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113942_0.png resize: (249, 138) 1338982898 -4.812041153307416 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113944_0.png resize: (153, 176) 1338982899 -2.8313091576692053 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113960_0.png resize: (138, 120) 1338982900 -1.9565093258686788 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113973_0.png resize: (114, 170) 1338982901 -3.599046234201562 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113955_0.png resize: (148, 179) 1338982902 -4.069245128549092 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113982_0.png resize: (213, 174) 1338982903 -3.474447593720896 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113936_0.png resize: (170, 91) 1338982904 -2.736081404476387 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113940_0.png resize: (202, 179) 1338982905 -4.47891671060908 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113949_0.png resize: (268, 248) 1338982906 -2.681483462075588 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113962_0.png resize: (228, 158) 1338982907 -3.8469932757401644 treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023_rle_crop_3683113969_0.png resize: (189, 129) 1338982908 -2.524985202069294 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495_rle_crop_3683113989_0.png resize: (432, 235) 1338982909 -2.5118027832950194 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495_rle_crop_3683113986_0.png resize: (182, 112) 1338982910 -1.5466536958693684 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495_rle_crop_3683113987_0.png resize: (753, 1008) 1338982911 -3.055540436960588 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495_rle_crop_3683113988_0.png resize: (215, 286) 1338982912 0.32379238060891724 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495_rle_crop_3683113990_0.png resize: (240, 291) 1338982913 -1.5190836196473272 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495_rle_crop_3683113985_0.png resize: (116, 108) 1338982914 -0.210302169975355 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495_rle_crop_3683113992_0.png resize: (189, 111) 1338982915 -0.3902113531754138 treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495_rle_crop_3683113984_0.png resize: (267, 258) 1338982916 -1.497661423771169 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114010_0.png resize: (149, 186) 1338982917 -2.970145822823905 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114009_0.png resize: (308, 387) 1338982918 -2.8550896007370246 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114001_0.png resize: (214, 175) 1338982919 -3.1185546143656473 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683113999_0.png resize: (211, 228) 1338982920 -1.6135250233600482 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683113998_0.png resize: (206, 241) 1338982921 -3.4811553902398784 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114016_0.png resize: (172, 104) 1338982922 -1.821109256187066 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683113994_0.png resize: (112, 96) 1338982923 -0.2700498732070843 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114002_0.png resize: (102, 79) 1338982924 -0.987184198580793 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114006_0.png resize: (110, 80) 1338982925 -0.711762891919401 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114008_0.png resize: (610, 191) 1338982926 -3.0093005431540916 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114004_0.png resize: (280, 430) 1338982927 -2.358306124204444 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114017_0.png resize: (287, 195) 1338982928 0.5830878495924007 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114012_0.png resize: (134, 222) 1338982929 -2.288360614403629 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683113995_0.png resize: (88, 74) 1338982930 -2.295055573818701 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683113993_0.png resize: (312, 159) 1338982931 -3.0740673274066737 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114011_0.png resize: (147, 76) 1338982932 -1.845928805466665 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683113997_0.png resize: (132, 134) 1338982933 -2.659137351507829 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114019_0.png resize: (112, 87) 1338982934 -0.4381944967964427 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114003_0.png resize: (348, 252) 1338982935 -4.5483179695580285 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683113996_0.png resize: (58, 148) 1338982936 -1.9489956739848724 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114013_0.png resize: (179, 128) 1338982938 -2.6071426125124946 treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f_rle_crop_3683114007_0.png resize: (191, 110) 1338982939 -1.2238888428345245 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114025_0.png resize: (160, 271) 1338982940 -1.4944700530160886 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114021_0.png resize: (464, 198) 1338982941 -0.9869998920812202 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114028_0.png resize: (137, 226) 1338982942 0.9880910862601225 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114026_0.png resize: (378, 163) 1338982943 -0.8860711154166985 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114024_0.png resize: (381, 371) 1338982944 -1.8327432822058294 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114020_0.png resize: (159, 122) 1338982945 -1.5512313908001536 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114022_0.png resize: (166, 259) 1338982946 -0.4723590103277142 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114031_0.png resize: (89, 164) 1338982947 -2.5184359002119754 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114030_0.png resize: (128, 161) 1338982948 -2.478752571589724 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114027_0.png resize: (231, 202) 1338982949 -0.2682380628991776 treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f_rle_crop_3683114023_0.png resize: (114, 100) 1338982950 -1.9915291730796743 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114064_0.png resize: (781, 247) 1338982951 -3.3504563126728426 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114050_0.png resize: (238, 149) 1338982952 -2.4335790755833258 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114075_0.png resize: (299, 288) 1338982953 -3.0322575540147945 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114086_0.png resize: (139, 133) 1338982954 -3.081803108745537 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114039_0.png resize: (352, 253) 1338982955 -3.3259894765684703 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114074_0.png resize: (183, 220) 1338982956 -1.7003216229094906 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114049_0.png resize: (208, 322) 1338982957 -2.885244673820478 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114070_0.png resize: (214, 294) 1338982958 -3.669767695905351 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114090_0.png resize: (147, 126) 1338982959 -2.0750281349054833 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114040_0.png resize: (154, 114) 1338982960 -1.37034943483777 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114093_0.png resize: (133, 122) 1338982961 -1.1605634483558342 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114052_0.png resize: (182, 143) 1338982962 -1.9702926883482055 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114080_0.png resize: (256, 173) 1338982963 -5.12264725164586 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114041_0.png resize: (144, 169) 1338982964 -1.3117788755969968 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114047_0.png resize: (109, 222) 1338982965 -3.076597811341956 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114038_0.png resize: (423, 458) 1338982966 -3.1109654522942085 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114061_0.png resize: (315, 268) 1338982967 -3.948694027445247 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114062_0.png resize: (157, 167) 1338982968 -4.230095817358092 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114057_0.png resize: (78, 134) 1338982969 -3.6556647620149767 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114059_0.png resize: (267, 215) 1338982970 -3.0540329432682105 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114067_0.png resize: (164, 86) 1338982971 -2.8987043652951927 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114055_0.png resize: (125, 89) 1338982972 -2.3504292569030594 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114048_0.png resize: (115, 107) 1338982973 -3.643405143122021 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114051_0.png resize: (69, 84) 1338982974 -1.0325218261084184 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114068_0.png resize: (181, 492) 1338982975 -3.7970582344976544 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114058_0.png resize: (200, 173) 1338982976 -4.667112204419766 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114046_0.png resize: (142, 237) 1338982977 -3.1709302032422357 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114069_0.png resize: (112, 135) 1338982978 -4.32295992163436 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114079_0.png resize: (460, 212) 1338982979 -2.788073773416028 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114042_0.png resize: (150, 150) 1338982980 -2.45321963349602 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114076_0.png resize: (134, 183) 1338982981 -4.105161077217639 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114060_0.png resize: (83, 88) 1338982982 -3.171843191309747 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114053_0.png resize: (81, 119) 1338982983 -3.263544892157949 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114073_0.png resize: (108, 225) 1338982984 -3.127191636038755 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114036_0.png resize: (75, 109) 1338982985 -1.5026617743456028 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114065_0.png resize: (251, 143) 1338982986 -2.8543490714528974 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114033_0.png resize: (245, 106) 1338982987 -3.4465307172631463 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114035_0.png resize: (276, 192) 1338982988 -3.2957545519698535 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114066_0.png resize: (260, 189) 1338982989 -3.4827542724236564 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114032_0.png resize: (133, 160) 1338982990 -3.589938002054876 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114044_0.png resize: (428, 311) 1338982991 -3.0190263413013496 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114043_0.png resize: (234, 245) 1338982992 -3.5691148106211723 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114056_0.png resize: (181, 124) 1338982993 -2.7386030390239853 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114037_0.png resize: (84, 157) 1338982994 0.268714591732864 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114034_0.png resize: (120, 124) 1338982995 -2.3152732531195346 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114088_0.png resize: (159, 159) 1338982996 -3.419836558516653 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114083_0.png resize: (265, 196) 1338982997 -3.1196489952995106 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114072_0.png resize: (222, 254) 1338982998 -3.6129607774630133 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114089_0.png resize: (429, 215) 1338982999 -4.25209426290067 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114045_0.png resize: (199, 151) 1338983000 -2.1524355474578254 treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33_rle_crop_3683114071_0.png resize: (159, 55) 1338983001 -2.566543878352506 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114158_0.png resize: (353, 279) 1338983002 -3.5223605393679347 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114136_0.png resize: (144, 107) 1338983003 -1.6843288547884616 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114135_0.png resize: (154, 192) 1338983004 -2.8895601828501087 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114109_0.png resize: (94, 164) 1338983005 -1.8639152481605872 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114139_0.png resize: (434, 386) 1338983006 -2.990808023627186 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114103_0.png resize: (175, 200) 1338983007 -2.906115823301885 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114107_0.png resize: (245, 199) 1338983008 -1.2756092549779567 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114125_0.png resize: (237, 248) 1338983009 -3.169628058269077 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114149_0.png resize: (102, 121) 1338983010 -1.554038637396488 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114119_0.png resize: (136, 171) 1338983011 -2.687143272108799 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114099_0.png resize: (104, 139) 1338983012 -2.2094063257622287 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114154_0.png resize: (210, 232) 1338983013 -3.806170311858941 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114144_0.png resize: (207, 212) 1338983015 -4.719937900379276 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114129_0.png resize: (151, 205) 1338983016 -3.6798770664825122 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114110_0.png resize: (104, 139) 1338983017 -2.082691809990688 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114115_0.png resize: (97, 263) 1338983018 -1.5631911369085 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114145_0.png resize: (208, 137) 1338983019 -3.6287785077370214 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114140_0.png resize: (440, 661) 1338983020 -4.078303659876709 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114104_0.png resize: (175, 125) 1338983021 -4.087132047292219 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114120_0.png resize: (209, 185) 1338983022 -3.477352054538427 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114126_0.png resize: (317, 136) 1338983023 -3.8498908244088574 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114105_0.png resize: (167, 151) 1338983024 -3.0619850771341244 treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52_rle_crop_3683114118_0.png resize: (210, 210) 1338983025 -3.574064653123527 treat image : 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: 46.98305654525757 step7:brightness Sat Feb 22 04:39:54 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1740195028_2145890_1338913054_d7a5fab30102c7b6560a23f63131acd8.jpg treat image : temp/1740195028_2145890_1338913046_2ceefd3762174f3c64e014de6aa1a298.jpg treat image : temp/1740195028_2145890_1338912999_de2864fd3db912b8bd8802b74a378c46.jpg treat image : temp/1740195028_2145890_1338912994_b44fb0938cb76f00ef18badb5216f023.jpg treat image : temp/1740195028_2145890_1338912953_2d3dab3866edaac85b6166d962ff3495.jpg treat image : temp/1740195028_2145890_1338765990_61fc1eee1751a939de6d2530bfc0c80f.jpg treat image : temp/1740195028_2145890_1338765987_1c4328a65d65f30ac09067e76cd1003f.jpg treat image : temp/1740195028_2145890_1338765911_a263185c42adf8762df579e4d6370c33.jpg treat image : temp/1740195028_2145890_1338765907_c129d0ca1b9ab5c201ea2aa2c348fd52.jpg treat image : temp/1740195028_2145890_1338765897_afc886add820ee618a13b564c609e233.jpg treat image : 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: 0 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 406 time used for this insertion : 0.1673574447631836 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 406 time used for this insertion : 1.8567571640014648 save missing photos in datou_result : time spend for datou_step_exec : 12.519387006759644 time spend to save output : 2.0299618244171143 total time spend for step 7 : 14.549348831176758 step8:velours_tree Sat Feb 22 04:40:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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 : 0.1076805591583252 time spend to save output : 4.100799560546875e-05 total time spend for step 8 : 0.10772156715393066 step9:send_mail_cod Sat Feb 22 04:40:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/workarea/git/Velours/python in order to get the selector url, please entre the license of selector results_Auto_P20744710_22-02-2025_04_40_09.pdf 20746604 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 .imagette207466041740195609 20746605 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 .imagette207466051740195610 20746606 imagette207466061740195612 20746607 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 .imagette207466071740195612 20746608 imagette207466081740195613 20746609 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 .imagette207466091740195613 20746610 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette207466101740195613 20746612 imagette207466121740195614 20746613 change filename to text .change filename to text .change filename to text .imagette207466131740195614 20746614 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 .imagette207466141740195614 SELECT h.hashtag,pcr.value FROM MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h where pcr.portfolio_id=20744710 and hashtag_type = 3594 and pcr.hashtag_id = h.hashtag_id; velour_link : https://www.fotonower.com/velours/20746604,20746605,20746606,20746607,20746608,20746609,20746610,20746611,20746612,20746613,20746614?tags=carton,autre,flou,papier,background,metal,pet_fonce,environnement,mal_croppe,pehd,pet_clair args[1338913054] : ((1338913054, -3.3308133976488654, 492609224), (1338913054, -0.18806300296740333, 496442774), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338913046] : ((1338913046, -5.251287219211992, 492609224), (1338913046, -0.4380164236006919, 496442774), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338912999] : ((1338912999, -3.8693647942848433, 492609224), (1338912999, -0.28339213158056015, 496442774), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338912994] : ((1338912994, -6.05281044255983, 492609224), (1338912994, -0.08038460490165272, 496442774), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338912953] : ((1338912953, -1.018765934457715, 492688767), (1338912953, 1.0068020899239645, 2107752395), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338765990] : ((1338765990, -3.1536540856768864, 492609224), (1338765990, -0.28113771408191446, 496442774), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338765987] : ((1338765987, -2.225710243451797, 492609224), (1338765987, 0.7495622989644798, 2107752395), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338765911] : ((1338765911, -5.85952920294326, 492609224), (1338765911, -0.008321820949091679, 2107752395), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338765907] : ((1338765907, -5.494774286768804, 492609224), (1338765907, -0.008441622332051391, 2107752395), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338765897] : ((1338765897, -4.061415492927072, 492609224), (1338765897, -0.16463598734488608, 496442774), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338765458] : ((1338765458, -3.9956594588051333, 492609224), (1338765458, 0.11160901347159827, 2107752395), '0.180908203125') We are sending mail with results at report@fotonower.com args[1338765330] : ((1338765330, -4.921552155850853, 492609224), (1338765330, 0.15189137037312903, 2107752395), '0.180908203125') We are sending mail with results at report@fotonower.com refus_total : 0.180908203125 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=20744710 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 SELECT photo_id, url FROM MTRBack.photos ph WHERE photo_id IN (1338913046,1338913054,1338765907,1338912994,1338765330,1338765458,1338765897,1338765911,1338765987,1338765990,1338912953,1338912999) Found this number of photos: 12 begin to download photo : 1338913046 begin to download photo : 1338912994 begin to download photo : 1338765897 begin to download photo : 1338765990 download finish for photo 1338765897 begin to download photo : 1338765911 download finish for photo 1338913046 begin to download photo : 1338913054 download finish for photo 1338912994 begin to download photo : 1338765330 download finish for photo 1338765990 begin to download photo : 1338912953 download finish for photo 1338765911 begin to download photo : 1338765987 download finish for photo 1338913054 begin to download photo : 1338765907 download finish for photo 1338912953 begin to download photo : 1338912999 download finish for photo 1338765330 begin to download photo : 1338765458 download finish for photo 1338765987 download finish for photo 1338912999 download finish for photo 1338765458 download finish for photo 1338765907 start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20744710_22-02-2025_04_40_09.pdf results_Auto_P20744710_22-02-2025_04_40_09.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20744710_22-02-2025_04_40_09.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','20744710','results_Auto_P20744710_22-02-2025_04_40_09.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20744710_22-02-2025_04_40_09.pdf','pdf','','1.04','0.180908203125') message_in_mail: Bonjour,
Veuillez trouver ci dessous les résultats du service carac on demand pour le portfolio: https://www.fotonower.com/view/20744710

https://www.fotonower.com/image?json=false&list_photos_id=1338913054
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338913046
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338912999
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338912994
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338912953
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338765990
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338765987
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338765911
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338765907
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338765897
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338765458
Bravo, la photo est bien prise.
https://www.fotonower.com/image?json=false&list_photos_id=1338765330
Bravo, la photo est bien prise.

Dans ces conditions,le taux de refus est: 18.09%
Veuillez trouver les photos des contaminants.

exemples de contaminants: carton: https://www.fotonower.com/view/20746604?limit=200
exemples de contaminants: autre: https://www.fotonower.com/view/20746605?limit=200
exemples de contaminants: papier: https://www.fotonower.com/view/20746607?limit=200
exemples de contaminants: metal: https://www.fotonower.com/view/20746609?limit=200
exemples de contaminants: pet_fonce: https://www.fotonower.com/view/20746610?limit=200
exemples de contaminants: pehd: https://www.fotonower.com/view/20746613?limit=200
exemples de contaminants: pet_clair: https://www.fotonower.com/view/20746614?limit=200
Veuillez trouver le rapport en pdf:https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20744710_22-02-2025_04_40_09.pdf.

Lien vers velours :https://www.fotonower.com/velours/20746604,20746605,20746606,20746607,20746608,20746609,20746610,20746611,20746612,20746613,20746614?tags=carton,autre,flou,papier,background,metal,pet_fonce,environnement,mal_croppe,pehd,pet_clair.


L'équipe Fotonower 202 b'' Server: nginx Date: Sat, 22 Feb 2025 03:40:20 GMT Content-Length: 0 Connection: close X-Message-Id: rb7rVZfTQ96bIvnM2w8mMQ 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 [1338913054, 1338913046, 1338912999, 1338912994, 1338912953, 1338765990, 1338765987, 1338765911, 1338765907, 1338765897, 1338765458, 1338765330] 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, '2606877') ('3318', '20744710', '1338913054', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338913046', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912999', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912994', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912953', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765990', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765987', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765911', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765907', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765897', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765458', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765330', None, None, None, None, None, '2606877') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 12 time used for this insertion : 0.08992791175842285 save_final save missing photos in datou_result : time spend for datou_step_exec : 10.770757675170898 time spend to save output : 0.0902101993560791 total time spend for step 9 : 10.860967874526978 step10:split_time_score Sat Feb 22 04:40:20 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed 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', 12),) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21022025 20744710 Nombre de photos uploadées : 12 / 23040 (0%) 21022025 20744710 Nombre de photos taguées (types de déchets): 0 / 12 (0%) 21022025 20744710 Nombre de photos taguées (volume) : 0 / 12 (0%) elapsed_time : load_data_split_time_score 1.1920928955078125e-06 elapsed_time : order_list_meta_photo_and_scores 3.814697265625e-06 ???????????? elapsed_time : fill_and_build_computed_from_old_data 0.0004448890686035156 elapsed_time : insert_dashboard_record_day_entry 0.02378058433532715 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 : 0.22463459872760835 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20739123_22-02-2025_01_03_14.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20739123 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`=20739123 AND mptpi.`type`=3594 To do Qualite : 0.180908203125 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20744710_22-02-2025_04_40_09.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20744710 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`=20744710 AND mptpi.`type`=3594 To do Qualite : 0.09458484434241583 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20739133_22-02-2025_02_05_15.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20739133 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`=20739133 AND mptpi.`type`=3726 To do Qualite : 0.23017996550472045 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20741325_22-02-2025_01_57_00.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20741325 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`=20741325 AND mptpi.`type`=3594 To do Qualite : 0.16375772636495675 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20729329_21-02-2025_17_39_37.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20729329 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`=20729329 AND mptpi.`type`=3594 To do Qualite : 0.16569577773238928 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20729332_21-02-2025_18_58_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20729332 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`=20729332 AND mptpi.`type`=3594 To do Qualite : 0.05800265082455208 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20729335_21-02-2025_16_59_26.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20729335 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`=20729335 AND mptpi.`type`=3726 To do Qualite : 0.22070641882010025 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20741329_22-02-2025_02_07_18.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20741329 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`=20741329 AND mptpi.`type`=3594 To do Qualite : 0.057652308327866095 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20739139_22-02-2025_00_37_10.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20739139 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`=20739139 AND mptpi.`type`=3726 To do Qualite : 0.24949062984831982 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20739145_22-02-2025_00_52_08.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20739145 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`=20739145 AND mptpi.`type`=3594 To do Qualite : 0.2231844589687727 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20739146_22-02-2025_00_33_06.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20739146 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`=20739146 AND mptpi.`type`=3594 To do Qualite : 0.20566999395507354 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_Auto_P20739147_22-02-2025_00_43_51.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 20739147 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`=20739147 AND mptpi.`type`=3594 To do NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21022025': {'nb_upload': 12, '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 [1338913054, 1338913046, 1338912999, 1338912994, 1338912953, 1338765990, 1338765987, 1338765911, 1338765907, 1338765897, 1338765458, 1338765330] Looping around the photos to save general results len do output : 1 /20744710Didn'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, '2606877') ('3318', '20744710', '1338913054', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338913046', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912999', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912994', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338912953', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765990', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765987', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765911', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765907', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765897', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765458', None, None, None, None, None, '2606877') ('3318', None, None, None, None, None, None, None, '2606877') ('3318', '20744710', '1338765330', None, None, None, None, None, '2606877') begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.0171053409576416 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.8825621604919434 time spend to save output : 0.017347097396850586 total time spend for step 10 : 0.899909257888794 caffe_path_current : About to save ! 2 After save, about to update current ! ret : 2 len(input) + len(total_photo_id_missing) : 12 set_done_treatment 277.96user 126.64system 9:56.40elapsed 67%CPU (0avgtext+0avgdata 8751148maxresident)k 1327152inputs+231856outputs (21675major+26018914minor)pagefaults 0swaps